role of literature review process

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Library Guide to Capstone Literature Reviews: Role of the Literature Review

The role of the literature review.

Your literature review gives readers an understanding of the scholarly research on your topic.

In your literature review you will:

• demonstrate that you are a well-informed scholar with expertise and knowledge in the field by giving an overview of the current state of the literature
• find a gap in the literature, or address a business or professional issue, depending on your doctoral study program; the literature review will illustrate how your research contributes to the scholarly conversation
• provide a synthesis of the issues, trends, and concepts surrounding your research

Be aware that the literature review is an iterative process. As you read and write initial drafts, you will find new threads and complementary themes, at which point you will return to search, find out about these new themes, and incorporate them into your review.

The purpose of this guide is to help you through the literature review process. Take some time to look over the resources in order to become familiar with them. The tabs on the left side of this page have additional information.

Short video: Research for the Literature Review

Short Video: Research for the Literature Review

(4 min 10 sec) Recorded August 2019 Transcript 

Literature review as a dinner party

To think about the role of the literature review, consider this analogy:  pretend that you throw a dinner party for the other researchers working in your topic area. First, you’d need to develop a guest list.

• The guests of honor would be early researchers or theorists; their work likely inspired subsequent studies, ideas, or controversies that the current researchers pursue.
• Then, think about the important current researchers to invite. Which guests might agree with each other?  Which others might provide useful counterpoints?
• You likely won’t be able to include everyone on the guest list, so you may need to choose carefully so that you don’t leave important figures out. 
• Alternatively, if there aren’t many researchers working in your topic area, then your guest list will need to include people working in other, related areas, who can still contribute to the conversation.

After the party, you describe the evening to a friend. You’ll summarize the evening’s conversation. Perhaps one guest made a comment that sparked a conversation, and then you describe who responded and how the topic evolved. There are other conversations to share, too. This is how you synthesize the themes and developments that you find in your research. Thinking about your literature research this way will help you to present your dinner party (and your literature review) in a lively and engaging way.

Short video: Empirical research

Video: How to locate and identify empirical research for your literature review

(6 min 16 sec) Recorded May 2020 Transcript 

Here are some useful resources from the Writing Center, the Office of Research and Doctoral Services, and other departments within the Office of Academic Support. Take some time to look at what is available to help you with your capstone/dissertation.

• Familiarize yourself with Walden support
• Doctoral Capstone Resources website
• Capstone writing resources
• Office of Student Research Administration
• Office of Research and Doctoral Services
• Visit the Writing Center

You can watch recorded webinars on the literature review in our Library Webinar Archives .

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Research Process :: Step by Step

• Introduction
• Select Topic
• Identify Keywords
• Background Information
• Develop Research Questions
• Refine Topic
• Search Strategy
• Popular Databases
• Evaluate Sources
• Types of Periodicals
• Reading Scholarly Articles
• Primary & Secondary Sources
• Organize / Take Notes
• Writing & Grammar Resources
• Annotated Bibliography
• Literature Review
• Citation Styles
• Paraphrasing
• Privacy / Confidentiality
• Research Process
• Selecting Your Topic
• Identifying Keywords
• Gathering Background Info
• Evaluating Sources

Organize the literature review into sections that present themes or identify trends, including relevant theory. You are not trying to list all the material published, but to synthesize and evaluate it according to the guiding concept of your thesis or research question.  

What is a literature review?

A literature review is an account of what has been published on a topic by accredited scholars and researchers. Occasionally you will be asked to write one as a separate assignment, but more often it is part of the introduction to an essay, research report, or thesis. In writing the literature review, your purpose is to convey to your reader what knowledge and ideas have been established on a topic, and what their strengths and weaknesses are. As a piece of writing, the literature review must be defined by a guiding concept (e.g., your research objective, the problem or issue you are discussing, or your argumentative thesis). It is not just a descriptive list of the material available, or a set of summaries

A literature review must do these things:

• be organized around and related directly to the thesis or research question you are developing
• synthesize results into a summary of what is and is not known
• identify areas of controversy in the literature
• formulate questions that need further research

Ask yourself questions like these:

• What is the specific thesis, problem, or research question that my literature review helps to define?
• What type of literature review am I conducting? Am I looking at issues of theory? methodology? policy? quantitative research (e.g. on the effectiveness of a new procedure)? qualitative research (e.g., studies of loneliness among migrant workers)?
• What is the scope of my literature review? What types of publications am I using (e.g., journals, books, government documents, popular media)? What discipline am I working in (e.g., nursing psychology, sociology, medicine)?
• How good was my information seeking? Has my search been wide enough to ensure I've found all the relevant material? Has it been narrow enough to exclude irrelevant material? Is the number of sources I've used appropriate for the length of my paper?
• Have I critically analyzed the literature I use? Do I follow through a set of concepts and questions, comparing items to each other in the ways they deal with them? Instead of just listing and summarizing items, do I assess them, discussing strengths and weaknesses?
• Have I cited and discussed studies contrary to my perspective?
• Will the reader find my literature review relevant, appropriate, and useful?

Ask yourself questions like these about each book or article you include:

• Has the author formulated a problem/issue?
• Is it clearly defined? Is its significance (scope, severity, relevance) clearly established?
• Could the problem have been approached more effectively from another perspective?
• What is the author's research orientation (e.g., interpretive, critical science, combination)?
• What is the author's theoretical framework (e.g., psychological, developmental, feminist)?
• What is the relationship between the theoretical and research perspectives?
• Has the author evaluated the literature relevant to the problem/issue? Does the author include literature taking positions she or he does not agree with?
• In a research study, how good are the basic components of the study design (e.g., population, intervention, outcome)? How accurate and valid are the measurements? Is the analysis of the data accurate and relevant to the research question? Are the conclusions validly based upon the data and analysis?
• In material written for a popular readership, does the author use appeals to emotion, one-sided examples, or rhetorically-charged language and tone? Is there an objective basis to the reasoning, or is the author merely "proving" what he or she already believes?
• How does the author structure the argument? Can you "deconstruct" the flow of the argument to see whether or where it breaks down logically (e.g., in establishing cause-effect relationships)?
• In what ways does this book or article contribute to our understanding of the problem under study, and in what ways is it useful for practice? What are the strengths and limitations?
• How does this book or article relate to the specific thesis or question I am developing?

Text written by Dena Taylor, Health Sciences Writing Centre, University of Toronto

http://www.writing.utoronto.ca/advice/specific-types-of-writing/literature-review

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YSN Doctoral Programs: Steps in Conducting a Literature Review

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What is a literature review?

A literature review is an integrated analysis -- not just a summary-- of scholarly writings and other relevant evidence related directly to your research question.  That is, it represents a synthesis of the evidence that provides background information on your topic and shows a association between the evidence and your research question.

A literature review may be a stand alone work or the introduction to a larger research paper, depending on the assignment.  Rely heavily on the guidelines your instructor has given you.

Why is it important?

A literature review is important because it:

• Explains the background of research on a topic.
• Demonstrates why a topic is significant to a subject area.
• Discovers relationships between research studies/ideas.
• Identifies major themes, concepts, and researchers on a topic.
• Identifies critical gaps and points of disagreement.
• Discusses further research questions that logically come out of the previous studies.

APA7 Style resources

APA Style Blog - for those harder to find answers

1. Choose a topic. Define your research question.

Your literature review should be guided by your central research question.  The literature represents background and research developments related to a specific research question, interpreted and analyzed by you in a synthesized way.

• Make sure your research question is not too broad or too narrow.  Is it manageable?
• Begin writing down terms that are related to your question. These will be useful for searches later.
• If you have the opportunity, discuss your topic with your professor and your class mates.

2. Decide on the scope of your review

How many studies do you need to look at? How comprehensive should it be? How many years should it cover? 

• This may depend on your assignment.  How many sources does the assignment require?

3. Select the databases you will use to conduct your searches.

Make a list of the databases you will search. 

Where to find databases:

• use the tabs on this guide
• Find other databases in the Nursing Information Resources web page
• More on the Medical Library web page
• ... and more on the Yale University Library web page

4. Conduct your searches to find the evidence. Keep track of your searches.

• Use the key words in your question, as well as synonyms for those words, as terms in your search. Use the database tutorials for help.
• Save the searches in the databases. This saves time when you want to redo, or modify, the searches. It is also helpful to use as a guide is the searches are not finding any useful results.
• Review the abstracts of research studies carefully. This will save you time.
• Use the bibliographies and references of research studies you find to locate others.
• Check with your professor, or a subject expert in the field, if you are missing any key works in the field.
• Ask your librarian for help at any time.
• Use a citation manager, such as EndNote as the repository for your citations. See the EndNote tutorials for help.

Review the literature

Some questions to help you analyze the research:

• What was the research question of the study you are reviewing? What were the authors trying to discover?
• Was the research funded by a source that could influence the findings?
• What were the research methodologies? Analyze its literature review, the samples and variables used, the results, and the conclusions.
• Does the research seem to be complete? Could it have been conducted more soundly? What further questions does it raise?
• If there are conflicting studies, why do you think that is?
• How are the authors viewed in the field? Has this study been cited? If so, how has it been analyzed?

Tips: 

• Review the abstracts carefully.  
• Keep careful notes so that you may track your thought processes during the research process.
• Create a matrix of the studies for easy analysis, and synthesis, across all of the studies.
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• Literature Review: The What, Why and How-to Guide
• Introduction

Literature Review: The What, Why and How-to Guide — Introduction

• Getting Started
• How to Pick a Topic
• Strategies to Find Sources
• Evaluating Sources & Lit. Reviews
• Tips for Writing Literature Reviews
• Writing Literature Review: Useful Sites
• Citation Resources
• Other Academic Writings

What are Literature Reviews?

So, what is a literature review? "A literature review is an account of what has been published on a topic by accredited scholars and researchers. In writing the literature review, your purpose is to convey to your reader what knowledge and ideas have been established on a topic, and what their strengths and weaknesses are. As a piece of writing, the literature review must be defined by a guiding concept (e.g., your research objective, the problem or issue you are discussing, or your argumentative thesis). It is not just a descriptive list of the material available, or a set of summaries." Taylor, D.  The literature review: A few tips on conducting it . University of Toronto Health Sciences Writing Centre.

Goals of Literature Reviews

What are the goals of creating a Literature Review?  A literature could be written to accomplish different aims:

• To develop a theory or evaluate an existing theory
• To summarize the historical or existing state of a research topic
• Identify a problem in a field of research 

Baumeister, R. F., & Leary, M. R. (1997). Writing narrative literature reviews .  Review of General Psychology , 1 (3), 311-320.

What kinds of sources require a Literature Review?

• A research paper assigned in a course
• A thesis or dissertation
• A grant proposal
• An article intended for publication in a journal

All these instances require you to collect what has been written about your research topic so that you can demonstrate how your own research sheds new light on the topic.

Types of Literature Reviews

What kinds of literature reviews are written?

Narrative review: The purpose of this type of review is to describe the current state of the research on a specific topic/research and to offer a critical analysis of the literature reviewed. Studies are grouped by research/theoretical categories, and themes and trends, strengths and weakness, and gaps are identified. The review ends with a conclusion section which summarizes the findings regarding the state of the research of the specific study, the gaps identify and if applicable, explains how the author's research will address gaps identify in the review and expand the knowledge on the topic reviewed.

• Example : Predictors and Outcomes of U.S. Quality Maternity Leave: A Review and Conceptual Framework:  10.1177/08948453211037398  

Systematic review : "The authors of a systematic review use a specific procedure to search the research literature, select the studies to include in their review, and critically evaluate the studies they find." (p. 139). Nelson, L. K. (2013). Research in Communication Sciences and Disorders . Plural Publishing.

• Example : The effect of leave policies on increasing fertility: a systematic review:  10.1057/s41599-022-01270-w

Meta-analysis : "Meta-analysis is a method of reviewing research findings in a quantitative fashion by transforming the data from individual studies into what is called an effect size and then pooling and analyzing this information. The basic goal in meta-analysis is to explain why different outcomes have occurred in different studies." (p. 197). Roberts, M. C., & Ilardi, S. S. (2003). Handbook of Research Methods in Clinical Psychology . Blackwell Publishing.

• Example : Employment Instability and Fertility in Europe: A Meta-Analysis:  10.1215/00703370-9164737

Meta-synthesis : "Qualitative meta-synthesis is a type of qualitative study that uses as data the findings from other qualitative studies linked by the same or related topic." (p.312). Zimmer, L. (2006). Qualitative meta-synthesis: A question of dialoguing with texts .  Journal of Advanced Nursing , 53 (3), 311-318.

• Example : Women’s perspectives on career successes and barriers: A qualitative meta-synthesis:  10.1177/05390184221113735

Literature Reviews in the Health Sciences

• UConn Health subject guide on systematic reviews Explanation of the different review types used in health sciences literature as well as tools to help you find the right review type
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Writing a Literature Review

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A literature review is a document or section of a document that collects key sources on a topic and discusses those sources in conversation with each other (also called synthesis ). The lit review is an important genre in many disciplines, not just literature (i.e., the study of works of literature such as novels and plays). When we say “literature review” or refer to “the literature,” we are talking about the research ( scholarship ) in a given field. You will often see the terms “the research,” “the scholarship,” and “the literature” used mostly interchangeably.

Where, when, and why would I write a lit review?

There are a number of different situations where you might write a literature review, each with slightly different expectations; different disciplines, too, have field-specific expectations for what a literature review is and does. For instance, in the humanities, authors might include more overt argumentation and interpretation of source material in their literature reviews, whereas in the sciences, authors are more likely to report study designs and results in their literature reviews; these differences reflect these disciplines’ purposes and conventions in scholarship. You should always look at examples from your own discipline and talk to professors or mentors in your field to be sure you understand your discipline’s conventions, for literature reviews as well as for any other genre.

A literature review can be a part of a research paper or scholarly article, usually falling after the introduction and before the research methods sections. In these cases, the lit review just needs to cover scholarship that is important to the issue you are writing about; sometimes it will also cover key sources that informed your research methodology.

Lit reviews can also be standalone pieces, either as assignments in a class or as publications. In a class, a lit review may be assigned to help students familiarize themselves with a topic and with scholarship in their field, get an idea of the other researchers working on the topic they’re interested in, find gaps in existing research in order to propose new projects, and/or develop a theoretical framework and methodology for later research. As a publication, a lit review usually is meant to help make other scholars’ lives easier by collecting and summarizing, synthesizing, and analyzing existing research on a topic. This can be especially helpful for students or scholars getting into a new research area, or for directing an entire community of scholars toward questions that have not yet been answered.

What are the parts of a lit review?

Most lit reviews use a basic introduction-body-conclusion structure; if your lit review is part of a larger paper, the introduction and conclusion pieces may be just a few sentences while you focus most of your attention on the body. If your lit review is a standalone piece, the introduction and conclusion take up more space and give you a place to discuss your goals, research methods, and conclusions separately from where you discuss the literature itself.

Introduction:

• An introductory paragraph that explains what your working topic and thesis is
• A forecast of key topics or texts that will appear in the review
• Potentially, a description of how you found sources and how you analyzed them for inclusion and discussion in the review (more often found in published, standalone literature reviews than in lit review sections in an article or research paper)
• Summarize and synthesize: Give an overview of the main points of each source and combine them into a coherent whole
• Analyze and interpret: Don’t just paraphrase other researchers – add your own interpretations where possible, discussing the significance of findings in relation to the literature as a whole
• Critically Evaluate: Mention the strengths and weaknesses of your sources
• Write in well-structured paragraphs: Use transition words and topic sentence to draw connections, comparisons, and contrasts.

Conclusion:

• Summarize the key findings you have taken from the literature and emphasize their significance
• Connect it back to your primary research question

How should I organize my lit review?

Lit reviews can take many different organizational patterns depending on what you are trying to accomplish with the review. Here are some examples:

• Chronological : The simplest approach is to trace the development of the topic over time, which helps familiarize the audience with the topic (for instance if you are introducing something that is not commonly known in your field). If you choose this strategy, be careful to avoid simply listing and summarizing sources in order. Try to analyze the patterns, turning points, and key debates that have shaped the direction of the field. Give your interpretation of how and why certain developments occurred (as mentioned previously, this may not be appropriate in your discipline — check with a teacher or mentor if you’re unsure).
• Thematic : If you have found some recurring central themes that you will continue working with throughout your piece, you can organize your literature review into subsections that address different aspects of the topic. For example, if you are reviewing literature about women and religion, key themes can include the role of women in churches and the religious attitude towards women.
• Qualitative versus quantitative research
• Empirical versus theoretical scholarship
• Divide the research by sociological, historical, or cultural sources
• Theoretical : In many humanities articles, the literature review is the foundation for the theoretical framework. You can use it to discuss various theories, models, and definitions of key concepts. You can argue for the relevance of a specific theoretical approach or combine various theorical concepts to create a framework for your research.

What are some strategies or tips I can use while writing my lit review?

Any lit review is only as good as the research it discusses; make sure your sources are well-chosen and your research is thorough. Don’t be afraid to do more research if you discover a new thread as you’re writing. More info on the research process is available in our "Conducting Research" resources .

As you’re doing your research, create an annotated bibliography ( see our page on the this type of document ). Much of the information used in an annotated bibliography can be used also in a literature review, so you’ll be not only partially drafting your lit review as you research, but also developing your sense of the larger conversation going on among scholars, professionals, and any other stakeholders in your topic.

Usually you will need to synthesize research rather than just summarizing it. This means drawing connections between sources to create a picture of the scholarly conversation on a topic over time. Many student writers struggle to synthesize because they feel they don’t have anything to add to the scholars they are citing; here are some strategies to help you:

• It often helps to remember that the point of these kinds of syntheses is to show your readers how you understand your research, to help them read the rest of your paper.
• Writing teachers often say synthesis is like hosting a dinner party: imagine all your sources are together in a room, discussing your topic. What are they saying to each other?
• Look at the in-text citations in each paragraph. Are you citing just one source for each paragraph? This usually indicates summary only. When you have multiple sources cited in a paragraph, you are more likely to be synthesizing them (not always, but often
• Read more about synthesis here.

The most interesting literature reviews are often written as arguments (again, as mentioned at the beginning of the page, this is discipline-specific and doesn’t work for all situations). Often, the literature review is where you can establish your research as filling a particular gap or as relevant in a particular way. You have some chance to do this in your introduction in an article, but the literature review section gives a more extended opportunity to establish the conversation in the way you would like your readers to see it. You can choose the intellectual lineage you would like to be part of and whose definitions matter most to your thinking (mostly humanities-specific, but this goes for sciences as well). In addressing these points, you argue for your place in the conversation, which tends to make the lit review more compelling than a simple reporting of other sources.

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Conducting a literature review: why do a literature review, why do a literature review.

• How To Find "The Literature"
• Found it -- Now What?

Besides the obvious reason for students -- because it is assigned! -- a literature review helps you explore the research that has come before you, to see how your research question has (or has not) already been addressed.

You identify:

• core research in the field
• experts in the subject area
• methodology you may want to use (or avoid)
• gaps in knowledge -- or where your research would fit in

It Also Helps You:

• Publish and share your findings
• Justify requests for grants and other funding
• Identify best practices to inform practice
• Set wider context for a program evaluation
• Compile information to support community organizing

Great brief overview, from NCSU

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Literature reviews, what is a literature review, learning more about how to do a literature review.

• Planning the Review
• The Research Question
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• Writing the Review

A literature review is a review and synthesis of existing research on a topic or research question. A literature review is meant to analyze the scholarly literature, make connections across writings and identify strengths, weaknesses, trends, and missing conversations. A literature review should address different aspects of a topic as it relates to your research question. A literature review goes beyond a description or summary of the literature you have read. 

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A literature review surveys prior research published in books, scholarly articles, and any other sources relevant to a particular issue, area of research, or theory, and by so doing, provides a description, summary, and critical evaluation of these works in relation to the research problem being investigated. Literature reviews are designed to provide an overview of sources you have used in researching a particular topic and to demonstrate to your readers how your research fits within existing scholarship about the topic.

Fink, Arlene. Conducting Research Literature Reviews: From the Internet to Paper . Fourth edition. Thousand Oaks, CA: SAGE, 2014.

Importance of a Good Literature Review

A literature review may consist of simply a summary of key sources, but in the social sciences, a literature review usually has an organizational pattern and combines both summary and synthesis, often within specific conceptual categories . A summary is a recap of the important information of the source, but a synthesis is a re-organization, or a reshuffling, of that information in a way that informs how you are planning to investigate a research problem. The analytical features of a literature review might:

• Give a new interpretation of old material or combine new with old interpretations,
• Trace the intellectual progression of the field, including major debates,
• Depending on the situation, evaluate the sources and advise the reader on the most pertinent or relevant research, or
• Usually in the conclusion of a literature review, identify where gaps exist in how a problem has been researched to date.

Given this, the purpose of a literature review is to:

• Place each work in the context of its contribution to understanding the research problem being studied.
• Describe the relationship of each work to the others under consideration.
• Identify new ways to interpret prior research.
• Reveal any gaps that exist in the literature.
• Resolve conflicts amongst seemingly contradictory previous studies.
• Identify areas of prior scholarship to prevent duplication of effort.
• Point the way in fulfilling a need for additional research.
• Locate your own research within the context of existing literature [very important].

Fink, Arlene. Conducting Research Literature Reviews: From the Internet to Paper. 2nd ed. Thousand Oaks, CA: Sage, 2005; Hart, Chris. Doing a Literature Review: Releasing the Social Science Research Imagination . Thousand Oaks, CA: Sage Publications, 1998; Jesson, Jill. Doing Your Literature Review: Traditional and Systematic Techniques . Los Angeles, CA: SAGE, 2011; Knopf, Jeffrey W. "Doing a Literature Review." PS: Political Science and Politics 39 (January 2006): 127-132; Ridley, Diana. The Literature Review: A Step-by-Step Guide for Students . 2nd ed. Los Angeles, CA: SAGE, 2012.

Types of Literature Reviews

It is important to think of knowledge in a given field as consisting of three layers. First, there are the primary studies that researchers conduct and publish. Second are the reviews of those studies that summarize and offer new interpretations built from and often extending beyond the primary studies. Third, there are the perceptions, conclusions, opinion, and interpretations that are shared informally among scholars that become part of the body of epistemological traditions within the field.

In composing a literature review, it is important to note that it is often this third layer of knowledge that is cited as "true" even though it often has only a loose relationship to the primary studies and secondary literature reviews. Given this, while literature reviews are designed to provide an overview and synthesis of pertinent sources you have explored, there are a number of approaches you could adopt depending upon the type of analysis underpinning your study.

Argumentative Review This form examines literature selectively in order to support or refute an argument, deeply embedded assumption, or philosophical problem already established in the literature. The purpose is to develop a body of literature that establishes a contrarian viewpoint. Given the value-laden nature of some social science research [e.g., educational reform; immigration control], argumentative approaches to analyzing the literature can be a legitimate and important form of discourse. However, note that they can also introduce problems of bias when they are used to make summary claims of the sort found in systematic reviews [see below].

Integrative Review Considered a form of research that reviews, critiques, and synthesizes representative literature on a topic in an integrated way such that new frameworks and perspectives on the topic are generated. The body of literature includes all studies that address related or identical hypotheses or research problems. A well-done integrative review meets the same standards as primary research in regard to clarity, rigor, and replication. This is the most common form of review in the social sciences.

Historical Review Few things rest in isolation from historical precedent. Historical literature reviews focus on examining research throughout a period of time, often starting with the first time an issue, concept, theory, phenomena emerged in the literature, then tracing its evolution within the scholarship of a discipline. The purpose is to place research in a historical context to show familiarity with state-of-the-art developments and to identify the likely directions for future research.

Methodological Review A review does not always focus on what someone said [findings], but how they came about saying what they say [method of analysis]. Reviewing methods of analysis provides a framework of understanding at different levels [i.e. those of theory, substantive fields, research approaches, and data collection and analysis techniques], how researchers draw upon a wide variety of knowledge ranging from the conceptual level to practical documents for use in fieldwork in the areas of ontological and epistemological consideration, quantitative and qualitative integration, sampling, interviewing, data collection, and data analysis. This approach helps highlight ethical issues which you should be aware of and consider as you go through your own study.

Systematic Review This form consists of an overview of existing evidence pertinent to a clearly formulated research question, which uses pre-specified and standardized methods to identify and critically appraise relevant research, and to collect, report, and analyze data from the studies that are included in the review. The goal is to deliberately document, critically evaluate, and summarize scientifically all of the research about a clearly defined research problem . Typically it focuses on a very specific empirical question, often posed in a cause-and-effect form, such as "To what extent does A contribute to B?" This type of literature review is primarily applied to examining prior research studies in clinical medicine and allied health fields, but it is increasingly being used in the social sciences.

Theoretical Review The purpose of this form is to examine the corpus of theory that has accumulated in regard to an issue, concept, theory, phenomena. The theoretical literature review helps to establish what theories already exist, the relationships between them, to what degree the existing theories have been investigated, and to develop new hypotheses to be tested. Often this form is used to help establish a lack of appropriate theories or reveal that current theories are inadequate for explaining new or emerging research problems. The unit of analysis can focus on a theoretical concept or a whole theory or framework.

NOTE: Most often the literature review will incorporate some combination of types. For example, a review that examines literature supporting or refuting an argument, assumption, or philosophical problem related to the research problem will also need to include writing supported by sources that establish the history of these arguments in the literature.

Baumeister, Roy F. and Mark R. Leary. "Writing Narrative Literature Reviews."  Review of General Psychology 1 (September 1997): 311-320; Mark R. Fink, Arlene. Conducting Research Literature Reviews: From the Internet to Paper . 2nd ed. Thousand Oaks, CA: Sage, 2005; Hart, Chris. Doing a Literature Review: Releasing the Social Science Research Imagination . Thousand Oaks, CA: Sage Publications, 1998; Kennedy, Mary M. "Defining a Literature." Educational Researcher 36 (April 2007): 139-147; Petticrew, Mark and Helen Roberts. Systematic Reviews in the Social Sciences: A Practical Guide . Malden, MA: Blackwell Publishers, 2006; Torracro, Richard. "Writing Integrative Literature Reviews: Guidelines and Examples." Human Resource Development Review 4 (September 2005): 356-367; Rocco, Tonette S. and Maria S. Plakhotnik. "Literature Reviews, Conceptual Frameworks, and Theoretical Frameworks: Terms, Functions, and Distinctions." Human Ressource Development Review 8 (March 2008): 120-130; Sutton, Anthea. Systematic Approaches to a Successful Literature Review . Los Angeles, CA: Sage Publications, 2016.

Structure and Writing Style

I.  Thinking About Your Literature Review

The structure of a literature review should include the following in support of understanding the research problem :

• An overview of the subject, issue, or theory under consideration, along with the objectives of the literature review,
• Division of works under review into themes or categories [e.g. works that support a particular position, those against, and those offering alternative approaches entirely],
• An explanation of how each work is similar to and how it varies from the others,
• Conclusions as to which pieces are best considered in their argument, are most convincing of their opinions, and make the greatest contribution to the understanding and development of their area of research.

The critical evaluation of each work should consider :

• Provenance -- what are the author's credentials? Are the author's arguments supported by evidence [e.g. primary historical material, case studies, narratives, statistics, recent scientific findings]?
• Methodology -- were the techniques used to identify, gather, and analyze the data appropriate to addressing the research problem? Was the sample size appropriate? Were the results effectively interpreted and reported?
• Objectivity -- is the author's perspective even-handed or prejudicial? Is contrary data considered or is certain pertinent information ignored to prove the author's point?
• Persuasiveness -- which of the author's theses are most convincing or least convincing?
• Validity -- are the author's arguments and conclusions convincing? Does the work ultimately contribute in any significant way to an understanding of the subject?

II.  Development of the Literature Review

Four Basic Stages of Writing 1.  Problem formulation -- which topic or field is being examined and what are its component issues? 2.  Literature search -- finding materials relevant to the subject being explored. 3.  Data evaluation -- determining which literature makes a significant contribution to the understanding of the topic. 4.  Analysis and interpretation -- discussing the findings and conclusions of pertinent literature.

