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: Hand washing with soap and water and scrubbing for at least 20 seconds is recommended, but a hand sanitizer that contains at least 60% alcohol is the best alternative

Abstract:
Infection by coronavirus (CoV-19) has led to emergence of a pandemic called as Coronavirus Disease (COVID-19) that has so far affected about 210 countries.
The dynamic data indicate that the pandemic by CoV-19 so far has infected 2,403,963 individuals, and among these 624,698 have recovered while, it has been fatal for 165,229.
Without much experience, currently, the medicines that are clinically being evaluated for COVID-19 include chloroquine, hydroxychloroquine, azithromycin, tocilizumab, lopinavir, ritonavir, tocilizumab and corticosteroids.
Therefore, countries such as Italy, USA, Spain and France with the most advanced health care system are partially successful to control CoV-19 infection.
India being the 2nd largest populous country, where, the healthcare system is underdeveloped, major portion of population follow unhygienic lifestyle, is able to restrict the rate of both infection and death of its citizens from COVID-19.
India has followed an early and a very strict social distancing by lockdown and has issued advisory to clean hands regularly by soap and/or by alcohol based sterilizers.
Rolling data on the global index of the CoV infection is 13,306, and the index of some countries such as USA (66,148), Italy (175,055), Spain (210,126), France (83,363) and Switzerland (262,122) is high.
The index of India has remained very low (161) so far, mainly due to early implementation of social lockdown, social distancing, and sanitizing hands.
However, articles on social lockdown as a preventive measure against COVID-19 in PubMed are scanty.
It has been observed that social lockdown has also drastic impacts on the environment especially on reduction of NO2 and CO2 emission.
Slow infection rate under strict social distancing will offer time to researchers to come up with exact medicines/vaccines against CoV-19.
Therefore, it is concluded that stringent social distancing via lockdown is highly important to control COVID-19 and also to contribute for self-regeneration of nature.

Answer: {"verdict": "SUPPORT", "evidence": ["The index of India has remained very low (161) so far, mainly due to early implementation of social lockdown, social distancing, and sanitizing hands."]}


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: Residual sars-cov-2 viral antigens detected in gastrointestinal and hepatic tissues from two recovered covid-19 patients

Abstract:
Residual SARS-CoV-2 RNA has been detected in stool samples and gastrointestinal tissues during the convalescence phase of COVID-19 infection.
This raises concern for persistence of SARS-CoV-2 virus particles and faecal-oral transmissibility in recovered COVID-19 patients.
Using multiplex immunohistochemistry, we unexpectedly detected SARS-CoV-2 viral antigens in intestinal and liver tissues, in surgical samples obtained from two patients who recovered from COVID-19.
We further validated the presence of virus by RT-PCR and flow cytometry to detect SARS-CoV-2-specific immunity in the tissues.
These findings might have important implications in terms of disease management and public health policy regarding transmission of COVID-19 via faecal-oral and iatrogenic routes during the convalescence phase.

Answer: {"verdict": "SUPPORT", "evidence": ["This raises concern for persistence of SARS-CoV-2 virus particles and faecal-oral transmissibility in recovered COVID-19 patients.", "Using multiplex immunohistochemistry, we unexpectedly detected SARS-CoV-2 viral antigens in intestinal and liver tissues, in surgical samples obtained from two patients who recovered from COVID-19.", "We further validated the presence of virus by RT-PCR and flow cytometry to detect SARS-CoV-2-specific immunity in the tissues.", "These findings might have important implications in terms of disease management and public health policy regarding transmission of COVID-19 via faecal-oral and iatrogenic routes during the convalescence phase."]}


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: Safety and immunogenicity study of 2019-ncov vaccine to prevent sars-cov-2 infection

