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: The furin cleavage site of sars-cov-2 spike protein is a key determinant for transmission due to enhanced infection in airway cells.

Abstract:
SARS-CoV-2 enters cells via its spike glycoprotein which must be cleaved sequentially at the S1/S2, then the S2’ cleavage sites (CS) to mediate membrane fusion.
SARS-CoV-2 has a unique polybasic insertion at the S1/S2 CS, which we demonstrate can be cleaved by furin.
Using lentiviral pseudotypes and a cell-culture adapted SARS-CoV-2 virus with a S1/S2 deletion, we show that the polybasic insertion is selected for in lung cells and primary human airway epithelial cultures but selected against in Vero E6, a cell line used for passaging SARS-CoV-2.
We find this selective advantage depends on expression of the cell surface protease, TMPRSS2, that allows virus entry independent of endosomes thus avoiding antiviral IFITM proteins.
SARS-CoV-2 virus lacking the S1/S2 furin CS was shed to lower titres from infected ferrets and was not transmitted to cohoused sentinel animals.
Thus, the polybasic CS is a key determinant for efficient SARS-CoV-2 transmission.

Answer: {"verdict": "CONTRADICT", "evidence": ["SARS-CoV-2 has a unique polybasic insertion at the S1/S2 CS, which we demonstrate can be cleaved by furin.", "Using lentiviral pseudotypes and a cell-culture adapted SARS-CoV-2 virus with a S1/S2 deletion, we show that the polybasic insertion is selected for in lung cells and primary human airway epithelial cultures but selected against in Vero E6, a cell line used for passaging SARS-CoV-2.", "We find this selective advantage depends on expression of the cell surface protease, TMPRSS2, that allows virus entry independent of endosomes thus avoiding antiviral IFITM proteins."]}


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: Yes, 5G radiation causes Covid-19

Abstract:
BACKGROUND: Traditional methods for cardiopulmonary assessment of Coronavirus Disease 2019 (COVID-19) patients pose risks to, both, patients and examiners.
This necessitates a remote examination of such patients without sacrificing information quality.
RESEARCH QUESTION: Assess the feasibility of a 5G-based robot-assisted remote ultrasound system in examining COVID-19 patients and establish an examination protocol for telerobotic ultrasound scanning.
STUDY DESIGN AND METHODS: Twenty-three COVID-19 patients were included and divided into two groups.
Twelve were non-severe cases, and 11 were severe cases.
All patients underwent a 5G-based robot-assisted remote ultrasound system examination of the lungs and heart following an established protocol.
Distribution characteristics and morphology of the lung and surrounding tissue lesions, left ventricular ejection fraction (LVEF), ventricular area ratio, pericardial effusion, and examination-related complications were recorded.
Bilateral lung lesions were evaluated by lung ultrasound score (LUS).
RESULTS: The remote ultrasound system successfully and safely performed cardiopulmonary examinations of all patients.
Peripheral lung lesions were clearly evaluated.
Severe cases had significantly more diseased regions [median (interquartile range), 6.0 (2.0-11.0) vs. 1.0 (0.0-2.8)] and higher LUSs [12.0 (4.0-24.0) vs. 2.0 (0.0-4.0)] than non-severe cases (both, P < 0.05 ).
One non-severe case (8.3%, 95%CI, 1.5% to 35.4%) and three severe cases (27.3%, 95%CI, 9.7% to 56.6%) were complicated by pleural effusions.
Four severe cases (36.4%, 95%CI, 15.2% to 64.6%) were complicated by pericardial effusions (vs 0% of non-severe cases, P < 0.05).
No patients had significant examination-related complications.
INTERPRETATION: 5G-based robot-assisted remote ultrasound system is feasible, and effectively obtains ultrasound characteristics for cardiopulmonary assessment of COVID-19 patients.
By following established protocols and considering medical history, clinical manifestations, and laboratory markers, it might help to evaluate the severity of COVID-19 remotely.

Answer: {"verdict": "CONTRADICT", "evidence": ["INTERPRETATION: 5G-based robot-assisted remote ultrasound system is feasible, and effectively obtains ultrasound characteristics for cardiopulmonary assessment of COVID-19 patients."]}


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

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

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

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

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

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


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

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

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

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

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

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


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

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

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

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

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

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


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

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

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

Claim: Severe COVID-19 outcomes decreases as the pandemic progressed from winter to the warmer months

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

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


Question:
You will be shown a claim 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: 5 x-linked agammaglobulinemia patients develop pneumonia as covid-19 manifestation but recover