Consider the following issues before writing the literature review: Clarify If your assignment is not specific about what form your literature review should take, seek clarification from your professor by asking these questions: 1.  Roughly how many sources would be appropriate to include? 2.  What types of sources should I review (books, journal articles, websites; scholarly versus popular sources)? 3.  Should I summarize, synthesize, or critique sources by discussing a common theme or issue? 4.  Should I evaluate the sources in any way beyond evaluating how they relate to understanding the research problem? 5.  Should I provide subheadings and other background information, such as definitions and/or a history? Find Models Use the exercise of reviewing the literature to examine how authors in your discipline or area of interest have composed their literature review sections. Read them to get a sense of the types of themes you might want to look for in your own research or to identify ways to organize your final review. The bibliography or reference section of sources you've already read, such as required readings in the course syllabus, are also excellent entry points into your own research. Narrow the Topic The narrower your topic, the easier it will be to limit the number of sources you need to read in order to obtain a good survey of relevant resources. Your professor will probably not expect you to read everything that's available about the topic, but you'll make the act of reviewing easier if you first limit scope of the research problem. A good strategy is to begin by searching the USC Libraries Catalog for recent books about the topic and review the table of contents for chapters that focuses on specific issues. You can also review the indexes of books to find references to specific issues that can serve as the focus of your research. For example, a book surveying the history of the Israeli-Palestinian conflict may include a chapter on the role Egypt has played in mediating the conflict, or look in the index for the pages where Egypt is mentioned in the text. Consider Whether Your Sources are Current Some disciplines require that you use information that is as current as possible. This is particularly true in disciplines in medicine and the sciences where research conducted becomes obsolete very quickly as new discoveries are made. However, when writing a review in the social sciences, a survey of the history of the literature may be required. In other words, a complete understanding the research problem requires you to deliberately examine how knowledge and perspectives have changed over time. Sort through other current bibliographies or literature reviews in the field to get a sense of what your discipline expects. You can also use this method to explore what is considered by scholars to be a "hot topic" and what is not.

III.  Ways to Organize Your Literature Review

Chronology of Events If your review follows the chronological method, you could write about the materials according to when they were published. This approach should only be followed if a clear path of research building on previous research can be identified and that these trends follow a clear chronological order of development. For example, a literature review that focuses on continuing research about the emergence of German economic power after the fall of the Soviet Union. By Publication Order your sources by publication chronology, then, only if the order demonstrates a more important trend. For instance, you could order a review of literature on environmental studies of brown fields if the progression revealed, for example, a change in the soil collection practices of the researchers who wrote and/or conducted the studies. Thematic [“conceptual categories”] A thematic literature review is the most common approach to summarizing prior research in the social and behavioral sciences. Thematic reviews are organized around a topic or issue, rather than the progression of time, although the progression of time may still be incorporated into a thematic review. For example, a review of the Internet’s impact on American presidential politics could focus on the development of online political satire. While the study focuses on one topic, the Internet’s impact on American presidential politics, it would still be organized chronologically reflecting technological developments in media. The difference in this example between a "chronological" and a "thematic" approach is what is emphasized the most: themes related to the role of the Internet in presidential politics. Note that more authentic thematic reviews tend to break away from chronological order. A review organized in this manner would shift between time periods within each section according to the point being made. Methodological A methodological approach focuses on the methods utilized by the researcher. For the Internet in American presidential politics project, one methodological approach would be to look at cultural differences between the portrayal of American presidents on American, British, and French websites. Or the review might focus on the fundraising impact of the Internet on a particular political party. A methodological scope will influence either the types of documents in the review or the way in which these documents are discussed.

Other Sections of Your Literature Review Once you've decided on the organizational method for your literature review, the sections you need to include in the paper should be easy to figure out because they arise from your organizational strategy. In other words, a chronological review would have subsections for each vital time period; a thematic review would have subtopics based upon factors that relate to the theme or issue. However, sometimes you may need to add additional sections that are necessary for your study, but do not fit in the organizational strategy of the body. What other sections you include in the body is up to you. However, only include what is necessary for the reader to locate your study within the larger scholarship about the research problem.

Here are examples of other sections, usually in the form of a single paragraph, you may need to include depending on the type of review you write:

• Current Situation : Information necessary to understand the current topic or focus of the literature review.
• Sources Used : Describes the methods and resources [e.g., databases] you used to identify the literature you reviewed.
• History : The chronological progression of the field, the research literature, or an idea that is necessary to understand the literature review, if the body of the literature review is not already a chronology.
• Selection Methods : Criteria you used to select (and perhaps exclude) sources in your literature review. For instance, you might explain that your review includes only peer-reviewed [i.e., scholarly] sources.
• Standards : Description of the way in which you present your information.
• Questions for Further Research : What questions about the field has the review sparked? How will you further your research as a result of the review?

IV.  Writing Your Literature Review

Once you've settled on how to organize your literature review, you're ready to write each section. When writing your review, keep in mind these issues.

Use Evidence A literature review section is, in this sense, just like any other academic research paper. Your interpretation of the available sources must be backed up with evidence [citations] that demonstrates that what you are saying is valid. Be Selective Select only the most important points in each source to highlight in the review. The type of information you choose to mention should relate directly to the research problem, whether it is thematic, methodological, or chronological. Related items that provide additional information, but that are not key to understanding the research problem, can be included in a list of further readings . Use Quotes Sparingly Some short quotes are appropriate if you want to emphasize a point, or if what an author stated cannot be easily paraphrased. Sometimes you may need to quote certain terminology that was coined by the author, is not common knowledge, or taken directly from the study. Do not use extensive quotes as a substitute for using your own words in reviewing the literature. Summarize and Synthesize Remember to summarize and synthesize your sources within each thematic paragraph as well as throughout the review. Recapitulate important features of a research study, but then synthesize it by rephrasing the study's significance and relating it to your own work and the work of others. Keep Your Own Voice While the literature review presents others' ideas, your voice [the writer's] should remain front and center. For example, weave references to other sources into what you are writing but maintain your own voice by starting and ending the paragraph with your own ideas and wording. Use Caution When Paraphrasing When paraphrasing a source that is not your own, be sure to represent the author's information or opinions accurately and in your own words. Even when paraphrasing an author’s work, you still must provide a citation to that work.

V.  Common Mistakes to Avoid

These are the most common mistakes made in reviewing social science research literature.

• Sources in your literature review do not clearly relate to the research problem;
• You do not take sufficient time to define and identify the most relevant sources to use in the literature review related to the research problem;
• Relies exclusively on secondary analytical sources rather than including relevant primary research studies or data;
• Uncritically accepts another researcher's findings and interpretations as valid, rather than examining critically all aspects of the research design and analysis;
• Does not describe the search procedures that were used in identifying the literature to review;
• Reports isolated statistical results rather than synthesizing them in chi-squared or meta-analytic methods; and,
• Only includes research that validates assumptions and does not consider contrary findings and alternative interpretations found in the literature.

Cook, Kathleen E. and Elise Murowchick. “Do Literature Review Skills Transfer from One Course to Another?” Psychology Learning and Teaching 13 (March 2014): 3-11; Fink, Arlene. Conducting Research Literature Reviews: From the Internet to Paper . 2nd ed. Thousand Oaks, CA: Sage, 2005; Hart, Chris. Doing a Literature Review: Releasing the Social Science Research Imagination . Thousand Oaks, CA: Sage Publications, 1998; Jesson, Jill. Doing Your Literature Review: Traditional and Systematic Techniques . London: SAGE, 2011; Literature Review Handout. Online Writing Center. Liberty University; Literature Reviews. The Writing Center. University of North Carolina; Onwuegbuzie, Anthony J. and Rebecca Frels. Seven Steps to a Comprehensive Literature Review: A Multimodal and Cultural Approach . Los Angeles, CA: SAGE, 2016; Ridley, Diana. The Literature Review: A Step-by-Step Guide for Students . 2nd ed. Los Angeles, CA: SAGE, 2012; Randolph, Justus J. “A Guide to Writing the Dissertation Literature Review." Practical Assessment, Research, and Evaluation. vol. 14, June 2009; Sutton, Anthea. Systematic Approaches to a Successful Literature Review . Los Angeles, CA: Sage Publications, 2016; Taylor, Dena. The Literature Review: A Few Tips On Conducting It. University College Writing Centre. University of Toronto; Writing a Literature Review. Academic Skills Centre. University of Canberra.

Writing Tip

Break Out of Your Disciplinary Box!

Thinking interdisciplinarily about a research problem can be a rewarding exercise in applying new ideas, theories, or concepts to an old problem. For example, what might cultural anthropologists say about the continuing conflict in the Middle East? In what ways might geographers view the need for better distribution of social service agencies in large cities than how social workers might study the issue? You don’t want to substitute a thorough review of core research literature in your discipline for studies conducted in other fields of study. However, particularly in the social sciences, thinking about research problems from multiple vectors is a key strategy for finding new solutions to a problem or gaining a new perspective. Consult with a librarian about identifying research databases in other disciplines; almost every field of study has at least one comprehensive database devoted to indexing its research literature.

Frodeman, Robert. The Oxford Handbook of Interdisciplinarity . New York: Oxford University Press, 2010.

Another Writing Tip

Don't Just Review for Content!

While conducting a review of the literature, maximize the time you devote to writing this part of your paper by thinking broadly about what you should be looking for and evaluating. Review not just what scholars are saying, but how are they saying it. Some questions to ask:

• How are they organizing their ideas?
• What methods have they used to study the problem?
• What theories have been used to explain, predict, or understand their research problem?
• What sources have they cited to support their conclusions?
• How have they used non-textual elements [e.g., charts, graphs, figures, etc.] to illustrate key points?

When you begin to write your literature review section, you'll be glad you dug deeper into how the research was designed and constructed because it establishes a means for developing more substantial analysis and interpretation of the research problem.

Hart, Chris. Doing a Literature Review: Releasing the Social Science Research Imagination . Thousand Oaks, CA: Sage Publications, 1 998.

Yet Another Writing Tip

When Do I Know I Can Stop Looking and Move On?

Here are several strategies you can utilize to assess whether you've thoroughly reviewed the literature:

• Look for repeating patterns in the research findings . If the same thing is being said, just by different people, then this likely demonstrates that the research problem has hit a conceptual dead end. At this point consider: Does your study extend current research?  Does it forge a new path? Or, does is merely add more of the same thing being said?
• Look at sources the authors cite to in their work . If you begin to see the same researchers cited again and again, then this is often an indication that no new ideas have been generated to address the research problem.
• Search Google Scholar to identify who has subsequently cited leading scholars already identified in your literature review [see next sub-tab]. This is called citation tracking and there are a number of sources that can help you identify who has cited whom, particularly scholars from outside of your discipline. Here again, if the same authors are being cited again and again, this may indicate no new literature has been written on the topic.

Onwuegbuzie, Anthony J. and Rebecca Frels. Seven Steps to a Comprehensive Literature Review: A Multimodal and Cultural Approach . Los Angeles, CA: Sage, 2016; Sutton, Anthea. Systematic Approaches to a Successful Literature Review . Los Angeles, CA: Sage Publications, 2016.

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Literature Reviews

• What is a literature review?
• Steps in the Literature Review Process
• Define your research question
• Determine inclusion and exclusion criteria
• Choose databases and search
• Review Results
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What is a Literature Review?

A literature or narrative review is a comprehensive review and analysis of the published literature on a specific topic or research question. The literature that is reviewed contains: books, articles, academic articles, conference proceedings, association papers, and dissertations. It contains the most pertinent studies and points to important past and current research and practices. It provides background and context, and shows how your research will contribute to the field. 

A literature review should: 

• Provide a comprehensive and updated review of the literature;
• Explain why this review has taken place;
• Articulate a position or hypothesis;
• Acknowledge and account for conflicting and corroborating points of view

From  S age Research Methods

Purpose of a Literature Review

A literature review can be written as an introduction to a study to:

• Demonstrate how a study fills a gap in research
• Compare a study with other research that's been done

Or it can be a separate work (a research article on its own) which:

• Organizes or describes a topic
• Describes variables within a particular issue/problem

Limitations of a Literature Review

Some of the limitations of a literature review are:

• It's a snapshot in time. Unlike other reviews, this one has beginning, a middle and an end. There may be future developments that could make your work less relevant.
• It may be too focused. Some niche studies may miss the bigger picture.
• It can be difficult to be comprehensive. There is no way to make sure all the literature on a topic was considered.
• It is easy to be biased if you stick to top tier journals. There may be other places where people are publishing exemplary research. Look to open access publications and conferences to reflect a more inclusive collection. Also, make sure to include opposing views (and not just supporting evidence).

Source: Grant, Maria J., and Andrew Booth. “A Typology of Reviews: An Analysis of 14 Review Types and Associated Methodologies.” Health Information & Libraries Journal, vol. 26, no. 2, June 2009, pp. 91–108. Wiley Online Library, doi:10.1111/j.1471-1842.2009.00848.x.

Meryl Brodsky : Communication and Information Studies

Hannah Chapman Tripp : Biology, Neuroscience

Carolyn Cunningham : Human Development & Family Sciences, Psychology, Sociology

Larayne Dallas : Engineering

Janelle Hedstrom : Special Education, Curriculum & Instruction, Ed Leadership & Policy ​

Susan Macicak : Linguistics

Imelda Vetter : Dell Medical School

For help in other subject areas, please see the guide to library specialists by subject .

Periodically, UT Libraries runs a workshop covering the basics and library support for literature reviews. While we try to offer these once per academic year, we find providing the recording to be helpful to community members who have missed the session. Following is the most recent recording of the workshop, Conducting a Literature Review. To view the recording, a UT login is required.

• October 26, 2022 recording
• Last Updated: Jun 18, 2024 1:00 PM
• URL: https://guides.lib.utexas.edu/literaturereviews

Frequently asked questions

What is the purpose of a literature review.

There are several reasons to conduct a literature review at the beginning of a research project:

• To familiarize yourself with the current state of knowledge on your topic
• To ensure that you’re not just repeating what others have already done
• To identify gaps in knowledge and unresolved problems that your research can address
• To develop your theoretical framework and methodology
• To provide an overview of the key findings and debates on the topic

Writing the literature review shows your reader how your work relates to existing research and what new insights it will contribute.

Frequently asked questions: Academic writing

A rhetorical tautology is the repetition of an idea of concept using different words.

Rhetorical tautologies occur when additional words are used to convey a meaning that has already been expressed or implied. For example, the phrase “armed gunman” is a tautology because a “gunman” is by definition “armed.”

A logical tautology is a statement that is always true because it includes all logical possibilities.

Logical tautologies often take the form of “either/or” statements (e.g., “It will rain, or it will not rain”) or employ circular reasoning (e.g., “she is untrustworthy because she can’t be trusted”).

You may have seen both “appendices” or “appendixes” as pluralizations of “ appendix .” Either spelling can be used, but “appendices” is more common (including in APA Style ). Consistency is key here: make sure you use the same spelling throughout your paper.

The purpose of a lab report is to demonstrate your understanding of the scientific method with a hands-on lab experiment. Course instructors will often provide you with an experimental design and procedure. Your task is to write up how you actually performed the experiment and evaluate the outcome.

In contrast, a research paper requires you to independently develop an original argument. It involves more in-depth research and interpretation of sources and data.

A lab report is usually shorter than a research paper.

The sections of a lab report can vary between scientific fields and course requirements, but it usually contains the following:

• Title: expresses the topic of your study
• Abstract: summarizes your research aims, methods, results, and conclusions
• Introduction: establishes the context needed to understand the topic
• Method: describes the materials and procedures used in the experiment
• Results: reports all descriptive and inferential statistical analyses
• Discussion: interprets and evaluates results and identifies limitations
• Conclusion: sums up the main findings of your experiment
• References: list of all sources cited using a specific style (e.g. APA)
• Appendices: contains lengthy materials, procedures, tables or figures

A lab report conveys the aim, methods, results, and conclusions of a scientific experiment . Lab reports are commonly assigned in science, technology, engineering, and mathematics (STEM) fields.

The abstract is the very last thing you write. You should only write it after your research is complete, so that you can accurately summarize the entirety of your thesis , dissertation or research paper .

If you’ve gone over the word limit set for your assignment, shorten your sentences and cut repetition and redundancy during the editing process. If you use a lot of long quotes , consider shortening them to just the essentials.

If you need to remove a lot of words, you may have to cut certain passages. Remember that everything in the text should be there to support your argument; look for any information that’s not essential to your point and remove it.

To make this process easier and faster, you can use a paraphrasing tool . With this tool, you can rewrite your text to make it simpler and shorter. If that’s not enough, you can copy-paste your paraphrased text into the summarizer . This tool will distill your text to its core message.

Revising, proofreading, and editing are different stages of the writing process .

• Revising is making structural and logical changes to your text—reformulating arguments and reordering information.
• Editing refers to making more local changes to things like sentence structure and phrasing to make sure your meaning is conveyed clearly and concisely.
• Proofreading involves looking at the text closely, line by line, to spot any typos and issues with consistency and correct them.

The literature review usually comes near the beginning of your thesis or dissertation . After the introduction , it grounds your research in a scholarly field and leads directly to your theoretical framework or methodology .

A literature review is a survey of scholarly sources (such as books, journal articles, and theses) related to a specific topic or research question .

It is often written as part of a thesis, dissertation , or research paper , in order to situate your work in relation to existing knowledge.

Avoid citing sources in your abstract . There are two reasons for this:

• The abstract should focus on your original research, not on the work of others.
• The abstract should be self-contained and fully understandable without reference to other sources.

There are some circumstances where you might need to mention other sources in an abstract: for example, if your research responds directly to another study or focuses on the work of a single theorist. In general, though, don’t include citations unless absolutely necessary.

An abstract is a concise summary of an academic text (such as a journal article or dissertation ). It serves two main purposes:

• To help potential readers determine the relevance of your paper for their own research.
• To communicate your key findings to those who don’t have time to read the whole paper.

Abstracts are often indexed along with keywords on academic databases, so they make your work more easily findable. Since the abstract is the first thing any reader sees, it’s important that it clearly and accurately summarizes the contents of your paper.

In a scientific paper, the methodology always comes after the introduction and before the results , discussion and conclusion . The same basic structure also applies to a thesis, dissertation , or research proposal .

Depending on the length and type of document, you might also include a literature review or theoretical framework before the methodology.

Whether you’re publishing a blog, submitting a research paper , or even just writing an important email, there are a few techniques you can use to make sure it’s error-free:

• Take a break : Set your work aside for at least a few hours so that you can look at it with fresh eyes.
• Proofread a printout : Staring at a screen for too long can cause fatigue – sit down with a pen and paper to check the final version.
• Use digital shortcuts : Take note of any recurring mistakes (for example, misspelling a particular word, switching between US and UK English , or inconsistently capitalizing a term), and use Find and Replace to fix it throughout the document.

If you want to be confident that an important text is error-free, it might be worth choosing a professional proofreading service instead.

Editing and proofreading are different steps in the process of revising a text.

Editing comes first, and can involve major changes to content, structure and language. The first stages of editing are often done by authors themselves, while a professional editor makes the final improvements to grammar and style (for example, by improving sentence structure and word choice ).

Proofreading is the final stage of checking a text before it is published or shared. It focuses on correcting minor errors and inconsistencies (for example, in punctuation and capitalization ). Proofreaders often also check for formatting issues, especially in print publishing.

The cost of proofreading depends on the type and length of text, the turnaround time, and the level of services required. Most proofreading companies charge per word or page, while freelancers sometimes charge an hourly rate.

For proofreading alone, which involves only basic corrections of typos and formatting mistakes, you might pay as little as $0.01 per word, but in many cases, your text will also require some level of editing , which costs slightly more.

It’s often possible to purchase combined proofreading and editing services and calculate the price in advance based on your requirements.

There are many different routes to becoming a professional proofreader or editor. The necessary qualifications depend on the field – to be an academic or scientific proofreader, for example, you will need at least a university degree in a relevant subject.

For most proofreading jobs, experience and demonstrated skills are more important than specific qualifications. Often your skills will be tested as part of the application process.

To learn practical proofreading skills, you can choose to take a course with a professional organization such as the Society for Editors and Proofreaders . Alternatively, you can apply to companies that offer specialized on-the-job training programmes, such as the Scribbr Academy .

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• Writing Tips

What is the Purpose of a Literature Review?

4-minute read

• 23rd October 2023

If you’re writing a research paper or dissertation , then you’ll most likely need to include a comprehensive literature review . In this post, we’ll review the purpose of literature reviews, why they are so significant, and the specific elements to include in one. Literature reviews can:

1. Provide a foundation for current research.

2. Define key concepts and theories.

3. Demonstrate critical evaluation.

4. Show how research and methodologies have evolved.

5. Identify gaps in existing research.

6. Support your argument.

Keep reading to enter the exciting world of literature reviews!

What is a Literature Review?

A literature review is a critical summary and evaluation of the existing research (e.g., academic journal articles and books) on a specific topic. It is typically included as a separate section or chapter of a research paper or dissertation, serving as a contextual framework for a study. Literature reviews can vary in length depending on the subject and nature of the study, with most being about equal length to other sections or chapters included in the paper. Essentially, the literature review highlights previous studies in the context of your research and summarizes your insights in a structured, organized format. Next, let’s look at the overall purpose of a literature review.

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Literature reviews are considered an integral part of research across most academic subjects and fields. The primary purpose of a literature review in your study is to:

Provide a Foundation for Current Research

Since the literature review provides a comprehensive evaluation of the existing research, it serves as a solid foundation for your current study. It’s a way to contextualize your work and show how your research fits into the broader landscape of your specific area of study.  

Define Key Concepts and Theories

The literature review highlights the central theories and concepts that have arisen from previous research on your chosen topic. It gives your readers a more thorough understanding of the background of your study and why your research is particularly significant .

Demonstrate Critical Evaluation 

A comprehensive literature review shows your ability to critically analyze and evaluate a broad range of source material. And since you’re considering and acknowledging the contribution of key scholars alongside your own, it establishes your own credibility and knowledge.

Show How Research and Methodologies Have Evolved

Another purpose of literature reviews is to provide a historical perspective and demonstrate how research and methodologies have changed over time, especially as data collection methods and technology have advanced. And studying past methodologies allows you, as the researcher, to understand what did and did not work and apply that knowledge to your own research.  

Identify Gaps in Existing Research

Besides discussing current research and methodologies, the literature review should also address areas that are lacking in the existing literature. This helps further demonstrate the relevance of your own research by explaining why your study is necessary to fill the gaps.

Support Your Argument

A good literature review should provide evidence that supports your research questions and hypothesis. For example, your study may show that your research supports existing theories or builds on them in some way. Referencing previous related studies shows your work is grounded in established research and will ultimately be a contribution to the field.  

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Literature Review: Purpose of a Literature Review

• Literature Review
• Purpose of a Literature Review
• Work in Progress
• Compiling & Writing
• Books, Articles, & Web Pages
• Types of Literature Reviews
• Departmental Differences
• Citation Styles & Plagiarism
• Know the Difference! Systematic Review vs. Literature Review

The purpose of a literature review is to:

• Provide a foundation of knowledge on a topic
• Identify areas of prior scholarship to prevent duplication and give credit to other researchers
• Identify inconstancies: gaps in research, conflicts in previous studies, open questions left from other research
• Identify the need for additional research (justifying your research)
• Identify the relationship of works in the context of their contribution to the topic and other works
• Place your own research within the context of existing literature, making a case for why further study is needed.

Videos & Tutorials

VIDEO: What is the role of a literature review in research? What's it mean to "review" the literature? Get the big picture of what to expect as part of the process. This video is published under a Creative Commons 3.0 BY-NC-SA US license. License, credits, and contact information can be found here: https://www.lib.ncsu.edu/tutorials/litreview/

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This paper is in the following e-collection/theme issue:

Published on 25.6.2024 in Vol 26 (2024)

Potential Roles of Large Language Models in the Production of Systematic Reviews and Meta-Analyses

Authors of this article:

• Xufei Luo 1, 2, 3, 4, 5   ; 
• Fengxian Chen 6 , PhD   ; 
• Di Zhu 7 , MPH   ; 
• Ling Wang 7 , MPH   ; 
• Zijun Wang 1, 2, 3, 4, 5   ; 
• Hui Liu 1, 2, 3, 4, 5   ; 
• Meng Lyu 7 , MPH   ; 
• Ye Wang 7 , MPH   ; 
• Qi Wang 8, 9 , PhD   ; 
• Yaolong Chen 1, 2, 3, 4, 5 , MD, PhD  

1 Evidence-Based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou, China

2 World Health Organization Collaboration Center for Guideline Implementation and Knowledge Translation, Lanzhou, China

3 Institute of Health Data Science, Lanzhou University, Lanzhou, China

4 Key Laboratory of Evidence Based Medicine and Knowledge Translation of Gansu Province, Lanzhou University, Lanzhou, China

5 Research Unit of Evidence-Based Evaluation and Guidelines, Chinese Academy of Medical Sciences (2021RU017), School of Basic Medical Sciences, Lanzhou University, Lanzhou, China

6 School of Information Science & Engineering, Lanzhou University, Lanzhou, China

7 School of Public Health, Lanzhou University, Lanzhou, China

8 Department of Health Research Methods, Evidence and Impact, Faculty of Health Sciences, McMaster University, Hamilton, ON, Canada

9 McMaster Health Forum, McMaster University, Hamilton, ON, Canada

Corresponding Author:

Yaolong Chen, MD, PhD

Evidence-Based Medicine Center

School of Basic Medical Sciences

Lanzhou University

No 199 Donggang West Road

Chengguan District

Lanzhou, 730000

Phone: 86 13893104140

Email: [email protected]

Large language models (LLMs) such as ChatGPT have become widely applied in the field of medical research. In the process of conducting systematic reviews, similar tools can be used to expedite various steps, including defining clinical questions, performing the literature search, document screening, information extraction, and language refinement, thereby conserving resources and enhancing efficiency. However, when using LLMs, attention should be paid to transparent reporting, distinguishing between genuine and false content, and avoiding academic misconduct. In this viewpoint, we highlight the potential roles of LLMs in the creation of systematic reviews and meta-analyses, elucidating their advantages, limitations, and future research directions, aiming to provide insights and guidance for authors planning systematic reviews and meta-analyses.

Introduction

A systematic review is the result of a systematic and rigorous evaluation of evidence, which may or may not include a meta-analysis [ 1 ]. Owing to the strict methodology and comprehensive summary of evidence, high-quality systematic reviews are considered the highest level of evidence, positioned at the top of the evidence pyramid [ 2 ]. Additionally, high-quality systematic reviews and meta-analyses are often used to support the development of clinical practice guidelines, aid clinical decision-making, and inform health care policy formulation [ 3 ]. Currently, the methods of systematic reviews and meta-analyses are applied in various disciplines in medicine and beyond such as law [ 4 ], management [ 5 ], and economics [ 6 ], and have yielded positive results, contributing to the continuous advancement of these fields [ 7 ].

The process of conducting systematic reviews demands a substantial investment in terms of time, resources, human effort, and financial capital [ 8 ]. To expedite the development of systematic reviews and meta-analyses, various automated or semiautomated tools such as Covidence have been developed [ 9 , 10 ]. However, the emergence of large language models (LLMs), particularly chatbots such as GPT, presents a set of both challenges and opportunities in the realm of systematic reviews and meta-analyses [ 11 ]. Based on the emerging literature in this field, we here provide our perspectives on the potential for harnessing the capabilities of LLMs to accelerate the production of systematic reviews and meta-analyses, while also scrutinizing the potential impacts and delineating the crucial steps involved in this process.

The Process and Challenges of Performing a Systematic Review and Meta-Analysis

The procedures and workflows for conducting systematic reviews and meta-analyses are well-established. Currently, researchers often refer to the Cochrane Handbooks recommended by the Cochrane Library for intervention or diagnostic reviews [ 12 , 13 ]. In addition, some scholars and institutions have developed detailed guidelines on the steps and methodology for performing systematic reviews and meta-analyses [ 14 - 17 ]. Generally speaking, researchers should take the following steps to produce a high-quality systematic review and meta-analysis: determine the clinical question, register and draft a protocol, set inclusion and exclusion criteria, develop and implement a search strategy, screen the literature, extract data from included studies, assess the quality and risk of bias of included studies, analyze and processed data, write up the full text of the manuscript, and submit the manuscript for publication, as illustrated in Figure 1 . These different steps contain many subtasks; therefore, conducting a complete systematic review and meta-analysis requires fairly complex and time-consuming work.

