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 warmer weather will slow the spread of SARS-CoV-2, the novel coronavirus that causes COVID-19

Abstract:
This paper investigates the correlation between the high level of coronavirus SARS-CoV-2 infection accelerated transmission and lethality, and surface air pollution in Milan metropolitan area, Lombardy region in Italy.
For January-April 2020 period, time series of daily average inhalable gaseous pollutants ozone (O3) and nitrogen dioxide (NO2), together climate variables (air temperature, relative humidity, wind speed, precipitation rate, atmospheric pressure field and Planetary Boundary Layer) were analyzed.
In spite of being considered primarily transmitted by indoor bioaerosols droplets and infected surfaces or direct human-to-human personal contacts, it seems that high levels of urban air pollution, and climate conditions have a significant impact on SARS-CoV-2 diffusion.
Exhibited positive correlations of ambient ozone levels and negative correlations of NO2 with the increased rates of COVID-19 infections (Total number, Daily New positive and Total Deaths cases), can be attributed to airborne bioaerosols distribution.
The results show positive correlation of daily averaged O3 with air temperature and inversely correlations with relative humidity and precipitation rates.
Viral genome contains distinctive features, including a unique N-terminal fragment within the spike protein, which allows coronavirus attachment on ambient air pollutants.
At this moment it is not clear if through airborne diffusion, in the presence of outdoor and indoor aerosols, this protein "spike" of the new COVID-19 is involved in the infectious agent transmission from a reservoir to a susceptible host during the highest nosocomial outbreak in some agglomerated industrialized urban areas like Milan is.
Also, in spite of collected data for cold season (winter-early spring) period, when usually ozone levels have lower values than in summer, the findings of this study support possibility as O3 can acts as a COVID-19 virus incubator.
Being a novel pandemic coronavirus version, it might be ongoing during summer conditions associated with higher air temperatures, low relative humidity and precipitation levels.

Answer: {"verdict": "CONTRADICT", "evidence": ["Exhibited positive correlations of ambient ozone levels and negative correlations of NO2 with the increased rates of COVID-19 infections (Total number, Daily New positive and Total Deaths cases), can be attributed to airborne bioaerosols distribution.", "The results show positive correlation of daily averaged O3 with air temperature and inversely correlations with relative humidity and precipitation rates.", "Being a novel pandemic coronavirus version, it might be ongoing during summer conditions associated with higher air temperatures, low relative humidity and precipitation levels."]}


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

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

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

Claim: The virus that causes COVID-19 (SARS-CoV-2), belongs to the betacoronaviruses, one of the four genera of coronaviruses.

Abstract:
The recent global outbreak of viral pneumonia designated as Coronavirus Disease 2019 (COVID-19) by coronavirus (SARS-CoV-2) has threatened global public health and urged to investigate its source.
Whole genome analysis of SARS-CoV-2 revealed ~96% genomic similarity with bat CoV (RaTG13) and clustered together in phylogenetic tree.
Furthermore, RaTGl3 also showed 97.43% spike protein similarity with SARS-CoV-2 suggesting that RaTGl3 is the closest strain.
However, RBD and key amino acid residues supposed to be crucial for human-to-human and cross-species transmission are homologues between SARS-CoV-2 and pangolin CoVs.
These results from our analysis suggest that SARS-CoV-2 is a recombinant virus of bat and pangolin CoVs.
Moreover, this study also reports mutations in coding regions of 125 SARS-CoV-2 genomes signifying its aptitude for evolution.
In short, our findings propose that homologous recombination has been occurred between bat and pangolin CoVs that triggered cross-species transmission and emergence of SARS-CoV-2, and, during the ongoing outbreak, SARS-CoV-2 is still evolving for its adaptability.

Answer: {"verdict": "SUPPORT", "evidence": ["The recent global outbreak of viral pneumonia designated as Coronavirus Disease 2019 (COVID-19) by coronavirus (SARS-CoV-2) has threatened global public health and urged to investigate its source."]}


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:
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 dog might be secondary host 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 route.
Based on these natural infection reports and experimental data, whether the pets are responsible for SARS-CoV-2 spread to human; needs to be deeply investigated.
Humans showing clinical symptoms of respiratory infections have been undergoing for 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 sero-prevalence studies especially in companion animals.

Answer: {"verdict": "CONTRADICT", "evidence": ["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 dog might be secondary host during the evolution of SARS-CoV-2 from bat to human.", "Therefore, there is a possibility of spreading SARS-CoV-2 through domestic pets.", "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 route."]}


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: adults who are hooked on cigarettes are 50% less likely to test positive for the illness

Abstract:
ABSTRACT Introduction: Recent studies show cigarette smokers are markedly under-represented among patients hospitalized for COVID-19 in over a dozen countries.
It is unclear if this may be related to confounding factors such as age distribution, access to care, and inaccurate records.
We hypothesized that these concerns could be avoided by studying smoking prevalence in relation to COVID-19 mortality.
Since climate has been identified as a factor in COVID-19, we studied groups of countries with relatively comparable temperatures.
Methods: The 20 hottest and 20 coldest countries in the Johns Hopkins Mortality Analysis database with a minimum mortality rate of .3 deaths/100,000 were selected on the basis of the average temperatures of their largest city.
Mortality rates were determined as of May 1, 2020 and correlated with national smoking rate adjusting for sex ratio, obesity, temperature, and elderly population.
Results: A highly significant inverse correlation between current daily smoking prevalence and COVID-19 mortality rate was noted for the group of hot countries (R=-.718, p = .0002), cold countries (R=-.567, p=.0046), and the combined group (R=-.324, p=.0207).
However, after adjustments only the regression for hot countries and the combined group remained significant.
In hot countries, for each percentage point increase in smoking rate mortality decreased by .147 per 100,000 population (95% CI .102- 192, p=.0066).
This resulted in mortality rates several-fold elevated in the countries with the lowest smoking rates relative to the highest smoking rates.
In the combined group, mortality decreased by .257 per 100,000 population (95% CI .175-.339, p=.0034).
Discussion: These findings add support to the finding of an inverse relationship between current smoking and seriously symptomatic COVID-19.
However, we conclude that the difference in mortality between the highest and lowest smoking countries appears too large to be due primarily to the effects of smoking per se.
A potentially beneficial effect of smoking is surprising, but compatible with a number of hypothetical mechanisms which deserve exploration: 1) Studies show smoking alters ACE2 expression which may affect COVID-19 infection or its progression to serious lung pathology.
2) Nicotine has anti-inflammatory activity and also appears to alter ACE2 expression.
3) Nitric oxide in cigarette smoke is known to be effective in treating pulmonary hypertension and has shown in vitro antiviral effects including against SARS-CoV-2.
4) Smoking has complicated effects on the immune system involving both up and down regulation, any of which might alone or in concert antagonize progression of COVID-19.
5) Smokers are exposed to hot vapors which may stimulate immunity in the respiratory tract by various heat-related mechanisms (e.g. heat shock proteins).
Studies of steam and sauna treatments have shown efficacy in other viral respiratory conditions.
At this time there is no clear evidence that smoking is protective against COVID-19, so the established recommendations to avoid smoking should be emphasized.
The interaction of smoking and COVID-19 will only be reliably determined by carefully designed prospective study, and there is reason to believe that there are unknown confounds that may be spuriously suggesting a protective effect of smoking.
However, the magnitude of the apparent inverse association of COVID-19 and smoking and its myriad clinical implications suggest the importance of further investigation.

