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Structure of Scholarly Articles and Peer Review: Structure of a Biomedical Research Article

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Title, Authors, Sources of Support and Acknowledgments

Structured abstract, introduction, results and discussion, international committee of medical journal editors (icmje).

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Medical research articles tend to be structured in similar ways. This standard structure helps assure that research is reported with the information readers need to critically appraise the research process and results.

This guide to the structure of a biomedical research article was informed by the description of standard manuscript sections found in the International Committee of Medical Journal Editors (ICMJE) Recommendations chapter on Manuscript Preparation: Preparing for Submission .

If you are writing an article for submisson to a particular journal be sure to obtain that journal's instructions for authors for specific guidelines.

Example Article: Lyons EJ, Tate DF, Ward DS, Wang X. Energy intake and expenditure during sedentary screen time and motion-controlled video gaming. Am J Clin Nutr. 2012 Aug;96(2):234-9. doi: 10.3945/ajcn.111.028423. Epub 2012 Jul 3. PubMed PMID: 22760571; PubMed Central PMCID: PMC3396440. (Free full text available)

Article title:  Should provide a succinct description of the purpose of the article using words that will help it be accurately retrieved by search engines. 

Author information:  Includes the author names and the institution(s) where each author was affiliated at the time the research was conducted. Full contact information is provided for the corresponding author. 

Source(s) of support: Specific information about grant funding or source of equipment, drugs, etc. obtained to support the research. 

Acknowledgments:  This section may found at the end of the article and is used to name people who contributed to the paper, but not fully enough to be named as an author. It may also include more information about the authors' specific roles.

The structure of quantitative research articles is derived from the scientifc process and includes sections covering introduction, methods, results, and discussion (IMRaD). The actual labels for the various parts may vary between journals.

Abstract:  A structured abstract reports a summary of each of the IMRaD sections. Enough information should be included to provide the purpose of the research; an outline of methods used; results with data; and conclusions that highlight the findings. 

The introduction provides background information about what is known from previous related research, citing the relevant studies, and points out the gap in previous research that is being addressed by the new study. Often, many of the references cited in a paper are in the introduction. The purpose of the research should be clearly stated in this section.

The sample paper's introduction links television watching to increased energy intake and obesity, notes that several studies have shown a similar link with video gaming, and states no study was identified that compared television and video gaming. Eighteen of the thirty-one references used in the paper are cited in the introduction. The final paragraph of the introduction has two sentences that clearly state the purpose and the hypothesized expected outcome of the study.

The methods section clearly explains how the study was conducted. The ICMJE recommends that this section include information about how participants were selected, detailed demographics about who the participants were, and explanations of why any particular populations were included or excluded from the study. The details of how the study was conducted should be described with enough detail that the study could be replicated. Selected statistical methods should be reported in enough detail that readers can evaluate their appropriateness to the data being gathered.

The sample paper’s methods section includes subsections covering:  recruitment; procedures used for each study subgroup (TV, VG, motion-controlled VG); what snacks and beverages were used and how they were made available; how energy intake and energy expenditure were measured; how the data was analyzed and the specific statistical analysis and secondary analysis that was used.

The results section reports the data gathered and the statistical analysis of the data. Tables and / or graphs are often used to clearly and compactly present the data.

The results section of the sample paper  has two subsections and two tables. One subsection and related table shows the analysis of participant characteristics, The other subsection and table covers the analysis of energy intake, expenditure, and surplus.

In order to critically appraise the quality of the study you need to be able to understand the statistical analysis of the data. Two articles that help with this task are:

• Greenhalgh Trisha.  How to read a paper: Statistics for the non-statistician. I: Different types of data need different statistical tests  BMJ 1997; 315:364
• Greenhalgh Trisha.  How to read a paper: Statistics for the non-statistician. II: “Significant” relations and their pitfalls  BMJ 1997; 315:422

Another aid to critically reading a paper is to see if it has been included and evaluated in a systematic review. Try searching for the article you are reading in Google Scholar and seeing if the cited references include a systematic review. The sample paper was critically reviewed in:

• Marsh S, Ni Mhurchu C, Maddison R. The non-advertising effects of screen-based sedentary activities on acute eating behaviours in children, adolescents, and young adults. A systematic review. Appetite. 2013 Dec;71:259-73. doi: 10.1016/j.appet.2013.08.017. Epub 2013 Aug 31. PubMed Abstract . Full-text for UNC-CH .

The  discussion section  clearly states the primary findings of the study, poses explanations for the findings and any conclusions that can be drawn from them. It may also include the author’s assessment of limitations in the research as conducted and suggestions for further research that is needed.

The structure of biomedical research articles has been standardized across different journals at least in part due to the work of the International Committee of Medical Journal Editors. This group first published the  Uniform Requirements for Manuscripts Submitted to Biomedical Journals  in 1978.

The  Recommendations for the Conduct, Reporting, Editing, and Publication of Scholarly work in Medical Journals  (2013) is the most recent update of ICMJE's work. 

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• Last Updated: Aug 14, 2023 12:15 PM
• URL: https://guides.lib.unc.edu/scholarly-articles

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Ethics and Scientific Integrity in Biomedical Research

Debates on Trust, Robustness, and Relevance

• Reference work entry
• First Online: 02 April 2020
• Cite this reference work entry

• Léo Coutellec 2  

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Because it is directly implicated in major social issues, biomedical research is a paradigmatic field for working in ethics to cross-reference epistemic, social, and political issues. This chapter shows that the ethics and scientific integrity of biomedical research has grasped this challenge by placing the transversal concern of trust at the heart of its approach. This question of trust is put into perspective with that of trustworthiness, which is closely linked to it, and which is described as a way of thinking together with the robustness of methods, evidence, results, and the social, ethical, and contextual relevance of trade-offs about them. In a context of increasing media coverage of scientific misconduct and profound changes in the scientific landscape, the ethics of biomedical research thus invites us to take up the complex question of the links between trust and trustworthiness.

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Coutellec, L. (2020). Ethics and Scientific Integrity in Biomedical Research. In: Iphofen, R. (eds) Handbook of Research Ethics and Scientific Integrity. Springer, Cham. https://doi.org/10.1007/978-3-030-16759-2_36

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Biomedical Research Leads Science’s 2021 Breakthroughs

Posted on January 4th, 2022 by Lawrence Tabak, D.D.S., Ph.D.

Hi everyone, I’m Larry Tabak. I’ve served as NIH’s Principal Deputy Director for over 11 years, and I will be the acting NIH director until a new permanent director is named. In my new role, my day-to-day responsibilities will certainly increase, but I promise to carve out time to blog about some of the latest research progress on COVID-19 and any other areas of science that catch my eye.

I’ve also invited the directors of NIH’s Institutes and Centers (ICs) to join me in the blogosphere and write about some of the cool science in their research portfolios. I will publish a couple of posts to start, then turn the blog over to our first IC director. From there, I envision alternating between posts from me and from various IC directors. That way, we’ll cover a broad array of NIH science and the tremendous opportunities now being pursued in biomedical research.

Since I’m up first, let’s start where the NIH Director’s Blog usually begins each year: by taking a look back at Science ’s Breakthroughs of 2021. The breakthroughs were formally announced in December near the height of the holiday bustle. In case you missed the announcement, the biomedical sciences accounted for six of the journal Science ’s 10 breakthroughs. Here, I’ll focus on four biomedical breakthroughs, the ones that NIH has played some role in advancing, starting with Science ’s editorial and People’s Choice top-prize winner:

Breakthrough of the Year: AI-Powered Predictions of Protein Structure

The biochemist Christian Anfinsen, who had a distinguished career at NIH, shared the 1972 Nobel Prize in Chemistry, for work suggesting that the biochemical interactions among the amino acid building blocks of proteins were responsible for pulling them into the final shapes that are essential to their functions. In his Nobel acceptance speech, Anfinsen also made a bold prediction: one day it would be possible to determine the three-dimensional structure of any protein based on its amino acid sequence alone. Now, with advances in applying artificial intelligence to solve biological problems—Anfinsen’s bold prediction has been realized.

But getting there wasn’t easy. Every two years since 1994, research teams from around the world have gathered to compete against each other in developing computational methods for predicting protein structures from sequences alone. A score of 90 or above means that a predicted structure is extremely close to what’s known from more time-consuming work in the lab. In the early days, teams more often finished under 60.

In 2020, a London-based company called DeepMind made a leap with their entry called AlphaFold. Their deep learning approach—which took advantage of 170,000 proteins with known structures—most often scored above 90, meaning it could solve most protein structures about as well as more time-consuming and costly experimental protein-mapping techniques. (AlphaFold was one of Science ’s runner-up breakthroughs last year.)

This year, the NIH-funded lab of David Baker and Minkyung Baek, University of Washington, Seattle, Institute for Protein Design, published that their artificial intelligence approach , dubbed RoseTTAFold, could accurately predict 3D protein structures from amino acid sequences with only a fraction of the computational processing power and time that AlphaFold required [1]. They immediately applied it to solve hundreds of new protein structures, including many poorly known human proteins with important implications for human health.

The DeepMind and RoseTTAFold scientists continue to solve more and more proteins [1,2], both alone and in complex with other proteins. The code is now freely available for use by researchers anywhere in the world. In one timely example, AlphaFold helped to predict the structural changes in spike proteins of SARS-CoV-2 variants Delta and Omicron [3]. This ability to predict protein structures, first envisioned all those years ago, now promises to speed fundamental new discoveries and the development of new ways to treat and prevent any number of diseases, making it this year’s Breakthrough of the Year.

Anti-Viral Pills for COVID-19

The development of the first vaccines to protect against COVID-19 topped Science ’s 2020 breakthroughs. This year, we’ve also seen important progress in treating COVID-19, including the development of anti-viral pills .

First, there was the announcement in October of interim data from Merck, Kenilworth, NJ, and Ridgeback Biotherapeutics, Miami, FL, of a significant reduction in hospitalizations for those taking the anti-viral drug molnupiravir [4] (originally developed with an NIH grant to Emory University, Atlanta). Soon after came reports of a Pfizer anti-viral pill that might target SARS-CoV-2, the novel coronavirus that causes COVID-19, even more effectively. Trial results show that, when taken within three days of developing COVID-19 symptoms, the pill reduced the risk of hospitalization or death in adults at high risk of progressing to severe illness by 89 percent [5].

On December 22, the Food and Drug Administration (FDA) granted Emergency Use Authorization (EUA) for Pfizer’s Paxlovid to treat mild-to-moderate COVID-19 in people age 12 and up at high risk for progressing to severe illness, making it the first available pill to treat COVID-19 [6]. The following day, the FDA granted an EUA for Merck’s molnupiravir to treat mild-to-moderate COVID-19 in unvaccinated, high-risk adults for whom other treatment options aren’t accessible or recommended, based on a final analysis showing a 30 percent reduction in hospitalization or death [7].

Additional promising anti-viral pills for COVID-19 are currently in development. For example, a recent NIH-funded preclinical study suggests that a drug related to molnupiravir, known as 4’-fluorouridine, might serve as a broad spectrum anti-viral with potential to treat infections with SARS-CoV-2 as well as respiratory syncytial virus (RSV) [8].

Artificial Antibody Therapies

Before anti-viral pills came on the scene, there’d been progress in treating COVID-19, including the development of monoclonal antibody infusions . Three monoclonal antibodies now have received an EUA for treating mild-to-moderate COVID-19, though not all are effective against the Omicron variant [9]. This is also an area in which NIH’s Accelerating COVID-19 Therapeutic Interventions and Vaccines (ACTIV ) public-private partnership has made big contributions.

Monoclonal antibodies are artificially produced versions of the most powerful antibodies found in animal or human immune systems, made in large quantities for therapeutic use in the lab. Until recently, this approach had primarily been put to work in the fight against conditions including cancer, asthma, and autoimmune diseases. That changed in 2021 with success using monoclonal antibodies against infections with SARS-CoV-2 as well as respiratory syncytial virus (RSV), human immunodeficiency virus (HIV), and other infectious diseases. This earned them a prominent spot among Science ’s breakthroughs of 2021.

Monoclonal antibodies delivered via intravenous infusions continue to play an important role in saving lives during the pandemic. But, there’s still room for improvement, including new formulations highlighted on the blog last year that might be much easier to deliver.

CRISPR Fixes Genes Inside the Body

One of the most promising areas of research in recent years has been gene editing, including CRISPR/Cas9, for fixing misspellings in genes to treat or even cure many conditions. This year has certainly been no exception.

CRISPR is a highly precise gene-editing system that uses guide RNA molecules to direct a scissor-like Cas9 enzyme to just the right spot in the genome to cut out or correct disease-causing misspellings. Science highlights a small study reported in The New England Journal of Medicine by researchers at Intellia Therapeutics, Cambridge, MA, and Regeneron Pharmaceuticals, Tarrytown, NY, in which six people with hereditary transthyretin (TTR) amyloidosis , a condition in which TTR proteins build up and damage the heart and nerves, received an infusion of guide RNA and CRISPR RNA encased in tiny balls of fat [10]. The goal was for the liver to take them up, allowing Cas9 to cut and disable the TTR gene. Four weeks later, blood levels of TTR had dropped by at least half.

In another study not yet published, researchers at Editas Medicine, Cambridge, MA, injected a benign virus carrying a CRISPR gene-editing system into the eyes of six people with an inherited vision disorder called Leber congenital amaurosis 10. The goal was to remove extra DNA responsible for disrupting a critical gene expressed in the eye. A few months later, two of the six patients could sense more light, enabling one of them to navigate a dimly lit obstacle course [11]. This work builds on earlier gene transfer studies begun more than a decade ago at NIH’s National Eye Institute.

Last year, in a research collaboration that included former NIH Director Francis Collins’s lab at the National Human Genome Research Institute (NHGRI), we also saw encouraging early evidence in mice that another type of gene editing, called DNA base editing, might one day correct Hutchinson-Gilford Progeria Syndrome, a rare genetic condition that causes rapid premature aging. Preclinical work has even suggested that gene-editing tools might help deliver long-lasting pain relief . The technology keeps getting better , too. This isn’t the first time that gene-editing advances have landed on Science ’s annual Breakthrough of the Year list, and it surely won’t be the last.

The year 2021 was a difficult one as the pandemic continued in the U.S. and across the globe, taking far too many lives far too soon. But through it all, science has been relentless in seeking and finding life-saving answers, from the rapid development of highly effective COVID-19 vaccines to the breakthroughs highlighted above.

As this list also attests, the search for answers has progressed impressively in other research areas during these difficult times. These groundbreaking discoveries are something in which we can all take pride—even as they encourage us to look forward to even bigger breakthroughs in 2022. Happy New Year!

References :

[1] Accurate prediction of protein structures and interactions using a three-track neural network . Baek M, DiMaio F, Anishchenko I, Dauparas J, Grishin NV, Adams PD, Read RJ, Baker D., et al. Science. 2021 Jul 15:eabj8754.

[2] Highly accurate protein structure prediction with AlphaFold . Jumper J, Evans R, Pritzel A, Green T, Senior AW, Kavukcuoglu K, Kohli P, Hassabis D. et al. Nature. 2021 Jul 15.

[3] Structural insights of SARS-CoV-2 spike protein from Delta and Omicron variants . Sadek A, Zaha D, Ahmed MS. preprint bioRxiv. 2021 Dec 9.

[4] Merck and Ridgeback’s investigational oral antiviral molnupiravir reduced the risk of hospitalization or death by approximately 50 Percent compared to placebo for patients with mild or moderate COVID-19 in positive interim analysis of phase 3 study . Merck. 1 Oct 2021.

[5] Pfizer’s novel COVID-19 oral antiviral treatment candidate reduced risk of hospitalization or death by 89% in interim analysis of phase 2/3 EPIC-HR Study . Pfizer. 5 November 52021.

[6] Coronavirus (COVID-19) Update: FDA authorizes first oral antiviral for treatment of COVID-19 . Food and Drug Administration. 22 Dec 2021.

[7] Coronavirus (COVID-19) Update: FDA authorizes additional oral antiviral for treatment of COVID-19 in certain adults . Food and Drug Administration. 23 Dec 2021.

[8] 4′-Fluorouridine is an oral antiviral that blocks respiratory syncytial virus and SARS-CoV-2 replication . Sourimant J, Lieber CM, Aggarwal M, Cox RM, Wolf JD, Yoon JJ, Toots M, Ye C, Sticher Z, Kolykhalov AA, Martinez-Sobrido L, Bluemling GR, Natchus MG, Painter GR, Plemper RK. Science. 2021 Dec 2.

