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: High Blood Pressure Doesn't Seem To Raise COVID-19 Risk

Abstract:
Hypertension is one of the most common comorbidities in patients with coronavirus disease 2019 (COVID-19).
This study aimed to clarify the impact of hypertension on COVID-19 and investigate whether the prior use of renin-angiotensin-aldosterone system (RAAS) inhibitors affects the prognosis of COVID-19.
A total of 996 patients with COVID-19 were enrolled, including 282 patients with hypertension and 714 patients without hypertension.
Propensity score-matched analysis (1:1 matching) was used to adjust the imbalanced baseline variables between the 2 groups.
Patients with hypertension were further divided into the RAAS inhibitor group (n=41) and non-RAAS inhibitor group (n=241) according to their medication history.
The results showed that COVID-19 patients with hypertension had more severe secondary infections, cardiac and renal dysfunction, and depletion of CD8+ cells on admission.
Patients with hypertension were more likely to have comorbidities and complications and were more likely to be classified as critically ill than those without hypertension.
Cox regression analysis revealed that hypertension (hazard ratio, 95% CI, unmatched cohort [1.80, 1.20-2.70]; matched cohort [2.24, 1.36-3.70]) was independently associated with all-cause mortality in patients with COVID-19.
In addition, hypertensive patients with a history of RAAS inhibitor treatment had lower levels of C-reactive protein and higher levels of CD4+ cells.
The mortality of patients in the RAAS inhibitor group (9.8% versus 26.1%) was significantly lower than that of patients in the non-RAAS inhibitor group.
In conclusion, hypertension may be an independent risk factor for all-cause mortality in patients with COVID-19.
Patients who previously used RAAS inhibitors may have a better prognosis.

Answer: {"verdict": "CONTRADICT", "evidence": ["The results showed that COVID-19 patients with hypertension had more severe secondary infections, cardiac and renal dysfunction, and depletion of CD8+ cells on admission.", "Patients with hypertension were more likely to have comorbidities and complications and were more likely to be classified as critically ill than those without hypertension.", "In conclusion, hypertension may be an independent risk factor for all-cause mortality in patients with COVID-19.", "Patients who previously used RAAS inhibitors may have a better prognosis."]}


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 d614g mutation in the sars-cov2 spike protein reduced infectivity in an ace2 receptor dependent manner

Abstract:
The SARS-CoV2 coronavirus responsible for the current COVID19 pandemic has been reported to have a relatively low mutation rate.
Nevertheless, a few prevalent variants have arisen that give the appearance of undergoing positive selection as they are becoming increasingly widespread over time.
Most prominent among these is the D614G amino acid substitution in the SARS-CoV2 Spike protein, which mediates viral entry.
The D614G substitution, however, is in linkage disequilibrium with the ORF1b P314L mutation where both mutations almost invariably co-occur, making functional inferences problematic.
In addition, the possibility of repeated new introductions of the mutant strain does not allow one to distinguish between a founder effect and an intrinsic genetic property of the virus.
Here, we synthesized and expressed the WT and D614G variant SARS-Cov2 Spike protein, and report that using a SARS-CoV2 Spike protein pseudotyped lentiviral vector we observe that the D614G variant Spike has >1/2 log(10) increased infectivity in human cells expressing the human ACE2 protein as the viral receptor.
The increased binding/fusion activity of the D614G Spike protein was corroborated in a cell fusion assay using Spike and ACE2 proteins expressed in different cells.
These results are consistent with the possibility that the Spike D614G mutant increases the infectivity of SARS-CoV2.

Answer: {"verdict": "CONTRADICT", "evidence": ["Most prominent among these is the D614G amino acid substitution in the SARS-CoV2 Spike protein, which mediates viral entry.", "The increased binding/fusion activity of the D614G Spike protein was corroborated in a cell fusion assay using Spike and ACE2 proteins expressed in different cells.", "These results are consistent with the possibility that the Spike D614G mutant increases the infectivity of SARS-CoV2."]}


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: Widely available lysosome targeting agents should be considered as a potential biomarker for covid-19