Answer: {"verdict": "CONTRADICT", "evidence": ["4) Smoking has complicated effects on the immune system involving both up and down regulation, any of which might alone or in concert antagonize progression of COVID-19.", "At this time there is no clear evidence that smoking is protective against COVID-19, so the established recommendations to avoid smoking should be emphasized."]}


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:
The rapid global spread of SARS-CoV-2 and resultant mortality and social disruption have highlighted the need to better understand coronavirus immunity to expedite vaccine development efforts.
Multiple candidate vaccines, designed to elicit protective neutralising antibodies targeting the viral spike glycoprotein, are rapidly advancing to clinical trial.
However, the immunogenic properties of the spike protein in humans are unresolved.
To address this, we undertook an in-depth characterisation of humoral and cellular immunity against SARS-CoV-2 spike in humans following mild to moderate SARS-CoV-2 infection.
We find serological antibody responses against spike are routinely elicited by infection and correlate with plasma neutralising activity and capacity to block ACE2/RBD interaction.
Expanded populations of spike-specific memory B cells and circulating T follicular helper cells (cTFH) were detected within convalescent donors, while responses to the receptor binding domain (RBD) constitute a minor fraction.
Using regression analysis, we find high plasma neutralisation activity was associated with increased spike-specific antibody, but notably also with the relative distribution of spike-specific cTFH subsets.
Thus both qualitative and quantitative features of B and T cell immunity to spike constitute informative biomarkers of the protective potential of novel SARS-CoV-2 vaccines.

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

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

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


Question:
You will be shown a claim 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: Eating garlic will protect me against getting the coronavirus.

Abstract:
In late December 2019 in Wuhan, China, several patients with viral pneumonia were identified as 2019 novel coronavirus (2019-nCoV).
So far, there are no specific treatments for patients with coronavirus disease-19 (COVID-19), and the treatments available today are based on previous experience with similar viruses such as severe acute respiratory syndrome-related coronavirus (SARS-CoV), Middle East respiratory syndrome coronavirus (MERS-CoV), and Influenza virus.
In this article, we have tried to reach a therapeutic window of drugs available to patients with COVID-19.
Cathepsin L is required for entry of the 2019-nCoV virus into the cell as target teicoplanin inhibits virus replication.
Angiotensin-converting-enzyme 2 (ACE2) in soluble form as a recombinant protein can prevent the spread of coronavirus by restricting binding and entry.
In patients with COVID-19, hydroxychloroquine decreases the inflammatory response and cytokine storm, but overdose causes toxicity and mortality.
Neuraminidase inhibitors such as oseltamivir, peramivir, and zanamivir are invalid for 2019-nCoV and are not recommended for treatment but protease inhibitors such as lopinavir/ritonavir (LPV/r) inhibit the progression of MERS-CoV disease and can be useful for patients of COVID-19 and, in combination with Arbidol, has a direct antiviral effect on early replication of SARS-CoV. Ribavirin reduces hemoglobin concentrations in respiratory patients, and remdesivir improves respiratory symptoms.
Use of ribavirin in combination with LPV/r in patients with SARS-CoV reduces acute respiratory distress syndrome and mortality, which has a significant protective effect with the addition of corticosteroids.
Favipiravir increases clinical recovery and reduces respiratory problems and has a stronger antiviral effect than LPV/r.
currently, appropriate treatment for patients with COVID-19 is an ACE2 inhibitor and a clinical problem reducing agent such as favipiravir in addition to hydroxychloroquine and corticosteroids.

Answer: {"verdict": "CONTRADICT", "evidence": ["In late December 2019 in Wuhan, China, several patients with viral pneumonia were identified as 2019 novel coronavirus (2019-nCoV).", "So far, there are no specific treatments for patients with coronavirus disease-19 (COVID-19), and the treatments available today are based on previous experience with similar viruses such as severe acute respiratory syndrome-related coronavirus (SARS-CoV), Middle East respiratory syndrome coronavirus (MERS-CoV), and Influenza virus."]}


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 causes mild alveolar inflammation and barrier dysfunction

Abstract:
Infections with SARS-CoV-2 lead to mild to severe coronavirus disease-19 (COVID-19) with systemic symptoms.
Although the viral infection originates in the respiratory system, it is unclear how the virus can overcome the alveolar barrier, which is observed in severe COVID-19 disease courses.
To elucidate the viral effects on the barrier integrity and immune reactions, we used mono-cell culture systems and a complex human alveolus-on-a-chip model composed of epithelial, endothelial, and mononuclear cells.
Our data show that SARS-CoV-2 efficiently infected epithelial cells with high viral loads and inflammatory response, including the interferon expression.
By contrast, the adjacent endothelial layer was no infected and did neither show productive virus replication or interferon release.
With prolonged infection, both cell types are damaged, and the barrier function is deteriorated, allowing the viral particles to overbear.
In our study, we demonstrate that although SARS-CoV-2 is dependent on the epithelium for efficient replication, the neighboring endothelial cells are affected, e.g., by the epithelial cytokine release, which results in the damage of the alveolar barrier function and viral dissemination.

