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

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

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

Claim: The viral protein nsp1 acts as a ribosome gatekeeper for shutting down host translation and fostering sars-cov-2 translation

Abstract:
SARS-CoV-2 coronavirus is responsible for Covid-19 pandemic.
In the early phase of infection, the single-strand positive RNA genome is translated into non-structural proteins (NSP).
One of the first proteins produced during viral infection, NSP1, binds to the host ribosome and blocks the mRNA entry channel.
This triggers translation inhibition of cellular translation.
In spite of the presence of NSP1 on the ribosome, viral translation proceeds however.
The molecular mechanism of the so-called viral evasion to NSP1 inhibition remains elusive.
Here, we confirm that viral translation is maintained in the presence of NSP1.
The evasion to NSP1-inhibition is mediated by the cis-acting RNA hairpin SL1 in the 5'UTR of SARS-CoV-2.
NSP1-evasion can be transferred on a reporter transcript by SL1 transplantation.
The apical part of SL1 is only required for viral translation.
We show that NSP1 remains bound on the ribosome during viral translation.
We suggest that the interaction between NSP1 and SL1 frees the mRNA accommodation channel while maintaining NSP1 bound to the ribosome.
Thus, NSP1 acts as a ribosome gatekeeper, shutting down host translation or fostering SARS-CoV-2 translation depending on the presence of the SL1 5'UTR hairpin.
SL1 is also present and necessary for translation of sub-genomic RNAs in the late phase of the infectious program.
Consequently, therapeutic strategies targeting SL1 should affect viral translation at early and late stages of infection.
Therefore, SL1 might be seen as a genuine 'Achille heel' of the virus.

Answer: {"verdict": "SUPPORT", "evidence": ["One of the first proteins produced during viral infection, NSP1, binds to the host ribosome and blocks the mRNA entry channel.", "In spite of the presence of NSP1 on the ribosome, viral translation proceeds however.", "We show that NSP1 remains bound on the ribosome during viral translation."]}


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-sign 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": "SUPPORT", "evidence": ["Thus, homozygosity for L-SIGN plays a protective role during SARS infection."]}


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

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

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

Claim: Lack of immune homology with vaccine preventable pathogens suggests childhood immunizations do also protect against sars-cov-2 through adaptive cross-immunity

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

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


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

Answer: {"verdict": "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: young peope are not at risk and do not die from covid-19

Abstract:
Currently (mid May 2020), most active cases of COVID-19 are found in Europe and North America while it is still in the initial phases in Africa.
As COVID-19 mortality occurs mainly in elderly and as Africa has a comparably young population, the death rates should be lower than on other continents.
We calculated standardised mortality ratios (SMR) using age-specific case fatality rates for COVID-19 and the age structure of the population of Africa and of other continents.
Compared to a European or Northern American population, the standardised mortality ratio was only 0.22 and 0.25, respectively, corresponding to reduction of deaths rates to a quarter.
Compared to the Asian and Latin American & Caribbean population, the SMR was 0.43 and 0.44, respectively, corresponding to half the death rate for Africa.
It is useful to quantify the isolated effect of the African age-structure on potential COVID-19 mortality for illustrative and communication purposes, keeping in mind the importance of public health measures that have been shown to be effective in reducing cases and deaths.
The different aspect of age pyramids of a European and an African population are striking and the potential implications for the pandemic are often discussed but rarely quantified.

Answer: {"verdict": "CONTRADICT", "evidence": ["As COVID-19 mortality occurs mainly in elderly and as Africa has a comparably young population, the death rates should be lower than on other continents.", "We calculated standardised mortality ratios (SMR) using age-specific case fatality rates for COVID-19 and the age structure of the population of Africa and of other continents.", "Compared to a European or Northern American population, the standardised mortality ratio was only 0.22 and 0.25, respectively, corresponding to reduction of deaths rates to a quarter.", "Compared to the Asian and Latin American & Caribbean population, the SMR was 0.43 and 0.44, respectively, corresponding to half the death rate for Africa."]}


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: Chadox1 ncov-19 vaccination induces sars-cov-2 pneumonia in rhesus macaques

Abstract:
Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) emerged in December 20191,2 and is responsible for the COVID-19 pandemic3.
Vaccines are an essential countermeasure urgently needed to control the pandemic4.
Here, we show that the adenovirus-vectored vaccine ChAdOx1 nCoV-19, encoding the spike protein of SARS-CoV-2, is immunogenic in mice, eliciting a robust humoral and cell-mediated response.
This response was not Th2 dominated, as demonstrated by IgG subclass and cytokine expression profiling.
A single vaccination with ChAdOx1 nCoV-19 induced a humoral and cellular immune response in rhesus macaques.
We observed a significantly reduced viral load in bronchoalveolar lavage fluid and respiratory tract tissue of vaccinated animals challenged with SARS-CoV-2 compared with control animals, and no pneumonia was observed in vaccinated rhesus macaques.
Importantly, no evidence of immune-enhanced disease following viral challenge in vaccinated animals was observed.
ChAdOx1 nCoV-19 is currently under investigation in a phase I clinical trial.
Safety, immunogenicity and efficacy against symptomatic PCR-positive COVID-19 disease will now be assessed in randomised controlled human clinical trials.

