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

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

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

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

Abstract:
PURPOSE OF REVIEW This article reviews 'no touch' methods for disinfection of the contaminated surface environment of hospitalized patients' rooms.
The focus is on studies that assessed the effectiveness of ultraviolet (UV) light devices, hydrogen peroxide systems, and self-disinfecting surfaces to reduce healthcare-associated infections (HAIs).
RECENT FINDINGS The contaminated surface environment in hospitals plays an important role in the transmission of several key nosocomial pathogens including methicillin-resistant Staphylococcus aureus, vancomycin-resistant Enterococcus spp.
, Clostridium difficile, Acinetobacter spp., and norovirus.
Multiple clinical trials have now demonstrated the effectiveness of UV light devices and hydrogen peroxide systems to reduce HAIs.
A limited number of studies have suggested that 'self-disinfecting' surfaces may also decrease HAIs.
SUMMARY Many studies have demonstrated that terminal cleaning and disinfection with germicides is often inadequate and leaves environmental surfaces contaminated with important nosocomial pathogens. 'No touch' methods of room decontamination (i.e., UV devices and hydrogen peroxide systems) have been demonstrated to reduce key nosocomial pathogens on inoculated test surfaces and on environmental surfaces in actual patient rooms.
Further UV devices and hydrogen peroxide systems have been demonstrated to reduce HAI.
A validated 'no touch' device or system should be used for terminal room disinfection following discharge of patients on contact precautions.
The use of a 'self-disinfecting' surface to reduce HAI has not been convincingly demonstrated.

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


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

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

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

Claim: Synergism of tnf-α and ifn-γ reduces inflammatory cell death, tissue damage, and mortality in sars-cov-2 infection and cytokine shock syndromes

Abstract:
The COVID-19 pandemic has caused significant morbidity and mortality.
Currently, there is a critical shortage of proven treatment options and an urgent need to understand the pathogenesis of multi-organ failure and lung damage.
Cytokine storm is associated with severe inflammation and organ damage during COVID-19.
However, a detailed molecular pathway defining this cytokine storm is lacking, and gaining mechanistic understanding of how SARS-CoV-2 elicits a hyperactive inflammatory response is critical to develop effective therapeutics.
Of the multiple inflammatory cytokines produced by innate immune cells during SARS-CoV-2 infection, we found that the combined production of TNF-α and IFN-γ specifically induced inflammatory cell death, PANoptosis, characterized by gasdermin–mediated pyroptosis, caspase-8–mediated apoptosis, and MLKL–mediated necroptosis.
Deletion of pyroptosis, apoptosis, or necroptosis mediators individually was not sufficient to protect against cell death.
However, cells deficient in both RIPK3 and caspase-8 or RIPK3 and FADD were resistant to this cell death.
Mechanistically, the STAT1/IRF1 axis activated by TNF-α and IFN-γ co-treatment induced iNOS for the production of nitric oxide.
Pharmacological and genetic deletion of this pathway inhibited pyroptosis, apoptosis, and necroptosis in macrophages.
Moreover, inhibition of PANoptosis protected mice from TNF-α and IFN-γ–induced lethal cytokine shock that mirrors the pathological symptoms of COVID-19.
In vivo neutralization of both TNF-α and IFN-γ in multiple disease models associated with cytokine storm showed that this treatment provided substantial protection against not only SARS-CoV-2 infection, but also sepsis, hemophagocytic lymphohistiocytosis, and cytokine shock models, demonstrating the broad physiological relevance of this mechanism.
Collectively, our findings reveal that blocking the COVID-19 cytokine-mediated inflammatory cell death signaling pathway identified in this study may benefit patients with COVID-19 or other cytokine storm-driven syndromes by limiting inflammation and tissue damage.
The findings also provide a molecular and mechanistic description for the term cytokine storm.
Additionally, these results open new avenues for the treatment of other infectious and autoinflammatory diseases and cancers where TNF-α and IFN-γ synergism play key pathological roles.

Answer: {"verdict": "CONTRADICT", "evidence": ["In vivo neutralization of both TNF-\u03b1 and IFN-\u03b3 in multiple disease models associated with cytokine storm showed that this treatment provided substantial protection against not only SARS-CoV-2 infection, but also sepsis, hemophagocytic lymphohistiocytosis, and cytokine shock models, demonstrating the broad physiological relevance of this mechanism."]}


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

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

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

Claim: Dogs can test positive for the virus.

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

Answer: {"verdict": "SUPPORT", "evidence": ["Cellular angiotensin converting enzyme 2 (ACE2) is the receptor of SARS-CoV-2 which is identical or similar in different species of animals such as pigs, ferrets, cats, orangutans, monkeys, and humans.", "Moreover, a recent study predicted that dog might be secondary host during the evolution of SARS-CoV-2 from bat to human.", "Therefore, there is a possibility of spreading SARS-CoV-2 through domestic pets.", "Experimental data showed ferrets and cats are highly susceptible to SARS-CoV-2 as infected by virus inoculation and can transmit the virus directly or indirectly by droplets or airborne route.", "Humans showing clinical symptoms of respiratory infections have been undergoing for COVID-19 diagnostic test but many infected people and few pets confirmed with SARS-CoV-2 remained asymptomatic."]}


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

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

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

Claim: In such a scenario, differentiating whether the cause of death is specifically due to COVID-19 or the result of treatment limitations can be difficult.