Answer: {"verdict": "CONTRADICT", "evidence": ["With prolonged infection, both cell types are damaged, and the barrier function is deteriorated, allowing the viral particles to overbear.", "In our study, we demonstrate that although SARS-CoV-2 is dependent on the epithelium for efficient replication, the neighboring endothelial cells are affected, e.g., by the epithelial cytokine release, which results in the damage of the alveolar barrier function and viral dissemination."]}


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: Exploratory analysis of immunization records highlights increasing sars-cov-2 rates in individuals with recent non-covid-19 vaccinations

Abstract:
Multiple clinical studies are ongoing to assess whether existing vaccines may afford protection against SARS-CoV-2 infection through trained immunity.
In this exploratory study, we analyze immunization records from 137,037 individuals who received SARS-CoV-2 PCR tests.
We find that polio, Hemophilus influenzae type-B (HIB), measles-mumps-rubella (MMR), varicella, pneumococcal conjugate (PCV13), geriatric flu, and hepatitis A / hepatitis B (HepA-HepB) vaccines administered in the past 1, 2, and 5 years are associated with decreased SARS-CoV-2 infection rates, even after adjusting for geographic SARS-CoV-2 incidence and testing rates, demographics, comorbidities, and number of other vaccinations.
Furthermore, age, race/ethnicity, and blood group stratified analyses reveal significantly lower SARS-CoV-2 rate among black individuals who have taken the PCV13 vaccine, with relative risk of 0.45 at the 5 year time horizon (n: 653, 95% CI: (0.32, 0.64), p-value: 6.9e-05).
These findings suggest that additional pre-clinical and clinical studies are warranted to assess the protective effects of existing non-COVID-19 vaccines and explore underlying immunologic mechanisms.
We note that the findings in this study are preliminary and are subject to change as more data becomes available and as further analysis is conducted.

Answer: {"verdict": "CONTRADICT", "evidence": ["We find that polio, Hemophilus influenzae type-B (HIB), measles-mumps-rubella (MMR), varicella, pneumococcal conjugate (PCV13), geriatric flu, and hepatitis A / hepatitis B (HepA-HepB) vaccines administered in the past 1, 2, and 5 years are associated with decreased SARS-CoV-2 infection rates, even after adjusting for geographic SARS-CoV-2 incidence and testing rates, demographics, comorbidities, and number of other vaccinations.", "Furthermore, age, race/ethnicity, and blood group stratified analyses reveal significantly lower SARS-CoV-2 rate among black individuals who have taken the PCV13 vaccine, with relative risk of 0.45 at the 5 year time horizon (n: 653, 95% CI: (0.32, 0.64), p-value: 6.9e-05).", "These findings suggest that additional pre-clinical and clinical studies are warranted to assess the protective effects of existing non-COVID-19 vaccines and explore underlying immunologic mechanisms."]}


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: Remdesivir proves effective against COVID-19

Abstract:
Currently, there is not any specific effective antiviral treatment for COVID-19.
Although most of the COVID-19 patients have mild or moderate courses, up to 5%10% can have severe, potentially life threatening course, there is an urgent need for effective drugs.
Optimized supportive care remains the mainstay of therapy.
There have been more than 300 clinical trials going on, various antiviral and immunomodulating agents are in various stages of evaluation for COVID-19 in those trials and some of them will be published in the next couple of months.
Despite the urgent need to find an effective antiviral treatment for COVID-19 through randomized controlled studies, certain agents are being used all over the world based on either in-vitro or extrapolated evidence or observational studies.
The most frequently used agents both in Turkey and all over the world including chloroquine, hydroxychloroquine, lopinavir/ritonavir, favipiravir and remdesivir will be reviewed here .Nitazoxanide and ivermectin were also included in this review as they have recently been reported to have an activity against SARS-CoV-2 in vitro and are licensed for the treatment of some other human infections.

Answer: {"verdict": "CONTRADICT", "evidence": ["Currently, there is not any specific effective antiviral treatment for 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: Dogs May Not Spread Covid-19, but Cats Can Pass It to Each Other

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

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


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: This correlation between antibody titers and neutralizing activity in sera from sars-cov-2 infected subjects

Abstract:
Plenty of serologic tests for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have been developed so far, thus documenting the importance of evaluating the relevant features of the immune response to this viral agent.
The performance of these assays is currently under investigation.
Amongst them, LIAISON® SARS-CoV-2 S1/S2 IgG by DiaSorin and Elecsys Anti-SARS-CoV-2 cobas® by Roche are currently used by laboratory medicine hospital departments in Italy and many other countries.
In the present study, we firstly compared two serologic tests on serum samples collected at two different time points from 46 laboratory-confirmed coronavirus disease-2019 (COVID-19) subjects.
Secondly, 85 negative serum samples collected before the SARS-CoV-2 pandemic were analyzed.
Thirdly, possible correlations between antibody levels and the resulting neutralizing activity against a clinical isolate of SARS-CoV-2 were evaluated.
Results revealed that both tests are endowed with low sensitivity on the day of hospital admission, which increased to 97.8% and 100% for samples collected after 15 days for DiaSorin and Roche tests, respectively.
The specificity evaluated for the two tests ranges from 96.5% to 100%, respectively.
Importantly, a poor direct correlation between antibody titers and neutralizing activity levels was evidenced in the present study.
These data further shed light on both potentials and possible limitations related to SARS-CoV-2 serology.
In this context, great efforts are still necessary for investigating antibody kinetics to develop novel diagnostic algorithms.
Moreover, further investigations on the role of neutralizing antibodies and their correlate of protection will be of paramount importance for the development of effective vaccines.