Abstract:
BACKGROUND: The recent SARS-CoV-2 pandemic, which has recently affected Italy since February 21, constitutes a threat to normal subjects, as the coronavirus disease-19 (COVID-19) can manifest with a broad spectrum of clinical phenotypes ranging from asymptomatic cases to pneumonia or even death.
There is evidence that older age and several comorbidities can affect the risk to develop severe pneumonia and possibly the need of mechanic ventilation in subjects infected with SARS-CoV-2.
Therefore, we evaluated the outcome of SARS-CoV-2 infection in patients with inborn errors of immunity (IEI) such as X-linked agammaglobulinemia (XLA).
METHODS: When the SARS-CoV-2 epidemic has reached Italy, we have activated a surveillance protocol of patients with IEI, to perform SARS-CoV-2 search by nasopharyngeal swab in patients presenting with symptoms that could be a manifestation of COVID-19, such as fever, cough, diarrhea, or vomiting.
RESULTS: We describe two patients with X-linked agammaglobulinemia (XLA) aged 34 and 26 years with complete absence of B cells from peripheral blood who developed COVID-19, as diagnosed by SARS-CoV-2 detection by nasopharyngeal swab, while receiving immunoglobulin infusions.
Both patients developed interstitial pneumonia characterized by fever, cough, and anorexia and associated with elevation of CRP and ferritin, but have never required oxygen ventilation or intensive care.
CONCLUSION: Our report suggests that XLA patients might present with high risk to develop pneumonia after SARS-CoV-2 infection, but can recover from infection, suggesting that B-cell response might be important, but is not strictly required to overcome the disease.
However, there is a need for larger observational studies to extend these conclusions to other patients with similar genetic immune defects.

Answer: {"verdict": "CONTRADICT", "evidence": ["Therefore, we evaluated the outcome of SARS-CoV-2 infection in patients with inborn errors of immunity (IEI) such as X-linked agammaglobulinemia (XLA)."]}


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: N95 masks are more effective than regular masks.

Abstract:
COVID-19, caused by SARS-CoV2 is a rapidly spreading global pandemic.
Although precise transmission routes and dynamics are unknown, SARS-CoV2 is thought primarily to spread via contagious respiratory droplets.
Unlike with SARS-CoV, maximal viral shedding occurs in the early phase of illness, and this is supported by models that suggest 40-80% of transmission events occur from pre- and asymptomatic individuals.
One widely-discussed strategy to limit transmission of SARS-CoV2, particularly from presymptomatic individuals, has been population-level wearing of masks.
Modelling for pandemic influenza suggests some benefit in reducing total numbers infected with even 50% mask-use.
COVID-19 has a higher hospitalization and mortality rate than influenza, and the impacts on these parameters, and critically, at what point in the pandemic trajectory mask-use might exert maximal benefit are completely unknown.
We derived a simplified SIR model to investigate the effects of near-universal mask-use on COVID-19 assuming 8 or 16% mask efficacy.
We decided to model, in particular, the impact of masks on numbers of critically-ill patients and cumulative mortality, since these are parameters that are likely to have the most severe consequences in the COVID-19 pandemic.
Whereas mask use had a relatively minor benefit on critical-care and mortality rates when transmissibility (Reff) was high, the reduction on deaths was dramatic as the effective R approached 1, as might be expected after aggressive social-distancing measures such as wide-spread lockdowns.
One major concern with COVID-19 is its potential to overwhelm healthcare infrastructures, even in resource-rich settings, with one third of hospitalized patients requiring critical-care.
We incorporated this into our model, increasing death rates for when critical-care resources have been exhausted.
Our simple model shows that modest efficacy of masks could avert substantial mortality in this scenario.
Importantly, the effects on mortality became hyper-sensitive to mask-wearing as the effective R approaches 1, i.e. near the tipping point of when the infection trajectory is expected to revert to exponential growth, as would be expected after effective lockdown.
Our model suggests that mask-wearing might exert maximal benefit as nations plan their post-lockdown strategies and suggests that mask-wearing should be included in further more sophisticated models of the current pandemic.

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


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

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

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

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

Abstract:
The recently emerged SARS-CoV-2 (Coronaviridae; Betacoronavirus) is the underlying cause of COVID-19 disease.
Here we assessed SARS-CoV2 from the Kingdom of Saudi Arabia alongside sequences of SARS-CoV, bat SARS-like CoVs and MERS-CoV, the latter currently detected in this region.
Phylogenetic analysis, natural selection investigation and genome recombination analysis were performed.
Our analysis showed that all Saudi SARS-CoV-2 sequences are of the same origin and closer proximity to bat SARS-like CoVs, followed by SARS-CoVs, however quite distant to MERS-CoV. Moreover, genome recombination analysis revealed two recombination events between SARS-CoV-2 and bat SARS-like CoVs.
This was further assessed by S gene recombination analysis.
These recombination events may be relevant to the emergence of this novel virus.
Moreover, positive selection pressure was detected between SARS-CoV-2, bat SL-CoV isolates and human SARS-CoV isolates.
However, the highest positive selection occurred between SARS-CoV-2 isolates and 2 bat-SL-CoV isolates (Bat-SL-RsSHC014 and Bat-SL-CoVZC45).
This further indicates that SARS-CoV-2 isolates were adaptively evolved from bat SARS-like isolates, and that a virus with originating from bats triggered this pandemic.
This study thuds sheds further light on the origin of this virus.
AUTHOR SUMMARY The emergence and subsequent pandemic of SARS-CoV-2 is a unique challenge to countries all over the world, including Saudi Arabia where cases of the related MERS are still being reported.
Saudi SARS-CoV-2 sequences were found to be likely of the same or similar origin.
In our analysis, SARS-CoV-2 were more closely related to bat SARS-like CoVs rather than to MERS-CoV (which originated in Saudi Arabia) or SARS-CoV, confirming other phylogenetic efforts on this pathogen.
Recombination and positive selection analysis further suggest that bat coronaviruses may be at the origin of SARS-CoV-2 sequences.
The data shown here give hints on the origin of this virus and may inform efforts on transmissibility, host adaptation and other biological aspects of this virus.

