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Published: 16 June 2020

COVID-19 impact on research, lessons learned from COVID-19 research, implications for pediatric research

Debra L. Weiner 1 , 2 ,
Vivek Balasubramaniam 3 ,
Shetal I. Shah 4 &
Joyce R. Javier 5 , 6

on behalf of the Pediatric Policy Council

Pediatric Research volume 88 , pages 148–150 ( 2020 ) Cite this article

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The COVID-19 pandemic has resulted in unprecedented research worldwide. The impact on research in progress at the time of the pandemic, the importance and challenges of real-time pandemic research, and the importance of a pediatrician-scientist workforce are all highlighted by this epic pandemic. As we navigate through and beyond this pandemic, which will have a long-lasting impact on our world, including research and the biomedical research enterprise, it is important to recognize and address opportunities and strategies for, and challenges of research and strengthening the pediatrician-scientist workforce.

The first cases of what is now recognized as SARS-CoV-2 infection, termed COVID-19, were reported in Wuhan, China in December 2019 as cases of fatal pneumonia. By February 26, 2020, COVID-19 had been reported on all continents except Antarctica. As of May 4, 2020, 3.53 million cases and 248,169 deaths have been reported from 210 countries. 1

Impact of COVID-19 on ongoing research

The impact on research in progress prior to COVID-19 was rapid, dramatic, and no doubt will be long term. The pandemic curtailed most academic, industry, and government basic science and clinical research, or redirected research to COVID-19. Most clinical trials, except those testing life-saving therapies, have been paused, and most continuing trials are now closed to new enrollment. Ongoing clinical trials have been modified to enable home administration of treatment and virtual monitoring to minimize participant risk of COVID-19 infection, and to avoid diverting healthcare resources from pandemic response. In addition to short- and long-term patient impact, these research disruptions threaten the careers of physician-scientists, many of whom have had to shift efforts from research to patient care. To protect research in progress, as well as physician-scientist careers and the research workforce, ongoing support is critical. NIH ( https://grants.nih.gov/policy/natural-disasters/corona-virus.htm ), PCORI ( https://www.pcori.org/funding-opportunities/applicant-and-awardee-faqs-related-covid-19 ), and other funders acted swiftly to provide guidance on proposal submission and award management, and implement allowances that enable grant personnel to be paid and time lines to be relaxed. Research institutions have also implemented strategies to mitigate the long-term impact of research disruptions. Support throughout and beyond the pandemic to retain currently well-trained research personnel and research support teams, and to accommodate loss of research assets, including laboratory supplies and study participants, will be required to complete disrupted research and ultimately enable new research.

In the long term, it is likely that the pandemic will force reallocation of research dollars at the expense of research areas funded prior to the pandemic. It will be more important than ever for the pediatric research community to engage in discussion and decisions regarding prioritization of funding goals for dedicated pediatric research and meaningful inclusion of children in studies. The recently released 2020 National Institute of Child Health and Development (NICHD) strategic plan that engaged stakeholders, including scientists and patients, to shape the goals of the Institute, will require modification to best chart a path toward restoring normalcy within pediatric science.

COVID-19 research

This global pandemic once again highlights the importance of research, stable research infrastructure, and funding for public health emergency (PHE)/disaster preparedness, response, and resiliency. The stakes in this worldwide pandemic have never been higher as lives are lost, economies falter, and life has radically changed. Ultimate COVID-19 mitigation and crisis resolution is dependent on high-quality research aligned with top priority societal goals that yields trustworthy data and actionable information. While the highest priority goals are treatment and prevention, biomedical research also provides data critical to manage and restore economic and social welfare.

Scientific and technological knowledge and resources have never been greater and have been leveraged globally to perform COVID-19 research at warp speed. The number of studies related to COVID-19 increases daily, the scope and magnitude of engagement is stunning, and the extent of global collaboration unprecedented. On January 5, 2020, just weeks after the first cases of illness were reported, the genetic sequence, which identified the pathogen as a novel coronavirus, SARS-CoV-2, was released, providing information essential for identifying and developing treatments, vaccines, and diagnostics. As of May 3, 2020 1133 COVID-19 studies, including 148 related to hydroxychloroquine, 13 to remdesivir, 50 to vaccines, and 100 to diagnostic testing, were registered on ClinicalTrials.gov, and 980 different studies on the World Health Organization’s International Clinical Trials Registry Platform (WHO ICTRP), made possible, at least in part, by use of data libraries to inform development of antivirals, immunomodulators, antibody-based biologics, and vaccines. On April 7, 2020, the FDA launched the Coronavirus Treatment Acceleration Program (CTAP) ( https://www.fda.gov/drugs/coronavirus-covid-19-drugs/coronavirus-treatment-acceleration-program-ctap ). On April 17, 2020, NIH announced a partnership with industry to expedite vaccine development ( https://www.nih.gov/news-events/news-releases/nih-launch-public-private-partnership-speed-covid-19-vaccine-treatment-options ). As of May 1, 2020, remdesivir (Gilead), granted FDA emergency use authorization, is the only approved therapeutic for COVID-19. 2

The pandemic has intensified research challenges. In a rush for data already thousands of manuscripts, news reports, and blogs have been published, but to date, there is limited scientifically robust data. Some studies do not meet published clinical trial standards, which now include FDA’s COVID-19-specific standards, 3 , 4 , 5 and/or are published without peer review. Misinformation from studies diverts resources from development and testing of more promising therapeutic candidates and has endangered lives. Ibuprofen, initially reported as unsafe for patients with COVID-19, resulted in a shortage of acetaminophen, endangering individuals for whom ibuprofen is contraindicated. Hydroxychloroquine initially reported as potentially effective for treatment of COVID-19 resulted in shortages for patients with autoimmune diseases. Remdesivir, in rigorous trials, showed decrease in duration of COVID-19, with greater effect given early. 6 Given the limited availability and safety data, the use outside clinical trials is currently approved only for severe disease. Vaccines typically take 10–15 years to develop. As of May 3, 2020, of nearly 100 vaccines in development, 8 are in trial. Several vaccines are projected to have emergency approval within 12–18 months, possibly as early as the end of the year, 7 still an eternity for this pandemic, yet too soon for long-term effectiveness and safety data. Antibody testing, necessary for diagnosis, therapeutics, and vaccine testing, has presented some of the greatest research challenges, including validation, timing, availability and prioritization of testing, interpretation of test results, and appropriate patient and societal actions based on results. 8 Relaxing physical distancing without data regarding test validity, duration, and strength of immunity to different strains of COVID-19 could have catastrophic results. Understanding population differences and disparities, which have been further exposed during this pandemic, is critical for response and long-term pandemic recovery. The “Equitable Data Collection and Disclosure on COVID-19 Act” calls for the CDC (Centers for Disease Control and Prevention) and other HHS (United States Department of Health & Human Services) agencies to publicly release racial and demographic information ( https://bass.house.gov/sites/bass.house.gov/files/Equitable%20Data%20Collection%20and%20Dislosure%20on%20COVID19%20Act_FINAL.pdf )

Trusted sources of up-to-date, easily accessible information must be identified (e.g., WHO https://www.who.int/emergencies/diseases/novel-coronavirus-2019/global-research-on-novel-coronavirus-2019-ncov , CDC https://www.cdc.gov/coronavirus/2019-nCoV/hcp/index.html , and for children AAP (American Academy of Pediatrics) https://www.aappublications.org/cc/covid-19 ) and should comment on quality of data and provide strategies and crisis standards to guide clinical practice.

Long-term, lessons learned from research during this pandemic could benefit the research enterprise worldwide beyond the pandemic and during other PHE/disasters with strategies for balancing multiple novel approaches and high-quality, time-efficient, cost-effective research. This challenge, at least in part, can be met by appropriate study design, collaboration, patient registries, automated data collection, artificial intelligence, data sharing, and ongoing consideration of appropriate regulatory approval processes. In addition, research to develop and evaluate innovative strategies and technologies to improve access to care, management of health and disease, and quality, safety, and cost effectiveness of care could revolutionize healthcare and healthcare systems. During PHE/disasters, crisis standards for research should be considered along with ongoing and just-in-time PHE/disaster training for researchers willing to share information that could be leveraged at time of crisis. A dedicated funded core workforce of PHE/disaster researchers and funded infrastructure should be considered, potentially as a consortium of networks, that includes physician-scientists, basic scientists, social scientists, mental health providers, global health experts, epidemiologists, public health experts, engineers, information technology experts, economists and educators to strategize, consult, review, monitor, interpret studies, guide appropriate clinical use of data, and inform decisions regarding effective use of resources for PHE/disaster research.

Differences between adult and pediatric COVID-19, the need for pediatric research

As reported by the CDC, from February 12 to April 2, 2020, of 149,760 cases of confirmed COVID-19 in the United States, 2572 (1.7%) were children aged <18 years, similar to published rates in China. 9 Severe illness has been rare. Of 749 children for whom hospitalization data is available, 147 (20%) required hospitalization (5.7% of total children), and 15 of 147 required ICU care (2.0%, 0.58% of total). Of the 95 children aged <1 year, 59 (62%) were hospitalized, and 5 (5.3%) required ICU admission. Among children there were three deaths. Despite children being relatively spared by COVID-19, spread of disease by children, and consequences for their health and pediatric healthcare are potentially profound with immediate and long-term impact on all of society.

We have long been aware of the importance and value of pediatric research on children, and society. COVID-19 is no exception and highlights the imperative need for a pediatrician-scientist workforce. Understanding differences in epidemiology, susceptibility, manifestations, and treatment of COVID-19 in children can provide insights into this pathogen, pathogen–host interactions, pathophysiology, and host response for the entire population. Pediatric clinical registries of COVID-infected, COVID-exposed children can provide data and specimens for immediate and long-term research. Of the 1133 COVID-19 studies on ClinicalTrials.gov, 202 include children aged ≤17 years. Sixty-one of the 681 interventional trials include children. With less diagnostic testing and less pediatric research, we not only endanger children, but also adults by not identifying infected children and limiting spread by children.

Pediatric considerations and challenges related to treatment and vaccine research for COVID-19 include appropriate dosing, pediatric formulation, and pediatric specific short- and long-term effectiveness and safety. Typically, initial clinical trials exclude children until safety has been established in adults. But with time of the essence, deferring pediatric research risks the health of children, particularly those with special needs. Considerations specific to pregnant women, fetuses, and neonates must also be addressed. Childhood mental health in this demographic, already struggling with a mental health pandemic prior to COVID-19, is now further challenged by social disruption, food and housing insecurity, loss of loved ones, isolation from friends and family, and exposure to an infodemic of pandemic-related information. Interestingly, at present mental health visits along with all visits to pediatric emergency departments across the United States are dramatically decreased. Understanding factors that mitigate and worsen psychiatric symptoms should be a focus of research, and ideally will result in strategies for prevention and management in the long term, including beyond this pandemic. Social well-being of children must also be studied. Experts note that the pandemic is a perfect storm for child maltreatment given that vulnerable families are now socially isolated, facing unemployment, and stressed, and that children are not under the watch of mandated reporters in schools, daycare, and primary care. 10 Many states have observed a decrease in child abuse reports and an increase in severity of emergency department abuse cases. In the short term and long term, it will be important to study the impact of access to care, missed care, and disrupted education during COVID-19 on physical and cognitive development.

Training and supporting pediatrician-scientists, such as through NIH physician-scientist research training and career development programs ( https://researchtraining.nih.gov/infographics/physician-scientist ) at all stages of career, as well as fostering research for fellows, residents, and medical students willing to dedicate their research career to, or at least understand implications of their research for, PHE/disasters is important for having an ongoing, as well as a just-in-time surge pediatric-focused PHE/disaster workforce. In addition to including pediatric experts in collaborations and consortiums with broader population focus, consideration should be given to pediatric-focused multi-institutional, academic, industry, and/or government consortiums with infrastructure and ongoing funding for virtual training programs, research teams, and multidisciplinary oversight.

The impact of the COVID-19 pandemic on research and research in response to the pandemic once again highlights the importance of research, challenges of research particularly during PHE/disasters, and opportunities and resources for making research more efficient and cost effective. New paradigms and models for research will hopefully emerge from this pandemic. The importance of building sustained PHE/disaster research infrastructure and a research workforce that includes training and funding for pediatrician-scientists and integrates the pediatrician research workforce into high-quality research across demographics, supports the pediatrician-scientist workforce and pipeline, and benefits society.

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Department of Pediatrics, Division of Emergency Medicine, Boston Children’s Hospital, Boston, MA, USA

Debra L. Weiner

Harvard Medical School, Boston, MA, USA

Department of Pediatrics, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA

Vivek Balasubramaniam

Department of Pediatrics and Division of Neonatology, Maria Fareri Children’s Hospital at Westchester Medical Center, New York Medical College, Valhalla, NY, USA

Shetal I. Shah

Division of General Pediatrics, Children’s Hospital Los Angeles, Los Angeles, CA, USA

Joyce R. Javier

Keck School of Medicine, University of Southern California, Los Angeles, CA, USA

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All authors made substantial contributions to conception and design, data acquisition and interpretation, drafting the manuscript, and providing critical revisions. All authors approve this final version of the manuscript.