Although systematic reviews and meta-analyses have been widely applied and play an important role in developing guidelines and informing clinical decision-making, their production process faces many challenges. One of these challenges is the long production time and large resource requirements. The average estimated time to complete and publish a systematic review is 67.3 weeks, requiring 5 researchers and costing approximately US $140,000 [ 18 , 19 ]. More recently, the development of automated and semiautomated tools using natural language processing and machine learning have accelerated systematic review and meta-analysis production to some extent [ 20 ], with studies showing that such tools can help to produce a systematic review and meta-analysis within 2 weeks [ 21 ]. However, these tools also have some limitations. First, no single tool can fully accelerate the entire production process of systematic reviews and meta-analyses. Second, these tools cannot process and analyze literature written in different languages. Finally, the reliability of the results generated by these automated and semiautomated tools needs further validation as they are not yet widely adopted for this purpose.

Applications of LLMs in Medical Research

Chatbots based on LLMs such as ChatGPT, Google Gemini, and Claude have become widely applied in medical research. These chatbots have proven to be valuable in tasks ranging from knowledge retrieval, language refinement, content generation, and medical exam preparation to literature assessment. ChatGPT has been shown to excel in accuracy, completeness, nuance, and speed when generating responses to clinical inquiries in psychiatry [ 22 ]. Moreover, LLMs such as ChatGPT play a pivotal role in automating the evaluation of medical literature, facilitating the identification of accurately reported research findings [ 23 ]. Despite their significant contributions, these chatbots are not without limitations. Challenges such as the potential for generating misleading content and susceptibility to academic deception necessitate further scholarly discourse on effective mitigation strategies. Standardized reporting practices may contribute to delineating the applications of ChatGPT and mitigating research biases [ 24 ].

ChatGPT has also demonstrated significant application potential and promise in the process of conducting systematic reviews and meta-analyses. Various studies [ 11 , 25 - 32 ] indicate that ChatGPT can play a pivotal role in formulating clinical questions, determining inclusion and exclusion criteria, screening literature, assessing publications, generating meta-analysis code, and assisting the full-text composition, among other relevant tasks. The details of these capabilities are summarized in Table 1 .

Potential Roles of LLMs in Producing Systematic Reviews and Meta-Analyses

Determine the research topic/question.

Determining the clinical question of interest represents the initial and paramount step in the process of conducting systematic reviews and meta-analyses. At this juncture, it is crucial to ascertain whether comparable systematic reviews and meta-analyses have already been published and to delineate the scope of the forthcoming review and meta-analysis. Generally, for interventional systematic reviews, the Patient, Intervention, Comparison, and Outcome (PICO) framework is considered for defining the scope and objectives of the research question [ 60 ]. In this context, ChatGPT serves a dual role. On the one hand, it expeditiously aids in searching for published systematic reviews and meta-analyses related to the relevant topics (see Multimedia Appendix 1 and Multimedia Appendix 2 ) [ 34 ]. On the other hand, ChatGPT assists in refining the clinical question that needs to be addressed (see Multimedia Appendix 3 ), facilitating prompt determination of the feasibility of undertaking the proposed study. However, it is important to be cautious of the retrieval of false literature [ 35 ].

Register and Write a Research Proposal

The registration and proposal writing process constitutes a pivotal preparatory phase for conducting systematic reviews and meta-analyses. Registration enhances research transparency, fosters collaboration among investigators, and mitigates the redundancy of research endeavors. Drafting a proposal helps in elucidating the research objectives and methods, providing robust support for the smooth execution of the study. For LLMs, generating preliminary registration information and initial proposal content is remarkably convenient and facile (see Multimedia Appendix 4 and Multimedia Appendix 5 ). For example, ChatGPT can assist researchers in generating the statistical methods for a research proposal [ 37 ]. However, considering that LLMs often generate fictitious literature, the content they produce may be inaccurate; thus, discernment and validation of the generated content remain essential considerations.

Define Inclusion and Exclusion Criteria

The inclusion and exclusion criteria for systematic reviews and meta-analyses are instrumental in determining the screening standards for studies. Therefore, strict and detailed inclusion and exclusion criteria contribute to the smooth and high-quality conduct of preparing systematic reviews and meta-analyses. The use of a chatbot based on LLMs can help in establishing the inclusion and exclusion criteria (see Multimedia Appendix 6 ) [ 38 ]; however, the inclusion criteria need to be optimized and adjusted according to the specific research objectives and the exclusion criteria should be based on the foundation of the inclusion criteria. Therefore, manual adjustments and optimizations are also necessary.

Develop a Search Strategy and Conduct Searches

ChatGPT can assist in formulating search strategies, using PubMed as an example [ 40 ]. Researchers can simply list their questions using the PICO framework and a search strategy can be quickly generated ( Multimedia Appendix 1 and Multimedia Appendix 2 ). Based on the generated search strategy, one method is to copy the strategy from ChatGPT and paste it into the PubMed search box for direct retrieval [ 40 , 41 ]. Another approach involves using the OpenAI application programming interfaces (APIs) to invoke PubMed APIs with the search strategy generated by ChatGPT. This facilitates searching the PubMed database, obtaining search results, and applying predetermined inclusion and exclusion criteria. Subsequently, ChatGPT can be used to filter the search results, exporting and recording the filtered results in JSON format. This integrated process encompasses search strategy formulation, retrieval, and filtering. However, the direct use of LLMs to generate search strategies and complete the one-stop process of searching and screening may not yet be mature, and this poses a significant challenge for generating the PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) flowchart. Therefore, we suggest using LLMs to generate search strategies, which should then be optimized and modified by librarians and computer experts (specializing in LLMs) before manually searching the databases. Additionally, to use search strategies transparently and reproducibly, the detailed prompts used should be reported [ 40 , 42 ].

Screen the Literature

Literature screening is one of the most time-consuming steps in the creation of systematic reviews and meta-analyses. Prior to the advent of ChatGPT, there were already many automated and semiautomated tools available for literature screening, such as Covidence, EPPI-Reviewer, DistillerSR, and others [ 39 ]. With the emergence of ChatGPT, researchers can now train the model based on predefined inclusion criteria. Subsequently, ChatGPT can be used to automatically screen records retrieved from databases and obtain the filtered results. Previous studies suggested that using ChatGPT in the literature selection process for a meta-analysis substantially diminishes the workload while preserving a recall rate on par with that of manual curation [ 28 , 44 - 47 ].

Extract the Data

Data extraction involves obtaining information from primary studies and serves as a primary source for systematic reviews and meta-analyses. Generally, when conducting systematic reviews and meta-analyses, basic information must be extracted from the original studies, such as publication date, country of conduct, and the journal of publication. Additionally, characteristics of the population, such as patient samples, age, gender/sex, and outcome data, are also extracted, including event occurrences, mean change values, and total sample size. Currently, tools based on natural language processing and LLMs, such as ChatGPT and Claude, demonstrate high accuracy in extracting information from PDF documents (see Multimedia Appendix 7 for an example) [ 47 - 50 ]. However, it is important to note that despite their promising capabilities, manual verification remains a necessary step in the data extraction process when using these artificial intelligence (AI) tools [ 61 ]. Using LLMs to extract data can help avoid random errors; however, caution is still required when extracting data from figures or tables [ 47 - 50 ].

Assess the Risk of Bias

Assessing the bias of risk involves evaluating the internal validity of studies included in research. For randomized controlled trials, tools such as Risk of Bias (RoB) [ 62 ] or its updated version RoB 2 [ 63 ] are typically used, with an estimated review time of 10-15 minutes per trial. However, automated tools such as RobotReviewer can streamline the extraction and evaluation process in batches [ 51 - 53 ], thereby improving efficiency, although manual verification is still necessary. Additionally, chatbots based on LLMs can aid in risk of bias assessment (see Multimedia Appendix 8 ), and their accuracy appears to be comparable to that of human evaluations [ 23 ].

Analyze the Data/Meta-Analysis

Data analysis serves as the source of systematic review results, typically encompassing basic information and outcome findings. The meta-analysis may be one outcome, along with potential components such as subgroup analysis, sensitivity analysis, meta-regression, and detection of publication bias. Numerous software options are available to facilitate these data analyses, including Stata, RevMan, Rstudio, and others [ 43 ]. Currently, it appears that chatbots based on LLMs may not fully execute data analysis independently, although they can extract the relevant information. Subsequently, one can employ corresponding software for comprehensive data analysis. Alternatively, after extracting information with chatbots, the ChatGPT Code Interpreter can assist in analysis and generating graphical results, although this requires a subscription to ChatGPT Plus. Moreover, an LLM markedly accelerates the data analysis process, empowering researchers to handle larger data sets with greater efficacy [ 54 ].

Draft the Full Manuscript

The complete drafting of systematic reviews and meta-analyses should adhere to the PRISMA reporting guidelines [ 64 ]. It is not advisable to use chatbots such as ChatGPT for article composition. On the one hand, the accuracy and integrity of content generated by ChatGPT require human verification. On the other hand, various research types and journals have different requirements for full-length articles, making it challenging to achieve uniformity in the generated content. However, using tools such as GPT for language refinement and adjusting the content logic can be considered to enhance the quality and readability of the article [ 33 , 55 ]. It is important to declare the use of GPT-related tools in the methods, acknowledgments, or appendices of the article to ensure transparency [ 24 , 65 ].

Submit and Publish

Submission and publication represent the final steps in the process of conducting systematic reviews and meta-analyses, aside from subsequent updates. At this stage, the potential role of LLM-based tools is to assist authors in recommending suitable journals (see Multimedia Appendix 9 ). These tools might also aid in crafting components required along with submission of the manuscript such as cover letters and highlights [ 59 ]. However, it is imperative to emphasize that the content generated by these tools requires manual verification to ensure accuracy, and all authors should be accountable for the content generated by LLMs.

Benefits and Drawbacks of Using LLMs

Systematic reviews and meta-analyses are crucial evidence types that support the development of guidelines [ 3 ]. The benefits of employing LLM-based chatbots in the production of systematic reviews and meta-analyses include increased speed, such as in the stages of evidence searching, data extraction, and assessment of bias risk; these tools can also enhance accuracy by reducing human errors such as those made while extracting essential information and pooling data. However, there are also drawbacks of these applications of LLMs, such as the potential for generating hallucinations, the requirement for human verification owing to the poor reliability of the models, and that the entire systematic review process is not replicable. Moreover, when interacting with LLM chatbots, it is important to manage data privacy. In particular, when using LLMs to analyze data, especially when including personal patient information, ethical approval and management must be properly addressed.

Challenges and Solutions

While LLMs can assist in accelerating the production of systematic reviews and meta-analyses in some steps, enhancing accuracy and transparency, and saving resources, they also face several challenges. For instance, LLMs cannot promptly update their versions and information. For example, ChatGPT 3.5 has been trained on data available in 2021. Thus, limitations such as the length of prompts and token constraints, as well as restrictions related to context associations, may potentially impact the overall results and user experience [ 25 ]. Although LLM-based autonomous agents have made strides in tasks related to systematic reviews and meta-analyses, their applications are still associated with various issues related to personalization, updating knowledge, strategic planning, and complex problem-solving. The development of LLM-driven autonomous agents adept at systematic reviews and meta-analyses warrants further exploration [ 66 ]. The use of LLMs as centrally controlled intelligent agents encompasses the ability to handle precise literature screening, extract and analyze complex data, and assist in manuscript composition, as highlighted by proof-of-concept demonstrations such as MetaGPT [ 67 ]. Moreover, the continuous growth of the use of LLMs can pose a significant challenge in ensuring the accuracy of information provided in systematic reviews, particularly if LLMs are indiscriminately overused.

To better facilitate the use of tools such as ChatGPT in systematic reviews and meta-analyses, we believe that, first and foremost, authors should understand the scope and scenarios for applying ChatGPT, clearly defining which steps can benefit from these tools. Second, for researchers, collaboration with computer scientists and AI engineers is crucial to optimize the prompts and develop integrated tools based on LLMs, such as web applications. These tools can assist in seamless transitions between different tasks in the systematic review process. Lastly, for journal editors, collaboration with authors and reviewers is essential to adhere to reporting and ethical principles associated with the use of GPT and similar tools [ 24 , 68 ]. This collaboration aims to promote transparency and integrity, while preventing indiscriminate overuse in the application of LLMs in systematic reviews and meta-analyses.

Future Perspectives and Conclusion

The emergence of LLMs could have a significant impact on the production of systematic reviews and meta-analyses. In this process, the application of chatbots such as ChatGPT has the potential to speed up certain steps such as literature screening, data extraction, and risk of bias assessment, which are processes that typically consume a considerable amount of time. However, it is important to note that if AI methods such as GPT are employed in performing systematic reviews, disclosure and declaration of the use of these tools are essential. This includes specifying the AI tools used, their roles, and the areas of application within the review process, among other relevant information for full disclosure [ 24 ]. In this context, developing a reporting guideline is warranted to guide the application of LLM tools in systematic reviews and meta-analyses. Although the PRISMA 2020 guideline briefly addresses the use of automation technologies, its coverage is limited to steps such as screening, and there is a lack of comprehensive guidance on the broader spectrum of applications [ 64 ].

Acknowledgments

ChatGPT 3.5 designed by OpenAI was used to help with language editing. The authors take the ultimate responsibility for the content of this publication.

Authors' Contributions

XL and YC were responsible for conceptualization of the article. XL, FC, DZ, and LW generated the examples with the large language models and wrote the first draft of the article. XL, ZW, HL, ML, YW, QW, and YC reviewed and edited the manuscript. YC supervised the study, takes full responsibility for the work and conduct of the study, has access to the data, and controlled the decision to publish. All authors read the final manuscript and approved the submission.

Conflicts of Interest

None declared.

Using ChatGPT 4.0 to assist in generating PubMed search strategies for assessing systematic reviews.

The results obtained after searching the PubMed database based on the search strategy generated by ChatGPT.

Using ChatGPT 4.0 to assist in optimizing the clinical question for conducting a systematic review and meta-analysis.

Using ChatGPT 4 to generate PROSPERO (International Prospective Register of Systematic Reviews) registration information.

Proposal of a systematic review and meta-analysis related to exercises for osteoarthritis generated by Claude 3 based on the provided prompts.

The inclusion and exclusion criteria for a systematic review and meta-analysis on exercise therapy for osteoarthritis based on GPT-4.

Using Claude 3 for data extraction from PDF documents: an example with three randomized controlled trials.

Using Claude 3 for risk of bias assessment: an example with two randomized controlled trials.

Using GPT-4 to assist in selecting target journals for submission of a systematic review and meta-analysis.

• Jahan N, Naveed S, Zeshan M, Tahir MA. How to conduct a systematic review: a narrative literature review. Cureus. Nov 04, 2016;8(11):e864. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Wallace SS, Barak G, Truong G, Parker MW. Hierarchy of evidence within the medical literature. Hosp Pediatr. Aug 01, 2022;12(8):745-750. [ CrossRef ] [ Medline ]
• Institute of Medicine, Board on Health Care Services, Committee on Standards for Developing Trustworthy Clinical Practice Guidelines, Graham R, Mancher M, Miller Wolman D, et al. Clinical Practice Guidelines We Can Trust. Washington, DC. National Academies Press; 2011.
• Bystranowski P, Janik B, Próchnicki M, Skórska P. Anchoring effect in legal decision-making: a meta-analysis. Law Hum Behav. Feb 2021;45(1):1-23. [ CrossRef ] [ Medline ]
• Geyskens I, Krishnan R, Steenkamp JEM, Cunha PV. A review and evaluation of meta-analysis practices in management research. J Management. Feb 05, 2008;35(2):393-419. [ CrossRef ]
• Bagepally BS, Chaikledkaew U, Chaiyakunapruk N, Attia J, Thakkinstian A. Meta-analysis of economic evaluation studies: data harmonisation and methodological issues. BMC Health Serv Res. Feb 15, 2022;22(1):202. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Gurevitch J, Koricheva J, Nakagawa S, Stewart G. Meta-analysis and the science of research synthesis. Nature. Mar 07, 2018;555(7695):175-182. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Tsertsvadze A, Chen Y, Moher D, Sutcliffe P, McCarthy N. How to conduct systematic reviews more expeditiously? Syst Rev. Nov 12, 2015;4:160. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Scott AM, Forbes C, Clark J, Carter M, Glasziou P, Munn Z. Systematic review automation tools improve efficiency but lack of knowledge impedes their adoption: a survey. J Clin Epidemiol. Oct 2021;138:80-94. [ CrossRef ] [ Medline ]
• Khalil H, Ameen D, Zarnegar A. Tools to support the automation of systematic reviews: a scoping review. J Clin Epidemiol. Apr 2022;144:22-42. [ CrossRef ] [ Medline ]
• Qureshi R, Shaughnessy D, Gill KAR, Robinson KA, Li T, Agai E. Are ChatGPT and large language models "the answer" to bringing us closer to systematic review automation? Syst Rev. Apr 29, 2023;12(1):72. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Higgins J, Thomas J, Chandler J, Cumpston M, Li T, Page M, et al. Cochrane Handbook for Systematic Reviews of Interventions version 6.4. Aug 2023. URL: https://training.cochrane.org/handbook [accessed 2024-06-11]
• Deeks J, Bossuyt P, Leeflang M, Takwoingi Y. Cochrane Handbook for Systematic Reviews of Diagnostic Test Accuracy. Jul 2023. URL: https://training.cochrane.org/handbook-diagnostic-test-accuracy/current [accessed 2024-06-11]
• Xiao Y, Watson M. Guidance on conducting a systematic literature review. J Plan Educ Res. Aug 28, 2017;39(1):93-112. [ CrossRef ]
• Muka T, Glisic M, Milic J, Verhoog S, Bohlius J, Bramer W, et al. A 24-step guide on how to design, conduct, and successfully publish a systematic review and meta-analysis in medical research. Eur J Epidemiol. Jan 2020;35(1):49-60. [ CrossRef ] [ Medline ]
• Siddaway AP, Wood AM, Hedges LV. How to do a systematic review: a best practice guide for conducting and reporting narrative reviews, meta-analyses, and meta-syntheses. Annu Rev Psychol. Jan 04, 2019;70:747-770. [ CrossRef ] [ Medline ]
• Tawfik GM, Dila KAS, Mohamed MYF, Tam DNH, Kien ND, Ahmed AM, et al. A step by step guide for conducting a systematic review and meta-analysis with simulation data. Trop Med Health. 2019;47:46. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Borah R, Brown AW, Capers PL, Kaiser KA. Analysis of the time and workers needed to conduct systematic reviews of medical interventions using data from the PROSPERO registry. BMJ Open. Feb 27, 2017;7(2):e012545. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Michelson M, Reuter K. The significant cost of systematic reviews and meta-analyses: a call for greater involvement of machine learning to assess the promise of clinical trials. Contemp Clin Trials Commun. Dec 2019;16:100443. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Marshall IJ, Wallace BC. Toward systematic review automation: a practical guide to using machine learning tools in research synthesis. Syst Rev. Jul 11, 2019;8(1):163. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Clark J, Glasziou P, Del Mar C, Bannach-Brown A, Stehlik P, Scott AM. A full systematic review was completed in 2 weeks using automation tools: a case study. J Clin Epidemiol. May 2020;121:81-90. [ CrossRef ] [ Medline ]
• Luykx JJ, Gerritse F, Habets PC, Vinkers CH. The performance of ChatGPT in generating answers to clinical questions in psychiatry: a two-layer assessment. World Psychiatry. Oct 15, 2023;22(3):479-480. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Roberts RH, Ali SR, Hutchings HA, Dobbs TD, Whitaker IS. Comparative study of ChatGPT and human evaluators on the assessment of medical literature according to recognised reporting standards. BMJ Health Care Inform. Oct 2023;30(1):e100830. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Luo X, Estill J, Chen Y. The use of ChatGPT in medical research: do we need a reporting guideline? Int J Surg. Dec 01, 2023;109(12):3750-3751. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Alshami A, Elsayed M, Ali E, Eltoukhy A, Zayed T. Harnessing the power of ChatGPT for automating systematic review process: methodology, case study, limitations, and future directions. Systems. Jul 09, 2023;11(7):351. [ CrossRef ]
• Mahuli SA, Rai A, Mahuli AV, Kumar A. Application ChatGPT in conducting systematic reviews and meta-analyses. Br Dent J. Jul 2023;235(2):90-92. [ CrossRef ] [ Medline ]
• van Dijk SHB, Brusse-Keizer MGJ, Bucsán CC, van der Palen J, Doggen CJM, Lenferink A. Artificial intelligence in systematic reviews: promising when appropriately used. BMJ Open. Jul 07, 2023;13(7):e072254. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Khraisha Q, Put S, Kappenberg J, Warraitch A, Hadfield K. Can large language models replace humans in systematic reviews? Evaluating GPT-4's efficacy in screening and extracting data from peer-reviewed and grey literature in multiple languages. Res Synth Methods. Mar 14, 2024:online ahead of print. [ CrossRef ] [ Medline ]
• Gwon YN, Kim JH, Chung HS, Jung EJ, Chun J, Lee S, et al. The use of generative AI for scientific literature searches for systematic reviews: ChatGPT and Microsoft Bing AI performance evaluation. JMIR Med Inform. May 14, 2024;12:e51187-e51187. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Hossain MM. Using ChatGPT and other forms of generative AI in systematic reviews: challenges and opportunities. J Med Imaging Radiat Sci. Mar 2024;55(1):11-12. [ CrossRef ] [ Medline ]
• Giunti G, Doherty CP. Cocreating an automated mHealth apps systematic review process with generative AI: design science research approach. JMIR Med Educ. Feb 12, 2024;10:e48949. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Nashwan AJ, Jaradat JH. Streamlining systematic reviews: harnessing large language models for quality assessment and risk-of-bias evaluation. Cureus. Aug 2023;15(8):e43023. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Huang J, Tan M. The role of ChatGPT in scientific communication: writing better scientific review articles. Am J Cancer Res. 2023;13(4):1148-1154. [ FREE Full text ] [ Medline ]
• Issaiy M, Ghanaati H, Kolahi S, Shakiba M, Jalali AH, Zarei D, et al. Methodological insights into ChatGPT's screening performance in systematic reviews. BMC Med Res Methodol. Mar 27, 2024;24(1):78. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Branum C, Schiavenato M. Can ChatGPT accurately answer a PICOT question? Assessing AI response to a clinical question. Nurse Educ. 2023;48(5):231-233. [ CrossRef ] [ Medline ]
• Macdonald C, Adeloye D, Sheikh A, Rudan I. Can ChatGPT draft a research article? An example of population-level vaccine effectiveness analysis. J Glob Health. Feb 17, 2023;13:01003. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Richard E, Pozzi A. Using ChatGPT to develop the statistical analysis plan for a randomized controlled trial: a case report. Zurich Open Repository and Archive. Preprint posted online on October 17, 2023. [ CrossRef ]
• Hutson M. How AI is being used to accelerate clinical trials. Nature. Mar 2024;627(8003):S2-S5. [ CrossRef ] [ Medline ]
• Van der Mierden S, Tsaioun K, Bleich A, Leenaars CHC. Software tools for literature screening in systematic reviews in biomedical research. Altex. 2019;36(3):508-517. [ CrossRef ] [ Medline ]
• Wang S, Scells H, Koopman B, Zuccon G. Can ChatGPT write a good Boolean query for systematic review literature search? arXiv. Preprint posted online on February 9, 2023. [ FREE Full text ] [ CrossRef ]
• Alaniz L, Vu C, Pfaff MJ. The utility of artificial intelligence for systematic reviews and Boolean query formulation and translation. Plast Reconstr Surg Glob Open. Oct 2023;11(10):e5339. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Guimarães NS, Joviano-Santos JV, Reis MG, Chaves RRM, Observatory of Epidemiology‚ Nutrition‚ Health Research (OPENS). Development of search strategies for systematic reviews in health using ChatGPT: a critical analysis. J Transl Med. Jan 02, 2024;22(1):1. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Tantry TP, Karanth H, Shetty PK, Kadam D. Self-learning software tools for data analysis in meta-analysis. Korean J Anesthesiol. Oct 2021;74(5):459-461. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Cai X, Geng Y, Du Y, Westerman B, Wang D, Ma C, et al. Utilizing ChatGPT to select literature for meta-analysis shows workload reduction while maintaining a similar recall level as manual curation. medRxiv. Preprint posted online on September 7, 2023. [ CrossRef ]
• Eugene S, Istvan D, Gauransh K. Assessing the ability of ChatGPT to screen articles for systematic reviews. arXiv. Preprint posted online on July 12, 2023. [ CrossRef ]
• Kohandel Gargari O, Mahmoudi MH, Hajisafarali M, Samiee R. Enhancing title and abstract screening for systematic reviews with GPT-3.5 turbo. BMJ Evid Based Med. Jan 19, 2024;29(1):69-70. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Guo E, Gupta M, Deng J, Park Y, Paget M, Naugler C. Automated paper screening for clinical reviews using large language models: data analysis study. J Med Internet Res. Jan 12, 2024;26:e48996. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Polak MP, Morgan D. Extracting accurate materials data from research papers with conversational language models and prompt engineering. Nat Commun. Feb 21, 2024;15(1):1569. [ CrossRef ] [ Medline ]
• Mahmoudi H, Chang D, Lee H, Ghaffarzadegan N, Jalali MS. A critical assessment of large language models for systematic reviews: utilizing ChatGPT for complex data extraction. SSRN. Preprint posted online on April 19, 2024. [ CrossRef ]
• Sun Z, Zhang R, Doi SA, Furuya-Kanamori L, Yu T, Lin L, et al. How good are large language models for automated data extraction from randomized trials? medRXiv. Preprint posted online on February 21, 2024. [ CrossRef ]
• Marshall IJ, Kuiper J, Wallace BC. RobotReviewer: evaluation of a system for automatically assessing bias in clinical trials. J Am Med Inform Assoc. Jan 2016;23(1):193-201. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Lai H, Ge L, Sun M, Pan B, Huang J, Hou L, et al. Assessing the risk of bias in randomized clinical trials with large language models. JAMA Netw Open. May 01, 2024;7(5):e2412687. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Pitre T, Jassal T, Talukdar JR, Shahab M, Ling M, Zeraatkar D. ChatGPT for assessing risk of bias of randomized trials using the RoB 2.0 tool: a methods study. medRXiv. Preprint posted online on November 22, 2023. [ CrossRef ]
• Rasheed Z, Waseem M, Ahmad A, Kemell KK, Xiaofeng W, Nguyen Duc A, et al. Can large language models serve as data analysts? A multi-agent assisted approach for qualitative data analysis. arXiv. Preprint posted online on February 2, 2024.
• Kim S. Using ChatGPT for language editing in scientific articles. Maxillofac Plast Reconstr Surg. Mar 08, 2023;45(1):13. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Gao CA, Howard FM, Markov NS, Dyer EC, Ramesh S, Luo Y, et al. Comparing scientific abstracts generated by ChatGPT to real abstracts with detectors and blinded human reviewers. NPJ Digit Med. Apr 26, 2023;6(1):75. [ CrossRef ] [ Medline ]
• Kim JK, Chua M, Rickard M, Lorenzo A. ChatGPT and large language model (LLM) chatbots: the current state of acceptability and a proposal for guidelines on utilization in academic medicine. J Pediatr Urol. Oct 2023;19(5):598-604. [ CrossRef ] [ Medline ]
• Mugaanyi J, Cai L, Cheng S, Lu C, Huang J. Evaluation of large language model performance and reliability for citations and references in scholarly writing: cross-disciplinary study. J Med Internet Res. Apr 05, 2024;26:e52935. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Firdausa Nuzula I, Miftahul Amri M. Will ChatGPT bring a new paradigm to HR World? A critical opinion article. J Manag Stud Devel. Apr 24, 2023;2(02):142-161. [ CrossRef ]
• Eriksen MB, Frandsen TF. The impact of patient, intervention, comparison, outcome (PICO) as a search strategy tool on literature search quality: a systematic review. J Med Libr Assoc. Oct 2018;106(4):420-431. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Roberts R. I tested how well ChatGPT can pull data out of messy PDFs. Source. Mar 01, 2023. URL: https://source.opennews.org/articles/testing-pdf-data-extraction-chatgpt/ [accessed 2024-06-11]
• Higgins JPT, Altman DG, Gøtzsche PC, Jüni P, Moher D, Oxman AD, Cochrane Bias Methods Group, et al. Cochrane Statistical Methods Group. The Cochrane Collaboration's tool for assessing risk of bias in randomised trials. BMJ. Oct 18, 2011;343:d5928. [ CrossRef ] [ Medline ]
• Sterne JAC, Savović J, Page MJ, Elbers RG, Blencowe NS, Boutron I, et al. RoB 2: a revised tool for assessing risk of bias in randomised trials. BMJ. Aug 28, 2019;366:l4898. [ CrossRef ] [ Medline ]
• Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. Mar 29, 2021;372:n71. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Gaggioli A. Ethics: disclose use of AI in scientific manuscripts. Nature. Feb 14, 2023;614(7948):413-413. [ CrossRef ] [ Medline ]
• Wang L, Ma C, Feng X, Zhang Z, Yang H, Zhang J, et al. A survey on large language model based autonomous agents. Front Comput Sci. Mar 22, 2024;18(6):186345. [ FREE Full text ] [ CrossRef ]
• Hong S, Zhuge M, Chen J, Zheng X, Cheng Y, Zhang C, et al. Metagpt: Meta programming for multi-agent collaborative framework. arXiv. Preprint posted online on November 6, 2023.
• Flanagin A, Pirracchio R, Khera R, Berkwits M, Hswen Y, Bibbins-Domingo K. Reporting use of AI in research and scholarly publication-JAMA Network Guidance. JAMA. Apr 02, 2024;331(13):1096-1098. [ CrossRef ] [ Medline ]

Abbreviations

Edited by G Eysenbach; submitted 20.02.24; peer-reviewed by A Jafarizadeh, M Chatzimina, AS Van Epps; comments to author 03.05.24; revised version received 21.05.24; accepted 29.05.24; published 25.06.24.

