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: It does not make a lot of sense that if somebody is otherwise healthy and young and they have hypertension alone, that they should be at increased risk

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

Abstract:
SARS-CoV-2 is the coronavirus agent of the COVID-19 pandemic causing high mortalities.
In contrast, the widely spread human coronaviruses OC43, HKU1, 229E, and NL63 tend to cause only mild symptoms.
The present study shows, by in silico analysis, that these common human viruses are expected to induce immune memory against SARS-CoV-2 by sharing protein fragments (antigen epitopes) for presentation to the immune system by MHC class I. A list of such epitopes is provided.
The number of these epitopes and the prevalence of the common coronaviruses suggest that a large part of the world population has some degree of specific immunity against SARS-CoV-2 already, even without having been infected by that virus.
For inducing protection, booster vaccinations enhancing existing immunity are less demanding than primary vaccinations against new antigens.
Therefore, for the discussion on vaccination strategies against COVID-19, the available immune memory against related viruses should be part of the consideration.

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:
OBJECTIVE.
To analyze the effectiveness of social distancing in the United States (U.S.).
METHODS.
A novel cell-phone ping data was used to quantify the measures of social distancing by all U.S. counties.
RESULTS.
Using a difference-in-difference approach results show that social distancing has been effective in slowing the spread of COVID-19.
CONCLUSIONS.
As policymakers face the very difficult question of the necessity and effectiveness of social distancing across the U.S., counties where the policies have been imposed have effectively increased social distancing and have seen slowing the spread of COVID-19.
These results might help policymakers to make the public understand the risks and benefits of the lockdown.

Answer: {"verdict": "SUPPORT", "evidence": ["As policymakers face the very difficult question of the necessity and effectiveness of social distancing across the U.S., counties where the policies have been imposed have effectively increased social distancing and have seen slowing the spread of 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: masks prevent me from spreading COVID-19

Abstract:
In the context of Coronavirus Disease (2019) (COVID-19) cases globally, there is a lack of consensus across cultures on whether wearing face masks is an effective physical intervention against disease transmission.
This study 1) illustrates transmission routes of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2); 2) addresses controversies surrounding the mask from perspectives of attitude, effectiveness, and necessity of wearing the mask with evidence that the use of mask would effectively interrupt the transmission of infectious diseases in both hospital settings and community settings; and 3) provides suggestion that the public should wear the mask during COVID-19 pandemic according to local context.
To achieve this goal, government should establish a risk adjusted strategy of mask use to scientifically publicize the use of masks, guarantee sufficient supply of masks, and cooperate for reducing health resources inequities.

Answer: {"verdict": "SUPPORT", "evidence": ["This study 1) illustrates transmission routes of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2); 2) addresses controversies surrounding the mask from perspectives of attitude, effectiveness, and necessity of wearing the mask with evidence that the use of mask would effectively interrupt the transmission of infectious diseases in both hospital settings and community settings; and 3) provides suggestion that the public should wear the mask during COVID-19 pandemic according to local context."]}


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: No, garlic won't prevent COVID-19. These 'home remedies' and 'cures' just don't work

Abstract:
OBJECTIVE To analyze the characteristics of YouTube videos in Spanish on the basic measures to prevent coronavirus disease 2019 (COVID-19).
METHODS On 18 March 2020, a search was conducted on YouTube using the terms "Prevencion Coronavirus" and "Prevencion COVID-19".
We studied the associations between the type of authorship and the country of publication with other variables (such as the number of likes and basic measures to prevent COVID-19 according to the World Health Organization, among others) with univariate analysis and a multiple logistic regression model.
RESULTS A total of 129 videos were evaluated; 37.2% were produced in Mexico (25.6%) and Spain (11.6%), and 56.6% were produced by mass media, including television and newspapers.
The most frequently reported basic preventive measure was hand washing (71.3%), and the least frequent was not touching the eyes, nose, and mouth (24.0%).
Hoaxes (such as eating garlic or citrus to prevent COVID-19) were detected in 15 videos (10.9%).
In terms of authorship, papers produced by health professionals had a higher probability of reporting hand hygiene (OR (95% CI) = 4.20 (1.17-15.09)) and respiratory hygiene (OR (95% CI) = 3.05 (1.22-7.62)) as preventive measures.
CONCLUSION Information from YouTube in Spanish on basic measures to prevent COVID-19 is usually not very complete and differs according to the type of authorship.
Our findings make it possible to guide Spanish-speaking users on the characteristics of the videos to be viewed in order to obtain reliable information.

Answer: {"verdict": "SUPPORT", "evidence": ["Hoaxes (such as eating garlic or citrus to prevent COVID-19) were detected in 15 videos (10.9%)."]}


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: Thrombotic microvascular injury is not mediated by thrombotic microangiopathy despite systemic complement activation in covid-19 patients

Abstract:
Hypoxemia and coagulopathy are common in severe symptomatic patients of coronavirus disease 2019 (COVID-19).
Histological evidence shows implication of complement activation and lung injury.
We research sign of complement activation and presence of thrombotic microangiopathy in 8 severe patients.
Six of them presented moderate elevation of final pathway of complement / sC5b-9 (median value : 350 ng/mL [IQR : 300,5-514,95 ng/mL]).
Two patients have been autopsied and presence of thrombotic microvascular injury have been found.
Interestingly, none the 8 patients had signs of mechanical hemolytic anemia (median value of hemoglobin : 10,5 gr/dL[IQR : 8,1-1,9], median value of haptoglobuline 4,49 [IQR 3,55-4,66], none of the patients has schistocyte) and thrombocytopenia (median value: 348000/mL [IQR : 266 000-401 000).
Finally, all 8 patients had elevated d-dimer (median value : 2226 microgr/l [IQR : 1493-2362]) and soluble fibrin monomer complex (median value : 8.5 mg/mL, IQR[ <6-10.6]).
In summary, this study show moderate activation of complement and coagulation with presence of thrombotic microvascular injury in patients with severe COVID-19 without evidence of systemic thrombotic microangiopathy.