[9] Anti-SARS-CoV-2 monoclonal antibodies . NIH COVID-19 Treatment Guidelines. 16 Dec 2021.

[10] CRISPR-Cas9 in vivo gene editing for transthyretin amyloidosis . Gillmore JD, Gane E, Taubel J, Kao J, Fontana M, Maitland ML, Seitzer J, O’Connell D, Walsh KR, Wood K, Phillips J, Xu Y, Amaral A, Boyd AP, Cehelsky JE, McKee MD, Schiermeier A, Harari O, Murphy A, Kyratsous CA, Zambrowicz B, Soltys R, Gutstein DE, Leonard J, Sepp-Lorenzino L, Lebwohl D. N Engl J Med. 2021 Aug 5;385(6):493-502.

[11] Editas Medicine announces positive initial clinical data from ongoing phase 1/2 BRILLIANCE clinical trial of EDIT-101 For LCA10 . Editas Medicine. 29 Sept 2021.

Structural Biology (National Institute of General Medical Sciences/NIH)

The Structures of Life (NIGMS)

COVID-19 Research (NIH)

2021 Science Breakthrough of the Year (American Association for the Advancement of Science, Washington, D.C)

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Tags: 4'-fluorouridine , Accelerating COVID-19 Therapeutic Interventions and Vaccines , ACTIV , AI , AlphaFold , amino acids , artificial antibodies , artificial intelligence , biochemistry , Christian Anfinsen , Chronic Pain , computational biology , coronavirus , COVID pill , COVID-19 , CRISPR , CRISPR/Cas9 , DeepMind , Delta variant , Editas Medicine , EUA , gene editing , hereditary transthyretin amyloidosis , Hutchinson-Gilford progeria syndrome , Intellia Therapeutics , Leber congenital amaurosis , Merck , molnupiravir , monoclonal antibodies , Omicron variant , pandemic , Pfizer , progeria , protein structure , rare disease , Regeneron , RoseTTAFold , SARS-CoV-2 , Science Breakthrough of the Year , Science Breakthroughs of 2021 , structural biology

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It would be remiss not to celebrate another NIH milestone in 2021: on March 1, 2021, the NIH finally took a stand against structural racism in biomedical research ( https://www.nih.gov/about-nih/who-we-are/nih-director/statements/nih-stands-against-structural-racism-biomedical-research ) — and given the rampant health inequities exposed by COVID-19 and also the profound issues of structural racism and economic adversity affecting the health and well-being of the US population, it is essential that this NIH breakthrough be celebrated, institutionalized, and supported with ample funding and leadership.

Biology is a fascinating thing. It’s amazing to think what a few nucleotides can do and how epigenetics can dictate very different outcomes. Something that is toxic in one species can be fairly benign in another. As the pandemic becomes endemic and there are other host animals (such as what is being seen with deer and certain zoo animals), one does have to ask about the evolutionary advantage of codon optimization and the value of pseudo-uridine. Perhaps there are lessons that can be gleaned from Gleevac?

Thank you for carrying on this important communication that I continue to share throughout my own circle. It is vital in combatting “qanonense” and more in our time.

Happy New Year Dr. Tabak, Congratulations on your important work in the NIH and thank you for this special explanation on medical advances being studied.

These goals deeply affect medical problems that are still difficult to manage, such as diseases of genetic origin. Even though they are sometimes rare diseases, we have to think that behind that statistical number is a face, a person, who begs for our help as doctors. I still remember today a young patient (visited decades ago) suffering from Leber’s congenital amaurosis who explained his tragedy to me: at the time I couldn’t find words to help him.

Looking at future treatments for chronic pain that is sometimes devastating to the affected person, it is encouraging to know that the CRISPR gene editing tool could help these patients. https://directorsblog.nih.gov/2021/04/01/could-crispr-gene-editing-technology-be-an-answer-to-chronic-pain/

Just yesterday, a patient who has been suffering from spinal damage for years, writes to me: he periodically updates me on the operations he is undergoing, unfortunately with poor results. Last planned, implantation of a medullary electro-stimulator. But the stress resulting from this situation also causes recurrent central serous chorioretinopathies, a source of serious fear for his sight.

I read tons of scientific ‘stuff’, mostly related to geo-hydromorphological, ecological and botanical, so reading outside these areas challenges my patience. I have to say, this presentation of accomplishments engages to most weary readers. So much of our science has become inaccessible from unnecessary jargon. Larry shows us how to reach out with clear, concise and understandable descriptions. It was a pleasure read! Thank you!

It’s delightful to read, and be reminded of the incredible work that’s been done over the past 2 years, with so much promise in the years soon to come.

Excellent, Dr. Tabak and colleagues….look forward to tracking this blog in the years ahead. Thank you from a colleague in the national and economic security world…

Science is a complicated thing to parlay to lay people without adequate training. The CDC serves to communicate public health concerns to the general public whereas the FDA serves as a regulatory body. Those with the background and training to make informed choices may make decisions that are different from the lay public. However with any of these organizations there needs to be a forum to communicate dissent without repercussion. If a non-majority voice is squashed either by intimidation or other measures, it hardly serves the purpose behind science. The difficult part lays in communicating the message to the majority of the “intended” population while respecting individual informed choices. Not an enviable position to be, and everyone should realize that. It’s like being given the job of a real time translator where nuances may have very different outcomes.

Welcome Larry Tabak. Best wishes for you & the NIH team.

I’m a hereditary TTR patient currently taking Tafamidis daily. My son is genetically positive. Good to read about advances. Need test subjects?

Biology is a fascinating thing. It’s amazing to think what a few nucleotides can do and how epigenetics can dictate very different outcomes.

Some solid-state metrology advances would improve medical equipment tools. I’ve been treating improving antenna design as replacing as much of its metal construction with solid-state lattice building processes, as is efficient. Before medical equipment, there is a need for metrology to improve. Solid-state mechanical reactions have no ground truth. X-ray diffraction, Raman Spectroscopy, NMR, XANES, manometry and thermometry, may all improve solid state manufacturing if improved. They can be improved themselves by solid state advances. This virtuous cycle will lead to soft medicine and now-metal equipment both being better prototyped by the above tools developed initially for solid state metrology. Seeing how many milli-seconds a ball bearing heats a mill surface will lead to the equipment will be quieter using more lattices than metals.

Sorry, I do not understand what the second sentence to stating. Please explain, restate. Thanks

My app was radar dishes and coils, improving them with better materials. The principle also works for say, a microscope. The eyepiece wheel on a Bruker Optics microscope is made of metal. Heavy enough to deaden some vibrations. Precision ground and buffed to have balance. Massive enough to take handling and minimize elastic forces handling wear as well as accidental impacts. But replace the metal with sapphire and 3/4 of the material properties improve with 1/4 being worse. Namely, a lighter microscope made in the shape of a hollow cell membrane will outperform CNC lasered metal or extruded plastic parts. Bigger lenses would throw the existing microscope off balance in months. Already I suggest making such equipment out of planetary mill constituents. I had a 1930s X-ray in 2007 and the machine took up 1/4 the room; consider how small baggage X-rays are now.

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Science, Medicine, and Animals (1991)

Chapter: conclusion.

People clearly want the benefits that derive from animal research. They also want animals to be well-treated and to undergo a minimum of pain and distress. These desires result from our values, from the importance we ascribe to both human and animal life.

But decisions about the use of animals should be based both on reason and values. It makes no sense to sacrifice future human health and well being by not using animals in research today. In fact, it would be immoral and selfish not to use animals in research today, given the harm that could accrue to future generations if such research were halted.

The promise that animal research holds for generations of humans remains undiminished

The majority of Americans agree that animal research must continue. But legislators rarely hear from this majority, whereas they are bombarded by appeals from the small minority who wish to stop or severely curtail such research. Many scientific, medical, and patient groups have come out strongly in favor of humanely conducted animal research. The National Academy of Sciences and Institute of Medicine would like to add their voices to the chorus of support for animal research.

We owe our good health to past investigators and the animals they studied. As we decide on the future of animal research, we should keep in mind the future generations who will look back at us and ask if we acted wisely.

The necessity for animal use in biomedical research is a hotly debated topic in classrooms throughout the country. Frequently teachers and students do not have access to balanced, factual material to foster an informed discussion on the topic. This colorful, 50-page booklet is designed to educate teenagers about the role of animal research in combating disease, past and present; the perspective of animal use within the whole spectrum of biomedical research; the regulations and oversight that govern animal research; and the continuing efforts to use animals more efficiently and humanely.

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• Research article
• Open access
• Published: 17 June 2014

How “moral” are the principles of biomedical ethics? – a cross-domain evaluation of the common morality hypothesis

• Markus Christen 1 ,
• Christian Ineichen 2 &
• Carmen Tanner 3  

BMC Medical Ethics volume  15 , Article number:  47 ( 2014 ) Cite this article

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The principles of biomedical ethics – autonomy, non-maleficence, beneficence, and justice – are of paradigmatic importance for framing ethical problems in medicine and for teaching ethics to medical students and professionals. In order to underline this significance, Tom L. Beauchamp and James F. Childress base the principles in the common morality, i.e. they claim that the principles represent basic moral values shared by all persons committed to morality and are thus grounded in human moral psychology. We empirically investigated the relationship of the principles to other moral and non-moral values that provide orientations in medicine. By way of comparison, we performed a similar analysis for the business & finance domain.

We evaluated the perceived degree of “morality” of 14 values relevant to medicine (n 1  = 317, students and professionals) and 14 values relevant to business & finance (n 2  = 247, students and professionals). Ratings were made along four dimensions intended to characterize different aspects of morality.

We found that compared to other values, the principles-related values received lower ratings across several dimensions that characterize morality. By interpreting our finding using a clustering and a network analysis approach, we suggest that the principles can be understood as “bridge values” that are connected both to moral and non-moral aspects of ethical dilemmas in medicine. We also found that the social domain (medicine vs. business & finance) influences the degree of perceived morality of values.

Conclusions

Our results are in conflict with the common morality hypothesis of Beauchamp and Childress, which would imply domain-independent high morality ratings of the principles. Our findings support the suggestions by other scholars that the principles of biomedical ethics serve primarily as instruments in deliberated justifications, but lack grounding in a universal “common morality”. We propose that the specific manner in which the principles are taught and discussed in medicine – namely by referring to conflicts requiring a balancing of principles – may partly explain why the degree of perceived “morality” of the principles is lower compared to other moral values.

Peer Review reports

The “ Principles of Biomedical Ethics ” by Tom L. Beauchamp and James F. Childress, which appeared for the first time in 1977, is a classic text in biomedical ethics. The authors’ contribution has been celebrated as one of the most important methodological inventions of modern practical ethics, particularly in Anglophone scholarship[ 1 ]. The core features of this so-called principlism are to locate moral principles (autonomy, non-maleficence, beneficence, and justice) pertinent to a particular moral situation and to use specification, balancing and (deductive) application to create a bridge between the moral situation and the relevant principles. In addition, the authors adopt a prescriptive common morality thesis as a theoretical justification for the methodological reasoning within principlism. This grounding of the principles in the common morality was emphasized in later editions. At the beginning of the most recent 7th edition, published in 2013, Beauchamp and Childress state that the common morality “refers to norms about right and wrong human conduct that are so widely shared that they form a stable social compact” ([ 2 ], p. 3). Thus, the common morality is not merely a specific morality, in contrast to other moralities, but is rather applicable to all persons in all places, and we rightly judge all human conduct by its standards. Examples include situations in which, for example, one knows not to lie, not to steal property, to keep promises, to respect the rights of others, not to kill or not to cause harm to innocent persons, and the like ([ 2 ], p. 3;[ 3 ]). It has been argued that there is far more consensus on common morality principles and rules than on any other moral theories. Hence, appealing to norms of the common morality will work better for practical decision-making[ 4 ].

However, principlism is not undisputed in bioethics. Its virtues are clarity, simplicity and universality, but its vices include neglect of emotional and personal factors that are inherent in specific decision situations, oversimplification of the issues, and excessive claims of universality[ 5 ]. The main focus of the critique concerns the completeness of the approach with respect to its practical use for dealing with moral problems in clinical practice given the challenge of ethical pluralism[ 6 ]. Opinions are conflicting in this regard. In support of principlism, Gillon[ 7 ] argues that the principles enable a clinician (and anybody else) to link professional guidance and rules with ethical aspects. They also allow new situations to be confronted in the light of these acceptable principles. There is some empirical support that the daily work of physicians and other professionals in biomedicine indeed reflects these four principles[ 8 ]. Others[ 9 ], however, argue that the principlist model is unreflective of how ethical decisions are taken in clinical practice and that the model is neither sufficiently action-guiding nor explicit about how to attain professional integrity. For example, based on an analysis of the communication process with Muslim parents, Westra and colleagues[ 10 ] concluded that the parties involved in disagreement may feel committed to seemingly similar, but actually quite different principles. In such cases, communication in terms of the principles may create a conflict within an apparently common conceptual framework. Page[ 11 ] found that psychology students value the principles but do not actually seem to use them directly in the decision-making process, which partly calls into question their practical relevance.

Despite such findings, the common morality has been emphasized by Beauchamp and Childress as the ultimate source of moral norms since the 5th edition of the “ Principles of Biomedical Ethics ”[ 4 ]. Karlsen and Solbakk[ 12 ] observed that with the publication of the 6th edition, the authors not only attempted to ground their theory in the common morality, but that there was also an increased tendency to align the former with the latter. While this strategy may give the impression of a more robust, and hence stable, foundation for the theoretical construct of principlism, Karlsen and Solbakk argue that this comes at the expense of theoretical and practical open-mindedness. In line with this reasoning, Beauchamp and Childress suggested that the question of whether the principles are indeed part of the common morality should be an object of empirical inquiry ([ 2 ], p. 416). Our research seeks to provide a first step of an empirical investigation of the common morality.

An empirical investigation of the common morality

For the purpose of the empirical investigation, we suggest making use of research in moral psychology. We posit that moral values or principles “so widely shared that they form a stable social compact” ([ 2 ], p. 3) are grounded in psychological processes that allow us to recognize that these values are at stake, and to align decisions and actions with these values. Furthermore, to the extent that these processes and values share some degree of universality, they are built into us by evolution. For example, beneficence or caring for others might be seen as being products of natural selection, adapted to allow survival[ 13 ].

In the following, we focus on those findings in moral research that align with the universality claim of the common morality in order to identify “dimensions” of morality that can then be empirically investigated. The findings posit that people have nearly instant reactions to situations of moral violations[ 14 – 16 ], indicating that moral concepts are “chronically accessible”[ 17 ] mental representations. This foundation of morality in human psychology developed to solve problems that faced our ancestors for millions of years. For example, generosity and sharing developed due to extreme mutual interdependence[ 18 ]. One can therefore expect that the common morality has a rich and long history of heritage across and beyond the hominid line[ 19 ], supporting the universality claim. Moreover, cognitive approaches of moral psychology, e.g. the moral-conventional distinction model of Turiel[ 20 ], have emphasized universal aspects of morality in the sense that the moral wrongness of an action does not depend on specific circumstances. However, beyond universality, current research in moral psychology and anthropology points out two further dimensions of morality, namely community orientation and cooperation[ 18 ]. Thus, it is plausible to relate the common morality, which should be “shared by all persons committed to morality” ([ 2 ], p. 3), to the features universality, community orientation and cooperation.

Certainly, there is also a line of research within moral psychology emphasizing the cultural and social grounding of morality, which calls this universality claim into question and tends to align with the tradition of moral relativism within philosophy[ 21 ]. Thus, an empirical investigation of the values that are claimed to be part of the common morality should also include a cross-cultural and/or cross-social comparison.

Research goals

The aim of the present research is twofold. First, we wish to empirically examine the extent to which various values are considered to be moral values and whether this evaluation is characterized by the features of universality, communion and cooperation orientation. With the term ‘value’ , we refer to stable beliefs about desirable states or conducts of behaviors, which serve as normative standards to assess and justify actions[ 22 , 23 ]. Second, we wish to test whether these evaluations of values generalize across different social domains. In doing so, we compare this evaluation in two domains: medicine and business & finance. We hypothesize that morality ratings coincide strongly with the features of universality, communion and cooperation orientation, and that these relations persist across different social domains. From this hypothesis, we deduce that: If the values that relate to the principles are, as expected, commonly characterized by these features, and if these evaluations persist across social domains of application, we have support for the claim that the principles are part of the common morality. If this is not the case, we have a conflicting result that requires further investigation.