Abstract:
While the COVID-19 pandemic advances, the scientific community struggles in the search for treatments.
Several improvements have been made, including the discovery of clinical efficacy of chloroquine (CQ) in COVID-19 patients, but the effective treatment protocols are still missing.
In order to find novel treatment options many scientists utilize the in silico approach to identify compounds that could interfere with the key molecules involved in entrance, replication, or dissemination of the SARS-CoV-2.
However, most of the identified molecules are currently not available as pharmacological agents, and assessing their safety and efficacy could take many months.
Here, we took a different approach based on the proposed pharmacodynamic model of CQ in COVID-19.
The main mechanism of action responsible for the favourable outcome of COVID-19 patients treated with CQ seems to be related to pH modulation-mediated effect on the endolysosomal trafficking, a characteristic of chemical compounds often called lysosomotropic agents because of the physico-chemical properties that enable them to passively diffuse through the endosomal membrane and undergo protonation-based trapping in the lumen of the acidic vesicles.
In this review, we discuss lysosomotropic and lysosome targeting drugs that are already in clinical use and are characterized by good safety profiles, low cost, and wide availability.
We emphasize that some of these drugs, in particular azithromycin and other macrolide antibiotics, indomethacin and some other non-steroidal anti-inflammatory drugs, proton pump inhibitors, and fluoxetine could provide additional therapeutic benefits in addition to the potential antiviral effect that still has to be confirmed by well-controlled clinical trials.
As some of these drugs, mostly antibiotics, were already empirically used in the treatment of COVID-19, we encourage our colleagues all over the world to publish patient data so potential efficacy of these agents can be evaluated in the clinical context and rapidly implemented in the therapeutic protocols if the beneficial effect on clinical outcome is observed.

Answer: {"verdict": "CONTRADICT", "evidence": ["The main mechanism of action responsible for the favourable outcome of COVID-19 patients treated with CQ seems to be related to pH modulation-mediated effect on the endolysosomal trafficking, a characteristic of chemical compounds often called lysosomotropic agents because of the physico-chemical properties that enable them to passively diffuse through the endosomal membrane and undergo protonation-based trapping in the lumen of the acidic vesicles.", "As some of these drugs, mostly antibiotics, were already empirically used in the treatment of COVID-19, we encourage our colleagues all over the world to publish patient data so potential efficacy of these agents can be evaluated in the clinical context and rapidly implemented in the therapeutic protocols if the beneficial effect on clinical outcome is observed."]}


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

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

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

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

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

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


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

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

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

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

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 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: Lack of immune homology with vaccine preventable pathogens suggests childhood immunizations do not protect against sars-cov-2 through adaptive cross-immunity

Abstract:
Recent epidemiological studies have investigated the potential effects of childhood immunization history on COVID-19 severity.
Specifically, prior exposure to Bacillus Calmette-Guerin (BCG) vaccine, oral poliovirus vaccine (OPV), or measles vaccine have been postulated to reduce COVID-19 severity-putative mechanism is via stimulation of the innate immune system to provide broader protection against non-specific pathogens.
While these epidemiological results remain inconclusive, we sought to investigate the potential role of adaptive immunity via cross-reactivity between vaccine preventable diseases (VPDs) with SARS-CoV-2.
We implemented a comprehensive exploration of immune homology (including sequence homology, immune epitopes, and glycosylation patterns) between SARS-CoV-2 and all pathogens with FDA-approved vaccines.
Sequence homology did not reveal significant alignments of protein sequences between SARS-CoV-2 with any VPD pathogens, including BCG-related strains.
We also could not identify any shared T or B cell epitopes between SARS-CoV-2 and VPD pathogens among either experimentally validated epitopes or predicted immune epitopes.
For N-glycosylation (N-glyc), while sites with the same tripeptides could be found between SARS-CoV-2 and certain VPD pathogens, their glycosylation potentials and positions were different.
In summary, lack of immune homology between SARS-CoV-2 and VPD pathogens suggests that childhood immunization history (i.e., BCG vaccination or others) does not provide protection from SARS-CoV-2 through adaptive cross-immunity.