Pediatric Policy Council

Scott C. Denne, MD, Chair, Pediatric Policy Council; Mona Patel, MD, Representative to the PPC from the Academic Pediatric Association; Jean L. Raphael, MD, MPH, Representative to the PPC from the Academic Pediatric Association; Jonathan Davis, MD, Representative to the PPC from the American Pediatric Society; DeWayne Pursley, MD, MPH, Representative to the PPC from the American Pediatric Society; Tina Cheng, MD, MPH, Representative to the PPC from the Association of Medical School Pediatric Department Chairs; Michael Artman, MD, Representative to the PPC from the Association of Medical School Pediatric Department Chairs; Shetal Shah, MD, Representative to the PPC from the Society for Pediatric Research; Joyce Javier, MD, MPH, MS, Representative to the PPC from the Society for Pediatric Research.

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Weiner, D.L., Balasubramaniam, V., Shah, S.I. et al. COVID-19 impact on research, lessons learned from COVID-19 research, implications for pediatric research. Pediatr Res 88 , 148–150 (2020). https://doi.org/10.1038/s41390-020-1006-3

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June 10, 2021

The COVID Lab-Leak Hypothesis: What Scientists Do and Do Not Know

An examination of the arguments that SARS-CoV-2 escaped from a lab in China and the science behind them

By Amy Maxmen , Smriti Mallapaty & Nature magazine

A security member keeps watch outside the Wuhan Institute of Virology.

Thomas Peter Alamy

Debate over the idea that the SARS-CoV-2 coronavirus emerged from a laboratory has escalated over the past few weeks, coinciding with the annual World Health Assembly, at which the World Health Organization (WHO) and officials from nearly 200 countries discussed the COVID-19 pandemic. After last year’s assembly, the WHO agreed to sponsor the first phase of an investigation into the pandemic’s origins, which took place in China in early 2021 .

Most scientists say SARS-CoV-2 probably has a natural origin, and was transmitted from an animal to humans. However, a lab leak has not been ruled out, and many are calling for a deeper investigation into the hypothesis that the virus emerged from the Wuhan Institute of Virology (WIV), located in the Chinese city where the first COVID-19 cases were reported. On 26 May, US President Joe Biden tasked the US Intelligence Community to join efforts to find SARS-CoV-2’s origins, whatever they might be, and report back in 90 days.

Australia, the European Union and Japan have also called for a robust investigation into SARS-CoV-2’s origins in China. The WHO has yet to reveal the next phase of its investigation. But China has asked that the probe examine other countries. Such reticence, and the fact that China has withheld information in the past, has fuelled suspicions of a ‘lab leak’. For instance, Chinese government officials suppressed crucial public-health data at the start of the COVID-19 pandemic, and during the 2002–04 severe acute respiratory syndrome (SARS) epidemic, according to high-level reports.

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At the assembly, Mike Ryan, director of health emergencies at the WHO, asked for less politicization of calls for an origin investigation , which have, in many ways, devolved into accusations. “Over the last number of days, we have seen more and more and more discourse in the media, with terribly little actual news, or evidence, or new material,” said Ryan. “This is disturbing.”

Nature looks at the key arguments that support a lab leak, and the extent to which research has answers.

There’s not yet any substantial evidence for a lab leak. Why are scientists still considering it?

Scientists don’t have enough evidence about the origins of SARS-CoV-2 to rule out the lab-leak hypothesis, or to prove the alternative — that the virus has a natural origin. Many infectious-disease researchers agree that the most probable scenario is that the virus evolved naturally and spread from a bat either directly to a person or through an intermediate animal. Most emerging infectious diseases begin with a spillover from nature, as was seen with HIV, influenza epidemics, Ebola outbreaks and the coronaviruses that caused the SARS epidemic beginning in 2002 and the Middle East respiratory syndrome (MERS) outbreak beginning in 2012.

Researchers have some leads that support a natural origin. Bats are known carriers of coronaviruses, and scientists have determined that the genome of SARS-CoV-2 is most similar to that of RATG13, a coronavirus that was first found in a horseshoe bat ( Rhinolophus affinis ) in the southern Chinese province of Yunnan in 2013. But RATG13’s genome is only 96% identical to SARS-CoV-2’s, suggesting that a closer relative of the virus—the one passed to humans—remains unknown.

Still, the possibility remains that SARS-CoV-2 escaped from a lab. Although lab leaks have never caused an epidemic, they have resulted in small outbreaks involving well-documented viruses. A relevant example happened in 2004, when two researchers were independently infected by the virus that causes SARS at a virology lab in Beijing that studied the disease. They spread the infection to seven others before the outbreak was contained.

What are the key arguments for a lab leak?

In theory, COVID-19 could have come from a lab in a few ways. Researchers might have collected SARS-CoV-2 from an animal and maintained it in their lab to study, or they might have created it by engineering coronavirus genomes. In these scenarios, a person in the lab might have then been accidentally or deliberately infected by the virus, and then spread it to others—sparking the pandemic. There is currently no clear evidence to back these scenarios, but they aren’t impossible.

People have made a number of arguments for a lab origin for SARS-CoV-2 that are currently conjecture.

One holds that it’s suspicious that, almost a year and a half into the pandemic, SARS-CoV-2’s closest relative still hasn’t been found in an animal. Another suggests it is no coincidence that COVID-19 was first detected in Wuhan, where a top lab studying coronaviruses, the WIV, is located.

Some lab-leak proponents contend that the virus contains unusual features and genetic sequences signalling that it was engineered by humans. And some say that SARS-CoV-2 spreads among people so readily that it must have been created with that intention. Another argument suggests that SARS-CoV-2 might have derived from coronaviruses found in an unused mine where WIV researchers collected samples from bats between 2012 and 2015.

So what do infectious disease researchers and evolutionary biologists say about these arguments?

Is it suspicious that no animal has been identified as transmitting the virus to humans?

Outbreak-origin investigations often take years, and some culprits remain unknown. It took 14 years to nail down the origin of the SARS epidemic, which began with a virus in bats that spread to humans, most likely through civets . To date, a complete Ebola virus has never been isolated from an animal in the region where the world’s largest outbreak occurred between 2013 and 2016.

Origin investigations are complicated because outbreaks among animals that aren't the main hosts of a particular virus, such as civets in the case of SARS, are often sporadic. Researchers must find the right animal before it dies or clears the infection. And, even if the animal tests positive, viruses found in saliva, faeces or blood are often degraded, making it difficult to sequence the pathogen’s whole genome.

Scientists have made some progress since the pandemic began, however. For example, a report, posted to the preprint server bioRxiv on 27 May, suggests that RmYN02, a coronavirus in bats in southern China, might be more closely related to SARS-CoV-2 than RATG13 is.

As for finding an intermediate host animal, researchers in China have tested more than 80,000 wild and domesticated animals; none have been positive for SARS-CoV-2. But this number is a tiny fraction of the animals in the country. To narrow the search down, researchers say, more strategic testing is needed to isolate animals that are most susceptible to infection and those that come in close contact with people. They also suggest using antibody tests to identify animals that have previously been infected with the virus.

Is it suspicious that the WIV is in Wuhan?

Virology labs tend to specialize in the viruses around them, says Vincent Munster, a virologist at the Rocky Mountain Laboratories, a division of the National Institutes of Health, in Hamilton, Montana. The WIV specializes in coronaviruses because many have been found in and around China. Munster names other labs that focus on endemic viral diseases: influenza labs in Asia, haemorrhagic fever labs in Africa and dengue-fever labs in Latin America, for example. “Nine out of ten times, when there’s a new outbreak, you’ll find a lab that will be working on these kinds of viruses nearby,” says Munster.

Researchers note that a coronavirus outbreak in Wuhan isn’t surprising, because it’s a city of 11 million people in a broader region where coronaviruses have been found. It contains an airport, train stations and markets selling goods and wildlife transported there from around the region — meaning a virus could enter the city and spread rapidly.

Does the virus have features that suggest it was created in a lab?

Several researchers have looked into whether features of SARS-CoV-2 signal that it was bioengineered. One of the first teams to do so, led by Kristian Andersen, a virologist at Scripps Research in La Jolla, California, determined that this was “improbable” for a few reasons, including a lack of signatures of genetic manipulation. Since then, others have asked whether the virus’s furin cleavage site—a feature that helps it to enter cells—is evidence of engineering, because SARS-CoV-2 has these sites but its closest relatives don’t. The furin cleavage site is important because it's in the virus's spike protein, and cleavage of the protein at that site is necessary for the virus to infect cells.

But many other coronaviruses have furin cleavage sites, such as coronaviruses that cause colds. Because viruses containing the site are scattered across the coronavirus family tree, rather than confined to a group of closely related viruses, Stephen Goldstein, a virologist at the University of Utah in Salt Lake City, says the site probably evolved multiple times because it provides an evolutionary advantage. Convergent evolution—the process by which organisms that aren’t closely related independently evolve similar traits as a result of adapting to similar environments—is incredibly common.

Another feature of SARS-CoV-2 that has drawn attention is a combination of nucleotides that underlie a segment of the furin cleavage site: CGG (these encode the amino acid arginine). A Medium article that speculates on a lab origin for SARS-CoV-2 quotes David Baltimore, a Nobel laureate and professor emeritus at the California Institute of Technology in Pasadena, as saying that viruses don’t usually have that particular code for arginine, but humans often do—a “smoking gun”, hinting that researchers might have tampered with SARS-CoV-2’s genome.

Andersen says that Baltimore was incorrect about that detail, however. In SARS-CoV-2, about 3% of the nucleotides encoding arginine are CGG, he says. And he points out that around 5% of those encoding arginine in the virus that caused the original SARS epidemic are CGG, too. In an e-mail to Nature , Baltimore says Andersen could be correct that evolution produced SARS-CoV-2, but adds that “there are other possibilities and they need careful consideration, which is all I meant to be saying”.

Is it true that SARS-CoV-2 must have been engineered, because it's perfect for causing a pandemic?

Many scientists say no. Just because the virus spreads among humans doesn't mean it was designed to do so. It also flourishes among mink and infects a host of carnivorous mammals . And it wasn’t optimally transmissible among humans for the better part of last year. Rather, new, more efficient variants have evolved around the world. To name one example, the highly transmissible variant of SARS-CoV-2 first reported in India (B.1.617.2, or Delta) has mutations in the nucleotides encoding its furin cleavage site that appear to make the virus better at infecting cells.

“This was not some supremely adapted pathogen,” says Joel Wertheim, a molecular epidemiologist at the University of California San Diego.

Did researchers collect SARS-CoV-2 from a mine?

Researchers from the WIV collected hundreds of samples from bats roosting in a mine between 2012 and 2015, after several miners working there had gotten sick with an unknown respiratory disease. (Last year, researchers reported that blood samples taken from the miners tested negative for antibodies against SARS-CoV-2, meaning that the sickness was probably not COVID-19.) Back at the lab, WIV researchers detected nearly 300 coronaviruses in the bat samples, but they were able to get whole or partial genomic sequences from fewer than a dozen , and none of those that were reported were SARS-CoV-2. During the WHO-led origins probe earlier this year, WIV researchers told investigators that they cultured only three coronaviruses at the lab, and none were closely related to SARS-CoV-2.

Although the investigators didn’t sift through freezers at the WIV to confirm this information, the low number of genomes and cultures doesn’t surprise virologists. Munster says it’s exceedingly difficult to extract intact coronaviruses from bat samples. Virus levels tend to be low in the animals, and viruses are often degraded in faeces, saliva and droplets of blood. Additionally, when researchers want to study or genetically alter viruses, they need to keep them (or synthetic mimics of them) alive, by finding the appropriate live animal cells for the viruses to inhabit in the lab, which can be a challenge.

So, for SARS-CoV-2 to have come from this mine in China, WIV researchers would have had to overcome some serious technical challenges—and they would have kept the information secret for a number of years and misled investigators on the WHO-led mission, scientists point out. There's no evidence of this, but it can't be ruled out.

What’s next for lab-leak investigations?

Biden asked the US Intelligence Community to report back to him in 90 days. Perhaps this investigation will shed light on undisclosed US intel reported by The Wall Street Journal suggesting that three staff members at the WIV were sick in November 2019, before the first cases of COVID-19 were reported in China. The article claims that US officials have different opinions on the quality of that intel. And researchers at the WIV have maintained that staff at the institute tested negative for antibodies that would indicate SARS-CoV-2 infection prior to January 2020.