Abstract:
BACKGROUND: The ongoing COVID-19 pandemic warrants accelerated efforts to test vaccine candidates.
We aimed to assess the safety and immunogenicity of an inactivated severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccine candidate, BBIBP-CorV, in humans.
METHODS: We did a randomised, double-blind, placebo-controlled, phase 1/2 trial at Shangqiu City Liangyuan District Center for Disease Control and Prevention in Henan Province, China.
In phase 1, healthy people aged 18-80 years, who were negative for serum-specific IgM/IgG antibodies against SARS-CoV-2 at the time of screening, were separated into two age groups (18-59 years and ≥60 years) and randomly assigned to receive vaccine or placebo in a two-dose schedule of 2 µg, 4 µg, or 8 µg on days 0 and 28.
In phase 2, healthy adults (aged 18-59 years) were randomly assigned (1:1:1:1) to receive vaccine or placebo on a single-dose schedule of 8 µg on day 0 or on a two-dose schedule of 4 µg on days 0 and 14, 0 and 21, or 0 and 28.
Participants within each cohort were randomly assigned by stratified block randomisation (block size eight) and allocated (3:1) to receive vaccine or placebo.
Group allocation was concealed from participants, investigators, and outcome assessors.
The primary outcomes were safety and tolerability.
The secondary outcome was immunogenicity, assessed as the neutralising antibody responses against infectious SARS-CoV-2.
This study is registered with www.chictr.org.cn, ChiCTR2000032459.
FINDINGS: In phase 1, 192 participants were enrolled (mean age 53·7 years [SD 15·6]) and were randomly assigned to receive vaccine (2 µg [n=24], 4 µg [n=24], or 8 µg [n=24] for both age groups [18-59 years and ≥60 years]) or placebo (n=24).
At least one adverse reaction was reported within the first 7 days of inoculation in 42 (29%) of 144 vaccine recipients.
The most common systematic adverse reaction was fever (18-59 years, one [4%] in the 2 µg group, one [4%] in the 4 µg group, and two [8%] in the 8 µg group; ≥60 years, one [4%] in the 8 µg group).
All adverse reactions were mild or moderate in severity.
No serious adverse event was reported within 28 days post vaccination.
Neutralising antibody geometric mean titres were higher at day 42 in the group aged 18-59 years (87·7 [95% CI 64·9-118·6], 2 µg group; 211·2 [158·9-280·6], 4 µg group; and 228·7 [186·1-281·1], 8 µg group) and the group aged 60 years and older (80·7 [65·4-99·6], 2 µg group; 131·5 [108·2-159·7], 4 µg group; and 170·87 [133·0-219·5], 8 µg group) compared with the placebo group (2·0 [2·0-2·0]).
In phase 2, 448 participants were enrolled (mean age 41·7 years [SD 9·9]) and were randomly assigned to receive the vaccine (8 µg on day 0 [n=84] or 4 µg on days 0 and 14 [n=84], days 0 and 21 [n=84], or days 0 and 28 [n=84]) or placebo on the same schedules (n=112).
At least one adverse reaction within the first 7 days was reported in 76 (23%) of 336 vaccine recipients (33 [39%], 8 µg day 0; 18 [21%], 4 µg days 0 and 14; 15 [18%], 4 µg days 0 and 21; and ten [12%], 4 µg days 0 and 28).
One placebo recipient in the 4 µg days 0 and 21 group reported grade 3 fever, but was self-limited and recovered.
All other adverse reactions were mild or moderate in severity.
The most common systematic adverse reaction was fever (one [1%], 8 µg day 0; one [1%], 4 µg days 0 and 14; three [4%], 4 µg days 0 and 21; two [2%], 4 µg days 0 and 28).
The vaccine-elicited neutralising antibody titres on day 28 were significantly greater in the 4 µg days 0 and 14 (169·5, 95% CI 132·2-217·1), days 0 and 21 (282·7, 221·2-361·4), and days 0 and 28 (218·0, 181·8-261·3) schedules than the 8 µg day 0 schedule (14·7, 11·6-18·8; all p<0·001).
INTERPRETATION: The inactivated SARS-CoV-2 vaccine, BBIBP-CorV, is safe and well tolerated at all tested doses in two age groups.
Humoral responses against SARS-CoV-2 were induced in all vaccine recipients on day 42.
Two-dose immunisation with 4 µg vaccine on days 0 and 21 or days 0 and 28 achieved higher neutralising antibody titres than the single 8 µg dose or 4 µg dose on days 0 and 14.
FUNDING: National Program on Key Research Project of China, National Mega projects of China for Major Infectious Diseases, National Mega Projects of China for New Drug Creation, and Beijing Science and Technology Plan.

Answer: {"verdict": "SUPPORT", "evidence": ["We aimed to assess the safety and immunogenicity of an inactivated severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccine candidate, BBIBP-CorV, in humans."]}


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

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

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

Claim: People with diabetes have not a higher risk for complications from coronavirus

Abstract:
AIMS: The 2019 novel coronavirus disease (COVID-19) emerged in Wuhan, China, and was characterized as a pandemic by the World Health Organization.
Diabetes is an established risk associated with poor clinical outcomes, but the association of diabetes with COVID-19 has not been reported yet.
METHODS: In this cohort study, we retrospectively reviewed 258 consecutive hospitalized COVID-19 patients with or without diabetes at the West Court of Union Hospital in Wuhan, China, recruited from January 29 to February 12, 2020.
The clinical features, treatment strategies and prognosis data were collected and analyzed.
Prognosis was followed up until March 12, 2020.
RESULTS: Of the 258 hospitalized patients (63 with diabetes) with COVID-19, the median age was 64 years (range 23-91), and 138 (53.5%) were male.
Common symptoms included fever (82.2%), dry cough (67.1%), polypnea (48.1%), and fatigue (38%).
Patients with diabetes had significantly higher leucocyte and neutrophil counts, and higher levels of fasting blood glucose, serum creatinine, urea nitrogen and creatine kinase isoenzyme MB at admission compared with those without diabetes.
COVID-19 patients with diabetes were more likely to develop severe or critical disease conditions with more complications, and had higher incidence rates of antibiotic therapy, non-invasive and invasive mechanical ventilation, and death (11.1% vs. 4.1%).
Cox proportional hazard model showed that diabetes (adjusted hazard ratio [aHR] = 3.64; 95% confidence interval [CI]: 1.09, 12.21) and fasting blood glucose (aHR = 1.19; 95% CI: 1.08, 1.31) were associated with the fatality due to COVID-19, adjusting for potential confounders.
CONCLUSIONS: Diabetes mellitus is associated with increased disease severity and a higher risk of mortality in patients with COVID-19.

