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: nearly half of coronavirus cases are people 20 to 44 years old

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

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


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

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

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

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

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

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


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

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

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

Claim: Dogs May Not Spread Covid-19, but Cats Can Pass It to Each Other

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

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

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


Question:
You will be shown a claim 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: Detection of antibodies to the sars-cov-2 spike glycoprotein in both serum and saliva enhances detection of infection

Abstract:
BACKGROUND: Detecting antibody responses during and after SARS-CoV-2 infection is essential in determining the seroepidemiology of the virus and the potential role of antibody in disease.
Scalable, sensitive and specific serological assays are essential to this process.
The detection of antibody in hospitalized patients with severe disease has proven straightforward; detecting responses in subjects with mild disease and asymptomatic infections has proven less reliable.
We hypothesized that the suboptimal sensitivity of antibody assays and the compartmentalization of the antibody response may contribute to this effect.
METHODS: We systemically developed an ELISA assay, optimising different antigens and amplification steps, in serum and saliva from symptomatic and asymptomatic SARS-CoV-2-infected subjects.
RESULTS: Using trimeric spike glycoprotein, rather than nucleocapsid enabled detection of responses in individuals with low antibody responses.
IgG1 and IgG3 predominate to both antigens, but more antispike IgG1 than IgG3 was detectable.
All antigens were effective for detecting responses in hospitalized patients.
Anti-spike, but not nucleocapsid, IgG, IgA and IgM antibody responses were readily detectable in saliva from non-hospitalized symptomatic and asymptomatic individuals.
Antibody responses in saliva and serum were largely independent of each other and symptom reporting.
CONCLUSIONS.
Detecting antibody responses in both saliva and serum is optimal for determining virus exposure and understanding immune responses after SARS-CoV-2 infection.
FUNDING.
This work was funded by the University of Birmingham, the National Institute for Health Research (UK), the NIH National Institute for Allergy and Infectious Diseases, the Bill and Melinda Gates Foundation and the University of Southampton.

Answer: {"verdict": "SUPPORT", "evidence": ["Anti-spike, but not nucleocapsid, IgG, IgA and IgM antibody responses were readily detectable in saliva from non-hospitalized symptomatic and asymptomatic individuals.", "Antibody responses in saliva and serum were largely independent of each other and symptom reporting."]}


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: Hospital readmissions of discharged patients with covid-19

Abstract:
Background.
COVID-19 infection has led to an overwhelming effort by health institutions to meet the high demand for hospital admissions.
Aim.
To analyse the clinical variables associated with readmission of patients who had previously been discharged after admission for COVID-19.
Design and methods.
We studied a retrospective cohort of patients with laboratory-confirmed SARS-CoV-2 infection who were admitted and subsequently discharged alive.
We then conducted a nested case-control study paired (1:1 ratio) by age, sex and period of admission.
Results.
Out of 1368 patients who were discharged during the study period, 61 patients (4.4%) were readmitted.
Immunocompromised patients were at increased risk for readmission.
There was also a trend towards a higher probability of readmission in hypertensive patients (p=0.07).
Cases had had a shorter hospital stay and a higher prevalence of fever during the 48 hours prior to discharge.
There were no significant differences in oxygen levels measured at admission and discharge by pulse oximetry intra-subject or between the groups.
Neutrophil/lymphocyte ratio at hospital admission tended to be higher in cases than in controls (p=0.06).
The motive for readmission in 10 patients (16.4%), was a thrombotic event in venous or arterial territory (p<0.001).
Neither glucocorticoids nor anticoagulants prescribed at hospital discharge were associated with a lower readmission rate.
Conclusions.
The rate of readmission after discharge from hospital for COVID-19 was low.
Immunocompromised patients and those presenting with fever during the 48 hours prior to discharge are at greater risk of readmission to hospital.

Answer: {"verdict": "SUPPORT", "evidence": ["Immunocompromised patients and those presenting with fever during the 48 hours prior to discharge are at greater risk of readmission to hospital."]}


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 new coronavirus can damage the lungs, heart and brain, which increases the risk of long-term health problems.