Answer: {"verdict": "SUPPORT", "evidence": ["In summary, lack of immune homology between SARS-CoV-2 and VPD pathogens suggests that childhood immunization history (i.e., BCG vaccination or others) does not provide protection from SARS-CoV-2 through adaptive cross-immunity."]}


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

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

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

Claim: Sars-cov-2 infection induces robust, neutralizing antibody responses that are stable for at least three weeks

Abstract:
SARS-CoV-2 has caused a global pandemic with millions infected and numerous fatalities.
Questions regarding the robustness, functionality and longevity of the antibody response to the virus remain unanswered.
Here we report that the vast majority of infected individuals with mild-to-moderate COVID-19 experience robust IgG antibody responses against the viral spike protein, based on a dataset of 19,860 individuals screened at Mount Sinai Health System in New York City.
We also show that titers are stable for at least a period approximating three months, and that anti-spike binding titers significantly correlate with neutralization of authentic SARS-CoV-2.
Our data suggests that more than 90% of seroconverters make detectible neutralizing antibody responses and that these titers are stable for at least the near-term future.

Answer: {"verdict": "CONTRADICT", "evidence": ["We also show that titers are stable for at least a period approximating three months, and that anti-spike binding titers significantly correlate with neutralization of authentic SARS-CoV-2."]}


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

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

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

Claim: the Covid-19 coronavirus can stay on various surfaces for a while

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


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: Both mutations in sars-cov-2 associate with severe and mild outcome

Abstract:
INTRODUCTION: Genomic alterations in a viral genome can lead to either better or worse outcome and identifying these mutations is of utmost importance.
Here, we correlated protein-level mutations in the SARS-CoV-2 virus to clinical outcome.
METHODS: Mutations in viral sequences from the GISAID virus repository were evaluated by using "hCoV-19/Wuhan/WIV04/2019" as the reference.
Patient outcomes were classified as mild disease, hospitalization and severe disease (death or documented treatment in an intensive-care unit).
Chi-square test was applied to examine the association between each mutation and patient outcome.
False discovery rate was computed to correct for multiple hypothesis testing and results passing FDR cutoff of 5% were accepted as significant.
RESULTS: Mutations were mapped to amino acid changes for 3,733 non-silent mutations.
Mutations correlated to mild outcome were located in the ORF8, NSP6, ORF3a, NSP4, and in the nucleocapsid phosphoprotein N. Mutations associated with inferior outcome were located in the surface (S) glycoprotein, in the RNA dependent RNA polymerase, in ORF3a, NSP3, ORF6 and N. Mutations leading to severe outcome with low prevalence were found in the ORF3A and in NSP7 proteins.
Four out of 22 of the most significant mutations mapped onto a 10 amino acid long phosphorylated stretch of N indicating that in spite of obvious sampling restrictions the approach can find functionally relevant sites in the viral genome.
CONCLUSIONS: We demonstrate that mutations in the viral genes may have a direct correlation to clinical outcome.
Our results help to quickly identify SARS-CoV-2 infections harboring mutations related to severe outcome.

Answer: {"verdict": "CONTRADICT", "evidence": ["Here, we correlated protein-level mutations in the SARS-CoV-2 virus to clinical outcome.", "Our results help to quickly identify SARS-CoV-2 infections harboring mutations related to severe outcome."]}


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

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

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

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

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

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


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

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

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

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

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

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


Question:
You will be shown a claim 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: Drinking hot ginger tea, with or without these additional ingredients, will cure COVID-19

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

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


Question:
You will be shown a claim 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: College students, many of whom are already stressed, reported an increase in depression and anxiety during the initial outbreak.

Abstract:
BACKGROUND: The coronavirus disease (COVID-19) pandemic has produced substantial health challenges from the perspective of both its direct health complications and the disruption to delivery of standard care for individuals with a range of acute and chronic health issues.
In parallel, the widespread application of social isolation initiatives in most countries raises the potential for significant mental health consequences and psychosocial impacts.
This has major implications for cardiovascular health care professionals and the management of their patients.
CHALLENGES: The COVID-19 pandemic and associated physical isolation practices are likely to result in a range of mental health and psychosocial challenges.
In addition to an increasing incidence of anxiety, depression, suicidal ideation and post-traumatic stress, the pandemic may also witness an increase in substance abuse, domestic violence and relationship discord.
The consequences of these complications will be further magnified, when considering their potential effect on cardiovascular disease and its management.
PURPOSE: This commentary aims to summarise some of the potential mental health and psychosocial challenges that may arise in the setting of the COVID-19 pandemic.