Last week, Anthony Fauci, Biden’s chief medical adviser, asked Chinese officials to release the hospital records of WIV staff members. Others have asked for blood samples from WIV staff members, and access to WIV bat and virus samples, laboratory notebooks and hard drives. But it’s unclear what such asks will yield because China has not conceded to demands for a full lab investigation. A spokesperson for the Ministry of Foreign Affairs of the People's Republic of China, Zhao Lijian, said that US labs should instead be investigated, and that some people in the United States “don't care about facts or truth and have zero interest in a serious science-based study of origins”.

As Biden's investigation commences and the WHO considers the next phase in its origin study, pandemic experts are bracing themselves for a long road ahead. “We want an answer,” says Jason Kindrachuk, a virologist at the University of Manitoba in Winnipeg, Canada. “But we may have to keep piecing bits of evidence together as weeks and months and years move forward.”

This article is reproduced with permission and was first published on June 8 2021.

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11 Questions to Ask About COVID-19 Research

Debates have raged on social media, around dinner tables, on TV, and in Congress about the science of COVID-19. Is it really worse than the flu? How necessary are lockdowns? Do masks work to prevent infection? What kinds of masks work best? Is the new vaccine safe?

You might see friends, relatives, and coworkers offer competing answers, often brandishing studies or citing individual doctors and scientists to support their positions. With so much disagreement—and with such high stakes—how can we use science to make the best decisions?

Here at Greater Good , we cover research into social and emotional well-being, and we try to help people apply findings to their personal and professional lives. We are well aware that our business is a tricky one.

Summarizing scientific studies and distilling the key insights that people can apply to their lives isn’t just difficult for the obvious reasons, like understanding and then explaining formal science terms or rigorous empirical and analytic methods to non-specialists. It’s also the case that context gets lost when we translate findings into stories, tips, and tools, especially when we push it all through the nuance-squashing machine of the Internet. Many people rarely read past the headlines, which intrinsically aim to be relatable and provoke interest in as many people as possible. Because our articles can never be as comprehensive as the original studies, they almost always omit some crucial caveats, such as limitations acknowledged by the researchers. To get those, you need access to the studies themselves.

And it’s very common for findings and scientists to seem to contradict each other. For example, there were many contradictory findings and recommendations about the use of masks, especially at the beginning of the pandemic—though as we’ll discuss, it’s important to understand that a scientific consensus did emerge.

Given the complexities and ambiguities of the scientific endeavor, is it possible for a non-scientist to strike a balance between wholesale dismissal and uncritical belief? Are there red flags to look for when you read about a study on a site like Greater Good or hear about one on a Fox News program? If you do read an original source study, how should you, as a non-scientist, gauge its credibility?

Here are 11 questions you might ask when you read about the latest scientific findings about the pandemic, based on our own work here at Greater Good.

1. Did the study appear in a peer-reviewed journal?

In peer review, submitted articles are sent to other experts for detailed critical input that often must be addressed in a revision prior to being accepted and published. This remains one of the best ways we have for ascertaining the rigor of the study and rationale for its conclusions. Many scientists describe peer review as a truly humbling crucible. If a study didn’t go through this process, for whatever reason, it should be taken with a much bigger grain of salt.

“When thinking about the coronavirus studies, it is important to note that things were happening so fast that in the beginning people were releasing non-peer reviewed, observational studies,” says Dr. Leif Hass, a family medicine doctor and hospitalist at Sutter Health’s Alta Bates Summit Medical Center in Oakland, California. “This is what we typically do as hypothesis-generating but given the crisis, we started acting on them.”

In a confusing, time-pressed, fluid situation like the one COVID-19 presented, people without medical training have often been forced to simply defer to expertise in making individual and collective decisions, turning to culturally vetted institutions like the Centers for Disease Control (CDC). Is that wise? Read on.

2. Who conducted the study, and where did it appear?

“I try to listen to the opinion of people who are deep in the field being addressed and assess their response to the study at hand,” says Hass. “With the MRNA coronavirus vaccines, I heard Paul Offit from UPenn at a UCSF Grand Rounds talk about it. He literally wrote the book on vaccines. He reviewed what we know and gave the vaccine a big thumbs up. I was sold.”

From a scientific perspective, individual expertise and accomplishment matters—but so does institutional affiliation.

Why? Because institutions provide a framework for individual accountability as well as safety guidelines. At UC Berkeley, for example , research involving human subjects during COVID-19 must submit a Human Subjects Proposal Supplement Form , and follow a standard protocol and rigorous guidelines . Is this process perfect? No. It’s run by humans and humans are imperfect. However, the conclusions are far more reliable than opinions offered by someone’s favorite YouTuber .

Recommendations coming from institutions like the CDC should not be accepted uncritically. At the same time, however, all of us—including individuals sporting a “Ph.D.” or “M.D.” after their names—must be humble in the face of them. The CDC represents a formidable concentration of scientific talent and knowledge that dwarfs the perspective of any one individual. In a crisis like COVID-19, we need to defer to that expertise, at least conditionally.

“If we look at social media, things could look frightening,” says Hass. When hundreds of millions of people are vaccinated, millions of them will be afflicted anyway, in the course of life, by conditions like strokes, anaphylaxis, and Bell’s palsy. “We have to have faith that people collecting the data will let us know if we are seeing those things above the baseline rate.”

3. Who was studied, and where?

Animal experiments tell scientists a lot, but their applicability to our daily human lives will be limited. Similarly, if researchers only studied men, the conclusions might not be relevant to women, and vice versa.

Many psychology studies rely on WEIRD (Western, educated, industrialized, rich and democratic) participants, mainly college students, which creates an in-built bias in the discipline’s conclusions. Historically, biomedical studies also bias toward gathering measures from white male study participants, which again, limits generalizability of findings. Does that mean you should dismiss Western science? Of course not. It’s just the equivalent of a “Caution,” “Yield,” or “Roadwork Ahead” sign on the road to understanding.

This applies to the coronavirus vaccines now being distributed and administered around the world. The vaccines will have side effects; all medicines do. Those side effects will be worse for some people than others, depending on their genetic inheritance, medical status, age, upbringing, current living conditions, and other factors.

For Hass, it amounts to this question: Will those side effects be worse, on balance, than COVID-19, for most people?

“When I hear that four in 100,000 [of people in the vaccine trials] had Bell’s palsy, I know that it would have been a heck of a lot worse if 100,000 people had COVID. Three hundred people would have died and many others been stuck with chronic health problems.”

4. How big was the sample?

In general, the more participants in a study, the more valid its results. That said, a large sample is sometimes impossible or even undesirable for certain kinds of studies. During COVID-19, limited time has constrained the sample sizes.

However, that acknowledged, it’s still the case that some studies have been much larger than others—and the sample sizes of the vaccine trials can still provide us with enough information to make informed decisions. Doctors and nurses on the front lines of COVID-19—who are now the very first people being injected with the vaccine—think in terms of “biological plausibility,” as Hass says.

Did the admittedly rushed FDA approval of the Pfizer-BioNTech vaccine make sense, given what we already know? Tens of thousands of doctors who have been grappling with COVID-19 are voting with their arms, in effect volunteering to be a sample for their patients. If they didn’t think the vaccine was safe, you can bet they’d resist it. When the vaccine becomes available to ordinary people, we’ll know a lot more about its effects than we do today, thanks to health care providers paving the way.

5. Did the researchers control for key differences, and do those differences apply to you?

Diversity or gender balance aren’t necessarily virtues in experimental research, though ideally a study sample is as representative of the overall population as possible. However, many studies use intentionally homogenous groups, because this allows the researchers to limit the number of different factors that might affect the result.

While good researchers try to compare apples to apples, and control for as many differences as possible in their analyses, running a study always involves trade-offs between what can be accomplished as a function of study design, and how generalizable the findings can be.

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You also need to ask if the specific population studied even applies to you. For example, when one study found that cloth masks didn’t work in “high-risk situations,” it was sometimes used as evidence against mask mandates.

However, a look beyond the headlines revealed that the study was of health care workers treating COVID-19 patients, which is a vastly more dangerous situation than, say, going to the grocery store. Doctors who must intubate patients can end up being splattered with saliva. In that circumstance, one cloth mask won’t cut it. They also need an N95, a face shield, two layers of gloves, and two layers of gown. For the rest of us in ordinary life, masks do greatly reduce community spread, if as many people as possible are wearing them.

6. Was there a control group?

One of the first things to look for in methodology is whether the population tested was randomly selected, whether there was a control group, and whether people were randomly assigned to either group without knowing which one they were in. This is especially important if a study aims to suggest that a certain experience or treatment might actually cause a specific outcome, rather than just reporting a correlation between two variables (see next point).

For example, were some people randomly assigned a specific meditation practice while others engaged in a comparable activity or exercise? If the sample is large enough, randomized trials can produce solid conclusions. But, sometimes, a study will not have a control group because it’s ethically impossible. We can’t, for example, let sick people go untreated just to see what would happen. Biomedical research often makes use of standard “treatment as usual” or placebos in control groups. They also follow careful ethical guidelines to protect patients from both maltreatment and being deprived necessary treatment. When you’re reading about studies of masks, social distancing, and treatments during the COVID-19, you can partially gauge the reliability and validity of the study by first checking if it had a control group. If it didn’t, the findings should be taken as preliminary.

7. Did the researchers establish causality, correlation, dependence, or some other kind of relationship?

We often hear “Correlation is not causation” shouted as a kind of battle cry, to try to discredit a study. But correlation—the degree to which two or more measurements seem connected—is important, and can be a step toward eventually finding causation—that is, establishing a change in one variable directly triggers a change in another. Until then, however, there is no way to ascertain the direction of a correlational relationship (does A change B, or does B change A), or to eliminate the possibility that a third, unmeasured factor is behind the pattern of both variables without further analysis.

In the end, the important thing is to accurately identify the relationship. This has been crucial in understanding steps to counter the spread of COVID-19 like shelter-in-place orders. Just showing that greater compliance with shelter-in-place mandates was associated with lower hospitalization rates is not as conclusive as showing that one community that enacted shelter-in-place mandates had lower hospitalization rates than a different community of similar size and population density that elected not to do so.

We are not the first people to face an infection without understanding the relationships between factors that would lead to more of it. During the bubonic plague, cities would order rodents killed to control infection. They were onto something: Fleas that lived on rodents were indeed responsible. But then human cases would skyrocket.

Why? Because the fleas would migrate off the rodent corpses onto humans, which would worsen infection. Rodent control only reduces bubonic plague if it’s done proactively; once the outbreak starts, killing rats can actually make it worse. Similarly, we can’t jump to conclusions during the COVID-19 pandemic when we see correlations.

8. Are journalists and politicians, or even scientists, overstating the result?

Language that suggests a fact is “proven” by one study or which promotes one solution for all people is most likely overstating the case. Sweeping generalizations of any kind often indicate a lack of humility that should be a red flag to readers. A study may very well “suggest” a certain conclusion but it rarely, if ever, “proves” it.

This is why we use a lot of cautious, hedging language in Greater Good , like “might” or “implies.” This applies to COVID-19 as well. In fact, this understanding could save your life.

When President Trump touted the advantages of hydroxychloroquine as a way to prevent and treat COVID-19, he was dramatically overstating the results of one observational study. Later studies with control groups showed that it did not work—and, in fact, it didn’t work as a preventative for President Trump and others in the White House who contracted COVID-19. Most survived that outbreak, but hydroxychloroquine was not one of the treatments that saved their lives. This example demonstrates how misleading and even harmful overstated results can be, in a global pandemic.

9. Is there any conflict of interest suggested by the funding or the researchers’ affiliations?

A 2015 study found that you could drink lots of sugary beverages without fear of getting fat, as long as you exercised. The funder? Coca Cola, which eagerly promoted the results. This doesn’t mean the results are wrong. But it does suggest you should seek a second opinion : Has anyone else studied the effects of sugary drinks on obesity? What did they find?

It’s possible to take this insight too far. Conspiracy theorists have suggested that “Big Pharma” invented COVID-19 for the purpose of selling vaccines. Thus, we should not trust their own trials showing that the vaccine is safe and effective.

But, in addition to the fact that there is no compelling investigative evidence that pharmaceutical companies created the virus, we need to bear in mind that their trials didn’t unfold in a vacuum. Clinical trials were rigorously monitored and independently reviewed by third-party entities like the World Health Organization and government organizations around the world, like the FDA in the United States.

Does that completely eliminate any risk? Absolutely not. It does mean, however, that conflicts of interest are being very closely monitored by many, many expert eyes. This greatly reduces the probability and potential corruptive influence of conflicts of interest.