Answer: {"verdict": "CONTRADICT", "evidence": ["COVID-19 patients with diabetes were more likely to develop severe or critical disease conditions with more complications, and had higher incidence rates of antibiotic therapy, non-invasive and invasive mechanical ventilation, and death (11.1% vs. 4.1%)."]}


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: hydroxychloroquine cures covid-19.

Abstract:
Hydroxychloroquine (HCQ) has sparked much interest in the therapeutics of the Coronavirus Disease 2019 (COVID-19) pandemic.
Its antiviral properties have been studied for years; regarding the Severe Acute Respiratory Syndrome-Corona Virus-2 (SARS-CoV-2), it has been shown that HCQ may act at multiple levels.
These extend from the initial attachment of the virus to the respiratory epithelium to the inhibition of its replication by the alkalinisation of the phagolysosome's microenvironment and the post-translational modification of certain viral proteins.
Preliminary clinical evidence from China and France showed significant virological and clinical benefit in HCQ-treated patients, while other studies, mostly including critically ill patients, did not show favorable results.
In this review, we critically appraise the existing evidence on HCQ against SARS-CoV-2 with particular emphasis on its protective and therapeutic role.
Safety concerns that are relevant to the short-term HCQ use are also discussed.
In the context of the rapid evolution of the COVID-19 pandemic that strains the health care systems worldwide and considering limited population-wide testing rates in most of the vulnerable countries, early empiric short-term administration of HCQ in symptomatic individuals, may be a promising, safe and low-cost strategy.

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: Higher risk if you have type 1 diabetes. Compared to people without diabetes, people with type 1 diabetes are approximately 3.5 times as likely to die in hospital with COVID-19, while people with type 2 are approximately twice as likely.

Abstract:
AIMS: The 2019 novel coronavirus disease (COVID-19) emerged in Wuhan, China, and was characterized as a pandemic by the World Health Organization.
Diabetes is an established risk associated with poor clinical outcomes, but the association of diabetes with COVID-19 has not been reported yet.
METHODS: In this cohort study, we retrospectively reviewed 258 consecutive hospitalized COVID-19 patients with or without diabetes at the West Court of Union Hospital in Wuhan, China, recruited from January 29 to February 12, 2020.
The clinical features, treatment strategies and prognosis data were collected and analyzed.
Prognosis was followed up until March 12, 2020.
RESULTS: Of the 258 hospitalized patients (63 with diabetes) with COVID-19, the median age was 64 years (range 23-91), and 138 (53.5%) were male.
Common symptoms included fever (82.2%), dry cough (67.1%), polypnea (48.1%), and fatigue (38%).
Patients with diabetes had significantly higher leucocyte and neutrophil counts, and higher levels of fasting blood glucose, serum creatinine, urea nitrogen and creatine kinase isoenzyme MB at admission compared with those without diabetes.
COVID-19 patients with diabetes were more likely to develop severe or critical disease conditions with more complications, and had higher incidence rates of antibiotic therapy, non-invasive and invasive mechanical ventilation, and death (11.1% vs. 4.1%).
Cox proportional hazard model showed that diabetes (adjusted hazard ratio [aHR] = 3.64; 95% confidence interval [CI]: 1.09, 12.21) and fasting blood glucose (aHR = 1.19; 95% CI: 1.08, 1.31) were associated with the fatality due to COVID-19, adjusting for potential confounders.
CONCLUSIONS: Diabetes mellitus is associated with increased disease severity and a higher risk of mortality in patients with COVID-19.

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: It appears that the virus that causes COVID-19 can spread from people to animals in some situations.

