Question:
You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:

- "verdict": The fact-checking verdict. If the information in the abstract supports the claim, write "SUPPORT". If the abstract contradicts the claim, write "CONTRADICT". If the abstract does not provide enough information to arrive at a verdict, write "NEI" (for "not enough information").
- "evidence": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was "NEI", then return an empty array.

For instance, if the model were given the claim "wearing masks can prevent the spread of COVID", the output might be
{
  "verdict": "SUPPORT",
  "evidence": ["Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19."]
}

Claim: The overall risk of dying in the hospital from COVID-19 among people with T1D is very low, however, one study found that this risk is higher for people with T1D (3.5 times higher) compared to people without diabetes.

Abstract:
Objective: To undertake a review and critical appraisal of published/preprint reports that offer methods of determining the effects of hypertension, diabetes, stroke, cancer, kidney issues, and high-cholesterol on COVID-19 disease severity.
Data sources: Google Scholar, PubMed, COVID-19 Open Research Dataset: a resource of over 128,000 scholarly articles, including over 59,000 articles with full text related to COVID-19, SARS-CoV-2, and coronaviruses.
Methods: A search was conducted by two authors independently on the freely available COVID-19 Open Research Dataset (CORD-19).
We developed an automated search engine to screen a total of 59,000 articles in a few seconds.
The search engine was built using a retrieval function that ranks a set of documents based on the query terms appearing in each document regardless of their proximity within the document.
Filtering of the articles was then undertaken using keywords and questions, e.g. "Effects of diabetes on COVID/normal coronavirus/SARS-CoV-2/nCoV/COVID-19 disease severity, mortality?".
The search terms were repeated for all the comorbidities considered in this paper.
Additional articles were retrieved by searching via Google Scholar and PubMed.
Findings: A total of 54 articles were considered for a full review.
It was observed that diabetes, hypertension, and cholesterol levels possess an apparent relation to COVID-19 severity.
Other comorbidities, such as cancer, kidney disease, and stroke, must be further evaluated to determine a strong relationship to the virus.
Reports associating cancer, kidney disease, and stroke with COVID-19 should be carefully interpreted, not only because of the size of the samples, but also because patients could be old, have a history of smoking, or have any other clinical condition suggesting that these factors might be associated with the poor COVID-19 outcomes rather than the comorbidity itself.
Such reports could lead many oncologists and physicians to change their treatment strategies without solid evidence and recommendations.
Further research regarding this relationship and its clinical management is warranted.
Additionally, treatment options must be examined further to provide optimal treatment and ensure better outcomes for patients suffering from these comorbidities.
It should be noted that, whether definitive measurements exist or not, the care of patients as well as the research involved should be largely prioritized to tackle this deadly pandemic.

Answer: {"verdict": "SUPPORT", "evidence": ["It was observed that diabetes, hypertension, and cholesterol levels possess an apparent relation to COVID-19 severity."]}


Question:
You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:

- "verdict": The fact-checking verdict. If the information in the abstract supports the claim, write "SUPPORT". If the abstract contradicts the claim, write "CONTRADICT". If the abstract does not provide enough information to arrive at a verdict, write "NEI" (for "not enough information").
- "evidence": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was "NEI", then return an empty array.

For instance, if the model were given the claim "wearing masks can prevent the spread of COVID", the output might be
{
  "verdict": "SUPPORT",
  "evidence": ["Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19."]
}

Claim: fever is beneficial to the body's natural immune response to fight covid-19;

Abstract:
Fever has been reported as a common symptom occurring in COVID-19 illness.
Over the counter antipyretics such as ibuprofen and acetaminophen are often taken by individuals to reduce the discomfort of fever.
Recently, the safety of ibuprofen in COVID-19 patients has been questioned due to anecdotal reports of worsening symptoms in previously healthy young adults.
Studies show that ibuprofen demonstrates superior efficacy in fever reduction compared to acetaminophen.
As fever may have benefit in shortening the duration of viral illness, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response when taken during the early stages of COVID-19 illness.

Answer: {"verdict": "CONTRADICT", "evidence": ["As fever may have benefit in shortening the duration of viral illness, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response when taken during the early stages of COVID-19 illness."]}


Question:
You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:

- "verdict": The fact-checking verdict. If the information in the abstract supports the claim, write "SUPPORT". If the abstract contradicts the claim, write "CONTRADICT".
- "evidence": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.

For instance, if the model were given the claim "the COVID vaccine is safe for healthy adults", the output might be
{
  "verdict": "SUPPORT",
  "evidence": ["Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults."]
}

Claim: Low vitamin k status predicts ards in a cohort of 138 hospitalized patients with covid-19.

Abstract:
It has recently been hypothesised that Vitamin K could play a role in COVID-19.
We aimed to test the hypothesis that low vitamin K status is a common characteristic of patients hospitalized with COVID-19 compared to population controls; and that low vitamin K status predicts mortality in COVID-19 patients.
In a cohort of 138 COVID-19 patients and 140 population controls, we measured plasma dephosphorylated-uncarboxylated Matrix Gla Protein (dp-ucMGP), which reflects the functional Vitamin K status in peripheral tissue.
Fourty-three patients died within 90-days from admission.
In patients, levels of dp-ucMGP differed significantly between survivors (mean 877; 95% CI: 778; 995) and non-survivors (mean 1445; 95% CI: 1148; 1820).
Furthermore, levels of dp-ucMGP (pmol/L) were considerably higher in patients (mean 1022; 95% CI: 912; 1151) compared to controls (mean 509; 95% CI: 485; 540).
Cox regression survival analysis showed that increasing levels of dp-ucMGP (reflecting low Vitamin K status) were associated with higher mortality risk (sex-and age-adjusted hazard ratio per doubling of dp-ucMGP was 1.50, 95% CI: 1.03; 2.18).
In conclusion, we found that low Vitamin K status predicted mortality in patients with COVID-19 supporting a potential role of Vitamin K in COVID-19.

Answer: {"verdict": "CONTRADICT", "evidence": ["We aimed to test the hypothesis that low vitamin K status is a common characteristic of patients hospitalized with COVID-19 compared to population controls; and that low vitamin K status predicts mortality in COVID-19 patients.", "In conclusion, we found that low Vitamin K status predicted mortality in patients with COVID-19 supporting a potential role of Vitamin K in COVID-19."]}


Question:
You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:

- "verdict": The fact-checking verdict. If the information in the abstract supports the claim, write "SUPPORT". If the abstract contradicts the claim, write "CONTRADICT".
- "evidence": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.

For instance, if the model were given the claim "the COVID vaccine is safe for healthy adults", the output might be
{
  "verdict": "SUPPORT",
  "evidence": ["Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults."]
}

Claim: Evolutionary arms race between virus and virus drives genetic diversity in bat sars related coronavirus spike genes

Abstract:
The Chinese horseshoe bat (Rhinolophus sinicus), reservoir host of severe acute respiratory syndrome coronavirus (SARS-CoV), carries many bat SARS-related CoVs (SARSr-CoVs) with high genetic diversity, particularly in the spike gene.
Despite these variations, some bat SARSr-CoVs can utilize the orthologs of the human SARS-CoV receptor, angiotensin-converting enzyme 2 (ACE2), for entry.
It is speculated that the interaction between bat ACE2 and SARSr-CoV spike proteins drives diversity.
Here, we identified a series of R. sinicus ACE2 variants with some polymorphic sites involved in the interaction with the SARS-CoV spike protein.
Pseudoviruses or SARSr-CoVs carrying different spike proteins showed different infection efficiencies in cells transiently expressing bat ACE2 variants.
Consistent results were observed by binding affinity assays between SARS-CoV and SARSr-CoV spike proteins and receptor molecules from bats and humans.
All tested bat SARSr-CoV spike proteins had a higher binding affinity to human ACE2 than to bat ACE2, although they showed a 10-fold lower binding affinity to human ACE2 compared with that of their SARS-CoV counterpart.
Structure modeling revealed that the difference in binding affinity between spike and ACE2 might be caused by the alteration of some key residues in the interface of these two molecules.
Molecular evolution analysis indicates that some key residues were under positive selection.
These results suggest that the SARSr-CoV spike protein and R. sinicus ACE2 may have coevolved over time and experienced selection pressure from each other, triggering the evolutionary arms race dynamics.
IMPORTANCE Evolutionary arms race dynamics shape the diversity of viruses and their receptors.
Identification of key residues which are involved in interspecies transmission is important to predict potential pathogen spillover from wildlife to humans.
Previously, we have identified genetically diverse SARSr-CoVs in Chinese horseshoe bats.
Here, we show the highly polymorphic ACE2 in Chinese horseshoe bat populations.
These ACE2 variants support SARS-CoV and SARSr-CoV infection but with different binding affinities to different spike proteins.
The higher binding affinity of SARSr-CoV spike to human ACE2 suggests that these viruses have the capacity for spillover to humans.
The positive selection of residues at the interface between ACE2 and SARSr-CoV spike protein suggests long-term and ongoing coevolutionary dynamics between them.
Continued surveillance of this group of viruses in bats is necessary for the prevention of the next SARS-like disease.