Answer: {"verdict": "CONTRADICT", "evidence": ["A single vaccination with ChAdOx1 nCoV-19 induced a humoral and cellular immune response in rhesus macaques.", "We observed a significantly reduced viral load in bronchoalveolar lavage fluid and respiratory tract tissue of vaccinated animals challenged with SARS-CoV-2 compared with control animals, and no pneumonia was observed in vaccinated rhesus macaques."]}


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:
The novel coronavirus Covid-19 follows transmission route and clinical presentation of all community-acquired coronaviruses.
Instead, the rate of transmission is significative higher, with a faster spread of the virus responsible of the worldwide outbreak and a significative higher mortality rate due to the development of a severe lung injury.
Most noteworthy is the distribution of death rate among age groups.
Children and younger people are almost protected from severe clinical presentation.
Possible explanation of this phenomenon could be the ability of past vaccinations (especially tetanic, diphtheria toxoids and inactivated bacteria as pertussis) to stimulate immune system and to generate a scattered immunity against non-self antigens in transit, as coronaviruses and other community-circulating viruses and make immune system readier to develop specific immunity against Covid-19.
The first support to this hypothesis is the distribution of mortality rate during historical pandemics ("Spanish flu" 1918, "Asian flu" 1956 and "the Hong Kong flu" 1968) among age groups before and after the introduction of vaccines.
The immunological support to the hypothesis derives from recent studies about immunotherapy for malignancies, which propose the use of oncolytic vaccines combined with toxoids in order to exploit CD4 + memory T cell recall in supporting the ongoing anti-tumour response.
According to this hypothesis vaccine formulations (tetanus, diphtheria, Bordetella pertussis) could be re-administrate after the first contact with Covid-19, better before the development of respiratory severe illness and of course before full-blown ARDS (Acute Respiratory Distress Syndrome).
The CD4 + memory exploiting could help immune system to recall immunity of already know antigens against coronaviruses, avoiding or limiting "lung crash" until virus specific immunity develops and making it faster and prolonged.
Finally, this administration could be helpful not only in already infected patients, but also before infection.
In fact, people could have an immune system more ready when the contact with the Covid-19 will occur.

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:
The effects of angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin receptor blockers (ARBs) on the risk of COVID-19 infection and disease progression are yet to be investigated.
The relationship between ACEI/ARB use and COVID-19 infection was systematically reviewed.
To identify relevant studies that met predetermined inclusion criteria, unrestricted searches of the PubMed, Embase, and Cochrane Library databases were conducted.
The search strategy included clinical date published until May 9, 2020.
Twelve articles involving more than 19,000 COVID-19 cases were included.
To estimate overall risk, random-effects models were adopted.
Our results showed that ACEI/ARB exposure was not associated with a higher risk of COVID-19 infection (OR = 0.99; 95 % CI, 0-1.04; P = 0.672).
Among those with COVID-19 infection, ACEI/ARB exposure was also not associated with a higher risk of having severe infection (OR = 0.98; 95 % CI, 0.87-1.09; P = 0.69) or mortality (OR = 0.73, 95 %CI, 0.5-1.07; P = 0.111).
However, ACEI/ARB exposure was associated with a lower risk of mortality compared to those on non-ACEI/ARB antihypertensive drugs (OR = 0.48, 95 % CI, 0.29-0.81; P = 0.006).
In conclusion, current evidence did not confirm the concern that ACEI/ARB exposure is harmful in patientswith COVID-19 infection.
This study supports the current guidelines that discourage discontinuation of ACEIs or ARBs in COVID-19 patients and the setting of the COVID-19 pandemic.

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


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

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

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

Claim: both dogs and cats can be infected by the virus that causes Covid-19 in humans, but none of the ten animals observed in the study showed clinical symptoms like coughing

Abstract:
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which originated in Wuhan, China, in 2019, is responsible for the COVID-19 pandemic.
It is now accepted that the wild fauna, probably bats, constitute the initial reservoir of the virus, but little is known about the role pets can play in the spread of the disease in human communities, knowing the ability of SARS-CoV-2 to infect some domestic animals.
We tested 21 domestic pets (9 cats and 12 dogs) living in close contact with their owners (belonging to a veterinary community of 20 students) in which two students tested positive for COVID-19 and several others (n = 11/18) consecutively showed clinical signs (fever, cough, anosmia, etc.) compatible with COVID-19 infection.
Although a few pets presented many clinical signs indicative for a coronavirus infection, no animal tested positive for SARS-CoV-2 by RT-PCR and no antibodies against SARS-CoV-2 were detectable in their blood using an immunoprecipitation assay.
These original data can serve a better evaluation of the host range of SARS-CoV-2 in natural environment exposure conditions.