10. Do the authors reference preceding findings and original sources?

The scientific method is based on iterative progress, and grounded in coordinating discoveries over time. Researchers study what others have done and use prior findings to guide their own study approaches; every study builds on generations of precedent, and every scientist expects their own discoveries to be usurped by more sophisticated future work. In the study you are reading, do the researchers adequately describe and acknowledge earlier findings, or other key contributions from other fields or disciplines that inform aspects of the research, or the way that they interpret their results?

Greater Good’s Guide to Well-Being During Coronavirus

Practices, resources, and articles for individuals, parents, and educators facing COVID-19

This was crucial for the debates that have raged around mask mandates and social distancing. We already knew quite a bit about the efficacy of both in preventing infections, informed by centuries of practical experience and research.

When COVID-19 hit American shores, researchers and doctors did not question the necessity of masks in clinical settings. Here’s what we didn’t know: What kinds of masks would work best for the general public, who should wear them, when should we wear them, were there enough masks to go around, and could we get enough people to adopt best mask practices to make a difference in the specific context of COVID-19 ?

Over time, after a period of confusion and contradictory evidence, those questions have been answered . The very few studies that have suggested masks don’t work in stopping COVID-19 have almost all failed to account for other work on preventing the disease, and had results that simply didn’t hold up. Some were even retracted .

So, when someone shares a coronavirus study with you, it’s important to check the date. The implications of studies published early in the pandemic might be more limited and less conclusive than those published later, because the later studies could lean on and learn from previously published work. Which leads us to the next question you should ask in hearing about coronavirus research…

11. Do researchers, journalists, and politicians acknowledge limitations and entertain alternative explanations?

Is the study focused on only one side of the story or one interpretation of the data? Has it failed to consider or refute alternative explanations? Do they demonstrate awareness of which questions are answered and which aren’t by their methods? Do the journalists and politicians communicating the study know and understand these limitations?

When the Annals of Internal Medicine published a Danish study last month on the efficacy of cloth masks, some suggested that it showed masks “make no difference” against COVID-19.

The study was a good one by the standards spelled out in this article. The researchers and the journal were both credible, the study was randomized and controlled, and the sample size (4,862 people) was fairly large. Even better, the scientists went out of their way to acknowledge the limits of their work: “Inconclusive results, missing data, variable adherence, patient-reported findings on home tests, no blinding, and no assessment of whether masks could decrease disease transmission from mask wearers to others.”

Unfortunately, their scientific integrity was not reflected in the ways the study was used by some journalists, politicians, and people on social media. The study did not show that masks were useless. What it did show—and what it was designed to find out—was how much protection masks offered to the wearer under the conditions at the time in Denmark. In fact, the amount of protection for the wearer was not large, but that’s not the whole picture: We don’t wear masks mainly to protect ourselves, but to protect others from infection. Public-health recommendations have stressed that everyone needs to wear a mask to slow the spread of infection.

“We get vaccinated for the greater good, not just to protect ourselves ”

As the authors write in the paper, we need to look to other research to understand the context for their narrow results. In an editorial accompanying the paper in Annals of Internal Medicine , the editors argue that the results, together with existing data in support of masks, “should motivate widespread mask wearing to protect our communities and thereby ourselves.”

Something similar can be said of the new vaccine. “We get vaccinated for the greater good, not just to protect ourselves,” says Hass. “Being vaccinated prevents other people from getting sick. We get vaccinated for the more vulnerable in our community in addition for ourselves.”

Ultimately, the approach we should take to all new studies is a curious but skeptical one. We should take it all seriously and we should take it all with a grain of salt. You can judge a study against your experience, but you need to remember that your experience creates bias. You should try to cultivate humility, doubt, and patience. You might not always succeed; when you fail, try to admit fault and forgive yourself.

Above all, we need to try to remember that science is a process, and that conclusions always raise more questions for us to answer. That doesn’t mean we never have answers; we do. As the pandemic rages and the scientific process unfolds, we as individuals need to make the best decisions we can, with the information we have.

This article was revised and updated from a piece published by Greater Good in 2015, “ 10 Questions to Ask About Scientific Studies .”

About the Authors

Jeremy Adam Smith

Uc berkeley.

Jeremy Adam Smith edits the GGSC’s online magazine, Greater Good . He is also the author or coeditor of five books, including The Daddy Shift , Are We Born Racist? , and (most recently) The Gratitude Project: How the Science of Thankfulness Can Rewire Our Brains for Resilience, Optimism, and the Greater Good . Before joining the GGSC, Jeremy was a John S. Knight Journalism Fellow at Stanford University.

Emiliana R. Simon-Thomas

Emiliana R. Simon-Thomas, Ph.D. , is the science director of the Greater Good Science Center, where she directs the GGSC’s research fellowship program and serves as a co-instructor of its Science of Happiness and Science of Happiness at Work online courses.

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Available Evidence and Ongoing Hypothesis on Corona Virus (COVID-19) and Psychosis: Is Corona Virus and Psychosis Related? A Narrative Review

Affiliations.

1 Department of Psychiatry, College of Health and Medical Sciences, Haramaya University, Harar, Ethiopia.
2 Department of Psychiatry, College of Health and Medical Sciences, Mettu University, Mettu, Ethiopia.
PMID: 32903810
PMCID: PMC7445510
DOI: 10.2147/PRBM.S264235

Background: Corona virus (COVID-19) is an outbreak of respiratory disease caused by a novel corona virus and declared to be a global health emergency and a pandemic by the World Health Organization (WHO) on March 11, 2020. Prevention strategies to control the transmission of the COVID-19 pandemic, such as closing of schools, refraining from gathering, and social distancing, have direct impacts on mental well-being. SARS-CoV-2 has a devastating psychological impact on the mental health status of the community and, particularly when associated with psychotic symptoms, it could affect the overall quality-of-life. The virus also has the potential to enter and infect the brain. As a result, psychosis symptoms could be an emerging phenomenon associated with the corona virus pandemic. The presence of psychotic symptoms may complicate the management options of patients with COVID-19.

Objective: The aim of this article review is to elaborate the relationships between COVID-19 and psychotic symptoms.

Methodology: We independently searched different electronic databases, such as Google scholar, PubMed, Medline, CINAHL, EMBASE, PsychInfo, and other relevant sources published in English globally, by using the search terms "psychosis and COVID-19", "corona virus", "brief psychotic", "schizophrenia", "organic psychosis", "infectious disease", "mental illness", "pandemics", and "psychiatry" in various permutations and combinations.

Results: The results of the included studies revealed that patients with a novel corona virus had psychotic symptoms, including hallucination in different forms of modality, delusion, disorganized speech, and grossly disorganized or catatonic behaviors. The patients with COVID-19-related psychotic symptoms had responded with a short-term administration of the antipsychotic medication.

Conclusion and recommendation: A corona virus-related psychosis has been identified in different nations, but it is difficult to conclude that a novel corona virus has been biologically related to psychosis or exacerbates psychotic symptoms. Therefore, to identify the causal relationships between COVID-19 and psychosis, the researchers should investigate the prospective study on the direct biological impacts of COVID-19 and psychosis, and the clinicians should pay attention for psychotic symptoms at the treatment center and isolation rooms in order to reduce the complication of a novel corona virus.

Keywords: 2020; COVID-19; SARS-CoV-2; psychosis.

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The authors report no conflicts of interest. The authors alone are responsible for the content and writing of this article.

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Hypothesis: The COVID-19 Pandemic is Signaling Humanity’s Global Overshoot

| February 8, 2022 | Leave a Comment

Image by Alex Borland / publicdomainpictures

Item Link: Access the Resource

File: Download

Date of Publication: April

Year of Publication: 2021

Publication City: San Francisco, CA

Publisher: Academia Inc.

Author(s): Alexander K. Lautensach, Sabina W. Lautensach

Journal: Academia Letters

Volume: Article 538

In the Anthropocene, the year 2020 marks a milestone in humanity’s learning process about how we are affecting the biosphere and how it affects us in return (Ulrich 2020). The COVID-19 pandemic is the first global event that changed every human life, if not yet actually then certainly potentially. For the first time, humanity experiences species-wide a planetary phenomenon that allows neither escape nor denial and that demands a collective response from all cultures and societies. That raises the question how this phenomenon is to be interpreted.

HYPOTHESIS:

The COVID-19 pandemic is a feedback signal from the biosphere that denotes the ecological overshoot of the human species (currently estimated at about 1.7 planets; GFN 2020). That means that efforts to control this pandemic, even if successful, will not solve the wider problem of overshoot and the prospect of further, more threatening signals or ‘transition events’ (including partial collapse) for as long as overshoot persists.

Read the full paper here or download it from the link above.

CORONAVIRUS COVERAGE

What you need to know about the COVID-19 lab-leak hypothesis

Newly reported information has revived scrutiny of this possible origin for the coronavirus, which experts still call unlikely though worth investigating.

Months after a World Health Organization investigation deemed it “extremely unlikely” that the novel coronavirus escaped accidentally from a laboratory in Wuhan, China, the idea is back in the news, giving new momentum to a hypothesis that many scientists believe is unlikely, and some have dismissed as a conspiracy theory .

The renewed attention comes on the heels of President Joe Biden’s ordering U.S. intelligence agencies on May 26 to “ redouble their efforts ” to investigate the origins of the coronavirus. On May 11, Biden’s chief medical adviser, Anthony Fauci, acknowledged he’s now “ not convinced ” the virus developed naturally—an apparent pivot from what he told National Geographic in an interview last year.

Also last month, more than a dozen scientists—top epidemiologists, immunologists, and biologists—wrote a letter published in the journal Science calling for a thorough investigation into two viable origin stories: natural spillover from animal to human, or an accident in which a wild laboratory sample containing SARS-CoV-2 was accidentally released. They urged that both hypotheses “be taken seriously until we have sufficient data,” writing that a proper investigation would be “transparent, objective, data-driven, inclusive of broad expertise, subject to independent oversight,” with conflicts of interest minimized, if possible.

“Anytime there is an infectious disease outbreak it is important to investigate its origin,” says Amesh Adalja, an infectious disease physician and senior scholar at the Johns Hopkins University Center for Health Security who did not contribute to the letter in Science . “The lab-leak hypothesis is possible—as is an animal spillover,” he says, “and I think that a thorough, independent investigation of its origins should be conducted.”

Unanswered questions

The origins of SARS-CoV-2, the virus that causes COVID-19 and has infected more than 171 million people, killing close to 3.7 million worldwide as of June 4, remain unclear. Many scientists, including those that participated in the WHO’s months-long investigation, believe the most likely explanation is that that it jumped from an animal to a person—potentially from a bat directly to a human, or through an intermediate host. Animal-to-human transmission is a common route for many viruses; at least two other coronaviruses, SARS and MERS , were spread through such zoonotic spillover.

Other scientists insist it’s worth investigating whether SARS-CoV-2 escaped from the Wuhan Institute of Virology, a laboratory that has studied coronaviruses in bats for more than a decade.

The WHO investigation —a joint effort between WHO-appointed scientists and Chinese officials—concluded it was “extremely unlikely” the highly transmissible virus escaped from a laboratory. But the WHO team suffered roadblocks that led some to question its conclusions; the scientists were not permitted to conduct an independent investigation and were denied access to any raw data. ( We still don’t know the origins of the coronavirus. Here are 4 scenarios .)

On March 30, when the WHO released its report, its director-general, Tedros Adhanom Ghebreyesus, called for further studies . “All hypotheses remain on the table,” he said at the time.

Then on May 11, Fauci told PolitiFact that while the virus most likely emerged via animal-to-human transmission, “it could have been something else, and we need to find that out.”

Recently disclosed evidence, first reported by the Wall Street Journal , has added fuel to the fire: Three researchers from the Wuhan Institute of Virology fell sick in November 2019 and sought hospital care, according to a U.S. intelligence report. In the final days of the Trump administration, the State Department released a statement that researchers at the institute had become ill with “symptoms consistent with both COVID-19 and common seasonal illness.”

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Most epidemiologists and virologists who have studied the novel coronavirus believe that it began spreading in November 2019. China says the first confirmed case was on December 8, 2019. During a briefing in Beijing this week, China’s foreign ministry spokesperson, Zhao Lijian, accused the U.S. of “ hyping up the theory of a lab leak ,” and asked, “does it really care about the study of origin tracing, or is it trying to divert attention?” Zhao also denied the Wall Street Journal report that three people had gotten sick.

Lab leak still ‘unlikely’

Some conservative politicians and commentators have embraced the lab-leak theory, while liberals more readily rejected it, especially early in the pandemic. The speculation has also heightened ongoing tensions between the U.S. and China.

On May 26, as the U.S. Senate passed a bill to declassify intelligence related to potential links between the Wuhan laboratory and COVID-19, Missouri Senator Josh Hawley, a Republican who sponsored the bill, said, “the world needs to know if this pandemic was the product of negligence at the Wuhan lab,” and lamented that “for over a year, anyone asking questions about the Wuhan Institute of Virology has been branded as a conspiracy theorist.”