Answer: {"verdict": "CONTRADICT", "evidence": ["It is speculated that the interaction between bat ACE2 and SARSr-CoV spike proteins drives diversity.", "These results suggest that the SARSr-CoV spike protein and R. sinicus ACE2 may have coevolved over time and experienced selection pressure from each other, triggering the evolutionary arms race dynamics."]}


Question:
You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:

- "verdict": The fact-checking verdict. If the information in the abstract supports the claim, write "SUPPORT". If the abstract contradicts the claim, write "CONTRADICT".
- "evidence": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.

For instance, if the model were given the claim "the COVID vaccine is safe for healthy adults", the output might be
{
  "verdict": "SUPPORT",
  "evidence": ["Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults."]
}

Claim: B cells and t cells mediate immunity to covid-19

Abstract:
Recent reports that antibodies to SARS-CoV-2 are not maintained in the serum following recovery from the virus have caused alarm.
However, the absence of specific antibodies in the serum does not necessarily mean an absence of immune memory.
Here, we discuss our current understanding of the relative contribution of B cells and T cells to immunity to SARS-CoV-2 and the implications for the development of effective treatments and vaccines for COVID-19.

Answer: {"verdict": "SUPPORT", "evidence": ["Here, we discuss our current understanding of the relative contribution of B cells and T cells to immunity to SARS-CoV-2 and the implications for the development of effective treatments and vaccines for COVID-19."]}


Question:
You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:

- "verdict": The fact-checking verdict. If the information in the abstract supports the claim, write "SUPPORT". If the abstract contradicts the claim, write "CONTRADICT".
- "evidence": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.

For instance, if the model were given the claim "the COVID vaccine is safe for healthy adults", the output might be
{
  "verdict": "SUPPORT",
  "evidence": ["Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults."]
}

Claim: Personalized screening approach for hospitalized patients with covid-19

Abstract:
Hospitalized patients with COVID-19 experiencing respiratory symptoms have different complications (inflammatory, co-infection and thrombotic) that are identifiable by analytics patterns.
Personalized treatment decisions decreased early mortality (OR 0.144, CI 0.03-0.686; p=0.015).
Increasing age (OR 1.06; p=0.038) and therapeutic effort limitation (OR 9.684; p<0.001) were associated with higher mortality.

Answer: {"verdict": "CONTRADICT", "evidence": ["Personalized treatment decisions decreased early mortality (OR 0.144, CI 0.03-0.686; p=0.015)."]}


Question:
You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:

- "verdict": The fact-checking verdict. If the information in the abstract supports the claim, write "SUPPORT". If the abstract contradicts the claim, write "CONTRADICT". If the abstract does not provide enough information to arrive at a verdict, write "NEI" (for "not enough information").
- "evidence": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was "NEI", then return an empty array.

For instance, if the model were given the claim "wearing masks can prevent the spread of COVID", the output might be
{
  "verdict": "SUPPORT",
  "evidence": ["Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19."]
}

Claim: In one study it was found that 100% of ICU COVID-19 patients less than 75 years old had vitamin D insufficiency.

Abstract:
The severity of coronavirus 2019 infection (COVID-19) is determined by the presence of pneumonia, severe acute respiratory distress syndrome (SARS-CoV-2), myocarditis, microvascular thrombosis and/or cytokine storms, all of which involve underlying inflammation.
A principal defence against uncontrolled inflammation, and against viral infection in general, is provided by T regulatory lymphocytes (Tregs).
Treg levels have been reported to be low in many COVID-19 patients and can be increased by vitamin D supplementation.
Low vitamin D levels have been associated with an increase in inflammatory cytokines and a significantly increased risk of pneumonia and viral upper respiratory tract infections.
Vitamin D deficiency is associated with an increase in thrombotic episodes, which are frequently observed in COVID-19.
Vitamin D deficiency has been found to occur more frequently in patients with obesity and diabetes.
These conditions are reported to carry a higher mortality in COVID-19.
If vitamin D does in fact reduce the severity of COVID-19 in regard to pneumonia/ARDS, inflammation, inflammatory cytokines and thrombosis, it is our opinion that supplements would offer a relatively easy option to decrease the impact of the pandemic.

Answer: {"verdict": "SUPPORT", "evidence": ["A principal defence against uncontrolled inflammation, and against viral infection in general, is provided by T regulatory lymphocytes (Tregs).", "Treg levels have been reported to be low in many COVID-19 patients and can be increased by vitamin D supplementation.", "Low vitamin D levels have been associated with an increase in inflammatory cytokines and a significantly increased risk of pneumonia and viral upper respiratory tract infections.", "Vitamin D deficiency is associated with an increase in thrombotic episodes, which are frequently observed in COVID-19.", "Vitamin D deficiency has been found to occur more frequently in patients with obesity and diabetes.", "These conditions are reported to carry a higher mortality in COVID-19.", "If vitamin D does in fact reduce the severity of COVID-19 in regard to pneumonia/ARDS, inflammation, inflammatory cytokines and thrombosis, it is our opinion that supplements would offer a relatively easy option to decrease the impact of the pandemic."]}


Question:
You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:

- "verdict": The fact-checking verdict. If the information in the abstract supports the claim, write "SUPPORT". If the abstract contradicts the claim, write "CONTRADICT". If the abstract does not provide enough information to arrive at a verdict, write "NEI" (for "not enough information").
- "evidence": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was "NEI", then return an empty array.

For instance, if the model were given the claim "wearing masks can prevent the spread of COVID", the output might be
{
  "verdict": "SUPPORT",
  "evidence": ["Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19."]
}

Claim: Further, the report enlightened on the use of vitamin D on COVID-19 patients

Abstract:
Background: Following emerge of a novel coronavirus from Wuhan, China, in December 2019, it has affected the whole world and after months of efforts by the medical communities, there is still no specific approach for prevention and treatment against the Coronavirus Disease 2019 (COVID-19).
Evidence recommends that vitamin D might be an important supportive agent for the immune system, mainly in cytokine response regulation against COVID-19.
Hence, we carried out a rapid systematic review and meta-analysis along with an ecological investigation in order to maximize the use of everything that exists about the role of vitamin D in the COVID-19.
Methods: A systematic search was performed in PubMed, Scopus, Embase, Cochrane Library, Web of Science and Google Scholar (intitle) as well as preprint database of medRxiv, bioRxiv, Research Square, preprints.org, search engine of ScienceDirect and a rapid search through famous journals up to May 26, 2020.
Studies focused on the role of vitamin D in confirmed COVID-19 patients were entered into the systematic review.
Along with our main aim, to find the second objective: correlation of global vitamin D status and COVID-19 recovery and mortality we carried out a literature search in PubMed database to identify the national or regional studies reported the vitamin D status globally.
CMA v. 2.2.064 and SPSS v.16 were used for data analysis.
Results: Out of nine studies entered into our systematic review, six studies containing 3,822 participants entered into the meta-analysis.
The meta-analysis indicated that 46.5% of COVID-19 patients were suffering from vitamin D deficiency (95% CI, 28.2%-65.8%) and in 43.3% of patients, levels of vitamin D were insufficient (95% CI, 27.4%-60.8%).
In regard to our ecological investigation on 51 countries including 408,748 participants, analyses indicated no correlation between vitamin D levels and recovery rate (r= 0.041) as well as mortality rate (r=-0.073) globally.
However, given latitude, a small reverse correlation between mortality rate and vitamin D status was observed throughout the globe (r= -0.177).
In Asia, a medium direct correlation was observed for recovery rate (r= 0.317) and a significant reveres correlation for mortality rate (r= -0.700) with vitamin D status in such patients.
In Europe, there were no correlations for both recovery (r= 0.040) and mortality rate (r= -0.035).
In Middle East, the recovery rate (r= 0.267) and mortality rate (r= -0.217) showed a medium correlation.
In North and Sought America, surprisingly, both recovery and mortality rate demonstrated a direct correlation respectively (r= 1.000, r=0.500).
In Oceania, unexpectedly, recovery (r= -1.000) and mortality (r= -1.000) rates were in considerable reverse correlation with vitamin D levels.
Conclusion: In this systematic review and meta-analysis with an ecological approach, we found a high percentage of COVID-19 patients who suffer from vitamin D deficiency or insufficiency.
Much more important, our ecological investigation resulted in substantial direct and reverse correlations between recovery and mortality rates of COVID-19 patients with vitamin D status in different countries.
Considering latitudes, a small reverse correlation between vitamin D status and mortality rate was found globally.
It seems that populations with lower levels of vitamin D might be more susceptible to the novel coronavirus infection.
Nevertheless, due to multiple limitations, if this study does not allow to quantify a value of the Vitamin D with full confidence, it allows at least to know what the Vitamin D might be and that it would be prudent to invest in this direction through comprehensive large randomized clinical trials.

Answer: {"verdict": "SUPPORT", "evidence": ["In Middle East, the recovery rate (r= 0.267) and mortality rate (r= -0.217) showed a medium correlation."]}


Question:
You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:

- "verdict": The fact-checking verdict. If the information in the abstract supports the claim, write "SUPPORT". If the abstract contradicts the claim, write "CONTRADICT".
- "evidence": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.