For the purpose of our research goals, we began with a qualitative step to identify the values that are considered as relevant in each domain. In the second, quantitative, step, we conducted two domain-specific surveys with the aim of investigating the evaluation of these values.

Step 1: value identification within two domains

We began by conducting literature reviews, interviews with experts, and a small survey among various professionals in Switzerland to identify a) the relevant values within the respective domain, and b) typical behavioral manifestations of these values. Using this procedure, we identified 14 values considered to be important within the respective domain. These values were not necessarily “genuinely moral”. The values identified in this manner in medicine were: autonomy (Autonomie), care (Fürsorge), cost-effectiveness (Wirtschaftlichkeit), feasibility (indicating that the physician should do whatever is technically possible; technischer Imperativ), honesty (Ehrlichkeit), integrity (Integrität), justice (Gerechtigkeit), loyalty (Loyalität), non-maleficence (Nichtschaden), performance (Leistung), professionalism (Professionalität), reputation (Reputation), respect (Respekt), and responsibility (Verantwortung). The 14 business & finance values were: engagement (Engagement), competition (Wettbewerb), compliance (Regelkonformität), fairness (Fairness), integrity (Integrität), loyalty (Loyalität), non-maleficence (Nichtschaden), performance (Leistung), professionalism (Professionalität), profitability (Profitabilität), reputation (Reputation), respect (Respekt), responsibility (Verantwortung), and transparency (Transparenz). As expected, the values of the two domains only partially overlapped.

To ensure that participants had a precise and common understanding of the values, which was necessary for making an accurate evaluation, each value was presented with three examples. These examples represented typical manifestations of the corresponding value in domain-specific settings and we used domain-specific terms (‘patients’ , ‘customers’, etc.) to adapt the descriptions to the respective domain. For instance, examples for autonomy in medicine were “A person or an institution a) respects the self-determination of others, b) avoids putting pressure on others to reach goals, and c) supports others such that they can make their own decisions.” Examples for profitability in business & finance were “A person or a company a) tries constantly to optimize the relationship between revenue and expenditure, b) defines success as the pursuit of profit, and c) grants a paramount importance to key financial indicators”. Table  1 outlines the value exemplifications used in our study (German original and English translation).

We note that the term ‘care’ (Fürsorge) and not ‘beneficence’ was used in the survey, since the German ‘Fürsorge’ is more common than the technical term ‘Benefizienz’. Furthermore, we used general descriptions of ‘fairness’ and ‘transparency’ for business & finance that were similar to the descriptions of ‘justice’ and ‘honesty’ in medicine. In other words, the semantics of these terms overlap, although there is a domain-specific tradition that, for example, what is considered a matter of honesty (Ehrlichkeit) in medicine is often a matter of transparency (Transparenz) in business & finance. Thus, overall, we had eight values that are commonly present in both domains and two domain-specific values that shared a large degree of semantic similarity. As expected, for the medicine domain, the experts considered all four principles to be important values; for the business & finance domain, two out of the four principles (non-maleficence and justice/fairness) were considered as relevant.

Step 2: value evaluations within domains

The following investigation was designed to examine the evaluations of the values identified in the previous step by conducting two domain-specific surveys.

We collected data from two samples through online surveys. In total, 455 participants composed of students and staff of the University of Zurich (the focus was on medical students, but students of other faculties could also participate) and members of a network of health professionals provided data for the medicine survey. Regarding the business & finance survey, the sample consisted of 333 economics students and staff (most of whom had work experience in business or finance). This study was cleared in accordance with the ethical review processes of the University of Zurich and within the “Ethical Guidelines for Psychologists of the Swiss Society for Psychology” ( http://www.ssp-sgp.ch/06_pdf/ersgp2003.pdf ). Furthermore, we followed the CHERRIES guidelines (The Checklist for Reporting Results of Internet E-Surveys; see http://www.jmir.org/2004/3/e34/ ) insofar as they were applicable to the surveys.

Each survey contained two parts. After participants had provided informed consent, we first assessed demographic information (gender, age, field of study) and information about the participants’ work experience in medicine or business & finance (whether they had work experience, and what kind of experience). Participants then rated each value (in a randomized order) along four dimensions using a 6-point Likert scale (see below). The participants were able to quit the survey whenever they wished. The participants who completed the whole survey were entered into a lottery to win an iPad. After quitting or after having rated all 14 values, the participants were asked whether they completed the survey with due diligence (this is a standard test question in psychological online surveys, but the answer did not influence whether the participants were entered into the lottery). Those participants who stated that they did not complete the survey with due diligence or did not answer the question were excluded from the data sets. We also compared the time needed to complete the survey between participants negating or affirming the due diligence question. Assuming that individuals who completed the survey with less care would need less time, we also excluded participants who responded as quickly as those who stated that they did not complete the survey with due diligence (this was only the case for participants who had only rated one single value).

After this quality check, the samples consisted of 317 participants in medicine (dropout: 30.3%) and 247 participants in business & finance (dropout: 25.8%). In the medicine sample, 71.9% of the participants were female, their mean age was M  = 25.4 years, and 27.4% reported having work experience (24.9% provided detailed descriptions of their profession, e.g. working as physician or nurse). In the business & finance sample, 54.3% of the participants were female, their mean age was M  = 26.6 years, and 31.6% reported having work experience (21.9% reported that they currently work between 50-100% in the business or finance domain). The over-representation of females in the medicine survey is mainly explained by the fact that 61% of medical students in Switzerland are female (final degree, data of 2010;[ 24 ]) and that among the professionals, more nurses than doctors answered the survey.

In the main part of the survey, we assessed the participants’ evaluation of the corresponding value along four dimensions (see Table  2 ): The “moral – non-moral” dimension was explicitly described as referring to universal principles and issues of right and wrong (MO-NMO)[ 20 ]. The “community-oriented – self-oriented” dimension referred to the social notion of morality (COM-SELF)[ 25 ]. The “cooperative – competitive” dimension was described as referring to collaborative or rivalry tendencies between human beings or institutions (COOP-COMP)[ 18 ]. Finally, we added the “principle-focused – consequentialist” dimension in order to include a reference to the classic teleological vs. deontological distinction in ethical theory (PRI-CON). This also served as a test to examine whether the notions of autonomy, care (beneficence), non-maleficence and justice are actually evaluated as “principles” as in the approach of biomedical ethics; we found no indications in this regard. Each dimension was rated on a 6-point scale (1 =  moral; community-oriented, cooperative, or principle-focused ; 6 =  non-moral, self-oriented, competitive, or consequentialist ).

We will first report the bivariate correlations among the dimensions, followed by the evaluation of single values across domains. Finally, we will examine how the values based on similarity analyses cluster within each domain. The data were analyzed using the software package Mathematica® version 9.

Correlational analyses

For each domain, we examined the pairwise Pearson product–moment correlations among the four dimensions at the aggregated level (across all values) and at the specific level (for each single value). Table  3 reports both the findings with the aggregated data, and the number of values with significant ( ps  < 0.05) bivariate correlations. As can be seen from Table  3 , the mutual correlations among dimensions MO-NMO, COM-SELF and COOP-COMP are about twice as high as the correlations among MO-NMO, COM-SELF, COOP-COMP and PRI-CON. At the aggregated level: The mean correlations among the first three dimensions in medicine and business & finance are 0.52 and 0.61, while the mean correlations of these with the fourth dimension are 0.25 and 0.34. A similar picture emerges at the specific level: The mean number of values with significant correlations among the first three dimensions in medicine and business & finance are 12.3 and 12.7, while the mean number of values with significant correlations with the fourth dimension are 5.0 and 5.0. These results demonstrate that the dimensions MO-NMO, COM-SELF and COOP-COMP are more closely associated among themselves than with dimension PRI-CON. We thus conclude that participants tend to associate a “moral value” with the attributions: universally valid, an issue of right and wrong, community and cooperation. In contrast, a “non-moral value” tends to be characterized by the features: non-universal, not an issue of right and wrong, but an issue of self-orientation and competition. Note that this “moral” versus “non-moral” distinction correlated only weakly with the “principle-focused” versus “consequentialist” distinction. One reason might be that, referring to the classic ethical traditions, a moral value can imply either a deontological or a consequentialist focus. Hence, weaker correlations are likely. We thus restrict the following cluster analysis to the dimensions MO-NMO, COM-SELF and COOP-COMP.

Evaluation of single values

Table  4 compares the ratings for the single values across medicine and business & finance (only for the eight values that were present in both domains). As can be seen, the analyses revealed significant differences with regard to five values. When contrasting medicine with business & finance, loyalty is less moral (dimension MO-NMO); responsibility is more self-centered (COM-SELF); and performance is more competitive (COOP-COMP) in medicine. Reputation and non-maleficence are less “moral” in medicine across all three dimensions.

Further analyses also revealed differences when contrasting participants with work experience to students without work experience, both in medicine and business & finance (data not shown; Mann–Whitney test, ps  < 0.05). Participants with professional experience in medicine consider loyalty to be significantly less moral (dimension MO-NMO) and justice to be significantly more cooperative (COOP-COMP). Participants with professional experience in business & finance consider engagement to be significantly more community-oriented (COM-SELF), reputation to be more non-moral (MO-NMO), self-centered (COM-SELF) and competitive (COOP-COMP), and integrity to be more cooperative (COOP-COMP).

Value classification

In a next step, we analyzed the classification of the values using two similarity metrics and two classification methods for each dimension MO-NMO, COM-SELF and COOP-COMP separately. For the similarity metrics, we used Mann–Whitney and Kolmogorov-Smirnov as two complementary nonparametric tests (the former has a higher power for rejecting the null hypothesis, the latter is more sensitive to the form of the distribution, e.g., bimodality). In the first classification method, two values are considered to be in the same group if the ratings along one dimension are not distinguishable for one of the two tests (i.e., ps  > 0.05). In the second classification method, the p-values of the two tests were used to create a distance matrix for either test (each matrix element is calculated as 1 minus the p-value of the corresponding value pair). Either the MW or the KS distance matrix for one dimension then served as input for a clustering algorithm that required no predefined specifications on cluster number and size[ 26 ]. In this way, two values X and Y could be in the same group a maximum of 12 times (3 dimensions × 2 similarity measures × 2 classification methods).This, in turn, resulted in a count matrix in which each matrix element stands for the number of times the two associated values have been put in the same group (Figure  1 ). The matrix rows can be ordered such that those values that are frequently grouped together are neighbors. Finally, these analyses revealed three classes of values with the following features: class-I (blue) and class-III (red) values are completely distinct; i.e. values from class I were never grouped together with values from class III or vice versa. In contrast, class-II (dark green) values tend to overlap with the other two classes, i.e. for some combination of dimension, similarity measure and classification method, a class-II value is grouped with a class-I value, and for some other combination, it is grouped with a class-III value.

Count matrix representing how often two values have been classified in the same group: the darker the entry, the more often two values have been grouped together (maximum 12 times). Yellow entries indicate values that have never been classified together; a) count matrix for medicine, b) count matrix for business & finance. The color bars on the left side indicate the two grouping options (blue: class-I, green: class-II, red: class-III). Value abbreviations: AUT = autonomy; CAR = care, CPT = competition, COM = compliance, CEF = cost-effectiveness, ENG = engagement, FAI = fairness, FEA = feasibility, HON = honesty, INT = integrity, JUS = justice, LOY = loyalty, NMA = non-maleficence, PER = performance, PRO = professionalism, PFT = profitability, REP = reputation, RES = respect, RPS = responsibility, TRA = transparency.

A closer analysis of the count matrix reveals two ways of forming these three value groups. Note that class I (blue) and class III (red) encompass the moral and non-moral group, respectively. In medicine, and with respect to grouping option 1, the blue class is composed of the values respect, loyalty, responsibility and honesty, while autonomy joins the red class. With respect to grouping option 2, the blue class is extended by the values care, non-maleficence and feasibility, while the red class is composed of the values cost-effectiveness, reputation, and performance. In business & finance, similar findings are discernible: In grouping option 1, the blue class is composed of the values respect, fairness, loyalty, responsibility, and non-maleficence, while the red class includes the values engagement and professionalism. In grouping option 2, the blue class is extended by the value integrity, while engagement and professionalism are excluded from the red class.

Taking the intersection of these two approaches of analyses reveals a “moral” core and a “non-moral” core for both social domains, which is partially domain-overlapping and partially domain-specific. In medicine, the moral core consists of respect, loyalty, responsibility and honesty; in business & finance, it consists of respect, fairness, loyalty, responsibility and non-maleficence. These values have been classified together in almost all cases. In medicine, the non-moral core consists of cost-effectiveness, reputation and performance; in business & finance, it is composed of the values reputation, competition, performance and profitability. Note that both the moral and non-moral cores share a high degree of overlap between the medicine and business & finance domain: respect, loyalty and responsibility for the moral core, and reputation and performance for the non-moral core.

Strikingly, none of the four values related to the principles of biomedical ethics (autonomy, non-maleficence, care, and justice) fall into the moral core; autonomy even received consistently low ratings among all three dimensions, making it almost a class-III value. In contrast, in the business & finance domain, two of the four values that represent the principles – non-maleficence and fairness – are in the moral core.The result of the count matrix can also be displayed as a network, with the size of the edges between two values reflecting the frequency with which the corresponding two values have been classified together (Figures  2 and 3 ). This representation motivates the notion of class-II values as “bridge values” (marked in green), i.e. these values can be grouped either with moral or with non-moral values depending on dimension, similarity metrics and classification method. Some “bridge values” have a stronger affinity to the moral core (marked in turquoise: care, feasibility and non-maleficence in medicine; integrity in business & finance), whereas others have a stronger affinity to the non-moral core (marked in green: autonomy and professionalism in medicine; engagement and professionalism in business & finance).

Network representation of the count matrix in medicine. The size of the edge between two values represents how often these values have been grouped together. Value abbreviations: see caption Figure  1 .

Network representation of the count matrix in business & finance. The size of the edge between two values represents how often these values have been grouped together. Value abbreviations: see caption Figure  1 .

We hypothesized that we can characterize the common morality using three dimensions which have been derived from current empirical research in morality. As expected, we found that these dimensions correlate strongly across the social domains medicine and business & finance. In addition, we identified values that form a moral core within both domains – respect, loyalty and responsibility. These data are consistent with the notion of a common morality, i.e. there are values that are perceived as being highly moral across social domains.

Strikingly, we found that the values associated with the principles of biomedical ethics are not part of this moral core. In particular, based on the ratings given by the participants, it is questionable whether non-maleficence and in particular autonomy are perceived as being part of the common morality. Interestingly, in the business & finance domain, non-maleficence is part of the moral core, indicating a domain-specificity of the perceived morality of this value. These findings are in conflict with the common morality hypothesis of Beauchamp and Childress. However, they are in line, for example, with Haidt and Joseph[ 19 ], who propose that innately prepared intuitions generate social-culturally variable values and virtues.

From the point of view of medical ethics, at first glance, our finding may be surprising, if not worrying, because one may consider it to be an indication of a failure to convey the desired normativity of values to the professionals who should work with them. Furthermore, the finding might indicate that the principles – in particular non-maleficence and autonomy – may not be grounded in the moral psychology of medical professionals in the same way as other moral values. This raises the question of how principles which are inherently not as moral-laden as assumed guide health care providers in conflict situations to find a helpful – and for their part “moral” – orientation that would generate guidance. We believe that to answer this question, one should analyze the function of the principles in practical moral decision making. Page[ 11 ] found an absence of predictive power of the principles in decision making, and concluded that this may be due to the absence of a behavioral model explaining how individuals cognitively use these principles in their decision making. According to our model of moral intelligence, which is proposed to provide an integrative framework for understanding moral behavior[ 27 ], moral values are, if internalized, part of the individual’s “moral compass” that helps to guide behavior. The weaker this grounding is – and lower “morality ratings” indicate this – the less likely it is that decision problems are framed as moral problems and that the corresponding values come into play in the decision-making process as moral values.