Answer: {"verdict": "SUPPORT", "evidence": ["In summary, this study show moderate activation of complement and coagulation with presence of thrombotic microvascular injury in patients with severe COVID-19 without evidence of systemic thrombotic microangiopathy."]}


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: Coronavirus nsp10/nsp16 methyltransferase can be targeted by nsp10-derived peptide in vitro and in vivo to reduce replication and pathogenesis

Abstract:
UNLABELLED The 5' cap structures of eukaryotic mRNAs are important for RNA stability and protein translation.
Many viruses that replicate in the cytoplasm of eukaryotes have evolved 2'-O-methyltransferases (2'-O-MTase) to autonomously modify their mRNAs and carry a cap-1 structure (m7GpppNm) at the 5' end, thereby facilitating viral replication and escaping innate immune recognition in host cells.
Previous studies showed that the 2'-O-MTase activity of severe acute respiratory syndrome coronavirus (SARS-CoV) nonstructural protein 16 (nsp16) needs to be activated by nsp10, whereas nsp16 of feline coronavirus (FCoV) alone possesses 2'-O-MTase activity (E. Decroly et al., J Virol 82:8071-8084, 2008, http://dx.doi.org/10.1128/JVI.00407-08; M. Bouvet et al., PLoS Pathog 6:e1000863, 2010, http://dx.doi.org/10.1371/journal.ppat.1000863; E. Decroly et al., PLoS Pathog 7:e1002059, 2011, http://dx.doi.org/10.1371/journal.ppat.1002059; Y. Chen et al., PLoS Pathog 7:e1002294, 2011, http://dx.doi.org/10.1371/journal.ppat.1002294) .
In this study, we demonstrate that stimulation of nsp16 2'-O-MTase activity by nsp10 is a universal and conserved mechanism in coronaviruses, including FCoV, and that nsp10 is functionally interchangeable in the stimulation of nsp16 of different coronaviruses.
Based on our current and previous studies, we designed a peptide (TP29) from the sequence of the interaction interface of mouse hepatitis virus (MHV) nsp10 and demonstrated that the peptide inhibits the 2'-O-MTase activity of different coronaviruses in biochemical assays and the viral replication in MHV infection and SARS-CoV replicon models.
Interestingly, the peptide TP29 exerted robust inhibitory effects in vivo in MHV-infected mice by impairing MHV virulence and pathogenesis through suppressing virus replication and enhancing type I interferon production at an early stage of infection.
Therefore, as a proof of principle, the current results indicate that coronavirus 2'-O-MTase activity can be targeted in vitro and in vivo.
IMPORTANCE Coronaviruses are important pathogens of animals and human with high zoonotic potential.
SARS-CoV encodes the 2'-O-MTase that is composed of the catalytic subunit nsp16 and the stimulatory subunit nsp10 and plays an important role in virus genome replication and evasion from innate immunity.
Our current results demonstrate that stimulation of nsp16 2'-O-MTase activity by nsp10 is a common mechanism for coronaviruses, and nsp10 is functionally interchangeable in the stimulation of nsp16 among different coronaviruses, which underlies the rationale for developing inhibitory peptides.
We demonstrate that a peptide derived from the nsp16-interacting domain of MHV nsp10 could inhibit 2'-O-MTase activity of different coronaviruses in vitro and viral replication of MHV and SARS-CoV replicon in cell culture, and it could strongly inhibit virus replication and pathogenesis in MHV-infected mice.
This work makes it possible to develop broad-spectrum peptide inhibitors by targeting the nsp16/nsp10 2'-O-MTase of coronaviruses.

Answer: {"verdict": "SUPPORT", "evidence": ["We demonstrate that a peptide derived from the nsp16-interacting domain of MHV nsp10 could inhibit 2'-O-MTase activity of different coronaviruses in vitro and viral replication of MHV and SARS-CoV replicon in cell culture, and it could strongly inhibit virus replication and pathogenesis in MHV-infected mice."]}


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: if you are low risk (healthy, young), you do not need social distancing.

Abstract:
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.
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.
This provides both evidence and impetus for governments considering similar measures in response to COVID-19 and other pandemics.

Answer: {"verdict": "CONTRADICT", "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.", "This provides both evidence and impetus for governments considering similar measures in response to COVID-19 and other pandemics."]}


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 testosterone levels predict clinical adverse outcomes in sars-cov-2 pneumonia patients

Abstract:
BACKGROUND: The pandemic of new severe acute respiratory syndrome (SARS) due to coronavirus (CoV) 2 (SARS-CoV-2) has stressed the importance of effective diagnostic and prognostic biomarkers of clinical worsening and mortality.
Epidemiological data showing a differential impact of SARS-CoV-2 infection on women and men have suggested a potential role for testosterone (T) in determining gender disparity in the SARS-CoV-2 clinical outcomes.
OBJECTIVES: To estimate the association between T level and SARS-CoV-2 clinical outcomes (defined as conditions requiring transfer to higher or lower intensity of care or death) in a cohort of patients admitted in the respiratory intensive care unit (RICU).
MATERIALS AND METHODS: A consecutive series of 31 male patients affected by SARS-CoV-2 pneumonia and recovered in the respiratory intensive care unit (RICU) of the "Carlo Poma" Hospital in Mantua were analyzed.
Several biochemical risk factors (ie, blood count and leukocyte formula, C-reactive protein (CRP), procalcitonin (PCT), lactate dehydrogenase (LDH), ferritin, D-dimer, fibrinogen, interleukin 6 (IL-6)) as well as total testosterone (TT), calculated free T (cFT), sex hormone-binding globulin (SHBG), and luteinizing hormone (LH) were determined.
RESULTS: Lower TT and cFT were found in the transferred to ICU/deceased in RICU group vs groups of patients transferred to IM or maintained in the RICU in stable condition.
Both TT and cFT showed a negative significant correlation with biochemical risk factors (ie, the neutrophil count, LDH, and PCT) but a positive association with the lymphocyte count.
Likewise, TT was also negatively associated with CRP and ferritin levels.
A steep increase in both ICU transfer and mortality risk was observed in men with TT < 5 nmol/L or cFT < 100 pmol/L. DISCUSSION AND CONCLUSION: Our study demonstrates for the first time that lower baseline levels of TT and cFT levels predict poor prognosis and mortality in SARS-CoV-2-infected men admitted to RICU.