For instance, if the model were given the claim "the COVID vaccine is safe for healthy adults", the output might be
{
  "verdict": "SUPPORT",
  "evidence": ["Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults."]
}

Claim: Bacterial modification of the host glycosaminoglycan heparan oxide modulates sars-cov-2 infectivity

Abstract:
The human microbiota has a close relationship with human disease and it remodels components of the glycocalyx including heparan sulfate (HS).
Studies of the severe acute respiratory syndrome coronavirus (SARS-CoV-2) spike protein receptor binding domain suggest that infection requires binding to HS and angiotensin converting enzyme 2 (ACE2) in a codependent manner.
Here, we show that commensal host bacterial communities can modify HS and thereby modulate SARS-CoV-2 spike protein binding and that these communities change with host age and sex.
Common human-associated commensal bacteria whose genomes encode HS-modifying enzymes were identified.
The prevalence of these bacteria and the expression of key microbial glycosidases in bronchoalveolar lavage fluid (BALF) was lower in adult COVID-19 patients than in healthy controls.
The presence of HS-modifying bacteria decreased with age in two large survey datasets, FINRISK 2002 and American Gut, revealing one possible mechanism for the observed increase in COVID-19 susceptibility with age.
In vitro, bacterial glycosidases from unpurified culture media supernatants fully blocked SARS-CoV-2 spike binding to human H1299 protein lung adenocarcinoma cells.
HS-modifying bacteria in human microbial communities may regulate viral adhesion, and loss of these commensals could predispose individuals to infection.
Understanding the impact of shifts in microbial community composition and bacterial lyases on SARS-CoV-2 infection may lead to new therapeutics and diagnosis of susceptibility.
Graphical Abstract.
Diagram of hypothesis for bacterial mediation of SARS-CoV-2 infection through heparan sulfate (HS).
It is well known that host microbes groom the mucosa where they reside.
Recent investigations have shown that HS, a major component of mucosal layers, is necessary for SARS-CoV-2 infection.
In this study we examine the impact of microbial modification of HS on viral attachment.

Answer: {"verdict": "CONTRADICT", "evidence": ["Understanding the impact of shifts in microbial community composition and bacterial lyases on SARS-CoV-2 infection may lead to new therapeutics and diagnosis of susceptibility."]}


Question:
You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:

- "verdict": The fact-checking verdict. If the information in the abstract supports the claim, write "SUPPORT". If the abstract contradicts the claim, write "CONTRADICT". If the abstract does not provide enough information to arrive at a verdict, write "NEI" (for "not enough information").
- "evidence": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was "NEI", then return an empty array.

For instance, if the model were given the claim "wearing masks can prevent the spread of COVID", the output might be
{
  "verdict": "SUPPORT",
  "evidence": ["Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19."]
}

Claim: the virus can stay on surfaces long enough to be a source of transmission

Abstract:
The ocular surface has been suggested as a site of infection with Coronavirus-2 (SARS-CoV-2) responsible for the coronavirus disease-19 (COVID-19).
This review examines the evidence for this hypothesis, and its implications for clinical practice.
Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2), responsible for the COVID-19 pandemic, is transmitted by person-to-person contact, via airborne droplets, or through contact with contaminated surfaces.
SARS-CoV-2 binds to angiotensin converting enzyme-2 (ACE2) to facilitate infection in humans.
This review sets out to evaluate evidence for the ocular surface as a route of infection.
A literature search in this area was conducted on 15 April 2020 using the Scopus database.
In total, 287 results were returned and reviewed.
There is preliminary evidence for ACE2 expression on corneal and conjunctival cells, but most of the other receptors to which coronaviruses bind appear to be found under epithelia of the ocular surface.
Evidence from animal studies is limited, with a single study suggesting viral particles on the eye can travel to the lung, resulting in very mild infection.
Coronavirus infection is rarely associated with conjunctivitis, with occasional cases reported in patients with confirmed COVID-19, along with isolated cases of conjunctivitis as a presenting sign.
Coronaviruses have been rarely isolated from tears or conjunctival swabs.
The evidence suggests coronaviruses are unlikely to bind to ocular surface cells to initiate infection.
Additionally, hypotheses that the virus could travel from the nasopharynx or through the conjunctival capillaries to the ocular surface during infection are probably incorrect.
Conjunctivitis and isolation of the virus from the ocular surface occur only rarely, and overwhelmingly in patients with confirmed COVID-19.
Necessary precautions to prevent person-to-person transmission should be employed in clinical practice throughout the pandemic, and patients should be reminded to maintain good hygiene practices.

Answer: {"verdict": "SUPPORT", "evidence": ["Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2), responsible for the COVID-19 pandemic, is transmitted by person-to-person contact, via airborne droplets, or through contact with contaminated surfaces."]}


Question:
You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:

- "verdict": The fact-checking verdict. If the information in the abstract supports the claim, write "SUPPORT". If the abstract contradicts the claim, write "CONTRADICT".
- "evidence": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.

For instance, if the model were given the claim "the COVID vaccine is safe for healthy adults", the output might be
{
  "verdict": "SUPPORT",
  "evidence": ["Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults."]
}

Claim: T cell anergy in covid-19 reflects virus persistence and poor outcomes

Abstract:
Coronavirus disease 2019 (COVID-19) can lead to severe pneumonia and hyperinflammation.
So far, insufficient or excessive T cell responses were described in patients.
We applied novel approaches to analyze T cell reactivity and showed that T anergy is already present in non-ventilated COVID-19 patients, very pronounced in ventilated patients, strongly associated with virus persistence and reversible with clinical recovery.
T cell activation was measured by downstream effects on responder cells like basophils, plasmacytoid dendritic cells, monocytes and neutrophils in whole blood and proved to be much more meaningful than classical readouts with PBMCs.
Monocytes responded stronger in males than females and IL-2 partially reversed T cell anergy.
Downstream markers of T cell anergy were also found in fresh blood samples of critically ill patients with severe T cell anergy.
Based on our data we were able to develop a score to predict fatal outcomes and to identify patients that may benefit from strategies to overcome T cell anergy.

Answer: {"verdict": "SUPPORT", "evidence": ["We applied novel approaches to analyze T cell reactivity and showed that T anergy is already present in non-ventilated COVID-19 patients, very pronounced in ventilated patients, strongly associated with virus persistence and reversible with clinical recovery.", "Downstream markers of T cell anergy were also found in fresh blood samples of critically ill patients with severe T cell anergy.", "Based on our data we were able to develop a score to predict fatal outcomes and to identify patients that may benefit from strategies to overcome T cell anergy."]}


Question:
You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:

- "verdict": The fact-checking verdict. If the information in the abstract supports the claim, write "SUPPORT". If the abstract contradicts the claim, write "CONTRADICT". If the abstract does not provide enough information to arrive at a verdict, write "NEI" (for "not enough information").
- "evidence": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was "NEI", then return an empty array.

For instance, if the model were given the claim "wearing masks can prevent the spread of COVID", the output might be
{
  "verdict": "SUPPORT",
  "evidence": ["Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19."]
}

Claim: the symptoms of COVID-19 are no worse than a cold

Abstract:
SARS-CoV-2 is a novel beta-coronavirus causing over 200.000 lethal cases within six months of first infecting humans.
SARS-CoV-2 causes COVID-19, a form of severe acute respiratory syndrome (SARS).
COVID-19 is characterized by two phases: the first resembles the flu with pneumonia, but after about seven or eight days the disease suddenly worsens to a sepsis-like syndrome.
It is difficult to explain this virus-immune-pathology sequence from virology or immunology only.
This paper hypothesizes that host-produced anti-spike protein antibodies are responsible for immune-induced viral dissemination.
Subsequently, systemic distribution of virus-antibodies complexes activates the immune pathology observed in severe COVID-19.
This hypothesis may be counterintuitive to immunologist that consider many anti-spike antibodies to be virus-neutralizing antibodies.
Although anti-spike antibodies may hinder infection of epithelial cells, antibody binding to the spike protein may facilitate virus infection of myeloid leukocytes.
If myeloid leukocytes reenter the circulation, they could spread the virus from a locoregional infection to a systemic disease.
Disseminated virus in combination with antibodies results in dispersed virus-antibody complexes that overstimulate the immune system.
The hypothesis aligns with the sequences of virus, immune and pathological events in COVID-19.
The delay in onset from both syndromes results from an immune system still naïve to the non-cross-reactive spike protein.
Details of this hypothesis are in concordance with many clinical characteristics of COVID-19, including its predominant lethality for the elderly, and the mostly asymptomatic course of disease in children.
It predicts putative detrimental effects of vaccines that induce virus-neutralizing antibodies against the spike protein, as has been shown for other coronaviruses.
This hypothesis has consequences for treatment of patients, evaluation of personal and herd immunity and vaccine development.
In patients, cellular immunity should be stimulated.
Neutralizing antibodies might not be indicative for immunity.
Vaccines should aim to stimulate cellular immunity COVID-19 and/or stimulate humoral immunity against viral proteins except for the immunodominant spike protein.