Answer: {"verdict": "SUPPORT", "evidence": ["CHALLENGES: The COVID-19 pandemic and associated physical isolation practices are likely to result in a range of mental health and psychosocial challenges."]}


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 patients taking hydroxychloroquine do not benefit

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

Answer: {"verdict": "CONTRADICT", "evidence": ["In the context of the rapid evolution of the COVID-19 pandemic that strains the health care systems worldwide and considering limited population-wide testing rates in most of the vulnerable countries, early empiric short-term administration of HCQ in symptomatic individuals, may be a promising, safe and low-cost strategy."]}


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 ground level ozone concentration is equivalently correlated with the number of covid-19 cases in warsaw, poland

Abstract:
COVID-19, which is a consequence of infection with the novel viral agent SARS-CoV-2, first identified in China (Hubei Province), has been declared a pandemic by the WHO.
As of September 10, 2020, over 70,000 cases and over 2,000 deaths have been recorded in Poland.
Of the many factors contributing to the level of transmission of the virus, the weather appears to be significant.
In this work we analyse the impact of weather factors such as temperature, relative humidity, wind speed and ground level ozone concentration on the number of COVID-19 cases in Warsaw, Poland.
The obtained results show an inverse correlation between ground level ozone concentration and the daily number of COVID-19 cases.

Answer: {"verdict": "CONTRADICT", "evidence": ["The obtained results show an inverse correlation between ground level ozone concentration and the daily number of COVID-19 cases."]}


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

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

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

Claim: Surgical masks are just a physical barrier that will protect you against "a visible splash or spray of fluid or large droplets

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

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


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

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

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

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

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

Abstract:
A novel coronavirus emerged in human populations and spread rapidly to cause the global coronavirus disease 2019 pandemic.
Although the origin of the associated virus (severe acute respiratory syndrome coronavirus 2 [SARS-CoV-2]) remains unclear, genetic evidence suggests that bats are a reservoir host of the virus, and pangolins are a probable intermediate.
SARS-CoV-2 has crossed the species barrier to infect humans and other animal species, and infected humans can facilitate reverse-zoonotic transmission to animals.
Considering the rapidly changing interconnections among people, animals, and ecosystems, traditional roles of veterinarians should evolve to include transdisciplinary roles.

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: Hypothesis: Angiotensin-converting enzyme inhibitors and angiotensin receptor blockers may increase the risk of severe COVID-19

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

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


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

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

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

Claim: Drinking hot ginger tea, with or without these additional ingredients, will cure COVID-19

Abstract:
The severity of coronavirus disease 2019 (COVID-19) infection is quite variable and the manifestations varies from asymptomatic disease to severe acute respiratory infection.
Fever, dry cough, dyspnea, myalgia, fatigue, loss of appetite, olfactory and gustatory dysfunctions are the most prevalent general symptoms.
Decreased immune system cells such as suppressed regulatory T cells, cytotoxic and helper T cells, natural killer cells, monocytes/macrophages and increased proinflammatory cytokines are the characteristic features.
Compounds derived from Allium sativum (garlic) have the potential to decrease the expression of proinflammatory cytokines and to reverse the immunological abnormalities to more acceptable levels.
Allium sativum is suggested as a beneficial preventive measure before being infected with SARS-CoV-2 virus.
Allium sativum is a functional food well-known for its immunomodulatory, antimicrobial, antiinflammatory, antimutagenic, antitumor properties.
Its antiviral efficiency was also demonstrated.
Some constituents of this plant were found to be active against protozoan parasites.
Within this context, it appears to reverse most immune system dysfunctions observed in patients with COVID-19 infection.
The relations among immune system parameters, leptin, leptin receptor, adenosin mono phosphate-activated protein kinase, peroxisome proliferator activated receptor-gamma have also been interpreted.
Leptin's role in boosting proinflammatory cytokines and in appetite decreasing suggest the possible beneficial effect of decreasing the concentration of this proinflammatory adipose tissue hormone in relieving some symptoms detected during COVID-19 infection.
In conclusion, Allium sativum may be an acceptable preventive measure against COVID-19 infection to boost immune system cells and to repress the production and secretion of proinflammatory cytokines as well as an adipose tissue derived hormone leptin having the proinflammatory nature.