Answer: {"verdict": "SUPPORT", "evidence": ["Epidemiological data showing a differential impact of SARS-CoV-2 infection on women and men have suggested a potential role for testosterone (T) in determining gender disparity in the SARS-CoV-2 clinical outcomes."]}


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: Covid-19 hits the old hardest, and young people do not die from it

Abstract:
Background: The coronavirus 2019 (COVID-19) pandemic has been spread-ing globally for months, yet the infection fatality ratio of the disease is still uncertain.
This is partly because of inconsistencies in testing and death reporting standards across countries.
Our purpose is to provide accurate estimates which do not rely on testing and death count data directly but only use population level statistics.
Methods: We collected demographic and death records data from the Italian Institute of Statistics.
We focus on the area in Italy that experienced the initial outbreak of COVID-19 and estimated a Bayesian model fitting age-stratified mortality data from 2020 and previous years.
We also assessed the sensitivity of our estimates to alternative assumptions on the proportion of population infected.
Findings: We estimate an overall infection fatality rate of 1.29% (95% credible interval [CrI] 0.89 - 2.01), as well as large differences by age, with a low infection fatality rate of 0.05% for under 60 year old (CrI 0-.19) and a substantially higher 4.25% (CrI 3.01-6.39) for people above 60 years of age.
In our sensitivity analysis, we found that even under extreme assumptions, our method delivered useful information.
For instance, even if only 10% of the population were infected, the infection fatality rate would not rise above 0.2% for people under 60.
Interpretation: Our empirical estimates based on population level data show a sharp difference in fatality rates between young and old people and firmly rule out overall fatality ratios below 0.5% in populations with more than 30% over 60 years old.

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's been much speculation about whether vitamin D might prevent or help survival with COVID-19, and two new studies appear to underscore the link.

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": "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: COVID-19: Hand sanitizers inactivate novel coronavirus

Abstract:
The recent emergence of Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causing COVID-19 is a major burden for health care systems worldwide.
It is important to address if the current infection control instructions based on active ingredients are sufficient.
We therefore determined the virucidal activity of two alcohol-based hand rub solutions for hand disinfection recommended by the World Health Organization (WHO), as well as commercially available alcohols.
Efficient SARS-CoV-2 inactivation was demonstrated for all tested alcohol-based disinfectants.
These findings show the successful inactivation of SARS-CoV-2 for the first time and provide confidence in its use for the control of COVID-19.
Importance The current COVID-19 outbreak puts a huge burden on the world’s health care systems.
Without effective therapeutics or vaccines being available, effective hygiene measure are of utmost importance to prevent viral spreading.
It is therefore crucial to evaluate current infection control strategies against SARS-CoV-2.
We show the inactivation of the novel coronavirus for the first time and endorse the importance of disinfectant-based hand hygiene to reduce SARS-CoV-2 transmission.

Answer: {"verdict": "SUPPORT", "evidence": ["Efficient SARS-CoV-2 inactivation was demonstrated for all tested alcohol-based disinfectants.", "These findings show the successful inactivation of SARS-CoV-2 for the first time and provide confidence in its use for the control of 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: 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:
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)--the virus that causes coronavirus disease (COVID-19)--has been detected in domestic dogs and cats, raising concerns of transmission from, to, or between these animals.
There is currently no indication that feline- or canine-to-human transmission can occur, though there is rising evidence of the reverse.
To explore the extent of animal-related transmission, we aggregated 17 case reports on confirmed SARS-CoV-2 infections in animals as of 15 May 2020.
All but two animals fully recovered and had only mild respiratory or digestive symptoms.
Using data from probable cat-to-cat transmission in Wuhan, China, we estimated the basic reproduction number R0 under this scenario at 1.09 (95% confidence interval: 1.05, 1.13).
This value is much lower than the R0 reported for humans and close to one, indicating that the sustained transmission between cats is unlikely to occur.
Our results support the view that the pet owners and other persons with COVID-19 in close contact with animals should be cautious of the way they interact with them.

Answer: {"verdict": "SUPPORT", "evidence": ["There is currently no indication that feline- or canine-to-human transmission can occur, though there is rising evidence of the reverse.", "Using data from probable cat-to-cat transmission in Wuhan, China, we estimated the basic reproduction number R0 under this scenario at 1.09 (95% confidence interval: 1.05, 1.13).", "This value is much lower than the R0 reported for humans and close to one, indicating that the sustained transmission between cats is unlikely to occur."]}


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: If your head is spinning, let medical experts provide some focus on one of the symptoms of the coronavirus.

Abstract:
INTRODUCTION: Neurological manifestations can occur during coronavirus disease 19 (COVID-19).
Several pathogenic mechanisms have been hypothesized, without conclusive results.
In this study, we evaluated the most frequent neurological symptoms in a cohort of hospitalized COVID-19 patients, and also investigated the possible relationship between plasmatic inflammatory indices and olfactory disorders (ODs) and between muscle pain and creatine kinase (CK).
METHODS: We consecutively enrolled hospitalized COVID-19 patients.
A structured questionnaire concerning typical and neurological symptoms, focusing on headache, dizziness, ODs, taste disorders (TDs), and muscle pain, was administrated by telephone interviews.
RESULTS: Common neurological symptoms were reported in the early phase of the disease, with a median onset ranging from 1 to 3 days.
Headache showed tension-type features and was more frequently associated with a history of headache.
Patients with ODs less frequently needed oxygen therapy.
Inflammatory indices did not significantly differ between patients with and without ODs.
Muscle pain did not show any association with CK level but was more frequently associated with arthralgia and headache.
CONCLUSION: In our cohort, ODs were an early symptom of COVID-19, more frequently reported by patients with milder forms of disease.
Headache in association with arthralgia and muscle pain seems to reflect the common symptoms of the flu-like syndrome, and not COVID-19 infection-specific.