Abstract:
The presence of cardiovascular co-morbidities and the known effects of coronaviruses on the cardiovascular system have called attention to the potential implications for patients with cardiovascular risk factors.
This evidence-based viewpoint will address two questions: (a) are individuals with underlying cardiovascular risk factors (e.g. high blood pressure or diabetes) or overt disease (e.g. coronary heart disease, heart failure, kidney disease) more likely to develop severe Covid-19 and to die than those without underlying conditions?
(b) does the regular use of angiotensin-converting enzyme inhibitors (ACE-i) or angiotensin-receptor blockers (ARB) make patients more likely to get infected and to die of Covid-19?
With a necessary cautionary note that the evidence around the links between Covid-19 and cardiovascular disease is accruing at a fast pace, to date we can conclude that: (a) the greater susceptibility of individuals with underlying cardiovascular conditions to develop more severe Covid-19 with higher mortality rate is likely to be confounded, in part, by age and the type of co-morbidities.
Patients with heart failure or chronic kidney disease might show an excess risk; (b) neither ACE-i nor ARB are associated with greater risk of SARS-Cov2 infection, or severity or risk of death in patients with Covid-19.
Patients on these drugs should not stop them, unless under strict medical supervision and with the addition of a suitable replacement medicine.

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: Wearing a surgical face mask probably won't keep you from getting sick

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


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

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

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

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

Abstract:
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 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: Mathematical modeling explains differential sars cov-2 kinetics in lung and nasal passages in remdesivir treated rhesus macaques

Abstract:
Remdesivir was recently demonstrated to decrease recovery time in hospitalized patients with SARS-CoV-2 infection.
In rhesus macaques, early initiation of remdesivir therapy prevented pneumonia and lowered viral loads in the lung, but viral loads increased in the nasal passages five days after therapy.
We developed mathematical models to explain these results.
We identified that 1) drug potency is slightly higher in nasal passages than in lungs, 2) viral load decrease in lungs relative to nasal passages during therapy because of infection-dependent generation of refractory cells in the lung, 3) incomplete drug potency in the lung that decreases viral loads even slightly may allow substantially less lung damage, and 4) increases in nasal viral load may occur due to a slight blunting of peak viral load and subsequent decrease of the intensity of the innate immune response, as well as a lack of refractory cells.
We also hypothesize that direct inoculation of the trachea in rhesus macaques may not recapitulate natural infection as lung damage occurs more abruptly in this model than in human infection.
We demonstrate with sensitivity analysis that a drug with higher potency could completely suppress viral replication and lower viral loads abruptly in the nasal passages as well as the lung.
One Sentence Summary We developed a mathematical model to explain why remdesivir has a greater antiviral effect on SARS CoV-2 in lung versus nasal passages in rhesus macaques.

Answer: {"verdict": "SUPPORT", "evidence": ["In rhesus macaques, early initiation of remdesivir therapy prevented pneumonia and lowered viral loads in the lung, but viral loads increased in the nasal passages five days after therapy.", "One Sentence Summary We developed a mathematical model to explain why remdesivir has a greater antiviral effect on SARS CoV-2 in lung versus nasal passages in rhesus macaques."]}


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: Structure of sars-cov-2 orf8 , a rapidly evolving coronavirus protein implicated in immune evasion

Abstract:
The molecular basis for the severity and rapid spread of the COVID-19 disease caused by SARS-CoV-2 is largely unknown.
ORF8 is a rapidly evolving accessory protein that has been proposed to interfere with immune responses.
The crystal structure of SARS-CoV-2 ORF8 was determined at 2.04 Å resolution by x-ray crystallography.
The structure reveals a ~60 residue core similar to SARS-CoV ORF7a with the addition of two dimerization interfaces unique to SARS-CoV-2 ORF8.
A covalent disulfide-linked dimer is formed through an N-terminal sequence specific to SARS-CoV-2, while a separate non-covalent interface is formed by another SARS-CoV-2-specific sequence, (73)YIDI(76).
Together the presence of these interfaces shows how SARS-CoV-2 ORF8 can form unique large-scale assemblies not possible for SARS-CoV, potentially mediating unique immune suppression and evasion activities.

Answer: {"verdict": "SUPPORT", "evidence": ["Together the presence of these interfaces shows how SARS-CoV-2 ORF8 can form unique large-scale assemblies not possible for SARS-CoV, potentially mediating unique immune suppression and evasion activities."]}


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

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

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

Claim: Cloth face coverings are most likely to reduce the spread of the COVID-19 virus when they are widely used by people in public settings

Abstract:
Controversy exists around the appropriate types of masks and the situations in which they should be used in community and health care settings for the prevention of SARS-CoV-2 infection.
In this article, the American College of Physicians (ACP) provides recommendations based on the best available evidence through 14 April 2020 on the effectiveness of N95 respirators, surgical masks, and cloth masks in reducing transmission of infection.
The ACP plans periodic updates of these recommendations on the basis of ongoing surveillance of the literature for 1 year from the initial search date.