We furthermore suggest that the way in which the principles are discussed and learned within biomedical ethics – namely as instruments to deal with dilemmatic situations – influences to some degree their grounding in the individual’s moral psychology. If values like non-maleficence or autonomy are regularly discussed in cases that involve a conflict between them, it is likely that the initial appeal of understanding autonomy as “moral” (i.e., providing unambiguous action guidance) is weakened. In this respect, it is interesting that professionals in our survey consider, for example, loyalty to be significantly less moral (dimension MO-NMO) compared to the students, although loyalty is considered to be among the moral foundations whose moral psychology has an evolutionary history[ 19 ]. As the moral complexity of many clinical problems can often be understood as conflicts in loyalties (e.g. between head physician and patient), it is possible that these experiences weaken the initial moral appeal of loyalty. That is, the social practice of dealing with the principles in a specific way in biomedical ethics (e.g., as instruments to teach ethics to students and health professionals) may at least to some degree foster or erode the foundation of the principles in common morality.

A prediction from our findings is, for example, that – compared to violations of honesty or respect – medical professionals will be less likely to quickly identify violations of autonomy in specific practical clinical problems as a moral issue (due to the particularly low morality ratings of autonomy). The multiple ways of understanding autonomy in medical decision problems[ 28 ] make such a prediction plausible.

In addition, our finding is in accordance with some of the recent critique of principlism raised by other scholars. For example, Lee[ 1 ] discussed the principles using the distinction between thin and thick concepts, where ‘thin’ and ‘thick’ both have two different meanings: One possibility is to view the terms based on their theoretical status: An ethical theory, method or principle is ‘thin’ in that it covers a theoretical area of morality but thick in that it provides guidance in practical moral realms. The other possibility is to view the terms from the standpoint of content: For example, a theory, method or principle is thin in that it deals with particular moral norms/virtues in a minimal sense but thick in that it utilizes a large number of moral norms/virtues including their cultural or traditional imprinting. Of course, these two ways of using thin and thick overlap in many instances. In the case of principlism, the method is, according to Lee, thick in status, since the method deals with practical moral issues, but thin in content, because it allows different individuals and cultures/traditions to use the four principles in their own way. In that sense, principlism would properly work primarily within Western culture.

In actual fact, our findings indicate an even stronger undermining of the psychological grounding of the principles in the common morality, because even within the same cultural frame we find that the degree of perceived morality of a value differs between social domains. The fact that non-maleficence is unambiguously in the moral values class in the domain business & finance may indicate that the participants in our study tended to frame harming a client or business partner as morally bad, whereas in the medical context, harming a patient is seen as morally justifiable in some contexts (e.g. in the case of vaccination or surgery; note that medical interventions are an incidence of bodily injury from a legal point of view). Thus, the professional training of medics requires them to question the primarily moral appeal of non-maleficence.

Several shortcomings of our study should be noted: First, we cannot completely rule out that the values varied in their generality. That is, the fact that our participants gave higher morality ratings to respect, loyalty, honesty and responsibility over more specific bioethical principles may simply reflect that some of the former values may already include some of the more specific principles. For example, Beauchamp and Childress call their first principle ‘respect for autonomy’ , and the notion of ‘responsibility’ may already involve a ‘duty of care’. Nonetheless, if a more specific value is inherently related to a more general one, we would expect this to also be reflected empirically in a higher similarity between these values regarding the various morality dimensions. In line with this, our results revealed, for example, that ‘responsibility’ is quite strongly associated with care, but that ‘respect’ is not at all associated with ‘autonomy’ (see Figure  2 ). A reason for this could be that the notion of “respecting a value” can be used for any value term, i.e. the meaning of “respecting x” is different from the general understanding of the value ‘respect’. Furthermore, the argument that in our study, some values were more fundamental that others cannot explain why there are still, and also domain-specific, differences between, for instance, loyalty, non-maleficence and justice/fairness – values for which there are good arguments[ 19 ] that they are generic. Of course, however, further research is needed to test the replicability and robustness of our findings.

Second, our approach did not include an intercultural comparison, which would allow for a more valid analysis of the common morality hypothesis. This could be an additional promising extension of this study, although it needs to be taken into account that the translations of value descriptions into different languages would have to be carefully validated in order to avoid shifts in meaning.

Based on our findings, we can conclude that the principles of biomedical ethics – in particular autonomy – have only a weak grounding in human moral psychology and thus in the common morality. Compared to other moral values, the principles do not appear to be as “inbuilt” or internalized values as expected. This might be unproblematic when people are able to engage in decision-making processes that involve effortful and reflective thinking. In such situations, the principles of biomedical ethics may serve as a useful framework and means for deliberate moral justifications[ 29 ]. Research has shown that expenditure of cognitive effort is, for example, more likely under conditions of opportunity (such as low time pressure or low mental workload). However, under conditions of lack of opportunity (such as high time pressure or high mental workload), individuals are more likely to rely on spontaneous processing, and therefore on values that are more internalized and quickly accessible[ 30 ]. In such situations, and to the extent that the principles of biomedical ethics are inbuilt in the human mind, they are less likely to affect decision making and behavior. Of course, future studies will have to examine more thoroughly the extent to which the famous biomedical principles really do influence moral decision making and behavior in practical contexts.

Authors’ information

MC is a researcher in neuroethics and empirical ethics, and research network manager at the University Research Priority Program Ethics of the University of Zurich. CI is a PhD student at the Institute of Biomedical Ethics of the University of Zurich and works in neuroethics. CT is a psychologist and conducts research in ethical decision making. She is currently head of the Center for Responsibility in Finance at the Department of Banking and Finance, University of Zurich, Switzerland.

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Acknowledgements

We thank Fabian Lienhard and Manuel Zehr for their support in executing and analyzing the business & finance survey. This study was supported by the “Käthe-Zingg-Schwichtenberg Fonds” of the Swiss Academy of Medical Sciences, Basel, Switzerland, and the “Stiftung Suzanne und Hans Biäsch zur Förderung der Angewandten Psychologie”, Zurich, Switzerland.

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MC and CT designed the study, MC and CI executed the study, MC performed the data analysis. All authors were involved in preparing the manuscript. All authors read and approved the final manuscript.

Christian Ineichen and Carmen Tanner contributed equally to this work.

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Christen, M., Ineichen, C. & Tanner, C. How “moral” are the principles of biomedical ethics? – a cross-domain evaluation of the common morality hypothesis. BMC Med Ethics 15 , 47 (2014). https://doi.org/10.1186/1472-6939-15-47

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• Published: 22 April 2024

T cell expressions of aberrant gene signatures and Co-inhibitory receptors (Co-IRs) as predictors of renal damage and lupus disease activity

• Chin-Man Wang 1 ,
• Yeong-Jian Jan Wu 2 ,
• Jian-Wen Zheng 2 ,
• Li Yu Huang 2 ,
• Keng Poo Tan 2   na1 &
• Ji-Yih Chen   ORCID: orcid.org/0000-0001-5661-6291 2   na1  

Journal of Biomedical Science volume  31 , Article number:  41 ( 2024 ) Cite this article

Metrics details

Systemic lupus erythematosus (SLE) is distinguished by an extensive range of clinical heterogeneity with unpredictable disease flares and organ damage. This research investigates the potential of aberrant signatures on T cell genes, soluble Co-IRs/ligands, and Co-IRs expression on T cells as biomarkers for lupus disease parameters.

Comparative transcriptome profiling analysis of non-renal and end-stage renal disease (ESRD) phenotypes of SLE was performed using CD4 + and CD8 + cDNA microarrays of sorted T cells. Comparing the expression of Co-IRs on T cells and serum soluble mediators among healthy and SLE phenotypes.

SLE patients with ESRD were downregulated CD38, PLEK, interferon-γ, CX3CR1, FGFBP2, and SLCO4C1 transcripts on CD4 + and CD8 + T cells simultaneously and NKG7, FCRL6, GZMB/H, FcγRIII, ITGAM, Fas ligand, TBX21, LYN, granulysin, CCL4L1, CMKLR1, HLA-DRβ, KIR2DL3, and KLRD1 in CD8 T cells. Pathway enrichment and PPI network analyses revealed that the overwhelming majority of Differentially Expressed Genes (DEGs) have been affiliated with novel cytotoxic, antigen presentation, and chemokine-cell migration signature pathways. CD8 + GZMK + T cells that are varied in nature, including CD161 + Mucosal-associated invariant T (MAIT) cells and CD161- aged-associated T (Taa) cells and CD161-GZMK + GZMB + T cells might account for a higher level of GZMK in CD8 + T cells associated with ESRD. SLE patients have higher TIGIT + , PD1 + , and lower CD127 + cell percentages on CD4 + T cells, higher TIM3 + , TIGIT + , HLA-DR + cell frequency, and lower MFI expression of CD127, CD160 in CD8 T cells. Co-IRs expression in T cells was correlated with soluble PD-1, PDL-2, and TIM3 levels, as well as SLE disease activity, clinical phenotypes, and immune-therapy responses.

The signature of dysfunctional pathways defines a distinct immunity pattern in LN ESRD patients. Expression levels of Co-IRs in peripheral blood T cells and serum levels of soluble PD1/PDL-2/TIM3 can serve as biomarkers for evaluating clinical parameters and therapeutic responses.

Introduction

Systemic lupus erythematosus (SLE) is a chronic, debilitating, autoimmune disorder that is defined by systemic inflammation. The innate and adaptive immune systems substantially contribute to the imbalanced immune reaction to self-antigens, which promotes immune tolerance loss and systemic autoimmunity toward nuclear autoantigens [ 1 ]. Recent research emphasizes understanding the biological processes enabling immune cell differentiation, which leads to the identification of novel potential prospects for immune-mediated blockade therapies and aids in the clinical course of SLE [ 2 , 3 ]. Diverse functional pathways and domains in proteins have been attributed to the organ-specific development of SLE. Emerging important research seeks to identify the precise mechanisms that contribute to the wide variety of clinical disease risks associated with SLE, which impacts multiple organs and ultimately results in a high mortality rate [ 4 ] . Lupus nephritis (LN) is defined by glomerulonephritis and tubulointerstitial inflammation among individuals diagnosed with SLE within 5 years, and 5–20% of those diagnosed with LN advance to end-stage renal disease (ESRD) [ 5 , 6 , 7 ]. LN is a potentially fatal autoimmune disease that necessitates early and accurate diagnosis as well as prompt treatment initiation for improving outcomes [ 6 , 8 ]. The purposes of therapy are for the individual's long-term survival, the prevention of flares and organ injury, and the improvement of quality of life [ 9 , 10 ]. Identifying the activity of SLE facilitates monitoring of the disease and the appraisal of therapeutic interventions. The enhanced comprehension of signaling and gene regulation deficiencies will result in the discovery of novel therapeutic targets and predictive biomarkers for LN.

T cells have become increasingly accepted as key contributors to the development of SLE [ 2 , 3 , 11 , 12 ]. The functions of varied effector, memory, exhausted and regulatory T cells are controlled by distinct pathways [ 13 ]. With the goal to improve the treatment and outcome of this complicated disease, it is necessary to identify the molecular and genetic defects of malfunctioning signaling pathways that lead to dysfunctional SLE T cells [ 2 ] . It is feasible to speculate that diverse aberrant gene expression and specific immune functional changes in T cells result in disruption of immune tolerance and, ultimately, autoimmune responses in SLE [ 14 ]. This study aimed to investigate novel immune-regulatory pathways that predict the heterogeneity and activity of lupus disease based on gene aberrant signatures and dysfunctions of Co-inhibitory receptors (Co-IRs) on T cells.

Materials and methods

Description of the characteristics of study populations and cohorts.

Patients for this study were recruited primarily from Chang Gung Memorial Hospital's Rheumatology Clinics. Rheumatologists who confirmed SLE in all examined patients based on American College of Rheumatology criteria [ 15 , 16 ]. Specific phenotypes of SLE were correlated with biological data. Ninety one patients with SLE (age, 23–80 years; 49.9 ± 11.7 median years) and 27 Health Controls (HCs) (age, 27–52 years; 36.3 ± 5.7 years) were enrolled. There were 68 patients with SLE without nephritis and 23 patients with nephritis; 25 had SLEDAI ≥ 6, compared to 66 < 6. Regarding the immunologic assays, this cohort observed Anti-dsDNA antibody: 35 ≧ 130 vs. 55 < 130; C3: 69 ≧ 70 vs. 22 < 70; C4: 74 ≧ 10 vs. 17 < 10; Anti-RNP antibody: 28 positive vs. 52 negative; Anti-SM antibody, 16 positive vs. 64 negative; Anti-SSA antibody, 51 positive vs. 31 negative; Anti-SSB antibody, 15 positive vs. 67 negative; Anti-ACA IgG antibody, 10 positive vs. 61 negative; Anti-ACA IgM antibody, 4 positive vs. 61 negative. Steroid (≥ 10 mg 67, 10-20 mg 23, > 20 mg 11), azathioprine (≥ 50 mg 13; > 50 mg 4), hydroxychloroquine (≥ 200 mg 35; > 200 mg 23), and mycophenolate (≤ 1000 mg 5; ≥ 1000 mg 8) are utilized as immune modulating drugs (Supplementary Table 1 ). Fourteen patients with SLE underwent a follow-up immune evaluation, with a median follow-up of 12.5 months after SLE diagnosis (range, 7–23; 13.0  ±  4.2 months).

Isolation and enrichment of CD4 and CD8 T cells

Peripheral blood mononuclear cells (PBMCs) from SLE patients were isolated using Ficoll-Hypaque gradients (GE Healthcare, Uppsala, Sweden) in accordance to the manufacturer's protocol. PBMCs were separated by flow cytometry toward CD4 + T cells and CD8 + T cells.

RNA isolation

The total RNA of mononuclear cells was isolated using the TRIzol total RNA isolation reagent. With the Superscript pre-amplification system VILO cDNA Synthesis reagent (Invitrogen Life Technologies), 5ug of total MNC RNA was used to synthesize complementary DNA (cDNA).

The cDNA microarray analysis

The CD4 + and CD8 + T-cell specimens that satisfied the criteria for RNA quality were sent for microarray analysis. The Bioanalyzer 2100 (Agilent Technologies, Santa Clara, California, United States) was used to evaluate the quantity and purity of RNA. Chang Gung Memorial Hospital (CGMH) utilized a GeneChip Human Genome U133 Plus2 array (Affymetrix, Santa Clara, California, United States) for microarray analysis. RNA was separated and incorporated into sequencing libraries with the TruSeq Stranded Total RNA Sample Preparation Kit KAPA mRNA HyperPrep Kit and KAPA Dual-Indexed Adapter Kit (Illumina, San Diego, California, United States) with ribosomal depletion using Ribo-Zero, and then analyzed on an Illumina NovaSeq 6000. On average, 50 million readings were generated per sample. Using FastQC Prefiltering, the integrity of raw reads was analyzed.

Functional annotation and pathway enrichment analysis of Differential Expression Gene (DEG)

The R package DESeq2 was used to adjust raw expression counts for library size (version 1.16.1). A pre-filtering of low-count genes was conducted to retain only genes with at least 50 total reads. Outliers were identified with the help of principal component analysis (PCA). The first five principal components (PCs) of each sample were extracted and correlated with clinical and technical information. Supervised hierarchical clustering of the SLE cohort utilizing subgroup-defining genes (> twofold statistically significant differential expression, p  < 0.05, FDR < 0.05). The subsequent analysis was conducted using the Database for Annotation, Visualization and Integrated Discovery (DAVID; http://david.abcc.ncifcrf.gov/ ), Kyoto Encyclopedia of Gene and Genomes (KEGG; http://www.genome.jp/kegg ) Bioinformatics databases as well as the UCSC genome browser tool. PPI (protein–protein interaction) network analysis was performed to identify the immune response gene modules of CD4 + and CD8 + T cells that contribute to LN pathogenesis based on the STRING v11.5 database (STRING,  https://www.string-db/org ). After data normalization and quality control, CD8 T cells prominent DEGs were identified between SLE patients with ESRD and those without nephritis. KEGG biopathways and Gene Ontology (GO) utilized all identified 52 CD8 T cells prominent DEGs to conduct pathway enrichment analyses for the specific biological functions of molecular function (MF), cellular component (CC), and biological process (BP). The GO terms and pathways were deemed significantly enriched when the FDR-adjusted P value was less than 0.05.