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: Blood pressure drugs may improve COVID-19 survival

Abstract:
Intravenous infusions of angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin receptor blockers (ARBs) in experimental animals increase the numbers of angiotensin-converting enzyme 2 (ACE2) receptors in the cardiopulmonary circulation.
ACE2 receptors serve as binding sites for SARS-CoV-2 virions in the lungs.
Patients who take ACEIs and ARBS may be at increased risk of severe disease outcomes due to SARS-CoV-2 infections.

Answer: {"verdict": "CONTRADICT", "evidence": ["Patients who take ACEIs and ARBS may be at increased risk of severe disease outcomes due to SARS-CoV-2 infections."]}


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 risk of pets spreading Covid-19 to humans is considered 'medium'

Abstract:
On April 22, CDC and the U.S. Department of Agriculture (USDA) reported cases of two domestic cats with confirmed infection with SARS-CoV-2, the virus that causes coronavirus disease 2019 (COVID-19).
These are the first reported companion animals (including pets and service animals) with SARS-CoV-2 infection in the United States, and among the first findings of SARS-CoV-2 symptomatic companion animals reported worldwide.
These feline cases originated from separate households and were epidemiologically linked to suspected or confirmed human COVID-19 cases in their respective households.
Notification of presumptive positive animal test results triggered a One Health* investigation by state and federal partners, who determined that no further transmission events to other animals or persons had occurred.
Both cats fully recovered.
Although there is currently no evidence that animals play a substantial role in spreading COVID-19, CDC advises persons with suspected or confirmed COVID-19 to restrict contact with animals during their illness and to monitor any animals with confirmed SARS-CoV-2 infection and separate them from other persons and animals at home (1).

Answer: {"verdict": "CONTRADICT", "evidence": ["Although there is currently no evidence that animals play a substantial role in spreading COVID-19, CDC advises persons with suspected or confirmed COVID-19 to restrict contact with animals during their illness and to monitor any animals with confirmed SARS-CoV-2 infection and separate them from other persons and animals at home (1)."]}


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: Iga dominates the early neutralizing antibody response to sars-cov-2

Abstract:
Humoral immune responses are typically characterized by primary IgM antibody responses followed by secondary antibody responses associated with immune memory and comprised of of IgG, IgA and IgE. Here we measured acute humoral responses to SARS-CoV-2, including the frequency of antibody-secreting cells and the presence of SARS-CoV-2-specific neutralizing antibodies in the serum, saliva and broncho-alveolar fluid of 159 patients with COVID-19.
Early SARS-CoV-2-specific humoral responses were dominated by IgA antibodies.
Peripheral expansion of IgA plasmablasts with mucosal-homing potential was detected shortly after the onset of symptoms and peaked during the third week of the disease.
The virus-specific antibody responses included IgG, IgM and IgA, but IgA contributed to virus neutralization to a greater extent compared with IgG. Specific IgA serum concentrations decreased notably one month after the onset of symptoms, but neutralizing IgA remained detectable in saliva for a longer time (days 49 to 73 post symptoms).
These results represent a critical observation given the emerging information as to the types of antibodies associated with optimal protection against re-infection, and whether vaccine regimens should consider targeting a potent but potentially short-lived IgA response.

Answer: {"verdict": "SUPPORT", "evidence": ["Early SARS-CoV-2-specific humoral responses were dominated by IgA 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: the Covid-19 coronavirus can stay on various surfaces for a while

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": "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: Coronaviruses cause respiratory illnesses, so the lungs are usually affected first. Early symptoms include fever, cough, and shortness of breath.

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 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: A novel in-cell elisa assay allows rapid and automated quantification of sars-cov-2 to analyze neutralizing antibodies and antiviral compounds

Abstract:
The coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is currently the most pressing medical and socioeconomic challenge.
Constituting important correlates of protection, the determination of virus-neutralizing antibodies (NAbs) is indispensable for convalescent plasma selection, vaccine candidate evaluation, and immunity certificates.
In contrast to standard serological ELISAs, plaque reduction neutralization tests (PRNTs) are laborious, time-consuming, expensive, and restricted to specialized laboratories.
To replace microscopic counting-based SARS-CoV-2 PRNTs by a novel assay exempt from genetically modified viruses, which are inapplicable in most diagnostics departments, we established a simple, rapid, and automated SARS-CoV-2 neutralization assay employing an in-cell ELISA (icELISA) approach.
After optimization of various parameters such as virus-specific antibodies, cell lines, virus doses, and duration of infection, SARS-CoV-2-infected cells became amenable as direct antigen source for quantitative icELISA.
Antiviral agents such as human sera containing NAbs or antiviral interferons dose dependently reduced the SARS-CoV-2-specific signal.
Applying increased infectious doses, the icELISA-based neutralization test (icNT) was superior to PRNT in discriminating convalescent sera with high from those with intermediate neutralizing capacities.
In addition, the icNT was found to be specific, discriminating between SARS-CoV-2-specific NAbs and those raised against other coronaviruses.
Altogether, the SARS-CoV-2 icELISA test allows rapid (<48 h in total, read-out in seconds) and automated quantification of virus infection in cell culture to evaluate the efficacy of NAbs and antiviral drugs using reagents and equipment present in most routine diagnostics departments.

Answer: {"verdict": "SUPPORT", "evidence": ["To replace microscopic counting-based SARS-CoV-2 PRNTs by a novel assay exempt from genetically modified viruses, which are inapplicable in most diagnostics departments, we established a simple, rapid, and automated SARS-CoV-2 neutralization assay employing an in-cell ELISA (icELISA) approach.", "Altogether, the SARS-CoV-2 icELISA test allows rapid (<48 h in total, read-out in seconds) and automated quantification of virus infection in cell culture to evaluate the efficacy of NAbs and antiviral drugs using reagents and equipment present in most routine diagnostics departments."]}


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: Vitamins and Minerals Help Fight Coronavirus

Abstract:
BACKGROUND The coronavirus disease 2019 (COVID-19) pandemic has affected almost 2.5 million people worldwide with almost 170 000 deaths reported to date.
So far, there is scarce evidence for the current treatment options available for COVID-19.
Vitamin C has previously been used for treatment of severe sepsis and septic shock.
We reviewed the feasibility of using vitamin C in the setting of COVID-19 in a series of patients.
METHODS We sequentially identified a series of patients who were requiring at least 30% of FiO2 or more who received IV vitamin C as part of the COVID-19 treatment and analyzed their demographic and clinical characteristics.
We compared inflammatory markers pre and post treatment including D-dimer and ferritin.
RESULTS We identified a total of 17 patients who received IV vitamin C for COVID-19.
The inpatient mortality rate in this series was 12% with 17.6% rates of intubation and mechanical ventilation.
We noted a significant decrease in inflammatory markers, including ferritin and D-dimer, and a trend to decreasing FiO2 requirements, after vitamin C administration.
CONCLUSION The use of IV vitamin C in patients with moderate to severe COVID-19 disease may be feasible.