Answer: {"verdict": "CONTRADICT", "evidence": ["Although a few pets presented many clinical signs indicative for a coronavirus infection, no animal tested positive for SARS-CoV-2 by RT-PCR and no antibodies against SARS-CoV-2 were detectable in their blood using an immunoprecipitation assay."]}


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: mix and drink as hot tea every afternoon

Abstract:
This review focused on the use of plant-based foods for enhancing the immunity of all aged groups against COVID-19.
In humans, coronaviruses are included in the spectrum of viruses that cause the common cold and, recently, severe acute respiratory syndrome (SARS).
Emerging infectious diseases, such as SARS present a major threat to public health.
The novel coronavirus has spread rapidly to multiple countries and has been declared a pandemic by the World Health Organization.
COVID-19 is usually caused a virus to which most probably the people with low immunity response are being affected.
Plant-based foods increased the intestinal beneficial bacteria which are helpful and make up of 85% of the immune system.
By the use of plenty of water, minerals like magnesium and Zinc, micronutrients, herbs, food rich in vitamins C, D and E, and better life style one can promote the health and can overcome this infection.
Various studies investigated that a powerful antioxidant glutathione and a bioflavonoid quercetin may prevent various infections including COVID-19.
In conclusion, the plant-based foods play a vital role to enhance the immunity of people to control 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: Hydroxychloroquine is an Effective Treatment for COVID-19

Abstract:
BACKGROUND: Chloroquine and hydroxychloroquine have been found to be efficient on SARS-CoV-2, and reported to be efficient in Chinese COV-19 patients.
We evaluate the role of hydroxychloroquine on respiratory viral loads.
PATIENTS AND METHODS: French Confirmed COVID-19 patients were included in a single arm protocol from early March to March 16th, to receive 600mg of hydroxychloroquine daily and their viral load in nasopharyngeal swabs was tested daily in a hospital setting.
Depending on their clinical presentation, azithromycin was added to the treatment.
Untreated patients from another center and cases refusing the protocol were included as negative controls.
Presence and absence of virus at Day6-post inclusion was considered the end point.
RESULTS: Six patients were asymptomatic, 22 had upper respiratory tract infection symptoms and eight had lower respiratory tract infection symptoms.
Twenty cases were treated in this study and showed a significant reduction of the viral carriage at D6-post inclusion compared to controls, and much lower average carrying duration than reported of untreated patients in the literature.
Azithromycin added to hydroxychloroquine was significantly more efficient for virus elimination.
CONCLUSION: Despite its small sample size our survey shows that hydroxychloroquine treatment is significantly associated with viral load reduction/disappearance in COVID-19 patients and its effect is reinforced by azithromycin.

Answer: {"verdict": "SUPPORT", "evidence": ["CONCLUSION: Despite its small sample size our survey shows that hydroxychloroquine treatment is significantly associated with viral load reduction/disappearance in COVID-19 patients and its effect is reinforced by azithromycin."]}


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: Microwave or autoclave cannot destroy the infectivity of infectious bronchitis virus and avian pneumovirus but allow detection by reverse transcriptase-polymerase chain reaction

Abstract:
A method is described for enabling safe transit of denatured virus samples for polymerase chain reaction (PCR) identification without the risk of unwanted viable viruses.
Cotton swabs dipped in avian infectious bronchitis virus (IBV) or avian pneumovirus (APV) were allowed to dry.
Newcastle disease virus and avian influenza viruses were used as controls.
Autoclaving and microwave treatment for as little as 20 sec destroyed the infectivity of all four viruses.
However, both IBV and APV could be detected by reverse transcriptase (RT)-PCR after autoclaving and as long as 5 min microwave treatment (Newcastle disease virus and avian influenza viruses were not tested).
Double microwave treatment of IBV and APV with an interval of 2 to 7 days between was tested.
After the second treatment, RT-PCR products were readily detected in all samples.
Swabs from the tracheas and cloacas of chicks infected with IBV shown to contain infectious virus were microwaved.
Swabs from both sources were positive by RT-PCR.
Microwave treatment appears to be a satisfactory method of inactivating virus while preserving nucleic acid for PCR identification.

Answer: {"verdict": "CONTRADICT", "evidence": ["Autoclaving and microwave treatment for as little as 20 sec destroyed the infectivity of all four viruses.", "However, both IBV and APV could be detected by reverse transcriptase (RT)-PCR after autoclaving and as long as 5 min microwave treatment (Newcastle disease virus and avian influenza viruses were not tested).", "Microwave treatment appears to be a satisfactory method of inactivating virus while preserving nucleic acid for PCR identification."]}


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: Melatonin is significantly associated with survival of intubated covid-19 patients

Abstract:
Background Respiratory distress requiring intubation is the most serious complication associated with coronavirus disease 2019 (COVID-19).
Methods In this retrospective study, we used survival analysis to determine whether or not mortality following intubation was associated with hormone exposure in patients treated at New York Presbyterian/ Columbia University Irving Medical Center.
Here, we report the overall hazards ratio for each hormone for exposure before and after intubation for intubated and mechanically ventilated patients.
Results Among the 189,987 patients, we identified 948 intubation periods across 791 patients who were diagnosed with COVID-19 or infected with SARS-CoV2 and 3,497 intubation periods across 2,981 patients who were not.
Melatonin exposure after intubation was statistically associated with a positive outcome in COVID-19 (demographics and comorbidities adjusted HR: 0.131, 95% CI: 7.76E-02-0.223, p-value = 8.19E-14) and non-COVID-19 (demographics and comorbidities adjusted HR: 0.278, 95% CI: 0.142-0.542, p-value = 1.72E-04) intubated patients.
Additionally, melatonin exposure after intubation was statically associated with a positive outcome in COVID-19 patients (demographics and comorbidities adjusted HR: 0.127, 95% CI: 6.01E-02-0.269, p-value = 7.15E-08).
Conclusions Melatonin exposure after intubation is significantly associated with a positive outcome in COVID-19 and non-COVID-19 patients.
Additionally, melatonin exposure after intubation is significantly associated with a positive outcome in COVID-19 patients requiring mechanical ventilation.
While our models account for many covariates, including clinical history and demographics, it is impossible to rule out confounding or collider biases within our population.
Further study into the possible mechanism of this observation is warranted.