Answer: {"verdict": "CONTRADICT", "evidence": ["SARS-CoV-2 causes COVID-19, a form of severe acute respiratory syndrome (SARS).", "COVID-19 is characterized by two phases: the first resembles the flu with pneumonia, but after about seven or eight days the disease suddenly worsens to a sepsis-like syndrome."]}


Question:
You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:

- "verdict": The fact-checking verdict. If the information in the abstract supports the claim, write "SUPPORT". If the abstract contradicts the claim, write "CONTRADICT". If the abstract does not provide enough information to arrive at a verdict, write "NEI" (for "not enough information").
- "evidence": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was "NEI", then return an empty array.

For instance, if the model were given the claim "wearing masks can prevent the spread of COVID", the output might be
{
  "verdict": "SUPPORT",
  "evidence": ["Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19."]
}

Claim: Hidden immune weakness found in gravely ill COVID-19 patients.

Abstract:
The WHO has declared SARS-CoV-2 outbreak a public health emergency of international concern.
However, to date, there was hardly any study in characterizing the immune responses, especially adaptive immune responses to SARS-CoV-2 infection.
In this study, we collected blood from COVID-19 patients who have recently become virus-free and therefore were discharged, and analyzed their SARS-CoV-2-specific antibody and T cell responses.
We observed SARS-CoV-2-specific humoral and cellular immunity in the patients.
Both were detected in newly discharged patients, suggesting both participate in immune-mediated protection to viral infection.
However, follow-up patients (2 weeks post discharge) exhibited high titers of IgG antibodies, but with low levels of virus-specific T cells, suggesting that they may enter a quiescent state.
Our work has thus provided a basis for further analysis of protective immunity to SARS-CoV-2, and understanding the pathogenesis of COVID-19, especially in the severe cases.
It has also implications in designing an effective vaccine to protect and treat SARS-CoV-2 infection.

Answer: {"verdict": "NEI", "evidence": []}


Question:
You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:

- "verdict": The fact-checking verdict. If the information in the abstract supports the claim, write "SUPPORT". If the abstract contradicts the claim, write "CONTRADICT". If the abstract does not provide enough information to arrive at a verdict, write "NEI" (for "not enough information").
- "evidence": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was "NEI", then return an empty array.

For instance, if the model were given the claim "wearing masks can prevent the spread of COVID", the output might be
{
  "verdict": "SUPPORT",
  "evidence": ["Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19."]
}

Claim: to reduce the spread of the virus: try to stay at least 2 metres (3 steps) away from anyone you do not live with (or anyone not in your support bubble); wash your hands with soap and water often - do this for at least 20 seconds; use hand sanitiser gel if soap and water are not available; wash your hands as soon as you get home; cover your mouth and nose with a tissue or your sleeve (not your hands) when you cough or sneeze; put used tissues in the bin immediately and wash your hands afterwards

Abstract:
Social distancing measures, with varying degrees of restriction, have been imposed around the world in order to stem the spread of COVID-19.
In this work we analyze the effect of current social distancing measures in the United States.
We quantify the reduction in doubling rate, by state, that is associated with social distancing.
We find that social distancing is associated with a statistically-significant reduction in the doubling rate for all but three states.
At the same time, we do not find significant evidence that social distancing has resulted in a reduction in the number of daily confirmed cases.
Instead, social distancing has merely stabilized the spread of the disease.
We provide an illustration of our findings for each state, including point estimates of the effective reproduction number, R, both with and without social distancing.
We also discuss the policy implications of our findings.

Answer: {"verdict": "SUPPORT", "evidence": ["We find that social distancing is associated with a statistically-significant reduction in the doubling rate for all but three states."]}


Question:
You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:

- "verdict": The fact-checking verdict. If the information in the abstract supports the claim, write "SUPPORT". If the abstract contradicts the claim, write "CONTRADICT". If the abstract does not provide enough information to arrive at a verdict, write "NEI" (for "not enough information").
- "evidence": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was "NEI", then return an empty array.

For instance, if the model were given the claim "wearing masks can prevent the spread of COVID", the output might be
{
  "verdict": "SUPPORT",
  "evidence": ["Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19."]
}

Claim: Vitamin D Supplementation Could Reduce Risk of Influenza and COVID-19 Infections and Deaths

Abstract:
The outbreak of COVID-19 has created a global public health crisis.
Little is known about the protective factors of this infection.
Therefore, preventive health measures that can reduce the risk of infection, progression and severity are desperately needed.
This review discussed the possible roles of vitamin D in reducing the risk of COVID-19 and other acute respiratory tract infections and severity.
Moreover, this study determined the correlation of vitamin D levels with COVID-19 cases and deaths in 20 European countries as of 20 May 2020.
A significant negative correlation (p=0.033) has been observed between mean vitamin D levels and COVID-19 cases per one million population in European countries.
However, the correlation of vitamin D with COVID-19 deaths of these countries was not significant.
Some retrospective studies demonstrated a correlation between vitamin D status and COVID-19 severity and mortality, while other studies did not find the correlation when confounding variables are adjusted.
Several studies demonstrated the role of vitamin D in reducing the risk of acute viral respiratory tract infections and pneumonia.
These include direct inhibition with viral replication or with anti-inflammatory or immunomodulatory ways.
In the meta-analysis, vitamin D supplementation has been shown as safe and effective against acute respiratory tract infections.
Thus, people who are at higher risk of vitamin D deficiency during this global pandemic should consider taking vitamin D supplements to maintain the circulating 25(OH)D in the optimal levels (75-125nmol/L).
In conclusion, there is not enough evidence on the association between vitamin D levels and COVID-19 severity and mortality.
Therefore, randomized control trials and cohort studies are necessary to test this hypothesis.

Answer: {"verdict": "SUPPORT", "evidence": ["A significant negative correlation (p=0.033) has been observed between mean vitamin D levels and COVID-19 cases per one million population in European countries.", "Several studies demonstrated the role of vitamin D in reducing the risk of acute viral respiratory tract infections and pneumonia.", "These include direct inhibition with viral replication or with anti-inflammatory or immunomodulatory ways.", "In the meta-analysis, vitamin D supplementation has been shown as safe and effective against acute respiratory tract infections.", "Thus, people who are at higher risk of vitamin D deficiency during this global pandemic should consider taking vitamin D supplements to maintain the circulating 25(OH)D in the optimal levels (75-125nmol/L)."]}


Question:
You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:

- "verdict": The fact-checking verdict. If the information in the abstract supports the claim, write "SUPPORT". If the abstract contradicts the claim, write "CONTRADICT". If the abstract does not provide enough information to arrive at a verdict, write "NEI" (for "not enough information").
- "evidence": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was "NEI", then return an empty array.

For instance, if the model were given the claim "wearing masks can prevent the spread of COVID", the output might be
{
  "verdict": "SUPPORT",
  "evidence": ["Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19."]
}

Claim: a popular treatment to tamp down the immune system in severely ill patients may help a few, but could harm many others.

Abstract:
Several related human coronaviruses (HCoVs) are endemic in the human population, causing mild respiratory infections1.
Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), the etiologic agent of Coronavirus disease 2019 (COVID-19), is a recent zoonotic infection that has quickly reached pandemic spread2,3.
Zoonotic introduction of novel coronaviruses is thought to occur in the absence of pre-existing immunity in the target human population.
Using diverse assays for detection of antibodies reactive with the SARS-CoV-2 Spike (S) glycoprotein, we demonstrate the presence of pre-existing immunity in uninfected and unexposed humans to the new coronavirus.
SARS-CoV-2 S-reactive antibodies, exclusively of the IgG class, were readily detectable by a sensitive flow cytometry-based method in SARS-CoV-2-uninfected individuals with recent HCoV infection and targeted the S2 subunit.
In contrast, SARS-CoV-2 infection induced higher titres of SARS-CoV-2 S-reactive IgG antibodies, as well as concomitant IgM and IgA antibodies throughout the observation period of 6 weeks since symptoms onset.
HCoV patient sera also variably reacted with SARS-CoV-2 S and nucleocapsid (N), but not with the S1 subunit or the receptor binding domain (RBD) of S on standard enzyme immunoassays.
Notably, HCoV patient sera exhibited specific neutralising activity against SARS-CoV-2 S pseudotypes, according to levels of SARS-CoV-2 S-binding IgG and with efficiencies comparable to those of COVID-19 patient sera.
Distinguishing pre-existing and de novo antibody responses to SARS-CoV-2 will be critical for serology, seroprevalence and vaccine studies, as well as for our understanding of susceptibility to and natural course of SARS-CoV-2 infection.

Answer: {"verdict": "NEI", "evidence": []}


Question:
You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:

- "verdict": The fact-checking verdict. If the information in the abstract supports the claim, write "SUPPORT". If the abstract contradicts the claim, write "CONTRADICT".
- "evidence": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.