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


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

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

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

Claim: Remdesivir proves effective against COVID-19

Abstract:
Effective therapeutics to treat COVID-19 are urgently needed.
While many investigational, approved, and repurposed drugs have been suggested, preclinical data from animal models can guide the search for effective treatments by ruling out treatments without in vivo efficacy.
Remdesivir (GS-5734) is a nucleotide analog prodrug with broad antiviral activity1,2, that is currently investigated in COVID-19 clinical trials and recently received Emergency Use Authorization from the US Food and Drug Administration3,4.
In animal models, remdesivir treatment was effective against MERS-CoV and SARS-CoV infection.2,5,6 In vitro, remdesivir inhibited replication of SARS-CoV-2.7,8 Here, we investigated the efficacy of remdesivir treatment in a rhesus macaque model of SARS-CoV-2 infection9.
In contrast to vehicle-treated animals, animals treated with remdesivir did not show signs of respiratory disease and had reduced pulmonary infiltrates on radiographs and reduced virus titers in bronchoalveolar lavages 12hrs after the first treatment administration.
Virus shedding from the upper respiratory tract was not reduced by remdesivir treatment.
At necropsy, lung viral loads of remdesivir-treated animals were lower and there was a reduction in damage to the lungs.
Thus, therapeutic remdesivir treatment initiated early during infection had a clinical benefit in SARS-CoV-2-infected rhesus macaques.
Although the rhesus macaque model does not represent the severe disease observed in a proportion of COVID-19 patients, our data support early remdesivir treatment initiation in COVID-19 patients to prevent progression to pneumonia.

Answer: {"verdict": "SUPPORT", "evidence": ["In animal models, remdesivir treatment was effective against MERS-CoV and SARS-CoV infection.2,5,6 In vitro, remdesivir inhibited replication of SARS-CoV-2.7,8 Here, we investigated the efficacy of remdesivir treatment in a rhesus macaque model of SARS-CoV-2 infection9."]}


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 patients taking hydroxychloroquine do not benefit

Abstract:
Background: There is no effective therapy for COVID-19.
Hydroxychloroquine (HCQ) and chloroquine (CQ) have been used for its treatment but their safety and efficacy remain uncertain.
Objective: We performed a systematic review to synthesize the available data on the efficacy and safety of CQ and HCQ for the treatment of COVID-19.
Methods: Two reviewers searched for published and pre-published relevant articles between December 2019 to 8th June 2020.
The data from the selected studies were abstracted and analyzed for efficacy and safety outcomes.
Critical appraisal of the evidence was done by Cochrane risk of bias tool and Newcastle Ottawa scale.
The quality of evidence was graded as per the GRADE approach.
Results: We reviewed 12 observational and 3 randomized trials which included 10659 patients of whom 5713 received CQ/HCQ and 4966 received only standard of care.
The efficacy of CQ/HCQ for COVID-19 was inconsistent across the studies.
Meta-analysis of included studies revealed no significant reduction in mortality with HCQ use [RR 0.98 95% CI 0.66-1.46] , time to fever resolution [mean difference -0.54 days (-1.19-011)] or clinical deterioration/development of ARDS with HCQ [RR 0.90 95% CI 0.47-1.71].
There was a higher risk of ECG abnormalities/arrhythmia with HCQ/CQ [RR 1.46 95% CI 1.04 to 2.06].
The quality of evidence was graded as very low for these outcomes.
Conclusions: The available evidence suggests that CQ or HCQ does not improve clinical outcomes in COVID-19.
Well-designed randomized trials are required for assessing the efficacy and safety of HCQ and CQ for COVID-19.

Answer: {"verdict": "SUPPORT", "evidence": ["Conclusions: The available evidence suggests that CQ or HCQ does not improve clinical outcomes 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: Coronavirus (COVID-19) Know how to protect yourself and others from COVID-19 and what to do if you are sick.