Abstract:
BACKGROUND: The 2019 novel coronavirus has caused the outbreak of the acute respiratory disease in Wuhan, Hubei Province of China since December 2019.
This study was performed to analyze the clinical characteristics of patients who succumbed to and who recovered from 2019 novel coronavirus disease (COVID-19).
METHODS: Clinical data were collected from two tertiary hospitals in Wuhan.
A retrospective investigation was conducted to analyze the clinical characteristics of fatal cases of COVID-19 (death group) and we compare them with recovered patients (recovered group).
Continuous variables were analyzed using the Mann-Whitney U test.
Categorical variables were analyzed by χ test or Fisher exact test as appropriate.
RESULTS: Our study enrolled 109 COVID-19 patients who died during hospitalization and 116 recovered patients.
The median age of the death group was older than the recovered group (69 [62, 74] vs. 40 [33, 57] years, Zâ=â9.738, Pâ<â0.001).
More patients in the death group had underlying diseases (72.5% vs. 41.4%, χâ=â22.105, Pâ<â0.001).
Patients in the death group had a significantly longer time of illness onset to hospitalization (10.0 [6.5, 12.0] vs. 7.0 [5.0, 10.0] days, Zâ=â3.216, Pâ=â0.001).
On admission, the proportions of patients with symptoms of dyspnea (70.6% vs. 19.0%, χâ=â60.905, Pâ<â0.001) and expectoration (32.1% vs. 12.1%, χâ=â13.250, Pâ<â0.001) were significantly higher in the death group.
The blood oxygen saturation was significantly lower in the death group (85 [77, 91]% vs. 97 [95, 98]%, Zâ=â10.625, Pâ<â0.001).
The white blood cell (WBC) in death group was significantly higher on admission (7.23 [4.87, 11.17] vs. 4.52 [3.62, 5.88] ×10/L, Zâ=â7.618, Pâ<â0.001).
Patients in the death group exhibited significantly lower lymphocyte count (0.63 [0.40, 0.79] vs. 1.00 [0.72, 1.27] ×10/L, Zâ=â8.037, Pâ<â0.001) and lymphocyte percentage (7.10 [4.45, 12.73]% vs. 23.50 [15.27, 31.25]%, Zâ=â10.315, Pâ<â0.001) on admission, and the lymphocyte percentage continued to decrease during hospitalization (7.10 [4.45, 12.73]% vs. 2.91 [1.79, 6.13]%, Zâ=â5.242, Pâ<â0.001).
Alanine transaminase (22.00 [15.00, 34.00] vs. 18.70 [13.00, 30.38] U/L, Zâ=â2.592, Pâ=â0.010), aspartate transaminase (34.00 [27.00, 47.00] vs. 22.00 [17.65, 31.75] U/L, Zâ=â7.308, Pâ<â0.001), and creatinine levels (89.00 [72.00, 133.50] vs. 65.00 [54.60, 78.75] µmol/L, Zâ=â6.478, Pâ<â0.001) were significantly higher in the death group than those in the recovered group.
C-reactive protein (CRP) levels were also significantly higher in the death group on admission (109.25 [35.00, 170.28] vs. 3.22 [1.04, 21.80] mg/L, Zâ=â10.206, Pâ<â0.001) and showed no significant improvement after treatment (109.25 [35.00, 170.28] vs. 81.60 [27.23, 179.08] mg/L, Zâ=â1.219, Pâ=â0.233).
The patients in the death group had more complications such as acute respiratory distress syndrome (ARDS) (89.9% vs. 8.6%, χâ=â148.105, Pâ<â0.001), acute cardiac injury (59.6% vs. 0.9%, χâ=â93.222, Pâ<â0.001), acute kidney injury (18.3% vs. 0%, χâ=â23.257, Pâ<â0.001), shock (11.9% vs. 0%, χâ=â14.618, Pâ<â0.001), and disseminated intravascular coagulation (DIC) (6.4% vs. 0%, χâ=â7.655, Pâ=â0.006).
CONCLUSIONS: Compared to the recovered group, more patients in the death group exhibited characteristics of advanced age, pre-existing comorbidities, dyspnea, oxygen saturation decrease, increased WBC count, decreased lymphocytes, and elevated CRP levels.
More patients in the death group had complications such as ARDS, acute cardiac injury, acute kidney injury, shock, and DIC.

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


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

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

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

Claim: Susceptible supply supports the role of climate in the early sars-cov-2 pandemic

Abstract:
Preliminary evidence suggests that climate may modulate the transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).
Yet it remains unclear whether seasonal and geographic variations in climate can substantially alter the pandemic trajectory, given that high susceptibility is a core driver.
Here, we use a climate-dependent epidemic model to simulate the SARS-CoV-2 pandemic by probing different scenarios based on known coronavirus biology.
We find that although variations in weather may be important for endemic infections, during the pandemic stage of an emerging pathogen, the climate drives only modest changes to pandemic size.
A preliminary analysis of nonpharmaceutical control measures indicates that they may moderate the pandemic-climate interaction through susceptible depletion.
Our findings suggest that without effective control measures, strong outbreaks are likely in more humid climates and summer weather will not substantially limit pandemic growth.