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


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

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

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

Claim: A booster dose mays immunogenicity of the covid-19 vaccine candidate chadox1 ncov-19 in aged mice

Abstract:
The spread of SARS-CoV-2 has caused a global pandemic that has affected almost every aspect of human life.
The development of an effective COVID-19 vaccine could limit the morbidity and mortality caused by infection, and may enable the relaxation of social distancing measures.
Age is one of the most significant risk factors for poor health outcomes after SARS-CoV-2 infection, therefore it is desirable that any new vaccine candidates should elicit a robust immune response in older adults.
Here, we test the immunogenicity of the adenoviral vectored vaccine ChAdOx1 nCoV-19 (AZD-1222) in aged mice.
We find that a single dose of this vaccine induces cellular and humoral immunity in aged mice, but at a reduced magnitude than in younger adult mice.
Furthermore, we report that a second dose enhances the immune response to this vaccine in aged mice, indicating that a primeboost strategy may be a rational approach to enhance immunogenicity in older persons.

Answer: {"verdict": "CONTRADICT", "evidence": ["We find that a single dose of this vaccine induces cellular and humoral immunity in aged mice, but at a reduced magnitude than in younger adult mice.", "Furthermore, we report that a second dose enhances the immune response to this vaccine in aged mice, indicating that a primeboost strategy may be a rational approach to enhance immunogenicity in older persons."]}


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

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

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

Claim: It is correct to call the virus that causes COVID-19, SARS.

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

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


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

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

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

Claim: Evidence is currently lacking and it is too early to make robust conclusions on any link between use of angiotensin-converting enzyme (ACE) inhibitors and angiotensin II type-I receptor blockers with risk or severity of novel coronavirus disease 2019 (COVID-19) infection.

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

Abstract:
The 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: The drugs have anti-inflammatory effects "in addition to their blood pressure benefits.

Abstract:
INTRODUCTION The present research aimed to determine the relation between the use of angiotensin-converting enzyme inhibitors (ACE inh) and angiotensinogen receptor blockers (ARBs) and in-hospital mortality of hypertensive patients diagnosed with Covid-19 pneumonia.
MATERIAL AND METHOD In this retrospective study, we included 113 consecutive hypertensive patients admitted due to Covid-19 infection.
In all patients, Covid-19 infection was confirmed with using reverse-transcription polymerase chain reaction.
All patients were on ACE inh/ARBs or other antihypertensive therapy unless no contraindication was present.
The primary outcome of the study was the in-hospital all-cause mortality.
RESULTS In total, 113 hypertensive Covid-19 patients were included, of them 74 patients were using ACE inh/ARBs.
During in-hospital follow up, 30.9% [n = 35 patients] of patients died.
The frequency of admission to the ICU and endotracheal intubation were significantly higher in patients using ACE inh/ARBs.
In a multivariable analysis, the use of ACE inh/ARBs was an independent predictor of in-hospital mortality (OR: 3.66; 95%CI: 1.11-18.18; p= .032).
Kaplan-Meir curve analysis displayed that patients on ACE inh/ARBs therapy had higher incidence of in-hospital death than those who were not.
CONCLUSION The present study has found that the use of ACE inh/ARBs therapy might be associated with an increased in-hospital mortality in patients who were diagnosed with Covid-19 pneumonia.
It is likely that ACE inh/ARBs therapy might not be beneficial in the subgroup of hypertensive Covid-19 patients despite the fact that there might be the possibility of some unmeasured residual confounders to affect the results of the study.