Flow cytometry measurement of T cell phenotype surface marker

Using multicolor calibration particles (BD Biosciences) in conjunction with saturated amounts of the following antibodies: CD279 (FITC Mouse Anti-Human CD279 Clone MIH4); Tim3 (PE Mouse Anti-Human TIM-3 (CD366) Clone 7D3); CD4 (PE-Cy™5 Mouse Anti-Human CD4 Clone RPA-T40; CD8 (APC Mouse Anti-Human CD8 Clone RPA-T8); CTLA-4 (Human CTLA-4 Alexa Fluor® 488-conjugated Antibody Clone # 2188A); LAG-3 (PE Mouse Anti-Human LAG-3 (CD223) Clone T47-530); CD127 (FITC Mouse Anti-Human CD127 Clone HIL-7R-M21); TIGIT (PE anti-human TIGIT (VSTM3) Antibody Clone A15153G); CD160 (Alexa Fluor® 488 Mouse Anti-Human CD160 Clone BY55); CD244 (PE Mouse Anti-Human CD244 Clone 2–69); HLA-DR (FITC Mouse Anti-Human HLA-DR Clone G46-6); CD38 (PE Mouse Anti-Human CD38 Clone HIT2); CD3 (PE-Cy™5 Mouse Anti-Human CD3 Clone HIT3a); CD103 (Brilliant Violet 421™ anti-human CD103 (Integrin αE) Clone Ber-ACT8); CD161(BD Horizon™ BV510 Mouse Anti-Human CD161 Clone DX12); Granzyme B (BD Pharmingen™ PE Mouse Anti-Human Granzyme B Clone GB11); Granzyme K (APC anti-human Granzyme K Antibody Clone GM26E7), CD197(CCR7) (BD Pharmingen™ PE-Cy™7 Rat Anti-Human CCR7 (CD197) Clone 3D12) and CD45RA (BD Horizon™ BV711 Mouse Anti-Human CD45RA Clone HI100), reactions were standardized. Following washing, the pellets were resuspended in cold staining buffer and analyzed using LSRFortessa and FACSCanto II flow cytometers (BD Biosciences). Using the software from FlowJo, LLC (Tree Star, Ashland, OR, USA) to collect and analyze the cells. The phenotype of immune cell subsets was determined using the HIP protocol of four color flow cytometric analysis.

Enzyme-linked immunosorbent assay (ELISA) for soluble mediators analysis

ELISA kits (R&D Systems) were used in accordance with the manufacturer's instructions to measure the serum/plasma levels of PD-1, PDL-2 and TIM3.

Human PBMC culture and in vitro IFN- \({\varvec{\beta}}\) stimulation

Ficoll-Paque Plus (Cytiva, Uppsala, Sweden) was utilized to generate PBMCs from whole blood of SLE-LN- and SLE-LN + patients. PBMCs were subsequently cultured in complete RPMI 1640 medium, which was supplemented with 10% FBS and 1% penicillin–streptomycin solution. PBMCs were inoculated into 6-well plates on day two, with 2.0 × 10 6 cells per well. The cells were then subjected to treatment with recombinant human IFN-β (200 ng/mL; R&D System), either with or without Tofacitinib (200 nM; Pfizer), or were left untreated. Following 120 h of IFN- stimulation, collected cells were subjected to co-IR antibody staining in preparation for flow cytometry analysis. For phosflow, cells were analyzed in the CD3 + lymphocyte population after being stained with p-STAT1 and total STAT1 antibodies according the manufacturer's instructions (BioLegend).

Statistical analysis

Using Graph Pad Prism 8.4.1, the statistical analyses were conducted. The data was presented as mean plus/minus standard deviation (S.D.) or as a percentage (%). The t-test was used to compare the differences between groups. Using Pearson's correlation coefficient, a correlation analysis was performed. The unpaired t-test was utilized to determine whether there were statistically significant differences between baseline data and those measured before and after therapy. Every aspect stated P -values were two-sided and not multiple testing adjusted. The degree of significance was set at P  < 0.05.

Profiling analysis revealed aberrant CD4  +  and CD8  +  T cell mRNA transcription in SLE phenotypes

Ten sorted CD4 + T cell and CD8 + T cell specimens from five SLE patients with ESRD (two for 4 years and three for 12 years more) and five patients without LN in the disease course were sent for microarray transcriptomes analysis. The expression levels on CD4 and CD8 T cells were used to execute a hierarchical clustering analysis of T cells. The Volcano plots and hierarchical clustering heat map revealed that LN with ESRD and SLE without LN phenotypes clustered significantly different gene expression levels. The volcano plot and heatmap of the most altered genes, as depicted in Fig.  1 , several critical unappreciated heterogeneity immune response genes that revealed a distinctive gene signature profile related to T cells differentiation, activation, and cytotoxicity on CD4 and CD8 T cells. In SLE with ESRD patients, CD38, PLEK (platelet and leukocyte C kinase substrate and the KSTR sequence of amino acids), interferon-γ, CX3CR1, fibroblast growth factor binding protein type 2 (FGFBP2), and solute carrier organic anion transporter family member 4C1 (SLCO4C1) were significantly downregulated on CD4 and CD8 T cells simultaneously. Fc receptor-like 6 (FCRL6), granzyme B (GZMB), GZMH, FcγR3A/3B, integrin subunit alpha M (ITGAM), FAS ligand, TBX21 (T-box transcription factors; also known as T-bet), LYN (Lck/Yes Novel tyrosine kinase, Src kinase family), granulysin, C–C motif chemokine ligand 4 like 1 (CCL4L1), chemerin chemokine-like receptor 1 (CMKLR1), HLA-DRβ1, KIR2DL3 and killer cell lectin like receptor D1 (CD94) transcripts were downregulated, whereas GZMK, NRCAM (Neuronal cell adhesion molecule) and DSEL (dermatan sulfate epimerase like) were upregulated in CD8 T cells. Pathway enrichment analysis of CD8 T cells revealed that Chemokine and cell migration were critical on BP (Fig.  2 A), cytolytic granule on CC (Fig.  2 B), Ig binding and platelet derived growth factor receptor binding on MP (Fig.  2 C) and graft versus host disease on KEGG were associated with the majority of genes and fold changes (Fig.  2 D). PPI network analysis uncovered the functional interdependence of these crucial pathways (Fig.  2 E). As depicted in the Supplemental Figures , the gene list best defining the pathways suggests that the NK cytotoxicity signature (KIR2DL3, CD94, FcγR, perforin, granzyme and Fas/Fas ligand induced apoptosis), graft versus host disease (MHC class II antigen processing and type I interferon and host target tissue injury), and chemokine-cytokine interaction (CCL4 and CX3CR1) significant influence the pathogenesis of advanced LN. Collectively, the aforementioned molecules are responsible for the phenomenon of inflammatory senescence commonly observed in repeatedly activated T cells, resulting in a substantial reduction of CD8 T cell cytotoxicity in LN patients with ESRD.

Differential expressed genes (DEGs) in SLE patients with ESRD (14, 15, 08, 20,16) and inactive SLE controls (09, 16, 06, 21, 17) ( A ) Volcano of the DEGs of CD4 T cells. B Heatmap of the CD4 T cells DEGs in SLE patients with ESRD and inactive SLE controls. C Volcano of the DEGs of CD8 T cells. D Heatmap of the 52 DEGs in SLE patients with ESRD and inactive SLE controls

Pathway enrichment analysis of CD8 T cells based on in-depth analyses of our DEGs ( A ) Chemokine mediated signal pathway and cell migration on biological process (BP) ( B ) Cytolytic granule on cellular component (CC) ( C ) Ig binding, platelet derived growth factor receptor binding and chemokine activity on molecular function (MF) ( D ) graft versus host disease on KEGG ( E ) The protein–protein interaction was obtained from the STRING database showed cytotoxicity signature, antigen presentation and chemokine-cytokine interaction influence the pathogenesis of advanced LN

GZMB and GZMK expressing CD8  +  T cells impact on SLE and clinical outcome

In order to gain greater comprehension of GZMK expression in SLE, we compared GZMK-expressing CD8 + T cells from normal controls, SLE with and without LN. As shown in Table  1 , there was no significant difference observed in the percentage and MFI expression of CD45RA-CD197-CD8 + GZMK + T cells between SLE patients and normal controls (20.32 ± 1.872%, ( N  = 38) vs. 19.96 ± 1.685% ( N  = 25), p  = 0.895; MFI: 3546 ± 273.9 ( N  = 38) vs. 3526 ± 192 ( N  = 25), p  = 0.9589). Yet, compared to normal controls, the proportion of CD45RA- CD103-CD8 + KLRB(CD161) + GZMK + Mucosal-associated invariant T (MAIT) cells decreased in SLE patients (6.200 ± 1.103%, ( N  = 25) vs. 1.158 ± 0.2279%, ( N  = 38), p  =  < 0.0001; MFI: 3709 ± 197.6 ( N  = 38) vs. 4323 ± 329.3 ( N  = 25), p  = 0.9589). The observation of a notable decrease in the quantity of CD8 + CD161 + GZMK + MAIT cells indicates that a segment of these cells may have escaped the bloodstream by migrating to inflamed tissues.

To determine the impact of GZMB and GZMK-expressing CD8 + T cells on ESRD, the expression levels of GZMB and GZMK on CD8 T cells from LN with ESRD, healthy controls, and SLE were compared. Table 2 presented that SLE patients possess a surge in CD45RA-CD197-8 + GZMB + CD8 T cells (9.320 ± 1.169%, ( N  = 25) vs. 26.32 ± 2.474%, ( N  = 38), p  =  < 0.0001), CD45RA-CD8 + CD103-CD161- GZMK + T cells (4.207 ± 0.6274%, ( N  = 25) vs. 6.989 ± 1.461%, ( N  = 38), p  = 0.1432), and CD45RA-CD8 + CD103-CD161- GZMK + GZMB + T cells (1.874 ± 0.2710%, ( N  = 25) vs. 6.989 ± 1.461%, ( N  = 38), p  = 0.0365) than healthy controls. It is noteworthy that ESRD patients exhibited greatly greater of CD45RA-CD8 + CD103-CD161- GZMK + T cells (1.874 ± 0.2710%, ( N  = 25) vs. 11.29 ± 4.934%, ( N  = 5), p  = 0.0082), and CD45RA-CD8 + CD103-CD161- GZMK + GZMB + T cell (1.874 ± 0.2710%, ( N  = 25) vs. 7.846 ± 4.233%, ( N  = 5), p  = 0.0033) were detected. Additionally, ESRD patients have a slightly higher quantity of CD45RA-CD8 + CD103-CD161 + GZMK + T MAIT cells than other SLE patients (2.070 ± 1.18 ( N  = 5) vs. 1.234 ± 0.2120 ( N  = 38)). Therefore, a diversity of GZMK + CD8 T cells may be responsible for an upsurge in GZMK expression in ESRD patients comparing to other SLE patients. Following this, the expression of Co-IRs (TIGIT, PD-1, and TIM3) on CD8 + cells was correlated with that of CD8 + cells expressing GZMK and GZMB. Figure  3 showed that there was a moderate negative correlation between the expressions of CD8 + TIGIT + T cells with CD45RA-CD8 + CD103-CD161- GZMK + T cells ( r  = -0.5806; p  = 0.0001) and CD45RA- CD8 + CD103-CD161-GZMK + GZMB + CD8 T cells ( r  = -0.5953; p  =  < 0.0001). Nevertheless, additional longitudinal research is necessary in order to ascertain the serial long-term effects.

The performance of Co-IRs expression on CD8 T cell correlation with varied CD8 + T cell expressing GZMK and GZMB. The p values are represented as follows: * P  < 0.05, ** P  < 0.01, *** P  < 0.001, **** P  < 0.0001, NS no significance ( P  ≥ 0.05)

Co-IRs expressions of T cell activation and exhaustion between SLE and healthy controls

The functionality of T cells is strictly controlled by an abundance of immune modulating signals from immunological inhibition and activation surface molecules, that include HLA-DR, CD38, inducible costimulatory molecule (ICOS), TIGIT, PD-1 and T cell immunoglobulin, and mucin domain-containing protein 3 (TIM3). In addition to upregulating PD-1, exhausted T cells lose the capacity to differentiate into memory cells, as indicated by the expression of the interleukin-7 receptor (IL-7R; CD127). On CD4 + and CD8 + T cells, we evaluate the frequency and intensity of T cell activation (CD38 and HLA-DR) and several Co-IRs, including TIGIT, PD-1, CD127, CD160, signaling lymphocytic activation molecule family member 4 (SLAMF4; CD244; 2B4) and TIM3 expression. Table 3 showed that the percentages of CD279 (8.027 ± 0.7983%, ( N  = 91) vs. 3.93 ± 0.7035%, ( N  = 27), p  = 0.008) and TIGIT (25.67 ± 1.119%, ( N  = 90) vs. 20.13 ± 0.8377% ( N  = 26), p  = 0.0109) expressions on CD4 + T cells were expanded in SLE patient. In contrast, CD127 expression on CD4 + T cells was reduced in SLE patients compared to HC (33.85 ± 2.343%, ( N  = 90) vs. 44.59 ± 5.149% ( N  = 27), p  = 0.0382; MFI: 394.1 ± 6.585 vs. 421.3 ± 12.36, p  = 0.0439). Also, TIM3 (12.56 ± 0.9711%, ( N  = 91) vs. 9.826 ± 1.19% ( N  = 27), p  = 0.1534; MFI: 444.3 ± 7.502 vs. 384.9 ± 12.23, p  = 0.0002), TIGIT (35.06 ± 1.65%, ( N  = 90) vs. 25.81 ± 1.774% ( N  = 27), p  = 0.0042) and HLA-DR (46.74 ± 2.319% ( N  = 51) vs. 18.6 ± 1.909% ( N  = 17), p  < 0.0001; MFI: 740.2 ± 31.93 vs. 547.2 ± 24.81, p  = 0.0013) expression on CD8 + T cells were higher in patients with SLE. In contrast, CD127 expression on CD8 + T cells was reduced in SLE patients compared to HC (22.46 ± 1.752%, ( N  = 90) vs. 40.86 ± 4.19% ( N  = 27), p  < 0.0001; MFI: 360.8 ± 7.882 vs. 421 ± 14.87, p  = 0.0003). CD160 expression on CD3 + T cells was higher in SLE patients compared to HC (16.92 ± 1.199, ( N  = 91) vs. 10.93 ± 1.137, ( N  = 27) p  = 0.0101). However, CD160 MFI expression on CD8 + T cells was lower in SLE patients compared to HC (MFI: 717.8 ± 14.43, ( N  = 86) vs. 791.4 ± 21.65, ( N  = 26) p  = 0.0121). As shown in Table  4 , CD8 + CD279 + TIM3 + (0.5922 ± 0.09517, ( N  = 90) vs. 0.08077 ± 0.01666, ( N  = 26), p  = 0.0048) and CD8 + HLA-DR + CD38 + (13.75 ± 1.162 ( N  = 87), vs. 4.519 ± 0.8416 ( N  = 27), p  < 0.0001) T cells were higher in patients with SLE.