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

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


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

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

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

Claim: covid-19 isn't a risk for young people

Abstract:
OBJECTIVES: To determine mortality rates among adults with critical illness from coronavirus disease 2019.
DESIGN: Observational cohort study of patients admitted from March 6, 2020, to April 17, 2020.
SETTING: Six coronavirus disease 2019 designated ICUs at three hospitals within an academic health center network in Atlanta, Georgia, United States.
PATIENTS: Adults greater than or equal to 18 years old with confirmed severe acute respiratory syndrome-CoV-2 disease who were admitted to an ICU during the study period.
INTERVENTIONS: None.
MEASUREMENTS AND MAIN RESULTS: Among 217 critically ill patients, mortality for those who required mechanical ventilation was 35.7% (59/165), with 4.8% of patients (8/165) still on the ventilator at the time of this report.
Overall mortality to date in this critically ill cohort is 30.9% (67/217) and 60.4% (131/217) patients have survived to hospital discharge.
Mortality was significantly associated with older age, lower body mass index, chronic renal disease, higher Sequential Organ Failure Assessment score, lower PaO2/FIO2 ratio, higher D-dimer, higher C-reactive protein, and receipt of mechanical ventilation, vasopressors, renal replacement therapy, or vasodilator therapy.
CONCLUSIONS: Despite multiple reports of mortality rates exceeding 50% among critically ill adults with coronavirus disease 2019, particularly among those requiring mechanical ventilation, our early experience indicates that many patients survive their critical illness.

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: Exposing yourself to the sun or to temperatures higher than 25C degrees does not prevent the coronavirus disease (COVID-19).

Abstract:
As the world economies get out of the lockdown imposed by the COVID-19 pandemic, there is an urgent need to assess the suitability of known technologies to mitigate COVID-19 transmission in confined spaces such as buildings.
This feasibility study looks at the method of upper-room ultraviolet (UV) air disinfection that has already proven its efficacy in preventing the transmission of airborne diseases such as measles and tuberculosis.
Using published data from various sources it is shown that the SARS-CoV-2 virus, which causes COVID-19, is highly likely to be susceptible to UV damage while suspended in air irradiated by UV-C at levels that are acceptable and safe for upper-room applications.
This is while humans are present in the room.
Both the expected and worst-case scenarios are investigated to show the efficacy of the upper-room UV-C approach to reduce COVID-19 air transmission in a confined space with moderate but sufficient height.
Discussion is given on the methods of analysis and the differences between virus susceptibility to UV-C when aerosolised or in liquid or on a surface.

Answer: {"verdict": "SUPPORT", "evidence": ["Using published data from various sources it is shown that the SARS-CoV-2 virus, which causes COVID-19, is highly likely to be susceptible to UV damage while suspended in air irradiated by UV-C at levels that are acceptable and safe for upper-room applications."]}


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: Cross-species transmission of the previously identified coronavirus 2019-ncov

Abstract:
The current outbreak of viral pneumonia in the city of Wuhan, China, was caused by a novel coronavirus designated 2019-nCoV by the World Health Organization, as determined by sequencing the viral RNA genome.
Many initial patients were exposed to wildlife animals at the Huanan seafood wholesale market, where poultry, snake, bats, and other farm animals were also sold.
To investigate possible virus reservoir, we have carried out comprehensive sequence analysis and comparison in conjunction with relative synonymous codon usage (RSCU) bias among different animal species based on the 2019-nCoV sequence.
Results obtained from our analyses suggest that the 2019-nCoV may appear to be a recombinant virus between the bat coronavirus and an origin-unknown coronavirus.
The recombination may occurred within the viral spike glycoprotein, which recognizes a cell surface receptor.
Additionally, our findings suggest that 2019-nCoV has most similar genetic information with bat coronovirus and most similar codon usage bias with snake.
Taken together, our results suggest that homologous recombination may occur and contribute to the 2019-nCoV cross-species transmission.

Answer: {"verdict": "CONTRADICT", "evidence": ["Results obtained from our analyses suggest that the 2019-nCoV may appear to be a recombinant virus between the bat coronavirus and an origin-unknown coronavirus."]}


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:
We remain largely without effective prophylactic/therapeutic interventions for COVID-19.
Although many human clinical trials are ongoing, there remains a deficiency of supportive preclinical drug efficacy studies.
Here we assessed the prophylactic/therapeutic efficacy of hydroxychloroquine (HCQ), a drug of interest for COVID-19 management, in two animal models.
When used for prophylaxis or treatment neither the standard human malaria dose (6.5 mg/kg) nor a high dose (50 mg/kg) of HCQ had any beneficial effect on clinical disease or SARS-CoV-2 kinetics (replication/shedding) in the Syrian hamster disease model.
Similarly, HCQ prophylaxis/treatment (6.5 mg/kg) did not significantly benefit clinical outcome nor reduce SARS-CoV-2 replication/shedding in the upper and lower respiratory tract in the rhesus macaque disease model.
In conclusion, our preclinical animal studies do not support the use of HCQ in prophylaxis/treatment of COVID-19.

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: 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:
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: with autoimmune conditions such as lupus, a person experiences "dysregulations of the immune system," meaning the immune system itself is compromised or malfunctioning

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

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

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


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

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

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

Claim: Diabetes is generally known to weaken the immune system, making it harder to protect against viral infections like COVID-19.