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:
In 2003, severe acute respiratory syndrome coronavirus (SARS-CoV) caused one of the most devastating epidemics known to the developed world.
There were two important lessons from this epidemic.
Firstly, coronaviruses, in addition to influenza viruses, can cause severe and rapidly spreading human infections.
Secondly, bats can serve as the origin and natural animal reservoir of deadly human viruses.
Since then, researchers around the world, especially those in Asia where SARS-CoV was first identified, have turned their focus to find novel coronaviruses infecting humans, bats, and other animals.
Two human coronaviruses, HCoV-HKU1 and HCoV-NL63, were identified shortly after the SARS-CoV epidemic as common causes of human respiratory tract infections.
In 2012, a novel human coronavirus, now called Middle East respiratory syndrome coronavirus (MERS-CoV), has emerged in the Middle East to cause fatal human infections in three continents.
MERS-CoV human infection is similar to SARS-CoV in having a high fatality rate and the ability to spread from person to person which resulted in secondary cases among close contacts including healthcare workers without travel history to the Middle East.
Both viruses also have close relationships with bat coronaviruses.
New cases of MERS-CoV infection in humans continue to occur with the origins of the virus still unknown in many cases.
A multifaceted approach is necessary to control this evolving MERS-CoV outbreak.
Source identification requires detailed epidemiological studies of the infected patients and enhanced surveillance of MERS-CoV or similar coronaviruses in humans and animals.
Early diagnosis of infected patients and appropriate infection control measures will limit the spread in hospitals, while social distancing strategies may be necessary to control the outbreak in communities if it remained uncontrolled as in the SARS epidemic.

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

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

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


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

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

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

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

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

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


Question:
You will be shown a claim 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-cov2 enables anaerobic bacteria to colonize the gut disrupting homeostasis

Abstract:
The oral cavity, as the entry point to the body, may play a critical role in the pathogenesis of SARS-CoV-2 infection that has caused a global outbreak of the coronavirus disease 2019 (COVID-19).
Available data indicate that the oral cavity may be an active site of infection and an important reservoir of SARS-CoV-2.
Considering that the oral surfaces are colonized by a diverse microbial community, it is likely that viruses have interactions with the host microbiota.
Patients infected by SARS-CoV-2 may have alterations in the oral and gut microbiota, while oral species have been found in the lung of COVID-19 patients.
Furthermore, interactions between the oral, lung, and gut microbiomes appear to occur dynamically whereby a dysbiotic oral microbial community could influence respiratory and gastrointestinal diseases.
However, it is unclear whether SARS-CoV-2 infection can alter the local homeostasis of the resident microbiota, actively cause dysbiosis, or influence cross-body sites interactions.
Here, we provide a conceptual framework on the potential impact of SARS-CoV-2 oral infection on the local and distant microbiomes across the respiratory and gastrointestinal tracts (‘oral-tract axes’), which remains largely unexplored.
Studies in this area could further elucidate the pathogenic mechanism of SARS-CoV-2 and the course of infection as well as the clinical symptoms of COVID-19 across different sites in the human host.

Answer: {"verdict": "CONTRADICT", "evidence": ["Patients infected by SARS-CoV-2 may have alterations in the oral and gut microbiota, while oral species have been found in the lung of COVID-19 patients."]}


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

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

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

Claim: Children, like adults, who have COVID-19 but have no symptoms (asymptomatic) can still spread the virus to others.

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

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


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

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

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

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

Abstract:
AIMS: To describe characteristics of COVID-19 patients with type 2 diabetes and to analyze risk factors for severity.
METHODS: Demographics, comorbidities, symptoms, laboratory findings, treatments and outcomes of COVID-19 patients with diabetes were collected and analyzed.
RESULTS: Seventy-fourCOVID-19 patients with diabetes were included.
Twenty-seven patients (36.5%) were severe and 10 patients (13.5%) died.
Higher levels of blood glucose, serum amyloid A (SAA), C reactive protein and interleukin 6 were associated with severe patients compared to non-severe ones (P<0.05).
Levels of albumin, cholesterol, high density lipoprotein, small and dense low density lipoprotein and CD4+T lymphocyte counts in severe patients were lower than those in non-severe patients (P<0.05).
Logistic regression analysis identified decreased CD4+T lymphocyte counts (odds ratio [OR]=0.988, 95%Confidence interval [95%CI] 0.979-0.997) and increased SAA levels (OR=1.029, 95%CI 1.002-1.058) as risk factors for severity of COVID-19 with diabetes (P<0.05).
CONCLUSIONS: Type 2 diabetic patients were more susceptible to COVID-19 than overall population, which might be associated with hyperglycemia and dyslipidemia.
Aggressive treatment should be suggested, especially when these patients had low CD4+T lymphocyte counts and high SAA levels.

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: Some models show that if people wear masks, death rates from COVID-19 stay very low.