©Xufei Luo, Fengxian Chen, Di Zhu, Ling Wang, Zijun Wang, Hui Liu, Meng Lyu, Ye Wang, Qi Wang, Yaolong Chen. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 25.06.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research (ISSN 1438-8871), is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.

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• v.59(1); 2021

Peer review guidance: a primer for researchers

Olena zimba.

1 Department of Internal Medicine No. 2, Danylo Halytsky Lviv National Medical University, Lviv, Ukraine

Armen Yuri Gasparyan

2 Departments of Rheumatology and Research and Development, Dudley Group NHS Foundation Trust (Teaching Trust of the University of Birmingham, UK), Russells Hall Hospital, Dudley, West Midlands, UK

The peer review process is essential for quality checks and validation of journal submissions. Although it has some limitations, including manipulations and biased and unfair evaluations, there is no other alternative to the system. Several peer review models are now practised, with public review being the most appropriate in view of the open science movement. Constructive reviewer comments are increasingly recognised as scholarly contributions which should meet certain ethics and reporting standards. The Publons platform, which is now part of the Web of Science Group (Clarivate Analytics), credits validated reviewer accomplishments and serves as an instrument for selecting and promoting the best reviewers. All authors with relevant profiles may act as reviewers. Adherence to research reporting standards and access to bibliographic databases are recommended to help reviewers draft evidence-based and detailed comments.

Introduction

The peer review process is essential for evaluating the quality of scholarly works, suggesting corrections, and learning from other authors’ mistakes. The principles of peer review are largely based on professionalism, eloquence, and collegiate attitude. As such, reviewing journal submissions is a privilege and responsibility for ‘elite’ research fellows who contribute to their professional societies and add value by voluntarily sharing their knowledge and experience.

Since the launch of the first academic periodicals back in 1665, the peer review has been mandatory for validating scientific facts, selecting influential works, and minimizing chances of publishing erroneous research reports [ 1 ]. Over the past centuries, peer review models have evolved from single-handed editorial evaluations to collegial discussions, with numerous strengths and inevitable limitations of each practised model [ 2 , 3 ]. With multiplication of periodicals and editorial management platforms, the reviewer pool has expanded and internationalized. Various sets of rules have been proposed to select skilled reviewers and employ globally acceptable tools and language styles [ 4 , 5 ].

In the era of digitization, the ethical dimension of the peer review has emerged, necessitating involvement of peers with full understanding of research and publication ethics to exclude unethical articles from the pool of evidence-based research and reviews [ 6 ]. In the time of the COVID-19 pandemic, some, if not most, journals face the unavailability of skilled reviewers, resulting in an unprecedented increase of articles without a history of peer review or those with surprisingly short evaluation timelines [ 7 ].

Editorial recommendations and the best reviewers

Guidance on peer review and selection of reviewers is currently available in the recommendations of global editorial associations which can be consulted by journal editors for updating their ethics statements and by research managers for crediting the evaluators. The International Committee on Medical Journal Editors (ICMJE) qualifies peer review as a continuation of the scientific process that should involve experts who are able to timely respond to reviewer invitations, submitting unbiased and constructive comments, and keeping confidentiality [ 8 ].

The reviewer roles and responsibilities are listed in the updated recommendations of the Council of Science Editors (CSE) [ 9 ] where ethical conduct is viewed as a premise of the quality evaluations. The Committee on Publication Ethics (COPE) further emphasizes editorial strategies that ensure transparent and unbiased reviewer evaluations by trained professionals [ 10 ]. Finally, the World Association of Medical Editors (WAME) prioritizes selecting the best reviewers with validated profiles to avoid substandard or fraudulent reviewer comments [ 11 ]. Accordingly, the Sarajevo Declaration on Integrity and Visibility of Scholarly Publications encourages reviewers to register with the Open Researcher and Contributor ID (ORCID) platform to validate and publicize their scholarly activities [ 12 ].

Although the best reviewer criteria are not listed in the editorial recommendations, it is apparent that the manuscript evaluators should be active researchers with extensive experience in the subject matter and an impressive list of relevant and recent publications [ 13 ]. All authors embarking on an academic career and publishing articles with active contact details can be involved in the evaluation of others’ scholarly works [ 14 ]. Ideally, the reviewers should be peers of the manuscript authors with equal scholarly ranks and credentials.

However, journal editors may employ schemes that engage junior research fellows as co-reviewers along with their mentors and senior fellows [ 15 ]. Such a scheme is successfully practised within the framework of the Emerging EULAR (European League Against Rheumatism) Network (EMEUNET) where seasoned authors (mentors) train ongoing researchers (mentees) how to evaluate submissions to the top rheumatology journals and select the best evaluators for regular contributors to these journals [ 16 ].

The awareness of the EQUATOR Network reporting standards may help the reviewers to evaluate methodology and suggest related revisions. Statistical skills help the reviewers to detect basic mistakes and suggest additional analyses. For example, scanning data presentation and revealing mistakes in the presentation of means and standard deviations often prompt re-analyses of distributions and replacement of parametric tests with non-parametric ones [ 17 , 18 ].

Constructive reviewer comments

The main goal of the peer review is to support authors in their attempt to publish ethically sound and professionally validated works that may attract readers’ attention and positively influence healthcare research and practice. As such, an optimal reviewer comment has to comprehensively examine all parts of the research and review work ( Table I ). The best reviewers are viewed as contributors who guide authors on how to correct mistakes, discuss study limitations, and highlight its strengths [ 19 ].

Structure of a reviewer comment to be forwarded to authors

Some of the currently practised review models are well positioned to help authors reveal and correct their mistakes at pre- or post-publication stages ( Table II ). The global move toward open science is particularly instrumental for increasing the quality and transparency of reviewer contributions.

Advantages and disadvantages of common manuscript evaluation models

Since there are no universally acceptable criteria for selecting reviewers and structuring their comments, instructions of all peer-reviewed journal should specify priorities, models, and expected review outcomes [ 20 ]. Monitoring and reporting average peer review timelines is also required to encourage timely evaluations and avoid delays. Depending on journal policies and article types, the first round of peer review may last from a few days to a few weeks. The fast-track review (up to 3 days) is practised by some top journals which process clinical trial reports and other priority items.

In exceptional cases, reviewer contributions may result in substantive changes, appreciated by authors in the official acknowledgments. In most cases, however, reviewers should avoid engaging in the authors’ research and writing. They should refrain from instructing the authors on additional tests and data collection as these may delay publication of original submissions with conclusive results.

Established publishers often employ advanced editorial management systems that support reviewers by providing instantaneous access to the review instructions, online structured forms, and some bibliographic databases. Such support enables drafting of evidence-based comments that examine the novelty, ethical soundness, and implications of the reviewed manuscripts [ 21 ].

Encouraging reviewers to submit their recommendations on manuscript acceptance/rejection and related editorial tasks is now a common practice. Skilled reviewers may prompt the editors to reject or transfer manuscripts which fall outside the journal scope, perform additional ethics checks, and minimize chances of publishing erroneous and unethical articles. They may also raise concerns over the editorial strategies in their comments to the editors.

Since reviewer and editor roles are distinct, reviewer recommendations are aimed at helping editors, but not at replacing their decision-making functions. The final decisions rest with handling editors. Handling editors weigh not only reviewer comments, but also priorities related to article types and geographic origins, space limitations in certain periods, and envisaged influence in terms of social media attention and citations. This is why rejections of even flawless manuscripts are likely at early rounds of internal and external evaluations across most peer-reviewed journals.

Reviewers are often requested to comment on language correctness and overall readability of the evaluated manuscripts. Given the wide availability of in-house and external editing services, reviewer comments on language mistakes and typos are categorized as minor. At the same time, non-Anglophone experts’ poor language skills often exclude them from contributing to the peer review in most influential journals [ 22 ]. Comments should be properly edited to convey messages in positive or neutral tones, express ideas of varying degrees of certainty, and present logical order of words, sentences, and paragraphs [ 23 , 24 ]. Consulting linguists on communication culture, passing advanced language courses, and honing commenting skills may increase the overall quality and appeal of the reviewer accomplishments [ 5 , 25 ].

Peer reviewer credits

Various crediting mechanisms have been proposed to motivate reviewers and maintain the integrity of science communication [ 26 ]. Annual reviewer acknowledgments are widely practised for naming manuscript evaluators and appreciating their scholarly contributions. Given the need to weigh reviewer contributions, some journal editors distinguish ‘elite’ reviewers with numerous evaluations and award those with timely and outstanding accomplishments [ 27 ]. Such targeted recognition ensures ethical soundness of the peer review and facilitates promotion of the best candidates for grant funding and academic job appointments [ 28 ].

Also, large publishers and learned societies issue certificates of excellence in reviewing which may include Continuing Professional Development (CPD) points [ 29 ]. Finally, an entirely new crediting mechanism is proposed to award bonus points to active reviewers who may collect, transfer, and use these points to discount gold open-access charges within the publisher consortia [ 30 ].

With the launch of Publons ( http://publons.com/ ) and its integration with Web of Science Group (Clarivate Analytics), reviewer recognition has become a matter of scientific prestige. Reviewers can now freely open their Publons accounts and record their contributions to online journals with Digital Object Identifiers (DOI). Journal editors, in turn, may generate official reviewer acknowledgments and encourage reviewers to forward them to Publons for building up individual reviewer and journal profiles. All published articles maintain e-links to their review records and post-publication promotion on social media, allowing the reviewers to continuously track expert evaluations and comments. A paid-up partnership is also available to journals and publishers for automatically transferring peer-review records to Publons upon mutually acceptable arrangements.

Listing reviewer accomplishments on an individual Publons profile showcases scholarly contributions of the account holder. The reviewer accomplishments placed next to the account holders’ own articles and editorial accomplishments point to the diversity of scholarly contributions. Researchers may establish links between their Publons and ORCID accounts to further benefit from complementary services of both platforms. Publons Academy ( https://publons.com/community/academy/ ) additionally offers an online training course to novice researchers who may improve their reviewing skills under the guidance of experienced mentors and journal editors. Finally, journal editors may conduct searches through the Publons platform to select the best reviewers across academic disciplines.

Peer review ethics

Prior to accepting reviewer invitations, scholars need to weigh a number of factors which may compromise their evaluations. First of all, they are required to accept the reviewer invitations if they are capable of timely submitting their comments. Peer review timelines depend on article type and vary widely across journals. The rules of transparent publishing necessitate recording manuscript submission and acceptance dates in article footnotes to inform readers of the evaluation speed and to help investigators in the event of multiple unethical submissions. Timely reviewer accomplishments often enable fast publication of valuable works with positive implications for healthcare. Unjustifiably long peer review, on the contrary, delays dissemination of influential reports and results in ethical misconduct, such as plagiarism of a manuscript under evaluation [ 31 ].

In the times of proliferation of open-access journals relying on article processing charges, unjustifiably short review may point to the absence of quality evaluation and apparently ‘predatory’ publishing practice [ 32 , 33 ]. Authors when choosing their target journals should take into account the peer review strategy and associated timelines to avoid substandard periodicals.

Reviewer primary interests (unbiased evaluation of manuscripts) may come into conflict with secondary interests (promotion of their own scholarly works), necessitating disclosures by filling in related parts in the online reviewer window or uploading the ICMJE conflict of interest forms. Biomedical reviewers, who are directly or indirectly supported by the pharmaceutical industry, may encounter conflicts while evaluating drug research. Such instances require explicit disclosures of conflicts and/or rejections of reviewer invitations.

Journal editors are obliged to employ mechanisms for disclosing reviewer financial and non-financial conflicts of interest to avoid processing of biased comments [ 34 ]. They should also cautiously process negative comments that oppose dissenting, but still valid, scientific ideas [ 35 ]. Reviewer conflicts that stem from academic activities in a competitive environment may introduce biases, resulting in unfair rejections of manuscripts with opposing concepts, results, and interpretations. The same academic conflicts may lead to coercive reviewer self-citations, forcing authors to incorporate suggested reviewer references or face negative feedback and an unjustified rejection [ 36 ]. Notably, several publisher investigations have demonstrated a global scale of such misconduct, involving some highly cited researchers and top scientific journals [ 37 ].

Fake peer review, an extreme example of conflict of interest, is another form of misconduct that has surfaced in the time of mass proliferation of gold open-access journals and publication of articles without quality checks [ 38 ]. Fake reviews are generated by manipulating authors and commercial editing agencies with full access to their own manuscripts and peer review evaluations in the journal editorial management systems. The sole aim of these reviews is to break the manuscript evaluation process and to pave the way for publication of pseudoscientific articles. Authors of these articles are often supported by funds intended for the growth of science in non-Anglophone countries [ 39 ]. Iranian and Chinese authors are often caught submitting fake reviews, resulting in mass retractions by large publishers [ 38 ]. Several suggestions have been made to overcome this issue, with assigning independent reviewers and requesting their ORCID IDs viewed as the most practical options [ 40 ].

Conclusions

The peer review process is regulated by publishers and editors, enforcing updated global editorial recommendations. Selecting the best reviewers and providing authors with constructive comments may improve the quality of published articles. Reviewers are selected in view of their professional backgrounds and skills in research reporting, statistics, ethics, and language. Quality reviewer comments attract superior submissions and add to the journal’s scientific prestige [ 41 ].

In the era of digitization and open science, various online tools and platforms are available to upgrade the peer review and credit experts for their scholarly contributions. With its links to the ORCID platform and social media channels, Publons now offers the optimal model for crediting and keeping track of the best and most active reviewers. Publons Academy additionally offers online training for novice researchers who may benefit from the experience of their mentoring editors. Overall, reviewer training in how to evaluate journal submissions and avoid related misconduct is an important process, which some indexed journals are experimenting with [ 42 ].

The timelines and rigour of the peer review may change during the current pandemic. However, journal editors should mobilize their resources to avoid publication of unchecked and misleading reports. Additional efforts are required to monitor published contents and encourage readers to post their comments on publishers’ online platforms (blogs) and other social media channels [ 43 , 44 ].

The authors declare no conflict of interest.

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Autophagy, oxidative stress, and alcoholic liver disease: a systematic review and potential clinical applications.

1. Introduction

2. materials and methods, 2.1. search strategy, 2.2. article selection and data extraction, 3. results and literature review, 3.1. studies selected, 3.2. ethanol, ald, and oxidative stress, 3.2.1. ethanol metabolism, 3.2.2. protein modifications, 3.2.3. lipid peroxidation and dna alteration, 3.2.4. mitochondrial damage, 3.2.5. inflammation, 3.2.6. iron overload, 3.2.7. protective mechanisms and the antioxidant response, 3.3. autophagy and ald, 3.3.1. the macroautophagy pathway, 3.3.2. mitophagy, 3.3.3. acute alcohol intake and autophagy in hepatocytes, 3.3.4. acute alcohol intake and autophagy in other cell types, 3.3.5. chronic ethanol intake and autophagy in hepatocytes, 3.3.6. chronic ethanol intake and autophagy in other cell types, 3.4. alcohol-induced organ–organ crosstalk and autophagy, 3.5. autophagy-targeting treatments for ald, 4. discussion, 5. conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