Abstract:
Background: Understanding and projecting the spread of COVID-19 requires reliable estimates of how weather components are associated with the transmission of the virus.
Prior research on this topic has been inconclusive.
Identifying key challenges to reliable estimation of weather impact on transmission we study this question using one of the largest assembled databases of COVID-19 infections and weather.
Methods: We assemble a dataset that includes virus transmission and weather data across 3,739 locations from December 12, 2019 to April 22, 2020.
Using simulation, we identify key challenges to reliable estimation of weather impacts on transmission, design a statistical method to overcome these challenges, and validate it in a blinded simulation study.
Using this method and controlling for location-specific response trends we estimate how different weather variables are associated with the reproduction number for COVID-19.
We then use the estimates to project the relative weather-related risk of COVID-19 transmission across the world and in large cities.
Results: We show that the delay between exposure and detection of infection complicates the estimation of weather impact on COVID-19 transmission, potentially explaining significant variability in results to-date.
Correcting for that distributed delay and offering conservative estimates, we find a negative relationship between temperatures above 25 degrees Celsius and estimated reproduction number ([R]), with each degree Celsius associated with a 3.1% (95% CI, 1.5% to 4.8%) reduction in [R].
Higher levels of relative humidity strengthen the negative effect of temperature above 25 degrees.
Moreover, one millibar of additional pressure increases [R] by approximately 0.8 percent (95% CI, 0.6% to 1%) at the median pressure (1016 millibars) in our sample.
We also find significant positive effects for wind speed, precipitation, and diurnal temperature on [R].
Sensitivity analysis and simulations show that results are robust to multiple assumptions.
Despite conservative estimates, weather effects are associated with a 43% change in [R] between the 5th and 95th percentile of weather conditions in our sample.
Conclusions: These results provide evidence for the relationship between several weather variables and the spread of COVID-19.
However, the (conservatively) estimated relationships are not strong enough to seasonally control the epidemic in most locations.

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: Salt-coated masks achieve strong viral deactivation rate

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

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


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

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

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

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

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

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


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

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

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

Claim: Vitamins C and D boost our immune systems, aiding in the fight against infectious diseases;

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

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


Question:
You will be shown a claim 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: Emergence of zoonoses such as covid-19 reveals the need for health sciences to embrace an explicit eco-social conceptual framework of health and security

Abstract:
An accurate understanding of why zoonoses such as SARS-CoV-2 are emerging at an increased rate, is vital to prevent future pandemics from the approximately 700,000 viruses with zoonotic potential.
Certain authors have argued that the consumption of wildlife, or human contact with bats was responsible for the emergence of SARS-CoV-2.
Others argue that a range of anthropogenic environmental degradations have played a vital role in the emergence of SARS-CoV-2 and other zoonoses.
In this opinion piece, I argue that these divergent viewpoints stem, in part, from different foundational conceptual frameworks-biomedical individualist and eco-social frameworks, respectively.
Based on the fact that the eco-social framework provides a more complete account of the different types of causal factors underpinning the emergence of zoonoses, I propose that the COVID-19 pandemic provides an additional reason for the health sciences to ground its theory of health and disease in an eco-social conceptual framework.

Answer: {"verdict": "CONTRADICT", "evidence": ["Others argue that a range of anthropogenic environmental degradations have played a vital role in the emergence of SARS-CoV-2 and other zoonoses.", "Based on the fact that the eco-social framework provides a more complete account of the different types of causal factors underpinning the emergence of zoonoses, I propose that the COVID-19 pandemic provides an additional reason for the health sciences to ground its theory of health and disease in an eco-social conceptual framework."]}


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: Many mammals around the world could have a role in spreading the disease, including dogs and cats.

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

Answer: {"verdict": "SUPPORT", "evidence": ["Experimental data showed ferrets and cats are highly susceptible to SARS-CoV-2 as infected by virus inoculation and can transmit the virus directly or indirectly by droplets or airborne route."]}


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

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

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

Claim: VITAMIN D HEALTHY LEVELS decrease COVID-19 MORTALITY RATES

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

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


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

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

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

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

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

Answer: {"verdict": "CONTRADICT", "evidence": ["Recent research results suggest that bats or pangolins might be the original hosts for the virus based on comparative studies using its genomic sequences.", "The results obtained from various AI-enabled experiments using clustering algorithms demonstrate that all examined COVID-19 virus genomes belong to a cluster that also contains bat and pangolin coronavirus genomes.", "This provides evidences strongly supporting scientific hypotheses that bats and pangolins are probable hosts for the COVID-19 virus.", "At the whole genome analysis level, our findings also indicate that bats are more likely the hosts for the COVID-19 virus than pangolins."]}


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

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

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

Claim: dexamethasone has only been shown to have positive effects in patients who require supplemental oxygen.