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

Answer: {"verdict": "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: Personalized surveillance approach for hospitalized patients with covid-19

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

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


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

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

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

Claim: vitamin D might be able to protect people from the coronavirus (COVID-19).

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

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


Question:
You will be shown a claim 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: Vinegar can kill microorganisms such as bacteria and viruses and treat yeast infections.

Abstract:
The global pandemic caused by the newly described severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused worldwide suffering and death of unimaginable magnitude from coronavirus disease 2019 (COVID-19).
The virus is transmitted through aerosol droplets, and causes severe acute respiratory syndrome.
SARS-CoV-2 uses the receptor-binding domain of its spike protein S1 to attach to the host angiotensin-converting enzyme 2 receptor in lung and airway cells.
Binding requires the help of another host protein, transmembrane protease serine S1 member 2.
Several factors likely contribute to the efficient transmission of SARS-CoV-2.
The receptor-binding domain of SARS-CoV-2 has a 10- to 20-fold higher receptor-binding capacity compared with previous pandemic coronaviruses.
In addition, because asymptomatic persons infected with SARS-CoV-2 have high viral loads in their nasal secretions, they can silently and efficiently spread the disease.
PCR-based tests have emerged as the criterion standard for the diagnosis of infection.
Caution must be exercised in interpreting antibody-based tests because they have not yet been validated, and may give a false sense of security of being "immune" to SARS-CoV-2.
We discuss how the development of some symptoms in allergic rhinitis can serve as clues for new-onset COVID-19.
There are mixed reports that asthma is a risk factor for severe COVID-19, possibly due to differences in asthma endotypes.
The rapid spread of COVID-19 has focused the efforts of scientists on repurposing existing Food and Drug Administration-approved drugs that inhibit viral entry, endocytosis, genome assembly, translation, and replication.
Numerous clinical trials have been launched to identify effective treatments for COVID-19.
Initial data from a placebo-controlled study suggest faster time to recovery in patients on remdesivir; it is now being evaluated in additional controlled studies.
As discussed in this review, till effective vaccines and treatments emerge, it is important to understand the scientific rationale of pandemic-mitigation strategies such as wearing facemasks and social distancing, and implement them.

Answer: {"verdict": "SUPPORT", "evidence": ["Numerous clinical trials have been launched to identify effective treatments for COVID-19.", "Initial data from a placebo-controlled study suggest faster time to recovery in patients on remdesivir; it is now being evaluated in additional controlled studies."]}


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

Answer: {"verdict": "SUPPORT", "evidence": ["Active smoking was associated with decreased odds 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: Unexpected Cause of Death in Younger COVID-19 Patients is Related to Blood Clotting

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": "SUPPORT", "evidence": ["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."]}


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

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

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

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

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

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

Abstract:
Importance.
Covid-19 infection has major international health and economic impacts and risk factors for infection are not completely understood.
Cannabis smoking is linked with poor respiratory health, immunosuppression and multiple contaminants.
Potential synergism between the two epidemics would represent a major public health convergence.
Cigarettes were implicated with disease severity in Wuhan, China.
Objective.
Is cannabis use epidemiologically associated with coronavirus incidence rate (CVIR)?
Design.
Cross-sectional state-based multivariable study.
Setting.
USA.
Primary and Secondary Outcome Measures.
CVIR.
Multivariable-adjusted geospatially-weighted regression models.
As the American cannabis epidemic is characterized by a recent doubling of daily cannabis use it was considered important to characterize the contribution of high intensity use.
Results.
Significant associations of daily cannabis use quintile with CVIR were identified with the highest quintile having a prevalence ratio 5.11 (95%C.I. 4.90-5.33), an attributable fraction in the exposed (AFE) 80.45% (79.61-81.25%) and an attributable fraction in the population of 77.80% (76.88-78.68%) with Chi-squared-for-trend (14,782, df=4) significant at P<10-500.
Similarly when cannabis legalization was considered decriminalization was associated with an elevated CVIR prevalence ratio 4.51 (95%C.I. 4.45-4.58), AFE 77.84% (77.50-78.17%) and Chi-squared-for-trend (56,679, df=2) significant at P<10-500.
Monthly and daily use were linked with CVIR in bivariate geospatial regression models (P=0.0027, P=0.0059).
In multivariable additive models number of flight origins and population density were significant.
In interactive geospatial models adjusted for international travel, ethnicity, income, population, population density and drug use, terms including last month cannabis were significant from P=7.3x10-15, daily cannabis use from P=7.3x10-11 and last month cannabis was independently associated (P=0.0365).
Conclusions and Relevance.
Data indicate CVIR demonstrates significant trends across cannabis use intensity quintiles and with relaxed cannabis legislation.
Recent cannabis use is independently predictive of CVIR in bivariate and multivariable adjusted models and intensity of use is interactively significant.
Cannabis thus joins tobacco as a SARS2-CoV-2 risk factor.