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

Answer: {"verdict": "SUPPORT", "evidence": ["Evidence that face masks provide effective protection against respiratory infections in the community is scarce.", "However, face masks are widely used by health workers as part of droplet precautions when caring for patients with respiratory infections.", "It would therefore be reasonable to suggest that consistent widespread use of face masks in the community could prevent further spread of the Severe Acute Respiratory Syndrome-Coronavirus 2 (SARS-CoV-2)."]}


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: Homozygous l-atpase plays a protective role in sars coronavirus infection

Abstract:
Severe acute respiratory syndrome (SARS) is caused by infection of a previously undescribed coronavirus (CoV).
L-SIGN, encoded by CLEC4M (also known as CD209L), is a SARS-CoV binding receptor that has polymorphism in its extracellular neck region encoded by the tandem repeat domain in exon 4.
Our genetic risk association study shows that individuals homozygous for CLEC4M tandem repeats are less susceptible to SARS infection.
L-SIGN is expressed in both non-SARS and SARS-CoV–infected lung.
Compared with cells heterozygous for L-SIGN, cells homozygous for L-SIGN show higher binding capacity for SARS-CoV, higher proteasome-dependent viral degradation and a lower capacity for trans infection.
Thus, homozygosity for L-SIGN plays a protective role during SARS infection.
SUPPLEMENTARY INFORMATION: The online version of this article (doi:10.1038/ng1698) contains supplementary material, which is available to authorized users.

Answer: {"verdict": "CONTRADICT", "evidence": ["Thus, homozygosity for L-SIGN plays a protective role during SARS infection."]}


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 immune system, noticing the infection, flares up, which can cause the lungs to fill with fluid and prevent adequate oxygen flow.

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

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


Question:
You will be shown a claim 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: Intradermal-delivered mva vaccine provides anamnestic protection in a rhesus macaque sars-cov-2 challenge model

Abstract:
Coronavirus disease 2019 (COVID-19), caused by the SARS-CoV-2 virus, has had a dramatic global impact on public health, social, and economic infrastructures.
Here, we assess immunogenicity and anamnestic protective efficacy in rhesus macaques of the intradermal (ID)-delivered SARS-CoV-2 spike DNA vaccine, INO-4800.
INO-4800 is an ID-delivered DNA vaccine currently being evaluated in clinical trials.
Vaccination with INO-4800 induced T cell responses and neutralizing antibody responses against both the D614 and G614 SARS-CoV-2 spike proteins.
Several months after vaccination, animals were challenged with SARS-CoV-2 resulting in rapid recall of anti-SARS-CoV-2 spike protein T and B cell responses.
These responses were associated with lower viral loads in the lung and with faster nasal clearance of virus.
These studies support the immune impact of INO-4800 for inducing both humoral and cellular arms of the adaptive immune system which are likely important for providing durable protection against COVID-19 disease.

Answer: {"verdict": "CONTRADICT", "evidence": ["Here, we assess immunogenicity and anamnestic protective efficacy in rhesus macaques of the intradermal (ID)-delivered SARS-CoV-2 spike DNA vaccine, INO-4800.", "Vaccination with INO-4800 induced T cell responses and neutralizing antibody responses against both the D614 and G614 SARS-CoV-2 spike proteins.", "Several months after vaccination, animals were challenged with SARS-CoV-2 resulting in rapid recall of anti-SARS-CoV-2 spike protein T and B cell responses.", "These studies support the immune impact of INO-4800 for inducing both humoral and cellular arms of the adaptive immune system which are likely important for providing durable protection against COVID-19 disease."]}


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

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

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

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

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: Seasonal human coronavirus antibodies are boosted upon sars-cov-2 infection but are associated with protection

Abstract:
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has rapidly spread within the human population.
Although SARS-CoV-2 is a novel coronavirus, most humans had been previously exposed to other antigenically distinct common seasonal human coronaviruses (hCoVs) before the COVID-19 pandemic.
Here, we quantified levels of SARS-CoV-2-reactive antibodies and hCoV-reactive antibodies in serum samples collected from 204 humans before the COVID-19 pandemic.
We then quantified pre-pandemic antibody levels in serum from a separate cohort of 252 individuals who became PCR-confirmed infected with SARS-CoV-2.
Finally, we longitudinally measured hCoV and SARS-CoV-2 antibodies in the serum of hospitalized COVID-19 patients.
Our studies indicate that most individuals possessed hCoV-reactive antibodies before the COVID-19 pandemic.
We determined that ~23% of these individuals possessed non-neutralizing antibodies that cross-reacted with SARS-CoV-2 spike and nucleocapsid proteins.
These antibodies were not associated with protection against SARS-CoV-2 infections or hospitalizations, but paradoxically these hCoV cross-reactive antibodies were boosted upon SARS-CoV-2 infection.