For instance, if the model were given the claim "the COVID vaccine is safe for healthy adults", the output might be
{
  "verdict": "SUPPORT",
  "evidence": ["Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults."]
}

Claim: Intradermal-delivered hiv vaccine provides anamnestic protection in a rhesus macaque sars-cov-2 challenge model

Abstract:
Coronavirus disease 2019 (COVID-19), caused by the SARS-CoV-2 virus, has had a dramatic global impact on public health, social, and economic infrastructures.
Here, we assess immunogenicity and anamnestic protective efficacy in rhesus macaques of the intradermal (ID)-delivered SARS-CoV-2 spike DNA vaccine, INO-4800.
INO-4800 is an ID-delivered DNA vaccine currently being evaluated in clinical trials.
Vaccination with INO-4800 induced T cell responses and neutralizing antibody responses against both the D614 and G614 SARS-CoV-2 spike proteins.
Several months after vaccination, animals were challenged with SARS-CoV-2 resulting in rapid recall of anti-SARS-CoV-2 spike protein T and B cell responses.
These responses were associated with lower viral loads in the lung and with faster nasal clearance of virus.
These studies support the immune impact of INO-4800 for inducing both humoral and cellular arms of the adaptive immune system which are likely important for providing durable protection against COVID-19 disease.

Answer: {"verdict": "CONTRADICT", "evidence": ["Here, we assess immunogenicity and anamnestic protective efficacy in rhesus macaques of the intradermal (ID)-delivered SARS-CoV-2 spike DNA vaccine, INO-4800.", "Vaccination with INO-4800 induced T cell responses and neutralizing antibody responses against both the D614 and G614 SARS-CoV-2 spike proteins.", "Several months after vaccination, animals were challenged with SARS-CoV-2 resulting in rapid recall of anti-SARS-CoV-2 spike protein T and B cell responses.", "These studies support the immune impact of INO-4800 for inducing both humoral and cellular arms of the adaptive immune system which are likely important for providing durable protection against COVID-19 disease."]}


Question:
You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:

- "verdict": The fact-checking verdict. If the information in the abstract supports the claim, write "SUPPORT". If the abstract contradicts the claim, write "CONTRADICT". If the abstract does not provide enough information to arrive at a verdict, write "NEI" (for "not enough information").
- "evidence": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was "NEI", then return an empty array.

For instance, if the model were given the claim "wearing masks can prevent the spread of COVID", the output might be
{
  "verdict": "SUPPORT",
  "evidence": ["Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19."]
}

Claim: For most patients, COVID-19 begins and ends in their lungs, because like the flu, coronaviruses are respiratory diseases

Abstract:
The outbreak of the 2019 Novel Coronavirus (SARS-CoV-2) rapidly spread from Wuhan, China to more than 150 countries, areas or territories, causing staggering number of infections and deaths.
A systematic profiling of the immune vulnerability landscape of SARS-CoV-2, which can bring critical insights into the immune clearance mechanism, peptide vaccine development, and antiviral antibody development, is lacking.
In this study, we investigated the potential of the SARS-CoV-2 viral proteins to induce class I and II MHC presentation and to form linear antibody epitopes.
We created an online database to broadly share the predictions as a resource for the research community.
Using this resource, we showed that genetic variations in SARS- CoV-2, though still few for the moment, already follow the pattern of mutations in related coronaviruses, and could alter the immune vulnerability landscape of this virus.
Importantly, we discovered evidence that SARS-CoV-2, along with related coronaviruses, used mutations to evade attack from the human immune system.
Overall, we present an immunological resource for SARS-CoV-2 that could promote both therapeutic development and mechanistic research.

Answer: {"verdict": "NEI", "evidence": []}


Question:
You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:

- "verdict": The fact-checking verdict. If the information in the abstract supports the claim, write "SUPPORT". If the abstract contradicts the claim, write "CONTRADICT". If the abstract does not provide enough information to arrive at a verdict, write "NEI" (for "not enough information").
- "evidence": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was "NEI", then return an empty array.

For instance, if the model were given the claim "wearing masks can prevent the spread of COVID", the output might be
{
  "verdict": "SUPPORT",
  "evidence": ["Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19."]
}

Claim: There are an urgent need for antivirals to treat the newly emerged SARS-CoV-2.

Abstract:
The outbreak of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has posed the world at a pandemic risk.
Coronavirus-19 disease (COVID-19) is an infectious disease caused by SARS-CoV-2, which causes pneumonia, requires intensive care unit hospitalization in about 10% of cases and can lead to a fatal outcome.
Several efforts are currently made to find a treatment for COVID-19 patients.
So far, several anti-viral and immunosuppressive or immunomodulating drugs have demonstrated some efficacy on COVID-19 both in vitro and in animal models as well as in cases series.
In COVID-19 patients a pro-inflammatory status with high levels of interleukin (IL)-1B, IL-1 receptor (R)A and tumor necrosis factor (TNF)-α has been demonstrated.
Moreover, high levels of IL-6 and TNF-α have been observed in patients requiring intensive-care-unit hospitalization.
This provided rationale for the use of anti-rheumatic drugs as potential treatments for this severe viral infection.
Other agents, such as hydroxychloroquine and chloroquine might have a direct anti-viral effect.
The anti-viral aspect of immunosuppressants towards a variety of viruses has been known since long time and it is herein discussed in the view of searching for a potential treatment for SARS-CoV-2 infection.

Answer: {"verdict": "NEI", "evidence": []}


Question:
You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:

- "verdict": The fact-checking verdict. If the information in the abstract supports the claim, write "SUPPORT". If the abstract contradicts the claim, write "CONTRADICT". If the abstract does not provide enough information to arrive at a verdict, write "NEI" (for "not enough information").
- "evidence": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was "NEI", then return an empty array.

For instance, if the model were given the claim "wearing masks can prevent the spread of COVID", the output might be
{
  "verdict": "SUPPORT",
  "evidence": ["Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19."]
}

Claim: dexamethasone reduced death rates

Abstract:
Recent announcements indicated, without sharing any distinct published set of results, that the corticosteroid dexamethasone may reduce mortality of severe COVID-19 patients only.
The recent Coronavirus [severe acute respiratory syndrome (SARS)-CoV-2]-associated multiorgan disease, called COVID-19, has high morbidity and mortality due to autoimmune destruction of the lungs stemming from the release of a storm of pro-inflammatory cytokines.
Defense against this Corona virus requires activated T cells and specific antibodies.
Instead, cytokines are responsible for the serious sequelae of COVID-19 that damage the lungs.
Dexamethasone is a synthetic corticosteroid approved by the FDA 1958 as a broad-spectrum immunosuppressor and it is about 30 times as active and with longer duration of action (2-3 days) than cortisone.
Dexamethasone would limit the production of and damaging effect of the cytokines, but will also inhibit the protective function of T cells and block B cells from making antibodies, potentially leading to increased plasma viral load that will persist after a patient survives SARS.
Moreover, dexamethasone would block macrophages from clearing secondary, nosocomial, infections.
Hence, dexamethasone may be useful for the short-term in severe, intubated, COVID-19 patients, but could be outright dangerous during recovery since the virus will not only persist, but the body will be prevented from generating protective antibodies.
Instead, a pulse of intravenous dexamethasone may be followed by administration of nebulized triamcinolone (6 times as active as cortisone) to concentrate in the lungs only.
These corticosteroids could be given together with the natural flavonoid luteolin because of its antiviral and anti-inflammatory properties, especially its ability to inhibit mast cells, which are the main source of cytokines in the lungs.
At the end, we should remember that "The good physician treats the disease; the great physician treats the patient who has the disease" [Sir William Osler's (1849-1919)].

Answer: {"verdict": "CONTRADICT", "evidence": ["Hence, dexamethasone may be useful for the short-term in severe, intubated, COVID-19 patients, but could be outright dangerous during recovery since the virus will not only persist, but the body will be prevented from generating protective antibodies."]}


Question:
You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:

- "verdict": The fact-checking verdict. If the information in the abstract supports the claim, write "SUPPORT". If the abstract contradicts the claim, write "CONTRADICT". If the abstract does not provide enough information to arrive at a verdict, write "NEI" (for "not enough information").
- "evidence": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was "NEI", then return an empty array.

For instance, if the model were given the claim "wearing masks can prevent the spread of COVID", the output might be
{
  "verdict": "SUPPORT",
  "evidence": ["Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19."]
}

Claim: Unfortunately, to date, no vaccines or antiviral drugs have been approved for the treatment of SARS-CoV-2 infection by regulatory agencies.

Abstract:
Different treatments are currently used for clinical management of SARS-CoV-2 infection, but little is known about their efficacy yet.
Here we present ongoing results to compare currently available drugs for a variety of diseases to find out if they counteract SARS-CoV-2-induced cytopathic effect in vitro.
Our goal is to prioritize antiviral activity to provide a solid evidence-driven rationale for forthcoming clinical trials.
Since the most effective antiviral approaches are usually based on combined therapies that tackle the viral life cycle at different stages, we are also testing combinations of drugs that may be critical to reduce the emergence of resistant viruses.
We will provide results as soon as they become available, so data should be interpreted with caution, clearly understanding the limitations of the in vitro model, that may not always reflect what could happen in vivo.
Thus, our goal is to test the most active antivirals identified in adequate animal models infected with SARS-CoV-2, to add more information about possible in vivo efficacy.
In turn, successful antivirals could be tested in clinical trials as treatments for infected patients, but also as pre-exposure prophylaxis to avoid novel infections until an effective and safe vaccine is developed.