Answer: {"verdict": "SUPPORT", "evidence": ["The recently emerged SARS-CoV-2 (Coronaviridae; Betacoronavirus) is the underlying cause of COVID-19 disease."]}


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

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

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

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

Abstract:
SARS-CoV-2, identified in Wuhan, China, for the first time in December 2019, is a new viral strain, which has not been previously identified in humans; it can be transmitted both by air and via direct and indirect contact; however, the most frequent way it spreads is via droplets.
Like the other viruses belonging to the same family of coronaviruses, it can cause from mild flu-like symptoms, such as cold, sore throat, cough and fever, to more severe ones such as pneumonia and breathing difficulties, and it can even lead to death.
Since no effective specific drug therapy has been found yet, nor any vaccine capable of limiting the spread of this pathogen, it is important for ways of preventing the spread of this infection to be established.
The purpose of our research was to provide a protocol to prevent the spread of SARS-CoV-2 infection in light of the limited information related to this coronavirus.
In detail, we analysed and searched targeted evidence-based guidelines issued in the various countries affected by this epidemic up till now.
In addition, we analyzed the recommendations for the prevention and control of other epidemics caused by other pathogens belonging to the same family of coronaviruses or others that present the same mechanisms of transmission.
General organizational measures regarding the containment and management of the epidemiological emergency of COVID-19 have been imposed by the competent authorities for an adequate and proportionate management of the evolution of the epidemiological situation.
The prevention and protection organizational measures therefore aim to minimize the probability of being exposed to SARS-CoV-2.
For this purpose, measures must also be taken at work to avoid new infections or even the spread of the virus where it has already been present.
Furthermore, environmental measures are aimed at reducing the risk of transmission of SARS-CoV-2 to individuals through contact with infected subjects, objects, equipment, or contaminated environmental surfaces.
Protective devices must be used whenever there is potentially close contact with a suspect case, especially when the potentially infected person does not wear a surgical mask that could reduce the spread of viruses in the environment.
By adopting this specific prevention and protection measures recommended in the workplace, it will be possible to help overcome this COVID-19 pandemic.

Answer: {"verdict": "SUPPORT", "evidence": ["SARS-CoV-2, identified in Wuhan, China, for the first time in December 2019, is a new viral strain, which has not been previously identified in humans; it can be transmitted both by air and via direct and indirect contact; however, the most frequent way it spreads is via droplets.", "Protective devices must be used whenever there is potentially close contact with a suspect case, especially when the potentially infected person does not wear a surgical mask that could reduce the spread of viruses in the environment."]}


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

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

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

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

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

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


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

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

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

Claim: Bn-cov2 antibody cocktail prevents and treats sars-cov-2 infection in rhesus macaques and hamsters

Abstract:
An urgent global quest for effective therapies to prevent and treat COVID-19 disease is ongoing.
We previously described REGN-COV2, a cocktail of two potent neutralizing antibodies (REGN10987+REGN10933) targeting non-overlapping epitopes on the SARS-CoV-2 spike protein.
In this report, we evaluate the in vivo efficacy of this antibody cocktail in both rhesus macaques and golden hamsters and demonstrate that REGN-COV-2 can greatly reduce virus load in lower and upper airway and decrease virus induced pathological sequalae when administered prophylactically or therapeutically.
Our results provide evidence of the therapeutic potential of this antibody cocktail.

Answer: {"verdict": "CONTRADICT", "evidence": ["We previously described REGN-COV2, a cocktail of two potent neutralizing antibodies (REGN10987+REGN10933) targeting non-overlapping epitopes on the SARS-CoV-2 spike protein.", "In this report, we evaluate the in vivo efficacy of this antibody cocktail in both rhesus macaques and golden hamsters and demonstrate that REGN-COV-2 can greatly reduce virus load in lower and upper airway and decrease virus induced pathological sequalae when administered prophylactically or therapeutically.", "Our results provide evidence of the therapeutic potential of this antibody cocktail."]}


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 weakened immune systems are at higher risk of getting severely sick from SARS-CoV-2, the virus that causes COVID-19.