Peter Navarro, Donald Trump’s former trade adviser, asserted in April 2020 that SARS-CoV-2 could have been engineered as a bioweapon, without citing any evidence.

The theory that SARS-CoV-2 was created as a bioweapon is “completely unlikely,” says William Schaffner, a professor of infectious diseases at Vanderbilt University Medical Center. For one thing, he explains, for a bioweapon to be successful, it must target an adversarial population without affecting one’s own. In contrast, SARS-CoV-2 “cannot be controlled,” he says. “It will spread, including back on your own population,” making it an extremely “counterproductive biowarfare agent.”

The more plausible lab-leak hypothesis, scientists say, is that the Wuhan laboratory isolated the novel coronavirus from an animal and was studying it when it accidentally escaped. “Not knowing the extent of its virulence and transmissibility, a lack of protective measures [could have] resulted in laboratory workers becoming infected,” initiating the transmission chain that ultimately resulted in the pandemic, says Rossi Hassad, an epidemiologist at Mercy College.

But Hassad adds he believes that this lab-leak theory is on the “extreme low end” of possibilities, and it “will quite likely remain only theoretical following any proper scientific investigation,” he says.

Biden ordered U.S. intelligence agencies to report back with their findings in 90 days, which would be August 26.

Based on the available information, Eyal Oren, an epidemiologist at San Diego State University, says it’s apparent why the most accepted hypothesis is that this virus originated in an animal and jumped to a human: “What is clear is that the genetic sequence of the COVID-19 virus is similar to other coronaviruses found in bats,” he says.

Some scientists remain skeptical that concrete conclusions can be drawn. “At the end, I anticipate that the question” of SARS-CoV-2’s origins “will remain unresolved,” Schaffner says.

In the meantime, science “moves much more slowly than the media and news cycles,” Oren says.

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Jennifer Pichierri 3 ,
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Giovanni Sotgiu 5 ,
Saverio Bellizzi 6 ,
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1 Public Health Department , Local Health Unit N. 2 "Marca Trevigiana" , Treviso , Veneto Region , Italy
2 IRCCS Materno Infantile Burlo Garofolo , Trieste , Friuli-Venezia Giulia Region , Italy
3 Children and Young People’s Diabetes , University College London Hospitals NHS Foundation Trust , London , UK
4 Cardiac, Thoracic and Vascular Sciences , Padua University Hospital , Padua , Veneto Region , Italy
5 Department of Medical, Surgical and Experimental Sciences , University of Sassari , Sassari , Sardinia Region , Italy
6 Partnership for Maternal, Newborn and Child Health , World Health Organization , Geneve , Switzerland
7 Microbiology Department , Kingston Hospital NHS Foundation Trust , Kingston upon Thames , London , UK
Correspondence to Dr Luca Cegolon; l.cegolon{at}gmail.com

https://doi.org/10.1136/bmjgh-2020-002564

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epidemiology

Summary box

What is already known about this subject.

Human coronaviruses are known to cause respiratory re-infections, regardless of pre-existing humoural immunity.

There is evidence suggesting that severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) had been circulating in Italy before the first COVID-19 case was detected in the country.

What are the new findings?

Prior infections with SARS-CoV-2 (or other viruses/coronaviruses) may arguably predispose to more severe forms of the disease following re-infection with SARS-CoV-2, with an immunological mechanism known as Antibody-Dependent-Enhancement, already observed with infections sustained by other coronaviruses (MERS-CoV and SARS-CoV) or other viruses such as the West Nile Virus and Dengue.

What are the recommendations for policy and practice?

If confirmed by in vivo studies, this hypothesis may have relevant implications for the treatment of severe forms of COVID-19, yet the possibility to produce an effective vaccine against SARS-CoV-2 might be hampered.

The ongoing COVID-19 pandemic, caused by the novel severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2), has affected 212 countries worldwide at various degrees as of 8 May 2020. 1

In this paper we discuss a hypothesis that prior viral infections—either by SARS-CoV-2 or different strains of coronaviruses, or potentially even other respiratory viruses—may predispose to more severe forms of COVID-19, following a secondary infection with SARS-CoV-2.

Most COVID-19 infections are asymptomatic or manifest with mild to moderate respiratory symptoms (fever, cough, sore throat, myalgia, fatigue and even non-severe pneumonia). Of patients with COVID-19, 14%–15% develop severe pneumonia and 5%–6% a critical condition requiring admission to intensive care unit (ICU). 2–4 Death may eventually occur after an average of 17.8 days since the onset of symptoms. 5

Among all countries, Italy (which was the first European COVID-19 cluster) presents a critical disease pattern as of 8 May 2020, having the third highest number of COVID-19 cases in the world after the USA and Spain, the fourth highest prevalence of the disease after Spain, Belgium and the USA, the third highest total number of deaths attributed to COVID-19 after the USA and the UK, and the third highest prevalence of COVID-19 mortality after Belgium and Spain, despite a current 1% rate of severe/critical disease among active cases, which has been progressively decreasing over time. 1

The cross-country discrepancies in the burden of COVID-19 observed so far across the globe cannot be explained only by differences in population age structures. 6–8 In fact, Japan has a population double that of Italy, with the proportion of subjects older than 65 being 28.8% in Japan vs 21.7% in Italy. 9 10 Nonetheless, as of 8 May 2020, the difference in COVID-19 prevalence between Japan (122 per million) and Italy (3570 per million) is massive. 1 Likewise, in Germany the percentage of individuals >65 is reportedly 22.1% (hence slightly higher than Italy), but the prevalence of COVID-19 is currently 2022 per million. 1 11 In Iran the proportion of people >65 is 5.5% (hence much younger than the Italian, German and Japanese populations), but the prevalence of COVID-19 is 1246 per million, as of 8 May 2020. 1 12

The mortality rate for COVID-19 is reportedly enhanced by 5.6%–10.5% in the presence of any comorbidities (hypertension, diabetes, cardiovascular diseases, cancer and/or chronic respiratory conditions) and becomes significantly and progressively higher after 50 years of age, 4 6 although the severe form of the disease increases linearly at any age stage. 5 Cold dry weather is a recognised risk factor for respiratory infections, rendering viruses as influenza more stable and individuals more susceptible. 13 14 This applies also to SARS-CoV-2, the viability and transmissibility of which reportedly reduce with hot and humid weather conditions. 14 Moreover, unfavourable disease progression and clinical outcomes of COVID-19 were found to be associated with cigarette smoking in a systematic review. 15

A number of factors may have contributed to enhancing the risk of infection with SARS-CoV-2 in Northern Italy. The age by which half of all young people leave parental home is higher in Italy than other European Union countries, 16 and such multigenerational cohabitation probably contributed to increase COVID-19 contagion among elderly individuals. The universal use of face masks was initially discouraged in Italy in order to preserve the limited supplies of personal protective equipment for professional use in healthcare settings; another argument initially was that face masks are ineffective in protecting against coronavirus infections. 17 Further major findings of the relevant literature have been summarised in figure 1 . An extraordinary elevated incidence of COVID-19 could have been the result of a perfect storm triggered by multiple interplaying factors. The affected areas in Northern Italy (regions of Lombardy, Emilia-Romagna, Piedmont and Veneto) are characterised by high population density 18 and recognised air pollution, 19 20 especially from fine particulate matter (PM2.5), which was found to increase the risk of death from COVID-19 in the USA. 21 Northern Italy includes several cities which, similarly to Philadelphia (USA) during the Spanish flu pandemic in 1918, 22 are historically important and densely populated, where social gatherings as well as business activities are certainly fundamental—the latter being vital to the economy of the entire country. These cultural and social dynamics might have influenced the initial resistance and reluctance of the general population to comply with the social restrictions progressively enforced by the Italian government (until full lockdown was established on 21 March). Moreover, the intense case finding in Italy was preceded by an initial overall underestimation of the COVID-19 threat by the Italian government and subsequently by the general population, who perceived the disease as just some sort of influenza, despite worrying news from the first affected country (China). 23 Thereafter SARS-CoV-2 was also going to spread to other European countries, which have also now been heavily affected by the disease. 1

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Conceptual framework explaining the relationships between various factors and incidence and severe/critical form of COVID-19. Established items: orange boxes; hypothetical items: blue boxes. ADE, antibody-dependent enhancement; ARDS, acute respiratory distress syndrome; COVID-19, coronavirus infectious disease 2019; IPC, infection prevention and control; SARS-CoV-2, severe acute respiratory syndrome coronavirus type.

The epidemiological investigations conducted by the Italian National Institute of Health ( Istituto Superiore di Sanità ) suggest that the vast majority of cases but the first three acquired the infection in Italy. It can therefore be reasonably argued that SARS-CoV-2 had been circulating in the country—especially in Lombardy, Emilia-Romagna, Piedmont and Veneto regions—for weeks before the first patient was found. 24 Human coronaviruses are known to cause respiratory reinfections regardless of pre-existing humoural immunity, both at the individual and community level. 25 At the same time, presumed hospital-associated transmission of SARS-CoV-2 has been reported since the initial stages of the COVID-19 outbreak in Wuhan (China) in 41% of the total number of patients, 70% of whom were healthcare staff. 26 This could have also occurred in Italy, where healthcare workers reportedly make up 9% out of all COVID-19 cases. 27 We therefore hypothesise (see blue boxes in figure 1 ) that repeated cycles of infection within a community (especially in older adults)—or even more worryingly in a healthcare setting—could have the potential to cause more severe forms of COVID-19, with acute respiratory distress syndrome (ARDS), the fundamental pathophysiology of severe viral pneumonia due to COVID-19, requiring admission to ICU. 28 ARDS associated with COVID-19 shares clinical features with the critical form of the 2003 severe acute respiratory syndrome coronavirus (SARS-CoV) epidemic, in particular lymphopaenia, massive infiltration of phagocytes and inflammation sustained by cytokines. 4 8 29

A plausible mechanistic hypothesis could be the antibody-dependent enhancement (ADE), sustained by prior exposure to SARS-CoV-2 or other viruses/coronaviruses. 8 Previous circulation and exposure to other coronaviruses similar to SARS-CoV-2 causing mild/asymptomatic cold-like symptoms shall in fact not be ruled out. 8 Binding and neutralising antibody response against other types of human coronaviruses was recently reported to increase with age in adult patients, 25 and this may explain the increased linear risk of severe COVID-19 with age, with death being significantly higher in patients older than 50. 5

Non-neutralising, subneutralising or even fully neutralising antibodies may play a key role in ADE. 30 Wan et al 30 have recently described a molecular mechanism for ADE involving the Middle East respiratory syndrome coronavirus (MERS-CoV), similar to what is already known for SARS-CoV and flaviviruses as Dengue and the West Nile virus. 31–35 While the entry of SARS-CoV into the phagocytes occurred principally through the human ACE2 receptor, the ADE mechanism was shown to be enhanced by antibodies specific for the spike (S) envelope glycoprotein binding with the macrophage receptor and subsequent enhancement of target cell infections. 33–35 Likewise, the antibody/opsonised SARS-CoV-2 particles may bind avidly with the IgFc receptors of target cells, increasing the virus yield as well as the production of cytokines. This may also explain the higher risk of thromboembolism reportedly associated with severe/critical COVID-19. 4 36 37

Anecdotal clinical reports of ‘biphasic infection’ and ‘cytokine storm’ seem to possibly point towards this direction, biphasic infection simply being the immunological result of a secondary infection by other coronaviruses or a reinfection due to SARS-CoV-2. 38–41 An early elevation of serum proinflammatory cytokines, suggesting a pathological mechanism mediated by cytokine storm, has been observed with both severe forms of SARS-CoV and MERS-CoV infections. The latter two viruses share a genomic similarity of about 79% and 50% with SARS-CoV-2, respectively. 41 42 Several Rhinolophus bats-related coronavirus strains have been found to share even higher sequence homology with SARS-CoV-2. 33 An abnormal humoural response due to ADE, in the early stages of a secondary infection by SARS-CoV-2, may delay the innate antiviral immune response relying on the production of type 1 interferon (IFN-1). This would compromise the initial antiviral response of the host, with subsequent elevated influx of proinflammatory cytokines, hyperinflammatory neutrophils and monocytes-macrophages and hypercoagulable state accountable for ARDS and typical pneumonia observed in patients affected by severe/critical COVID-19 ( figure 1 ). 4 41 43 44

If confirmed, this hypothesis would have relevant implications for the treatment of COVID-19 and the development of an effective vaccine. The licensing of a vaccine against human coronaviruses has failed thus far, partly because immunised individuals could potentially be at higher risk of ADE sustained by facilitated uptake of viral antigen-antibody complexes by target cells. 4 31 33 44 The approval of a vaccine against SARS-CoV-2 may encounter similar obstacles. Likewise, herd immunity would not be a possibility with COVID-19. WHO recommends passive immunotherapy when vaccine and antivirals are not available for emerging infections. 45 In a preliminary uncontrolled case study on five critically ill patients with COVID-19 who developed ARDS, the administration of convalescent plasma—drawn from five patients who recovered from COVID-19 and containing SARS-CoV-2-specific neutralising antibodies (IgG)—between 10 and 22 days since admission significantly improved the clinical status of all, resolving ARDS in four of them within 12 days since transfusion. 46 On the other hand, the treatment of severe COVID-19 may also benefit from monoclonal antibodies targeting proinflammatory cytokines 4 as well as supplements of IFN-1 in combination with other antiviral drugs. 47–50

Whether secondary infections from other coronaviruses or repeated community reinfections of SARS-CoV-2 may account for more severe presentations of COVID-19 observed in some countries compared with others, 8 and whether it is only a matter of time for the virus to circulate and infect a significant proportion of the population before causing reinfections and therefore more severe clinical features, are something which will require more indepth epidemiological and immunological/serological investigations. A better understanding of any underlying immunological mechanism or any additional risk factor which could explain cross-country differences in the rates of severe disease and mortality attributable to COVID-19 will help to guide international public health responses during this ongoing pandemic. It will be important to clarify if the ARDS mechanism responsible for the severe respiratory infection could potentially be attributable to ADE.

