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 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": ["Various psychological problems and important consequences in terms of mental health including stress, anxiety, depression, frustration, uncertainty during COVID-19 outbreak emerged progressively."]}


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

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


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

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

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

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

Abstract:
Angiotensin-converting enzyme (ACE) inhibitors (ACEIs) and angiotensin II type1 receptor blockers (ARBs) are among the most widely prescribed drugs for the treatment of arterial hypertension, heart failure and chronic kidney disease.
A number of studies, mainly in animals and not involving the lungs, have indicated that these drugs can increase expression of angiotensin-converting enzyme 2 (ACE2).
ACE2 is the cell entry receptor of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of coronavirus disease 2019 (COVID-19) that is currently battering the globe.
This has led to the hypothesis that use of ACEIs and ARBs may increase the risk of developing severe COVID-19.
In this point of view paper, possible scenarios regarding the impact of ACEI/ARB pharmacotherapy on COVID-19 are discussed in relation to the currently available evidence.
Although further research on the influence of blood-pressure-lowering drugs, including those not targeting the renin-angiotensin system, is warranted, there are presently no compelling clinical data showing that ACEIs and ARBs increase the likelihood of contracting COVID-19 or worsen the outcome of SARS-CoV2 infections.
Thus, unless contraindicated, use of ACEIs/ARBs in COVID-19 patients should be continued in line with the recent recommendations of medical societies.

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


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

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

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

Claim: Although smoking is the most common way to use marijuana, some people bake it into a brownie or other food. Eating pot might spare you the lung effects of this drug, but that doesn't mean it's safe.

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

Answer: {"verdict": "SUPPORT", "evidence": ["CONCLUSION: Male, aged over 65, smoking patients might face a greater risk of developing into the critical or mortal condition and the comorbidities such as hypertension, diabetes, cardiovascular disease, and respiratory diseases could also greatly affect the prognosis of the COVID-19."]}


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

Answer: {"verdict": "SUPPORT", "evidence": ["Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)--the virus that causes coronavirus disease (COVID-19)--has been detected in domestic dogs and cats, raising concerns of transmission from, to, or between these animals.", "There is currently no indication that feline- or canine-to-human transmission can occur, though there is rising evidence of the reverse."]}


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

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

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

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

Abstract:
SARS-CoV-2 is the coronavirus agent of the COVID-19 pandemic causing high mortalities.
In contrast, the widely spread human coronaviruses OC43, HKU1, 229E, and NL63 tend to cause only mild symptoms.
The present study shows, by in silico analysis, that these common human viruses are expected to induce immune memory against SARS-CoV-2 by sharing protein fragments (antigen epitopes) for presentation to the immune system by MHC class I. A list of such epitopes is provided.
The number of these epitopes and the prevalence of the common coronaviruses suggest that a large part of the world population has some degree of specific immunity against SARS-CoV-2 already, even without having been infected by that virus.
For inducing protection, booster vaccinations enhancing existing immunity are less demanding than primary vaccinations against new antigens.
Therefore, for the discussion on vaccination strategies against COVID-19, the available immune memory against related viruses should be part of the consideration.

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: Overall, funds that the hospital receives for COVID-19 deaths are more the greater good and the good of the patients' families.

Abstract:
The ongoing outbreak of COVID-19 has been expanding worldwide.
As of 17 April 2020, the death toll stands at a sobering 147,027 and over two million cases, this has been straining the health care systems all over.
Respiratory failure has been cited as the major cause of death but here we present a case about a patient who instead succumbed to severe metabolic acidosis with multiple organ failure.