Co-IRs expression on T cell correlated to SLE clinical disease parameters

We next determined several Co-IRs and activation markers including TIGIT, PD1, TIM3, CD160, HLA-DR, CD38 and CD127 expression on CD3 + /CD4 + /CD8 + T cells and analyzed the performance of their correlation with a series of clinical manifestations, disease activity and laboratory features including presence of nephritis (proteinuria < 0.5gm vs. > 0.5gm), decreased complement component 3 (C3) and/or complement component 4 (C4), disease activity (SLEDAI > 6) and ds-DNA antibody production. Figure  4 A to E showed SLE patients with nephritis exhibited higher frequencies of CD4 + T cells expressing PD1, CTLA4, TIM3, CD127 and TIGIT [CD4 + CD279 + (6.162 ± 0.6218%, ( N  = 65) vs. 10.57 ± 1.938%, ( N  = 23), p  = 0.0056); CD4 + CTLA4 + (0.1545 ± 0.04463%, ( N  = 66) vs. 0.5565 ± 0.1915%, ( N  = 23), p  = 0.0038); CD4 + TIM3 + (3.279 ± 0.4234%, ( N  = 66) vs. 7.378 ± 1.095%, ( N  = 23), p  < 0.0001); CD4 + CD127 + (28.55 ± 2.72%, ( N  = 67) vs. 49.3 ± 2.767%, ( N  = 23), p  < 0.0001); CD4 + TIGIT + (24.13 ± 1.297%, ( N  = 67) vs. 30.14 ± 1.978%, ( N  = 23), p  = 0.0184)]. CD8 + T cells (Fig.  4 F to K) express higher TIM3, CTLA4, CD127, TIGIT, CD160 and CD244 [CD8 + TIM3 + (10.59 ± 1.041%, ( N  = 68) vs. 18.38 ± 1.858%, ( N  = 23), p  = 0.0003); CD8 + CTLA4 + (0.1538 ± 0.0323%, ( N  = 65) vs. 0.313 ± 0.04415%, ( N  = 23), p  = 0.0099); CD8 + CD127 + (19.94 ± 2.074%, ( N  = 67) vs. 29.82 ± 2.773%, ( N  = 23), p  = 0.013); CD8 + TIGIT + (31.86 ± 1.76%, ( N  = 67) vs. 44.38 ± 3.271%, ( N  = 23), p  = 0.0007); CD8 + CD160 + (31.2 ± 1.77%, ( N  = 66) vs. 40.82 ± 2.944%, ( N  = 23), p  = 0.0068); CD8 + CD244 + (1.025 ± 0.2473%, ( N  = 65) vs. 2.523 ± 0.7825%, ( N  = 22), p  = 0.0178)]. SLE patients with high disease activity (SLEDAI < 6 vs. SLEDAI ≥ 6) have expanded cell numbers of CD3 + CD160 + (14.32 ± 1.012%, ( N  = 64) vs 22.22 ± 3.09%, ( N  = 23), p  = 0.0022). SLE patients with C3 depression have significant higher expression cells number (%) of CD4 + CD279 + (7.109 ± 0.8234%, ( N  = 69) vs 10.91 ± 1.973%, ( N  = 22), p  = 0.0408) and CD3 + CD279 + (5.245 ± 0.5207%, ( N  = 66) vs 9.191 ± 1.728%, ( N  = 22), p  = 0.0041). C4 depression showed lower expression of CD4 + CTLA4 + % (0.1681 ± 0.03076, ( N  = 72) vs. 0.4125 ± 0.1793, ( N  = 16), p  = 0.023 and CD8 + LAG3 + (0.23 ± 0.04366, ( N  = 70) vs. 0.5063 ± 0.134, ( N  = 16), p  = 0.0149). SLE patients with positive dsDNA demonstrated higher expression cells number (%) of CD4 + CD279 + (5.516 ± 0.6812%, ( N  = 49) vs 9.756 ± 1.44%, ( N  = 32), p  = 0.0041). The detail of Co-IRs and activation markers correlation with a series of clinical manifestations, disease activity and laboratory features were listed in Supplemental Tables  2 , 3 , 4 , 5 and 6 .

The performance of Co-IRs expression on T cell correlation with a series of clinical manifestations, disease activity and laboratory features including presence of nephritis (proteinuria < 0.5gm vs. > 0.5gm), decreased complement component 3 (C3) and/or complement component 4 (C4), disease activity (SLEDAI > 6) and ds-DNA antibody production. The p values presentation as above Fig.  3

Regarding two Co-IRs analysis, Fig.  5 A to E demonstrated nephritis patients exhibited a statistically significant increase in the cell numbers of CD4 + CD279 + TIM3 + (0.5075 ± 0.09351%, ( N  = 67) vs. 1.127 ± 0.2388%, ( N  = 22), p  = 0.0045); CD4 + CD127 + TIGIT + (3.634 ± 0.4631%, ( N  = 67) vs. 5.587 ± 0.6844%, ( N  = 23), p  = 0.0303); CD8 + HLA-DR + CD127 + (1.322 ± 0.1797%, ( N  = 46) vs. 5.183 ± 1.346%, ( N  = 6), p  < 0.0001); CD8 + HLA-DR + CD38 + (11.23 ± 0.9701%, ( N  = 63) vs. 18.41 ± 2.934%, ( N  = 22), p  = 0.0033). SLE patients with high disease activity (Fig.  5 F and G) showed expanded CD8 + HLA-DR + CD38 + (11.81 ± 1.076%, ( N  = 63) vs. 18.55 ± 3.281%, ( N  = 21), p  = 0.0127; CD8 + HLA-DR + CD127 + 1.417 ± 0.1921%, ( N  = 42) vs. 3.24 ± 1.105%, ( N  = 10), p  = 0.0076). C3 depression (Fig.  5 H to K) had low CD8 + HLA-DR + CD38 + (11.32 ± 0.9845%, ( N  = 64) vs. 18.26 ± 2.912%, ( N  = 21), p  = 0.0047); CD8 + CTLA4 + LAG3 + (0.007246 ± 0.004762%, ( N  = 69) vs. 0.03636 ± 0.01239%, ( N  = 22), p  = 0.0092); CD3 + CD160 + CD244 + (0.3254 ± 0.04261%, ( N  = 67) vs. 0.655 ± 0.1496%, ( N  = 20), p  = 0.0043) and CD3 + CD279 + TIGIT + (3.312 ± 0.4021 (69) vs. 5.757 ± 0.9143 (21), p  = 0.007). SLE patients with positive dsDNA (Fig.  5 L and M) showed higher expression of CD4 + CD279 + TIM3 + (0.3531 ± 0.059%, ( N  = 49) vs. 0.87 ± 0.199%, ( N  = 30), p  = 0.0037).

The performance of two Co-IRs expression on T cell correlation with a series of clinical manifestations, disease activity and laboratory features. The p values presentation as above Fig.  3

Subsequently, we compared the levels of activated (CD38, HLA-DR), functioning (CD127), and exhaustion (PD-1, CTLA4, TIGIT, Tim-3, CD160, and CD244) markers on T cells prior to and following seven to twenty-three moths immunotherapy. Notably, higher Co-IRs expression levels of most SLE patients decreased after treatment (Fig.  6 ), indicating that Co-IRs monitor are useful tools in determining the treatment response, including CD4 + CD279 + ( p  = 0.0405, N  = 14), CD4 + TIM3 + ( p  = 0.0043, N  = 14), CD4 + CTLA4 + ( p  = 0.0494, N  = 13), CD4 + CD127 + ( p  < 0.0001, N  = 13), CD8 + CD279 + ( p  = 0.0317, N  = 14), CD8 + TIM3 + ( p  < 0.0001, N  = 14), CD8 + CTLA4 + ( p  = 0.0371, N  = 13), CD8 + LAG3 + ( p  = 0.0061, N  = 12), CD8 + CD127 + ( p  < 0.0001, N  = 13), CD8 + TIGIT + ( p  = 0.0034, N  = 13), CD8 + CD160 + ( p  = 0.0024, N  = 14), CD8 + CD244 + ( p  = 0.0305, N  = 14), CD4 + CD279 + TIM3 + ( p  = 0.0159, N  = 13), CD4 + CD127 + TIGIT + ( p  = 0.0002, N  = 12), CD8 + CD279 + TIM3 + ( p  = 0.037, N  = 14), CD8 + CD127 + TIGIT + ( p  = 0.0027, N  = 12). Our research suggests that abnormal immune activation with Co-IRs expression may contribute to the immune dysregulation observed in SLE disease courses. The detail information of fourteen patients were listed in Supplemental Table  7 .

The performance of Co-IRs expression on T cell changes after treatment. The p values presentation as above Fig.  3

Soluble PD-1, PDL-2 and TIM3 levels in SLE

PD-L2 is one of the ligands of PD-1 expressed by T cells, and its binding to PD-1 blocks activation signals from the T cell receptor and CD28 in typical T cells. TIM3 modulates Th1 immunity through eliciting apoptosis, prompts the generation of a disintegrin and metalloproteinase, and limits autoimmunity [ 17 ]. We then asked whether these soluble mediators can correlate to disease phenotypes. As shown in Fig.  7 , SLE patients have significant elevated serum levels of soluble PD-1, PDL-2 and TIM3 than HCs (sPD-1: 396.2 ± 69.71, N  = 77 vs. 111.1 ± 32.92, N  = 92 p  = 0.0001; sPDL2: 16.97 ± 0.9238, N  = 87 vs 12.78 ± 0.2314, N  = 92 p  < 0.0001; sTIM3: 6.822 ± 1.5367, N  = 91 vs 3.675 ± 0.08441, N  = 92 p  < 0.0001). Figure  8 indicated sPD-1 levels were correlated to CD4 + CD279 + % ( p  = 0.0027, N  = 75), CD8 + CD279 + % ( p  = 0.0009, N  = 75) and CD3 + CD279 + % ( p  = 0.001, N  = 75). sPD-L2 levels were correlated to CD4 + CD279 + (% p  = 0.0111, MFI = 0.0089, N  = 87), CD4 + CD279 + (% p  = 0.0187, MFI < 0.0001, N  = 87) and CD3 + CD279 + (% p  = 0.0209, MFI =  < 0.0001, N  = 87). sTIM3 levels were correlated to CD8 + TIM3 + % ( p  = 0.0041, N  = 90). High sPD-L2 levels correlated with SLE proteinuria (14.37 ± 0.9327, ( N  = 67) vs 21.99 ± 1.797, ( N  = 25), p  < 0.0001) whereas inverse to C4 depression (17.69 ± 1.055, ( N  = 71) vs 12.31 ± 1.392, (N = 21), p  = 0.0116). High sTIM3 levels correlated with SLE proteinuria (4.966 ± 0.2901, ( N  = 68) vs 11.63 ± 1.341, ( N  = 27), p  < 0.0001; high (SLEDAI ≥ 6) disease activity (5.274 ± 0.344, ( N  = 68) vs 10.06 ± 1.157, ( N  = 27), p  < 0.0001 and C3 depression (6.294 ± 0.587, ( N  = 73) vs 9.872 ± 1.778, ( N  = 26), p  = 0.0148. They functioned as serologic indicators of disease activity and organ involvement.

Soluble PD-1, PDL-2 and Tim3 serum levels showed significant elevated in SLE patients and correlation with the performance of Co-IRs expression on T cell. The p values presentation as above Fig.  3

Soluble PD-1, PDL-2 and Tim3 serum levels showed correlation with the presence of nephritis (proteinuria < 0.5gm vs. > 0.5gm), decreased complement component 3 (C3) and/or complement component 4 (C4), and disease activity (SLEDAI > 6). The p values presentation as above Fig.  3

Type I interferon (IFN-β) and JAK inhibitor (JAKi) effects on SLE T cells

The transcriptional regulation of Co-IR expression such as TIGIT, PD-1, TIM-3, LAG-3, and others can be controlled by the Type I IFN-JAK-STAT axis pathway. In an effort to elucidate the Co-IR expression discrepancy found in our study, we applied exogenous IFN-β together with or without the JAK inhibitor Tofacitinib to the PMBC cultures obtained from LN- ( n  = 3) and LN + ( n  = 4) SLE patients. Following stimulation, we employed the FACS analysis to examine the expressions of co-IRs on CD4 + or CD8 + T cell subsets of LN- and LN + patients, including TIGIT, CD38, HLA-DR, CD366, CD279 and CD38 + DR + , as well as the levels of phosphorylated STAT1 (Tyr701) and total STAT1 in CD3 + T cell subsets. As shown in Fig.  9 , in response to IFN-β stimulation, both LN- and LN + derived PBMCs exhibited a comparable range of percentages for p-STAT1 + /STAT1 + double positive CD3 + T cells (19.5% to 38.5% in LN- vs 18.3% to 33.1% in LN +). Furthermore, the relative MFI of p-STAT1 (Tyr701) and total STAT1 in these PBMCs increased by four to sixfold in comparison to unstimulated T cells (Fig.  8 C). More importantly, we revealed that the CD3 + T cell subsets from LN + patients exhibited a greater degree of responsiveness to Tofacitinib than of those from LN- patients (Fig.  8 C and D). With the exception of CD279 on CD4 + T cells, the expression of the majority of co-IRs does not differ significantly in response to IFN- (Supplementary Fig.  4 ). It is worth noting that LN- patients exhibit reduced levels of CD38 expression, whereas CD38 expression surged following IFN-β induction and declined after JAKi addition. Tofacitinib is more likely to inhibit the expression of CD38 on CD4 + or CD8 + subsets, as well as the expression of CD38 + DR + on CD8 + subsets; therefore, the preponderance of hyperactive p-STAT1 signaling in LN + T lymphocytes might occur in these subpopulations.

Flow cytometry analysis of phospho and total STAT1 in CD3 + lymphocytes from SLE-LN- and SLE-LN + patients. A  Prominent proportions of total STAT1 + or p-STAT1 + double-positive lymphocytes were collected from three LN- and three LN + patients were underwent stimulation for 48 h with 200 ng/mL IFN- \(\upbeta\) in the presence or absence of 200 nM Tofacitinib. B Representative histograms of the levels of p-STAT1 and total STAT1 in LN- and LN + lymphocytes treated, as described in ( A ). The unstimulated cell populations are represented by the solid black lines, whereas the cell populations that were treated with IFN-β and IFN-β + Tofacitinib were denoted by the solid and dashed red lines, respectively. Histograms that were gray-filled depict negative controls (FMO with isotypes). C Comparative statistical evaluation of p-STAT1 and total STAT1 relative MFI in B. Bar graphs show the fold change MFI of p-STAT1 in IFN- \(\upbeta\) -stimulated CD3 + T cells with or without JAKi Tofacitinib between LN- and LN + patients. The results were found to be statistically significant using an unpaired t-test, and the data are presented as mean ± SEM. D The percentages of p-STAT1 + /STAT1 + double positive T cells (CD3 +) from LN- and LN + patients with or without IFN- \(\upbeta\) stimulation in the presence or absence of Tofacitinib. An unpaired t-test was employed to assess the difference between the two groups, and statistical bar graphs with mean ± SEM were utilized for presenting the quantitative results

The molecular basis of SLE is obscure due to its heterogeneity; however, a combination of genotyping with gene networks, mRNA sequencing, and cellular phenotyping analysis may detect distinct signatures conferring susceptibility to SLE, disease activity, and disease severity [ 18 , 19 , 20 ]. Using multidimensional cytometry and transcriptomics to identify SLE-specific phenotypes, this study investigated the unmet need for optimal personalized therapy for SLE. Due to their ability to coordinate and facilitate B cells in promoting autoantibody production, T cells were identified as a key factor in the development of SLE. Several phenotypic and physiological modifications in T cell populations that increase the probability of lupus-related inflammation are being identified [ 21 ]. SLE patients with ESRD exhibited a novel transcriptional and phenotypic profile with minimal expression of cytotoxic granules, CD38, and HLA-DR on CD4 + and CD8 + T cells. Long-term, continuous antigenic priming generated specific subsets of exhausted CD4 + and CD8 + T cells that promote functional T cell silencing of lupus nephritis. The flaws of T cells cytotoxicity in SLE patients explain not only the origin of autoimmunity due to the inability to eradicate autoreactive B cells but also the markedly reduced antiviral responses [ 22 ] that contribute to uncontrolled Epstein-Barr virus infection [ 23 , 24 , 25 ] and EBV reactivation in SLE disease activity [ 26 ].

Several identified genes are indispensable for cytotoxicity, signaling, and inflammatory factor production. NKG7 is necessary for cytotoxic degranulation in their mobilization and transport of perforin and GZMB cytotoxicity granules comprising vesicles, which entails the transfer of CD107a to the cell surface and the eradication of targeted cells [ 27 ]. FCRL6 possesses inhibitory properties with cytosolic cysteine-rich motif and engages SHP-2 through phosphorylation of ITIM. It interacts extracellularly with MHCII/HLA-DR and is uniquely expressed on cytotoxic T and NK cells [ 28 , 29 , 30 ]. In a cross-ancestry meta-analysis, pleckstrin homology domain containing family F member 2 gene (PLEKHF2) loci was identified as a functional locus related to IFN-α production in dendritic cells and NK cells in patients with SLE [ 31 ]. ITGAM modulates the immune functions of CD8 T cells and macrophages [ 32 ].