Answer: {"verdict": "CONTRADICT", "evidence": ["Although SARS-CoV-2 is a novel coronavirus, most humans had been previously exposed to other antigenically distinct common seasonal human coronaviruses (hCoVs) before the COVID-19 pandemic.", "We then quantified pre-pandemic antibody levels in serum from a separate cohort of 252 individuals who became PCR-confirmed infected with SARS-CoV-2.", "These antibodies were not associated with protection against SARS-CoV-2 infections or hospitalizations, but paradoxically these hCoV cross-reactive antibodies were boosted upon SARS-CoV-2 infection."]}


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 is infecting quite a few people, many with vicious effects.

Abstract:
BACKGROUND: An epidemic of Coronavirus Disease 2019 (COVID-19) began in December 2019 and triggered a Public Health Emergency of International Concern (PHEIC).
We aimed to find risk factors for the progression of COVID-19 to help reducing the risk of critical illness and death for clinical help.
METHODS: The data of COVID-19 patients until March 20, 2020 were retrieved from four databases.
We statistically analyzed the risk factors of critical/mortal and non-critical COVID-19 patients with meta-analysis.
RESULTS: Thirteen studies were included in Meta-analysis, including a total number of 3027 patients with SARS-CoV-2 infection.
Male, older than 65, and smoking were risk factors for disease progression in patients with COVID-19 (male: ORâ¯=â¯1.76, 95% CI (1.41, 2.18), P < 0.00001; age over 65 years old: OR =6.06, 95% CI(3.98, 9.22), P < 0.00001; current smoking: OR =2.51, 95% CI(1.39, 3.32), Pâ¯=â¯0.0006).
The proportion of underlying diseases such as hypertension, diabetes, cardiovascular disease, and respiratory disease were statistically significant higher in critical/mortal patients compared to the non-critical patients (diabetes: OR=3.68, 95% CI (2.68, 5.03), P < 0.00001; hypertension: ORâ¯=â¯2.72, 95% CI (1.60,4.64), Pâ¯=â¯0.0002; cardiovascular disease: ORâ¯=â¯5.19, 95% CI(3.25, 8.29), P < 0.00001; respiratory disease: ORâ¯=â¯5.15, 95% CI(2.51, 10.57), P < 0.00001).
Clinical manifestations such as fever, shortness of breath or dyspnea were associated with the progression of disease [fever: 0Râ¯=â¯0.56, 95% CI (0.38, 0.82), Pâ¯=â¯0.003;shortness of breath or dyspnea: 0R=4.16, 95% CI (3.13, 5.53), P < 0.00001].
Laboratory examination such as aspartate amino transferase(AST) > 40U/L, creatinine(Cr) ≥ 133mol/L, hypersensitive cardiac troponin I(hs-cTnI) > 28pg/mL, procalcitonin(PCT) > 0.5ng/mL, lactatede hydrogenase(LDH) > 245U/L, and D-dimer > 0.5mg/L predicted the deterioration of disease while white blood cells(WBC)<4â¯×â¯109/L meant a better clinical status[AST > 40U/L:OR=4.00, 95% CI (2.46, 6.52), P < 0.00001; Cr ≥ 133µmol/L: ORâ¯=â¯5.30, 95% CI (2.19, 12.83), Pâ¯=â¯0.0002; hs-cTnI > 28 pg/mL: ORâ¯=â¯43.24, 95% CI (9.92, 188.49), P < 0.00001; PCT > 0.5 ng/mL: ORâ¯=â¯43.24, 95% CI (9.92, 188.49), P < 0.00001;LDH > 245U/L: ORâ¯=â¯43.24, 95% CI (9.92, 188.49), P < 0.00001; D-dimer > 0.5mg/L: ORâ¯=â¯43.24, 95% CI (9.92, 188.49), P < 0.00001; WBC < 4â¯×â¯109/L: ORâ¯=â¯0.30, 95% CI (0.17, 0.51), P < 0.00001].
CONCLUSION: Male, aged over 65, smoking patients might face a greater risk of developing into the critical or mortal condition and the comorbidities such as hypertension, diabetes, cardiovascular disease, and respiratory diseases could also greatly affect the prognosis of the COVID-19.
Clinical manifestation such as fever, shortness of breath or dyspnea and laboratory examination such as WBC, AST, Cr, PCT, LDH, hs-cTnI and D-dimer could imply the progression of COVID-19.