Two different in vivo strategies could be employed to endorse this hypothesis.

First (observational design), all healthcare workers and blood donors should undergo serum test for COVID-19. Individuals presenting SARS-CoV-2 IgG antibodies should be included in a local/regional/national ad-hoc register and monitored over time for the possible development of severe disease sustained by ADE, which would need to be confirmed by blood count, dosage of IFN and proinflammatory cytokines, in addition to chest CT scan. The risk of developing severe COVID-19 should be estimated and stratified by relevant risk factors, including baseline serum level of SARS-CoV-2-specific IgG antibodies, age, sex, potential occupational exposure, medical history (particularly previous infections and vaccination status), any comorbidities, area of residence and health status of household members, among others.

Second (experimental laboratory design), animal models (hamsters, rodents, palm civets, monkeys, ferrets) 33 51 52 could be infected by SARS-CoV-2 (or other viruses/coronaviruses) and subsequently re-exposed to SARS-CoV-2 to verify the possibility of onset of ARDS sustained by ADE.

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Handling editor Seye Abimbola

Contributors LC, JP, SB, GP and GM equally contributed to conceiving the idea and drafting the manuscript. SC and GS contributed to drafting the manuscript.

Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

Competing interests None declared.

Patient consent for publication Not required.

Provenance and peer review Not commissioned; externally peer reviewed.

Data availability statement There are no data in this work.

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The Impact of COVID-19 on the Careers of Women in Academic Sciences, Engineering, and Medicine (2021)

Chapter: 8 major findings and research questions, 8 major findings and research questions, introduction.

The COVID-19 pandemic, which began in late 2019, created unprecedented global disruption and infused a significant level of uncertainty into the lives of individuals, both personally and professionally, around the world throughout 2020. The significant effect on vulnerable populations, such as essential workers and the elderly, is well documented, as is the devastating effect the COVID-19 pandemic had on the economy, particularly brick-and-mortar retail and hospitality and food services. Concurrently, the deaths of unarmed Black people at the hands of law enforcement officers created a heightened awareness of the persistence of structural injustices in U.S. society.

Against the backdrop of this public health crisis, economic upheaval, and amplified social consciousness, an ad hoc committee was appointed to review the potential effects of the COVID-19 pandemic on women in academic science, technology, engineering, mathematics, and medicine (STEMM) during 2020. The committee’s work built on the National Academies of Sciences, Engineering, and Medicine report Promising Practices for Addressing the Underrepresentation of Women in Science, Engineering, and Medicine: Opening Doors (the Promising Practices report), which presents evidence-based recommendations to address the well-established structural barriers that impede the advancement of women in STEMM. However, the committee recognized that none of the actions identified in the Promising Practices report were conceived within the context of a pandemic, an economic downturn, or the emergence of national protests against structural racism. The representation and vitality of academic women in STEMM had already warranted national attention prior to these events, and the COVID-19

pandemic appeared to represent an additional risk to the fragile progress that women had made in some STEMM disciplines. Furthermore, the future will almost certainly hold additional, unforeseen disruptions, which underscores the importance of the committee’s work.

In times of stress, there is a risk that the divide will deepen between those who already have advantages and those who do not. In academia, senior and tenured academics are more likely to have an established reputation, a stable salary commitment, and power within the academic system. They are more likely, before the COVID-19 pandemic began, to have established professional networks, generated data that can be used to write papers, and achieved financial and job security. While those who have these advantages may benefit from a level of stability relative to others during stressful times, those who were previously systemically disadvantaged are more likely to experience additional strain and instability.

As this report has documented, during 2020 the COVID-19 pandemic had overall negative effects on women in academic STEMM in areas such productivity, boundary setting and boundary control, networking and community building, burnout rates, and mental well-being. The excessive expectations of caregiving that often fall on the shoulders of women cut across career timeline and rank (e.g., graduate student, postdoctoral scholar, non-tenure-track and other contingent faculty, tenure-track faculty), institution type, and scientific discipline. Although there have been opportunities for innovation and some potential shifts in expectations, increased caregiving demands associated with the COVID-19 pandemic in 2020, such as remote working, school closures, and childcare and eldercare, had disproportionately negative outcomes for women.

The effects of the COVID-19 pandemic on women in STEMM during 2020 are understood better through an intentionally intersectional lens. Productivity, career, boundary setting, mental well-being, and health are all influenced by the ways in which social identities are defined and cultivated within social and power structures. Race and ethnicity, sexual orientation, gender identity, academic career stage, appointment type, institution type, age, and disability status, among many other factors, can amplify or diminish the effects of the COVID-19 pandemic for a given person. For example, non-cisgender women may be forced to return to home environments where their gender identity is not accepted, increasing their stress and isolation, and decreasing their well-being. Women of Color had a higher likelihood of facing a COVID-19–related death in their family compared with their white, non-Hispanic colleagues. The full extent of the effects of the COVID-19 pandemic for women of various social identities was not fully understood at the end of 2020.

Considering the relative paucity of women in many STEMM fields prior to the COVID-19 pandemic, women are more likely to experience academic isolation, including limited access to mentors, sponsors, and role models that share gender, racial, or ethnic identities. Combining this reality with the physical isolation stipulated by public health responses to the COVID-19 pandemic,

women in STEMM were subject to increasing isolation within their fields, networks, and communities. Explicit attention to the early indicators of how the COVID-19 pandemic affected women in academic STEMM careers during 2020, as well as attention to crisis responses throughout history, may provide opportunities to mitigate some of the long-term effects and potentially develop a more resilient and equitable academic STEMM system.

MAJOR FINDINGS

Given the ongoing nature of the COVID-19 pandemic, it was not possible to fully understand the entirety of the short- or long-term implications of this global disruption on the careers of women in academic STEMM. Having gathered preliminary data and evidence available in 2020, the committee found that significant changes to women’s work-life boundaries and divisions of labor, careers, productivity, advancement, mentoring and networking relationships, and mental health and well-being have been observed. The following findings represent those aspects that the committee agreed have been substantiated by the preliminary data, evidence, and information gathered by the end of 2020. They are presented either as Established Research and Experiences from Previous Events or Impacts of the COVID-19 Pandemic during 2020 that parallel the topics as presented in the report.

Established Research and Experiences from Previous Events

Leading up to the COVID-19 pandemic, the representation of women has slowly increased in STEMM fields, from acquiring Ph.D.s to holding leadership positions, but with caveats to these limited steps of progress; for example, women representation in leadership positions tends to be at institutions with less prestige and fewer resources. While promising and encouraging, such progress is fragile and prone to setbacks especially in times of crisis (see ).

Social crises (e.g., terrorist attacks, natural disasters, racialized violence, and infectious diseases) and COVID-19 pandemic-related disruptions to workload and schedules, added to formerly routine job functions and health risks, have the potential to exacerbate mental health conditions such as insomnia, depression, anxiety, and posttraumatic stress. All of these conditions occur more frequently among women than men. As multiple crises coincided during 2020, there is a greater chance that women will be affected mentally and physically (see and ).

___________________

1 This finding is primarily based on research on cisgender women and men.

Structural racism is an omnipresent stressor for Women of Color, who already feel particularly isolated in many fields and disciplines. Attempts to ensure equity for all women may not necessarily create equity for women across various identities if targeted interventions designed to promote gender equity do not account for the racial and ethnic heterogeneity of women in STEMM (see , , and ).

Impacts of the COVID-19 Pandemic during 2020

	While some research indicates consistency in publications authored by women in specific STEMM disciplines, like Earth and space sciences, during 2020, several other preliminary measures of productivity suggest that COVID-19 disruptions have disproportionately affected women compared with men. Reduced productivity may be compounded by differences in the ways research is conducted, such as whether field research or face-to-face engagement with human subjects is required (see ).
	Many administrative decisions regarding institutional supports made during 2020, such as work-from-home provisions and extensions on evaluations or deliverables, are likely to exacerbate underlying gender-based inequalities in academic advancement rather than being gender neutral as assumed. For example, while colleges and universities have offered extensions for those on the tenure track and federal and private funders have offered extensions on funding and grants, these changes do not necessarily align with the needs expressed by women, such as the need for flexibility to contend with limited availability of caregiving and requests for a reduced workload, nor do they generally benefit women faculty who are not on the tenure track. Furthermore, provision of institutional support may be insufficient if it does not account for the challenges faced by those with multiple marginalized identities (see and ).
	Organizational-level approaches may be needed to address challenges that have emerged as a result of the COVID-19 pandemic in 2020, as well as those challenges that may have existed before the pandemic but are now more visible and amplified. Reliance on individual coping strategies may be insufficient (see and ).
	The COVID-19 pandemic has intensified complications related to worklife boundaries that largely affect women. Preliminary evidence

	from 2020 suggests women in academic STEMM are experiencing increased workload, decreased productivity, changes in interactions, and difficulties from remote work caused by the COVID-19 pandemic and associated disruptions. Combined with the gendered division of nonemployment labor that affected women before the pandemic, these challenges have been amplified, as demonstrated by a lack of access to childcare, children’s heightened behavioral and academic needs, increased eldercare demands, and personal physical and mental health concerns. These are particularly salient for women who are parents or caregivers (see ).
	During the COVID-19 pandemic, technology has allowed for the continuation of information exchange and many collaborations. In some cases technology has facilitated the increased participation of women and underrepresented groups. However, preliminary indicators also show gendered impacts on science and scientific collaborations during 2020. These arise because some collaborations cannot be facilitated online and some collaborations face challenges including finding time in the day to engage synchronously, which presents a larger burden for women who manage the larger share of caregiving and other household duties, especially during the first several months of the COVID-19 pandemic (see ).
	During the COVID-19 pandemic in 2020, some professional societies adapted to the needs of members as well as to broader interests of individuals engaged in the disciplines they serve. Transitioning conferences to virtual platforms has produced both positive outcomes, such as lower attendance costs and more open access to content, and negative outcomes, including over-flexibility (e.g., scheduling meetings at non-traditional work hours; last-minute changes) and opportunities for bias in virtual environments (see ).
	During the COVID-19 pandemic in 2020, many of the decision-making processes, including financial decisions like lay-offs and furloughs, that were quickly implemented contributed to unilateral decisions that frequently deviated from effective practices in academic governance, such as those in crisis and equity-minded leadership. Fast decisions greatly affected contingent and nontenured faculty members—positions that are more often occupied by women and People of Color. In 2020, these financial decisions already had negative, short-term effects and may portend long-term consequences (see ).
	Social support, which is particularly important during stressful situations, is jeopardized by the physical isolation and restricted social interactions that have

	been imposed during the COVID-19 pandemic. For women who are already isolated within their specific fields or disciplines, additional social isolation may be an important contributor to added stress (see ).
	For women in the health professions, major risk factors during the COVID-19 pandemic in 2020 included unpredictability in clinical work, evolving clinical and leadership roles, the psychological demands of unremitting and stressful work, and heightened health risks to family and self (see ).

RESEARCH QUESTIONS

While this report compiled much of the research, data, and evidence available in 2020 on the effects of the COVID-19 pandemic, future research is still needed to understand all the potential effects, especially any long-term implications. The research questions represent areas the committee identified for future research, rather than specific recommendations. They are presented in six categories that parallel the chapters of the report: Cross-Cutting Themes; Academic Productivity and Institutional Responses; Work-Life Boundaries and Gendered Divisions of Labor; Collaboration, Networking, and Professional Societies; Academic Leadership and Decision-Making; and Mental Health and Well-being. The committee hopes the report will be used as a basis for continued understanding of the impact of the COVID-19 pandemic in its entirety and as a reference for mitigating impacts of future disruptions that affect women in academic STEMM. The committee also hopes that these research questions may enable academic STEMM to emerge from the pandemic era a stronger, more equitable place for women. Therefore, the committee identifies two types of research questions in each category; listed first are those questions aimed at understanding the impacts of the disruptions from the COVID-19 pandemic, followed by those questions exploring the opportunities to help support the full participation of women in the future.