Answer: {"verdict": "CONTRADICT", "evidence": ["Importantly, a poor direct correlation between antibody titers and neutralizing activity levels was evidenced in the present study."]}


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

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

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

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

Abstract:
: The COVID-19 pandemic is challenging our cardiovascular care of patients with heart diseases.
In the setting of pericardial diseases, there are two possible different scenarios to consider: the patient being treated for pericarditis who subsequently becomes infected with SARS-CoV-2, and the patient with COVID-19 who develops pericarditis or pericardial effusion.
In both conditions, clinicians may be doubtful regarding the safety of nonsteroidal anti-inflammatory drugs (NSAIDs), corticosteroids, colchicine, and biological agents, such as anti-IL1 agents (e.g. anakinra), that are the mainstay of therapy for pericarditis.
For NSAIDs, there is no clear scientific evidence linking ibuprofen and other NSAIDs to worsening of COVID-19; however, it seems prudent to continue them, if necessary to control pericarditis, and on the other hand, to prefer paracetamol for fever and systemic symptoms related to COVID-19.
Treatments with corticosteroids, colchicine, and anakinra appear well tolerated in the context of COVID-19 infection and are currently actively evaluated as potential therapeutic options for COVID infection at different stages of the disease.
On this basis, currently most treatments for pericarditis do not appear contraindicated also in the presence of possible COVID-19 infection and should not be discontinued, and some (corticosteroids, colchicine, and anakinra) can be considered to treat both conditions.

Answer: {"verdict": "CONTRADICT", "evidence": ["For NSAIDs, there is no clear scientific evidence linking ibuprofen and other NSAIDs to worsening of COVID-19; however, it seems prudent to continue them, if necessary to control pericarditis, and on the other hand, to prefer paracetamol for fever and systemic symptoms related to 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: Young people as diligent about Covid measures as older people

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: smokers have a very much lower probability of developing symptomatic or severe SARA-CoV-2 [COVID-19] infection as compared to the general population

Abstract:
Introduction Epidemiological and laboratory research seems to suggest that smoking and perhaps nicotine alone could reduce the severity of COVID-19.
Likewise, there is some evidence that inhaled corticosteroids could also reduce its severity, opening the possibility that nicotine and inhaled steroids could be used as treatments.
Methods In this prospective cohort study, we will link English general practice records from the QResearch database to Public Health England's database of SARS-CoV-2 positive tests, Hospital Episode Statistics, admission to intensive care units, and death from COVID-19 to identify our outcomes: hospitalisation, ICU admission, and death due to COVID.
Using Cox regression, we will perform sequential adjustment for potential confounders identified by separate directed acyclic graphs to: 1.
Assess the association between smoking and COVID-19 disease severity, and how that changes on adjustment for smoking-related comorbidity.
2. More closely characterise the association between smoking and severe COVID-19 disease by assessing whether the association is modified by age (as a proxy of length of smoking), gender, ethnic group, and whether people have asthma or COPD.
3. Assess for evidence of a dose-response relation between smoking intensity and disease severity, which would help create a case for causality.
4.
Examine the association between former smokers who are using NRT or are vaping and disease severity.
5. Examine whether pre-existing respiratory disease is associated with severe COVID-19 infection.
6. Assess whether the association between chronic obstructive pulmonary disease (COPD) and asthma and COVID-19 disease severity is modified by age, gender, ethnicity, and smoking status.
7. Assess whether the use of inhaled corticosteroids is associated with severity of COVID-19 disease.
8. To assess whether the association between use of inhaled corticosteroids and severity of COVID-19 disease is modified by the number of other airways medications used (as a proxy for severity of condition) and whether people have asthma or COPD.
Conclusions This representative population sample will, to our knowledge, present the first comprehensive examination of the association between smoking, nicotine use without smoking, respiratory disease, and severity of COVID-19.
We will undertake several sensitivity analyses to examine the potential for bias in these associations.

Answer: {"verdict": "CONTRADICT", "evidence": ["Introduction Epidemiological and laboratory research seems to suggest that smoking and perhaps nicotine alone could reduce the severity 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: Coronaviruses cause respiratory illnesses, so the lungs are usually affected first. Early symptoms include fever, cough, and shortness of breath.

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 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-cov1 frequent mask use in public venues, frequent hand washing, and disinfecting the living quarters were no protective factors

Abstract:
We analyzed information obtained from 1,192 patients with probable severe acute respiratory syndrome (SARS) reported in Hong Kong.
Among them, 26.6% were hospital workers, 16.1% were household members of SARS patients and had probable secondary infections, 14.3% were Amoy Garden residents, 4.9% were inpatients, and 20.1% were contacts of SARS patients who were not family members.
The remaining 347 case-patients (29.1%) did not have “known” sources of infection.
Excluding those <16 years of age, 330 patients with cases from “undefined” sources were used in a 1:2 matched case-control study.
Multivariate analysis of this case-control study showed that having visited mainland China, hospitals, or the Amoy Gardens were risk factors (odds ratio [OR] 1.95 to 7.63).
In addition, frequent mask use in public venues, frequent hand washing, and disinfecting the living quarters were significant protective factors (OR 0.36 to 0.58).
In Hong Kong, therefore, community-acquired infection did not make up most transmissions, and public health measures have contributed substantially to the control of the SARS epidemic.

Answer: {"verdict": "CONTRADICT", "evidence": ["In addition, frequent mask use in public venues, frequent hand washing, and disinfecting the living quarters were significant protective factors (OR 0.36 to 0.58)."]}


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: camostat mesylate cure coronavirus.

Abstract:
SARS-CoV-2 rapidly spread around the globe after its emergence in Wuhan in December 2019.
With no specific therapeutic and prophylactic options available, the virus was able to infect millions of people.
To date, close to half a million patients succumbed to the viral disease, COVID-19.
The high need for treatment options, together with the lack of small animal models of infection has led to clinical trials with repurposed drugs before any preclinical in vivo evidence attesting their efficacy was available.
We used Syrian hamsters to establish a model to evaluate antiviral activity of small molecules in both an infection and a transmission setting.
Upon intranasal infection, the animals developed high titers of SARS-CoV-2 in the lungs and pathology similar to that observed in mild COVID-19 patients.
Treatment of SARS-CoV-2-infected hamsters with favipiravir or hydroxychloroquine (with and without azithromycin) resulted in respectively a mild or no reduction in viral RNA and infectious virus.
Micro-CT scan analysis of the lungs showed no improvement compared to non-treated animals, which was confirmed by histopathology.
In addition, both compounds did not prevent virus transmission through direct contact and thus failed as prophylactic treatments.
By modelling the PK profile of hydroxychloroquine based on the trough plasma concentrations, we show that the total lung exposure to the drug was not the limiting factor.
In conclusion, we here characterized a hamster infection and transmission model to be a robust model for studying in vivo efficacy of antiviral compounds.
The information acquired using hydroxychloroquine and favipiravir in this model is of critical value to those designing (current and) future clinical trials.
At this point, the data here presented on hydroxychloroquine either alone or combined with azithromycin (together with previously reported in vivo data in macaques and ferrets) provide no scientific basis for further use of the drug in humans.