Answer: {"verdict": "NEI", "evidence": []}


Question:
You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:

- "verdict": The fact-checking verdict. If the information in the abstract supports the claim, write "SUPPORT". If the abstract contradicts the claim, write "CONTRADICT". If the abstract does not provide enough information to arrive at a verdict, write "NEI" (for "not enough information").
- "evidence": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was "NEI", then return an empty array.

For instance, if the model were given the claim "wearing masks can prevent the spread of COVID", the output might be
{
  "verdict": "SUPPORT",
  "evidence": ["Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19."]
}

Claim: Can coronavirus spread through food or packaging? No Absolutely not

Abstract:
Agriculture and the food sector are critical to food and nutrition security because they not only produce food but also contribute to economic empowerment by employing a large share of female and male workers, especially in developing countries.
Food systems at all levels―globally, domestically, locally, and in the home― are expected to be highly affected by the COVID-19 crisis.
Women and men work as food producers, processors, and traders and will likely be impacted differently.
Shocks or crises can exacerbate or reduce gender gaps, and so can policy responses to mitigate the impact of these crises or shocks.
We offer some perspectives and available country examples on how the COVID-19 crisis and responses to the crisis could be a setback or offer opportunities for gender equality in the food system.

Answer: {"verdict": "NEI", "evidence": []}


Question:
You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:

- "verdict": The fact-checking verdict. If the information in the abstract supports the claim, write "SUPPORT". If the abstract contradicts the claim, write "CONTRADICT". If the abstract does not provide enough information to arrive at a verdict, write "NEI" (for "not enough information").
- "evidence": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was "NEI", then return an empty array.

For instance, if the model were given the claim "wearing masks can prevent the spread of COVID", the output might be
{
  "verdict": "SUPPORT",
  "evidence": ["Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19."]
}

Claim: diabetes significantly increases coronavirus sufferers risk of dying

Abstract:
Some comorbidities are associated with severe coronavirus disease (Covid-19) but it is unclear whether some increase susceptibility to Covid-19.
In this case-control Mexican study we found that obesity represents the strongest predictor for Covid-19 followed by diabetes and hypertension in both sexes and chronic renal failure in females only.
Active smoking was associated with decreased odds of Covid-19.
These findings indicate that these comorbidities are not only associated with severity of disease but also predispose for getting Covid-19.
Future research is needed to establish the mechanisms involved in each comorbidity and the apparent "protective" effect of cigarette smoking.

Answer: {"verdict": "SUPPORT", "evidence": ["In this case-control Mexican study we found that obesity represents the strongest predictor for Covid-19 followed by diabetes and hypertension in both sexes and chronic renal failure in females only."]}


Question:
You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:

- "verdict": The fact-checking verdict. If the information in the abstract supports the claim, write "SUPPORT". If the abstract contradicts the claim, write "CONTRADICT". If the abstract does not provide enough information to arrive at a verdict, write "NEI" (for "not enough information").
- "evidence": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was "NEI", then return an empty array.

For instance, if the model were given the claim "wearing masks can prevent the spread of COVID", the output might be
{
  "verdict": "SUPPORT",
  "evidence": ["Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19."]
}

Claim: Can coronavirus spread through food or packaging? No Absolutely not

Abstract:
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is easily transmitted from person to person, which has fueled the ongoing pandemic.
Governments in different countries have taken drastic actions such as complete lockdown.
However, little attention has been paid to food safety and its potential linkage with the coronavirus disease (COVID-19) pandemic.
SARS-CoV-2 spread from staff to food products or food surfaces is conceivable.
At least, instead of consuming unpackaged or uncovered foods, consumption of boiled or canned foods processed at high temperatures should be preferred.
Before consumption, consumers should clean the surface of canned foods.
In addition to recommending or enforcing simple precautions, such as using masks, governments must conduct mandatory SARS-CoV-2 tests regularly and intermittently for personnel who handle food materials or supporting materials (e.g., plastic pouches).
Local markets, such as those in Wuhan, which sell live animals and exotic foods for consumption, are a concern.
Trade of exotic or wild animals, unhygienic marketplace conditions, and not cooking at high temperatures ought to be prohibited.
The consumption of vitamins, minerals, and other food-derived compounds such as omega fatty acids is a prudent way to improve the performance of the immune system.
In addition, nano-encapsulated materials with controlled release properties may be useful in protecting food products and packaging from SARS-CoV-2 contamination.

Answer: {"verdict": "SUPPORT", "evidence": ["Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is easily transmitted from person to person, which has fueled the ongoing pandemic."]}


Question:
You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:

- "verdict": The fact-checking verdict. If the information in the abstract supports the claim, write "SUPPORT". If the abstract contradicts the claim, write "CONTRADICT". If the abstract does not provide enough information to arrive at a verdict, write "NEI" (for "not enough information").
- "evidence": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was "NEI", then return an empty array.

For instance, if the model were given the claim "wearing masks can prevent the spread of COVID", the output might be
{
  "verdict": "SUPPORT",
  "evidence": ["Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19."]
}

Claim: People with Diabetes May Have Higher Risk for COVID-19

Abstract:
AIMS: We aimed to briefly review the general characteristics of the novel coronavirus (SARS-CoV-2) and provide a better understanding of the coronavirus disease (COVID-19) in people with diabetes, and its management.
METHODS: We searched for articles in PubMed and Google Scholar databases till 02 April 2020, with the following keywords: "SARS-CoV-2", "COVID-19", "infection", "pathogenesis", "incubation period", "transmission", "clinical features", "diagnosis", "treatment", "diabetes", with interposition of the Boolean operator "AND".
RESULTS: The clinical spectrum of COVID-19 is heterogeneous, ranging from mild flu-like symptoms to acute respiratory distress syndrome, multiple organ failure and death.
Older age, diabetes and other comorbidities are reported as significant predictors of morbidity and mortality.
Chronic inflammation, increased coagulation activity, immune response impairment, and potential direct pancreatic damage by SARS-CoV-2 might be among the underlying mechanisms of the association between diabetes and COVID-19.
No conclusive evidence exists to support the discontinuation of angiotensin-converting enzyme inhibitors (ACEI), angiotensin receptor blockers or thiazolidinediones because of COVID-19 in people with diabetes.
Caution should be taken to potential hypoglycemic events with the use of chloroquine in these subjects.
Patient tailored therapeutic strategies, rigorous glucose monitoring and careful consideration of drug interactions might reduce adverse outcomes.
CONCLUSIONS: Suggestions are made on the possible pathophysiological mechanisms of the relationship between diabetes and COVID-19, and its management.
No definite conclusions can be made based on current limited evidence.
Further research regarding this relationship and its clinical management is warranted.

Answer: {"verdict": "SUPPORT", "evidence": ["Older age, diabetes and other comorbidities are reported as significant predictors of morbidity and mortality.", "Chronic inflammation, increased coagulation activity, immune response impairment, and potential direct pancreatic damage by SARS-CoV-2 might be among the underlying mechanisms of the association between diabetes and COVID-19."]}


Question:
You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:

- "verdict": The fact-checking verdict. If the information in the abstract supports the claim, write "SUPPORT". If the abstract contradicts the claim, write "CONTRADICT". If the abstract does not provide enough information to arrive at a verdict, write "NEI" (for "not enough information").
- "evidence": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was "NEI", then return an empty array.

For instance, if the model were given the claim "wearing masks can prevent the spread of COVID", the output might be
{
  "verdict": "SUPPORT",
  "evidence": ["Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19."]
}

Claim: In severe cases of COVID-19, activation patterns of B cells resemble those seen in systemic lupus erythematosus, an autoimmune disease. Emory researchers want to see how far that resemblance extends.

Abstract:
The WHO has declared SARS-CoV-2 outbreak a public health emergency of international concern.
However, to date, there was hardly any study in characterizing the immune responses, especially adaptive immune responses to SARS-CoV-2 infection.
In this study, we collected blood from COVID-19 patients who have recently become virus-free and therefore were discharged, and analyzed their SARS-CoV-2-specific antibody and T cell responses.
We observed SARS-CoV-2-specific humoral and cellular immunity in the patients.
Both were detected in newly discharged patients, suggesting both participate in immune-mediated protection to viral infection.
However, follow-up patients (2 weeks post discharge) exhibited high titers of IgG antibodies, but with low levels of virus-specific T cells, suggesting that they may enter a quiescent state.
Our work has thus provided a basis for further analysis of protective immunity to SARS-CoV-2, and understanding the pathogenesis of COVID-19, especially in the severe cases.
It has also implications in designing an effective vaccine to protect and treat SARS-CoV-2 infection.

Answer: {"verdict": "NEI", "evidence": []}


Question:
You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:

- "verdict": The fact-checking verdict. If the information in the abstract supports the claim, write "SUPPORT". If the abstract contradicts the claim, write "CONTRADICT". If the abstract does not provide enough information to arrive at a verdict, write "NEI" (for "not enough information").
- "evidence": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was "NEI", then return an empty array.