Answer: {"verdict": "CONTRADICT", "evidence": ["A preliminary analysis of nonpharmaceutical control measures indicates that they may moderate the pandemic-climate interaction through susceptible depletion."]}


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

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

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

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

Abstract:
The aim in this study was to assess the effectiveness of a quaternary ammonium chloride (QAC) surfactant in reducing surface staphylococcal contamination in a routinely operating medical ward occupied by patients who had tested positive for methicillin-resistant Staphylococcus aureus (MRSA).
The QAC being tested is an antibacterial film that is sprayed onto a surface and can remain active for up to 8 h. A field experimental study was designed with the QAC plus daily hypochlorite cleaning as the experimental group and hypochlorite cleaning alone as the control group.
The method of swabbing on moistened surfaces was used for sampling.
It was found that 83% and 77% of the bedside surfaces of MRSA-positive and MRSA-negative patients respectively were contaminated with staphylococci at 08:00 hours, and that the staphylococcal concentrations increased by 80% at 1200 h over a 4-hour period with routine ward and clinical activities.
Irrespective of the MRSA status of the patients, high-touch surfaces around the bed-units within the studied medical ward were heavily contaminated (ranged 1 to 276 cfu/cm(2) amongst the sites with positive culture) with staphylococcal bacteria including MRSA, despite the implementation of daily hypochlorite wiping.
However, the contamination rate dropped significantly from 78% to 11% after the application of the QAC polymer.
In the experimental group, the mean staphylococcal concentration of bedside surfaces was significantly (p < 0.0001) reduced from 4.4 ± 8.7 cfu/cm(2) at 08:00 hours to 0.07 ± 0.26 cfu/cm(2) at 12:00 hours by the QAC polymer.
The results of this study support the view that, in addition to hypochlorite wiping, the tested QAC surfactant is a potential environmental decontamination strategy for preventing the transmission of clinically important pathogens in medical wards.

Answer: {"verdict": "SUPPORT", "evidence": ["It was found that 83% and 77% of the bedside surfaces of MRSA-positive and MRSA-negative patients respectively were contaminated with staphylococci at 08:00 hours, and that the staphylococcal concentrations increased by 80% at 1200 h over a 4-hour period with routine ward and clinical activities."]}


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

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

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

Claim: Can COVID-19 be spread from pets to people or other pets? According to the latest information from the CDC , the risk of animals spreading COVID-19 to people is very low. However, because all animals can carry germs that can make people sick, it's always a good idea to practice healthy habits around pets and other animals.

Abstract:
Little information on the SARS-CoV-2 virus in animals is available to date.
Whereas no one husbandry animal case has been reported to date, which would have significant implications in food safety, companion animals play a role in COVID-19 epidemiology that opens up new questions.
There is evidence that SARS-CoV-2 can infect felines, dogs and minks, and there is evidence of human-to-animal infection.
Likewise, the S protein nucleotide sequence of the SARS-CoV-2 virus isolated in domestic animals and humans is identical, and the replication of the SARS-CoV-2 in cats is efficient.
Besides, the epidemiological evidence for this current pandemic indicates that the spillover to humans was associated with close contact between man and exotic animals, very probably in Chinese wet markets, thus there is a growing general consensus that the exotic animal markets, should be strictly regulated.
The examination of these findings and the particular role of animals in COVID-19 should be carefully analyzed in order to establish preparation and containment measures.
Animal management and epidemiological surveillance must be also considered for COVID-19 control, and it can open up new questions regarding COVID-19 epidemiology and the role that animals play in it.

Answer: {"verdict": "SUPPORT", "evidence": ["There is evidence that SARS-CoV-2 can infect felines, dogs and minks, and there is evidence of human-to-animal infection.", "Likewise, the S protein nucleotide sequence of the SARS-CoV-2 virus isolated in domestic animals and humans is identical, and the replication of the SARS-CoV-2 in cats is efficient."]}


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: Hidden immune weakness found in gravely ill COVID-19 patients.

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

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


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

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

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

Claim: Vitamin B could help prevent the 'worst outcomes' in covid-19 cases

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

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


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

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

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

Claim: Thrombotic microvascular injury is primarily mediated by thrombotic microangiopathy despite systemic complement activation in covid-19 patients

Abstract:
Hypoxemia and coagulopathy are common in severe symptomatic patients of coronavirus disease 2019 (COVID-19).
Histological evidence shows implication of complement activation and lung injury.
We research sign of complement activation and presence of thrombotic microangiopathy in 8 severe patients.
Six of them presented moderate elevation of final pathway of complement / sC5b-9 (median value : 350 ng/mL [IQR : 300,5-514,95 ng/mL]).
Two patients have been autopsied and presence of thrombotic microvascular injury have been found.
Interestingly, none the 8 patients had signs of mechanical hemolytic anemia (median value of hemoglobin : 10,5 gr/dL[IQR : 8,1-1,9], median value of haptoglobuline 4,49 [IQR 3,55-4,66], none of the patients has schistocyte) and thrombocytopenia (median value: 348000/mL [IQR : 266 000-401 000).
Finally, all 8 patients had elevated d-dimer (median value : 2226 microgr/l [IQR : 1493-2362]) and soluble fibrin monomer complex (median value : 8.5 mg/mL, IQR[ <6-10.6]).
In summary, this study show moderate activation of complement and coagulation with presence of thrombotic microvascular injury in patients with severe COVID-19 without evidence of systemic thrombotic microangiopathy.