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: Wear masks with two or more layers to stop the spread of 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: Severe COVID-19 outcomes decreases as the pandemic progressed from winter to the warmer months

Abstract:
This paper investigates the correlation between the high level of coronavirus SARS-CoV-2 infection accelerated transmission and lethality, and surface air pollution in Milan metropolitan area, Lombardy region in Italy.
For January-April 2020 period, time series of daily average inhalable gaseous pollutants ozone (O3) and nitrogen dioxide (NO2), together climate variables (air temperature, relative humidity, wind speed, precipitation rate, atmospheric pressure field and Planetary Boundary Layer) were analyzed.
In spite of being considered primarily transmitted by indoor bioaerosols droplets and infected surfaces or direct human-to-human personal contacts, it seems that high levels of urban air pollution, and climate conditions have a significant impact on SARS-CoV-2 diffusion.
Exhibited positive correlations of ambient ozone levels and negative correlations of NO2 with the increased rates of COVID-19 infections (Total number, Daily New positive and Total Deaths cases), can be attributed to airborne bioaerosols distribution.
The results show positive correlation of daily averaged O3 with air temperature and inversely correlations with relative humidity and precipitation rates.
Viral genome contains distinctive features, including a unique N-terminal fragment within the spike protein, which allows coronavirus attachment on ambient air pollutants.
At this moment it is not clear if through airborne diffusion, in the presence of outdoor and indoor aerosols, this protein "spike" of the new COVID-19 is involved in the infectious agent transmission from a reservoir to a susceptible host during the highest nosocomial outbreak in some agglomerated industrialized urban areas like Milan is.
Also, in spite of collected data for cold season (winter-early spring) period, when usually ozone levels have lower values than in summer, the findings of this study support possibility as O3 can acts as a COVID-19 virus incubator.
Being a novel pandemic coronavirus version, it might be ongoing during summer conditions associated with higher air temperatures, low relative humidity and precipitation levels.

Answer: {"verdict": "CONTRADICT", "evidence": ["Being a novel pandemic coronavirus version, it might be ongoing during summer conditions associated with higher air temperatures, low relative humidity and precipitation levels."]}


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

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

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

Claim: In such a scenario, differentiating whether the cause of death is specifically due to COVID-19 or the result of treatment limitations can be difficult.

Abstract:
The ongoing outbreak of COVID-19 has been expanding worldwide.
As of 17 April 2020, the death toll stands at a sobering 147,027 and over two million cases, this has been straining the health care systems all over.
Respiratory failure has been cited as the major cause of death but here we present a case about a patient who instead succumbed to severe metabolic acidosis with multiple organ failure.

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: Saliva sampling is an excellent option to determine the number of sars cov2 diagnostic tests in settings with supplies shortages

Abstract:
As part of any plan to lift or ease the confinement restrictions that are in place in many different countries, there is an urgent need to increase the capacity of laboratory testing for SARS CoV-2.
Detection of the viral genome through RT-qPCR is the golden standard for this test, however, the high demand of the materials and reagents needed to sample individuals, purify the viral RNA, and perform the RT-qPCR test has resulted in a worldwide shortage of several of these supplies.
Here, we show that directly lysed saliva samples can serve as a suitable source for viral RNA detection that is cheaper and can be as efficient as the classical protocol that involves column purification of the viral RNA.
In addition, it surpasses the need for swab sampling, decreases the risk of the healthcare personnel involved in this process, and accelerates the diagnostic procedure.

Answer: {"verdict": "CONTRADICT", "evidence": ["Detection of the viral genome through RT-qPCR is the golden standard for this test, however, the high demand of the materials and reagents needed to sample individuals, purify the viral RNA, and perform the RT-qPCR test has resulted in a worldwide shortage of several of these supplies."]}


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: Hot weather can spread the virus more as it may get you more out there, make you more mobile and you would actually interact with more people

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": "CONTRADICT", "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: As the novel coronavirus sweeps the globe, those with high blood pressure are at heightened risk for more severe complications should they contract Covid-19

Abstract:
OBJECTIVE: To investigate the association between hypertension and outcome in patients with Coronavirus Disease 2019 (COVID-19) pneumonia.
METHODS: We performed a systematic literature search from several databases on studies that assess hypertension and outcome in COVID-19.
Composite of poor outcome, comprising of mortality, severe COVID-19, acute respiratory distress syndrome (ARDS), need for intensive care unit (ICU) care and disease progression were the outcomes of interest.
RESULTS: A total of 6560 patients were pooled from 30 studies.
Hypertension was associated with increased composite poor outcome (risk ratio (RR) 2.11 (95% confidence interval (CI) 1.85, 2.40), p < 0.001; I2, 44%) and its sub-group, including mortality (RR 2.21 (1.74, 2.81), p < 0.001; I2, 66%), severe COVID-19 (RR 2.04 (1.69, 2.47), p < 0.001; I2 31%), ARDS (RR 1.64 (1.11, 2.43), p = 0.01; I2,0%, p = 0.35), ICU care (RR 2.11 (1.34, 3.33), p = 0.001; I2 18%, p = 0.30), and disease progression (RR 3.01 (1.51, 5.99), p = 0.002; I2 0%, p = 0.55).
Meta-regression analysis showed that gender (p = 0.013) was a covariate that affects the association.
The association was stronger in studies with a percentage of males < 55% compared to â©¾ 55% (RR 2.32 v. RR 1.79).
CONCLUSION: Hypertension was associated with increased composite poor outcome, including mortality, severe COVID-19, ARDS, need for ICU care and disease progression in patients with COVID-19.