Abstract:
Background: Dexamethasone, a synthetic glucocorticoid, has anti-inflammatory and immunosuppressive properties.
There is a hyperinflammatory response involved in the clinical course of patients with pneumonia due to SARS-CoV2.
To date, there has been no definite therapy for COVID-19.
We reviewed the charts of SARS-CoV2 patients with pneumonia and moderate to severely elevated CRP and worsening hypoxemia who were treated with early, short-term dexamethasone.
Methods: We describe a series of 21 patients who tested positive for SARS-CoV2 and were admitted to The Miriam Hospital in Providence and were treated with a short course of dexamethasone, either alone or in addition to current investigative therapies.
Results: CRP levels decreased significantly following the start of dexamethasone from mean initial levels of 129.52 to 40.73 mg/L at time of discharge.
71% percent of the patients were discharged home with a mean length of stay of 7.8 days.
None of the patients had escalation of care, leading to mechanical ventilation.
Two patients were transferred to inpatient hospice facilities on account of persistent hypoxemia, in line with their documented goals of care.
Conclusions: A short course of systemic corticosteroids among inpatients with SARS-CoV2 with hypoxic respiratory failure was well tolerated, and most patients had improved outcomes.
This limited case series may not offer concrete evidence towards the benefit of corticosteroids in COVID-19.
However, patients positive response to short-term corticosteroids demonstrates that they may help blunt the severity of inflammation and prevent a severe hyperinflammatory phase, in turn reducing the length of stay, ICU admissions, and healthcare costs.

Answer: {"verdict": "CONTRADICT", "evidence": ["To date, there has been no definite therapy for COVID-19."]}


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

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

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

Claim: Masks do reduce spread of flu and some COVID-19

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


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: Ferrets can catch the coronavirus and might give it to other ferrets. But poultry and pigs don't appear to be at risk.

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

Answer: {"verdict": "CONTRADICT", "evidence": ["Using data from probable cat-to-cat transmission in Wuhan, China, we estimated the basic reproduction number R0 under this scenario at 1.09 (95% confidence interval: 1.05, 1.13).", "This value is much lower than the R0 reported for humans and close to one, indicating that the sustained transmission between cats is unlikely to occur."]}


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

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

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

Claim: 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:
Objectives: To evaluate SARS-CoV-2 surface and air contamination during the peak of the COVID-19 pandemic in London.
Design: Prospective cross-sectional observational study.
Setting: An acute NHS healthcare provider.
Participants: All inpatient wards were fully occupied by patients with COVID-19 at the time of sampling.
Interventions: Air and surface samples were collected from a range of clinical areas and a public area of the hospital.
An active air sampler was used to collect three or four 1.0 m3 air samples in each area.
Surface samples were collected by swabbing approximately 25 cm2 of items in the immediate vicinity of each air sample.
SARS-CoV-2 was detected by RT-qPCR and viral culture using Vero E6 and Caco2 cells; additionally the limit of detection for culturing SARS-CoV-2 dried onto surfaces was determined.
Main outcome measures: SARS-CoV-2 detected by PCR or culture.
Results: Viral RNA was detected on 114/218 (52.3%) of surface and 14/31 (38.7%) air samples but no virus was cultured.
The proportion of surface samples contaminated with viral RNA varied by item sampled and by clinical area.
Viral RNA was detected on surfaces and in air in public areas of the hospital but was more likely to be found in areas immediately occupied by COVID-19 patients (67/105 (63.8%) in areas immediately occupied by COVID-19 patients vs. 29/64 (45.3%) in other areas (odds ratio 0.5, 95% confidence interval 0.2-0.9, p=0.025, Fishers exact test).
The PCR Ct value for all surface and air samples (>30) indicated a viral load that would not be culturable.
Conclusions: Our findings of extensive viral RNA contamination of surfaces and air across a range of acute healthcare settings in the absence of cultured virus underlines the potential risk from surface and air contamination in managing COVID-19, and the need for effective use of PPE, social distancing, and hand/surface hygiene.

Answer: {"verdict": "SUPPORT", "evidence": ["Conclusions: Our findings of extensive viral RNA contamination of surfaces and air across a range of acute healthcare settings in the absence of cultured virus underlines the potential risk from surface and air contamination in managing COVID-19, and the need for effective use of PPE, social distancing, and hand/surface hygiene."]}


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

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

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

Claim: Vitamins C and D boost our immune systems, aiding in the fight against infectious 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": []}