Answer: {"verdict": "SUPPORT", "evidence": ["Melatonin exposure after intubation was statistically associated with a positive outcome in COVID-19 (demographics and comorbidities adjusted HR: 0.131, 95% CI: 7.76E-02-0.223, p-value = 8.19E-14) and non-COVID-19 (demographics and comorbidities adjusted HR: 0.278, 95% CI: 0.142-0.542, p-value = 1.72E-04) intubated patients.", "Additionally, melatonin exposure after intubation was statically associated with a positive outcome in COVID-19 patients (demographics and comorbidities adjusted HR: 0.127, 95% CI: 6.01E-02-0.269, p-value = 7.15E-08).", "Conclusions Melatonin exposure after intubation is significantly associated with a positive outcome in COVID-19 and non-COVID-19 patients.", "Additionally, melatonin exposure after intubation is significantly associated with a positive outcome in COVID-19 patients requiring mechanical ventilation."]}


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: taking medication to lower fever, such as paracetamol (tylenol) and ibuprofen (advil) worsen COVID-19

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

Answer: {"verdict": "CONTRADICT", "evidence": ["As fever may have benefit in shortening the duration of viral illness, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response when taken during the early stages of COVID-19 illness."]}


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

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

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

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

Abstract:
Coronavirus disease 2019 (COVID-19) is a pandemic with no specific drugs and high fatality.
The most urgent need is to find effective treatments.
We sought to determine whether hydroxychloroquine (HCQ) application may reduce the death risk of critically ill COVID-19 patients.
In this retrospective study, we included 550 critically ill COVID-19 patients who need mechanical ventilation in Tongji Hospital, Wuhan, from February 1, 2020 to April 4, 2020.
All 550 patients received comparable basic treatments including antiviral drugs and antibiotics, and 48 of them were treated with oral HCQ treatment (200 mg twice a day for 7-10 days) in addition to the basic treatments.
Primary endpoint is fatality of patients, and inflammatory cytokine levels were compared between HCQ and non-hydroxychloroquine (NHCQ) treatments.
We found that fatalities are 18.8% (9/48) in HCQ group, which is significantly lower than 47.4% (238/502) in the NHCQ group (P<0.001).
The time of hospital stay before patient death is 15 (10-21) days and 8 (4-14) days for the HCQ and NHCQ groups, respectively (P<0.05).
The levels of inflammatory cytokine IL-6 were significantly reduced from 22.2 (8.3-118.9) pg mL-1 at the beginning of the treatment to 5.2 (3.0-23.4) pg mL-1 (P<0.05) at the end of the treatment in the HCQ group but there is no change in the NHCQ group.
These data demonstrate that addition of HCQ on top of the basic treatments is highly effective in reducing the fatality of critically ill patients of COVID-19 through attenuation of inflammatory cytokine storm.
Therefore, HCQ should be prescribed as a part of treatment for critically ill COVID-19 patients, with possible outcome of saving lives.
hydroxychloroquine, IL-6, mortalities, COVID-19.

Answer: {"verdict": "SUPPORT", "evidence": ["We found that fatalities are 18.8% (9/48) in HCQ group, which is significantly lower than 47.4% (238/502) in the NHCQ group (P<0.001).", "The time of hospital stay before patient death is 15 (10-21) days and 8 (4-14) days for the HCQ and NHCQ groups, respectively (P<0.05).", "The levels of inflammatory cytokine IL-6 were significantly reduced from 22.2 (8.3-118.9) pg mL-1 at the beginning of the treatment to 5.2 (3.0-23.4) pg mL-1 (P<0.05) at the end of the treatment in the HCQ group but there is no change in the NHCQ group.", "Therefore, HCQ should be prescribed as a part of treatment for critically ill COVID-19 patients, with possible outcome of saving lives."]}


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 mask  may offer some protection for the covid-19

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

Answer: {"verdict": "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: Can coronavirus spread through food or packaging? No Absolutely not

Abstract:
BACKGROUND: The COVID-19 pandemic has generated a new era in the world while we still figure out the consequences in different aspects of our daily life.
The food supply chain and the food industry do not comprise an exception.
SCOPE AND APPROACH: This review summarizes the possible transmission ways of COVID-19 through the foods, food supply chain, surfaces, and environment before exploring the development of corresponding detection tools of SARS-CoV-2.
For the time being, the possibility of transmission through the food sector is considered negligible, and tracing of SARS-CoV-2 in working environments is not considered as a priority by public authorities.
However, the adverse effects on the environment, food systems, and people along the food supply chain are already evident.
KEY FINDINGS AND CONCLUSIONS: As long as we move from farm to fork, more safety measures are needed since more people (and subsequently more potential sources of infection) are involved in the process.
The need for developing respective bioanalytical protocols for food and environmental safety applications to adapt in the post-lockdown period is also highlighted.