Answer: {"verdict": "CONTRADICT", "evidence": ["In summary, this study show moderate activation of complement and coagulation with presence of thrombotic microvascular injury in patients with severe COVID-19 without evidence of systemic thrombotic microangiopathy."]}


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

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

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

Claim: Surgical Masks Stop Transmission Of COVID-19 From Symptomatic People

Abstract:
Background As the coronavirus (COVID-19) pandemic spreads globally, hospitals are rushing to adapt their facilities which may not have been designed to deal with infections adequately.
We present the management of a patient with suspected COVID-19 pneumonia.
Case A 66-years-old man presented to the hospital and his recent travel history, infective symptoms and CXR made him a possible COVID-19 suspect.
Emergency surgery was decided considering the septic condition.
The patient was transported to operating theatre with supplemental oxygen over a face mask and plastic covering over the trolley.
Rapid sequence intubation was performed by an experienced anesthetist using a videolaryngoscope.
After surgery, the patient remained intubated to avoid re-intubation due to initial presentation of respiratory distress.
Droplet, contact and airborne infection precautions were instituted.
Conclusions Our objective was to facilitate surgical management of patients with known or suspected COVID-19 while minimising risk of nosocomial transmission to healthcare workers and other patients.

Answer: {"verdict": "CONTRADICT", "evidence": ["The patient was transported to operating theatre with supplemental oxygen over a face mask and plastic covering over the trolley."]}


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

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

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

Claim: College students, many of whom are already stressed, reported an increase in depression and anxiety during the initial outbreak.

Abstract:
As a result of the emergence of coronavirus disease 2019 (COVID-19) outbreak caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in the Chinese city of Wuhan, a situation of socio-economic crisis and profound psychological distress rapidly occurred worldwide.
Various psychological problems and important consequences in terms of mental health including stress, anxiety, depression, frustration, uncertainty during COVID-19 outbreak emerged progressively.
This work aimed to comprehensively review the current literature about the impact of COVID-19 infection on the mental health in the general population.
The psychological impact of quarantine related to COVID-19 infection has been additionally documented together with the most relevant psychological reactions in the general population related to COVID-19 outbreak.
The role of risk and protective factors against the potential to develop psychiatric disorders in vulnerable individuals has been addressed as well.
The main implications of the present findings have been discussed.

Answer: {"verdict": "SUPPORT", "evidence": ["As a result of the emergence of coronavirus disease 2019 (COVID-19) outbreak caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in the Chinese city of Wuhan, a situation of socio-economic crisis and profound psychological distress rapidly occurred worldwide."]}


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

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

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

Claim: Imbalanced antiviral response to sars-cov-2 drives development of covid-19

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

Answer: {"verdict": "CONTRADICT", "evidence": ["Cell and animal models of SARS-CoV-2 infection, in addition to transcriptional and serum profiling of COVID-19 patients, consistently revealed a unique and inappropriate inflammatory response.", "We propose that reduced innate antiviral defenses coupled with exuberant inflammatory cytokine production are the defining and driving features of COVID-19."]}


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

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

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

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

Answer: {"verdict": "SUPPORT", "evidence": ["Cellular angiotensin converting enzyme 2 (ACE2) is the receptor of SARS-CoV-2 which is identical or similar in different species of animals such as pigs, ferrets, cats, orangutans, monkeys, and humans.", "Moreover, a recent study predicted that dog might be secondary host during the evolution of SARS-CoV-2 from bat to human.", "Therefore, there is a possibility of spreading SARS-CoV-2 through domestic pets.", "Experimental data showed ferrets and cats are highly susceptible to SARS-CoV-2 as infected by virus inoculation and can transmit the virus directly or indirectly by droplets or airborne route."]}


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

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

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

Claim: Unexpected Cause of Death in Younger COVID-19 Patients is Related to Blood Clotting

Abstract:
OBJECTIVE: The COVID-19 pandemic has caused much morbidity and mortality to patients but also health care providers.
We tabulated the cases of physician deaths from COVID-19 associated with front-line work in hopes of mitigating future events.
METHOD: On April 5, 2020, Google internet search was performed using the keywords doctor, physician, death, COVID, COVID-19, and coronavirus in English and Farsi, and in Chinese using the Baidu search engine.
RESULTS: We found 198 physician deaths from COVID-19, but complete details were missing for 49 individuals.
The average age of the physicians that died was 63.4 years (range 28 to 90 years) and the median age was 66 years of age.
Ninety percent of the deceased physicians were male (175/194).
General practitioners and emergency room doctors (78/192), respirologists (5/192), internal medicine specialists (11/192) and anesthesiologists (6/192) comprised 52% of those dying.
Two percent of the deceased were epidemiologists (4/192), 2% were infectious disease specialists (4/192), 5% were dentists (9/192), 4% were ENT (8/192), and 4% were ophthalmologists (7/192).
The countries with the most reported physician deaths were Italy (79/198), Iran (43/198), China (16/198), Philippines (14/198), United States (9/192) and Indonesia (7/192).
CONCLUSION: Physicians from all specialties may die from COVID, and these deaths will likely increase as the pandemic progresses.
Lack of personal protective equipment was cited as a common cause of death.
Consideration should be made to exclude older physicians from front-line work.

Answer: {"verdict": "CONTRADICT", "evidence": ["Lack of personal protective equipment was cited as a common cause of death."]}


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

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

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

Claim: Coronavirus disease (COVID-19) is a novel (new) coronavirus, one that has not been previously identified in humans, and is responsible for an outbreak of respiratory illness that became a global pandemic in 2020.