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

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


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

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

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

Claim: There is also evidence that smokers in hospital who have coronavirus are at a higher risk than non-smokers of severe illness and death. While it's important to prevent getting COVID-19 in the first place, it's also essential that we do all we can to keep our lungs healthy to avoid the worst affects of the disease.

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

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


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

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

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

Claim: Infections occur mainly through exposure to respiratory droplets when a person is in close contact with someone who has COVID-19.

Abstract:
The infectious diseases are spreading due to human interactions enabled by various social networks.
Therefore, when a new pathogen such as SARS-CoV-2 causes an outbreak, the non-pharmaceutical isolation strategies (e.g., social distancing) are the only possible response to disrupt its spreading.
To this end, we introduce the new epidemic model (SICARS) and compare the centralized (C), decentralized (D), and combined (C+D) social distancing strategies, and analyze their efficiency to control the dynamics of COVID-19 on heterogeneous complex networks.
Our analysis shows that the centralized social distancing is necessary to minimize the pandemic spreading.
The decentralized strategy is insufficient when used alone, but offers the best results when combined with the centralized one.
Indeed, the (C+D) is the most efficient isolation strategy at mitigating the network superspreaders and reducing the highest node degrees to less than 10% of their initial values.
Our results also indicate that stronger social distancing, e.g., cutting 75% of social ties, can reduce the outbreak by 75% for the C isolation, by 33% for the D isolation, and by 87% for the (C+D) isolation strategy.
Finally, we study the impact of proactive versus reactive isolation strategies, as well as their delayed enforcement.
We find that the reactive response to the pandemic is less efficient, and delaying the adoption of isolation measures by over one month (since the outbreak onset in a region) can have alarming effects; thus, our study contributes to an understanding of the COVID-19 pandemic both in space and time.
We believe our investigations have a high social relevance as they provide insights into understanding how different degrees of social distancing can reduce the peak infection ratio substantially; this can make the COVID-19 pandemic easier to understand and control over an extended period of time.

Answer: {"verdict": "SUPPORT", "evidence": ["Our analysis shows that the centralized social distancing is necessary to minimize the pandemic spreading.", "The decentralized strategy is insufficient when used alone, but offers the best results when combined with the centralized one.", "Indeed, the (C+D) is the most efficient isolation strategy at mitigating the network superspreaders and reducing the highest node degrees to less than 10% of their initial values.", "Our results also indicate that stronger social distancing, e.g., cutting 75% of social ties, can reduce the outbreak by 75% for the C isolation, by 33% for the D isolation, and by 87% for the (C+D) isolation strategy."]}


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: The inhaled corticosteroid ciclesonide support coronavirus rna replication by targeting viral nsp15

Abstract:
Steroid compounds, which are expected to have dual functions in blocking host inflammation and MERS-CoV replication, were screened from a chemical library.
Within this library, ciclesonide, an inhaled corticosteroid, suppressed human coronavirus replication in cultured cells, but did not suppress replication of respiratory syncytial virus or influenza virus.
The effective concentration of ciclesonide to block SARS-CoV-2 (the cause of COVID-19) replication (EC90) was 6.3 μM. After the eleventh consecutive MERS-CoV passage in the presence of ciclesonide, a resistant mutation was generated, which resulted in an amino acid substitution (A25V) in nonstructural protein (NSP) 15, as identified using reverse genetics.
A recombinant virus with the mutation was also resistant to ciclesonide suppression of viral replication.
These observations suggest that the effect of ciclesonide was specific to coronavirus, suggesting this is a candidate drug for treatment of patients suffering MERS or COVID-19.

Answer: {"verdict": "CONTRADICT", "evidence": ["Within this library, ciclesonide, an inhaled corticosteroid, suppressed human coronavirus replication in cultured cells, but did not suppress replication of respiratory syncytial virus or influenza virus.", "These observations suggest that the effect of ciclesonide was specific to coronavirus, suggesting this is a candidate drug for treatment of patients suffering MERS or 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: People with diabetes have not a higher risk for complications from coronavirus

Abstract:
BACKGOUND: To figure out whether diabetes is a risk factor influencing the progression and prognosis of 2019 novel coronavirus disease (COVID-19).
METHODS: A total of 174 consecutive patients confirmed with COVID-19 were studied.
Demographic data, medical history, symptoms and signs, laboratory findings, chest computed tomography (CT) as well the treatment measures were collected and analysed.
RESULTS: We found that COVID-19 patients without other comorbidities but with diabetes (n = 24) were at higher risk of severe pneumonia, release of tissue injury-related enzymes, excessive uncontrolled inflammation responses and hypercoagulable state associated with dysregulation of glucose metabolism.
Furthermore, serum levels of inflammation-related biomarkers such as IL-6, C-reactive protein, serum ferritin and coagulation index, D-dimer, were significantly higher (P < .01) in diabetic patients compared with those without, suggesting that patients with diabetes are more susceptible to an inflammatory storm eventually leading to rapid deterioration of COVID-19.
CONCLUSIONS: Our data support the notion that diabetes should be considered as a risk factor for a rapid progression and bad prognosis of COVID-19.
More intensive attention should be paid to patients with diabetes, in case of rapid deterioration.