Answer: {"verdict": "SUPPORT", "evidence": ["For the time being, the possibility of transmission through the food sector is considered negligible, and tracing of SARS-CoV-2 in working environments is not considered as a priority by public authorities."]}


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

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

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

Claim: Many readers have written in to ask whether ibuprofen or other non-steroidal anti-inflammatory drugs, or NSAIDs, can worsen COVID-19, the disease caused by the novel coronavirus.

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

Answer: {"verdict": "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: Vitamin D Does Not Cure Covid-19 But It Plays an Important Role

Abstract:
Importance: Vitamin D treatment has been found to decrease incidence of viral respiratory tract infection, especially in vitamin D deficiency.
It is unknown whether COVID-19 incidence is associated with vitamin D deficiency and treatment.
Objective: To examine whether vitamin D deficiency and treatment are associated with testing positive for COVID-19.
Design: Retrospective cohort study Setting: University of Chicago Medicine Participants: Patients tested for COVID-19 from 3/3/2020-4/10/2020.
Vitamin D deficiency was defined by the most recent 25-hydroxycholecalciferol <20ng/ml or 1,25-dihydroxycholecalciferol <18pg/ml within 1 year before COVID-19 testing.
Treatment was defined by the most recent vitamin D type and dose, and treatment changes between the time of the most recent vitamin D level and time of COVID-19 testing.
Vitamin D deficiency and treatment changes were combined to categorize vitamin D status at the time of COVID-19 testing as likely deficient(last-level-deficient/treatment-not-increased), likely sufficient(last-level-not-deficient/treatment-not-decreased), or uncertain deficiency(last-level-deficient/treatment-increased or last-level-not-deficient/treatment-decreased).
Main Outcomes and Measures: The main outcome was testing positive for COVID-19.
Multivariable analysis tested whether the most recent vitamin D level and treatment changes after that level were associated with testing positive for COVID-19 controlling for demographic and comorbidity indicators.
Bivariate analyses of associations of treatment with vitamin D deficiency and COVID-19 were performed.
Results: Among 4,314 patients tested for COVID-19, 499 had a vitamin D level in the year before testing.
Vitamin D status at the time of COVID-19 testing was categorized as likely deficient for 127(25%) patients, likely sufficient for 291(58%) patients, and uncertain for 81(16%) patients.
In multivariate analysis, testing positive for COVID-19 was associated with increasing age(RR(age<50)=1.05,p<0.021;RR(age[≥]50)=1.02,p<0.064)), non-white race(RR=2.54,p<0.01) and being likely vitamin D deficient (deficient/treatment-not-increased:RR=1.77,p<0.02) as compared to likely vitamin D sufficient(not-deficient/treatment-not-decreased), with predicted COVID-19 rates in the vitamin D deficient group of 21.6%(95%CI[14.0%-29.2%] ) versus 12.2%(95%CI[8.9%-15.4%]) in the vitamin D sufficient group.
Vitamin D deficiency declined with increasing vitamin D dose, especially of vitamin D3.
Vitamin D dose was not significantly associated with testing positive for COVID-19.
Conclusions and Relevance: Vitamin D deficiency that is not sufficiently treated is associated with COVID-19 risk.
Testing and treatment for vitamin D deficiency to address COVID-19 warrant aggressive pursuit and study.

Answer: {"verdict": "SUPPORT", "evidence": ["Conclusions and Relevance: Vitamin D deficiency that is not sufficiently treated is associated with COVID-19 risk."]}


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: Cats may be able to spread coronavirus to humans despite showing no symptoms

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

Answer: {"verdict": "CONTRADICT", "evidence": ["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."]}


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 and kids: What can happen when children get the coronavirus. A rare but sometimes deadly syndrome poses extra risk for COVID's youngest victims.

Abstract:
AIM: Many countries have closed schools and kindergartens to minimise COVID-19, but the role that children play in disease transmission is unclear.
METHODS: A systematic literature review of the MEDLINE and EMBASE databases and medRxiv/bioRxiv preprint servers to 11 May 2020 identified published and unpublished papers on COVID-19 transmission by children.
RESULTS: We identified 700 scientific papers and letters and 47 full texts were studied in detail.
Children accounted for a small fraction of COVID-19 cases and mostly had social contacts with peers or parents, rather than older people at risk of severe disease.
Data on viral loads were scarce, but indicated that children may have lower levels than adults, partly because they often have fewer symptoms, and this should decrease the transmission risk.
Household transmission studies showed that children were rarely the index case and case studies suggested that children with COVID-19 seldom caused outbreaks.
However, it is highly likely that children can transmit the SARS-COV-2 virus, which causes COVID-19, and even asymptomatic children can have viral loads.
CONCLUSION: Children are unlikely to be the main drivers of the pandemic.
Opening up schools and kindergartens is unlikely to impact COVID-19 mortality rates in older people.

Answer: {"verdict": "SUPPORT", "evidence": ["Data on viral loads were scarce, but indicated that children may have lower levels than adults, partly because they often have fewer symptoms, and this should decrease the transmission risk."]}


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

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

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


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

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

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

Claim: If you're worried about corona, only the N95 mask will protect you

Abstract:
In the context of Coronavirus Disease (2019) (COVID-19) cases globally, there is a lack of consensus across cultures on whether wearing face masks is an effective physical intervention against disease transmission.
This study 1) illustrates transmission routes of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2); 2) addresses controversies surrounding the mask from perspectives of attitude, effectiveness, and necessity of wearing the mask with evidence that the use of mask would effectively interrupt the transmission of infectious diseases in both hospital settings and community settings; and 3) provides suggestion that the public should wear the mask during COVID-19 pandemic according to local context.
To achieve this goal, government should establish a risk adjusted strategy of mask use to scientifically publicize the use of masks, guarantee sufficient supply of masks, and cooperate for reducing health resources inequities.