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: Blood pressure drugs may improve COVID-19 survival

Abstract:
The effects of angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin receptor blockers (ARBs) on the risk of COVID-19 infection and disease progression are yet to be investigated.
The relationship between ACEI/ARB use and COVID-19 infection was systematically reviewed.
To identify relevant studies that met predetermined inclusion criteria, unrestricted searches of the PubMed, Embase, and Cochrane Library databases were conducted.
The search strategy included clinical date published until May 9, 2020.
Twelve articles involving more than 19,000 COVID-19 cases were included.
To estimate overall risk, random-effects models were adopted.
Our results showed that ACEI/ARB exposure was not associated with a higher risk of COVID-19 infection (OR = 0.99; 95 % CI, 0-1.04; P = 0.672).
Among those with COVID-19 infection, ACEI/ARB exposure was also not associated with a higher risk of having severe infection (OR = 0.98; 95 % CI, 0.87-1.09; P = 0.69) or mortality (OR = 0.73, 95 %CI, 0.5-1.07; P = 0.111).
However, ACEI/ARB exposure was associated with a lower risk of mortality compared to those on non-ACEI/ARB antihypertensive drugs (OR = 0.48, 95 % CI, 0.29-0.81; P = 0.006).
In conclusion, current evidence did not confirm the concern that ACEI/ARB exposure is harmful in patientswith COVID-19 infection.
This study supports the current guidelines that discourage discontinuation of ACEIs or ARBs in COVID-19 patients and the setting of the COVID-19 pandemic.

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: Officials believe that the virus was present in meat sold at the said market.

Abstract:
Coronavirus disease-19 (COVID-19) caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is now a pandemic threat.
This virus is supposed to be spread by human to human transmission.
Cellular angiotensin-converting enzyme 2 (ACE2) is the receptor of SARS-CoV-2 which is identical or similar in different species of animals such as pigs, ferrets, cats, orangutans, monkeys, and humans.
Moreover, a recent study predicted that dogs might be secondary hosts during the evolution of SARS-CoV-2 from bat to human.
Therefore, there is a possibility of spreading SARS-CoV-2 through domestic pets.
There are now many reports of SARS-CoV-2 positive cases in dogs, cats, tigers, lion, and minks.
Experimental data showed ferrets and cats are highly susceptible to SARS-CoV-2 as infected by virus inoculation and can transmit the virus directly or indirectly by droplets or airborne routes.
Based on these natural infection reports and experimental data, whether the pets are responsible for SARS-CoV-2 spread to humans; needs to be deeply investigated.
Humans showing clinical symptoms of respiratory infections have been undergoing for the COVID-19 diagnostic test but many infected people and few pets confirmed with SARS-CoV-2 remained asymptomatic.
In this review, we summarize the natural cases of SARS-CoV-2 in animals with the latest researches conducted in this field.
This review will be helpful to think insights of SARS-CoV-2 transmissions, spread, and demand for seroprevalence studies, especially in companion animals.

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

Abstract:
Importance: Vitamin D treatment has been found to decrease incidence of viral respiratory tract infection, especially in vitamin D deficiency.
It is unknown whether COVID-19 incidence is associated with vitamin D deficiency and treatment.
Objective: To examine whether vitamin D deficiency and treatment are associated with testing positive for COVID-19.
Design: Retrospective cohort study Setting: University of Chicago Medicine Participants: Patients tested for COVID-19 from 3/3/2020-4/10/2020.
Vitamin D deficiency was defined by the most recent 25-hydroxycholecalciferol <20ng/ml or 1,25-dihydroxycholecalciferol <18pg/ml within 1 year before COVID-19 testing.
Treatment was defined by the most recent vitamin D type and dose, and treatment changes between the time of the most recent vitamin D level and time of COVID-19 testing.
Vitamin D deficiency and treatment changes were combined to categorize vitamin D status at the time of COVID-19 testing as likely deficient(last-level-deficient/treatment-not-increased), likely sufficient(last-level-not-deficient/treatment-not-decreased), or uncertain deficiency(last-level-deficient/treatment-increased or last-level-not-deficient/treatment-decreased).
Main Outcomes and Measures: The main outcome was testing positive for COVID-19.
Multivariable analysis tested whether the most recent vitamin D level and treatment changes after that level were associated with testing positive for COVID-19 controlling for demographic and comorbidity indicators.
Bivariate analyses of associations of treatment with vitamin D deficiency and COVID-19 were performed.
Results: Among 4,314 patients tested for COVID-19, 499 had a vitamin D level in the year before testing.
Vitamin D status at the time of COVID-19 testing was categorized as likely deficient for 127(25%) patients, likely sufficient for 291(58%) patients, and uncertain for 81(16%) patients.
In multivariate analysis, testing positive for COVID-19 was associated with increasing age(RR(age<50)=1.05,p<0.021;RR(age[≥]50)=1.02,p<0.064)), non-white race(RR=2.54,p<0.01) and being likely vitamin D deficient (deficient/treatment-not-increased:RR=1.77,p<0.02) as compared to likely vitamin D sufficient(not-deficient/treatment-not-decreased), with predicted COVID-19 rates in the vitamin D deficient group of 21.6%(95%CI[14.0%-29.2%] ) versus 12.2%(95%CI[8.9%-15.4%]) in the vitamin D sufficient group.
Vitamin D deficiency declined with increasing vitamin D dose, especially of vitamin D3.
Vitamin D dose was not significantly associated with testing positive for COVID-19.
Conclusions and Relevance: Vitamin D deficiency that is not sufficiently treated is associated with COVID-19 risk.
Testing and treatment for vitamin D deficiency to address COVID-19 warrant aggressive pursuit and study.