Answer: {"verdict": "CONTRADICT", "evidence": ["Cannabis smoking is linked with poor respiratory health, immunosuppression and multiple contaminants.", "Potential synergism between the two epidemics would represent a major public health convergence.", "Cigarettes were implicated with disease severity in Wuhan, China."]}


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: Vinegar can kill microorganisms such as bacteria and viruses and treat yeast infections.

Abstract:
PURPOSE: SARS-CoV-2 is a new pandemic influenza caused by a coronavirus which main route of transmission is through exhaled droplets that primarily infect the nose and the nasopharynx.
The aim of this paper is to evaluate the effect of acetic acid, the active component of vinegar, as a potential disinfectant agent for upper airways.
METHODS: Twenty-nine patients were enrolled and divided into two groups: group 1 (14 patients) was composed of patients treated with off-label hydroxychloroquine and lopinavir/ritonavir, whereas group 2 (15 patients) was composed of patients treated with hydroxychloroquine only, combined with the inhalation of acetic acid disinfectant at a 0.34% concentration.
A questionnaire-based evaluation of symptoms was performed after 15 days in both groups.
RESULTS: It appears that the number of patients treated with acetic acid (group 2) that experienced improvement in individual symptoms was double that of the other group of patients (group 1), although numbers are too small for robust statistical analysis.
CONCLUSIONS: Considering its potential benefits and high availability, acetic acid disinfection appears to be a promising adjunctive therapy in cases of non-severe COVID-19 and deserves further investigation.

Answer: {"verdict": "SUPPORT", "evidence": ["CONCLUSIONS: Considering its potential benefits and high availability, acetic acid disinfection appears to be a promising adjunctive therapy in cases of non-severe COVID-19 and deserves further investigation."]}


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: Supplements and products unlikely to help with coronavirus and could be dangerous

Abstract:
OBJECTIVE Coronavirus disease 2019 (COVID-19) is a fatal and fast-spreading viral infection.
To date, the number of COVID-19 patients worldwide has crossed over six million with over three hundred and seventy thousand deaths (according to the data from World Health Organization; updated on 2 June 2020).
Although COVID-19 can be rapidly diagnosed, efficient clinical treatment of COVID-19 remains unavailable, resulting in high fatality.
Some clinical trials have identified vitamin C (VC) as a potent compound pneumonia management.
In addition, glycyrrhizic acid (GA) is clinically as an anti-inflammatory medicine against pneumonia-induced inflammatory stress.
We hypothesized that the combination of VC and GA is a potential option for treating COVID-19.
METHODS The aim of this study was to determine pharmacological targets and molecular mechanisms of VC + GA treatment for COVID-19, using bioinformational network pharmacology.
RESULTS We uncovered optimal targets, biological processes and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways of VC + GA against COVID-19.
Our findings suggested that combinatorial VC and GA treatment for COVID-19 was associated with elevation of immunity and suppression of inflammatory stress, including activation of the T cell receptor signaling pathway, regulation of Fc gamma R-mediated phagocytosis, ErbB signaling pathway and vascular endothelial growth factor signaling pathway.
We also identified 17 core targets of VC + GA, which suggest as antimicrobial function.
CONCLUSIONS For the first time, our study uncovered the pharmacological mechanism underlying combined VC and GA treatment for COVID-19.
These results should benefit efforts to address the most pressing problem currently facing the world.

Answer: {"verdict": "SUPPORT", "evidence": ["Although COVID-19 can be rapidly diagnosed, efficient clinical treatment of COVID-19 remains unavailable, resulting in high fatality."]}


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

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

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

Claim: A prefusion sars-cov-2 spike rna vaccine is highly immunogenic and prevents lung infection in non-human primates

Abstract:
To contain the coronavirus disease 2019 (COVID-19) pandemic, a safe and effective vaccine against the new severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is urgently needed in quantities sufficient to immunise large populations.
In this study, we report the design, preclinical development, immunogenicity and anti-viral protective effect in rhesus macaques of the BNT162b2 vaccine candidate.
BNT162b2 contains an LNP-formulated nucleoside-modified mRNA that encodes the spike glycoprotein captured in its prefusion conformation.
After expression of the BNT162b2 coding sequence in cells, approximately 20% of the spike molecules are in the one-RBD ‘up’, two-RBD ‘down’ state.
Immunisation of mice with a single dose of BNT162b2 induced dose level-dependent increases in pseudovirus neutralisation titers.
Prime-boost vaccination of rhesus macaques elicited authentic SARS-CoV-2 neutralising geometric mean titers 10.2 to 18.0 times that of a SARS-CoV-2 convalescent human serum panel.
BNT162b2 generated strong TH1 type CD4+ and IFNγ+ CD8+ T-cell responses in mice and rhesus macaques.
The BNT162b2 vaccine candidate fully protected the lungs of immunised rhesus macaques from infectious SARS-CoV-2 challenge.
BNT162b2 is currently being evaluated in a global, pivotal Phase 2/3 trial (NCT04368728).