Cross-Cutting Themes

What are the short- and long-term effects of the COVID-19 pandemic on the career trajectories, job stability, and leadership roles of women, particularly of Black women and other Women of Color? How do these effects vary across institutional characteristics, 2 discipline, and career stage?

2 Institutional characteristics include different institutional types (e.g., research university, liberal arts college, community college), locales (e.g., urban, rural), missions (e.g., Historically Black Colleges and Universities, Hispanic-Serving Institutions, Asian American/Native American/Pacific Islander-Serving Institutions, Tribal Colleges and Universities), and levels of resources.

How did the confluence of structural racism, economic hardships, and environmental disruptions affect Women of Color during the COVID-19 pandemic? Specifically, how did the murder of George Floyd, Breonna Taylor, and other Black citizens impact Black women academics’ safety, ability to be productive, and mental health?
How has the inclusion of women in leadership and other roles in the academy influenced the ability of institutions to respond to the confluence of major social crises during the COVID-19 pandemic?
How can institutions build on the involvement women had across STEMM disciplines during the COVID-19 pandemic to increase the participation of women in STEMM and/or elevate and support women in their current STEMM-related positions?
How can institutions adapt, leverage, and learn from approaches developed during 2020 to attend to challenges experienced by Women of Color in STEMM in the future?

Academic Productivity and Institutional Responses

How did the institutional responses (e.g., policies, practices) that were outlined in the Major Findings impact women faculty across institutional characteristics and disciplines?
What are the short- and long-term effects of faculty evaluation practices and extension policies implemented during the COVID-19 pandemic on the productivity and career trajectories of members of the academic STEMM workforce by gender?
What adaptations did women use during the transition to online and hybrid teaching modes? How did these techniques and adaptations vary as a function of career stage and institutional characteristics?
What are examples of institutional changes implemented in response to the COVID-19 pandemic that have the potential to reduce systemic barriers to participation and advancement that have historically been faced by academic women in STEMM, specifically Women of Color and other marginalized women in STEMM? How might positive institutional responses be leveraged to create a more resilient and responsive higher education ecosystem?
How can or should funding arrangements be altered (e.g., changes in funding for research and/or mentorship programs) to support new ways of interaction for women in STEMM during times of disruption, such as the COVID-19 pandemic?

Work-Life Boundaries and Gendered Divisions of Labor

How do different social identities (e.g., racial; socioeconomic status; culturally, ethnically, sexually, or gender diverse; immigration status; parents of young children and other caregivers; women without partners) influence the management of work-nonwork boundaries? How did this change during the COVID-19 pandemic?
How have COVID-19 pandemic-related disruptions affected progress toward reducing the gender gap in academic STEMM labor-force participation? How does this differ for Women of Color or women with caregiving responsibilities?
How can institutions account for the unique challenges of women faculty with parenthood and caregiving responsibilities when developing effective and equitable policies, practices, or programs?
How might insights gained about work-life boundaries during the COVID-19 pandemic inform how institutions develop and implement supportive resources (e.g., reductions in workload, on-site childcare, flexible working options)?

Collaboration, Networking, and Professional Societies

What were the short- and long-term effects of the COVID-19 pandemic-prompted switch from in-person conferences to virtual conferences on conference culture and climate, especially for women in STEMM?
How will the increase in virtual conferences specifically affect women’s advancement and career trajectories? How will it affect women’s collaborations?
How has the shift away from attending conferences and in-person networking changed longer-term mentoring and sponsoring relationships, particularly in terms of gender dynamics?
How can institutions maximize the benefits of digitization and the increased use of technology observed during the COVID-19 pandemic to continue supporting women, especially marginalized women, by increasing accessibility, collaborations, mentorship, and learning?
How can organizations that support, host, or facilitate online and virtual conferences and networking events (1) ensure open and fair access to participants who face different funding and time constraints; (2) foster virtual connections among peers, mentors, and sponsors; and (3) maintain an inclusive environment to scientists of all backgrounds?
What policies, practices, or programs can be developed to help women in STEMM maintain a sense of support, structure, and stability during and after periods of disruption?

Academic Leadership and Decision-Making

What specific interventions did colleges and universities initiate or prioritize to ensure that women were included in decision-making processes during responses to the COVID-19 pandemic?
How effective were colleges and universities that prioritized equity-minded leadership, shared leadership, and crisis leadership styles at mitigating emerging and potential negative effects of the COVID-19 pandemic on women in their communities?
What specific aspects of different leadership models translated to more effective strategies to advance women in STEMM, particularly during the COVID-19 pandemic?
How can examples of intentional inclusion of women in decision-making processes during the COVID-19 pandemic be leveraged to develop the engagement of women as leaders at all levels of academic institutions?
What are potential “top-down” structural changes in academia that can be implemented to mitigate the adverse effects of the COVID-19 pandemic or other disruptions?
How can academic leadership, at all levels, more effectively support the mental health needs of women in STEMM?

Mental Health and Well-being

What is the impact of the COVID-19 pandemic and institutional responses on the mental health and well-being of members of the academic STEMM workforce as a function of gender, race, and career stage?
How are tools and diagnostic tests to measure aspects of wellbeing, including burnout and insomnia, used in academic settings? How does this change during times of increased stress, such as the COVID-19 pandemic?
How might insights gained about mental health during the COVID-19 pandemic be used to inform preparedness for future disruptions?
How can programs that focus on changes in biomarkers of stress and mood dysregulation, such as levels of sleep, activity, and texting patterns, be developed and implemented to better engage women in addressing their mental health?
What are effective interventions to address the health of women academics in STEMM that specifically account for the effects of stress on women? What are effective interventions to mitigate the excessive levels of stress for Women of Color?

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The spring of 2020 marked a change in how almost everyone conducted their personal and professional lives, both within science, technology, engineering, mathematics, and medicine (STEMM) and beyond. The COVID-19 pandemic disrupted global scientific conferences and individual laboratories and required people to find space in their homes from which to work. It blurred the boundaries between work and non-work, infusing ambiguity into everyday activities. While adaptations that allowed people to connect became more common, the evidence available at the end of 2020 suggests that the disruptions caused by the COVID-19 pandemic endangered the engagement, experience, and retention of women in academic STEMM, and may roll back some of the achievement gains made by women in the academy to date.

The Impact of COVID-19 on the Careers of Women in Academic Sciences, Engineering, and Medicine identifies, names, and documents how the COVID-19 pandemic disrupted the careers of women in academic STEMM during the initial 9-month period since March 2020 and considers how these disruptions - both positive and negative - might shape future progress for women. This publication builds on the 2020 report Promising Practices for Addressing the Underrepresentation of Women in Science, Engineering, and Medicine to develop a comprehensive understanding of the nuanced ways these disruptions have manifested. The Impact of COVID-19 on the Careers of Women in Academic Sciences, Engineering, and Medicine will inform the academic community as it emerges from the pandemic to mitigate any long-term negative consequences for the continued advancement of women in the academic STEMM workforce and build on the adaptations and opportunities that have emerged.

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J Korean Med Sci
v.36(50); 2021 Dec 27

Formulating Hypotheses for Different Study Designs

Durga prasanna misra.

1 Department of Clinical Immunology and Rheumatology, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow, India.

Armen Yuri Gasparyan

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

Olena Zimba

3 Department of Internal Medicine #2, Danylo Halytsky Lviv National Medical University, Lviv, Ukraine.

Marlen Yessirkepov

4 Department of Biology and Biochemistry, South Kazakhstan Medical Academy, Shymkent, Kazakhstan.

Vikas Agarwal

George d. kitas.

5 Centre for Epidemiology versus Arthritis, University of Manchester, Manchester, UK.

Generating a testable working hypothesis is the first step towards conducting original research. Such research may prove or disprove the proposed hypothesis. Case reports, case series, online surveys and other observational studies, clinical trials, and narrative reviews help to generate hypotheses. Observational and interventional studies help to test hypotheses. A good hypothesis is usually based on previous evidence-based reports. Hypotheses without evidence-based justification and a priori ideas are not received favourably by the scientific community. Original research to test a hypothesis should be carefully planned to ensure appropriate methodology and adequate statistical power. While hypotheses can challenge conventional thinking and may be controversial, they should not be destructive. A hypothesis should be tested by ethically sound experiments with meaningful ethical and clinical implications. The coronavirus disease 2019 pandemic has brought into sharp focus numerous hypotheses, some of which were proven (e.g. effectiveness of corticosteroids in those with hypoxia) while others were disproven (e.g. ineffectiveness of hydroxychloroquine and ivermectin).

Graphical Abstract

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DEFINING WORKING AND STANDALONE SCIENTIFIC HYPOTHESES

Science is the systematized description of natural truths and facts. Routine observations of existing life phenomena lead to the creative thinking and generation of ideas about mechanisms of such phenomena and related human interventions. Such ideas presented in a structured format can be viewed as hypotheses. After generating a hypothesis, it is necessary to test it to prove its validity. Thus, hypothesis can be defined as a proposed mechanism of a naturally occurring event or a proposed outcome of an intervention. 1 , 2

Hypothesis testing requires choosing the most appropriate methodology and adequately powering statistically the study to be able to “prove” or “disprove” it within predetermined and widely accepted levels of certainty. This entails sample size calculation that often takes into account previously published observations and pilot studies. 2 , 3 In the era of digitization, hypothesis generation and testing may benefit from the availability of numerous platforms for data dissemination, social networking, and expert validation. Related expert evaluations may reveal strengths and limitations of proposed ideas at early stages of post-publication promotion, preventing the implementation of unsupported controversial points. 4

Thus, hypothesis generation is an important initial step in the research workflow, reflecting accumulating evidence and experts' stance. In this article, we overview the genesis and importance of scientific hypotheses and their relevance in the era of the coronavirus disease 2019 (COVID-19) pandemic.

DO WE NEED HYPOTHESES FOR ALL STUDY DESIGNS?

Broadly, research can be categorized as primary or secondary. In the context of medicine, primary research may include real-life observations of disease presentations and outcomes. Single case descriptions, which often lead to new ideas and hypotheses, serve as important starting points or justifications for case series and cohort studies. The importance of case descriptions is particularly evident in the context of the COVID-19 pandemic when unique, educational case reports have heralded a new era in clinical medicine. 5

Case series serve similar purpose to single case reports, but are based on a slightly larger quantum of information. Observational studies, including online surveys, describe the existing phenomena at a larger scale, often involving various control groups. Observational studies include variable-scale epidemiological investigations at different time points. Interventional studies detail the results of therapeutic interventions.

Secondary research is based on already published literature and does not directly involve human or animal subjects. Review articles are generated by secondary research. These could be systematic reviews which follow methods akin to primary research but with the unit of study being published papers rather than humans or animals. Systematic reviews have a rigid structure with a mandatory search strategy encompassing multiple databases, systematic screening of search results against pre-defined inclusion and exclusion criteria, critical appraisal of study quality and an optional component of collating results across studies quantitatively to derive summary estimates (meta-analysis). 6 Narrative reviews, on the other hand, have a more flexible structure. Systematic literature searches to minimise bias in selection of articles are highly recommended but not mandatory. 7 Narrative reviews are influenced by the authors' viewpoint who may preferentially analyse selected sets of articles. 8

In relation to primary research, case studies and case series are generally not driven by a working hypothesis. Rather, they serve as a basis to generate a hypothesis. Observational or interventional studies should have a hypothesis for choosing research design and sample size. The results of observational and interventional studies further lead to the generation of new hypotheses, testing of which forms the basis of future studies. Review articles, on the other hand, may not be hypothesis-driven, but form fertile ground to generate future hypotheses for evaluation. Fig. 1 summarizes which type of studies are hypothesis-driven and which lead on to hypothesis generation.

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STANDARDS OF WORKING AND SCIENTIFIC HYPOTHESES

A review of the published literature did not enable the identification of clearly defined standards for working and scientific hypotheses. It is essential to distinguish influential versus not influential hypotheses, evidence-based hypotheses versus a priori statements and ideas, ethical versus unethical, or potentially harmful ideas. The following points are proposed for consideration while generating working and scientific hypotheses. 1 , 2 Table 1 summarizes these points.