Answer: {"verdict": "SUPPORT", "evidence": ["Conclusions and Relevance: Vitamin D deficiency that is not sufficiently treated is associated with COVID-19 risk."]}


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: Hydroxychloroquine safety outcome within approved therapeutic protocol for covid-19 outpatients in saudi arabia

Abstract:
Background: Healthcare systems globally has been challenged following the COVID-19 pandemic, since late 2019.
Multiple approaches and strategies have been performed to relieve the pressure and support existing healthcare systems.
The Saudi Arabian Ministry of Health (MOH) launched an initiative to support the National Healthcare System.
Since the 5th of June 2020, 238 outpatient fever clinics were established across Saudi Arabia.
Methods: A cross-sectional study included 2,733 eligible patients subjected to MOH treatment protocol (hydroxychloroquine and zinc) and revisited the clinics within 3-7 days after treatment initiation.
This study aimed to assess the safety outcome and reported adverse events from hydroxychloroquine use among suspected COVID-19 patients.
The data was collected through an electronic link and cross-checked with that of the national database (Health Electronic Surveillance Network, HESN) and reports from the MOH Morbidity and Mortality (M&M) Committee.
Results: Majority of the cases were males (70.4%).
Upon reassessing the studied participants within 3-7 days, 240 patients (8.8%) discontinued the treatment protocol because of the development of side effects (4.1%) and for non-clinical reasons in the remaining (4.7%).
Medication side effects overall were reported among (6.7%) of all studied participants, including mainly cardiovascular adverse events (2.5%), followed by gastrointestinal (GI) symptoms (2.4%).
No Intensive Care Unit (ICU) admission or death were reported among these patients.
Conclusion: In our study, results show that the use of hydroxychloroquine for COVID-19 patients in mild to moderate cases in an outpatient setting, within the protocol recommendation and inclusion/exclusion criteria, is safe, highly tolerable, and with minimum side effects.

Answer: {"verdict": "SUPPORT", "evidence": ["Conclusion: In our study, results show that the use of hydroxychloroquine for COVID-19 patients in mild to moderate cases in an outpatient setting, within the protocol recommendation and inclusion/exclusion criteria, is safe, highly tolerable, and with minimum side effects."]}


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: Severe covid-19 infection is associated with increased antibody-mediated platelet apoptosis

Abstract:
The pathophysiology of COVID-19 associated thrombosis seems to be multifactorial, involving interplay between cellular and plasmatic elements of the hemostasis.
We hypothesized that COVID-19 is accompanied by platelet apoptosis with subsequent alteration of the coagulation system.
We investigated depolarization of mitochondrial inner transmembrane potential ({Delta}{Psi}m), cytosolic calcium (Ca2+) concentration, and phosphatidylserine (PS) externalization by flow cytometry.
Platelets from intensive care unit (ICU) COVID-19 patients (n=21) showed higher {Delta}{Psi}m depolarization, cytosolic Ca2+ concentration and PS externalization, compared to healthy controls (n=18) and COVID-19 non-ICU patients (n=4).
Moreover significant higher cytosolic Ca2+ concentration and PS was observed compared to septic ICU control group (ICU control).
In ICU control group (n=5; non-COVID-19 ICU) cytosolic Ca2+ concentration and PS externalization was comparable to healthy control, with an increase in {Delta}{Psi}m depolarization.
Sera from ICU COVID-19 patients induced significant increase in apoptosis markers ({Delta}{Psi}m depolarization, cytosolic Ca2+ concentration and PS externalization) compared to healthy volunteer and septic ICU control.
Interestingly, immunoglobulin G (IgG) fractions from COVID-19 patients induced an Fc gamma receptor IIA dependent platelet apoptosis ({Delta}{Psi}m depolarization, cytosolic Ca2+ concentration and PS externalization).
Enhanced PS externalization in platelets from ICU COVID-19 patients was associated with increased sequential organ failure assessment (SOFA) score (r=0.5635) and D-Dimer (r=0.4473).
Most importantly, patients with thrombosis had significantly higher PS externalization compared to those without.
The strong correlations between apoptosis markers and increased D-Dimer levels as well as the incidence of thrombosis may indicate that antibody-mediated platelet apoptosis potentially contributes to sustained increased thromboembolic risk in ICU COVID-19 patients.

Answer: {"verdict": "SUPPORT", "evidence": ["The strong correlations between apoptosis markers and increased D-Dimer levels as well as the incidence of thrombosis may indicate that antibody-mediated platelet apoptosis potentially contributes to sustained increased thromboembolic risk in ICU COVID-19 patients."]}


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: Coronavirus remains stable on metals and plastic for three days. Outside a lab, however, the virus might last considerably longer: its genetic material could be detected on surfaces 17 days after a cruise ship was empty of passengers (although it's not clear whether that material represents infectious virus particles).

Abstract:
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a highly contagious virus that can transmit through respiratory droplets, aerosols, or contacts.
Frequent touching of contaminated surfaces in public areas is therefore a potential route of SARS-CoV-2 transmission.
The inanimate surfaces have often been described as a source of nosocomial infections.
However, summaries on the transmissibility of coronaviruses from contaminated surfaces to induce the coronavirus disease 2019 are rare at present.
This review aims to summarize data on the persistence of different coronaviruses on inanimate surfaces.
The literature was systematically searched on Medline without language restrictions.
All reports with experimental evidence on the duration persistence of coronaviruses on any type of surface were included.
Most viruses from the respiratory tract, such as coronaviruses, influenza, SARS-CoV, or rhinovirus, can persist on surfaces for a few days.
Persistence time on inanimate surfaces varied from minutes to up to one month, depending on the environmental conditions.
SARSCoV-2 can be sustained in air in closed unventilated buses for at least 30 min without losing infectivity.
The most common coronaviruses may well survive or persist on surfaces for up to one month.
Viruses in respiratory or fecal specimens can maintain infectivity for quite a long time at room temperature.
Absorbent materials like cotton are safer than unabsorbent materials for protection from virus infection.
The risk of transmission via touching contaminated paper is low.
Preventive strategies such as washing hands and wearing masks are critical to the control of coronavirus disease 2019.