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


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

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

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

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

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

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


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

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

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

Claim: there are few novel sars-cov-2 cases in malaria countries because of the use of the antimalarial drug hydroxychloroquine.

Abstract:
Hydroxychloroquine has been promoted for its use in treatment of COVID-19 patients based on in-vitro evidences.
We searched the databases to include randomized and observational studies evaluating the effect of Hydroxychloroquine on mortality in COVID-19 patients.
The outcome was summarized as odds ratios (OR) with a 95% confidence interval (CI).We used the inverse-variance method with a random effect model and assessed the heterogeneity using I2 test.
We used ROBINS-I tool to assess methodological quality of the included studies.
We performed the meta-analysis using 'Review manager software version 5.3'.
We identified 6 observationalstudies satisfying the selection criteria.
In all studies, Hydroxychloroquine was given as add on to the standard care and effect was compared with the standard care alone.
A pooled analysis observed 251 deaths in 1331 participants of the Hydroxychloroquine arm and 363 deaths in 1577 participants of the control arm.
There was no difference in odds of mortality events amongst Hydroxychloroquine and supportive care arm [1.25 (95% CI: 0.65, 2.38); I2 = 80%].
A similar trend was observed with moderate risk of bias studies [0.95 (95% CI: 0.44, 2.06); I2 = 85%].
The odds of mortality were significantly higher in patients treated with Hydroxychloroquine + Azithromycin than supportive care alone [2.34 (95% CI: 1.63, 3.34); I2 = 0%].
A pooled analysis of recently published studies suggests no additional benefit for reducing mortality in COVID-19 patients when Hydroxychloroquine is given as add-on to the standard care.
Graphical Abstract.

Answer: {"verdict": "CONTRADICT", "evidence": ["The odds of mortality were significantly higher in patients treated with Hydroxychloroquine + Azithromycin than supportive care alone [2.34 (95% CI: 1.63, 3.34); I2 = 0%].", "A pooled analysis of recently published studies suggests no additional benefit for reducing mortality in COVID-19 patients when Hydroxychloroquine is given as add-on to the standard care."]}


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: Like other viruses with a lipid envelope, SARS-CoV-2 is probably sensitive to temperature, humidity, and solar radiation

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

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


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

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

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

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

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

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


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

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

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

Claim: Wearing the N95 respirator mask can protect against coronavirus

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


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

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

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

Claim: Hospital readmissions of sars patients with covid-19

Abstract:
Background.
COVID-19 infection has led to an overwhelming effort by health institutions to meet the high demand for hospital admissions.
Aim.
To analyse the clinical variables associated with readmission of patients who had previously been discharged after admission for COVID-19.
Design and methods.
We studied a retrospective cohort of patients with laboratory-confirmed SARS-CoV-2 infection who were admitted and subsequently discharged alive.
We then conducted a nested case-control study paired (1:1 ratio) by age, sex and period of admission.
Results.
Out of 1368 patients who were discharged during the study period, 61 patients (4.4%) were readmitted.
Immunocompromised patients were at increased risk for readmission.
There was also a trend towards a higher probability of readmission in hypertensive patients (p=0.07).
Cases had had a shorter hospital stay and a higher prevalence of fever during the 48 hours prior to discharge.
There were no significant differences in oxygen levels measured at admission and discharge by pulse oximetry intra-subject or between the groups.
Neutrophil/lymphocyte ratio at hospital admission tended to be higher in cases than in controls (p=0.06).
The motive for readmission in 10 patients (16.4%), was a thrombotic event in venous or arterial territory (p<0.001).
Neither glucocorticoids nor anticoagulants prescribed at hospital discharge were associated with a lower readmission rate.
Conclusions.
The rate of readmission after discharge from hospital for COVID-19 was low.
Immunocompromised patients and those presenting with fever during the 48 hours prior to discharge are at greater risk of readmission to hospital.

Answer: {"verdict": "CONTRADICT", "evidence": ["Immunocompromised patients and those presenting with fever during the 48 hours prior to discharge are at greater risk of readmission to hospital."]}