• WHO. Global Status Report on Alcohol and Health 2018 ; Licence: CC BY-NC-SA 3.0 IGO; World Health Organization: Geneva, Switzerland, 2018. [ Google Scholar ]
• Zakhari, S.; Li, T.-K. Determinants of Alcohol Use and Abuse: Impact of Quantity and Frequency Patterns on Liver Disease. Hepatology 2007 , 46 , 2032–2039. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Mathurin, P.; Bataller, R. Trends in the Management and Burden of Alcoholic Liver Disease. J. Hepatol. 2015 , 62 , S38–S46. [ Google Scholar ] [ CrossRef ] [ PubMed ] [ Green Version ]
• Huang, D.Q.; Mathurin, P.; Cortez-Pinto, H.; Loomba, R. Global Epidemiology of Alcohol-Associated Cirrhosis and HCC: Trends, Projections and Risk Factors. Nat. Rev. Gastroenterol. Hepatol. 2023 , 20 , 37–49. [ Google Scholar ] [ CrossRef ]
• Williams, J.A.; Manley, S.; Ding, W.-X. New Advances in Molecular Mechanisms and Emerging Therapeutic Targets in Alcoholic Liver Diseases. World J. Gastroenterol. 2014 , 20 , 12908–12933. [ Google Scholar ] [ CrossRef ]
• Venkatraman, A.; Landar, A.; Davis, A.J.; Chamlee, L.; Sanderson, T.; Kim, H.; Page, G.; Pompilius, M.; Ballinger, S.; Darley-Usmar, V.; et al. Modification of the Mitochondrial Proteome in Response to the Stress of Ethanol-Dependent Hepatotoxicity. J. Biol. Chem. 2004 , 279 , 22092–22101. [ Google Scholar ] [ CrossRef ] [ PubMed ] [ Green Version ]
• Das, S.K.; Vasudevan, D.M. Alcohol-Induced Oxidative Stress. Life Sci. 2007 , 81 , 177–187. [ Google Scholar ] [ CrossRef ]
• Manzo-Avalos, S.; Saavedra-Molina, A. Cellular and Mitochondrial Effects of Alcohol Consumption. Int. J. Environ. Res. Public Health 2010 , 7 , 4281–4304. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Yang, W.S.; Kim, K.J.; Gaschler, M.M.; Patel, M.; Shchepinov, M.S.; Stockwell, B.R. Peroxidation of Polyunsaturated Fatty Acids by Lipoxygenases Drives Ferroptosis. Proc. Natl. Acad. Sci. USA 2016 , 113 , E4966–E4975. [ Google Scholar ] [ CrossRef ]
• Mueller, S.; Peccerella, T.; Qin, H.; Glassen, K.; Waldherr, R.; Flechtenmacher, C.; Straub, B.K.; Millonig, G.; Stickel, F.; Bruckner, T.; et al. Carcinogenic Etheno DNA Adducts in Alcoholic Liver Disease: Correlation with Cytochrome P-4502E1 and Fibrosis. Alcohol. Clin. Exp. Res. 2018 , 42 , 252–259. [ Google Scholar ] [ CrossRef ]
• Vijayakumar, K.; Cho, G.-W. Autophagy: An Evolutionarily Conserved Process in the Maintenance of Stem Cells and Aging. Cell Biochem. Funct. 2019 , 37 , 452–458. [ Google Scholar ] [ CrossRef ]
• Eid, N.; Ito, Y.; Horibe, A.; Otsuki, Y. Ethanol-Induced Mitophagy in Liver Is Associated with Activation of the PINK1-Parkin Pathway Triggered by Oxidative DNA Damage. Histol. Histopathol. 2016 , 31 , 1143–1159. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Rasineni, K.; Donohue, T.M.; Thomes, P.G.; Yang, L.; Tuma, D.J.; McNiven, M.A.; Casey, C.A. Ethanol-induced Steatosis Involves Impairment of Lipophagy, Associated with Reduced Dynamin2 Activity. Hepatol. Commun. 2017 , 1 , 501–512. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Ding, W.-X.; Li, M.; Yin, X.-M. Selective Taste of Ethanol-Induced Autophagy for Mitochondria and Lipid Droplets. Autophagy 2011 , 7 , 248–249. [ Google Scholar ] [ CrossRef ] [ PubMed ] [ Green Version ]
• Khambu, B.; Wang, L.; Zhang, H.; Yin, X.-M. The Activation and Function of Autophagy in Alcoholic Liver Disease. Curr. Mol. Pharmacol. 2017 , 10 , 165–171. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Chao, X.; Ding, W.-X. Role and Mechanisms of Autophagy in Alcohol-Induced Liver Injury. Adv. Pharmacol. 2019 , 85 , 109–131. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Williams, J.A.; Ding, W.-X. Role of Autophagy in Alcohol and Drug-Induced Liver Injury. Food Chem. Toxicol. Int. J. Publ. Br. Ind. Biol. Res. Assoc. 2020 , 136 , 111075. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Yang, L.; Liu, C.; Zhao, W.; He, C.; Ding, J.; Dai, R.; Xu, K.; Xiao, L.; Luo, L.; Liu, S.; et al. Impaired Autophagy in Intestinal Epithelial Cells Alters Gut Microbiota and Host Immune Responses. Appl. Environ. Microbiol. 2018 , 84 , e00880-18. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Foerster, E.G.; Mukherjee, T.; Cabral-Fernandes, L.; Rocha, J.D.B.; Girardin, S.E.; Philpott, D.J. How Autophagy Controls the Intestinal Epithelial Barrier. Autophagy 2022 , 18 , 86–103. [ Google Scholar ] [ CrossRef ]
• Li, Y.; Ding, W.-X. Adipose Tissue Autophagy and Homeostasis in Alcohol-Induced Liver Injury. Liver Res. 2017 , 1 , 54–62. [ Google Scholar ] [ CrossRef ]
• Rasineni, K.; Srinivasan, M.P.; Balamurugan, A.N.; Kaphalia, B.S.; Wang, S.; Ding, W.-X.; Pandol, S.J.; Lugea, A.; Simon, L.; Molina, P.E.; et al. Recent Advances in Understanding the Complexity of Alcohol-Induced Pancreatic Dysfunction and Pancreatitis Development. Biomolecules 2020 , 10 , 669. [ Google Scholar ] [ CrossRef ]
• Yang, F.; Luo, J. Endoplasmic Reticulum Stress and Ethanol Neurotoxicity. Biomolecules 2015 , 5 , 2538–2553. [ Google Scholar ] [ CrossRef ] [ PubMed ] [ Green Version ]
• Peng, H.; Qin, X.; Chen, S.; Ceylan, A.F.; Dong, M.; Lin, Z.; Ren, J. Parkin Deficiency Accentuates Chronic Alcohol Intake-Induced Tissue Injury and Autophagy Defects in Brain, Liver and Skeletal Muscle. Acta Biochim. Biophys. Sin. 2020 , 52 , 665–674. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Li, Y.; Wang, S.; Ni, H.-M.; Huang, H.; Ding, W.-X. Autophagy in Alcohol-Induced Multiorgan Injury: Mechanisms and Potential Therapeutic Targets. BioMed Res. Int. 2014 , 2014 , 498491. [ Google Scholar ] [ CrossRef ] [ PubMed ] [ Green Version ]
• Armutcu, F. Organ Crosstalk: The Potent Roles of Inflammation and Fibrotic Changes in the Course of Organ Interactions. Inflamm. Res. 2019 , 68 , 825–839. [ Google Scholar ] [ CrossRef ]
• Albillos, A.; de Gottardi, A.; Rescigno, M. The Gut-Liver Axis in Liver Disease: Pathophysiological Basis for Therapy. J. Hepatol. 2020 , 72 , 558–577. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Ferrell, J.M.; Chiang, J.Y.L. Bile Acid Receptors and Signaling Crosstalk in the Liver, Gut and Brain. Liver Res. 2021 , 5 , 105–118. [ Google Scholar ] [ CrossRef ]
• Poole, L.G.; Dolin, C.E.; Arteel, G.E. Organ-Organ Crosstalk and Alcoholic Liver Disease. Biomolecules 2017 , 7 , 62. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Martinez-Lopez, N.; Garcia-Macia, M.; Sahu, S.; Athonvarangkul, D.; Liebling, E.; Merlo, P.; Cecconi, F.; Schwartz, G.J.; Singh, R. Autophagy in the CNS and Periphery Coordinate Lipophagy and Lipolysis in the Brown Adipose Tissue and Liver. Cell Metab. 2016 , 23 , 113–127. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Ouzzani, M.; Hammady, H.; Fedorowicz, Z.; Elmagarmid, A. Rayyan—A Web and Mobile App for Systematic Reviews. Syst. Rev. 2016 , 5 , 210. Available online: https://www.rayyan.ai/ (accessed on 13 January 2023). [ CrossRef ] [ Green Version ]
• Lu, X.; Xuan, W.; Li, J.; Yao, H.; Huang, C.; Li, J. AMPK Protects against Alcohol-Induced Liver Injury through UQCRC2 to up-Regulate Mitophagy. Autophagy 2021 , 17 , 3622–3643. [ Google Scholar ] [ CrossRef ]
• Chen, C.; Wang, S.; Yu, L.; Mueller, J.; Fortunato, F.; Rausch, V.; Mueller, S. H 2 O 2 -Mediated Autophagy during Ethanol Metabolism. Redox Biol. 2021 , 46 , 102081. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Zhao, Y.; Lu, J.; Mao, A.; Zhang, R.; Guan, S. Autophagy Inhibition Plays a Protective Role in Ferroptosis Induced by Alcohol via the P62–Keap1–Nrf2 Pathway. J. Agric. Food Chem. 2021 , 69 , 9671–9683. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Liu, L.; Xie, P.; Li, W.; Wu, Y.; An, W. Augmenter of Liver Regeneration Protects against Ethanol-Induced Acute Liver Injury by Promoting Autophagy. Am. J. Pathol. 2019 , 189 , 552–567. [ Google Scholar ] [ CrossRef ] [ PubMed ] [ Green Version ]
• You, Y.; Li, W.-Z.; Zhang, S.; Hu, B.; Li, Y.-X.; Li, H.-D.; Tang, H.-H.; Li, Q.-W.; Guan, Y.-Y.; Liu, L.-X.; et al. SNX10 Mediates Alcohol-Induced Liver Injury and Steatosis by Regulating the Activation of Chaperone-Mediated Autophagy. J. Hepatol. 2018 , 69 , 129–141. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Mahli, A.; Thasler, W.E.; Patsenker, E.; Müller, S.; Stickel, F.; Müller, M.; Seitz, H.K.; Cederbaum, A.I.; Hellerbrand, C. Identification of Cytochrome CYP2E1 as Critical Mediator of Synergistic Effects of Alcohol and Cellular Lipid Accumulation in Hepatocytes in Vitro. Oncotarget 2015 , 6 , 41464–41478. [ Google Scholar ] [ CrossRef ] [ PubMed ] [ Green Version ]
• Thomes, P.G.; Ehlers, R.A.; Trambly, C.S.; Clemens, D.L.; Fox, H.S.; Tuma, D.J.; Donohue, T.M. Multilevel Regulation of Autophagosome Content by Ethanol Oxidation in HepG2 Cells. Autophagy 2013 , 9 , 63–73. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Ding, W.; Li, M.; Chen, X.; Ni, H.; Lin, C.; Gao, W.; Lu, B.; Stolz, D.B.; Clemens, D.L.; Yin, X. Autophagy Reduces Acute Ethanol-Induced Hepatotoxicity and Steatosis in Mice. Gastroenterology 2010 , 139 , 1740–1752. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Guo, R.; Xu, X.; Babcock, S.A.; Zhang, Y.; Ren, J. Aldehyde Dedydrogenase-2 Plays a Beneficial Role in Ameliorating Chronic Alcohol-Induced Hepatic Steatosis and Inflammation through Regulation of Autophagy. J. Hepatol. 2015 , 62 , 647–656. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Wu, D.; Wang, X.; Zhou, R.; Yang, L.; Cederbaum, A.I. Alcohol Steatosis and Cytotoxicity: The Role of Cytochrome P4502E1 and Autophagy. Free Radic. Biol. Med. 2012 , 53 , 1346–1357. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Wu, D.; Wang, X.; Zhou, R.; Cederbaum, A. CYP2E1 Enhances Ethanol-Induced Lipid Accumulation but Impairs Autophagy in HepG2 E47 Cells. Biochem. Biophys. Res. Commun. 2010 , 402 , 116–122. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Samuvel, D.J.; Li, L.; Krishnasamy, Y.; Gooz, M.; Takemoto, K.; Woster, P.M.; Lemasters, J.J.; Zhong, Z. Mitochondrial Depolarization after Acute Ethanol Treatment Drives Mitophagy in Living Mice. Autophagy 2022 , 18 , 2671–2685. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Yan, S.; Zhou, J.; Chen, X.; Dong, Z.; Yin, X.-M. Diverse Consequences in Liver Injury in Mice with Different Autophagy Functional Status Treated with Alcohol. Am. J. Pathol. 2019 , 189 , 1744–1762. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Williams, J.A.; Ni, H.-M.; Ding, Y.; Ding, W.-X. Parkin Regulates Mitophagy and Mitochondrial Function to Protect against Alcohol-Induced Liver Injury and Steatosis in Mice. Am. J. Physiol. Gastrointest. Liver Physiol. 2015 , 309 , G324–G340. [ Google Scholar ] [ CrossRef ] [ PubMed ] [ Green Version ]
• Manley, S.; Ni, H.-M.; Williams, J.A.; Kong, B.; DiTacchio, L.; Guo, G.; Ding, W.-X. Farnesoid X Receptor Regulates Forkhead Box O3a Activation in Ethanol-Induced Autophagy and Hepatotoxicity. Redox Biol. 2014 , 2 , 991–1002. [ Google Scholar ] [ CrossRef ] [ PubMed ] [ Green Version ]
• Ni, H.-M.; Du, K.; You, M.; Ding, W.-X. Critical Role of FoxO3a in Alcohol-Induced Autophagy and Hepatotoxicity. Am. J. Pathol. 2013 , 183 , 1815–1825. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Lin, C.-W.; Zhang, H.; Li, M.; Xiong, X.; Chen, X.; Chen, X.; Dong, X.C.; Yin, X.-M. Pharmacological Promotion of Autophagy Alleviates Steatosis and Injury in Alcoholic and Non-Alcoholic Fatty Liver Conditions in Mice. J. Hepatol. 2013 , 58 , 993–999. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Yang, L.; Wu, D.; Wang, X.; Cederbaum, A.I. Cytochrome P4502E1, Oxidative Stress, JNK and Autophagy in Acute Alcohol-Induced Fatty Liver. Free Radic. Biol. Med. 2012 , 53 , 1170–1180. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Thomes, P.G.; Trambly, C.S.; Thiele, G.M.; Duryee, M.J.; Fox, H.S.; Haorah, J.; Donohue, T.M. Proteasome Activity and Autophagosome Content in Liver Are Reciprocally Regulated by Ethanol Treatment. Biochem. Biophys. Res. Commun. 2012 , 417 , 262–267. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Guo, W.; Zhong, W.; Hao, L.; Sun, X.; Zhou, Z. Activation of MTORC1 by Free Fatty Acids Suppresses LAMP2 and Autophagy Function via ER Stress in Alcohol-Related Liver Disease. Cells 2021 , 10 , 2730. [ Google Scholar ] [ CrossRef ]
• Guo, W.; Zhong, W.; Hao, L.; Dong, H.; Sun, X.; Yue, R.; Li, T.; Zhou, Z. Fatty Acids Inhibit LAMP2-Mediated Autophagy Flux via Activating ER Stress Pathway in Alcohol-Related Liver Disease. Cell. Mol. Gastroenterol. Hepatol. 2021 , 12 , 1599–1615. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Babuta, M.; Furi, I.; Bala, S.; Bukong, T.N.; Lowe, P.; Catalano, D.; Calenda, C.; Kodys, K.; Szabo, G. Dysregulated Autophagy and Lysosome Function Are Linked to Exosome Production by Micro-RNA 155 in Alcoholic Liver Disease. Hepatology 2019 , 70 , 2123–2141. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Zhou, H.; Zhu, P.; Wang, J.; Toan, S.; Ren, J. DNA-PKcs Promotes Alcohol-Related Liver Disease by Activating Drp1-Related Mitochondrial Fission and Repressing FUNDC1-Required Mitophagy. Signal Transduct. Target. Ther. 2019 , 4 , 56. [ Google Scholar ] [ CrossRef ] [ PubMed ] [ Green Version ]
• Menk, M.; Graw, J.A.; Poyraz, D.; Möbius, N.; Spies, C.D.; von Haefen, C. Chronic Alcohol Consumption Inhibits Autophagy and Promotes Apoptosis in the Liver. Int. J. Med. Sci. 2018 , 15 , 682–688. [ Google Scholar ] [ CrossRef ] [ PubMed ] [ Green Version ]
• Chao, X.; Wang, S.; Zhao, K.; Li, Y.; Williams, J.A.; Li, T.; Chavan, H.; Krishnamurthy, P.; He, X.C.; Li, L.; et al. Impaired TFEB-Mediated Lysosome Biogenesis and Autophagy Promote Chronic Ethanol-Induced Liver Injury and Steatosis in Mice. Gastroenterology 2018 , 155 , 865–879.e12. [ Google Scholar ] [ CrossRef ]
• Kong, X.; Yang, Y.; Ren, L.; Shao, T.; Li, F.; Zhao, C.; Liu, L.; Zhang, H.; McClain, C.J.; Feng, W. Activation of Autophagy Attenuates EtOH-LPS-Induced Hepatic Steatosis and Injury through MD2 Associated TLR4 Signaling. Sci. Rep. 2017 , 7 , 9292. [ Google Scholar ] [ CrossRef ]
• Lu, Y.; Cederbaum, A.I. Autophagy Protects against CYP2E1/Chronic Ethanol-Induced Hepatotoxicity. Biomolecules 2015 , 5 , 2659–2674. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• King, A.L.; Swain, T.M.; Mao, Z.; Udoh, U.S.; Oliva, C.R.; Betancourt, A.M.; Griguer, C.E.; Crowe, D.R.; Lesort, M.; Bailey, S.M. Involvement of the Mitochondrial Permeability Transition Pore in Chronic Ethanol-Mediated Liver Injury in Mice. Am. J. Physiol. Gastrointest. Liver Physiol. 2014 , 306 , G265–G277. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Tan, T.C.H.; Crawford, D.H.G.; Jaskowski, L.A.; Subramaniam, V.N.; Clouston, A.D.; Crane, D.I.; Bridle, K.R.; Anderson, G.J.; Fletcher, L.M. Excess Iron Modulates Endoplasmic Reticulum Stress-Associated Pathways in a Mouse Model of Alcohol and High-Fat Diet-Induced Liver Injury. Lab. Investig. 2013 , 93 , 1295–1312. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Liang, S.; Zhong, Z.; Kim, S.Y.; Uchiyama, R.; Roh, Y.S.; Matsushita, H.; Gottlieb, R.A.; Seki, E. Murine Macrophage Autophagy Protects against Alcohol-Induced Liver Injury by Degrading Interferon Regulatory Factor 1 (IRF1) and Removing Damaged Mitochondria. J. Biol. Chem. 2019 , 294 , 12359–12369. [ Google Scholar ] [ CrossRef ]
• Ilyas, G.; Cingolani, F.; Zhao, E.; Tanaka, K.; Czaja, M.J. Decreased Macrophage Autophagy Promotes Liver Injury and Inflammation from Alcohol. Alcohol. Clin. Exp. Res. 2019 , 43 , 1403–1413. [ Google Scholar ] [ CrossRef ]
• Xie, Z.-Y.; Xiao, Z.-H.; Wang, F.-F. Inhibition of Autophagy Reverses Alcohol-Induced Hepatic Stellate Cells Activation through Activation of Nrf2-Keap1-ARE Signaling Pathway. Biochimie 2018 , 147 , 55–62. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Denaës, T.; Lodder, J.; Chobert, M.-N.; Ruiz, I.; Pawlotsky, J.-M.; Lotersztajn, S.; Teixeira-Clerc, F. The Cannabinoid Receptor 2 Protects Against Alcoholic Liver Disease Via a Macrophage Autophagy-Dependent Pathway. Sci. Rep. 2016 , 6 , 28806. [ Google Scholar ] [ CrossRef ] [ PubMed ] [ Green Version ]
• Hernández-Gea, V.; Hilscher, M.; Rozenfeld, R.; Lim, M.P.; Nieto, N.; Werner, S.; Devi, L.A.; Friedman, S.L. Endoplasmic Reticulum Stress Induces Fibrogenic Activity in Hepatic Stellate Cells through Autophagy. J. Hepatol. 2013 , 59 , 98–104. [ Google Scholar ] [ CrossRef ] [ PubMed ] [ Green Version ]
• Park, H.-S.; Jo, E.; Han, J.-H.; Jung, S.-H.; Lee, D.-H.; Park, I.; Heo, K.-S.; Na, M.; Myung, C.-S. Hepatoprotective Effects of an Acer Tegmentosum Maxim Extract through Antioxidant Activity and the Regulation of Autophagy. J. Ethnopharmacol. 2019 , 239 , 111912. [ Google Scholar ] [ CrossRef ]
• Liu, Y.; Wang, J.; Li, L.; Hu, W.; Qu, Y.; Ding, Y.; Meng, L.; Teng, L.; Wang, D. Hepatoprotective Effects of Antrodia cinnamomea : The Modulation of Oxidative Stress Signaling in a Mouse Model of Alcohol-Induced Acute Liver Injury. Oxid. Med. Cell. Longev. 2017 , 2017 , e7841823. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Yu, Y.; Tian, Z.; Liang, L.; Yang, X.; Sheng, D.; Zeng, J.; Li, X.; Shi, R.; Han, Z.; Wei, L. Babao Dan Attenuates Acute Ethanol-Induced Liver Injury via Nrf2 Activation and Autophagy. Cell Biosci. 2019 , 9 , 80. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Kim, Y.-J.; Hwang, S.H.; Jia, Y.; Seo, W.-D.; Lee, S.-J. Barley Sprout Extracts Reduce Hepatic Lipid Accumulation in Ethanol-Fed Mice by Activating Hepatic AMP-Activated Protein Kinase. Food Res. Int. 2017 , 101 , 209–217. [ Google Scholar ] [ CrossRef ]
• Yuan, F.; Xu, Y.; You, K.; Zhang, J.; Yang, F.; Li, Y. Calcitriol Alleviates Ethanol-Induced Hepatotoxicity via AMPK/MTOR-Mediated Autophagy. Arch. Biochem. Biophys. 2021 , 697 , 108694. [ Google Scholar ] [ CrossRef ]
• Yang, L.; Rozenfeld, R.; Wu, D.; Devi, L.A.; Zhang, Z.; Cederbaum, A. Cannabidiol Protects Liver from Binge Alcohol-Induced Steatosis by Mechanisms Including Inhibition of Oxidative Stress and Increase in Autophagy. Free Radic. Biol. Med. 2014 , 68 , 260–267. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Khan, I.; Bhardwaj, M.; Shukla, S.; Min, S.-H.; Choi, D.K.; Bajpai, V.K.; Huh, Y.S.; Kang, S.C. Carvacrol Inhibits Cytochrome P450 and Protects against Binge Alcohol-Induced Liver Toxicity. Food Chem. Toxicol. 2019 , 131 , 110582. [ Google Scholar ] [ CrossRef ]
• Zeng, T.; Zhang, C.-L.; Song, F.-Y.; Zhao, X.-L.; Xie, K.-Q. CMZ Reversed Chronic Ethanol-Induced Disturbance of PPAR-α Possibly by Suppressing Oxidative Stress and PGC-1α Acetylation, and Activating the MAPK and GSK3β Pathway. PLoS ONE 2014 , 9 , e98658. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Chen, Y.-Y.; Zhang, C.-L.; Zhao, X.-L.; Xie, K.-Q.; Zeng, T. Inhibition of Cytochrome P4502E1 by Chlormethiazole Attenuated Acute Ethanol-Induced Fatty Liver. Chem. Biol. Interact. 2014 , 222 , 18–26. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Lee, Y.J.; Shu, M.-S.; Kim, J.-Y.; Kim, Y.-H.; Sim, K.H.; Sung, W.J.; Eun, J.R. Cilostazol Protects Hepatocytes against Alcohol-Induced Apoptosis via Activation of AMPK Pathway. PLoS ONE 2019 , 14 , e0211415. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Guo, X.; Cui, R.; Zhao, J.; Mo, R.; Peng, L.; Yan, M. Corosolic Acid Protects Hepatocytes against Ethanol-Induced Damage by Modulating Mitogen-Activated Protein Kinases and Activating Autophagy. Eur. J. Pharmacol. 2016 , 791 , 578–588. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Qiu, P.; Dong, Y.; Li, B.; Kang, X.; Gu, C.; Zhu, T.; Luo, Y.; Pang, M.; Du, W.; Ge, W. Dihydromyricetin Modulates P62 and Autophagy Crosstalk with the Keap-1/Nrf2 Pathway to Alleviate Ethanol-Induced Hepatic Injury. Toxicol. Lett. 2017 , 274 , 31–41. [ Google Scholar ] [ CrossRef ] [ PubMed ] [ Green Version ]
• Zhang, Y.; Jin, Q.; Li, X.; Jiang, M.; Cui, B.-W.; Xia, K.-L.; Wu, Y.-L.; Lian, L.-H.; Nan, J.-X. Amelioration of Alcoholic Liver Steatosis by Dihydroquercetin through the Modulation of AMPK-Dependent Lipogenesis Mediated by P2X7R–NLRP3-Inflammasome Activation. J. Agric. Food Chem. 2018 , 66 , 4862–4871. [ Google Scholar ] [ CrossRef ]
• Zhou, Z.-S.; Kong, C.-F.; Sun, J.-R.; Qu, X.-K.; Sun, J.-H.; Sun, A.-T. Fisetin Ameliorates Alcohol-Induced Liver Injury through Regulating SIRT1 and SphK1 Pathway. Am. J. Chin. Med. 2022 , 50 , 2171–2184. [ Google Scholar ] [ CrossRef ]
• Xue, M.; Liang, H.; Zhou, Z.; Liu, Y.; He, X.; Zhang, Z.; Sun, T.; Yang, J.; Qin, Y.; Qin, K. Effect of Fucoidan on Ethanol-Induced Liver Injury and Steatosis in Mice and the Underlying Mechanism. Food Nutr. Res. 2021 , 65 , 5384. [ Google Scholar ] [ CrossRef ]
• Xue, M.; Tian, Y.; Sui, Y.; Zhao, H.; Gao, H.; Liang, H.; Qiu, X.; Sun, Z.; Zhang, Y.; Qin, Y. Protective Effect of Fucoidan against Iron Overload and Ferroptosis-Induced Liver Injury in Rats Exposed to Alcohol. Biomed. Pharmacother. 2022 , 153 , 113402. [ Google Scholar ] [ CrossRef ]
• Song, X.; Yin, S.; Huo, Y.; Liang, M.; Fan, L.; Ye, M.; Hu, H. Glycycoumarin Ameliorates Alcohol-Induced Hepatotoxicity via Activation of Nrf2 and Autophagy. Free Radic. Biol. Med. 2015 , 89 , 135–146. [ Google Scholar ] [ CrossRef ]
• Zhang, Y.-P.; Yang, X.-Q.; Yu, D.-K.; Xiao, H.-Y.; Du, J.-R. Nrf2 Signalling Pathway and Autophagy Impact on the Preventive Effect of Green Tea Extract against Alcohol-Induced Liver Injury. J. Pharm. Pharmacol. 2021 , 73 , 986–995. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Zhu, Q.; Zhuo, H.; Yang, L.; Ouyang, H.; Chen, J.; Liu, B.; Huang, H. A Peptide HEPFYGNEGALR from Apostichopus Japonicus Alleviates Acute Alcoholic Liver Injury by Enhancing Antioxidant Response in Male C57BL/6J Mice. Molecules 2022 , 27 , 5839. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Gao, L.; Chen, X.; Fu, Z.; Yin, J.; Wang, Y.; Sun, W.; Ren, H.; Zhang, Y. Kinsenoside Alleviates Alcoholic Liver Injury by Reducing Oxidative Stress, Inhibiting Endoplasmic Reticulum Stress, and Regulating AMPK-Dependent Autophagy. Front. Pharmacol. 2022 , 12 , 747325. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Li, R.; Yu, L.; Qin, Y.; Zhou, Y.; Liu, W.; Li, Y.; Chen, Y.; Xu, Y. Protective Effects of Rare Earth Lanthanum on Acute Ethanol-Induced Oxidative Stress in Mice via Keap 1/Nrf2/P62 Activation. Sci. Total Environ. 2021 , 758 , 143626. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Kurhaluk, N.; Tkachenko, H.; Lukash, O. Melatonin Modulates Oxidative Phosphorylation, Hepatic and Kidney Autophagy-Caused Subclinical Endotoxemia and Acute Ethanol-Induced Oxidative Stress. Chronobiol. Int. 2020 , 37 , 1709–1724. [ Google Scholar ] [ CrossRef ]
• Lin, G.-S.; Zhao, M.-M.; Fu, Q.-C.; Zhao, S.-Y.; Ba, T.-T.; Yu, H.-X. Palmatine Attenuates Hepatocyte Injury by Promoting Autophagy via the AMPK/MTOR Pathway after Alcoholic Liver Disease. Drug Dev. Res. 2022 , 83 , 1613–1622. [ Google Scholar ] [ CrossRef ]
• Zhao, X.; Zhou, M.; Deng, Y.; Guo, C.; Liao, L.; He, L.; Peng, C.; Li, Y. Functional Teas from Penthorum Chinense Pursh Alleviates Ethanol-Induced Hepatic Oxidative Stress and Autophagy Impairment in Zebrafish via Modulating the AMPK/P62/Nrf2/MTOR Signaling Axis. Plant Foods Hum. Nutr. 2022 , 77 , 514–520. [ Google Scholar ] [ CrossRef ]
• Cao, P.; Zhang, Y.; Huang, Z.; Sullivan, M.A.; He, Z.; Wang, J.; Chen, Z.; Hu, H.; Wang, K. The Preventative Effects of Procyanidin on Binge Ethanol-Induced Lipid Accumulation and ROS Overproduction via the Promotion of Hepatic Autophagy. Mol. Nutr. Food Res. 2019 , 63 , 1801255. [ Google Scholar ] [ CrossRef ]
• Yu, X.; Xu, Y.; Zhang, S.; Sun, J.; Liu, P.; Xiao, L.; Tang, Y.; Liu, L.; Yao, P. Quercetin Attenuates Chronic Ethanol-Induced Hepatic Mitochondrial Damage through Enhanced Mitophagy. Nutrients 2016 , 8 , 27. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Zhao, X.; Xue, X.; Wang, J.; Dai, S.; Peng, C.; Li, Y. Quercetin Alleviates Ethanol-Induced Hepatic Steatosis in L02 Cells by Activating TFEB Translocation to Compensate for Inadequate Autophagy. Phytother. Res. 2023 , 37 , 62–76. [ Google Scholar ] [ CrossRef ]
• Lin, H.; Guo, X.; Liu, J.; Liu, P.; Mei, G.; Li, H.; Li, D.; Chen, H.; Chen, L.; Zhao, Y.; et al. Improving Lipophagy by Restoring Rab7 Cycle: Protective Effects of Quercetin on Ethanol-Induced Liver Steatosis. Nutrients 2022 , 14 , 658. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Zhu, M.; Zhou, X.; Zhao, J. Quercetin Prevents Alcohol-induced Liver Injury through Targeting of PI3K/Akt/Nuclear Factor-κB and STAT3 Signaling Pathway. Exp. Ther. Med. 2017 , 14 , 6169–6175. [ Google Scholar ] [ CrossRef ] [ PubMed ] [ Green Version ]
• Zeng, H.; Guo, X.; Zhou, F.; Xiao, L.; Liu, J.; Jiang, C.; Xing, M.; Yao, P. Quercetin Alleviates Ethanol-Induced Liver Steatosis Associated with Improvement of Lipophagy. Food Chem. Toxicol. Int. J. Publ. Br. Ind. Biol. Res. Assoc. 2019 , 125 , 21–28. [ Google Scholar ] [ CrossRef ]
• Zhao, X.; Gong, L.; Wang, C.; Liu, M.; Hu, N.; Dai, X.; Peng, C.; Li, Y. Quercetin Mitigates Ethanol-Induced Hepatic Steatosis in Zebrafish via P2X7R-Mediated PI3K/ Keap1/Nrf2 Signaling Pathway. J. Ethnopharmacol. 2021 , 268 , 113569. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Tang, L.; Yang, F.; Fang, Z.; Hu, C. Resveratrol Ameliorates Alcoholic Fatty Liver by Inducing Autophagy. Am. J. Chin. Med. 2016 , 44 , 1207–1220. [ Google Scholar ] [ CrossRef ]
• You, M.; Liang, X.; Ajmo, J.M.; Ness, G.C. Involvement of Mammalian Sirtuin 1 in the Action of Ethanol in the Liver. Am. J. Physiol. Gastrointest. Liver Physiol. 2008 , 294 , G892–G898. [ Google Scholar ] [ CrossRef ] [ PubMed ] [ Green Version ]
• Shi, X.; Sun, R.; Zhao, Y.; Fu, R.; Wang, R.; Zhao, H.; Wang, Z.; Tang, F.; Zhang, N.; Tian, X.; et al. Promotion of Autophagosome–Lysosome Fusion via Salvianolic Acid A-Mediated SIRT1 up-Regulation Ameliorates Alcoholic Liver Disease. RSC Adv. 2018 , 8 , 20411–20422. [ Google Scholar ] [ CrossRef ]
• Fu, X.; Zhong, Z.; Hu, F.; Zhang, Y.; Li, C.; Yan, P.; Feng, L.; Shen, J.; Huang, B. The Protective Effects of Selenium-Enriched Spirulina Platensis on Chronic Alcohol-Induced Liver Injury in Mice. Food Funct. 2018 , 9 , 3155–3165. [ Google Scholar ] [ CrossRef ]
• Song, X.-Y.; Liu, P.-C.; Liu, W.-W.; Zhou, J.; Hayashi, T.; Mizuno, K.; Hattori, S.; Fujisaki, H.; Ikejima, T. Silibinin Inhibits Ethanol- or Acetaldehyde-Induced Ferroptosis in Liver Cell Lines. Toxicol. Vitro Int. J. Publ. Assoc. BIBRA 2022 , 82 , 105388. [ Google Scholar ] [ CrossRef ]
• Atef, M.M.; Hafez, Y.M.; Alshenawy, H.A.; Emam, M.N. Ameliorative Effects of Autophagy Inducer, Simvastatin on Alcohol-Induced Liver Disease in a Rat Model. J. Cell. Biochem. 2019 , 120 , 7679–7688. [ Google Scholar ] [ CrossRef ]
• Zhou, R.; Lin, J.; Wu, D. Sulforaphane Induces Nrf2 and Protects against CYP2E1-Dependent Binge Alcohol-Induced Liver Steatosis. Biochim. Biophys. Acta 2014 , 1840 , 209–218. [ Google Scholar ] [ CrossRef ] [ PubMed ] [ Green Version ]
• Guo, J.; Chen, Y.; Yuan, F.; Peng, L.; Qiu, C. Tangeretin Protects Mice from Alcohol-Induced Fatty Liver by Activating Mitophagy through the AMPK–ULK1 Pathway. J. Agric. Food Chem. 2022 , 70 , 11236–11244. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Zhou, Y.; Wu, R.; Wang, X.; Jiang, Y.; Xu, W.; Shao, Y.; Yue, C.; Shi, W.; Jin, H.; Ge, T.; et al. Activation of UQCRC2-Dependent Mitophagy by Tetramethylpyrazine Inhibits MLKL-Mediated Hepatocyte Necroptosis in Alcoholic Liver Disease. Free Radic. Biol. Med. 2022 , 179 , 301–316. [ Google Scholar ] [ CrossRef ]