Answer: {"verdict": "SUPPORT", "evidence": ["CONCLUSION: Hypertension was associated with increased composite poor outcome, including mortality, severe COVID-19, ARDS, need for ICU care and disease progression in patients with COVID-19."]}


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

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

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

Claim: Saliva is more sensitive for sars-cov-2 detection in covid-19 patients than nasopharyngeal swabs

Abstract:
Rapid and accurate SARS-CoV-2 diagnostic testing is essential for controlling the ongoing COVID-19 pandemic.
The current gold standard for COVID-19 diagnosis is real-time RT-PCR detection of SARS-CoV-2 from nasopharyngeal swabs.
Low sensitivity, exposure risks to healthcare workers, and global shortages of swabs and personal protective equipment, however, necessitate the validation of new diagnostic approaches.
Saliva is a promising candidate for SARS-CoV-2 diagnostics because (1) collection is minimally invasive and can reliably be self-administered and (2) saliva has exhibited comparable sensitivity to nasopharyngeal swabs in detection of other respiratory pathogens, including endemic human coronaviruses, in previous studies.
To validate the use of saliva for SARS-CoV-2 detection, we tested nasopharyngeal and saliva samples from confirmed COVID-19 patients and self-collected samples from healthcare workers on COVID-19 wards.
When we compared SARS-CoV-2 detection from patient-matched nasopharyngeal and saliva samples, we found that saliva yielded greater detection sensitivity and consistency throughout the course of infection.
Furthermore, we report less variability in self-sample collection of saliva.
Taken together, our findings demonstrate that saliva is a viable and more sensitive alternative to nasopharyngeal swabs and could enable at-home self-administered sample collection for accurate large-scale SARS-CoV-2 testing.

Answer: {"verdict": "SUPPORT", "evidence": ["Saliva is a promising candidate for SARS-CoV-2 diagnostics because (1) collection is minimally invasive and can reliably be self-administered and (2) saliva has exhibited comparable sensitivity to nasopharyngeal swabs in detection of other respiratory pathogens, including endemic human coronaviruses, in previous studies.", "To validate the use of saliva for SARS-CoV-2 detection, we tested nasopharyngeal and saliva samples from confirmed COVID-19 patients and self-collected samples from healthcare workers on COVID-19 wards.", "When we compared SARS-CoV-2 detection from patient-matched nasopharyngeal and saliva samples, we found that saliva yielded greater detection sensitivity and consistency throughout the course of infection.", "Taken together, our findings demonstrate that saliva is a viable and more sensitive alternative to nasopharyngeal swabs and could enable at-home self-administered sample collection for accurate large-scale SARS-CoV-2 testing."]}


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: Smoking is a risk factor for COVID-19 patients, but one particular substance in cigarettes - nicotine - might prevent infection in some people, or improve COVID-19 prognosis

Abstract:
Objectives: To investigate whether there is a causal effect of cardiometabolic traits on risk of sepsis and severe covid-19.
Design: Mendelian randomisation analysis.
Setting: UK Biobank and HUNT study population-based cohorts for risk of sepsis, and genome-wide association study summary data for risk of severe covid-19 with respiratory failure.
Participants: 12,455 sepsis cases (519,885 controls) and 1,610 severe covid-19 with respiratory failure cases (2,205 controls).
Exposure: Genetic variants that proxy body mass index (BMI), lipid traits, systolic blood pressure, lifetime smoking score, and type 2 diabetes liability - derived from studies considering between 188,577 to 898,130 participants.
Main outcome measures: Risk of sepsis and severe covid-19 with respiratory failure.
Results: Higher genetically proxied BMI and lifetime smoking score were associated with increased risk of sepsis in both UK Biobank (BMI: odds ratio 1.38 per standard deviation increase, 95% confidence interval [CI] 1.27 to 1.51; smoking: odds ratio 2.81 per standard deviation increase, 95% CI 2.09-3.79) and HUNT (BMI: 1.41, 95% CI 1.18 to 1.69; smoking: 1.93, 95% CI 1.02-3.64).
Higher genetically proxied BMI and lifetime smoking score were also associated with increased risk of severe covid-19, although with wider confidence intervals (BMI: 1.75, 95% CI 1.20 to 2.57; smoking: 3.94, 95% CI 1.13 to 13.75).
There was limited evidence to support associations of genetically proxied lipid traits, systolic blood pressure or type 2 diabetes liability with risk of sepsis or severe covid-19.
Similar findings were generally obtained when using Mendelian randomization methods that are more robust to the inclusion of pleiotropic variants, although the precision of estimates was reduced.
Conclusions: Our findings support a causal effect of elevated BMI and smoking on risk of sepsis and severe covid-19.
Clinical and public health interventions targeting obesity and smoking are likely to reduce sepsis and covid-19 related morbidity, along with the plethora of other health-related outcomes that these traits adversely affect.

Answer: {"verdict": "CONTRADICT", "evidence": ["Results: Higher genetically proxied BMI and lifetime smoking score were associated with increased risk of sepsis in both UK Biobank (BMI: odds ratio 1.38 per standard deviation increase, 95% confidence interval [CI] 1.27 to 1.51; smoking: odds ratio 2.81 per standard deviation increase, 95% CI 2.09-3.79) and HUNT (BMI: 1.41, 95% CI 1.18 to 1.69; smoking: 1.93, 95% CI 1.02-3.64).", "Conclusions: Our findings support a causal effect of elevated BMI and smoking on risk of sepsis and severe 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: cloth face mask with a filter can help prevent the spread of COVID-19