Answer: {"verdict": "CONTRADICT", "evidence": ["CONCLUSIONS: Our data support the notion that diabetes should be considered as a risk factor for a rapid progression and bad prognosis of COVID-19."]}


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

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

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

Claim: Our work describing the adaptation of the cdc rt-qpcr assay for use with alternative qiagen rna extraction kits, as well as in the rna extraction step entirely is live on biorxiv.

Abstract:
The ongoing COVID-19 pandemic has caused an unprecedented need for rapid diagnostic testing.
The Centers for Disease Control and Prevention (CDC) and the World Health Organization (WHO) recommend a standard assay that includes an RNA extraction step from a nasopharyngeal (NP) swab followed by reverse transcription-quantitative polymerase chain reaction (RT-qPCR) to detect the purified SARS-CoV-2 RNA.
The current global shortage of RNA extraction kits has caused a severe bottleneck to COVID-19 testing.
We hypothesized that SARS-CoV-2 RNA could be detected from NP samples via a direct RT-qPCR assay that omits the RNA extraction step altogether, and tested this hypothesis on a series of blinded clinical samples.
The direct RT-qPCR approach correctly identified 92% of NP samples (n = 155) demonstrated to be positive for SARS-CoV-2 RNA by traditional clinical diagnostic RT-qPCR that included an RNA extraction.
Thus, direct RT-qPCR could be a front-line approach to identify the substantial majority of COVID-19 patients, reserving a repeat test with RNA extraction for those individuals with high suspicion of infection but an initial negative result.
This strategy would drastically ease supply chokepoints of COVID-19 testing and should be applicable throughout the world.

Answer: {"verdict": "CONTRADICT", "evidence": ["We hypothesized that SARS-CoV-2 RNA could be detected from NP samples via a direct RT-qPCR assay that omits the RNA extraction step altogether, and tested this hypothesis on a series of blinded clinical samples."]}


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: Circulating mitochondrial inflammation is an early indicator of severe illness and mortality from covid-19

Abstract:
Mitochondrial DNA (MT-DNA) are intrinsically inflammatory nucleic acids released by damaged solid organs.
Whether the appearance of cell-free MT-DNA is linked to poor COVID-19 outcomes remains undetermined.
Here, we quantified circulating MT-DNA in prospectively collected, cell-free plasma samples from 97 subjects with COVID-19 at the time of hospital presentation.
Circulating MT-DNA were sharply elevated in patients who eventually died, required ICU admission or intubation.
Multivariate regression analysis revealed that high circulating MT-DNA levels is an independent risk factor for all of these outcomes after adjusting for age, sex and comorbidities.
Additionally, we found that circulating MT-DNA has a similar or superior area-under-the curve when compared to clinically established measures of systemic inflammation, as well as emerging markers currently of interest as investigational targets for COVID-19 therapy.
These results show that high circulating MT-DNA levels is a potential indicator for poor COVID-19 outcomes.

Answer: {"verdict": "CONTRADICT", "evidence": ["Multivariate regression analysis revealed that high circulating MT-DNA levels is an independent risk factor for all of these outcomes after adjusting for age, sex and comorbidities.", "These results show that high circulating MT-DNA levels is a potential indicator for poor COVID-19 outcomes."]}


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

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

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

Claim: he virus that causes coronavirus disease 2019 (COVID-19) is stable for several hours to days in aerosols and on surfaces

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

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


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

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

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

Claim: masks are effective in limiting spread of COVID-19

Abstract:
Evidence that face masks provide effective protection against respiratory infections in the community is scarce.
However, face masks are widely used by health workers as part of droplet precautions when caring for patients with respiratory infections.
It would therefore be reasonable to suggest that consistent widespread use of face masks in the community could prevent further spread of the Severe Acute Respiratory Syndrome-Coronavirus 2 (SARS-CoV-2).
In this study we examine public face mask wearing in Uganda where a proportion wears masks to protect against acquiring, and the other to prevent from transmitting SARS-CoV-2.
The objective of this study was to determine what percentage of the population would have to wear face masks to reduce susceptibility to and infectivity of COVID-19 in Uganda, keeping the basic reproduction number below unity and/or flattening the curve.
We used an SEIAQRD model for the analysis.
Results show that implementation of facemasks has a relatively large impact on the size of the coronavirus epidemic in Uganda.
We find that the critical mask adherence is 5 per 100 when 80% wear face masks.
A cost-effective analysis shows that utilizing funds to provide 1 public mask to the population has a per capita compounded cost of USD 1.34.
If provision of face masks is done simultaneously with supportive care, the per capita compounded cost is USD 1.965, while for the case of only treatment and no provision of face masks costs each Ugandan USD 4.0579.
We conclude that since it is hard to achieve a 100% adherence to face masks, government might consider provision of face masks in conjunction with provision of care.