Answer: {"verdict": "SUPPORT", "evidence": ["Most viruses from the respiratory tract, such as coronaviruses, influenza, SARS-CoV, or rhinovirus, can persist on surfaces for a few days.", "The most common coronaviruses may well survive or persist on surfaces for up to one month."]}


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: Several people, including the US president, have suggested that the novel coronavirus SARS-CoV-2 and the disease it causes, COVID-19, will go away on its own in the warmer weather that will come.

Abstract:
The coronavirus disease 2019 (COVID-19) outbreak has become a severe public health issue.
The novelty of the virus prompts a search for understanding of how ecological factors affect the transmission and survival of the virus.
Several studies have robustly identified a relationship between temperature and the number of cases.
However, there is no specific study for a tropical climate such as Brazil.
This work aims to determine the relationship of temperature to COVID-19 infection for the state capital cities of Brazil.
Cumulative data with the daily number of confirmed cases was collected from February 27 to April 1, 2020, for all 27 state capital cities of Brazil affected by COVID-19.
A generalized additive model (GAM) was applied to explore the linear and nonlinear relationship between annual average temperature compensation and confirmed cases.
Also, a polynomial linear regression model was proposed to represent the behavior of the growth curve of COVID-19 in the capital cities of Brazil.
The GAM dose-response curve suggested a negative linear relationship between temperatures and daily cumulative confirmed cases of COVID-19 in the range from 16.8 °C to 27.4 °C.
Each 1 °C rise of temperature was associated with a -4.8951% (t = -2.29, p = 0.0226) decrease in the number of daily cumulative confirmed cases of COVID-19.
A sensitivity analysis assessed the robustness of the results of the model.
The predicted R-squared of the polynomial linear regression model was 0.81053.
In this study, which features the tropical temperatures of Brazil, the variation in annual average temperatures ranged from 16.8 °C to 27.4 °C.
Results indicated that temperatures had a negative linear relationship with the number of confirmed cases.
The curve flattened at a threshold of 25.8 °C.
There is no evidence supporting that the curve declined for temperatures above 25.8 °C.
The study had the goal of supporting governance for healthcare policymakers.

Answer: {"verdict": "CONTRADICT", "evidence": ["Results indicated that temperatures had a negative linear relationship with the number of confirmed cases."]}


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: If Fever Helps Fight Infection, Should I Avoid Fever-Reducing Drugs

Abstract:
Ibuprofen is an over-the-counter medication that is used widely for the treatment of pain and fever during COVID-19 pandemic.
A concern was raised regarding the safety of ibuprofen use because of its role in increasing ACE2 levels within the Renin-Angiotensin-Aldosterone system.
ACE2 is the coreceptor for the entry of SARS-CoV-2 into cells, and so, a potential increased risk of contracting COVID-19 disease and/or worsening of COVID-19 infection was feared with ibuprofen use.
However, available data from limited studies show administration of recombinant ACE2 improves lung damage caused by respiratory viruses, suggesting ibuprofen use may be beneficial in COVID-19 disease.
At this time, there is no supporting evidence to discourage the use of ibuprofen.

Answer: {"verdict": "CONTRADICT", "evidence": ["Ibuprofen is an over-the-counter medication that is used widely for the treatment of pain and fever during COVID-19 pandemic.", "However, available data from limited studies show administration of recombinant ACE2 improves lung damage caused by respiratory viruses, suggesting ibuprofen use may be beneficial in COVID-19 disease.", "At this time, there is no supporting evidence to discourage the use of ibuprofen."]}


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 is no clear evidence that using ibuprofen to treat symptoms such as a high temperature can make coronavirus (covid-19) worse

Abstract:
OBJECTIVE: It was recently suggested that Ibuprofen might increase the risk for severe and fatal COVID-19 disease and should therefore be avoided in this patient population.
We aimed to evaluate whether ibuprofen use in patients with COVID-19 was associated with more severe disease, compared to patients using paracetamol or no antipyretics.
METHODS: In a retrospective cohort study of patients with COVID-19 from Shamir Medical Center, Israel, we monitored any use of ibuprofen from a week prior to diagnosis of COVID-19 throughout the disease.
Primary outcomes were mortality and the need for respiratory support, including oxygen administration and mechanical ventilation.
RESULTS: The study included 403 confirmed cases of COVID-19, with a median age of 45 years.
Of the entire cohort, 44 patients (11%) needed respiratory support and 12 (3%) patients died.
One hundred and seventy-nine (44%) patients had fever, with 32% using paracetamol and 22% using ibuprofen, for symptom-relief.
In the Ibuprofen group, 3 (3.4%) patients died, while in the non-Ibuprofen group 9 (2.8%) patients died (P=0.95).
Nine (10.3%) patients from the Ibuprofen group needed respiratory support, as compared with 35 (11%) from the non-Ibuprofen group (P=1).
When compared to exclusive paracetamol users, no differences were observed in mortality rates or the need for respiratory support among patients using ibuprofen.
CONCLUSIONS: In this cohort of COVID-19 patients, Ibuprofen use was not associated with worse clinical outcomes, compared to paracetamol or no antipyretic.