Abstract:
Background Protecting Health Care Workers (HCWs) during routine care of suspected or confirmed COVID-19 patients is of paramount importance to halt the SARS-CoV-2 (Severe Acute Respiratory Syndrome-Coronavirus-2) pandemic.
The WHO, ECDC and CDC have issued conflicting guidelines on the use of respiratory filters (N95) by HCWs.
Methods We searched PubMed, Embase and The Cochrane Library from the inception to March 21, 2020 to identify randomized controlled trials (RCTs) comparing N95 respirators versus surgical masks for prevention of COVID-19 or any other respiratory infection among HCWs.
The grading of recommendations, assessment, development, and evaluation (GRADE) was used to evaluate the quality of evidence.
Findings Four RCTs involving 8736 HCWs were included.
We did not find any trial specifically on prevention of COVID-19.
However, wearing N95 respirators can prevent 73 more (95% CI 46-91) clinical respiratory infections per 1000 HCWs compared to surgical masks (2 RCTs; 2594 patients; low quality of evidence).
A protective effect of N95 respirators in laboratory-confirmed bacterial colonization (RR= 0.41; 95%CI 0.28-0.61) was also found.
A trend in favour of N95 respirators was observed in preventing laboratory-confirmed respiratory viral infections, laboratory-confirmed respiratory infection, and influenza like illness.
Interpretation We found no direct high quality evidence on whether N95 respirators are better than surgical masks for HCWs protection from SARS-CoV-2.
However, low quality evidence suggests that N95 respirators protect HCWs from clinical respiratory infections.
This finding should be contemplated to decide the best strategy to support the resilience of healthcare systems facing the potentially catastrophic SARS-CoV-2 pandemic.

Answer: {"verdict": "SUPPORT", "evidence": ["However, wearing N95 respirators can prevent 73 more (95% CI 46-91) clinical respiratory infections per 1000 HCWs compared to surgical masks (2 RCTs; 2594 patients; low quality of 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: Salt-coated masks achieve low viral deactivation rate

Abstract:
Aerosolized pathogens are a leading cause of respiratory infection and transmission.
Currently used protective measures pose potential risk of primary/secondary infection and transmission.
Here, we report the development of a universal, reusable virus deactivation system by functionalization of the main fibrous filtration unit of surgical mask with sodium chloride salt.
The salt coating on the fiber surface dissolves upon exposure to virus aerosols and recrystallizes during drying, destroying the pathogens.
When tested with tightly sealed sides, salt-coated filters showed remarkably higher filtration efficiency than conventional mask filtration layer, and 100% survival rate was observed in mice infected with virus penetrated through salt-coated filters.
Viruses captured on salt-coated filters exhibited rapid infectivity loss compared to gradual decrease on bare filters.
Salt-coated filters proved highly effective in deactivating influenza viruses regardless of subtypes and following storage in harsh environmental conditions.
Our results can be applied in obtaining a broad-spectrum, airborne pathogen prevention device in preparation for epidemic and pandemic of respiratory diseases.

Answer: {"verdict": "CONTRADICT", "evidence": ["Here, we report the development of a universal, reusable virus deactivation system by functionalization of the main fibrous filtration unit of surgical mask with sodium chloride salt.", "Salt-coated filters proved highly effective in deactivating influenza viruses regardless of subtypes and following storage in harsh environmental conditions."]}


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: As many deaths due to COVID-19 may be assigned to other causes of deaths (for example, if COVID-19 was not mentioned on the death certificate as a suspected cause of death), tracking all-cause mortality can provide information about whether an excess number of deaths is observed, even when COVID-19 mortality may be undercounted.

Abstract:
BACKGROUND: The 2019 novel coronavirus has caused the outbreak of the acute respiratory disease in Wuhan, Hubei Province of China since December 2019.
This study was performed to analyze the clinical characteristics of patients who succumbed to and who recovered from 2019 novel coronavirus disease (COVID-19).
METHODS: Clinical data were collected from two tertiary hospitals in Wuhan.
A retrospective investigation was conducted to analyze the clinical characteristics of fatal cases of COVID-19 (death group) and we compare them with recovered patients (recovered group).
Continuous variables were analyzed using the Mann-Whitney U test.
Categorical variables were analyzed by χ test or Fisher exact test as appropriate.
RESULTS: Our study enrolled 109 COVID-19 patients who died during hospitalization and 116 recovered patients.
The median age of the death group was older than the recovered group (69 [62, 74] vs. 40 [33, 57] years, Zâ=â9.738, Pâ<â0.001).
More patients in the death group had underlying diseases (72.5% vs. 41.4%, χâ=â22.105, Pâ<â0.001).
Patients in the death group had a significantly longer time of illness onset to hospitalization (10.0 [6.5, 12.0] vs. 7.0 [5.0, 10.0] days, Zâ=â3.216, Pâ=â0.001).
On admission, the proportions of patients with symptoms of dyspnea (70.6% vs. 19.0%, χâ=â60.905, Pâ<â0.001) and expectoration (32.1% vs. 12.1%, χâ=â13.250, Pâ<â0.001) were significantly higher in the death group.
The blood oxygen saturation was significantly lower in the death group (85 [77, 91]% vs. 97 [95, 98]%, Zâ=â10.625, Pâ<â0.001).
The white blood cell (WBC) in death group was significantly higher on admission (7.23 [4.87, 11.17] vs. 4.52 [3.62, 5.88] ×10/L, Zâ=â7.618, Pâ<â0.001).
Patients in the death group exhibited significantly lower lymphocyte count (0.63 [0.40, 0.79] vs. 1.00 [0.72, 1.27] ×10/L, Zâ=â8.037, Pâ<â0.001) and lymphocyte percentage (7.10 [4.45, 12.73]% vs. 23.50 [15.27, 31.25]%, Zâ=â10.315, Pâ<â0.001) on admission, and the lymphocyte percentage continued to decrease during hospitalization (7.10 [4.45, 12.73]% vs. 2.91 [1.79, 6.13]%, Zâ=â5.242, Pâ<â0.001).
Alanine transaminase (22.00 [15.00, 34.00] vs. 18.70 [13.00, 30.38] U/L, Zâ=â2.592, Pâ=â0.010), aspartate transaminase (34.00 [27.00, 47.00] vs. 22.00 [17.65, 31.75] U/L, Zâ=â7.308, Pâ<â0.001), and creatinine levels (89.00 [72.00, 133.50] vs. 65.00 [54.60, 78.75] µmol/L, Zâ=â6.478, Pâ<â0.001) were significantly higher in the death group than those in the recovered group.
C-reactive protein (CRP) levels were also significantly higher in the death group on admission (109.25 [35.00, 170.28] vs. 3.22 [1.04, 21.80] mg/L, Zâ=â10.206, Pâ<â0.001) and showed no significant improvement after treatment (109.25 [35.00, 170.28] vs. 81.60 [27.23, 179.08] mg/L, Zâ=â1.219, Pâ=â0.233).
The patients in the death group had more complications such as acute respiratory distress syndrome (ARDS) (89.9% vs. 8.6%, χâ=â148.105, Pâ<â0.001), acute cardiac injury (59.6% vs. 0.9%, χâ=â93.222, Pâ<â0.001), acute kidney injury (18.3% vs. 0%, χâ=â23.257, Pâ<â0.001), shock (11.9% vs. 0%, χâ=â14.618, Pâ<â0.001), and disseminated intravascular coagulation (DIC) (6.4% vs. 0%, χâ=â7.655, Pâ=â0.006).
CONCLUSIONS: Compared to the recovered group, more patients in the death group exhibited characteristics of advanced age, pre-existing comorbidities, dyspnea, oxygen saturation decrease, increased WBC count, decreased lymphocytes, and elevated CRP levels.
More patients in the death group had complications such as ARDS, acute cardiac injury, acute kidney injury, shock, and DIC.