For instance, if the model were given the claim "wearing masks can prevent the spread of COVID", the output might be
{
  "verdict": "SUPPORT",
  "evidence": ["Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19."]
}

Claim: To help slow the spread and reduce your risk of COVID-19, stay at least 6 feet away from others. Keeping physical distance is important, even if you are not sick.

Abstract:
BACKGROUND: The Australian and New Zealand governments both initiated strict social distancing measures in response to the COVID-19 pandemic in late March.
It remains difficult to quantify the impact this had in reducing the spread of the virus.
METHODS: Bayesian structural time series model provide a model to quantify the scenario in which these government-level interventions were not placed.
Our models predict these strict social distancing measures caused a 79% and 61% reduction in the daily cases of COVID-19 across Australia and New Zealand respectively.
CONCLUSION: This provides both evidence and impetus for governments considering similar measures in response to COVID-19 and other pandemics.

Answer: {"verdict": "SUPPORT", "evidence": ["Our models predict these strict social distancing measures caused a 79% and 61% reduction in the daily cases of COVID-19 across Australia and New Zealand respectively."]}


Question:
You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:

- "verdict": The fact-checking verdict. If the information in the abstract supports the claim, write "SUPPORT". If the abstract contradicts the claim, write "CONTRADICT".
- "evidence": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.

For instance, if the model were given the claim "the COVID vaccine is safe for healthy adults", the output might be
{
  "verdict": "SUPPORT",
  "evidence": ["Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults."]
}

Claim: Antibody responses to sars-cov2 are similar in children with mis-c compared to adults with covid-19

Abstract:
Clinical manifestations of COVID-19 caused by the novel coronavirus SARS-CoV-2 are associated with age.
While children are largely spared from severe respiratory disease, they can present with a SARS-CoV-2-associated multisystem inflammatory syndrome (MIS-C) similar to Kawasaki's disease.
Here, we show distinct antibody (Ab) responses in children with MIS-C compared to adults with severe COVID-19 causing acute respiratory distress syndrome (ARDS), and those who recovered from mild disease.
There was a reduced breadth and specificity of anti-SARS-CoV-2-specific antibodies in MIS-C patients compared to the COVID patient groups; MIS-C predominantly generated IgG Abs specific for the Spike (S) protein but not for the nucleocapsid (N) protein, while both COVID-19 cohorts had anti-S IgG, IgM and IgA Abs, as well as anti-N IgG Abs.
Moreover, MIS-C patients had reduced neutralizing activity compared to COVID-19 cohorts, indicating a reduced protective serological response.
These results suggest a distinct infection course and immune response in children and adults who develop severe disease, with implications for optimizing treatments based on symptom and age.

Answer: {"verdict": "CONTRADICT", "evidence": ["Here, we show distinct antibody (Ab) responses in children with MIS-C compared to adults with severe COVID-19 causing acute respiratory distress syndrome (ARDS), and those who recovered from mild disease.", "These results suggest a distinct infection course and immune response in children and adults who develop severe disease, with implications for optimizing treatments based on symptom and age."]}


Question:
You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:

- "verdict": The fact-checking verdict. If the information in the abstract supports the claim, write "SUPPORT". If the abstract contradicts the claim, write "CONTRADICT".
- "evidence": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.

For instance, if the model were given the claim "the COVID vaccine is safe for healthy adults", the output might be
{
  "verdict": "SUPPORT",
  "evidence": ["Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults."]
}

Claim: Single-hybrid analyses reveal sars-cov-2 interference with intrinsic immune response in the human gut

Abstract:
Objective Exacerbated pro-inflammatory immune response contributes to COVID-19 pathology.
Despite the evidence about SARS-CoV-2 infecting the human gut, little is known about the importance of the enteric phase of SARS-CoV-2 for the viral lifecycle and for the development of COVID-19-associated pathologies.
Similarly, it remains unknown whether the innate immune response triggered in this organ to combat viral infection is similar or distinct compared to the one triggered in other organs.
Design We exploited human ileum-and colon-derived organoids as a non-transformed culture model supporting SARS-CoV-2 infection.
We characterized the replication kinetics of SARS-CoV-2 in intestinal epithelial cells and correlated the expression of the viral receptor ACE2 with infection.
We performed conventional and targeted single-cell transcriptomics and multiplex single-molecule RNA fluorescence in situ hybridization and used IFN-reporter bioassays to characterize the response of primary human intestinal epithelial cells to SARS-CoV-2 infection.
Results We identified a subpopulation of enterocytes as the prime target of SARS-CoV-2.
We found the lack of positive correlation between susceptibility to infection and the expression of ACE2 and revealed that SARS-CoV-2 downregulates ACE2 expression upon infection.
Infected cells activated strong proinflammatory programs and produced interferon, while expression of interferon-stimulated genes was limited to bystander cells due to SARS-CoV-2 suppressing the autocrine action of interferon in infected cells.
Conclusion Our findings reveal that SARS-CoV-2 curtails the immune response in primary human intestinal epithelial cells to promote its replication and spread and this highlights the gut as a proinflammatory reservoir that should be considered to fully understand SARS-CoV-2 pathogenesis.
Significance of the study What is already known about this subject?
COVID-19 patients have gastrointestinal symptoms which likely correlates with SARS-CoV-2 infection of the intestinal epithelium SARS-CoV-2 replicates in human intestinal epithelial cells.
Intestinal organoids are a good model to study SARS-CoV-2 infection of the gastrointestinal tract There is a limited interferon response in human lung epithelial cells upon SARS-CoV-2 infection.
What are the new findings?
A specific subpopulation of enterocytes are the prime targets of SARS-CoV-2 infection of the human gut.
There is a lack of correlation between ACE2 expression and susceptibility to SARS-CoV-2 infection.
SARS-CoV-2 downregulates ACE2 expression upon infection.
Human intestinal epithelium cells produce interferon upon SARS-CoV-2 infection.
Interferon acts in a paracrine manner to induce interferon stimulated genes that control viral infection only in bystander cells.
SARS-CoV-2 actively blocks interferon signaling in infected cells.
How might it impact on clinical practice in the foreseeable future?
The absence of correlation between ACE2 levels and susceptibility suggest that medications influencing ACE2 levels (e.g. high blood pressure drugs) will not make patients more susceptible to SARS-CoV-2 infection.
The restricted cell tropism and the distinct immune response mounted by the GI tract, suggests that specific cellular restriction/replication factors and organ specific intrinsic innate immune pathways can represent unique therapeutic targets to treat COVD-19 patients by considering which organ is most infected/impacted by SARS-CoV-2.
The strong pro-inflammatory signal mounted by the intestinal epithelium can fuel the systemic inflammation observed in COVID-19 patients and is likely participating in the lung specific pathology.

Answer: {"verdict": "CONTRADICT", "evidence": ["Conclusion Our findings reveal that SARS-CoV-2 curtails the immune response in primary human intestinal epithelial cells to promote its replication and spread and this highlights the gut as a proinflammatory reservoir that should be considered to fully understand SARS-CoV-2 pathogenesis.", "Human intestinal epithelium cells produce interferon upon SARS-CoV-2 infection."]}


Question:
You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:

- "verdict": The fact-checking verdict. If the information in the abstract supports the claim, write "SUPPORT". If the abstract contradicts the claim, write "CONTRADICT". If the abstract does not provide enough information to arrive at a verdict, write "NEI" (for "not enough information").
- "evidence": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was "NEI", then return an empty array.

For instance, if the model were given the claim "wearing masks can prevent the spread of COVID", the output might be
{
  "verdict": "SUPPORT",
  "evidence": ["Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19."]
}

Claim: a small number of pets worldwide, including cats and dogs, can be infected with the virus that causes COVID-19, mostly after close contact with people with COVID-19.

Abstract:
Social distancing measures, with varying degrees of restriction, have been imposed around the world in order to stem the spread of COVID-19.
In this work we analyze the effect of current social distancing measures in the United States.
We quantify the reduction in doubling rate, by state, that is associated with social distancing.
We find that social distancing is associated with a statistically-significant reduction in the doubling rate for all but three states.
At the same time, we do not find significant evidence that social distancing has resulted in a reduction in the number of daily confirmed cases.
Instead, social distancing has merely stabilized the spread of the disease.
We provide an illustration of our findings for each state, including point estimates of the effective reproduction number, R, both with and without social distancing.
We also discuss the policy implications of our findings.

Answer: {"verdict": "NEI", "evidence": []}


Question:
You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:

- "verdict": The fact-checking verdict. If the information in the abstract supports the claim, write "SUPPORT". If the abstract contradicts the claim, write "CONTRADICT". If the abstract does not provide enough information to arrive at a verdict, write "NEI" (for "not enough information").
- "evidence": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was "NEI", then return an empty array.