Points to be considered while evaluating the validity of hypotheses
Backed by evidence-based data
Testable by relevant study designs
Supported by preliminary (pilot) studies
Testable by ethical studies
Maintaining a balance between scientific temper and controversy

Evidence-based data

A scientific hypothesis should have a sound basis on previously published literature as well as the scientist's observations. Randomly generated (a priori) hypotheses are unlikely to be proven. A thorough literature search should form the basis of a hypothesis based on published evidence. 7

Unless a scientific hypothesis can be tested, it can neither be proven nor be disproven. Therefore, a scientific hypothesis should be amenable to testing with the available technologies and the present understanding of science.

Supported by pilot studies

If a hypothesis is based purely on a novel observation by the scientist in question, it should be grounded on some preliminary studies to support it. For example, if a drug that targets a specific cell population is hypothesized to be useful in a particular disease setting, then there must be some preliminary evidence that the specific cell population plays a role in driving that disease process.

Testable by ethical studies

The hypothesis should be testable by experiments that are ethically acceptable. 9 For example, a hypothesis that parachutes reduce mortality from falls from an airplane cannot be tested using a randomized controlled trial. 10 This is because it is obvious that all those jumping from a flying plane without a parachute would likely die. Similarly, the hypothesis that smoking tobacco causes lung cancer cannot be tested by a clinical trial that makes people take up smoking (since there is considerable evidence for the health hazards associated with smoking). Instead, long-term observational studies comparing outcomes in those who smoke and those who do not, as was performed in the landmark epidemiological case control study by Doll and Hill, 11 are more ethical and practical.

Balance between scientific temper and controversy

Novel findings, including novel hypotheses, particularly those that challenge established norms, are bound to face resistance for their wider acceptance. Such resistance is inevitable until the time such findings are proven with appropriate scientific rigor. However, hypotheses that generate controversy are generally unwelcome. For example, at the time the pandemic of human immunodeficiency virus (HIV) and AIDS was taking foot, there were numerous deniers that refused to believe that HIV caused AIDS. 12 , 13 Similarly, at a time when climate change is causing catastrophic changes to weather patterns worldwide, denial that climate change is occurring and consequent attempts to block climate change are certainly unwelcome. 14 The denialism and misinformation during the COVID-19 pandemic, including unfortunate examples of vaccine hesitancy, are more recent examples of controversial hypotheses not backed by science. 15 , 16 An example of a controversial hypothesis that was a revolutionary scientific breakthrough was the hypothesis put forth by Warren and Marshall that Helicobacter pylori causes peptic ulcers. Initially, the hypothesis that a microorganism could cause gastritis and gastric ulcers faced immense resistance. When the scientists that proposed the hypothesis themselves ingested H. pylori to induce gastritis in themselves, only then could they convince the wider world about their hypothesis. Such was the impact of the hypothesis was that Barry Marshall and Robin Warren were awarded the Nobel Prize in Physiology or Medicine in 2005 for this discovery. 17 , 18

DISTINGUISHING THE MOST INFLUENTIAL HYPOTHESES

Influential hypotheses are those that have stood the test of time. An archetype of an influential hypothesis is that proposed by Edward Jenner in the eighteenth century that cowpox infection protects against smallpox. While this observation had been reported for nearly a century before this time, it had not been suitably tested and publicised until Jenner conducted his experiments on a young boy by demonstrating protection against smallpox after inoculation with cowpox. 19 These experiments were the basis for widespread smallpox immunization strategies worldwide in the 20th century which resulted in the elimination of smallpox as a human disease today. 20

Other influential hypotheses are those which have been read and cited widely. An example of this is the hygiene hypothesis proposing an inverse relationship between infections in early life and allergies or autoimmunity in adulthood. An analysis reported that this hypothesis had been cited more than 3,000 times on Scopus. 1

LESSONS LEARNED FROM HYPOTHESES AMIDST THE COVID-19 PANDEMIC

The COVID-19 pandemic devastated the world like no other in recent memory. During this period, various hypotheses emerged, understandably so considering the public health emergency situation with innumerable deaths and suffering for humanity. Within weeks of the first reports of COVID-19, aberrant immune system activation was identified as a key driver of organ dysfunction and mortality in this disease. 21 Consequently, numerous drugs that suppress the immune system or abrogate the activation of the immune system were hypothesized to have a role in COVID-19. 22 One of the earliest drugs hypothesized to have a benefit was hydroxychloroquine. Hydroxychloroquine was proposed to interfere with Toll-like receptor activation and consequently ameliorate the aberrant immune system activation leading to pathology in COVID-19. 22 The drug was also hypothesized to have a prophylactic role in preventing infection or disease severity in COVID-19. It was also touted as a wonder drug for the disease by many prominent international figures. However, later studies which were well-designed randomized controlled trials failed to demonstrate any benefit of hydroxychloroquine in COVID-19. 23 , 24 , 25 , 26 Subsequently, azithromycin 27 , 28 and ivermectin 29 were hypothesized as potential therapies for COVID-19, but were not supported by evidence from randomized controlled trials. The role of vitamin D in preventing disease severity was also proposed, but has not been proven definitively until now. 30 , 31 On the other hand, randomized controlled trials identified the evidence supporting dexamethasone 32 and interleukin-6 pathway blockade with tocilizumab as effective therapies for COVID-19 in specific situations such as at the onset of hypoxia. 33 , 34 Clues towards the apparent effectiveness of various drugs against severe acute respiratory syndrome coronavirus 2 in vitro but their ineffectiveness in vivo have recently been identified. Many of these drugs are weak, lipophilic bases and some others induce phospholipidosis which results in apparent in vitro effectiveness due to non-specific off-target effects that are not replicated inside living systems. 35 , 36

Another hypothesis proposed was the association of the routine policy of vaccination with Bacillus Calmette-Guerin (BCG) with lower deaths due to COVID-19. This hypothesis emerged in the middle of 2020 when COVID-19 was still taking foot in many parts of the world. 37 , 38 Subsequently, many countries which had lower deaths at that time point went on to have higher numbers of mortality, comparable to other areas of the world. Furthermore, the hypothesis that BCG vaccination reduced COVID-19 mortality was a classic example of ecological fallacy. Associations between population level events (ecological studies; in this case, BCG vaccination and COVID-19 mortality) cannot be directly extrapolated to the individual level. Furthermore, such associations cannot per se be attributed as causal in nature, and can only serve to generate hypotheses that need to be tested at the individual level. 39

IS TRADITIONAL PEER REVIEW EFFICIENT FOR EVALUATION OF WORKING AND SCIENTIFIC HYPOTHESES?

Traditionally, publication after peer review has been considered the gold standard before any new idea finds acceptability amongst the scientific community. Getting a work (including a working or scientific hypothesis) reviewed by experts in the field before experiments are conducted to prove or disprove it helps to refine the idea further as well as improve the experiments planned to test the hypothesis. 40 A route towards this has been the emergence of journals dedicated to publishing hypotheses such as the Central Asian Journal of Medical Hypotheses and Ethics. 41 Another means of publishing hypotheses is through registered research protocols detailing the background, hypothesis, and methodology of a particular study. If such protocols are published after peer review, then the journal commits to publishing the completed study irrespective of whether the study hypothesis is proven or disproven. 42 In the post-pandemic world, online research methods such as online surveys powered via social media channels such as Twitter and Instagram might serve as critical tools to generate as well as to preliminarily test the appropriateness of hypotheses for further evaluation. 43 , 44

Some radical hypotheses might be difficult to publish after traditional peer review. These hypotheses might only be acceptable by the scientific community after they are tested in research studies. Preprints might be a way to disseminate such controversial and ground-breaking hypotheses. 45 However, scientists might prefer to keep their hypotheses confidential for the fear of plagiarism of ideas, avoiding online posting and publishing until they have tested the hypotheses.

SUGGESTIONS ON GENERATING AND PUBLISHING HYPOTHESES

Publication of hypotheses is important, however, a balance is required between scientific temper and controversy. Journal editors and reviewers might keep in mind these specific points, summarized in Table 2 and detailed hereafter, while judging the merit of hypotheses for publication. Keeping in mind the ethical principle of primum non nocere, a hypothesis should be published only if it is testable in a manner that is ethically appropriate. 46 Such hypotheses should be grounded in reality and lend themselves to further testing to either prove or disprove them. It must be considered that subsequent experiments to prove or disprove a hypothesis have an equal chance of failing or succeeding, akin to tossing a coin. A pre-conceived belief that a hypothesis is unlikely to be proven correct should not form the basis of rejection of such a hypothesis for publication. In this context, hypotheses generated after a thorough literature search to identify knowledge gaps or based on concrete clinical observations on a considerable number of patients (as opposed to random observations on a few patients) are more likely to be acceptable for publication by peer-reviewed journals. Also, hypotheses should be considered for publication or rejection based on their implications for science at large rather than whether the subsequent experiments to test them end up with results in favour of or against the original hypothesis.

Points to be considered before a hypothesis is acceptable for publication
Experiments required to test hypotheses should be ethically acceptable as per the World Medical Association declaration on ethics and related statements
Pilot studies support hypotheses
Single clinical observations and expert opinion surveys may support hypotheses
Testing hypotheses requires robust methodology and statistical power
Hypotheses that challenge established views and concepts require proper evidence-based justification

Hypotheses form an important part of the scientific literature. The COVID-19 pandemic has reiterated the importance and relevance of hypotheses for dealing with public health emergencies and highlighted the need for evidence-based and ethical hypotheses. A good hypothesis is testable in a relevant study design, backed by preliminary evidence, and has positive ethical and clinical implications. General medical journals might consider publishing hypotheses as a specific article type to enable more rapid advancement of science.

Disclosure: The authors have no potential conflicts of interest to disclose.

Author Contributions:

Data curation: Gasparyan AY, Misra DP, Zimba O, Yessirkepov M, Agarwal V, Kitas GD.

ORFAN: FAI Score Obtained During CCTA, AI-Risk Algorithm Refine Risk Reclassification

Jun 26, 2024

ACC News Story

Coronary computed tomography (CCTA), used for first-line investigation of chest pain, has revealed there is a large group of individuals without obstructive coronary artery disease (CAD) for whom there is an unclear prognosis and management. Defining inflammatory risk using the perivascular fat attenuation index (FAI) Score has been shown to enhance clinical risk stratification and CCTA interpretation, according to results from the ORFAN study published May 29 in The Lancet .

The ORFAN study sought to evaluate the risk profile and event rates in patients undergoing CCTA as part of routine clinical care in the UK National Health Service, as well as test the hypothesis that coronary arterial inflammation drives cardiac mortality or major adverse cardiac events (MACE), defined as myocardial infarction, new onset heart failure or cardiac death, and to externally validate the performance of the previously trained artificial intelligence (AI)-Risk prognostic algorithm and the related AI-Risk classification system.

Kenneth Chan, MRCP , et al., conducted the longitudinal study in Cohort A, consisting of 40,091 consecutive patients in eight hospitals from January 2010 to March 2021 (Cohort A). The prognostic value of the FAI Score was evaluated in Cohort B, a subset of 3,393 patients from the two hospitals with the longest follow-up (7.7 years), as well as the AI-enhanced risk prediction algorithm that included the FAI Score, coronary plaque metrics and clinical risk factors.

Results showed that 81.1% (32,533) of patients did not have obstructive CAD and that over the 2.7-year median follow-up they accounted for 66.3% of the total 4,307 MACE and 63.7% of the total cardiac deaths. Furthermore, in Cohort B over the median 7.7 follow-up, an increased FAI Score in all the three coronary arteries enhanced risk prediction for cardiac mortality (hazard ratio [HR], 29.8; p<0.001) and MACE (HR, 12.6; p<0.001). Additionally, the FAI Score predicted cardiac mortality and MACE independently from cardiovascular risk factors and presence or extent of CAD.

Regarding the AI-Risk classification, for patients with very high risk vs. low or medium risk, there was a positive association with cardiac mortality (HR, 6.75; p<0.001), and MACE (HR, 4.68; p<0.001). Moreover, the AI-risk model was “well-calibrated against true events.”

Noting the unmet need to improve risk stratification and management in the population without obstructive CAD, the authors write that this study shows that measuring coronary inflammation from routine CCTA captures cardiovascular inflammatory risk, even in those without visible plaque or coronary calcification. “An AI-assisted risk prediction tool incorporating FAI Score, atherosclerotic plaque burden and the patient risk factor profile provides clinically meaningful risk reclassification in patients undergoing routine CCTA that could guide the more precise use of preventative treatments, including anti-inflammatory therapies,” they write.

Clinical Topics: Heart Failure and Cardiomyopathies, Vascular Medicine, Atherosclerotic Disease (CAD/PAD), Acute Heart Failure

Keywords: Heart Failure, Anti-Inflammatory Agents, Arteritis, Heart Disease Risk Factors, Chest Pain, Risk Assessment, Myocardial Infarction, Cholangiopancreatography, Magnetic Resonance, Artificial Intelligence, Plaque, Atherosclerotic, Cardiovascular Diseases, Coronary Artery Disease

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