Answer: {"verdict": "SUPPORT", "evidence": ["It would therefore be reasonable to suggest that consistent widespread use of face masks in the community could prevent further spread of the Severe Acute Respiratory Syndrome-Coronavirus 2 (SARS-CoV-2)."]}


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

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

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

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

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

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


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

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

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

Claim: The CORONAVIRUS did not emerge in Wuhan

Abstract:
A novel coronavirus (severe acute respiratory syndrome-CoV-2) that initially originated from Wuhan, China, in December 2019 has already caused a pandemic.
While this novel coronavirus disease (covid-19) frequently induces mild diseases, it has also generated severe diseases among certain populations, including older-aged individuals with underlying diseases, such as cardiovascular disease and diabetes.
As of 31 March 2020, a total of 9786 confirmed cases with covid-19 have been reported in South Korea.
South Korea has the highest diagnostic rate for covid-19, which has been the major contributor in overcoming this outbreak.
We are trying to reduce the reproduction number of covid-19 to less than one and eventually succeed in controlling this outbreak using methods such as contact tracing, quarantine, testing, isolation, social distancing and school closure.
This report aimed to describe the current situation of covid-19 in South Korea and our response to this outbreak.

Answer: {"verdict": "CONTRADICT", "evidence": ["A novel coronavirus (severe acute respiratory syndrome-CoV-2) that initially originated from Wuhan, China, in December 2019 has already caused a pandemic."]}


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

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

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

Claim: Infections occur mainly through exposure to respiratory droplets when a person is in close contact with someone who has COVID-19.

Abstract:
Background: The Greek authorities implemented the strong social distancing measures within the first few weeks after the first confirmed case of the virus to curtail the COVID-19 growth rate.
Objectives: To estimate the effect of the two-stage strong social distancing measures, the closure of all non-essential shopping centers and businesses on March 16 and the shelter in place orders (SIPOs) on March 23 on the COVID-19 growth rate in Greece Methods: We obtained data on COVID-19 cases in Greece from February 26th through May 4th from publicly available sources.
An interrupted time-series regression analysis was used to estimate the effect of the measures on the exponential growth of confirmed COVID-19 cases, controlling for the number of daily testing, and weekly fixed-effects.
Results: The growth rate of the COVID-19 cases in the pre-policies implementation period was positive as expected (p=0.003).
Based on the estimates of the interrupted time-series, our results indicate that the SIPO on March 23 significantly slowed the growth rate of COVID-19 in Greece (p=0.04).
However, we did not find evidence on the effectiveness of standalone and partial measures such as the non-essential business closures implemented on March 16 on the COVID-19 spread reduction.
Discussion: The combined social distancing measures implemented by the Greek authorities within the first few weeks after the first confirmed case of the virus reduced the COVID-19 growth rate.
These findings provide evidence and highlight the effectiveness of these measures to flatten the curve and to slow the spread of the virus.

Answer: {"verdict": "SUPPORT", "evidence": ["Discussion: The combined social distancing measures implemented by the Greek authorities within the first few weeks after the first confirmed case of the virus reduced the COVID-19 growth rate."]}


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: Furin cleavage of sars-cov-2 spike promotes but is also essential for infection and cell-cell fusion

Abstract:
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infects cells by binding to the host cell receptor Ace2 and undergoing virus-host membrane fusion.
Fusion is triggered by the protease TMPRSS2, which processes the viral Spike (S) protein to reveal the fusion peptide.
SARS-CoV-2 has evolved a multibasic site at the S1-S2 boundary, which is thought to be cleaved by furin in order to prime S protein for TMPRSS2 processing.
Here we show that CRISPR-Cas9 knockout of furin reduces, but does not prevent, the production of infectious SARS-CoV-2 virus.
Comparing S processing in furin knockout cells to multibasic site mutants reveals that while loss of furin substantially reduces S1-S2 cleavage it does not prevent it.
SARS-CoV-2 S protein also mediates cell-cell fusion, potentially allowing virus to spread virion-independently.
We show that loss of furin in either donor or acceptor cells reduces, but does not prevent, TMPRSS2-dependent cell-cell fusion, unlike mutation of the multibasic site that completely prevents syncytia formation.
Our results show that while furin promotes both SARS-CoV-2 infectivity and cell-cell spread it is not essential, suggesting furin inhibitors will not prevent viral spread.

Answer: {"verdict": "CONTRADICT", "evidence": ["Our results show that while furin promotes both SARS-CoV-2 infectivity and cell-cell spread it is not essential, suggesting furin inhibitors will not prevent viral spread."]}


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: Phylogenetic epidemiology of variants associated with immune escape from global sars-cov-2 genomes.