CD8 T cells promote lupus disease activity through generating IFN-γ and directly inducing tissue damage. CD8 T cells produce an excess of perforin and GZMB, but their responsiveness is reduced. CD8 + cytotoxic T lymphocytes protect against lupus-like disorder by eliminating activated autoreactive B cells via perforin-mediated killing [ 33 ]. In diseased kidneys, cytotoxic CD8 + T cells that express high levels of GZMB, perforin, or GZMK have been identified [ 34 ]. We observed that CD8 +  T cells cytotoxic signatures (GZMB, GZMH, granulysin, perforin, IFN-γ, FcγRIII, KIRs and CD94) downregulation may contribute to alternations with poor cytotoxic capacity (Fig.  2 ), whereas FcγRIII (3A/3B), TBX21, LYN, CCL4L1, and CMKLR1 decrease production in CD8 + T cell, indicating a potentially central role in inactive inflammatory pathways and cell trafficking ( Supplemental Figures ). SLE with ESRD is associated with a significant loss of functional specific immune responses. Immune senescence markers PD-1 and CD57 did not differ in our two distinct SLE phenotypes, whereas GZMK transcript increased in ESRD indicating potential age-associated GZMK-expressing CD8 +  T (Taa) cells of exhaustion and tissue homing, which address potential immune system dysfunctions [ 35 ] . The elevated numbers of CD8 + CD161-GZMK + T cells and CD8 + CD161-GZMK + GZMB + T cells observed in patients with ESRD LN may be major contributing variables. Additional analysis proposed predominant migratory cells to inflammatory tissue are MAIT cells [ 36 ] that are CD8 + CD161 + GZMK + , whereas ESRD patients exhibited diminished tissue damage accompanied by the circulation of GZMK + T cells. These novel findings revealed impaired cytotoxicity, dysfunctions of effector T cells and potential immune senescent modulation in end stage LN. The findings of GO and pathway enrichment analyses support the notion that dysfunctions in cytotoxicity, antigen processing and presentation, and chemokine-cytokine pathways are major risk factors. The present study identified the key genes, revealing potential targets for predicting the disease progression and infectious risk of LN. Nonetheless, SLE has a complex blood transcriptome due to heterogeneous cellular origins, and single-cell RNA sequencing (scRNA-seq) that resolves the SLE transcriptional signatures heterogeneity origination could eventually result in precision medicine implementations [ 37 ].

Higher Co-IRs expressions on T cells might be related to T cell exhaustion, in which dysfunctional effector cytotoxicity reactions are gradually extinguished to prevent noteworthy collateral tissue damage as a fundamental concept of T cell dysfunction that evolved to limit immunopathology. CD8 + T cells that are exhausted are unable to eliminating their intended targets. Global exhaustion signatures in CD8 + T cells have been coupled with a long-term disease silence and beneficial responses to therapy in lupus patients [ 38 ]. In exhausted T cells, the genes 4–1BB (CD137), CTLA4, PD-1, Leukocyte immunoglobulin-like receptor subunit B member 4 (LILRB4), and KLG1 are upregulated [ 39 ]. Variable PD-1 and ICOS co-expression T cells are elevated, whereas PD1 + ICOS + Tem cells correlate with SLE disease progression and are encompassed by exhausted T cells, which correspond to a lupus-silent course [ 40 ]. In lupus-affected mice, kidney-infiltrating T cells become activated effector cells that cause injury to tissues and, eventually, failure of the organs. Tissue parenchyma is able to suppress T-cell responses and limit self-damage [ 41 ]. Our research identified high expressions of PD1, CTLA4, TIM3, and TIGIT on T cells were correlated with disease activity, nephritis, and treatment response downregulation in SLE. T cell-depleted Co-IRs execute a dynamic role in effector T cells differentiation throughout the heterogeneous course of SLE.

We observed an increase in CD3 + CD160 + CD244 + T cells in SLE patients with persistent proteinuria, high disease activity, and C3/C4 depression. SLE patients had decreased levels of SLAMF7 and SLAMF4 (CD244) on memory CD8 + T cells, suggesting deficient antiviral effector function with inadequate effector CD8 + T cell degranulation capacity and a proportion of IFN-producing cells in response to antigen stimulation [ 42 , 43 ]. The expression of SLAMF4 on monocytes was diminished in patients with SLE, which inversely associated with serum autoantibody quantities [ 44 ]. Substantial reduced SLAMF4 + CD8 + T cells were detected in SLE patients, resulting in weakened cytotoxic capacity and an impaired ability to combat infection [ 43 , 45 ]. The inhibition of CD160 to CD160-ligand coupling revived CD8 T-cell proliferation, and the degree of restoration was proportional to the ex vivo CD160 + CD8 T cells, demonstrating that CD160-associated CD8 T-cell dysfunction exists independently of PD-1 expression [ 46 ].

CD38, HLA-DR, and CD127 (IL-7R) are distinct markers of chronically activated T-cell phenotypes. T-bet, RUNX3, and EOMES are attenuated in CD8 + CD38 + T cells from SLE patients, resulting in a decrease in CD8 T cell-mediated cytotoxicity and an increase in susceptibility to infection [ 47 ]. The CD8 + HLA-DR + CD38 + T cells that have been linked in this study to LN, C3 depression, and SLE disease activity are believed to be the cause of SLE's persistent immune activation. Hyper-activated HLA-DR + CD38 + T cells facilitate viral persistence and loss of immunologic competence in chronic infections. In SLE, CD38 expression in T cells generates Th1 and Th2 inflammatory cytokines, which are correlated with disease activity [ 48 ] but may be exhausted for predicting an improved prognosis in lupus [ 49 ]. In contrast, transcriptional analysis of blood pathological CD8 + T cells from patients with autoimmune disease indicated a correlation between an activated, non-exhausted CD8 + T cell phenotype and a poor prognosis [ 49 , 50 ]. We hypothesize that SLE with end-organ injury is associated with a loss of CD38 + HLA-DR + T cells with chronic immune activation. In SLE, type I interferon may increase CD38 levels, whereas JAKi inhibits T cell activation via the CD38 pathway, according to the in vitro study. These results suggest that monitoring T cell exhaustion and cytotoxicity status is beneficial for assessing renal damage and infection risk. Our findings support the need to devise strategies that enhance T cell exhaustion.

SLE patients exhibiting hematologic manifestations and positive anti-dsDNA antibodies showed higher proportions of HLA-DR + T cells and ICOS + T cells [ 51 ]. The expression of HLA-DR + T cells had a positive correlation with SLEDAI, and number of TIGIT + T cells was reduced in patients with renal involvement [ 51 ]. HLA-DR, costimulatory molecules on activation-related circulating T cell subsets, and relevant chemokines and cytokines all contribute to the onset of SLE. In SLE patients, we observed a decrease in CD127, whereas CD127 + TIGIT + T cells have a strong correlation with therapeutic responses. CD127 + memory T cells suppress CD244 and cytotoxic granules expression [ 52 ]. CD127 restitution and TIGIT co-expression on nascent CD4 + and CD8 + memory T cells [ 49 ] correlate with the reduction of pathogenic T cell subsets and are valuable for assessing and predicting lupus treatment efficacy.

Following activation, diverse costimulatory and co-inhibitory molecules are dynamically expressed on the surface of T cells. ICOS is a costimulatory receptor, whereas PD-1, TIGIT, and TIM3 are Co-IRs that inhibit CD4 + and CD8 + T cell responses. PD-1 is extensively expressed on a wide variety of T cell subtypes. Circulating PD-1 + ICOS + Tfh, PD-1 + ICOS + Tcm, and PD-1 + ICOS + Tem were all significantly increased in patients with SLE [ 40 , 53 ]. Additionally, CXCR5-CXCR3 + PD-1 hi CD4 + helper T cells were observed [ 54 ], as were CXCR5 hi ICOS hi PD-1 hi Tfh-like T cells [ 55 ]. PD-1 + CXCR5-CD4 + T peripheral helper (Tph) cells correlated with SLE disease parameters and progress of the disease [ 56 ]. The investigation into the potential efficacy of the PD-1-PD-L1 pathway as an inhibitory mechanism in the progression and development of LN in mice, and the search for a potential efficient treatment approach for SLE [ 57 ]. Significant to the pathogenesis of SLE, two critical signaling pathways, type I interferon and toll-like receptor, influence the expression of PD-1 and its ligands (PD-L1, PD-L2) via activation of NF-κB and/or STAT1 [ 58 ]. However, the inability of inhibitory receptors to inhibit the excessive activation of T cells might have been due to the upregulation of alternative activation pathways.

Patients with active SLE who screened confirmed for anti-dsDNA autoantibodies had elevated serum levels of sPD-1 and sPD-L2 [ 59 ]. TIM3 have been co-expressed and co-regulated on dysfunctional or 'exhausted' T cells during protracted viral infections and cancers [ 60 ]. Using the SLEDAI-2 K score, sTIM3 is associated with disease activity, organ damage, and active renal disease [ 61 ]. Expression of TIM3 and co-expression of TIM3 and Fas on particular peripheral T populations has been linked to disease activity in patients with SLE [ 62 ]. The bioactivity of sTIM3 and sPD-1 ensures that they maintain the capacity to bind to the respective receptors or ligands. In competition with the ligand, these proteins impede the inhibitory effects of PD-1 and TIM3 signals that are bound to membranes, thereby facilitating T-cell activation [ 63 ]. It can be difficult to predict episodes and remission of cyclical diseases like SLE and to develop an accurate biomarker for disease assessment. According to our findings, CD4 + T cells and CD8 + T cells that co-express PD-1 and TIM3 were substantially more prevalent in SLE patients with LN, elevated disease activity, C3 depression, and anti-dsDNA antibodies nevertheless diminished following immunotherapy. Age-associated CD8 + TIM3 + PD-1 + T cells evidenced more prominent signs of exhaustion, proliferation defects in response to either homeostatic or TCR stimulation, and altered cytokine secretion, while producing the immunosuppressive cytokine IL-10 [ 64 ]. Throughout chronic infection, virus-specific CD8 T cells preserved strong TIM3 expression, expressed together PD-1, and displayed diminished levels of the effector cytokines IFN-γ, TNF and IL-2 [ 65 ]. This population represents the most dysfunctional exhausted cells. These findings suggested that the expression of co-inhibitory receptors is a crucial determinant of autoimmunity. Our research provides vital insights into T cell exhaustion for the creation of a more accurate disease severity prediction profile. These immune cell surface markers could serve as diagnostic biomarkers for SLE, and this specific pathway could be a therapeutic target for SLE. Moreover, our results suggest that the elevated sPD1/PD-L2/Tim3 levels associated with SLE disease activity might be utilized as a therapeutic biomarker for response evaluation.

The levels of TIGIT expression on CD4 + T cells increased substantially in SLE patients and had a strong correlation with disease activity [ 66 , 67 ]; however, activation, proliferation, and production of cytokines were decreased [ 67 ]. It was recently reported that type 1 interferon (IFN-I) stimulates LAG-3 expression while inhibiting TIGIT expression on human naive CD4 + and CD8 + T cells [ 66 ]. However, no substantial alterations in TIGIT expression were observed upon induction of IFN-β. Furthermore, JAKi provided evidence that the regulation of CO-IRs is carried out by specific transcription networks that are linked to Type I interferon. However, it is conceivable that the lymphocytes of LN + patients employed a unique stimulus to regulate the JAK-STAT pathway, as evidenced by their greater reactivity to Tofacitinib. Treg cells are an additional designation given to CD4 + TIGIT + cells that are co-expressed with Foxp3 and Helio as a result of the effector function of these cells [ 68 , 69 ]. T cells that are CD8 + TIGIT + which are suggestive of hyperactivated or exhausted T cells, merit additional investigation.

SLE is a relapsing, refractory disease with limitations due to clinical heterogeneity resulting from cellular, serologic, and other abnormalities. We identify distinct subject subgroups and predict long-term prognosis by establishing a stratification of lupus patients based on a specific transcriptome signature and pathologically altered T cells that lead to active and progressive disease. This research is particularly beneficial in the clinical setting for identifying potentially blood-based markers essential for the initiation of SLE, classifying the extent of disease or predicting disease outcome, and minimizing treatment-related complications.

Availability of data and materials

The data presented in this study are available on request from the corresponding author.

Abbreviations

Systemic lupus erythematosus

Lupus nephritis

End renal stage disease

Differential Expression Gene

Principal component analysis

Platelet and leukocyte C kinase substrate and the KSTR sequence of amino acids

Fibroblast growth factor binding protein type 2

Fibroblast growth factor

Solute carrier organic anion transporter family member 4C1

Fc receptor-like 6

Integrin subunit alpha M

T-box transcription factors

C–C motif chemokine ligand 4 like 1

Chemerin chemokine-like receptor 1

Killer cell lectin like receptor D1

Neuronal cell adhesion molecule

Dermatan sulfate epimerase like

T cell immune-receptor with Ig and immune-receptor tyrosine-based inhibitory domains

Programmed cell death 1

Programmed death-ligand 2

T cell immunoglobulin, and mucin domain-containing protein 3

Signaling lymphocytic activation molecule family member 4

Interleukin-7 receptor (CD127)

C-X-C chemokine receptor type 3

Inducible T-cell costimulatory

C-X-C chemokine receptor type 5

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Acknowledgements

We greatly appreciate the genomic core of Chang Gung Memorial Hospital for the microarray technical support.

This work was supported by funding from the Chang Gung Memorial Hospital (Grant numbers: CMRPG 5K0131, CMRPG 5L0133 and CMRPG 3J1422) and the Ministry of Science and Technology, Taiwan (Grant numbers: MOST 109–2314-B-182–068-MY3 and 112–2314-B-182–057).

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Keng Poo Tan and Ji-Yih Chen contributed equally to this work.

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Department of Rehabilitation, Chang Gung Memorial Hospital, Chang Gung University College of Medicine, No. 5, Fu‐Shin St. Kwei‐Shan, Taoyuan, Republic of China

Chin-Man Wang

Department of Medicine, Division of Allergy, Immunology and Rheumatology, Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Taoyuan, Taiwan, No. 5, Fu-Shin St. Kwei-Shan, Republic of China

Yeong-Jian Jan Wu, Jian-Wen Zheng, Li Yu Huang, Keng Poo Tan & Ji-Yih Chen

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Conceptualization, resources, funding acquisition C.M W. and J.Y.C. methodology, data curation, investigation Y.J.J.W., L.Y.H. K.P.T. and J.W.Z., original draft preparation C.M W. J.Y.C. K.P.T. and Y.J.J.W. All authors have read and agreed to the published version of the manuscript.

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Additional file 1: .

Supplementary Figures 1 to 3.  The gene list best defining the pathways influence the pathogenesis of LN in long term ESRD.  Supplementary Figure 4. Co-IRs expressions on CD4+ or CD8+ subsets subsequent to stimulation with IFN-β and JAKi.  Supplementary Table 1.  The clinical characteristics of SLE patients and normal controls.  Supplementary Table 2.   The comparison one Co-IRs expression on T cells between SLE nephritis negative and positive.  Supplementary Table 3. The comparison one Co-IRs expression on T cells between SLE SLEDAI <6 and ≧ 6. Supplementary Table 4. The comparison one Co-IRs expression on T cells between SLE C3 ≧ 70 and <70. Supplementary Table 5. The comparison one Co-IRs expression on T cells between SLE C4 ≧ 10 and <10. Supplementary Table 6. The comparison one Co-IRs expression on T cells between SLE dsDNA <130 and ≧ 130. Supplementary Table 7. The comparison one Co-IRs expression on T cells between SLE before and following therapy.

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Wang, CM., Jan Wu, YJ., Zheng, JW. et al. T cell expressions of aberrant gene signatures and Co-inhibitory receptors (Co-IRs) as predictors of renal damage and lupus disease activity. J Biomed Sci 31 , 41 (2024). https://doi.org/10.1186/s12929-024-01024-7

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Biomedical Waste Management and Its Importance: A Systematic Review

Himani s bansod.