Answer: {"verdict": "CONTRADICT", "evidence": ["This study 1) illustrates transmission routes of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2); 2) addresses controversies surrounding the mask from perspectives of attitude, effectiveness, and necessity of wearing the mask with evidence that the use of mask would effectively interrupt the transmission of infectious diseases in both hospital settings and community settings; and 3) provides suggestion that the public should wear the mask during COVID-19 pandemic according to local context."]}


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

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

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

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

Abstract:
Ibuprofen is an over-the-counter medication that is used widely for the treatment of pain and fever during COVID-19 pandemic.
A concern was raised regarding the safety of ibuprofen use because of its role in increasing ACE2 levels within the Renin-Angiotensin-Aldosterone system.
ACE2 is the coreceptor for the entry of SARS-CoV-2 into cells, and so, a potential increased risk of contracting COVID-19 disease and/or worsening of COVID-19 infection was feared with ibuprofen use.
However, available data from limited studies show administration of recombinant ACE2 improves lung damage caused by respiratory viruses, suggesting ibuprofen use may be beneficial in COVID-19 disease.
At this time, there is no supporting evidence to discourage the use of ibuprofen.

Answer: {"verdict": "CONTRADICT", "evidence": ["Ibuprofen is an over-the-counter medication that is used widely for the treatment of pain and fever during COVID-19 pandemic.", "However, available data from limited studies show administration of recombinant ACE2 improves lung damage caused by respiratory viruses, suggesting ibuprofen use may be beneficial in COVID-19 disease.", "At this time, there is no supporting evidence to discourage the use of ibuprofen."]}


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 Can Be Treated By Gargling With Warm Water Mixed With Salt And Vinegar

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

Answer: {"verdict": "CONTRADICT", "evidence": ["Numerous clinical trials have been launched to identify effective treatments for COVID-19.", "Initial data from a placebo-controlled study suggest faster time to recovery in patients on remdesivir; it is now being evaluated in additional controlled studies.", "As discussed in this review, till effective vaccines and treatments emerge, it is important to understand the scientific rationale of pandemic-mitigation strategies such as wearing facemasks and social distancing, and implement them."]}


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: headaches are rarely the sole symptom present in a coronavirus patient.

Abstract:
OBJECTIVE To analyze the epidemiological characteristics and clinical features of the patients with coronavirus disease 2019 (COVID-19), so as to provide basis for clinical diagnosis.
METHODS The epidemiology, clinical symptoms, laboratory and radiologic data of 23 patients with COVID-19 admitted to the Fifth People's Hospital of Xinyang City from January 22nd to January 29th, 2020 were retrospectively analyzed.
RESULTS There was 23 patients with COVID-19, with 15 men and 8 women, and the median age was 46.0 (40.5, 52.0) years old (ranged from 27 years old to 80 years old).
Nine patients had basic disease (39.1%), including hypertension (17.4%), cardiovascular diseases (17.4%), diabetes (8.7%), hypothyroidism (4.3%) and past history of tuberculosis (4.3%).
All the 23 patients had contact history in Wuhan area or with confirmed cases.
Clinical symptoms included fever (100%), cough (69.6%), expectoration (43.5%), myalgia (26.1%), headache (17.4%) and dyspnea (17.4%), and the less common symptom was diarrhea (4.3%).
Blood routine tests showed leukocytopenia in 11 patients (47.8%), normal leukocyte counts in 10 patients (43.5%), and leukocytosis in 2 patients (8.7%); lymphopenia was found in 13 patients (56.5%).
All 23 patients had different degrees of infective lesions in chest CT, with 7 patients (30.4%) on one side and 16 patients (69.6%) on both sides.
There were 19 mild patients, 4 severe patients, and no critical or death case.
Complications included acute respiratory distress syndrome (17.4%).
No patient was reported with liver, kidney or heart dysfunction or secondary infection.
CONCLUSIONS Epidemic history of contact, fever, pneumonia signs of chest CT, normal or decreased count of leukocyte and lymphopenia are the clinical basis for diagnosis of COVID-19.
However, at present, the treatment of patients has not been completed, and the effective treatment strategy and final prognosis are unclear.