Answer: {"verdict": "CONTRADICT", "evidence": ["OBJECTIVE: It was recently suggested that Ibuprofen might increase the risk for severe and fatal COVID-19 disease and should therefore be avoided in this patient population."]}


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: Sars-cov-2 d614g variant exhibits enhanced replication ex vivo and earlier transmission in vivo

Abstract:
The D614G substitution in the S protein is most prevalent SARS-CoV-2 strain circulating globally, but its effects in viral pathogenesis and transmission remain unclear.
We engineered SARS-CoV-2 variants harboring the D614G substitution with or without nanoluciferase.
The D614G variant replicates more efficiency in primary human proximal airway epithelial cells and is more fit than wildtype (WT) virus in competition studies.
With similar morphology to the WT virion, the D614G virus is also more sensitive to SARS-CoV-2 neutralizing antibodies.
Infection of human ACE2 transgenic mice and Syrian hamsters with the WT or D614G viruses produced similar titers in respiratory tissue and pulmonary disease.
However, the D614G variant exhibited significantly faster droplet transmission between hamsters than the WT virus, early after infection.
Our study demonstrated the SARS-CoV2 D614G substitution enhances infectivity, replication fitness, and early transmission.

Answer: {"verdict": "SUPPORT", "evidence": ["Our study demonstrated the SARS-CoV2 D614G substitution enhances infectivity, replication fitness, and early transmission."]}


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 survival 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": "CONTRADICT", "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 high temperature and low relative humidity lead to high evaporation rates of saliva-contaminated droplets, thus significantly reducing the coronavirus viability

Abstract:
The 2020 coronavirus pandemic is developing at different paces throughout the world.
Some areas, like the Caribbean Basin, have yet to see the virus strike at full force.
When it does, there is reasonable evidence to suggest the consequent COVID-19 outbreaks will overwhelm healthcare systems and economies.
This is particularly concerning in the Caribbean as pandemics can have disproportionately higher mortality impacts on lower and middle-income countries.
Preliminary observations from our team and others suggest that temperature and climatological factors could influence the spread of this novel coronavirus, making spatiotemporal predictions of its infectiousness possible.
This review studies geographic and time-based distribution of known respiratory viruses in the Caribbean Basin in an attempt to foresee how the pandemic will develop in this region.
This review is meant to aid in planning short- and long-term interventions to manage outbreaks at the international, national, and subnational levels in the region.

Answer: {"verdict": "SUPPORT", "evidence": ["Preliminary observations from our team and others suggest that temperature and climatological factors could influence the spread of this novel coronavirus, making spatiotemporal predictions of its infectiousness possible."]}


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: Most children with COVID-19 have mild symptoms or have no symptoms at all.

Abstract:
Background: As the novel coronavirus triggering COVID-19 has broken out in Wuhan, China and spread rapidly worldwide, it threatens the lives of thousands of people and poses a global threat on the economies of the entire world.
However, infection with COVID-19 is currently rare in children.
Objective To discuss the latest findings and research focus on the basis of characteristics of children confirmed with COVID-19, and provide an insight into the future treatment and research direction.
Methods: We searched the terms "COVID-19 OR coronavirus OR SARS-CoV-2" AND "Pediatric OR children" on PubMed, Embase, Cochrane library, NIH, CDC, and CNKI.
The authors also reviewed the guidelines published on Chinese CDC and Chinese NHC.
Results: We included 25 published literature references related to the epidemiology, clinical manifestation, accessary examination, treatment, and prognosis of pediatric patients with COVID-19.
Conclusion: The numbers of children with COVID-19 pneumonia infection are small, and most of them come from family aggregation.
Symptoms are mainly mild or even asymptomatic, which allow children to be a risk factor for transmission.
Thus, strict epidemiological history screening is needed for early diagnosis and segregation.
This holds especially for infants, who are more susceptible to infection than other age groups in pediatric age, but have most likely subtle and unspecific symptoms.
They need to be paid more attention to.
CT examination is a necessity for screening the suspected cases, because most of the pediatric patients are mild cases, and plain chest X-ray do not usually show the lesions or the detailed features.
Therefore, early chest CT examination combined with pathogenic detection is a recommended clinical diagnosis scheme in children.
The risk factors which may suggest severe or critical progress for children are: Fast respiratory rate and/or; lethargy and drowsiness mental state and/or; lactate progressively increasing and/or; imaging showed bilateral or multi lobed infiltration, pleural effusion or rapidly expending of lesions in a short period of time and/or; less than 3 months old or those who underly diseases.
For those critical pediatric patients with positive SARS-CoV-2 diagnosis, polypnea may be the most common symptom.
For treatment, the elevated PCT seen in children in contrast to adults suggests that the underlying coinfection/secondary infection may be more common in pediatric patients and appropriate antibacterial treatment should be considered.
Once cytokine storm is found in these patients, anti-autoimmune or blood-purifying therapy should be given in time.
Furthermore, effective isolation measures and appropriate psychological comfort need to be provided timely.

Answer: {"verdict": "SUPPORT", "evidence": ["Conclusion: The numbers of children with COVID-19 pneumonia infection are small, and most of them come from family aggregation.", "Symptoms are mainly mild or even asymptomatic, which allow children to be a risk factor for transmission."]}


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 risk of animals spreading COVID-19 to people is considered to be low.

Abstract:
On April 22, CDC and the U.S. Department of Agriculture (USDA) reported cases of two domestic cats with confirmed infection with SARS-CoV-2, the virus that causes coronavirus disease 2019 (COVID-19).
These are the first reported companion animals (including pets and service animals) with SARS-CoV-2 infection in the United States, and among the first findings of SARS-CoV-2 symptomatic companion animals reported worldwide.
These feline cases originated from separate households and were epidemiologically linked to suspected or confirmed human COVID-19 cases in their respective households.
Notification of presumptive positive animal test results triggered a One Health* investigation by state and federal partners, who determined that no further transmission events to other animals or persons had occurred.
Both cats fully recovered.
Although there is currently no evidence that animals play a substantial role in spreading COVID-19, CDC advises persons with suspected or confirmed COVID-19 to restrict contact with animals during their illness and to monitor any animals with confirmed SARS-CoV-2 infection and separate them from other persons and animals at home (1).

Answer: {"verdict": "SUPPORT", "evidence": ["Although there is currently no evidence that animals play a substantial role in spreading COVID-19, CDC advises persons with suspected or confirmed COVID-19 to restrict contact with animals during their illness and to monitor any animals with confirmed SARS-CoV-2 infection and separate them from other persons and animals at home (1)."]}