1 Community Medicine, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, IND

Prasad Deshmukh

2 Head and Neck Surgery, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, IND

The waste generated in various hospitals and healthcare facilities, including the waste of industries, can be grouped under biomedical waste (BMW). The constituents of this type of waste are various infectious and hazardous materials. This waste is then identified, segregated, and treated scientifically. There is an inevitable need for healthcare professionals to have adequate knowledge and a proper attitude towards BMW and its management. BMW generated can either be solid or liquid waste comprising infectious or potentially infectious materials, such as medical, research, or laboratory waste. There is a high possibility that inappropriate management of BMW can cause infections to healthcare workers, the patients visiting the facilities, and the surrounding environment and community. BMW can also be classified into general, pathological, radioactive, chemical, infectious, sharps, pharmaceuticals, or pressurized wastes. India has well-established rules for the proper handling and management of BMW. Biomedical Waste Management Rules, 2016 (BMWM Rules, 2016) specify that every healthcare facility shall take all necessary steps to ensure that BMW is handled without any adverse effect on human and environmental health. This document contains six schedules, including the category of BMW, the color coding and type of containers, and labels for BMW containers or bags, which should be non-washable and visible. A label for the transportation of BMW containers, the standard for treatment and disposal, and the schedule for waste treatment facilities such as incinerators and autoclaves are included in the schedule. The new rules established in India are meant to improve the segregation, transportation, disposal methods, and treatment of BMW. This proper management is intended to decrease environmental pollution because, if not managed properly, BMW can cause air, water, and land pollution. Collective teamwork with committed government support in finance and infrastructure development is a very important requirement for the effective disposal of BMW. Devoted healthcare workers and facilities are also significant. Further, the proper and continuous monitoring of BMW is a vital necessity. Therefore, developing environmentally friendly methods and the right plan and protocols for the disposal of BMW is very important to achieve a goal of a green and clean environment. The aim of this review article is to provide systematic evidence-based information along with a comprehensive study of BMW in an organized manner.

Introduction and background

The amount of daily biomedical waste (BMW) produced in India is enormous [ 1 ]. People from all segments of society, regardless of age, sex, ethnicity, or religion, visit hospitals, which results in the production of BMW, which is becoming increasingly copious and heterogeneous [ 2 ]. BMW produced in India is about 1.5-2 kg/bed/day [ 3 ]. BMW include anatomical waste, sharps, laboratory waste, and others and, if not carefully segregated, can be fatal. Additionally, inappropriate segregation of dirty plastic, a cytotoxic and recyclable material, might harm our ecosystem [ 4 ]. Earlier, BMW was not considered a threat to humans and the environment. In the 1980s and 1990s, fears about contact with infectious microorganisms such as human immunodeficiency virus (HIV) and hepatitis B virus (HBV) prompted people to consider the potential risks of BMW [ 5 ]. BMW is hazardous in nature as it consists of potential viruses or other disease-causing microbial particles; it may be present in human samples, blood bags, needles, cotton swabs, dressing material, beddings, and others. Therefore, the mismanagement of BMW is a community health problem. The general public must also take specific actions to mitigate the rising environmental degradation brought on by negligent BMW management. On July 20, 1998, BMW (Management and Handling) Rules were framed. On March 28, 2016, under the Environment (Protection) Act, 1986, the Ministry of Environment and Forest (MoEF) implemented the new BMW Rules (2016) and replaced the earlier one (1988). BMW produced goes through a new protocol or approach that helps in its appropriate management in terms of its characterization, quantification, segregation, storage, transport, and treatment.

According to Chapter 2 of the Medical Waste Management and Processing Rules, 2016, “The BMW could not be mixed with other wastes at any stage while producing inside hospitals, while collecting from hospitals, while transporting, and should be processed separately based on classification.” The COVID-19 pandemic has now transformed healthy societies worldwide into diseased ones, resulting in a very high number of deaths. It also created one significant problem: improper handling of the medical waste produced in the testing and treatment of the disease [ 6 ]. In India, BMW generated due to COVID-19 contributed to about 126 tonnes per day out of the 710 tonnes of waste produced daily [ 7 ]. 

The basic principle of the management of BMW is Reduce, Reuse, and Recycle-the 3Rs. Out of the total amount of BMW generated, 85% is general (non-hazardous) waste, and the remaining 15% is hazardous. As BMW contains sharps and syringes, the pathogens can enter the human body through cuts, abrasions, puncture wounds, and other ways. There might also be chances of ingestion and inhalation of BMW, which can lead to infections. Some examples of infections are Salmonella, Shigella, Mycobacterium tuberculosis, Streptococcus pneumonia, acquired immunodeficiency syndrome (AIDS), hepatitis A, B, and C, and helminthic infections [ 8 ]. This systematic review is conducted to obtain essential, up-to-date information on BMW for the practical application of its management. The highlight of the management of BMW is that the “success of BMW management depends on segregation at the point of generation” [ 9 ].

The findings have been reported following the principles and criteria of the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA). The systematic review has been conducted according to these standards and principles.

Search Sources/Search Strategy

We used the MeSH strategy to obtain articles from PubMed and ResearchGate employing the following terms: (“Biomedical/waste” [Majr] OR “Biomedical Waste/source” [Majr] OR “Biomedical Waste/hazards” [Majr] OR “Biomedical Waste/segregation” [Majr] OR “Biomedical Waste/rules” [Majr] OR “Biomedical Waste/laws” [Majr] OR “Biomedical Waste/environment” [Majr]). Specifically, for management-related studies, the search terms (“Management/steps” [Majr] OR “Management/handling” [Majr] OR “Management/coding” [Majr] OR “Color coding/segregation” [Majr] OR “Treatment/method” [Majr] OR “Autoclaving/waste” [Majr] OR “Incineration/waste” [Majr]) were used. We obtained the most pertinent research papers and used them in different arrangements using the Boolean operators “AND” and “OR.”

Inclusion and exclusion criteria

We focused on papers written in the English language, published within the last decade, relevant to the central questions of this review article, and that are systematic reviews such as randomized clinical trials and observational studies. We, however, excluded papers published in languages other than English, irrelevant to the questions, and related to topics other than BMW.

Search outcomes

After the initial screening, we narrowed the search results down to 264 papers. A total of 42 duplicate papers were removed. Subsequently, publications were refined by the title/abstract, and we eliminated a few studies due to the lack of full text and/or related articles. Finally, after assessing 27 items for eligibility, we included 11 papers in our review. Figure ​ Figure1 1 is the flow chart for article selection formulated on PRISMA.

PRISMA: Preferred Reporting Items for Systematic Review and Meta-analysis, PMC: PubMed Central

Need for BMW management in hospitals

BMW threatens the health of medical staff, hospital-visiting patients, and people in the nearby community. Improper disposal leads to severe hospital-acquired diseases along with an increased risk of air and water pollution. Due to open-space waste disposal practices, animals and scavengers might get infected, leading to the scattering of waste and the spreading of infections. In countering such activities, four major principle functions of BMW management are applicable: the placement of bins at the source of generation of BMW, segregation of BMW, removal or mutilation of the recyclable waste, and disinfection of the waste [ 10 ]. BMW management methods aim predominantly to avoid the generation of waste and, if generated, then recover as much as possible [ 11 ].

BMW management rules in India

On March 28, 2016, under the Environment (Protection) Act, 1986, the MoEF notified the new BMW Rules, 2016 and replaced the earlier Rules (1988). BMW produced goes through a new protocol or approach which helps in the appropriate management of waste, i.e., its characterization, quantification, segregation, storage, transport, and treatment, all of which aim to decrease environmental pollution [ 12 ]. Problems with the improper management of BMW also shed light on the scavengers who, for recycling, segregate the potentially hazardous BMW without using gloves or masks. Strict rules have been implemented to ensure that there is no stealing of recyclable materials or spillage by some humans or animals and that it is transported to the common BMW treatment facility [ 10 ]. The first solution to stop the spread of hazardous and toxic waste was incineration. Incineration is required in all hospitals and healthcare facilities that produce BMW. However, due to the absence of services that provide certified incinerators in a few countries, BMW has to be sent to landfills, which leads to land contamination and harms the environment [ 13 ]. Incinerators used for disposal might also lead to environmental pollution. Numerous toxins are formed during incineration, which are the products of incomplete combustion. Thus, some new standards have been issued to resolve this problem and safeguard the environment and public health [ 14 ].

Steps in the management of BMW

BMW management needs to be organized, as even a single mistake can cause harm to the people in charge. There are six steps in the management of BMW [ 15 ]: surveying the waste produced; segregating, collecting, and categorizing the waste; storing, transporting, and treating the waste. Segregation is the separation of different types of waste generated, which helps reduce the risks resulting from the improper management of BMW. When the waste is simply disposed of, there is an increased risk of the mixture of waste such as sharps with general waste. These sharps can be infectious to the handler of the waste. Further, if not segregated properly, there is a huge chance of syringes and needles disposed of in the hospitals being reused. Segregation prevents this and helps in achieving the goal of recycling the plastic and metal waste generated [ 16 ]. According to Schedule 2, waste must be segregated into containers at the source of its generation, and according to Schedule 3, the container used must be labeled. The schedules of BMW (Management and Handling) Rules, 1998, which were initially ten in number, have now been reduced to four [ 17 ]. The collection of BMW involves the use of different colors of bins for waste disposal. The color is an important indicator for the segregation and identification of different categories of waste into suitable-colored containers. They must be labeled properly based on the place they have been generated, such as hospital wards, rooms, and operation theatres. It is also very important to remember that the waste must be stored for less than 8-10 hours in hospitals with around 250 beds and 24 hours in nursing homes. The storage bag or area must be marked with a sign [ 16 ]. 

Figure ​ Figure1 1 shows the biohazard signs that symbolize the nature of waste to the general public.

Biohazards are substances that threaten all living things on earth. The biohazard symbol presented in Figure ​ Figure1 1 was remarked as an important public sign, signaling the harms and hazards of entering the specified zone or room [ 18 ]. Along with the biohazard sign, the room door must have a label saying “AUTHORISED PERSONNEL ONLY.” The temporary storage room must always be locked and away from the general public's reach. The waste is then collected by the vehicles daily. A ramp must be present for easy transportation. The waste collected is then taken for treatment. The loading of wastes should not be done manually. It is very vital to properly close or tie the bag or the container to avoid any spillage and harm to the handlers, the public, and the environment. The transport vehicle or trolley must be properly covered, and the route used must be the one with less traffic flow [ 19 ].

BMW handling staff should be provided with personal protective equipment (PPE), gloves, masks, and boots. BMW retrievers must be provided with rubber gloves that should be bright yellow. After usage, the importance of disinfecting or washing the gloves twice should be highlighted. The staff working in or near the incinerator chamber must be provided with a non-inflammable kit. This kit consists of a gas mask that should cover the nose and mouth of the staff member. The boots should cover the leg up to the ankle to protect from splashes and must be anti-skid [ 16 ]. According to the revised BMW management rules, 2016, it is mandatory to provide proper training to healthcare facility staff members on handling BMW. The training should be mandatorily conducted annually. Along with the management step of the color coding for segregation, it is also important for the staff to be trained in record keeping. This practice of record-keeping helps track the total amount of waste generated and the problems that occurred during the management process, thus helping improve segregation, treatment, and disposal [ 20 ].

Color coding for segregation of BMW

Color coding is the first step of BMW management. Different wastes are classified into different types, and therefore, they must be handled and disposed of according to their classification. The bins used for waste disposal in all healthcare facilities worldwide are always color-coded. Based on the rule of universality, bins are assigned a specific color, according to which the waste is segregated. This step helps avoid the chaos that occurs when all types of waste are jumbled, which can lead to improper handling and disposal and further result in the contraction of several diseases [ 21 ]. The different kinds of categories of waste include sharp waste such as scalpels, blades, needles, and objects that can cause a puncture wound, anatomical waste, recyclable contaminated waste, chemicals, laboratory waste such as specimens, blood bags, vaccines, and medicines that are discarded. All the above-mentioned wastes are segregated in different colored bins and sent for treatment [ 22 ]. Yellow bins collect anatomical waste, infectious waste, chemical waste, laboratory waste, and pharmaceutical waste, covering almost all types of BMW. Different bins and various types of sterilization methods are used depending on how hazardous the waste is. The best tools for sterilization are autoclaves. Red bins collect recyclable contaminated wastes, and non-chlorinated plastic bags are used for BMW collection. Blue containers collect hospital glassware waste such as vials and ampoules. White bins are translucent where discarded and contaminated sharps are disposed of. Sharp wastes must always be disposed of in puncture-proof containers to avoid accidents leading to handlers contracting diseases [ 23 , 24 ]. 

Figure ​ Figure3 3 illustrates the different colored bins used for the segregation of BMW.

BMW management refers to completely removing all the hazardous and infectious waste generated from hospital settings. The importance of waste treatment is to remove all the pathogenic organisms by decontaminating the waste generated. This helps in the prevention of many severe health-related issues that can be caused because of the infective waste. It is a method used to prevent all environmental hazards [ 25 ].

Methods for the treatment of BMW

There are many methods that are used for the treatment of BMW. One of the most economical ways of waste treatment is incineration, which is just not some simple “burning” but the burning of waste at very high temperatures ranging from 1800℉ to 2000℉ to decrease the total mass of decontaminated waste by converting it into ash and gases, which is then further disposed of in landfills [ 25 , 26 ]. Important instructions associated with the use of incinerators are as follows: chlorinated plastic bags must not be put inside the incinerators as they can produce dioxin [ 26 ]. Metals should not be destroyed in an incinerator. The metals present in BMW are made of polyvinyl chloride. When these metals are burned, they produce a huge amount of dioxin. Dioxins are very toxic chlorinated chemical compounds, as dioxins, when released into the environment, can lead to environmental pollution and a higher incidence of cancer and respiratory manifestations [ 14 ].

Autoclaving is an alternate method of incineration. The mechanism of this process involved sterilization using steam and moisture. Operating temperatures and time of autoclaving is 121℃ for 20-30 minutes. The steam destroys pathogenic agents present in the waste and also sterilizes the equipment used in the healthcare facility [ 25 ]. Autoclaving has no health impacts and is very cost-friendly. It is recommended for the treatment of disposables and sharps, but the anatomical, radioactive, and chemical wastes must not be treated in an autoclave [ 27 ]. Chemical methods are the commonest methods that include chemicals such as chlorine, hydrogen peroxide, and Fenton’s reagent. They are used to kill the microorganisms present in the waste and are mainly used for liquid waste, such as blood, urine, and stool. They can also be used to treat solid waste and disinfect the equipment used in hospital settings and surfaces such as floors and walls [ 28 ]. Thermal inactivation is a method that uses high temperatures to kill the microorganisms present in the waste and reduce the waste generated in larger volumes. The temperature differs according to the type of pathogen present in the waste. After the treatment is done, the contents are then discarded into sewers [ 29 ].

Very serious environmental and health hazards can be triggered if hospital waste is mixed with normal garbage, which can lead to poor health and incurable diseases such as AIDS [ 30 ]. The needle sticks can be highly infectious if discarded inappropriately. Injury by these contaminated needles can lead to a high risk of active infection of HBV or HIV [ 31 ]. The groups at increased risk of getting infected accidentally are the medical waste handlers and scavengers. Sharps must properly be disposed of in a translucent thin-walled white bin. If sharps are discarded in a thin plastic bag, there is a high chance that the sharps might puncture the bag and injure the waste handler [ 32 ]. It can also be the main cause of severe air, water, and land pollution. Air pollutants in BMW can remain in the air as spores. These are known as biological air pollutants. Chemical air pollutants are released because of incinerators and open burning. Another type of threat is water pollutants. BMW containing heavy metals when disposed of in water bodies results in severe water contamination. The landfills where the disposal takes place must be constructed properly, or the waste inside might contaminate the nearby water bodies, thus contaminating the drinking water. Land pollution is caused due to open dumping [ 33 ]. BMW must also be kept away from the reach of rodents such as black rats and house mice, which can spread the pathogens to the people living nearby [ 34 ].

Many promising steps were taken to minimize the volume of waste discarded from the source, its treatment, and disposal. The 3R system encourages the waste generators to reuse, reduce, and recycle. Everyone must be aware of the 3Rs because this approach can help achieve a better and cleaner environment [ 35 ]. Unfortunately, most economically developing countries cannot correctly manage BMW. Very few staff members of healthcare facilities are educated about proper waste management. The waste handlers are also poorly educated about the hazards of waste [ 36 ]. Every member helping in the waste management process must be made aware of the dangers of BMW to avoid accidents that harm the environment and living beings [ 37 ].

Conclusions

BMW is generated by healthcare facilities and can be hazardous and infectious. Improper handling can lead to health hazards. Collection, segregation, transportation, treatment, and disposal of BMW are important steps in its management. The color coding of bins, the use of technologies such as incineration and autoclaving, and attention to environmental impacts are also highly crucial. BMW management aims to reduce waste volume and ensure proper disposal. All those involved should strive to make the environment safer.

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The authors have declared that no competing interests exist.