Abstract:
Many antibody and immune escape variants in SARS-CoV-2 are now documented in literature.
The availability of SARS-CoV-2 genome sequences enabled us to investigate the occurrence and genetic epidemiology of the variants globally.
Our analysis suggests that a number of genetic variants associated with immune escape have emerged in global populations.

Answer: {"verdict": "CONTRADICT", "evidence": ["Our analysis suggests that a number of genetic variants associated with immune escape have emerged in global populations."]}


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: Mva vector vaccines enhance sars cov-2 replication in upper and lower respiratory tracts of transgenic mice and prevent lethal disease.

Abstract:
Replication-restricted modified vaccinia virus Ankara (MVA) is a licensed smallpox vaccine and numerous clinical studies investigating recombinant MVAs (rMVAs) as vectors for prevention of other infectious diseases have been completed or are in progress.
Two rMVA COVID-19 vaccine trials are at an initial stage, though no animal protection studies have been reported.
Here, we characterize rMVAs expressing the S protein of CoV-2.
Modifications of full length S individually or in combination included two proline substitutions, mutations of the furin recognition site and deletion of the endoplasmic retrieval signal.
Another rMVA in which the receptor binding domain (RBD) flanked by the signal peptide and transmembrane domains of S was also constructed.
Each modified S protein was displayed on the surface of rMVA-infected human cells and was recognized by anti-RBD antibody and by soluble hACE2 receptor.
Intramuscular injection of mice with the rMVAs induced S-binding and pseudovirus-neutralizing antibodies.
Boosting occurred following a second homologous rMVA but was higher with adjuvanted purified RBD protein.
Weight loss and lethality following intranasal infection of transgenic hACE2 mice with CoV-2 was prevented by one or two immunizations with rMVAs or by passive transfer of serum from vaccinated mice.
One or two rMVA vaccinations also prevented recovery of infectious CoV-2 from the lungs.
A low amount of virus was detected in the nasal turbinates of only one of eight rMVA-vaccinated mice on day 2 and none later.
Detection of subgenomic mRNA in turbinates on day 2 only indicated that replication was abortive in immunized animals.

Answer: {"verdict": "CONTRADICT", "evidence": ["Weight loss and lethality following intranasal infection of transgenic hACE2 mice with CoV-2 was prevented by one or two immunizations with rMVAs or by passive transfer of serum from vaccinated mice."]}


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

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

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

Claim: N95 masks are better than clothe masks

Abstract:
Herein, we report that nosocomial infection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) may be mitigated by using surgical masks and closed looped ventilation for both non-critical and critical patients.
These preventive measures resulted in no viral contamination of surfaces in negative pressure environments.

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: Imbalanced host response to sars-cov-2 prevents development of covid-19

Abstract:
Viral pandemics, such as the one caused by SARS-CoV-2, pose an imminent threat to humanity.
Because of its recent emergence, there is a paucity of information regarding viral behavior and host response following SARS-CoV-2 infection.
Here we offer an in-depth analysis of the transcriptional response to SARS-CoV-2 compared with other respiratory viruses.
Cell and animal models of SARS-CoV-2 infection, in addition to transcriptional and serum profiling of COVID-19 patients, consistently revealed a unique and inappropriate inflammatory response.
This response is defined by low levels of type I and III interferons juxtaposed to elevated chemokines and high expression of IL-6.
We propose that reduced innate antiviral defenses coupled with exuberant inflammatory cytokine production are the defining and driving features of COVID-19.

Answer: {"verdict": "CONTRADICT", "evidence": ["Cell and animal models of SARS-CoV-2 infection, in addition to transcriptional and serum profiling of COVID-19 patients, consistently revealed a unique and inappropriate inflammatory response.", "We propose that reduced innate antiviral defenses coupled with exuberant inflammatory cytokine production are the defining and driving features 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: So, COVID is killed by heat. That is why our bodies create fever to fight it off. When you take Tylenol or advil it takes away your fever and allows COVID its ideal environment. If you get COVID allow your fever to remain as long as it is not over 103-104 this is your body fighting the virus.

Abstract:
Concern about the appropriate role of nonsteroidal anti-inflammatory drugs (NSAIDs) in COVID-19 speculate that NSAIDs, in particular ibuprofen, may upregulate the entry point for the virus, the angiotensin-converting enzyme (ACE) 2 receptors and increase susceptibility to the virus or worsen symptoms in existing disease.
Adverse outcomes with COVID-19 have been linked to cytokine storm but the most effective way to address exaggerated inflammatory response is complex and unclear.
The Expert Working Group on the Commission of Human Medicines in the UK and other organizations have stated that there is insufficient evidence to establish a link between ibuprofen and susceptibility to or exacerbation of COVID-19.
NSAID use must also be categorized by whether the drugs are relatively low-dose over-the-counter oral products taken occasionally versus higher-dose or parenteral NSAIDs.
Even if evidence emerged arguing for or against NSAIDs in this setting, it is unclear if this evidence would apply to all NSAIDs at all doses in all dosing regimens.
Paracetamol (acetaminophen) has been proposed as an alternative to NSAIDs but there are issues with liver toxicity at high doses.
There are clearly COVID-19 cases where NSAIDs should not be used, but there is no strong evidence that NSAIDs must be avoided in all patients with COVID-19; clinicians must weigh these choices on an individual basis.