For instance, if the model were given the claim "wearing masks can prevent the spread of COVID", the output might be
{
  "verdict": "SUPPORT",
  "evidence": ["Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19."]
}

Claim: The coronavirus came from the local wet market in China

Abstract:
Coronavirus disease 2019 (COVID-19) originated in the city of Wuhan, Hubei Province, Central China, and has spread quickly to 72 countries to date.
COVID-19 is caused by a novel coronavirus, named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) [previously provisionally known as 2019 novel coronavirus (2019-nCoV)].
At present, the newly identified SARS-CoV-2 has caused a large number of deaths with tens of thousands of confirmed cases worldwide, posing a serious threat to public health.
However, there are no clinically approved vaccines or specific therapeutic drugs available for COVID-19.
Intensive research on the newly emerged SARS-CoV-2 is urgently needed to elucidate the pathogenic mechanisms and epidemiological characteristics and to identify potential drug targets, which will contribute to the development of effective prevention and treatment strategies.
Hence, this review will focus on recent progress regarding the structure of SARS-CoV-2 and the characteristics of COVID-19, such as the aetiology, pathogenesis and epidemiological characteristics.

Answer: {"verdict": "SUPPORT", "evidence": ["Coronavirus disease 2019 (COVID-19) originated in the city of Wuhan, Hubei Province, Central China, and has spread quickly to 72 countries to date."]}


Question:
You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:

- "verdict": The fact-checking verdict. If the information in the abstract supports the claim, write "SUPPORT". If the abstract contradicts the claim, write "CONTRADICT". If the abstract does not provide enough information to arrive at a verdict, write "NEI" (for "not enough information").
- "evidence": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was "NEI", then return an empty array.

For instance, if the model were given the claim "wearing masks can prevent the spread of COVID", the output might be
{
  "verdict": "SUPPORT",
  "evidence": ["Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19."]
}

Claim: Unfortunately, to date, no vaccines or antiviral drugs have been approved for the treatment of SARS-CoV-2 infection by regulatory agencies.

Abstract:
The outbreak of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has posed the world at a pandemic risk.
Coronavirus-19 disease (COVID-19) is an infectious disease caused by SARS-CoV-2, which causes pneumonia, requires intensive care unit hospitalization in about 10% of cases and can lead to a fatal outcome.
Several efforts are currently made to find a treatment for COVID-19 patients.
So far, several anti-viral and immunosuppressive or immunomodulating drugs have demonstrated some efficacy on COVID-19 both in vitro and in animal models as well as in cases series.
In COVID-19 patients a pro-inflammatory status with high levels of interleukin (IL)-1B, IL-1 receptor (R)A and tumor necrosis factor (TNF)-α has been demonstrated.
Moreover, high levels of IL-6 and TNF-α have been observed in patients requiring intensive-care-unit hospitalization.
This provided rationale for the use of anti-rheumatic drugs as potential treatments for this severe viral infection.
Other agents, such as hydroxychloroquine and chloroquine might have a direct anti-viral effect.
The anti-viral aspect of immunosuppressants towards a variety of viruses has been known since long time and it is herein discussed in the view of searching for a potential treatment for SARS-CoV-2 infection.

Answer: {"verdict": "NEI", "evidence": []}


Question:
You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:

- "verdict": The fact-checking verdict. If the information in the abstract supports the claim, write "SUPPORT". If the abstract contradicts the claim, write "CONTRADICT".
- "evidence": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.

For instance, if the model were given the claim "the COVID vaccine is safe for healthy adults", the output might be
{
  "verdict": "SUPPORT",
  "evidence": ["Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults."]
}

Claim: Antibodies to sars-cov-2 are associated without protection against reinfection

Abstract:
BackgroundIt is critical to understand whether infection with Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) protects from subsequent reinfection.
MethodsWe investigated the incidence of SARS-CoV-2 PCR-positive results in seropositive and seronegative healthcare workers (HCWs) attending asymptomatic and symptomatic staff testing at Oxford University Hospitals, UK.
Baseline antibody status was determined using anti-spike and/or anti-nucleocapsid IgG assays and staff followed for up to 30 weeks.
We used Poisson regression to estimate the relative incidence of PCR-positive results and new symptomatic infection by antibody status, accounting for age, gender and changes in incidence over time.
ResultsA total of 12219 HCWs participated and had anti-spike IgG measured, 11052 were followed up after negative and 1246 after positive antibody results including 79 who seroconverted during follow up.
89 PCR-confirmed symptomatic infections occurred in seronegative individuals (0.46 cases per 10,000 days at risk) and no symptomatic infections in those with anti-spike antibodies.
Additionally, 76 (0.40/10,000 days at risk) anti-spike IgG seronegative individuals had PCR-positive tests in asymptomatic screening, compared to 3 (0.21/10,000 days at risk) seropositive individuals.
Overall, positive baseline anti-spike antibodies were associated with lower rates of PCR-positivity (with or without symptoms) (adjusted rate ratio 0.24 [95%CI 0.08-0.76, p=0.015]).
Rate ratios were similar using anti-nucleocapsid IgG alone or combined with anti-spike IgG to determine baseline status.
ConclusionsPrior SARS-CoV-2 infection that generated antibody responses offered protection from reinfection for most people in the six months following infection.
Further work is required to determine the long-term duration and correlates of post-infection immunity.

Answer: {"verdict": "CONTRADICT", "evidence": ["Overall, positive baseline anti-spike antibodies were associated with lower rates of PCR-positivity (with or without symptoms) (adjusted rate ratio 0.24 [95%CI 0.08-0.76, p=0.015])."]}


Question:
You will be shown a claim related to the COVID-19 pandemic, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:

- "verdict": The fact-checking verdict. If the information in the abstract supports the claim, write "SUPPORT". If the abstract contradicts the claim, write "CONTRADICT".
- "evidence": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.

For instance, if the model were given the claim "the COVID vaccine is safe for healthy adults", the output might be
{
  "verdict": "SUPPORT",
  "evidence": ["Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults."]
}

Claim: Social distancing = > 80 % of the population, covid-19 may be curbed 13 weeks, < = 70 % may always be curbed.

Abstract:
In this paper we develop an agent-based model for a fine-grained computational simulation of the ongoing COVID-19 pandemic in Australia.
This model is calibrated to reproduce several characteristics of COVID-19 transmission, accounting for its reproductive number, the length of incubation and generation periods, age-dependent attack rates, and the growth rate of cumulative incidence during a sustained and unmitigated local transmission.
An important calibration outcome is the age-dependent fraction of symptomatic cases, with this fraction for children found to be one-fifth of such fraction for adults.
We then apply the model to compare several intervention strategies, including restrictions on international air travel, case isolation, social distancing with varying levels of compliance, and school closures.
School closures are not found to bring decisive benefits.
We report an important transition across the levels of social distancing compliance, in the range between 70% and 80% levels.
This suggests that a compliance of below 70% is unlikely to succeed for any duration of social distancing, while a compliance at the 90% level is likely to control the disease within 13-14 weeks, when coupled with effective case isolation and international travel restrictions.

Answer: {"verdict": "CONTRADICT", "evidence": ["We report an important transition across the levels of social distancing compliance, in the range between 70% and 80% levels.", "This suggests that a compliance of below 70% is unlikely to succeed for any duration of social distancing, while a compliance at the 90% level is likely to control the disease within 13-14 weeks, when coupled with effective case isolation and international travel restrictions."]}


Question:
You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:

- "verdict": The fact-checking verdict. If the information in the abstract supports the claim, write "SUPPORT". If the abstract contradicts the claim, write "CONTRADICT". If the abstract does not provide enough information to arrive at a verdict, write "NEI" (for "not enough information").
- "evidence": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was "NEI", then return an empty array.

For instance, if the model were given the claim "wearing masks can prevent the spread of COVID", the output might be
{
  "verdict": "SUPPORT",
  "evidence": ["Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19."]
}

Claim: Early studies have found that cats are the animals most likely to catch the new coronavirus. They can also show symptoms of COVID-19 and might be able to pass it to other cats.

Abstract:
Little information on the SARS-CoV-2 virus in animals is available to date.
Whereas no one husbandry animal case has been reported to date, which would have significant implications in food safety, companion animals play a role in COVID-19 epidemiology that opens up new questions.
There is evidence that SARS-CoV-2 can infect felines, dogs and minks, and there is evidence of human-to-animal infection.
Likewise, the S protein nucleotide sequence of the SARS-CoV-2 virus isolated in domestic animals and humans is identical, and the replication of the SARS-CoV-2 in cats is efficient.
Besides, the epidemiological evidence for this current pandemic indicates that the spillover to humans was associated with close contact between man and exotic animals, very probably in Chinese wet markets, thus there is a growing general consensus that the exotic animal markets, should be strictly regulated.
The examination of these findings and the particular role of animals in COVID-19 should be carefully analyzed in order to establish preparation and containment measures.
Animal management and epidemiological surveillance must be also considered for COVID-19 control, and it can open up new questions regarding COVID-19 epidemiology and the role that animals play in it.

Answer: {"verdict": "SUPPORT", "evidence": ["There is evidence that SARS-CoV-2 can infect felines, dogs and minks, and there is evidence of human-to-animal infection."]}