Answer: {"verdict": "SUPPORT", "evidence": ["Clinical symptoms included fever (100%), cough (69.6%), expectoration (43.5%), myalgia (26.1%), headache (17.4%) and dyspnea (17.4%), and the less common symptom was diarrhea (4.3%)."]}


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: Brilacidin , a covid-19 drug candidate , exhibits potent in vitro antiviral activity against sars-cov-2

Abstract:
Summary Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), the newly emergent causative agent of coronavirus disease-19 (COVID-19), has resulted in more than one million deaths worldwide since it was first detected in 2019.
There is a critical global need for therapeutic intervention strategies that can be deployed to safely treat COVID-19 disease and reduce associated morbidity and mortality.
Increasing evidence shows that both natural and synthetic antimicrobial peptides (AMPs), also referred to as Host Defense Proteins/Peptides (HDPs), can inhibit SARS-CoV-2, paving the way for the potential clinical use of these molecules as therapeutic options.
In this manuscript, we describe the potent antiviral activity exerted by brilacidin—a de novo designed synthetic small molecule that captures the biological properties of HDPs—on SARS-CoV-2 in a human lung cell line (Calu-3) and a monkey cell line (Vero).
These data suggest that SARS-CoV-2 inhibition in these cell culture models is primarily a result of the impact of brilacidin on viral entry and its disruption of viral integrity.
Brilacidin has demonstrated synergistic antiviral activity when combined with remdesivir.
Collectively, our data demonstrate that brilacidin exerts potent inhibition of SARS-CoV-2 and thus supports brilacidin as a promising COVID-19 drug candidate.
Highlights Brilacidin potently inhibits SARS-CoV-2 in an ACE2 positive human lung cell line.
Brilacidin achieved a high Selectivity Index of 426 (CC50=241μM/IC50=0.565μM).
Brilacidin’s main mechanism appears to disrupt viral integrity and impact viral entry.
Brilacidin and remdesivir exhibit excellent synergistic activity against SARS-CoV-2.
Significance Statement SARS-CoV-2, the emergent novel coronavirus, has led to the current global COVID-19 pandemic, characterized by extreme contagiousness and high mortality rates.
There is an urgent need for effective therapeutic strategies to safely and effectively treat SARS-CoV-2 infection.
We demonstrate that brilacidin, a synthetic small molecule with peptide-like properties, is capable of exerting potent in vitro antiviral activity against SARS-CoV-2, both as a standalone treatment and in combination with remdesivir, which is currently the only FDA-approved drug for the treatment of COVID-19.

Answer: {"verdict": "SUPPORT", "evidence": ["Collectively, our data demonstrate that brilacidin exerts potent inhibition of SARS-CoV-2 and thus supports brilacidin as a promising COVID-19 drug candidate.", "Brilacidin and remdesivir exhibit excellent synergistic activity against SARS-CoV-2.", "We demonstrate that brilacidin, a synthetic small molecule with peptide-like properties, is capable of exerting potent in vitro antiviral activity against SARS-CoV-2, both as a standalone treatment and in combination with remdesivir, which is currently the only FDA-approved drug for the treatment of COVID-19."]}


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

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

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

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

Abstract:
We use the synthetic control method to analyze the effect of face masks on the spread of Covid-19 in Germany.
Our identification approach exploits regional variation in the point in time when face masks became compulsory.
Depending on the region we analyse, we find that face masks reduced the cumulative number of registered Covid-19 cases between 2.3% and 13% over a period of 10 days after they became compulsory.
Assessing the credibility of the various estimates, we conclude that face masks reduce the daily growth rate of reported infections by around 40%.

Answer: {"verdict": "SUPPORT", "evidence": ["Depending on the region we analyse, we find that face masks reduced the cumulative number of registered Covid-19 cases between 2.3% and 13% over a period of 10 days after they became compulsory.", "Assessing the credibility of the various estimates, we conclude that face masks reduce the daily growth rate of reported infections by around 40%."]}


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: scientists do not recommend that people should begin smoking simply to attempt to avoid a severe case of COVID-19. Smoking is still a leading cause of preventable death across the globe.

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

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


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

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

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

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

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 mechanism behind the protective effects of smoking could be found in nicotine

Abstract:
Statistical surveys of COVID-19 patients indicate, against all common logic, that people who smoke are less prone to the infection and/or exhibit less severe respiratory symptoms than non-smokers.
This suggests that nicotine may have some preventive or modulatory effect on the inflammatory response in the lungs.
Because it is known that the response to, and resolution of the SARS-CoV-2 infection depends mainly on the lung macrophages, we discuss the recent scientific findings, which may explain why and how nicotine may modulate lung macrophage response during COVID-19 infection.

Answer: {"verdict": "SUPPORT", "evidence": ["Statistical surveys of COVID-19 patients indicate, against all common logic, that people who smoke are less prone to the infection and/or exhibit less severe respiratory symptoms than non-smokers.", "This suggests that nicotine may have some preventive or modulatory effect on the inflammatory response in the lungs."]}


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 N95 respirator is thicker than a surgical mask, but neither Schaffner

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

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


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

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

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

Claim: A report indicates that Acetaminophen (Tylenol) may be preferred over Ibuprofen (Advil) for coronavirus (fever)

Abstract:
Coronavirus Disease 2019 (COVID-19) can cause severe respiratory failure and distressing symptoms including fever, cough, breathlessness and anxiety.
Symptomatic (palliative) treatment is of fundamental importance both in conjuncture with life-sustaining interventions and in end of life care.
Based on the evidence to date, there are several treatment options to consider for the relief of fever (acetaminophen, NSAID, oral glucocorticoids), cough (morphine), breathlessness (morphine, oxygen, fan), anxiety (benzodiazepines) and pain (NSAID, morphine).
Top priorities include precautions to protect staff and people at-risk from infection and planning how to provide adequate treatment for each individual depending on setting, including palliative care.