• Thoreen, C.C.; Kang, S.A.; Chang, J.W.; Liu, Q.; Zhang, J.; Gao, Y.; Reichling, L.J.; Sim, T.; Sabatini, D.M.; Gray, N.S. An ATP-Competitive Mammalian Target of Rapamycin Inhibitor Reveals Rapamycin-Resistant Functions of MTORC1. J. Biol. Chem. 2009 , 284 , 8023–8032. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Wu, W.-B.; Chen, Y.-Y.; Zhu, B.; Peng, X.-M.; Zhang, S.-W.; Zhou, M.-L. Excessive Bile Acid Activated NF-Kappa B and Promoted the Development of Alcoholic Steatohepatitis in Farnesoid X Receptor Deficient Mice. Biochimie 2015 , 115 , 86–92. [ Google Scholar ] [ CrossRef ]
• Liuzzi, J.P.; Yoo, C. Role of Zinc in the Regulation of Autophagy During Ethanol Exposure in Human Hepatoma Cells. Biol. Trace Elem. Res. 2013 , 156 , 350–356. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Li, S.; Tan, H.-Y.; Wang, N.; Zhang, Z.-J.; Lao, L.; Wong, C.-W.; Feng, Y. The Role of Oxidative Stress and Antioxidants in Liver Diseases. Int. J. Mol. Sci. 2015 , 16 , 26087–26124. [ Google Scholar ] [ CrossRef ] [ PubMed ] [ Green Version ]
• Wu, D.; Cederbaum, A.I. Oxidative Stress and Alcoholic Liver Disease. Semin. Liver Dis. 2009 , 29 , 141–154. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Zhu, H.; Jia, Z.; Misra, H.; Li, Y.R. Oxidative Stress and Redox Signaling Mechanisms of Alcoholic Liver Disease: Updated Experimental and Clinical Evidence. J. Dig. Dis. 2012 , 13 , 133–142. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Balbo, S.; Hashibe, M.; Gundy, S.; Brennan, P.; Canova, C.; Simonato, L.; Merletti, F.; Richiardi, L.; Agudo, A.; Castellsagué, X.; et al. N2-Ethyldeoxyguanosine as a Potential Biomarker for Assessing Effects of Alcohol Consumption on DNA. Cancer Epidemiol. Biomark. Prev. Publ. Am. Assoc. Cancer Res. Cosponsored Am. Soc. Prev. Oncol. 2008 , 17 , 3026–3032. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Lucey, M.R.; Mathurin, P.; Morgan, T.R. Alcoholic Hepatitis. N. Engl. J. Med. 2009 , 360 , 2758–2769. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Tan, H.K.; Yates, E.; Lilly, K.; Dhanda, A.D. Oxidative Stress in Alcohol-Related Liver Disease. World J. Hepatol. 2020 , 12 , 332–349. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Yang, Y.M.; Cho, Y.E.; Hwang, S. Crosstalk between Oxidative Stress and Inflammatory Liver Injury in the Pathogenesis of Alcoholic Liver Disease. Int. J. Mol. Sci. 2022 , 23 , 774. [ Google Scholar ] [ CrossRef ]
• Cederbaum, A.I. Alcohol Metabolism. Clin. Liver Dis. 2012 , 16 , 667–685. [ Google Scholar ] [ CrossRef ] [ PubMed ] [ Green Version ]
• Cederbaum, A.I.; Lu, Y.; Wu, D. Role of Oxidative Stress in Alcohol-Induced Liver Injury. Arch. Toxicol. 2009 , 83 , 519–548. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Jiang, Y.; Zhang, T.; Kusumanchi, P.; Han, S.; Yang, Z.; Liangpunsakul, S. Alcohol Metabolizing Enzymes, Microsomal Ethanol Oxidizing System, Cytochrome P450 2E1, Catalase, and Aldehyde Dehydrogenase in Alcohol-Associated Liver Disease. Biomedicines 2020 , 8 , 50. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Galli, A.; Pinaire, J.; Fischer, M.; Dorris, R.; Crabb, D.W. The Transcriptional and DNA Binding Activity of Peroxisome Proliferator-Activated Receptor Alpha Is Inhibited by Ethanol Metabolism. A Novel Mechanism for the Development of Ethanol-Induced Fatty Liver. J. Biol. Chem. 2001 , 276 , 68–75. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• You, M.; Fischer, M.; Deeg, M.A.; Crabb, D.W. Ethanol Induces Fatty Acid Synthesis Pathways by Activation of Sterol Regulatory Element-Binding Protein (SREBP). J. Biol. Chem. 2002 , 277 , 29342–29347. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Donohue, T.M.; Osna, N.A.; Trambly, C.S.; Whitaker, N.P.; Thomes, P.G.; Todero, S.L.; Davis, J.S. Early Growth Response-1 Contributes to Steatosis Development after Acute Ethanol Administration. Alcohol. Clin. Exp. Res. 2012 , 36 , 759–767. [ Google Scholar ] [ CrossRef ]
• Sacitharan, P.K.; Bou-Gharios, G.; Edwards, J.R. SIRT1 Directly Activates Autophagy in Human Chondrocytes. Cell Death Discov. 2020 , 6 , 41. [ Google Scholar ] [ CrossRef ]
• Zhao, R.-Z.; Jiang, S.; Zhang, L.; Yu, Z.-B. Mitochondrial Electron Transport Chain, ROS Generation and Uncoupling (Review). Int. J. Mol. Med. 2019 , 44 , 3–15. [ Google Scholar ] [ CrossRef ] [ PubMed ] [ Green Version ]
• Seitz, H.K.; Stickel, F. Risk Factors and Mechanisms of Hepatocarcinogenesis with Special Emphasis on Alcohol and Oxidative Stress. Biol. Chem. 2006 , 387 , 349–360. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Lu, Y.; Cederbaum, A.I. CYP2E1 and Oxidative Liver Injury by Alcohol. Free Radic. Biol. Med. 2008 , 44 , 723–738. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Ambade, A.; Mandrekar, P. Oxidative Stress and Inflammation: Essential Partners in Alcoholic Liver Disease. Int. J. Hepatol. 2012 , 2012 , 853175. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Song, Q.; Chen, Y.; Wang, J.; Hao, L.; Huang, C.; Griffiths, A.; Sun, Z.; Zhou, Z.; Song, Z. ER Stress-Induced Upregulation of NNMT Contributes to Alcohol-Related Fatty Liver Development. J. Hepatol. 2020 , 73 , 783–793. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Ding, Q.; Ma, Y.; Lai, S.; Dou, X.; Li, S. NNMT Aggravates Hepatic Steatosis, but Alleviates Liver Injury in Alcoholic Liver Disease. J. Hepatol. 2021 , 74 , 1248–1250. [ Google Scholar ] [ CrossRef ]
• Campagna, R.; Vignini, A. NAD + Homeostasis and NAD + -Consuming Enzymes: Implications for Vascular Health. Antioxidants 2023 , 12 , 376. [ Google Scholar ] [ CrossRef ]
• Shin, J.H.; Park, C.W.; Yoon, G.; Hong, S.M.; Choi, K.Y. NNMT Depletion Contributes to Liver Cancer Cell Survival by Enhancing Autophagy under Nutrient Starvation. Oncogenesis 2018 , 7 , 58. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Yu, H.; Zhou, X.; Wang, Y.; Huang, X.; Yang, J.; Zeng, J.; Li, G.; Xie, X.; Zhang, J. Nicotinamide N-Methyltransferase Inhibits Autophagy Induced by Oxidative Stress through Suppressing the AMPK Pathway in Breast Cancer Cells. Cancer Cell Int. 2020 , 20 , 191. [ Google Scholar ] [ CrossRef ]
• Campagna, R.; Mateuszuk, Ł.; Wojnar-Lason, K.; Kaczara, P.; Tworzydło, A.; Kij, A.; Bujok, R.; Mlynarski, J.; Wang, Y.; Sartini, D.; et al. Nicotinamide N-Methyltransferase in Endothelium Protects against Oxidant Stress-Induced Endothelial Injury. Biochim. Biophys. Acta Mol. Cell Res. 2021 , 1868 , 119082. [ Google Scholar ] [ CrossRef ]
• Guarino, M.; Dufour, J.-F. Nicotinamide and NAFLD: Is There Nothing New Under the Sun? Metabolites 2019 , 9 , 180. [ Google Scholar ] [ CrossRef ] [ PubMed ] [ Green Version ]
• Filomeni, G.; Rotilio, G.; Ciriolo, M.R. Disulfide Relays and Phosphorylative Cascades: Partners in Redox-Mediated Signaling Pathways. Cell Death Differ. 2005 , 12 , 1555–1563. [ Google Scholar ] [ CrossRef ] [ PubMed ] [ Green Version ]
• Lu, L.; Jiang, J.; Zhan, M.; Zhang, H.; Wang, Q.-T.; Sun, S.-N.; Guo, X.-K.; Yin, H.; Wei, Y.; Liu, J.O.; et al. Targeting Neoantigens in Hepatocellular Carcinoma for Immunotherapy: A Futile Strategy? Hepatology 2021 , 73 , 414–421. [ Google Scholar ] [ CrossRef ] [ PubMed ] [ Green Version ]
• Zapotoczny, B.; Braet, F.; Kus, E.; Ginda-Mäkelä, K.; Klejevskaja, B.; Campagna, R.; Chlopicki, S.; Szymonski, M. Actin-spectrin Scaffold Supports Open Fenestrae in Liver Sinusoidal Endothelial Cells. Traffic Cph. Den. 2019 , 20 , 932–942. [ Google Scholar ] [ CrossRef ]
• Kimata, Y.; Kohno, K. Endoplasmic Reticulum Stress-Sensing Mechanisms in Yeast and Mammalian Cells. Curr. Opin. Cell Biol. 2011 , 23 , 135–142. [ Google Scholar ] [ CrossRef ]
• Bailey, S.M. A Review of the Role of Reactive Oxygen and Nitrogen Species in Alcohol-Induced Mitochondrial Dysfunction. Free Radic. Res. 2003 , 37 , 585–596. [ Google Scholar ] [ CrossRef ]
• Haldar, S.M.; Stamler, J.S. S-Nitrosylation at the Interface of Autophagy and Disease. Mol. Cell 2011 , 43 , 1–3. [ Google Scholar ] [ CrossRef ]
• Rizza, S.; Cardaci, S.; Montagna, C.; Di Giacomo, G.; De Zio, D.; Bordi, M.; Maiani, E.; Campello, S.; Borreca, A.; Puca, A.A.; et al. S-Nitrosylation Drives Cell Senescence and Aging in Mammals by Controlling Mitochondrial Dynamics and Mitophagy. Proc. Natl. Acad. Sci. USA 2018 , 115 , E3388–E3397. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Dalleau, S.; Baradat, M.; Guéraud, F.; Huc, L. Cell Death and Diseases Related to Oxidative Stress: 4-Hydroxynonenal (HNE) in the Balance. Cell Death Differ. 2013 , 20 , 1615–1630. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Linhart, K.; Bartsch, H.; Seitz, H.K. The Role of Reactive Oxygen Species (ROS) and Cytochrome P-450 2E1 in the Generation of Carcinogenic Etheno-DNA Adducts. Redox Biol. 2014 , 3 , 56–62. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Abdelmegeed, M.A.; Choi, Y.; Ha, S.-K.; Song, B.-J. Cytochrome P450-2E1 Promotes Aging-Related Hepatic Steatosis, Apoptosis and Fibrosis through Increased Nitroxidative Stress. Free Radic. Biol. Med. 2016 , 91 , 188–202. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Nair, J.; Srivatanakul, P.; Haas, C.; Jedpiyawongse, A.; Khuhaprema, T.; Seitz, H.K.; Bartsch, H. High Urinary Excretion of Lipid Peroxidation-Derived DNA Damage in Patients with Cancer-Prone Liver Diseases. Mutat. Res. 2010 , 683 , 23–28. [ Google Scholar ] [ CrossRef ]
• Neeley, W.L.; Essigmann, J.M. Mechanisms of Formation, Genotoxicity, and Mutation of Guanine Oxidation Products. Chem. Res. Toxicol. 2006 , 19 , 491–505. [ Google Scholar ] [ CrossRef ]
• Nassir, F.; Ibdah, J.A. Role of Mitochondria in Alcoholic Liver Disease. World J. Gastroenterol. 2014 , 20 , 2136–2142. [ Google Scholar ] [ CrossRef ]
• Grattagliano, I.; Russmann, S.; Diogo, C.; Bonfrate, L.; Oliveira, P.J.; Wang, D.Q.-H.; Portincasa, P. Mitochondria in Chronic Liver Disease. Curr. Drug Targets 2011 , 12 , 879–893. [ Google Scholar ] [ CrossRef ]
• García-Ruiz, C.; Kaplowitz, N.; Fernandez-Checa, J.C. Role of Mitochondria in Alcoholic Liver Disease. Curr. Pathobiol. Rep. 2013 , 1 , 159–168. [ Google Scholar ] [ CrossRef ] [ PubMed ] [ Green Version ]
• Bratton, S.B.; Salvesen, G.S. Regulation of the Apaf-1–Caspase-9 Apoptosome. J. Cell Sci. 2010 , 123 , 3209–3214. [ Google Scholar ] [ CrossRef ] [ PubMed ] [ Green Version ]
• Harijith, A.; Ebenezer, D.L.; Natarajan, V. Reactive Oxygen Species at the Crossroads of Inflammasome and Inflammation. Front. Physiol. 2014 , 5 , 352. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Seki, E.; Brenner, D.A.; Karin, M. A Liver Full of JNK: Signaling in Regulation of Cell Function and Disease Pathogenesis, and Clinical Approaches. Gastroenterology 2012 , 143 , 307–320. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Pastorino, J.G.; Shulga, N.; Hoek, J.B. TNF-α-Induced Cell Death in Ethanol-Exposed Cells Depends on P38 MAPK Signaling but Is Independent of Bid and Caspase-8. Am. J. Physiol. Gastrointest. Liver Physiol. 2003 , 285 , G503–G516. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Wallace, D.C. Mitochondrial DNA Mutations in Disease and Aging. Environ. Mol. Mutagen. 2010 , 51 , 440–450. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Lenaz, G. Mitochondria and Reactive Oxygen Species. Which Role in Physiology and Pathology? Adv. Exp. Med. Biol. 2012 , 942 , 93–136. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Kim, I.; Lemasters, J.J. Mitophagy Selectively Degrades Individual Damaged Mitochondria after Photoirradiation. Antioxid. Redox Signal. 2011 , 14 , 1919–1928. [ Google Scholar ] [ CrossRef ] [ PubMed ] [ Green Version ]
• Bergamini, E.; Cavallini, G.; Donati, A.; Gori, Z. The Anti-Ageing Effects of Caloric Restriction May Involve Stimulation of Macroautophagy and Lysosomal Degradation, and Can Be Intensified Pharmacologically. Biomed. Pharmacother. 2003 , 57 , 203–208. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Dolganiuc, A.; Thomes, P.G.; Ding, W.-X.; Lemasters, J.J.; Donohue, T.M. Autophagy in Alcohol-Induced Liver Diseases. Alcohol. Clin. Exp. Res. 2012 , 36 , 1301–1308. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Wu, D.; Cederbaum, A. Glutathione Depletion in CYP2E1-Expressing Liver Cells Induces Toxicity Due to the Activation of P38 Mitogen-Activated Protein Kinase and Reduction of Nuclear Factor-ΚB DNA Binding Activity. Mol. Pharmacol. 2004 , 66 , 749–760. [ Google Scholar ] [ CrossRef ]
• Albano, E. Oxidative Mechanisms in the Pathogenesis of Alcoholic Liver Disease. Mol. Aspects Med. 2008 , 29 , 9–16. [ Google Scholar ] [ CrossRef ]
• Micheau, O.; Tschopp, J. Induction of TNF Receptor I-Mediated Apoptosis via Two Sequential Signaling Complexes. Cell 2003 , 114 , 181–190. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Kim, Y.-S.; Morgan, M.J.; Choksi, S.; Liu, Z.-G. TNF-Induced Activation of the Nox1 NADPH Oxidase and Its Role in the Induction of Necrotic Cell Death. Mol. Cell 2007 , 26 , 675–687. [ Google Scholar ] [ CrossRef ]
• Wang, C.; Li, X.; Wang, H.; Xie, Q.; Xu, Y. Notch1-Nuclear Factor ΚB Involves in Oxidative Stress-Induced Alcoholic Steatohepatitis. Alcohol Alcohol. 2014 , 49 , 10–16. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Tilg, H.; Moschen, A.R.; Szabo, G. Interleukin-1 and Inflammasomes in Alcoholic Liver Disease/Acute Alcoholic Hepatitis and Nonalcoholic Fatty Liver Disease/Nonalcoholic Steatohepatitis. Hepatology 2016 , 64 , 955–965. [ Google Scholar ] [ CrossRef ] [ PubMed ] [ Green Version ]
• Tschopp, J.; Schroder, K. NLRP3 Inflammasome Activation: The Convergence of Multiple Signalling Pathways on ROS Production? Nat. Rev. Immunol. 2010 , 10 , 210–215. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Sun, Q.; Zhang, W.; Zhong, W.; Sun, X.; Zhou, Z. Pharmacological Inhibition of NOX4 Ameliorates Alcohol-Induced Liver Injury in Mice through Improving Oxidative Stress and Mitochondrial Function. Biochim. Biophys. Acta Gen. Subj. 2017 , 1861 , 2912–2921. [ Google Scholar ] [ CrossRef ] [ PubMed ] [ Green Version ]
• Kono, H.; Rusyn, I.; Yin, M.; Gäbele, E.; Yamashina, S.; Dikalova, A.; Kadiiska, M.B.; Connor, H.D.; Mason, R.P.; Segal, B.H.; et al. NADPH Oxidase-Derived Free Radicals Are Key Oxidants in Alcohol-Induced Liver Disease. J. Clin. Investig. 2000 , 106 , 867–872. [ Google Scholar ] [ CrossRef ] [ PubMed ] [ Green Version ]
• Thakur, V.; Pritchard, M.T.; McMullen, M.R.; Wang, Q.; Nagy, L.E. Chronic Ethanol Feeding Increases Activation of NADPH Oxidase by Lipopolysaccharide in Rat Kupffer Cells: Role of Increased Reactive Oxygen in LPS-Stimulated ERK1/2 Activation and TNF-Alpha Production. J. Leukoc. Biol. 2006 , 79 , 1348–1356. [ Google Scholar ] [ CrossRef ] [ PubMed ] [ Green Version ]
• Milic, S.; Mikolasevic, I.; Orlic, L.; Devcic, E.; Starcevic-Cizmarevic, N.; Stimac, D.; Kapovic, M.; Ristic, S. The Role of Iron and Iron Overload in Chronic Liver Disease. Med. Sci. Monit. Int. Med. J. Exp. Clin. Res. 2016 , 22 , 2144–2151. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Purohit, V.; Russo, D.; Salin, M. Role of Iron in Alcoholic Liver Disease: Introduction and Summary of the Symposium. Alcohol Fayettev. N 2003 , 30 , 93–97. [ Google Scholar ] [ CrossRef ]
• Wen, Q.; Liu, J.; Kang, R.; Zhou, B.; Tang, D. The Release and Activity of HMGB1 in Ferroptosis. Biochem. Biophys. Res. Commun. 2019 , 510 , 278–283. [ Google Scholar ] [ CrossRef ]
• Filomeni, G.; De Zio, D.; Cecconi, F. Oxidative Stress and Autophagy: The Clash between Damage and Metabolic Needs. Cell Death Differ. 2015 , 22 , 377–388. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Choi, D.W.; Kim, S.Y.; Kim, S.K.; Kim, Y.C. Factors Involved in Hepatic Glutathione Depletion Induced by Acute Ethanol Administration. J. Toxicol. Environ. Health A 2000 , 60 , 459–469. [ Google Scholar ] [ CrossRef ]
• Mantena, S.K.; King, A.L.; Andringa, K.K.; Landar, A.; Darley-Usmar, V.; Bailey, S.M. Novel Interactions of Mitochondria and Reactive Oxygen/Nitrogen Species in Alcohol Mediated Liver Disease. World J. Gastroenterol. 2007 , 13 , 4967–4973. [ Google Scholar ] [ CrossRef ] [ PubMed ] [ Green Version ]
• Nogueira, V.; Hay, N. Molecular Pathways: Reactive Oxygen Species Homeostasis in Cancer Cells and Implications for Cancer Therapy. Clin. Cancer Res. Off. J. Am. Assoc. Cancer Res. 2013 , 19 , 4309–4314. [ Google Scholar ] [ CrossRef ] [ PubMed ] [ Green Version ]
• Zhao, Y.; Hu, X.; Liu, Y.; Dong, S.; Wen, Z.; He, W.; Zhang, S.; Huang, Q.; Shi, M. ROS Signaling under Metabolic Stress: Cross-Talk between AMPK and AKT Pathway. Mol. Cancer 2017 , 16 , 79. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Meijer, A.J.; Codogno, P. Regulation and Role of Autophagy in Mammalian Cells. Int. J. Biochem. Cell Biol. 2004 , 36 , 2445–2462. [ Google Scholar ] [ CrossRef ]
• Mijaljica, D.; Prescott, M.; Devenish, R.J. Microautophagy in Mammalian Cells: Revisiting a 40-Year-Old Conundrum. Autophagy 2011 , 7 , 673–682. [ Google Scholar ] [ CrossRef ] [ PubMed ] [ Green Version ]
• Kaushik, S.; Cuervo, A.M. Chaperone-Mediated Autophagy: A Unique Way to Enter the Lysosome World. Trends Cell Biol. 2012 , 22 , 407–417. [ Google Scholar ] [ CrossRef ] [ PubMed ] [ Green Version ]
• Williams, J.A.; Ding, W.-X. Mechanisms, Pathophysiological Roles and Methods for Analyzing Mitophagy—Recent Insights. Biol. Chem. 2018 , 399 , 147–178. [ Google Scholar ] [ CrossRef ] [ PubMed ] [ Green Version ]
• Gatica, D.; Lahiri, V.; Klionsky, D.J. Cargo Recognition and Degradation by Selective Autophagy. Nat. Cell Biol. 2018 , 20 , 233–242. [ Google Scholar ] [ CrossRef ]
• Garcia-Macia, M.; Santos-Ledo, A.; Leslie, J.; Paish, H.L.; Collins, A.L.; Scott, R.S.; Watson, A.; Burgoyne, R.A.; White, S.; French, J.; et al. A Mammalian Target of Rapamycin-Perilipin 3 (MTORC1-Plin3) Pathway Is Essential to Activate Lipophagy and Protects Against Hepatosteatosis. Hepatology 2021 , 74 , 3441–3459. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Green, D.R.; Levine, B. To Be or Not to Be? How Selective Autophagy and Cell Death Govern Cell Fate. Cell 2014 , 157 , 65–75. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Kumar, A.V.; Mills, J.; Lapierre, L.R. Selective Autophagy Receptor P62/SQSTM1, a Pivotal Player in Stress and Aging. Front. Cell Dev. Biol. 2022 , 10 , 793328. [ Google Scholar ] [ CrossRef ]
• Mizushima, N. The Role of the Atg1/ULK1 Complex in Autophagy Regulation. Curr. Opin. Cell Biol. 2010 , 22 , 132–139. [ Google Scholar ] [ CrossRef ]
• Kim, J.; Kundu, M.; Viollet, B.; Guan, K.-L. AMPK and MTOR Regulate Autophagy through Direct Phosphorylation of Ulk1. Nat. Cell Biol. 2011 , 13 , 132–141. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Høyer-Hansen, M.; Jäättelä, M. AMP-Activated Protein Kinase: A Universal Regulator of Autophagy? Autophagy 2007 , 3 , 381–383. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Liang, J.; Shao, S.H.; Xu, Z.-X.; Hennessy, B.; Ding, Z.; Larrea, M.; Kondo, S.; Dumont, D.J.; Gutterman, J.U.; Walker, C.L.; et al. The Energy Sensing LKB1-AMPK Pathway Regulates P27(Kip1) Phosphorylation Mediating the Decision to Enter Autophagy or Apoptosis. Nat. Cell Biol. 2007 , 9 , 218–224. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Hamasaki, M.; Furuta, N.; Matsuda, A.; Nezu, A.; Yamamoto, A.; Fujita, N.; Oomori, H.; Noda, T.; Haraguchi, T.; Hiraoka, Y.; et al. Autophagosomes Form at ER–Mitochondria Contact Sites. Nature 2013 , 495 , 389–393. [ Google Scholar ] [ CrossRef ]
• Itakura, E.; Kishi, C.; Inoue, K.; Mizushima, N. Beclin 1 Forms Two Distinct Phosphatidylinositol 3-Kinase Complexes with Mammalian Atg14 and UVRAG. Mol. Biol. Cell 2008 , 19 , 5360–5372. [ Google Scholar ] [ CrossRef ] [ PubMed ] [ Green Version ]
• Matsunaga, K.; Saitoh, T.; Tabata, K.; Omori, H.; Satoh, T.; Kurotori, N.; Maejima, I.; Shirahama-Noda, K.; Ichimura, T.; Isobe, T.; et al. Two Beclin 1-Binding Proteins, Atg14L and Rubicon, Reciprocally Regulate Autophagy at Different Stages. Nat. Cell Biol. 2009 , 11 , 385–396. [ Google Scholar ] [ CrossRef ]
• Russell, R.C.; Tian, Y.; Yuan, H.; Park, H.W.; Chang, Y.-Y.; Kim, J.; Kim, H.; Neufeld, T.P.; Dillin, A.; Guan, K.-L. ULK1 Induces Autophagy by Phosphorylating Beclin-1 and Activating VPS34 Lipid Kinase. Nat. Cell Biol. 2013 , 15 , 741–750. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Obara, K.; Ohsumi, Y. Dynamics and Function of PtdIns(3)P in Autophagy. Autophagy 2008 , 4 , 952–954. [ Google Scholar ] [ CrossRef ] [ PubMed ] [ Green Version ]
• Polson, H.E.J.; de Lartigue, J.; Rigden, D.J.; Reedijk, M.; Urbé, S.; Clague, M.J.; Tooze, S.A. Mammalian Atg18 (WIPI2) Localizes to Omegasome-Anchored Phagophores and Positively Regulates LC3 Lipidation. Autophagy 2010 , 6 , 506–522. [ Google Scholar ] [ CrossRef ] [ PubMed ] [ Green Version ]
• Yoshii, S.R.; Mizushima, N. Monitoring and Measuring Autophagy. Int. J. Mol. Sci. 2017 , 18 , 1865. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Ohsumi, Y. Molecular Dissection of Autophagy: Two Ubiquitin-like Systems. Nat. Rev. Mol. Cell Biol. 2001 , 2 , 211–216. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Lamb, C.A.; Yoshimori, T.; Tooze, S.A. The Autophagosome: Origins Unknown, Biogenesis Complex. Nat. Rev. Mol. Cell Biol. 2013 , 14 , 759–774. [ Google Scholar ] [ CrossRef ]
• Tan, C.T.; Soh, N.J.H.; Chang, H.-C.; Yu, V.C. P62/SQSTM1 in Liver Diseases: The Usual Suspect with Multifarious Identities. FEBS J. 2021 , 290 , 892–912. [ Google Scholar ] [ CrossRef ]
• Takahashi, Y.; He, H.; Tang, Z.; Hattori, T.; Liu, Y.; Young, M.M.; Serfass, J.M.; Chen, L.; Gebru, M.; Chen, C.; et al. An Autophagy Assay Reveals the ESCRT-III Component CHMP2A as a Regulator of Phagophore Closure. Nat. Commun. 2018 , 9 , 2855. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Takahashi, Y.; Liang, X.; Hattori, T.; Tang, Z.; He, H.; Chen, H.; Liu, X.; Abraham, T.; Imamura-Kawasawa, Y.; Buchkovich, N.J.; et al. VPS37A Directs ESCRT Recruitment for Phagophore Closure. J. Cell Biol. 2019 , 218 , 3336–3354. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Itakura, E.; Kishi-Itakura, C.; Mizushima, N. The Hairpin-Type Tail-Anchored SNARE Syntaxin 17 Targets to Autophagosomes for Fusion with Endosomes/Lysosomes. Cell 2012 , 151 , 1256–1269. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Shen, Q.; Shi, Y.; Liu, J.; Su, H.; Huang, J.; Zhang, Y.; Peng, C.; Zhou, T.; Sun, Q.; Wan, W.; et al. Acetylation of STX17 (Syntaxin 17) Controls Autophagosome Maturation. Autophagy 2021 , 17 , 1157–1169. [ Google Scholar ] [ CrossRef ]
• Hubert, V.; Peschel, A.; Langer, B.; Gröger, M.; Rees, A.; Kain, R. LAMP-2 Is Required for Incorporating Syntaxin-17 into Autophagosomes and for Their Fusion with Lysosomes. Biol. Open 2016 , 5 , 1516–1529. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Settembre, C.; Di Malta, C.; Polito, V.A.; Garcia Arencibia, M.; Vetrini, F.; Erdin, S.; Erdin, S.U.; Huynh, T.; Medina, D.; Colella, P.; et al. TFEB Links Autophagy to Lysosomal Biogenesis. Science 2011 , 332 , 1429–1433. [ Google Scholar ] [ CrossRef ] [ PubMed ] [ Green Version ]
• Iorio, R.; Celenza, G.; Petricca, S. Mitophagy: Molecular Mechanisms, New Concepts on Parkin Activation and the Emerging Role of AMPK/ULK1 Axis. Cells 2021 , 11 , 30. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Geisler, S.; Holmström, K.M.; Skujat, D.; Fiesel, F.C.; Rothfuss, O.C.; Kahle, P.J.; Springer, W. PINK1/Parkin-Mediated Mitophagy Is Dependent on VDAC1 and P62/SQSTM1. Nat. Cell Biol. 2010 , 12 , 119–131. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Youle, R.J.; Narendra, D.P. Mechanisms of Mitophagy. Nat. Rev. Mol. Cell Biol. 2011 , 12 , 9–14. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Jin, S.M.; Lazarou, M.; Wang, C.; Kane, L.A.; Narendra, D.P.; Youle, R.J. Mitochondrial Membrane Potential Regulates PINK1 Import and Proteolytic Destabilization by PARL. J. Cell Biol. 2010 , 191 , 933–942. [ Google Scholar ] [ CrossRef ] [ PubMed ] [ Green Version ]
• Iguchi, M.; Kujuro, Y.; Okatsu, K.; Koyano, F.; Kosako, H.; Kimura, M.; Suzuki, N.; Uchiyama, S.; Tanaka, K.; Matsuda, N. Parkin-Catalyzed Ubiquitin-Ester Transfer Is Triggered by PINK1-Dependent Phosphorylation. J. Biol. Chem. 2013 , 288 , 22019–22032. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Bjørkøy, G.; Lamark, T.; Johansen, T. P62/SQSTM1: A Missing Link between Protein Aggregates and the Autophagy Machinery. Autophagy 2006 , 2 , 138–139. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Ichimura, Y.; Kominami, E.; Tanaka, K.; Komatsu, M. Selective Turnover of P62/A170/SQSTM1 by Autophagy. Autophagy 2008 , 4 , 1063–1066. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Rodriguez, Y.; Dunfield, J.; Roderique, T.; Ni, H.-M. Liver-Adipose Tissue Crosstalk in Alcohol-Associated Liver Disease: The Role of MTOR. Liver Res. 2022 , 6 , 227–237. [ Google Scholar ] [ CrossRef ]
• Li, Y.; Chao, X.; Wang, S.; Williams, J.A.; Ni, H.-M.; Ding, W.-X. Role of Mechanistic Target of Rapamycin and Autophagy in Alcohol-Induced Adipose Atrophy and Liver Injury. Am. J. Pathol. 2020 , 190 , 158–175. [ Google Scholar ] [ CrossRef ]
• Sakane, S.; Hikita, H.; Shirai, K.; Myojin, Y.; Sasaki, Y.; Kudo, S.; Fukumoto, K.; Mizutani, N.; Tahata, Y.; Makino, Y.; et al. White Adipose Tissue Autophagy and Adipose-Liver Crosstalk Exacerbate Nonalcoholic Fatty Liver Disease in Mice. Cell. Mol. Gastroenterol. Hepatol. 2021 , 12 , 1683–1699. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Sid, B.; Verrax, J.; Calderon, P.B. Role of AMPK Activation in Oxidative Cell Damage: Implications for Alcohol-Induced Liver Disease. Biochem. Pharmacol. 2013 , 86 , 200–209. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Zhao, R.; Zhu, M.; Zhou, S.; Feng, W.; Chen, H. Rapamycin-Loaded MPEG-PLGA Nanoparticles Ameliorate Hepatic Steatosis and Liver Injury in Non-Alcoholic Fatty Liver Disease. Front. Chem. 2020 , 8 , 407. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Zhao, X.; Xue, X.; Wang, C.; Wang, J.; Peng, C.; Li, Y. Emerging Roles of Sirtuins in Alleviating Alcoholic Liver Disease: A Comprehensive Review. Int. Immunopharmacol. 2022 , 108 , 108712. [ Google Scholar ] [ CrossRef ]
• Rafiee, S.; Mohammadi, H.; Ghavami, A.; Sadeghi, E.; Safari, Z.; Askari, G. Efficacy of Resveratrol Supplementation in Patients with Nonalcoholic Fatty Liver Disease: A Systematic Review and Meta-Analysis of Clinical Trials. Complement. Ther. Clin. Pract. 2021 , 42 , 101281. [ Google Scholar ] [ CrossRef ]
• Nguyen-Khac, E.; Thevenot, T.; Piquet, M.-A.; Benferhat, S.; Goria, O.; Chatelain, D.; Tramier, B.; Dewaele, F.; Ghrib, S.; Rudler, M.; et al. Glucocorticoids plus N -Acetylcysteine in Severe Alcoholic Hepatitis. N. Engl. J. Med. 2011 , 365 , 1781–1789. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Morley, K.C.; Baillie, A.; Van Den Brink, W.; Chitty, K.E.; Brady, K.; Back, S.E.; Seth, D.; Sutherland, G.; Leggio, L.; Haber, P.S. N-Acetyl Cysteine in the Treatment of Alcohol Use Disorder in Patients with Liver Disease: Rationale for Further Research. Expert Opin. Investig. Drugs 2018 , 27 , 667–675. [ Google Scholar ] [ CrossRef ]
• Ni, Y.-H.; Huo, L.-J.; Li, T.-T. Antioxidant Axis Nrf2-Keap1-ARE in Inhibition of Alcoholic Liver Fibrosis by IL-22. World J. Gastroenterol. 2017 , 23 , 2002–2011. [ Google Scholar ] [ CrossRef ]
• Lu, C.; Xu, W.; Zhang, F.; Shao, J.; Zheng, S. Nrf2 Knockdown Disrupts the Protective Effect of Curcumin on Alcohol-Induced Hepatocyte Necroptosis. Mol. Pharm. 2016 , 13 , 4043–4053. [ Google Scholar ] [ CrossRef ]
• Ospina, R.; Marmolejo-Ramos, F. Performance of Some Estimators of Relative Variability. Front. Appl. Math. Stat. 2019 , 5 , 43. [ Google Scholar ] [ CrossRef ]