Answer: {"verdict": "SUPPORT", "evidence": ["In this study, we report the design, preclinical development, immunogenicity and anti-viral protective effect in rhesus macaques of the BNT162b2 vaccine candidate.", "Prime-boost vaccination of rhesus macaques elicited authentic SARS-CoV-2 neutralising geometric mean titers 10.2 to 18.0 times that of a SARS-CoV-2 convalescent human serum panel.", "The BNT162b2 vaccine candidate fully protected the lungs of immunised rhesus macaques from infectious SARS-CoV-2 challenge."]}


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

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

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

Claim: vitamin D might help protect against becoming infected with, and developing serious symptoms of, COVID-19

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

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


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

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

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

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

Abstract:
Coronavirus disease 2019 (COVID-19) has become a global concern and public health issue due to its higher infection and mortality rate; particularly, the risk is very higher among the patients who have cardiovascular diseases (CVD) and/or diabetes mellitus (DM).
In this review, we analyzed the recently published literature on CVD and DM associated with COVD-19 infections and highlight their association with potential mechanisms.
The findings revealed that without any previous history of CVD, the COVID-19 patients have developed some CVD complications like myocardial injury, cardiomyopathy, and venous thromboembolism after being infected with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and required for those patients an emergency clinical support to be aware to manage those complications.
Though the association between DM and COVID-19-induced severe complications is still unclear, the limited data predict that different markers like interleukin (IL)-1, IL-6, C-reactive protein, and D-dimer linked with the severity of COVID-19 infection in diabetic individuals.
Further studies on a large scale are urgently needed to explore the underlying mechanisms between CVD, DM, and COVID-19 for better treatment.

Answer: {"verdict": "CONTRADICT", "evidence": ["Though the association between DM and COVID-19-induced severe complications is still unclear, the limited data predict that different markers like interleukin (IL)-1, IL-6, C-reactive protein, and D-dimer linked with the severity of COVID-19 infection in diabetic individuals."]}


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

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

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

Claim: COVID-19 spreads by contact with respiratory droplets that spread when an infected person coughs or sneezes.

Abstract:
Coronavirus Disease 2019 (COVID-19) is a pandemic affecting many countries worldwide.
Given the increasing incidence especially in elderly and individuals with comorbid conditions, it is advised by health authorities to stay home if possible, maintain social distancing and stay away from those who are sick or could be infected.
Patients with comorbidities especially cardiovascular disease are at higher risk of getting infected with COVID-19 and have worse prognosis.
Among efforts to safely manage warfarin patients during this pandemic, we introduced a hospital drive-up anticoagulation testing service.
This service can reduce the risk of exposure of anticoagulation patients to COVID-19 by reducing the contact time with the different personnel at the hospital and by maintaining those patients at a safe distance from others.

Answer: {"verdict": "SUPPORT", "evidence": ["Given the increasing incidence especially in elderly and individuals with comorbid conditions, it is advised by health authorities to stay home if possible, maintain social distancing and stay away from those who are sick or could be infected."]}


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: Only an N95 mask will help me from getting covid-19.

Abstract:
We identified seasonal human coronaviruses, influenza viruses and rhinoviruses in exhaled breath and coughs of children and adults with acute respiratory illness.
Surgical face masks significantly reduced detection of influenza virus RNA in respiratory droplets and coronavirus RNA in aerosols, with a trend toward reduced detection of coronavirus RNA in respiratory droplets.
Our results indicate that surgical face masks could prevent transmission of human coronaviruses and influenza viruses from symptomatic individuals.

Answer: {"verdict": "CONTRADICT", "evidence": ["Surgical face masks significantly reduced detection of influenza virus RNA in respiratory droplets and coronavirus RNA in aerosols, with a trend toward reduced detection of coronavirus RNA in respiratory droplets.", "Our results indicate that surgical face masks could prevent transmission of human coronaviruses and influenza viruses from symptomatic individuals."]}


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: Covid-2019 associated overexpressed prevotella proteins mediated host-pathogen interactions and their role in coronavirus outbreak