Abstract:
Since Dec 2019, a cluster of pneumonia outbreak in Wuhan, Hubei province, China, and soon spread to all province of China.
The pathogen was proved to be a novel betacoronavirus called 2019 novel coronavirus (officially named by the World Health Organization as COVID-19).
The typical clinical manifestations were fever, cough, dyspnea, and myalgia or fatigue.
Less common symptoms included headache, diarrhea, nausea and vomiting.
However diarrhea as the first symptom is rarely reported.
Here we reported a case of 2019 novel coronavirus-infected patient (NCIP) with diarrhea as the initial symptom.
Image of CT scan and laboratory examination and careful collected as well as detection of viral RNA in pharynx.
The case demonstrate that gastrointestinal symptoms ware not rare in NCIP, and diarrhea could be the initial symptom.

Answer: {"verdict": "SUPPORT", "evidence": ["The pathogen was proved to be a novel betacoronavirus called 2019 novel coronavirus (officially named by the World Health Organization as COVID-19).", "The typical clinical manifestations were fever, cough, dyspnea, and myalgia or fatigue.", "Less common symptoms included headache, diarrhea, nausea and vomiting."]}


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: While there are a lot of unknowns with the novel virus, there's no evidence of COVID-19 transmitting through food or packaging, according to the Food and Drug Administration.

Abstract:
Agriculture and the food sector are critical to food and nutrition security because they not only produce food but also contribute to economic empowerment by employing a large share of female and male workers, especially in developing countries.
Food systems at all levels―globally, domestically, locally, and in the home― are expected to be highly affected by the COVID-19 crisis.
Women and men work as food producers, processors, and traders and will likely be impacted differently.
Shocks or crises can exacerbate or reduce gender gaps, and so can policy responses to mitigate the impact of these crises or shocks.
We offer some perspectives and available country examples on how the COVID-19 crisis and responses to the crisis could be a setback or offer opportunities for gender equality in the food system.

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


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

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

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

Claim: A simple in-cell elisa assay allows rapid and automated quantification of sars-cov-2 to analyze neutralizing antibodies and antiviral compounds

Abstract:
The coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is currently the most pressing medical and socioeconomic challenge.
Constituting important correlates of protection, the determination of virus-neutralizing antibodies (NAbs) is indispensable for convalescent plasma selection, vaccine candidate evaluation, and immunity certificates.
In contrast to standard serological ELISAs, plaque reduction neutralization tests (PRNTs) are laborious, time-consuming, expensive, and restricted to specialized laboratories.
To replace microscopic counting-based SARS-CoV-2 PRNTs by a novel assay exempt from genetically modified viruses, which are inapplicable in most diagnostics departments, we established a simple, rapid, and automated SARS-CoV-2 neutralization assay employing an in-cell ELISA (icELISA) approach.
After optimization of various parameters such as virus-specific antibodies, cell lines, virus doses, and duration of infection, SARS-CoV-2-infected cells became amenable as direct antigen source for quantitative icELISA.
Antiviral agents such as human sera containing NAbs or antiviral interferons dose dependently reduced the SARS-CoV-2-specific signal.
Applying increased infectious doses, the icELISA-based neutralization test (icNT) was superior to PRNT in discriminating convalescent sera with high from those with intermediate neutralizing capacities.
In addition, the icNT was found to be specific, discriminating between SARS-CoV-2-specific NAbs and those raised against other coronaviruses.
Altogether, the SARS-CoV-2 icELISA test allows rapid (<48 h in total, read-out in seconds) and automated quantification of virus infection in cell culture to evaluate the efficacy of NAbs and antiviral drugs using reagents and equipment present in most routine diagnostics departments.

Answer: {"verdict": "CONTRADICT", "evidence": ["To replace microscopic counting-based SARS-CoV-2 PRNTs by a novel assay exempt from genetically modified viruses, which are inapplicable in most diagnostics departments, we established a simple, rapid, and automated SARS-CoV-2 neutralization assay employing an in-cell ELISA (icELISA) approach.", "Altogether, the SARS-CoV-2 icELISA test allows rapid (<48 h in total, read-out in seconds) and automated quantification of virus infection in cell culture to evaluate the efficacy of NAbs and antiviral drugs using reagents and equipment present in most routine diagnostics departments."]}


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: Enjoy ginger, but it's not a 'cure' for COVID-19

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

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


Question:
You will be shown a claim 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: Serological surveys in reunion island of the first hospitalized patients revealed that long-lived immunoglobulin g antibodies specific against sars-cov2 virus are rapidly transmitted in severe cases