Answer: {"verdict": "CONTRADICT", "evidence": ["Based on the evidence to date, there are several treatment options to consider for the relief of fever (acetaminophen, NSAID, oral glucocorticoids), cough (morphine), breathlessness (morphine, oxygen, fan), anxiety (benzodiazepines) and pain (NSAID, morphine)."]}


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: Berberine and obatoclax induce sars-cov-2 replication in primary human nasal epithelial cells in vitro.

Abstract:
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged as a new human pathogen in late 2019 and has infected an estimated 10% of the global population in less than a year.
There is a clear need for effective antiviral drugs to complement current preventive measures including vaccines.
In this study, we demonstrate that berberine and obatoclax, two broad-spectrum antiviral compounds, are effective against multiple isolates of SARS-CoV-2.
Berberine, a plant-derived alkaloid, inhibited SARS-CoV-2 at low micromolar concentrations and obatoclax, originally developed as an anti-apoptotic protein antagonist, was effective at sub-micromolar concentrations.
Time-of-addition studies indicated that berberine acts on the late stage of the viral life cycle.
In agreement, berberine mildly affected viral RNA synthesis, but strongly reduced infectious viral titers, leading to an increase in the particle-to-pfu ratio.
In contrast, obatoclax acted at the early stage of the infection, in line with its activity to neutralize the acidic environment in endosomes.
We assessed infection of primary human nasal epithelial cells cultured on an air-liquid interface and found that SARS-CoV-2 infection induced and repressed expression of a specific set of cytokines and chemokines.
Moreover, both obatoclax and berberine inhibited SARS-CoV-2 replication in these primary target cells.
We propose berberine and obatoclax as potential antiviral drugs against SARS-CoV-2 that could be considered for further efficacy testing.

Answer: {"verdict": "CONTRADICT", "evidence": ["In this study, we demonstrate that berberine and obatoclax, two broad-spectrum antiviral compounds, are effective against multiple isolates of SARS-CoV-2.", "Berberine, a plant-derived alkaloid, inhibited SARS-CoV-2 at low micromolar concentrations and obatoclax, originally developed as an anti-apoptotic protein antagonist, was effective at sub-micromolar concentrations.", "Moreover, both obatoclax and berberine inhibited SARS-CoV-2 replication in these primary target cells."]}


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: Avoid medications to lower fever if sick with the new coronavirus

Abstract:
Ibuprofen is an over-the-counter medication that is used widely for the treatment of pain and fever during COVID-19 pandemic.
A concern was raised regarding the safety of ibuprofen use because of its role in increasing ACE2 levels within the Renin-Angiotensin-Aldosterone system.
ACE2 is the coreceptor for the entry of SARS-CoV-2 into cells, and so, a potential increased risk of contracting COVID-19 disease and/or worsening of COVID-19 infection was feared with ibuprofen use.
However, available data from limited studies show administration of recombinant ACE2 improves lung damage caused by respiratory viruses, suggesting ibuprofen use may be beneficial in COVID-19 disease.
At this time, there is no supporting evidence to discourage the use of ibuprofen.

Answer: {"verdict": "CONTRADICT", "evidence": ["Ibuprofen is an over-the-counter medication that is used widely for the treatment of pain and fever during COVID-19 pandemic.", "However, available data from limited studies show administration of recombinant ACE2 improves lung damage caused by respiratory viruses, suggesting ibuprofen use may be beneficial in COVID-19 disease.", "At this time, there is no supporting evidence to discourage the use of ibuprofen."]}


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

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

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

Claim: covid-19 patients taking hydroxychloroquine do not benefit

Abstract:
Hydroxychloroquine (HCQ) garnered scientific attention in early February following publication of reports showing in vitro activity of chloroquine (CQ) against COVID‐19.
While studies are mixed on this topic, the therapeutic effect of HCQ or CQ still need more valid clinical evidence.
In this descriptive observational study, we aimed to discuss the treatment response of HCQ in COVID‐19 infected patients and 30 cases were included.
The demographic, treatment, laboratory parameters of C‐reactive protein (CRP) and interleukin‐6 (IL‐6) before and after HCQ therapy and clinical outcome in the 30 COVID‐19 patients were assessed.
In order to evaluate the effect of mediation time point, we also divided these cases into two groups, patients began administrated with HCQ within 7 days hospital (defined as early delivery group) and 7 days after hospital (defined as later delivery group).
We found that, the elevated IL‐6, a risk factor in severe patients were reduced to normal level after HCQ treatment.
More importantly, patients treated with HCQ at the time of early hospital recovered faster than those who treated later or taken as second line choose for their obvious shorter hospitalization time.
In summary, early use of HCQ was better than later use and the effect of IL‐6 and CRP level can not be ruled out.
This article is protected by copyright.
All rights reserved.

Answer: {"verdict": "CONTRADICT", "evidence": ["More importantly, patients treated with HCQ at the time of early hospital recovered faster than those who treated later or taken as second line choose for their obvious shorter hospitalization time."]}


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: Hydroxychloroquine proves activity in hamsters and macaques infected with sars-cov-2

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

Answer: {"verdict": "CONTRADICT", "evidence": ["Similarly, HCQ prophylaxis/treatment (6.5 mg/kg) did not significantly benefit clinical outcome nor reduce SARS-CoV-2 replication/shedding in the upper and lower respiratory tract in the rhesus macaque disease model."]}