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Salete-Granado, D.; Carbonell, C.; Puertas-Miranda, D.; Vega-Rodríguez, V.-J.; García-Macia, M.; Herrero, A.B.; Marcos, M. Autophagy, Oxidative Stress, and Alcoholic Liver Disease: A Systematic Review and Potential Clinical Applications. Antioxidants 2023 , 12 , 1425. https://doi.org/10.3390/antiox12071425

Salete-Granado D, Carbonell C, Puertas-Miranda D, Vega-Rodríguez V-J, García-Macia M, Herrero AB, Marcos M. Autophagy, Oxidative Stress, and Alcoholic Liver Disease: A Systematic Review and Potential Clinical Applications. Antioxidants . 2023; 12(7):1425. https://doi.org/10.3390/antiox12071425

Salete-Granado, Daniel, Cristina Carbonell, David Puertas-Miranda, Víctor-José Vega-Rodríguez, Marina García-Macia, Ana Belén Herrero, and Miguel Marcos. 2023. "Autophagy, Oxidative Stress, and Alcoholic Liver Disease: A Systematic Review and Potential Clinical Applications" Antioxidants 12, no. 7: 1425. https://doi.org/10.3390/antiox12071425

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A Novel Fuzzy Model for Knowledge-Driven Process Optimization in Renewable Energy Projects

• Open access
• Published: 24 June 2024

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• Chicheng Huang 1 ,
• Serhat Yüksel 2 , 3 &
• Hasan Dinçer   ORCID: orcid.org/0000-0002-8072-031X 2 , 4  

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This study is aimed at identifying key indicators to increase knowledge-based process optimization for renewable energy projects. Within this context, a novel fuzzy decision-making model is introduced that has two different stages. The first stage is related to the weighting of the knowledge-based determinants of process optimization in investment decisions by using quantum picture fuzzy rough sets (QPFR)-based multi-step wise weight assessment ratio analysis (M-SWARA). On the other side, the second stage consists of ranking the investment alternatives for process optimization in renewable energy projects via the QPFR-based technique for order preference by similarity (TOPSIS) methodology. The main contribution of this study is that a priority analysis is conducted for information-based factors affecting the performance of renewable energy projects. This situation provides an opportunity for the investments to implement appropriate strategies to increase the optimization of these investments. It is concluded that quality is the most essential indicator with respect to the process optimization of these projects. It can be possible to increase the efficiency of these projects by using better quality products. Innovation has an important role in ensuring the use of quality products in environmental sustainability. Owing to new technologies, it is easier to use more effective and innovative products. This condition also contributes to increasing the efficiency of the energy production process. Furthermore, the findings also denote that the most appropriate energy innovation alternative is the variety of clean energy sources. By focusing on different clean energy alternatives, the risk of interruptions in energy generation can be minimized. In other words, the negative impact of climatic conditions on energy production can be lowered significantly with the help of this situation.

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Introduction

Making correct investment decisions in renewable energy projects is vital. The main purpose of these projects is to produce energy without harming the environment. In this context, owing to the success of these investments, the fight against climate change can be much easier. On the other hand, investments in these projects also contribute to increasing energy supply security. In other words, with the help of these projects, countries are not dependent on others for energy (Dong et al., 2023a ). Furthermore, the right investments in renewable energy projects support economic development. Owing to these investments, new employment opportunities emerge in the country (Dzwigol et al., 2023 ). With the right investment decision in these types of energy, the costs can be reduced to more reasonable levels. This situation can help these investments to be sustainable. Moreover, the correct investments in these projects encourage the development of innovative technologies that allows the industry to develop (Shang et al., 2023 ). In summary, making the right investment decisions in renewable energy projects provides a wide range of benefits, both socially and economically.

Knowledge-based process optimization is essential for performance improvements of renewable energy projects. This situation allows the processes of energy projects to be analyzed comprehensively. In this way, deficiencies in the processes of the projects can be detected (Abbas et al., 2023 ). This issue allows to increase the efficiency of the projects by taking specific actions. Knowledge-based process optimization also helps to reduce the risks of projects (Peng et al., 2023 ). Owing to a detailed analysis, it is possible to make the processes in the projects safer. On the other hand, analyzing processes in this context encourages the emergence of new ideas and innovative solutions. Thus, it can be easier to discover new technologies for these projects and to develop new business models (Jiang et al., 2023 ). Moreover, the analysis of processes in this way requires the use of information based on real data. In this way, it is possible to make objective decisions (Pan et al., 2023 ). Additionally, opportunities for improvement can be identified by analyzing processes in an information-based manner. This ensures that projects evolve continuously over time. Consequently, knowledge-based process optimization is essential to increase the effectiveness of renewable energy projects.

There are various knowledge-based determinants of process optimization in renewable energy investment decisions. In this scope, providing efficiency is a key issue for this condition. The quality of energy generation equipment also contributes significantly to increasing the performance of these projects (Martínez et al., 2023 ). Owing to the preference of high-quality materials, less resources will be used in the process of obtaining energy. This contributes significantly to the achievement of the environmental sustainability targets of the projects. Flexible services contribute to the successful fight against this problem by providing quick responses to the energy system. Flexible service delivery also enables more efficient use of energy resources. In this way, energy demand management will be carried out more successfully (Li & Umair, 2023 ). Meeting customer expectations correctly is another issue that plays an important role in this process. Since customers may prefer companies that can meet their expectations more, businesses need to take action to ensure customer satisfaction demands (Sirr et al., 2023 ).

It is necessary to improve the knowledge-based factors to increase the performance of renewable energy projects. This will contribute to achieving process optimization in these projects. Otherwise, renewable energy projects will not be able to compete with other types of energy investment, and as a result, the sustainability of these projects will become increasingly difficult. However, there are many knowledge-based variables that can affect the development of these projects (Razzaq et al., 2023 ). Moreover, improvements to these factors also create new costs. As it can be understood from here, making too many improvements in the first place causes the costs of the enterprises to increase very much. Since this situation will make the projects financially difficult, it is more reasonable for businesses to start the process with limited improvements. Therefore, it is necessary to conduct a priority analysis for information-based factors affecting the performance of renewable energy projects (Wan et al., 2023 ).

The objective of this study is to identify key indicators to increase knowledge-based process optimization for renewable energy projects. Hence, the main research question of this study is to understand which determinants play the most critical role to provide optimization in these projects. For this purpose, a novel fuzzy decision-making model is introduced by the integration of the quantum picture fuzzy rough sets (QPFR), multi-step wise weight assessment ratio analysis (M-SWARA), and technique for order preference by similarity (TOPSIS). In Stage 1, the knowledge-based determinants of process optimization in investment decisions are identified through a thorough analysis using M-SWARA based on QPFRs. In Stage 2, investment alternatives for process optimization in renewable energy projects are identified and evaluated using linguistic evaluations from decision-makers with QPFR-TOPSIS. The main motivation for making this study is the need for a new decision-making model to examine the optimization of renewable energy projects. Most of the existing models in the literature cannot consider the causal directions of the determinants. However, the indicators of renewable energy optimization may have an influence on each other. Hence, to make a more appropriate evaluation, an impact relation map should be taken into consideration. To satisfy this situation, the M-SWARA methodology is used in this proposed model to weight the indicators.

The main contributions of the study are denoted below.

Conducting a priority analysis for information-based factors affecting the performance of renewable energy projects is the main contribution of the study. Knowledge-based factors should be improved to increase the performance of renewable energy projects (Peng et al., 2023 ). With the help of this issue, project optimization can be achieved more effectively. In this process, there are a lot of knowledge-based variables that can affect the development of these projects. However, the main problem is that improvements to these factors also create new costs. Therefore, because of the budget limitation, it is quite difficult to make improvement for many of these variables. Thus, this priority evaluation helps to increase the performance of renewable energy projects. Additionally, the best investment alternatives for process optimization can be provided to the investors. Hence, more specific strategies can be presented for investors so that effectiveness of the investments can be achieved more easily (Abbas et al., 2023 ).

Another contribution of this study is considering a new decision-making technique with the name of M-SWARA. In spite of many advantages of classical SWARA, the causal directions cannot be identified (Yüksel & Dinçer, 2023 ). This situation is accepted as the main drawback of this technique. To minimize this problem, Xu et al. ( 2023 ) implemented some improvements to the classical SWARA approach, and a new methodology (M-SWARA) is generated so that causal directions can be evaluated. Knowledge-based determinants of process optimization in renewable energy investment decisions may have an influence on each other (Wan et al., 2023 ). Hence, the causality relationship between these items should be considered to make more effective evaluation.

Considering picture fuzzy rough sets in the analysis process provides significant advantages. In this context, the concepts of picture fuzzy sets and rough sets are combined. Hence, uncertainty in the analysis process can be handled more effectively. Using picture fuzzy sets helps to represent membership degrees as intervals. With the help of this issue, vague information can be modeled in a more effective manner. However, in the traditional fuzzy sets, a single membership degree is taken into consideration. Therefore, it can be understood that PFRs help to achieve more accurate findings.

The second section includes the evaluation of literature. The third section focuses on methodology. The fourth section gives information about analysis results. The final parts make discussion and conclusion.

Literature Review

It is of great importance that the projects be efficient to achieve process optimization in renewable energy projects. Ensuring efficiency in these projects means less loss in energy production. Vásquez-Ordóñez et al. ( 2023 ) stated that the efficient use of materials taken into consideration in energy production allows these projects to be more economical and sustainable. On the other hand, Sharif et al. ( 2023 ) identified that an efficient energy investment project contributes to cost savings (Li & Shao, 2023 ). Efficiency is possible by using less amount of resources in energy production. This situation also enables less labor requirement. In this way, it is possible to reduce the costs of energy investment projects (Ainou et al., 2023 ; Iqbal et al., 2023 ). The competitiveness of the projects with less cost also increases significantly. Moreover, Adebayo et al. ( 2023 ) determined that the efficiency of the project also helps to increase performance. In this way, it is possible for renewable energy projects to generate more income. Alharbi et al. ( 2023 ) concluded that the efficiency of projects is also directly related to environmental sustainability. By ensuring efficiency, renewable energy projects can be increased and thus both natural resources and the environment can be successfully protected.

Customer expectations must be met correctly to achieve process optimization in renewable energy projects. Customers have different expectations from renewable energy projects such as safety and low cost. Meeting these expectations increases customers’ satisfaction with the project (Kolte et al., 2023 ). On the other hand, Dong et al. ( 2023b ) claimed that meeting customer expectations also ensures the acceptance of the project by the community. Increasing competitiveness is one of the important benefits of customer satisfaction. The energy sector is a highly competitive field. In this context, Ramzan et al. ( 2023 ) identified that businesses need to meet the expectations of their customers correctly to increase their competitiveness. Since customers may prefer companies that can meet their expectations more, businesses need to take actions to ensure customer satisfaction (Zhu et al., 2023 ; Abid et al., 2024 ). Furthermore, Wang et al. ( 2023 ) stated that meeting customer expectations positively affects the image and reputation of the project. This allows businesses to have an environmentally friendly image. Moreover, according to Hille and Oelker ( 2023 ), taking into consideration customer feedback and requests is a factor that increases customer loyalty. This increases the sustainability and long-term success of the projects.

Flexible service is required to ensure process optimization in renewable energy projects. In this way, energy supply and demand balance can be achieved. Yang and Song ( 2023 ) defined that since renewable energy types are affected by weather conditions, differences in production capacity may occur. To minimize this problem, flexible services are needed (Chien et al., 2023 ; You et al., 2023 ). This variability in renewable energies can cause fluctuations in energy production. Lee et al. ( 2023 ) mentioned that flexible services contribute to the successful fight against this problem by providing quick responses to the energy system. On the other hand, Prokopenko et al. ( 2023 ) identified that flexible service provision also enables more efficient use of energy resources. In this way, energy demand management will be carried out more successfully. Moreover, Temesgen Hordofa et al. ( 2023 ) defined that flexible service delivery is important to meet customer. At different times, there may be variability in the demands of customers for energy. This increases customer satisfaction and enables more effective management of energy demand.

Quality products play a critical role to ensure process optimization in renewable energy projects. In this context, it is possible to increase the efficiency of the project thanks to the quality of energy production equipment (Zhong et al., 2024 ; Zhang et al., 2023a ). Additionally, Taghizadeh-Hesary et al. ( 2023 ) concluded that since this will also mean that more energy can be produced, the performance of the project will also be increased. In addition to this issue, Ai et al. ( 2023 ) claimed that quality products also increase the reliability of the project. Preferring higher quality power generation equipment reduces the possibility of failure in projects. In this way, it is much more possible to ensure the sustainability of the projects (Karimi Alavijeh et al., 2024 ; Gao et al., 2023 ). Moreover, Norouzi et al. ( 2023 ) determined that cost savings can be achieved by using quality products in the energy production process. Also, Liu et al. ( 2023 ) identified that quality products are also important in terms of using advanced technology in the energy sector. In other words, quality products also encourage businesses to use innovative products. Another benefit of choosing quality products is that the negative effects on the environment can be reduced. According to Hou et al. ( 2023 ), owing to the preference of high-quality materials, less resources will be used in the process of obtaining energy. This contributes significantly to the achievement of the environmental sustainability targets of the projects.

As a result of the literature evaluation, the following points can be reached. Renewable energy projects have some significant contributions to both social and economic improvements of the companies. However, there are some barriers for the developments of these projects. In this process, high initial investment costs make the investors worried about these projects. Hence, necessary actions should be taken to solve this problem. Within this framework, optimization plays a critical role to increase the performance of these investments. However, it is quite important to identify which factors play the critical role in this optimization process. Therefore, a comprehensive decision-making model should be generated to make this evaluation effectively.

Methodology

The details of the approaches used in the proposed model are given in this section.

Modeling Uncertainty with QPFRs with Golden Cuts

Managing uncertainty in decision-making processes is of great importance. Otherwise, this uncertainty prevents the analysis results from being accurately predicted. Therefore, there is a risk that the results will be wrong. QPFRs with golden cuts is introduced in this study for decreasing the subjective and uncertain evaluations of decision-making process (Al-Binali et al., 2023 ). In this process, the lower and upper limits as well as a rough boundary interval in the concept of quantum mechanics and golden ratio are taken into consideration. Various probabilities are considered in Quantum theory (Dinçer et al., 2023 ). These sets are adapted to the PFRs. Equations  1 , 2 , and 3  indicate these issues (Carayannis et al., 2023 ).

In this scope, C demonstrates the collection of events,  \({\varphi}^{2}\) is the amplitude results, u demonstrates the event and \({\theta}^{2}\) refers to the phase angle. Picture fuzzy numbers (A) are an evolution of traditional numbers (He & Wang, 2023 ). Owing to these numbers, it is aimed to minimize the uncertainties in the decision-making processes. In these numbers, each element is represented by a degree (Kaya, 2023 ). This also contributes to increasing the flexibility in the analysis process. Equation (4) indicates classical sets in which \(\mu_A\)  shows the membership degree (Zhao et al., 2023 ).

Intuitionistic fuzzy sets are explained in Eq.  5  where non-membership function is identified as \({v}_{A}\) .

PFRs are explained in Eq. (6) in which  \(n_A\) and  \(h_A\) denote neutral and refusal degrees. The details of the condition are given as \(\mu_A\left(x\right)+n_A\left(x\right)+v_A+h_A\left(x\right)=1\) .

Equations 7 , 8 , 9 , 10 , and 11  identify the details of the operations.

Lower \(\left(\underline{Apr}\left(C_i\right)\right)\) , upper \(\left(\overline{Apr}\left({C}_{i}\right)\right)\)  approximation, and boundary region \(\left(Bnd\left({C}_{i}\right)\right)\) are identified in Eqs.  12 , 13 , and  14 .

Equations  15 , 16 , and 17  refer to the lower \(\left(\underline{Lim}\left({C}_{i}\right)\right)\) , upper \(\left(\overline{Lim}\left({C}_{i}\right)\right)\) limits, and the rough number \(\left(RN\left({C}_{i}\right)\right)\) .

QPFRSs are given in Eq.  18 . In this context, \({C}_{i{\mu }_{A}}\) , \({C}_{i{n}_{A}}\) , \({C}_{i{v}_{A}}\) , and \({C}_{i{h}_{A}}\) indicate membership, neutral, non-membership, and refusal degrees (Qahtan et al., 2023 ).

The definitions of these issues are denoted in Eqs.  19 , 20 , 21 , 22 , 23 , 24 , 25 , and 26  (Dinçer et al., 2022a ).

\({N}_{L{\mu }_{A}}\) ,  \({N}_{L{n}_{A}}\) , \({N}_{L{v}_{A}}\) , and  \({N}_{L{h}_{A}}\) are the number of elements in \(\underline{Apr}\left({C}_{i{\mu }_{A}}\right)\) , \(\underline{Apr}\left(C_{in_A}\right)\) , \(\underline{Apr}\left({C}_{i{v}_{A}}\right)\) , and  \(\underline{Apr}\left({C}_{i{h}_{A}}\right)\) , respectively, while \({N}_{U{\mu }_{A}}\) ,  \({N}_{U{n}_{A}}\) , \({N}_{U{v}_{A}}\) , and  \({N}_{U{h}_{A}}\) are defined for \(\overline{Apr}\left({C}_{i{\mu }_{A}}\right)\) , \(\overline{Apr}\left({C}_{i{n}_{A}}\right)\) , \(\overline{Apr}\left({C}_{i{v}_{A}}\right)\) , and  \(\overline{Apr}\left({C}_{i{h}_{A}}\right)\) as in Eqs.  27 , 28 , 29 , 30 , 31 , 32 , 33 , and 34 .

The formulation of QPFSs with the amplitude and the angle results are demonstrated in Eqs.  35  and 36 .

Golden ratio ( G ) is used in the proposed model to compute degrees more appropriately (Kou et al., 2023b ). Equations  37 , 38 , 39 , 40 , and 41  give information about these details in which b and a show high and low values (Moiseev et al., 2023 ).

\({X}_{1}\) and \({X}_{2}\) are two universes, and \({\stackrel{\sim}{A}}_{c}\) and \({\stackrel{\sim}{B}}_{c}\) , respectively, represented by

and they are two QPFRs derived from the universes of discourse \({X}_{1}\) and \({X}_{2}\) . The operations are denoted by Eqs.  42 , 43 , 44 , and 45 .

M-SWARA with QPFRs

SWARA is used to define the weights of different factors. This technique has some significant advantages. First, weights can be easily calculated with the SWARA method. In addition, the SWARA method can perform consistency analysis of comparisons (Kou et al., 2023a ). In this way, it can be checked whether the data received from users is consistent. However, causality analysis of the criteria cannot be determined with this technique. Some improvements have been made in the classical SWARA method in this study. A new technique called multi-SWARA (M-SWARA) has been developed (Xu et al., 2023 ). The decision matrix is formed from the expert opinions obtained as in Eq.  46 .

Aggregated values are explained in Eq.  47 .

Equation  48  is taken into consideration to compute defuzzified items.

Next, \({s}_{j}\) (importance rate), \({k}_{j}\) (coefficient), \({q}_{j}\) (recomputed weight), and \({w}_{j}\) (weight) are identified as in Eqs.  49 , 50 , 51  (Niu et al., 2023 ).

If  \(s_{j-1}=s_j,q_{j-1}=q_j\) ; If  \(s_j=0,k_{j-1}=k_j\)    

After that, the values are transposed and limited to the power of 2 t  + 1 to define the weights. Moreover, with the help of the threshold values, impact-relation degrees can be identified (Mikhaylov et al., 2023 ).

TOPSIS with QPFRs

TOPSIS technique is taken into consideration to rank various alternatives. In this process, the distances to the optimal solutions are used (Pour et al., 2023 ). This proposed model includes the extension of the classical TOPSIS with QPFRNs. Hence, more effective evaluations can be conducted (Bhatia & Diaz-Elsayed, 2023 ). The evaluations are obtained from the experts and decision matrix is generated as in Eq.  52 .

The values are defuzzified and normalized with Eq.  53 .

Equation 54 is used to define weighted values (Aydoğdu et al., 2023 ).

The positive and negative ideal solutions, \({A}^{+}\) and \({A}^{-}\) , can be identified as in Eqs. 55 and 56 .

Next, by Eqs. 57 and 58 , the distances to the best and worst alternatives, \({D}_{i}^{+}\) and \({D}_{i}^{-}\) , are computed.

The relative closeness to the ideal solutions, \({RC}_{i}\) is used to rank the alternatives as in Eq. ( 59 ) (Pour et al., 2023 ).

Model Construction

Existing models in the literature are criticized due to some conditions. For instance, in some of the models, analytical hierarchy process was taken into consideration to weigh the determinants (Moslem, 2024 ; Yorulmaz & Eti, 2024 ). Similarly, some scholars also used analytical network process methodology to compute the weights of the items (Nalbant, 2024 ; Quaiser & Srivastava, 2024 ). However, these studies could not consider the causality relationship between these indicators. On the other side, decision-making techniques were integrated with the fuzzy sets in some of the previously generated models. In this process, the main reason is to minimize the uncertainties in this process. For this purpose, some studies considered triangular fuzzy sets (Dinçer et al., 2022b ; Dong et al., 2021 ; Wang, 2020 ), whereas some other scholars used trapezoidal fuzzy numbers (Kou et al., 2021 ; Zhao et al., 2021 ; Dombi & Jónás, 2020 ). However, these sets are also criticized because a single membership degree is taken into consideration. These results indicate that there is a strong need for a new decision-making model to evaluate the optimization of renewable energy investments. While integrating previously explained techniques, a new model is defined. In this process, the M-SWARA methodology is used to weigh the indicators so that the causality relationship between these factors can be considered. Similar to this issue, for the purpose of handling uncertainties more effectively, picture fuzzy rough sets are used in the analysis process that was introduced by Cuong and Kreinovich ( 2013 ). With the help of this situation, the concepts of picture fuzzy sets and rough sets can be combined. Owing to using picture fuzzy sets, membership degrees can be represented as intervals. Moreover, rough sets can help understand patterns in the data set and sort them into specific categories. The details of the proposed model are shown in Fig.  1 .

The algorithm of hybrid model

In the first stage, the knowledge-based determinants of process optimization in investment decisions are identified. For this purpose, an analysis is performed by using DEMATEL based on QPFRs. In this process, a decision matrix is generated, the values are defuzzified and normalized, and stable matrix is constructed. In the next stage, investment alternatives for process optimization in renewable energy projects are defined. In this context, QPFR-TOPSIS is taken into consideration. Within this framework, the values are defuzzified, normalized, and weighted. Finally, the most significant alternatives can be defined. In the second stage, the weights computed in the first stage are taken into consideration. Hence, there is a progressive relationship between these two stages.

Analysis Results

The proposed model has two different parts. In this section, the results are given for each part separately.

Weighting the Knowledge-Based Determinants of Process Optimization

First, the knowledge-based determinants of process optimization in renewable energy investment decisions are selected. They are detailed in Table  1 .

Ensuring efficiency in renewable energy projects means less loss in energy production. On the other hand, an efficient energy investment project contributes to cost savings. In this way, it is possible to reduce the costs of energy investment projects. Customers have different expectations from renewable energy projects such as safety and low cost. Meeting these expectations increases customers’ satisfaction with the project. Flexible service delivery is another essential element for process optimization in renewable energy projects. In this way, energy supply and demand balance can be achieved. On the other hand, flexible service provision also enables more efficient use of energy resources. In this way, energy demand management will be carried out more successfully. Owing to the quality of energy production equipment, it will be easier to increase the efficiency of the project. Preferring higher quality power generation equipment reduces the possibility of failure in projects. In this way, it is much more possible to ensure the sustainability of the projects.

After that, evaluations are obtained from the expert team that consists of three different people. The first expert is the general manager in an energy production company. He has 25 years working experience with a master’s degree. The second expert is an academician that has lots of publications related to the energy transition. He has more than 20 years of working experience. The final expert works as a chief finance officer in a renewable energy company who has 23 years of working experience. While they evaluate the questions related to the criteria, the values in Table  2 are considered.

The evaluations are denoted in Table  3 .