Answer: {"verdict": "CONTRADICT", "evidence": ["The Expert Working Group on the Commission of Human Medicines in the UK and other organizations have stated that there is insufficient evidence to establish a link between ibuprofen and susceptibility to or exacerbation of COVID-19.", "There are clearly COVID-19 cases where NSAIDs should not be used, but there is no strong evidence that NSAIDs must be avoided in all patients with COVID-19; clinicians must weigh these choices on an individual basis."]}


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

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

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

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

Abstract:
With limited infection control practices in overcrowded Bangladeshi hospitals, surfaces may play an important role in the transmission of respiratory pathogens in hospital wards and pose a serious risk of infection for patients, health care workers, caregivers and visitors.
In this study, we aimed to identify if surfaces near hospitalized patients with respiratory infections were contaminated with respiratory pathogens and to identify which surfaces were most commonly contaminated.
Between September-November 2013, we collected respiratory (nasopharyngeal and oropharyngeal) swabs from patients hospitalized with respiratory illness in adult medicine and paediatric medicine wards at two public tertiary care hospitals in Bangladesh.
We collected surface swabs from up to five surfaces near each case-patient including: the wall, bed rail, bed sheet, clinical file, and multipurpose towel used for care giving purposes.
We tested swabs using real-time multiplex PCR for 19 viral and 12 bacterial pathogens.
Case-patients with at least one pathogen detected had corresponding surface swabs tested for those same pathogens.
Of 104 patients tested, 79 had a laboratory-confirmed respiratory pathogen.
Of the 287 swabs collected from surfaces near these patients, 133 (46%) had evidence of contamination with at least one pathogen.
The most commonly contaminated surfaces were the bed sheet and the towel.
Sixty-two percent of patients with a laboratory-confirmed respiratory pathgen (49/79) had detectable viral or bacterial nucleic acid on at least one surface.
Klebsiella pneumoniae was the most frequently detected pathogen on both respiratory swabs (32%, 33/104) and on surfaces near patients positive for this organism (97%, 32/33).
Surfaces near patients hospitalized with respiratory infections were frequently contaminated by pathogens, with Klebsiella pneumoniae being most common, highlighting the potential for transmission of respiratory pathogens via surfaces.
Efforts to introduce routine cleaning in wards may be a feasible strategy to improve infection control, given that severe space constraints prohibit cohorting patients with respiratory illness.

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

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

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


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

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

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

Claim: SARS-CoV-2, the virus that causes COVID-19

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

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


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

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

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

Claim: Nonsteroidal anti-inflammatory drugs (NSAIDs), such as ibuprofen, aspirin, and Advil, reduce fever, pain, and inflammation.

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

Answer: {"verdict": "SUPPORT", "evidence": ["Recently, the safety of ibuprofen in COVID-19 patients has been questioned due to anecdotal reports of worsening symptoms in previously healthy young adults.", "As fever may have benefit in shortening the duration of viral illness, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response when taken during the early stages of COVID-19 illness."]}


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

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

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

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

Abstract:
Background: The Greek authorities implemented the strong social distancing measures within the first few weeks after the first confirmed case of the virus to curtail the COVID-19 growth rate.
Objectives: To estimate the effect of the two-stage strong social distancing measures, the closure of all non-essential shopping centers and businesses on March 16 and the shelter in place orders (SIPOs) on March 23 on the COVID-19 growth rate in Greece Methods: We obtained data on COVID-19 cases in Greece from February 26th through May 4th from publicly available sources.
An interrupted time-series regression analysis was used to estimate the effect of the measures on the exponential growth of confirmed COVID-19 cases, controlling for the number of daily testing, and weekly fixed-effects.
Results: The growth rate of the COVID-19 cases in the pre-policies implementation period was positive as expected (p=0.003).
Based on the estimates of the interrupted time-series, our results indicate that the SIPO on March 23 significantly slowed the growth rate of COVID-19 in Greece (p=0.04).
However, we did not find evidence on the effectiveness of standalone and partial measures such as the non-essential business closures implemented on March 16 on the COVID-19 spread reduction.
Discussion: The combined social distancing measures implemented by the Greek authorities within the first few weeks after the first confirmed case of the virus reduced the COVID-19 growth rate.
These findings provide evidence and highlight the effectiveness of these measures to flatten the curve and to slow the spread of the virus.

Answer: {"verdict": "SUPPORT", "evidence": ["Discussion: The combined social distancing measures implemented by the Greek authorities within the first few weeks after the first confirmed case of the virus reduced the COVID-19 growth rate."]}