Abstract:
Both cellular and humoral immunities are critically important to control COVID19 infection but little is known about the kinetics of those responses and, in particular, in patients who will go on to develop a severe form of the disease over several weeks.
We herein report the first set of data of our prospective cohort study of 90 hospitalized cases.
Serological surveys were thoroughly performed over 2 month period by assessing IgG and IgM responses by immunofluorescence, immunoblot, Western blot and conventional ELISA using clinical RUN isolates of SARS-CoV-2 immobilized on 96 well plates.
While the IgM and, unexpectedly, the IgG responses were readily detected early during the course of the disease (5-7 days post-first symptoms), our results (n=3-5 and over the full dilution set of the plasma 1/200 to 1/12800) demonstrated a significant decrease (over 2.5-fold) of IgG levels in severe (ICU) hospitalized patients (exemplified in patient 1) by WB and ELISA.
In contrast, mild non-ICU patients had a steady and yet robust rise in their specific IgG levels against the virus.
Interestingly, both responses (IgM and IgG) were initially against the nucleocapsid (50kDa band on the WB) and spreading to other major viral protein S and domains (S1 and S2.
In conclusion, serological testing may be helpful for the diagnosis of patients with negative RT-PCR results and for the identification of asymptomatic cases.
Moreover, medical care and protections should be maintained particularly for recovered patients (severe cases) who may remain at risk of relapsing or reinfection.
Experiments to ascertain T cell responses but although their kinetics overtime are now highly warranted.
All in all, these studies will help to delineate the best routes for vaccination.

Answer: {"verdict": "CONTRADICT", "evidence": ["Serological surveys were thoroughly performed over 2 month period by assessing IgG and IgM responses by immunofluorescence, immunoblot, Western blot and conventional ELISA using clinical RUN isolates of SARS-CoV-2 immobilized on 96 well plates.", "While the IgM and, unexpectedly, the IgG responses were readily detected early during the course of the disease (5-7 days post-first symptoms), our results (n=3-5 and over the full dilution set of the plasma 1/200 to 1/12800) demonstrated a significant decrease (over 2.5-fold) of IgG levels in severe (ICU) hospitalized patients (exemplified in patient 1) by WB and ELISA.", "In conclusion, serological testing may be helpful for the diagnosis of patients with negative RT-PCR results and for the identification of asymptomatic cases."]}


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

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

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

Claim: 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:
BACKGROUND Novel coronavirus disease (COVID-19) is spreading globally.
Little is known about the risk factors for the clinical outcomes of COVID-19 in children.
METHODS A retrospective case-control study was taken in children with severe acute respiratory syndrome coronary virus-2 infection in Wuhan Children's Hospital.
Risk factors associated with the development of COVID-19 and progression were collected and analyzed.
RESULTS Eight out of 260 children diagnosed with severe COVID-19 pneumonia were included in the study.
Thirty-five children with COVID-19 infection matched for age, sex and date of admission, and who classified as non-severe type, were randomly selected from the hospital admissions.
For cases with severe pneumonia caused by COVID-19, the most common symptoms were dyspnea (87.5%), fever (62.5%) and cough (62.5%).
In laboratory, white blood cells count was significantly higher in severe children than non-severe children.
Levels of inflammation bio-makers such as hsCRP, IL-6, IL-10 and D-dimer elevated in severe children compared with non-severe children on admission.
The level of total bilirubin and uric acid clearly elevated in severe children compared with non-severe children on admission.
All of severe children displayed the lesions on chest CT, more lung segments were involved in severe children than in non-severe children, which was only risk factor associated with severe COVID-19 pneumonia in multivariable analysis.
CONCLUSIONS More than 3 lung segments involved were associated with greater risk of development of severe COVID-19 in children.
Moreover, the possible risk of the elevation of IL-6, high total bilirubin and D-dimer with univariable analysis could identify patients to be severe earlier.

Answer: {"verdict": "SUPPORT", "evidence": ["BACKGROUND Novel coronavirus disease (COVID-19) is spreading globally.", "Little is known about the risk factors for the clinical outcomes of COVID-19 in children.", "RESULTS Eight out of 260 children diagnosed with severe COVID-19 pneumonia were included in the study.", "Thirty-five children with COVID-19 infection matched for age, sex and date of admission, and who classified as non-severe type, were randomly selected from the hospital admissions.", "CONCLUSIONS More than 3 lung segments involved were associated with greater risk of development of severe COVID-19 in children.", "Moreover, the possible risk of the elevation of IL-6, high total bilirubin and D-dimer with univariable analysis could identify patients to be severe earlier."]}


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:
OBJECTIVE: It was recently suggested that Ibuprofen might increase the risk for severe and fatal COVID-19 disease and should therefore be avoided in this patient population.
We aimed to evaluate whether ibuprofen use in patients with COVID-19 was associated with more severe disease, compared to patients using paracetamol or no antipyretics.
METHODS: In a retrospective cohort study of patients with COVID-19 from Shamir Medical Center, Israel, we monitored any use of ibuprofen from a week prior to diagnosis of COVID-19 throughout the disease.
Primary outcomes were mortality and the need for respiratory support, including oxygen administration and mechanical ventilation.
RESULTS: The study included 403 confirmed cases of COVID-19, with a median age of 45 years.
Of the entire cohort, 44 patients (11%) needed respiratory support and 12 (3%) patients died.
One hundred and seventy-nine (44%) patients had fever, with 32% using paracetamol and 22% using ibuprofen, for symptom-relief.
In the Ibuprofen group, 3 (3.4%) patients died, while in the non-Ibuprofen group 9 (2.8%) patients died (P=0.95).
Nine (10.3%) patients from the Ibuprofen group needed respiratory support, as compared with 35 (11%) from the non-Ibuprofen group (P=1).
When compared to exclusive paracetamol users, no differences were observed in mortality rates or the need for respiratory support among patients using ibuprofen.
CONCLUSIONS: In this cohort of COVID-19 patients, Ibuprofen use was not associated with worse clinical outcomes, compared to paracetamol or no antipyretic.