QPFRs for the relation matrix are computed as in Table  4 .

Moreover, the QPFRs for the relation matrix are provided in Table  5 .

Defuzzified values are calculated as in Table  6 .

Normalized relation matrix is constructed as in Table  7 .

Critical values for the relationship degrees of each criterion are computed as in Table  8 .

Relation matrix can be created as in Table  9 . In this table, impact directions are also presented.

Table  9 demonstrates that flexibility is the most influencing factor because it affects both efficiency and quality. Furthermore, quality is the most influenced indicator since it is influenced by both customer needs and flexibility. The stable matrix is created in the following step as in Table  10 .

The weights of the indicators are also demonstrated in Fig.  2 .

Quality is the most important factor for project optimization in renewable energy investments. In addition, customer expectations are another prominent variable in this process. Efficiency and flexibility, on the other hand, have a lower weight of importance compared to the others. As it can be understood from here, it is important that the materials used in renewable energy investments are of high quality. This will contribute to minimizing the disruptions in the energy production process. Thus, it will be possible to produce uninterrupted energy. Furthermore, investors should also pay attention to issues that increase customer satisfaction. In this respect, meeting these expectations increases the satisfaction and trust of the customers in the project. This allows the products to be preferred more by customers.

Ranking the Investment Alternatives for Process Optimization of Renewable Energy Projects

This part of the analysis is related to the alternative ranking. In this context, first, investment alternatives are selected regarding the process optimization of renewable energy projects. They are explained in Table  11 .

Environmental regulations and incentives play an important role in increasing the optimization of renewable energy investment projects. These practices can reduce the costs of projects or shorten their turnaround times. This reduces the risks of investors and ensures that projects are competitive. On the other hand, environmental regulations and incentives encourage innovation in renewable energy technologies. This process also contributes significantly to increasing energy efficiency. Diversification of renewable energy projects plays an important role in increasing the optimization of renewable energy investment projects. This type of diversification will allow for the distribution of risks. Similarly, diversifying renewable energy projects helps secure energy supply. The development of technological infrastructure has a key importance in increasing the optimization of renewable energy investment projects. Technological advances can improve the efficiency and performance of renewable energy technologies. More efficient technologies also allow for higher productivity. On the other hand, to increase the optimization of renewable energy investment projects, effective financing resources are required. Appropriate and sufficient financing ensures the realization of the projects and initiates the optimization process. Similarly, effective sources of finance play an important role in reducing the financial risks of renewable energy projects. A successful supply chain also plays an important role in increasing the optimization of renewable energy investment projects. Timely and accurate sourcing of materials and equipment required for renewable energy projects is critical. This, in turn, contributes significantly to increasing the efficiency of the processes of the projects. Table  12 identifies the evaluations for these alternatives.

QPFRs for the decision matrix are constructed and the details are presented in Table  13 .

Next, QPFRs for the decision matrix are demonstrated in Table  14 .

Defuzzified decision values are given in Table  15 .

Normalized decision values are created as in Table  16 .

Weighted decision values are identified in Table  17 .

Investment alternatives for process optimization of renewable energy projects are ranked in the following step. These results are indicated in Table  18 .

They are also illustrated in Fig.  3 .

Ranking results

Variety of the renewable energy sources is the most critical alternative to improve the optimization of this source. Additionally, technological infrastructure is the second most important alternative for this condition. Financial and environmental regulations are in the last ranks. Diversification of renewable energy projects is very important in terms of distributing risks. In this way, dependency on a single energy source or technology can be reduced. Diversification of renewable energy projects also ensures that the energy supply is secured. The development of technological infrastructure has an important role in increasing the optimization of renewable energy investment projects. Technological advances can improve the efficiency and performance of renewable energy technologies. More efficient technologies allow investment projects to achieve higher energy production and therefore higher efficiency.

Discussions

Quality is found as the most critical determinant for project optimization in renewable energy investments. Quality products increase the performance and efficiency of renewable energy projects. In this context, power generation equipment should be designed to provide high performance. On the other hand, quality products increase the reliability and durability of the project. High-quality power generation equipment reduces the probability of failure. This contributes significantly to uninterrupted electricity generation. Thus, energy security and continuity of the project are possible. Furthermore, quality products provide long-term cost savings. In this way, the products will need less maintenance and repair. This allows the project to save costs in the long run. On the other hand, high-quality equipment helps to use less resources during power generation. Thus, less waste and lower emissions can occur.

Some strategies should be implemented to use quality products in renewable energy projects. Choosing the right supplier is vital in this process. For the products used to be of high quality, it is necessary to do business with reliable suppliers. According to Alonso-Travesset et al. ( 2023 ), this condition helps to ensure a continuous quality in the products used. In this context, compliance with standards is also necessary for the use of quality products. Ottonelli et al. ( 2023 ) identified that it may be easier for the products that comply with certain standards to have high quality and performance. In this context, the conformity of the products to the standards should be checked periodically. Karakislak et al. ( 2023 ) mentioned that to achieve this goal, it is very important to have an effective control mechanism. On the other hand, Naqvi et al. ( 2023 ) discussed that technological developments in the renewable energy sector are advancing rapidly. Therefore, it is very important to follow these current developments in the sector effectively for the quality of the products used in the projects.

According to the results obtained, the diversification of renewable energy projects plays an important role in increasing the optimization of renewable energy investment projects. In this context, projects based on different technologies and energy sources need to create a portfolio. Van Song et al. ( 2023 ) claimed that this condition reduces the dependency on a single energy source. Additionally, Zhang et al. ( 2023b ) concluded that the diversification of renewable energy projects ensures that the energy supply is secured. In this framework, projects should depend on different energy sources. This situation supports a more balanced energy production in different climatic and weather conditions. Moreover, Yi et al. ( 2023 ) denoted that the diversification of renewable energy projects supports innovation and technological development. In other words, by focusing on different technologies, it is possible to develop more innovative products.

In this study, it is aimed to determine the most critical knowledge-based determinants and the most suitable investment alternatives of process optimization in renewable energy investment decisions. For this purpose, this study introduces a novel integration of the QPFR-M-SWARA and QPFR-TOPSIS frameworks to optimize investment decisions in the renewable energy sector. In Stage 1, the knowledge-based determinants of process optimization in investment decisions are identified through a thorough analysis using DEMATEL based on QPFRs. In Stage 2, investment alternatives for process optimization in renewable energy projects are identified and evaluated using linguistic evaluations from decision-makers with QPFR-TOPSIS. Quality is defined as the most important factor for project optimization in renewable energy investments. Moreover, customer expectations also play a critical role in this process. However, efficiency and flexibility have a lower weight of importance compared to the others. With respect to the alternative ranking results, it is found that a variety of renewable energy sources is the most convenient alternative to improve the optimization of this source. Furthermore, technological infrastructure is the second most important alternative for this condition.

The main contribution of the study is that a priority analysis is conducted for information-based factors affecting the performance of renewable energy projects. Knowledge-based factors should be improved to increase the performance of renewable energy projects. However, the main problem is that improvements to these factors also create new costs. Therefore, because of the budget limitation, it is quite difficult to make improvements for many of these variables. Thus, this priority evaluation helps to increase the performance of renewable energy projects. Another critical contribution of this study is that a new decision-making technique is proposed with the name of M-SWARA. In classical SWARA, an impact relation map of the factors cannot be created. To solve this problem, the SWARA technique is enhanced in this study with some improvements. As a result, the M-SWARA methodology is created so that the causal directions of the criteria can be identified. In addition, considering picture fuzzy rough sets helps to achieve more accurate findings because they represent membership degrees as intervals.

In this study, different types of renewable energy investments are not analyzed separately. Instead, a general analysis of renewable energy projects was carried out. This situation can be addressed in more detail in future studies. In other words, a more specific analysis of the process optimization of solar projects can be performed. Similarly, a comparative analysis can be performed to identify important knowledge-based variables in different types of renewable energy. In this way, it will be possible to develop different strategy proposals for each type of clean energy. On the other hand, a new model suitable for the subject of the study is proposed. A new decision-making model can be developed in future studies. It will also be possible to determine the direction of the relations between the variables with a new model to be created using sine trigonometric numbers. CRITIC technique can also be taken into consideration to consider the correlation between the items. Similarly, the innovation theories and concepts of triple helix, quadruple helix, and quintuple helix can be considered for future studies (Carayannis & Campbell, 2010 ; Carayannis et al., 2022 ).

Abbas, J., Wang, L., Belgacem, S. B., Pawar, P. S., Najam, H., & Abbas, J. (2023). Investment in renewable energy and electricity output: Role of green finance, environmental tax, and geopolitical risk: Empirical evidence from China. Energy, 269 , 126683.

Article   Google Scholar  

Abdulkader, R., Ghanimi, H. M., Dadheech, P., Alharbi, M., El-Shafai, W., Fouda, M. M. ,..., & Sengan, S. (2023). Soft computing in smart grid with decentralized generation and renewable energy storage system planning. Energies, 16 (6), 2655.

Abid, L., Kacem, S., & Saadaoui, H. (2024). Addressing the environmental Kuznets curve in the West African countries: Exploring the roles of FDI, corruption, and renewable energy. Journal of the Knowledge Economy , 1–25.

Adebayo, T. S., Ullah, S., Kartal, M. T., Ali, K., Pata, U. K., & Ağa, M. (2023). Endorsing sustainable development in BRICS: The role of technological innovation, renewable energy consumption, and natural resources in limiting carbon emission. Science of the Total Environment, 859 , 160181.

Ai, R., Zheng, Y., Yüksel, S., & Dinçer, H. (2023). Investigating the components of fintech ecosystem for distributed energy investments with an integrated quantum spherical decision support system. Financial Innovation, 9 (1), 27.

Ainou, F. Z., Ali, M., & Sadiq, M. (2023). Green energy security assessment in Morocco: Green finance as a step toward sustainable energy transition. Environmental Science and Pollution Research, 30 (22), 61411–61429.

Akbari, E., Shabestari, S. F. M., Pirouzi, S., & Jadidoleslam, M. (2023). Network flexibility regulation by renewable energy hubs using flexibility pricing-based energy management. Renewable Energy, 206 , 295–308.

Al-Binali, T., Aysan, A. F., Dinçer, H., Unal, I. M., & Yüksel, S. (2023). New horizons in bank mergers: A quantum spherical fuzzy decision-making framework for analyzing Islamic and conventional bank mergers and enhancing resilience. Sustainability, 15 (10), 7822.

Alharbi, S. S., Mamun, A., Boubaker, M., S., & Rizvi, S. K. (2023). A. Green finance and renewable energy: A worldwide evidence. Energy Economics , 106499.

Alonso-Travesset, À., Coppitters, D., Martín, H., & de la Hoz, J. (2023). Economic and regulatory uncertainty in renewable energy system design: A review. Energies, 16 (2), 882.

Andriani, D. P., & Tseng, F. S. (2023). Pricing and investment decisions when facing heterogeneous customers under different supply chain power structures. Alexandria Engineering Journal, 78 , 390–405.

Aydoğdu, E., Güner, E., Aldemir, B., & Aygün, H. (2023). Complex spherical fuzzy TOPSIS based on entropy. Expert Systems with Applications , 215 , 119331.

Bhatia, P., & Diaz-Elsayed, N. (2023). Facilitating decision-making for the adoption of smart manufacturing technologies by SMEs via fuzzy TOPSIS. International Journal of Production Economics , 108762.

Carayannis, E. G., & Campbell, D. F. (2010). Triple Helix, Quadruple Helix and Quintuple Helix and how do knowledge, innovation and the environment relate to each other? A proposed framework for a trans-disciplinary analysis of sustainable development and social ecology. International Journal of Social Ecology and Sustainable Development (IJSESD) , 1 (1), 41–69.

Carayannis, E. G., Campbell, D. F., & Grigoroudis, E. (2022). Helix trilogy: The triple, quadruple, and quintuple innovation helices from a theory, policy, and practice set of perspectives. Journal of the Knowledge Economy , 13 (3), 2272–2301.

Carayannis, E., Kostis, P., Dinçer, H., & Yüksel, S. (2023). Quality function deployment-oriented strategic outlook to sustainable energy policies based on quintuple innovation helix. Journal of the Knowledge Economy , 1–19.

Chien, F., Huang, L., & Zhao, W. (2023). The influence of sustainable energy demands on energy efficiency: Evidence from China. Journal of Innovation & Knowledge , 8 (1), 100298.

Cuong, B. C., & Kreinovich, V. (2013). Picture fuzzy sets-A new concept for computational intelligence problems. In 2013 third world congress on information and communication technologies (WICT 2013) (pp. 1–6). IEEE.

Dinçer, H., Yüksel, S., Mikhaylov, A., Pinter, G., & Shaikh, Z. A. (2022a). Analysis of renewable-friendly smart grid technologies for the distributed energy investment projects using a hybrid picture fuzzy rough decision-making approach. Energy Reports , 8 , 11466–11477.

Dinçer, H., Yüksel, S., & Martínez, L. (2022b). Collaboration enhanced hybrid fuzzy decision-making approach to analyze the renewable energy investment projects. Energy Reports , 8 , 377–389.

Dinçer, H., Yüksel, S., Mikhaylov, A., Muyeen, S. M., Chang, T., Barykin, S., & Kalinina, O. (2023). CO2 emissions integrated fuzzy model: A case of seven emerging economies. Energy Reports , 9 , 5741–5751.

Dombi, J., & Jónás, T. (2020). Ranking trapezoidal fuzzy numbers using a parametric relation pair. Fuzzy sets and Systems , 399 , 20–43.

Dong, J., Wan, S., & Chen, S. M. (2021). Fuzzy best-worst method based on triangular fuzzy numbers for multi-criteria decision-making. Information Sciences , 547 , 1080–1104.

Dong, T., Yin, S., & Zhang, N. (2023a). New energy-driven construction industry: Digital green innovation investment project selection of photovoltaic building materials enterprises using an integrated fuzzy decision approach. Systems, 11 (1), 11.

Dong, W., Li, Y., Gao, P., & Sun, Y. (2023b). Role of trade and green bond market in renewable energy deployment in Southeast Asia. Renewable Energy .

Dzwigol, H., Kwilinski, A., Lyulyov, O., & Pimonenko, T. (2023). Renewable Energy, Knowledge Spillover and Innovation: Capacity of Environmental Regulation. Energies , 16 (3), 1117.

Gao, J., Feng, Q., Guan, T., & Zhang, W. (2023). Unlocking paths for transforming green technological innovation in manufacturing industries. Journal of Innovation & Knowledge , 8 (3), 100394.

He, S., & Wang, Y. (2023). Evaluating new energy vehicles by picture fuzzy sets based on sentiment analysis from online reviews. Artificial Intelligence Review , 56 (3), 2171–2192.

Hille, E., & Oelker, T. J. (2023). International expansion of renewable energy capacities: The role of innovation and choice of policy instruments. Ecological Economics , 204 , 107658.

Hou, H., Zhu, Y., Wang, J., & Zhang, M. (2023). Will green financial policy help improve China’s environmental quality? The role of digital finance and green technology innovation. Environmental Science and Pollution Research , 30 (4), 10527–10539.

Hu, J., Tang, Q., Wu, Z., Zhang, B., He, C., & Chen, Q. (2023). Optimization and assessment method for total energy system retrofit in the petrochemical industry considering clean energy substitution for fossil fuel. Energy Conversion and Management , 284 , 116967.

Iqbal, S., Wang, Y., Ali, S., Amin, N., & Kausar, S. (2023). Asymmetric determinants of renewable energy production in Pakistan: Do economic development, environmental technology, and financial development matter? Journal of the Knowledge Economy , 1–18.

Jiang, Y., Hossain, M. R., Khan, Z., Chen, J., & Badeeb, R. A. (2023). Revisiting research and development expenditures and trade adjusted emissions: Green innovation and renewable energy R&D role for developed countries. Journal of the Knowledge Economy , 1–36.

Karakislak, I., Sadat-Razavi, P., & Schweizer-Ries, P. (2023). A cooperative of their own: Gender implications on renewable energy cooperatives in Germany. Energy Research & Social Science , 96 , 102947.

Karimi Alavijeh, N., Ahmadi Shadmehri, M. T., Esmaeili, P., & Dehdar, F. (2024). Asymmetric impacts of renewable energy on human development: Exploring the role of carbon emissions, economic growth, and urbanization in European Union countries. Journal of the Knowledge Economy , 1–25.

Kaya, S. K. (2023). A novel two-phase group decision-making model for circular supplier selection under picture fuzzy environment. Environmental Science and Pollution Research , 30 (12), 34135–34157.

Kolte, A., Festa, G., Ciampi, F., Meissner, D., & Rossi, M. (2023). Exploring corporate venture capital investments in clean energy—a focus on the Asia-Pacific region. Applied Energy , 334 , 120677.

Kou, G., Olgu Akdeniz, Ö., Dinçer, H., & Yüksel, S. (2021). Fintech investments in European banks: A hybrid IT2 fuzzy multidimensional decision-making approach. Financial Innovation , 7 (1), 39.

Kou, G., Dinçer, H., Yüksel, S., & Alotaibi, F. S. (2023a). Imputed expert decision recommendation system for QFD-based omnichannel strategy selection for financial services. International Journal of Information Technology & Decision Making , 2330003.

Kou, G., Pamucar, D., Dinçer, H., & Yüksel, S. (2023b). From risks to rewards: A comprehensive guide to sustainable investment decisions in renewable energy using a hybrid facial expression-based fuzzy decision-making approach. Applied Soft Computing , 142 , 110365.

Lee, J. Y., Choi, J. W., Choi, J. H., & Lee, B. H. (2023). Text-mining analysis using national R&D project data of South Korea to investigate innovation in graphene environment technology. International Journal of Innovation Studies , 7 (1), 87–99.

Li, S., & Shao, Q. (2023). How do financial development and environmental policy stringency affect renewable energy innovation? The Porter hypothesis and beyond. Journal of Innovation & Knowledge , 8 (3), 100369.

Li, C., & Umair, M. (2023). Does green finance development goals affects renewable energy in China. Renewable Energy , 203 , 898–905.

Liu, Y., Xu, L., Sun, H., Chen, B., & Wang, L. (2023). Optimization of carbon performance evaluation and its application to strategy decision for investment of green technology innovation. Journal of Environmental Management , 325 , 116593.

Martínez, L., Dinçer, H., & Yüksel, S. (2023). A hybrid decision making approach for new service development process of renewable energy investment. Applied Soft Computing , 133 , 109897.

Masoomi, B., Sahebi, I. G., Ghobakhloo, M., & Mosayebi, A. (2023). Do industry 5.0 advantages address the sustainable development challenges of the renewable energy supply chain? Sustainable Production and Consumption , 43 , 94–112.

Mikhaylov, A., Dinçer, H., & Yüksel, S. (2023). Analysis of financial development and open innovation oriented fintech potential for emerging economies using an integrated decision-making approach of MF-X-DMA and golden cut bipolar q-ROFSs. Financial Innovation , 9 (1), 1–34.

Moiseev, N., Mikhaylov, A., Dinçer, H., & Yüksel, S. (2023). Market capitalization shock effects on open innovation models in e-commerce: Golden cut q-rung orthopair fuzzy multicriteria decision-making analysis. Financial Innovation , 9 (1), 55.

Moslem, S. (2024). A novel parsimonious spherical fuzzy analytic hierarchy process for sustainable urban transport solutions. Engineering Applications of Artificial Intelligence , 128 , 107447.

Nalbant, K. G. (2024). A methodology for personnel selection in business development: An interval type 2-based fuzzy DEMATEL-ANP approach. Heliyon , 10 (1).

Naqvi, B., Rizvi, S. K. A., Mirza, N., & Umar, M. (2023). Financial market development: A potentiating policy choice for the green transition in G7 economies. International Review of Financial Analysis , 87 , 102577.

Niu, X., Yüksel, S., & Dinçer, H. (2023). Emission strategy selection for the circular economy-based production investments with the enhanced decision support system. Energy , 274 , 127446.

Norouzi, F., Hoppe, T., Kamp, L. M., Manktelow, C., & Bauer, P. (2023). Diagnosis of the implementation of smart grid innovation in the Netherlands and corrective actions. Renewable and Sustainable Energy Reviews , 175 , 113185.

Ottonelli, J., Lazaro, L. L. B., Andrade, J. C. S., & Abram, S. (2023). Do solar photovoltaic clean development mechanism projects contribute to sustainable development in Latin America? Prospects for the Paris Agreement. Energy Policy , 174 , 113428.

Pan, W., Cao, H., & Liu, Y. (2023). Green innovation, privacy regulation and environmental policy. Renewable Energy , 203 , 245–254.

Peng, B., Zhao, Y., Elahi, E., & Wan, A. (2023). Can third-party market cooperation solve the dilemma of emissions reduction? A case study of energy investment project conflict analysis in the context of carbon neutrality. Energy , 264 , 126280.

Pour, P. D., Ahmed, A. A., Nazzal, M. A., & Darras, B. M. (2023). An industry 4.0 Technology Selection Framework for Manufacturing Systems and firms using fuzzy AHP and fuzzy. TOPSIS Methods Systems , 11 (4), 192.

Google Scholar  

Prokopenko, O., Kurbatova, T., Khalilova, M., Zerkal, A., Prause, G., Binda, J., & Komarnitskyi, I. (2023). Impact of investments and R&D costs in Renewable Energy Technologies on companies’ profitability indicators: Assessment and Forecast. Energies , 16 (3), 1021.

Qahtan, S., Alsattar, H. A., Zaidan, A. A., Deveci, M., Pamucar, D., & Delen, D. (2023). Performance assessment of sustainable transportation in the shipping industry using a q-rung orthopair fuzzy rough sets-based decision making methodology. Expert Systems with Applications , 223 , 119958.

Quaiser, R. M., & Srivastava, P. R. (2024). Outbound open innovation effectiveness measurement between big organizations and startups using Fuzzy MCDM. Management Decision .

Ramzan, M., Razi, U., Quddoos, M. U., & Adebayo, T. S. (2023). Do green innovation and financial globalization contribute to the ecological sustainability and energy transition in the United Kingdom? Policy insights from a bootstrap rolling window approach. Sustainable Development , 31 (1), 393–414.

Razzaq, A., Sharif, A., Ozturk, I., & Skare, M. (2023). Asymmetric influence of digital finance, and renewable energy technology innovation on green growth in China. Renewable Energy , 202 , 310–319.

Shang, Y., Zhu, L., Qian, F., & Xie, Y. (2023). Role of green finance in renewable energy development in the tourism sector. Renewable Energy , 206 , 890–896.

Sharif, A., Kocak, S., Khan, H. H. A., Uzuner, G., & Tiwari, S. (2023). Demystifying the links between green technology innovation, economic growth, and environmental tax in ASEAN-6 countries: The dynamic role of green energy and green investment. Gondwana Research , 115 , 98–106.

Sirr, G., Power, B., Ryan, G., Eakins, J., O’Connor, E., & le Maitre, J. (2023). An analysis of the factors affecting Irish citizens’ willingness to invest in wind energy projects. Energy Policy , 173 , 113364.

Taghizadeh-Hesary, F., Phoumin, H., & Rasoulinezhad, E. (2023). Assessment of role of green bond in renewable energy resource development in Japan. Resources Policy , 80 , 103272.

Temesgen Hordofa, T., Vu, M., Maneengam, H., Mughal, A., Cong, N. T., P., & Liying, S. (2023). Does eco-innovation and green investment limit the CO2 emissions in China? Economic Research-Ekonomska Istraživanja , 36 (1), 1–16.

Thirunavukkarasu, M., Sawle, Y., & Lala, H. (2023). A comprehensive review on optimization of hybrid renewable energy systems using various optimization techniques. Renewable and Sustainable Energy Reviews , 176 , 113192.

Van Song, N., Que, N. D., Tiep, N. C., van Tien, D., Van Ha, T., Phuong, P. T. L., & Oanh, T. T. K. (2023). The influence of economic and non-economic determinants on the sustainable energy consumption: Evidence from Vietnam economy. Environmental Science and Pollution Research , 1–14.

Vásquez-Ordóñez, L. R., Lassala, C., Ulrich, K., & Ribeiro-Navarrete, S. (2023). Efficiency factors in the financing of renewable energy projects through crowdlending. Journal of Business Research , 155 , 113389.

Wan, Q., Miao, X., Wang, C., Dinçer, H., & Yüksel, S. (2023). A hybrid decision support system with golden cut and bipolar q-ROFSs for evaluating the risk-based strategic priorities of fintech lending for clean energy projects. Financial Innovation , 9 (1), 1–25.

Wang, Z. J. (2020). A novel triangular fuzzy analytic hierarchy process. IEEE Transactions on Fuzzy Systems , 29 (7), 2032–2046.

Wang, H., Ergu, D., & Zai, W. (2023). Effect of Chinese Currency Appreciation on Investments in Renewable Energy Projects in Countries along the Belt and Road. Sustainability , 15 (3), 1784.

Xu, X., Yüksel, S., & Dinçer, H. (2023). An integrated decision-making approach with golden cut and bipolar q-ROFSs to renewable energy storage investments. International Journal of Fuzzy Systems , 25 (1), 168–181.

Yan, R., Wang, J., Huo, S., Qin, Y., Zhang, J., Tang, S., & Zhou, L. (2023). Flexibility improvement and stochastic multi-scenario hybrid optimization for an integrated energy system with high-proportion renewable energy. Energy , 263 , 125779.

Yang, C., & Song, X. (2023). Assessing the determinants of renewable energy and energy efficiency on technological innovation: Role of human capital development and investement. Environmental Science and Pollution Research , 1–21.

Yi, S., Raghutla, C., Chittedi, K. R., & Fareed, Z. (2023). How economic policy uncertainty and financial development contribute to renewable energy consumption? The importance of economic globalization. Renewable Energy , 202 , 1357–1367.

Yorulmaz, H., & Eti, S. (2024). Building telework capability in the new business era for SMEs, using spherical fuzzy AHP methodology for prioritizing the actions. Future Business Journal , 10 (1), 1–11.

You, C., Khattak, S. I., & Ahmad, M. (2023). Impact of innovation in solar photovoltaic energy generation, distribution, or transmission-related technologies on carbon dioxide emissions in China. Journal of the Knowledge Economy , 1–35.

Yüksel, S., & Dinçer, H. (2023). Sustainability analysis of digital transformation and circular industrialization with quantum spherical fuzzy modeling and golden cuts. Applied Soft Computing , 138 , 110192.

Zhang, Q., Adebayo, T. S., Ibrahim, R. L., & Al-Faryan, M. A. S. (2023a). Do the asymmetric effects of technological innovation amidst renewable and nonrenewable energy make or mar carbon neutrality targets? International Journal of Sustainable Development & World Ecology, 30 (1), 68–80.

Zhang, M., Tang, Y., Liu, L., Jin, J., & Zhou, D. (2023b). Is asset securitization an effective means of financing China’s renewable energy enterprises? A systematic overview. Energy Reports, 9 , 859–872.

Zhao, Y., Xu, Y., Yüksel, S., Dinçer, H., & Ubay, G. G. (2021). Hybrid IT2 fuzzy modelling with alpha cuts for hydrogen energy investments. International Journal of Hydrogen Energy , 46 (13), 8835–8851.

Zhao, X. K., Zhu, X. M., Bai, K. Y., & Zhang, R. T. (2023). A novel failure model and effect analysis method using a flexible knowledge acquisition framework based on picture fuzzy sets. Engineering Applications of Artificial Intelligence , 117 , 105625.

Zhong, X., Ali, A., & Zhang, L. (2024). The influence of green finance and renewable energy sources on renewable energy investment and carbon emission: COVID-19 pandemic effects on Chinese economy. Journal of the Knowledge Economy , 1–24.

Zhu, J., Lin, N., Zhu, H., & Liu, X. (2023). Role of sharing economy in energy transition and sustainable economic development in China. Journal of Innovation & Knowledge , 8 (2), 100314.

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Huang, C., Yüksel, S. & Dinçer, H. A Novel Fuzzy Model for Knowledge-Driven Process Optimization in Renewable Energy Projects. J Knowl Econ (2024). https://doi.org/10.1007/s13132-024-02074-w

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