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: Coronavirus (COVID-19) Know how to protect yourself and others from COVID-19 and what to do if you are sick.

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": "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: Drugs widely used to treat high blood pressure appear to make COVID-19 dangerously worse.

Abstract:
Coronavirus disease 2019 (COVID-19) is a viral pandemic precipitated by the severe acute respiratory syndrome coronavirus 2.
Since previous reports suggested that viral entry into cells may involve angiotensin converting enzyme 2, there has been growing concern that angiotensin converting enzyme inhibitor (ACEI) and angiotensin II receptor blocker (ARB) use may exacerbate the disease severity.
In this retrospective, single-center US study of adult patients diagnosed with COVID-19, we evaluated the association of ACEI/ARB use with hospital admission.
Secondary outcomes included: ICU admission, mechanical ventilation, length of hospital stay, use of inotropes, and all-cause mortality.
Propensity score matching was performed to account for potential confounders.
Among 590 unmatched patients diagnosed with COVID-19, 78 patients were receiving ACEI/ARB (median age 63 years and 59.7% male) and 512 patients were non-users (median age 42 years and 47.1% male).
In the propensity matched population, multivariate logistic regression analysis adjusting for age, gender and comorbidities demonstrated that ACEI/ARB use was not associated with hospital admission (OR 1.2, 95% CI 0.5-2.7, p = 0.652).
CAD and CKD/ESRD remained independently associated with admission to hospital.
All-cause mortality, ICU stay, need for ventilation, and inotrope use was not significantly different between the 2 study groups.
In conclusion, among patients who were diagnosed with COVID-19, ACEI/ARB use was not associated with increased risk of hospital admission.

Answer: {"verdict": "CONTRADICT", "evidence": ["In conclusion, among patients who were diagnosed with COVID-19, ACEI/ARB use was not associated with increased risk of hospital admission."]}


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

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

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

Claim: Social distancing = > 80 % of the population , covid-19 may be curbed 13 weeks , < = 70 % may not be curbed .

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

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


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

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

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

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

Abstract:
Coronaviruses are a genetically highly variable family of viruses that infect vertebrates and have succeeded in infecting humans many times by overcoming the species barrier.
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which initially appeared in China at the end of 2019, exhibits a high infectivity and pathogenicity compared to other coronaviruses.
As the viral coat and other viral components are recognized as being foreign by the immune system, this can lead to initial symptoms, which are induced by the very efficiently working immune defense system via the respiratory epithelium.
During severe courses a systemically expressed proinflammatory cytokine storm and subsequent changes in the coagulation and complement systems can occur.
Virus-specific antibodies, the long-term expression of which is ensured by the formation of B memory cell clones, generate a specific immune response that is also detectable in blood (seroconversion).
Specifically effective cytotoxic CD8+ Tcell populations are also formed, which recognize viral epitopes as pathogen-specific patterns in combination with MHC presentation on the cell surface of virus-infected cells and destroy these cells.
At the current point in time it is unclear how regular, robust and durable this immune status is constructed.
Experiences with other coronavirus infections (SARS and Middle East respiratory syndrome, MERS) indicate that the immunity could persist for several years.
Based on animal experiments, already acquired data on other coronavirus types and plausibility assumptions, it can be assumed that seroconverted patients have an immunity of limited duration and only a very low risk of reinfection.
Knowledge of the molecular mechanisms of viral cycles and immunity is an important prerequisite for the development of vaccination strategies and development of effective drugs.

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: Remdesivir proves effective against COVID-19

Abstract:
Remdesivir is one of the most promising drugs to treat COVID-19 based on the following facts: remdesivir has a broad-spectrum antiviral mechanism of action; it demonstrated in vitro activity against SARS-CoV-2 and in vivo efficacy in animal models against the similar coronavirus MERS-CoV; its safety profile has been tested in Ebola patients and in compassionate use in COVID-19 patients.
Currently, remdesivir is being investigated in ten randomized controlled trials against COVID-19.
The dose regimen of remdesivir is an IV loading dose of 200 mg on day 1 followed by daily IV maintenance doses of 100 mg for 5-9 days.
Based on our data analysis, however, remdesivir with IV administration alone is unlikely to achieve excellent clinical efficacy.
This analysis is based on the following observations: plasma exposures of remdesivir and its active metabolite are unlikely to be correlated with its clinical efficacy; remdesivir and its active metabolites are unlikely to be adequate in the lung to kill the SARS-CoV-2 virus.
Even if remdesivir demonstrates benefits in the current randomized controlled trials, its efficacy may be limited.
We suggest that a combination of an IV and pulmonary delivery dose regimen should be studied immediately to realize a potentially more effective antiviral therapy against COVID-19.
Graphical abstract.

Answer: {"verdict": "CONTRADICT", "evidence": ["This analysis is based on the following observations: plasma exposures of remdesivir and its active metabolite are unlikely to be correlated with its clinical efficacy; remdesivir and its active metabolites are unlikely to be adequate in the lung to kill the SARS-CoV-2 virus."]}