Abstract:
MOTIVATION: The outbreak of COVID-2019 initiated at Wuhan, China has become a global threat by rapid transmission and severe fatalities.
Recent studies have uncovered whole genome sequence of SARS-CoV-2 (causing COVID-2019).
In addition, lung metagenomic studies on infected patients revealed overrepresented Prevotella spp.
producing certain proteins in abundance.
We performed host-pathogen protein-protein interaction analysis between SARS-CoV-2 and overrepresented Prevotella proteins with human proteome.
We also performed functional overrepresentation analysis of interacting proteins to understand their role in COVID-2019 severity.
RESULTS: It was found that over-expressed Prevotella proteins can promote viral infection.
As per the results, Prevotella proteins, but not viral proteins are involved in multiple interactions with NF-kB, which is involved in increasing clinical severity of COVID-2019.
Prevotella may have role in COVID-2019 outbreak and should be given importance for understanding disease mechanisms and improving treatment outcomes.
SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Answer: {"verdict": "SUPPORT", "evidence": ["As per the results, Prevotella proteins, but not viral proteins are involved in multiple interactions with NF-kB, which is involved in increasing clinical severity of COVID-2019.", "Prevotella may have role in COVID-2019 outbreak and should be given importance for understanding disease mechanisms and improving treatment outcomes."]}


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 reduces severe 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 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:
Aims: The question of interactions between the renin angiotensin aldosterone system drugs and the incidence and prognosis of COVID-19 infection has been raised by the medical community.
We hypothesised that if patients treated with ACE inhibitors (ACEI) or AT1 receptor blockers (ARB) were more prone to SARS-CoV2 infection and had a worse prognosis than untreated patients, the prevalence of consumption of these drugs would be higher in patients with COVID-19 compared to the general population.
Methods and results: We used a clinical epidemiology approach based on the estimation of standardised prevalence ratio (SPR) of consumption of ACEI and ARB in four groups of patients (including 187 COVID-19 positive) with increasing severity referred to the University hospital of Lille and in three French reference samples (the exhaustive North population (n=1,569,968), a representative sample of the French population (n=414,046), a random sample of Lille area (n=1,584)).
The SPRs of ACEI and ARB did not differ as the severity of the COVID-19 patients increased, being similar to the regular consumption of these drugs in the North of France population with the same non-significant increase for both treatment (1.17 [0.83-1.67]).
A statistically significant increase in the SPR of ARB (1.56 [1.02-2.39]) was observed in intensive care unit patients only.
After stratification on obesity, this increase was limited to the high risk subgroup of obese patients.
Conclusions: Our results strongly support the recommendation that ACEI and ARB should be continued in the population and in COVID-19 positive patients, reinforcing the position of several scientific societies.

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: Supplementation is unlikely to show an effect in people whose vitamin C levels are already high

Abstract:
Optimal nutrition can improve well-being and might mitigate the risk and morbidity associated with coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).
This review summarizes nutritional guidelines to support dietary counseling provided by dietitians and health-related professionals.
The majority of documents encouraged the consumption of fruits, vegetables, and whole grain foods.
Thirty-one percent of the guidelines highlighted the importance of minerals and vitamins such as zinc and vitamins C, A, and D to maintain a well-functioning immune system.
Dietary supplementation has not been linked to COVID-19 prevention.
However, supplementation with vitamins C and D, as well as with zinc and selenium, was highlighted as potentially beneficial for individuals with, or at risk of, respiratory viral infections or for those in whom nutrient deficiency is detected.
There was no convincing evidence that food or food packaging is associated with the transmission of COVID-19, but good hygiene practices for handling and preparing foods were recommended.
No changes to breastfeeding recommendations have been made, even in women diagnosed with COVID-19.

Answer: {"verdict": "SUPPORT", "evidence": ["Dietary supplementation has not been linked to COVID-19 prevention.", "However, supplementation with vitamins C and D, as well as with zinc and selenium, was highlighted as potentially beneficial for individuals with, or at risk of, respiratory viral infections or for those in whom nutrient deficiency is detected."]}


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 disease (COVID-19) is an infectious disease caused by a newly discovered coronavirus

Abstract:
Origin of the COVID-19 virus has been intensely debated in the scientific community since the first infected cases were detected in December 2019.
The disease has caused a global pandemic, leading to deaths of thousands of people across the world and thus finding origin of this novel coronavirus is important in responding and controlling the pandemic.
Recent research results suggest that bats or pangolins might be the original hosts for the virus based on comparative studies using its genomic sequences.
This paper investigates the COVID-19 virus origin by using artificial intelligence (AI) and raw genomic sequences of the virus.
More than 300 genome sequences of COVID-19 infected cases collected from different countries are explored and analysed using unsupervised clustering methods.
The results obtained from various AI-enabled experiments using clustering algorithms demonstrate that all examined COVID-19 virus genomes belong to a cluster that also contains bat and pangolin coronavirus genomes.
This provides evidences strongly supporting scientific hypotheses that bats and pangolins are probable hosts for the COVID-19 virus.
At the whole genome analysis level, our findings also indicate that bats are more likely the hosts for the COVID-19 virus than pangolins.

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