Answer: {"verdict": "CONTRADICT", "evidence": ["CONCLUSIONS: In this cohort of COVID-19 patients, Ibuprofen use was not associated with worse clinical outcomes, compared to paracetamol or no antipyretic."]}


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

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

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


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

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

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

Claim: Peptide vaccine candidate mimics the heterogeneity of natural sars-cov-2 immunity in convalescent humans and induces broad t cell responses in mice models

Abstract:
We developed a global peptide vaccine against SARS-CoV-2 that addresses the dual challenges of heterogeneity in the immune responses of different individuals and potential heterogeneity of the infecting virus.
PolyPEPI-SCoV-2 is a polypeptide vaccine containing nine 30-mer peptides derived from all four major structural proteins of the SARS-CoV-2.
Vaccine peptides were selected based on their frequency as HLA class I and class II personal epitopes (PEPIs) restricted to multiple autologous HLA alleles of individuals in an in silico cohort of 433 subjects of different ethnicities.
PolyPEPI-SCoV-2 vaccine administered with Montanide ISA 51VG adjuvant generated robust, Th1-biased CD8+ and CD4+ T cell responses against all four structural proteins of the virus, as well as binding antibodies upon subcutaneous injection into BALB/c and CD34+ transgenic mice.
In addition, PolyPEPI-SCoV-2-specific, polyfunctional CD8+ and CD4+ T cells were detected ex vivo in each of the 17 asymptomatic/mild COVID-19 convalescents’ blood investigated, 1–5 months after symptom onset.
The PolyPEPI-SCoV-2-specific T cell repertoire used for recovery from COVID-19 was extremely diverse: donors had an average of seven different peptide-specific T cells, against the SARS-CoV-2 proteins; 87% of donors had multiple targets against at least three SARS-CoV-2 proteins and 53% against all four.
In addition, PEPIs determined based on the complete HLA class I genotype of the convalescent donors were validated, with 84% accuracy, to predict PEPI-specific CD8+ T cell responses measured for the individuals.
Extrapolation of the above findings to a US bone marrow donor cohort of 16,000 HLA-genotyped individuals with 16 different ethnicities (n=1,000 each ethnic group) suggest that PolyPEPI-SCoV-2 vaccination in a general population will likely elicit broad, multi-antigenic CD8+ and CD4+ T cell responses in 98% of individuals, independent of ethnicity, including Black, Asian, and Minority Ethnic (BAME) cohorts.

Answer: {"verdict": "SUPPORT", "evidence": ["Vaccine peptides were selected based on their frequency as HLA class I and class II personal epitopes (PEPIs) restricted to multiple autologous HLA alleles of individuals in an in silico cohort of 433 subjects of different ethnicities.", "PolyPEPI-SCoV-2 vaccine administered with Montanide ISA 51VG adjuvant generated robust, Th1-biased CD8+ and CD4+ T cell responses against all four structural proteins of the virus, as well as binding antibodies upon subcutaneous injection into BALB/c and CD34+ transgenic mice."]}


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

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

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

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

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: masks prevent me from spreading COVID-19

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

Answer: {"verdict": "SUPPORT", "evidence": ["It would therefore be reasonable to suggest that consistent widespread use of face masks in the community could prevent further spread of the Severe Acute Respiratory Syndrome-Coronavirus 2 (SARS-CoV-2).", "We find that the critical mask adherence is 5 per 100 when 80% wear face masks."]}


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 increases risk of heart attacks and stroke

Abstract:
OBJECTIVE: The COVID-19 pandemic has caused much morbidity and mortality to patients but also health care providers.
We tabulated the cases of physician deaths from COVID-19 associated with front-line work in hopes of mitigating future events.
METHOD: On April 5, 2020, Google internet search was performed using the keywords doctor, physician, death, COVID, COVID-19, and coronavirus in English and Farsi, and in Chinese using the Baidu search engine.
RESULTS: We found 198 physician deaths from COVID-19, but complete details were missing for 49 individuals.
The average age of the physicians that died was 63.4 years (range 28 to 90 years) and the median age was 66 years of age.
Ninety percent of the deceased physicians were male (175/194).
General practitioners and emergency room doctors (78/192), respirologists (5/192), internal medicine specialists (11/192) and anesthesiologists (6/192) comprised 52% of those dying.
Two percent of the deceased were epidemiologists (4/192), 2% were infectious disease specialists (4/192), 5% were dentists (9/192), 4% were ENT (8/192), and 4% were ophthalmologists (7/192).
The countries with the most reported physician deaths were Italy (79/198), Iran (43/198), China (16/198), Philippines (14/198), United States (9/192) and Indonesia (7/192).
CONCLUSION: Physicians from all specialties may die from COVID, and these deaths will likely increase as the pandemic progresses.
Lack of personal protective equipment was cited as a common cause of death.
Consideration should be made to exclude older physicians from front-line work.

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


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

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

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

Claim: Sars-cov-2 epidemic after social and economic reopening in two us states reveals shifts in age structure and clinical characteristics

Abstract:
In the United States, state-level re-openings in spring 2020 presented an opportunity for the resurgence of SARS-CoV-2 transmission.
One important question during this time was whether human contact and mixing patterns could increase gradually without increasing viral transmission, the rationale being that new mixing patterns would likely be associated with improved distancing, masking, and hygiene practices.
A second key question to follow during this time was whether clinical characteristics of the epidemic would improve after the initial surge of cases.
Here, we analyze age-structured case, hospitalization, and death time series from three states – Rhode Island, Massachusetts, and Pennsylvania – that had successful reopenings in May 2020 without summer waves of infection.
Using a Bayesian inference framework on eleven daily data streams and flexible daily population contact parameters, we show that population-average mixing rates dropped by >50% during the lockdown period in March/April, and that the correlation between overall population mobility and transmission-capable mixing was broken in May as these states partially re-opened.
We estimate the reporting rates (fraction of symptomatic cases reporting to health system) at 96.3% (RI), 62.5% (MA), and 98.9% (PA).
We show that elderly individuals were less able to reduce contacts during the lockdown period when compared to younger individuals, leading to the outbreak being concentrated in elderly congregate settings despite the lockdown.
Attack rate estimates through August 31 2020 are 6.2% (95% CI: 5.7% – 6.8%) of the total population infected for Rhode Island, 6.7% (95% CI: 5.4% – 7.6%) in Massachusetts, and 2.7% (95% CI: 2.5% – 3.1%) in Pennsylvania, with some validation available through published seroprevalence studies.
Infection fatality rates (IFR) estimates are higher in our analysis (>2%) than previously reported values, likely resulting from the epidemics in these three states affecting the most vulnerable sub-populations and the close matches between the states’ reported COVID-19 deaths and excess deaths.
Data in Pennsylvania may have been underreported for both non-hospitalized and hospitalized patients, casting substantial uncertainty on estimates of attack rate and infection fatality rate.

Answer: {"verdict": "CONTRADICT", "evidence": ["A second key question to follow during this time was whether clinical characteristics of the epidemic would improve after the initial surge of cases."]}


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

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

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

Claim: Salt-coated masks achieve lower viral deactivation rate

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

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


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

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

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

Claim: Most children with COVID-19 have mild symptoms or have no symptoms at all.

Abstract:
BACKGROUND Novel coronavirus disease (COVID-19) is spreading globally.
Little is known about the risk factors for the clinical outcomes of COVID-19 in children.
METHODS A retrospective case-control study was taken in children with severe acute respiratory syndrome coronary virus-2 infection in Wuhan Children's Hospital.
Risk factors associated with the development of COVID-19 and progression were collected and analyzed.
RESULTS Eight out of 260 children diagnosed with severe COVID-19 pneumonia were included in the study.
Thirty-five children with COVID-19 infection matched for age, sex and date of admission, and who classified as non-severe type, were randomly selected from the hospital admissions.
For cases with severe pneumonia caused by COVID-19, the most common symptoms were dyspnea (87.5%), fever (62.5%) and cough (62.5%).
In laboratory, white blood cells count was significantly higher in severe children than non-severe children.
Levels of inflammation bio-makers such as hsCRP, IL-6, IL-10 and D-dimer elevated in severe children compared with non-severe children on admission.
The level of total bilirubin and uric acid clearly elevated in severe children compared with non-severe children on admission.
All of severe children displayed the lesions on chest CT, more lung segments were involved in severe children than in non-severe children, which was only risk factor associated with severe COVID-19 pneumonia in multivariable analysis.
CONCLUSIONS More than 3 lung segments involved were associated with greater risk of development of severe COVID-19 in children.
Moreover, the possible risk of the elevation of IL-6, high total bilirubin and D-dimer with univariable analysis could identify patients to be severe earlier.

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


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

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

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

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

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

Answer: {"verdict": "CONTRADICT", "evidence": ["Findings: We estimate an overall infection fatality rate of 1.29% (95% credible interval [CrI] 0.89 - 2.01), as well as large differences by age, with a low infection fatality rate of 0.05% for under 60 year old (CrI 0-.19) and a substantially higher 4.25% (CrI 3.01-6.39) for people above 60 years of age."]}


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

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

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

Claim: It's worth noting that there isn't a lot of data on what can kill SARS-CoV-2-the virus that causes coronavirus COVID-19-on surfaces

Abstract:
Since December 2019, a respiratory pandemic named as coronavirus disease 2019 (Covid-19) caused by a new coronavirus named as SARS-CoV-2, has taken the world by storm.
The symptoms are fever, malaise, and cough which resolve in a few days in most cases; but may progress to respiratory distress and organ failure.
Transmission is through droplet infection or fomites, but other modes such as airborne transmission and oro-fecal transmission are also speculated.
Research is underway to develop effective vaccines and medicines for the disease.
In such a scenario, we present the measures described in Unani system of medicine for health protection during epidemics.
Unani is a traditional system of medicine developed during the middle ages, which employs natural drugs of herbal, animal and mineral origin for treatment.
In Unani medicine, during an epidemic, apart from isolation and quarantine, three measures are of utmost importance, (i) purification of surroundings using certain herbal drugs as fumigants or sprays, (ii) health promotion and immune-modulation, and (iii) use of health-protecting drugs and symptom-specific drugs.
Drugs such as loban (Styrax benzoides W. G. Craib), sandroos (Hymenaea verrucosa Gaertn.) za'fran (Crocus sativus L.), vinegar etc.
are prescribed in various forms.
Scientific researches on these drugs reveal the presence of a number of pharmacologically active substances, which may provide a new insight into the management of infections and epidemics.

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