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: Increase in sars-cov-2 viral rna identified in wastewater 48 hours before covid-19 clinical tests and 96 hours before hospitalizations

Abstract:
Curtailing the Spring 2020 COVID-19 surge required sweeping and stringent interventions by governments across the world.
Wastewater-based COVID-19 epidemiology programs have been initiated in many countries to provide public health agencies with a complementary disease tracking metric and facile surveillance tool.
However, their efficacy in prospectively capturing resurgence following a period of low prevalence is unclear.
In this study, the SARS-CoV-2 viral signal was measured in primary clarified sludge harvested every two days at the City of Ottawa's water resource recovery facility during the summer of 2020, when clinical testing recorded daily percent positivity below 1%.
In late July, increases of >400% in normalized SARS-CoV-2 RNA signal in wastewater were identified 48 hours prior to reported >300% increases in positive cases that were retrospectively attributed to community-acquired infections.
During this resurgence period, SARS-CoV-2 RNA signal in wastewater preceded the reported >160% increase in community hospitalizations by approximately 96 hours.
This study supports wastewater-based COVID-19 surveillance of populations in augmenting the efficacy of diagnostic testing, which can suffer from sampling biases or timely reporting as in the case of hospitalization census.

Answer: {"verdict": "SUPPORT", "evidence": ["In late July, increases of >400% in normalized SARS-CoV-2 RNA signal in wastewater were identified 48 hours prior to reported >300% increases in positive cases that were retrospectively attributed to community-acquired infections.", "During this resurgence period, SARS-CoV-2 RNA signal in wastewater preceded the reported >160% increase in community hospitalizations by approximately 96 hours."]}


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: Hand washing with soap and water and scrubbing for at least 20 seconds is recommended, but a hand sanitizer that contains at least 60% alcohol is the best alternative

Abstract:
The recent emergence of Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causing COVID-19 is a major burden for health care systems worldwide.
It is important to address if the current infection control instructions based on active ingredients are sufficient.
We therefore determined the virucidal activity of two alcohol-based hand rub solutions for hand disinfection recommended by the World Health Organization (WHO), as well as commercially available alcohols.
Efficient SARS-CoV-2 inactivation was demonstrated for all tested alcohol-based disinfectants.
These findings show the successful inactivation of SARS-CoV-2 for the first time and provide confidence in its use for the control of COVID-19.
Importance The current COVID-19 outbreak puts a huge burden on the world’s health care systems.
Without effective therapeutics or vaccines being available, effective hygiene measure are of utmost importance to prevent viral spreading.
It is therefore crucial to evaluate current infection control strategies against SARS-CoV-2.
We show the inactivation of the novel coronavirus for the first time and endorse the importance of disinfectant-based hand hygiene to reduce SARS-CoV-2 transmission.

Answer: {"verdict": "SUPPORT", "evidence": ["Efficient SARS-CoV-2 inactivation was demonstrated for all tested alcohol-based disinfectants.", "These findings show the successful inactivation of SARS-CoV-2 for the first time and provide confidence in its use for the control 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: young peope are not at risk and do not die from covid-19

Abstract:
Objective: Severity of the coronavirus disease 2019 (covid-19) has been assessed in terms of absolute mortality in SARS-CoV-2 positive cohorts.
An assessment of mortality relative to mortality in the general population is presented.
Design: Retrospective population-based study.
Setting: Individual information on symptomatic confirmed SARS-CoV-2 patients and subsequent deaths from any cause were compared to the all-cause mortality in the Swiss population of 2018.
Starting February 23, 2020, mortality in covid-19 patients was monitored for 80 days and compared to the population mortality observed in the same time-of-year starting February 23, 2018.
Participants: 5 160 595 inhabitants of Switzerland aged 35 to 95 without covid-19 (general population in spring 2018) and 20 769 persons tested positively for covid-19 (spring 2020).
Measurements: Sex- and age-specific mortality rates were estimated using Cox proportional hazards models.
Absolute probabilities of death were predicted and risk was assessed in terms of relative mortality by taking the ratio between the sex- and age-specific absolute mortality in covid19 patients and the corresponding mortality in the 2018 general population.
Results: A confirmed SARS-CoV-2 infection substantially increased the probability of death across all patient groups, ranging from nine (6 to 15) times the population mortality in 35-year old infected females to a 53-fold increase (46 to 59) for 95 year old infected males.
The highest relative risks were observed among males and older patients.
The magnitude of these effects was smaller compared to increases observed in absolute mortality risk.
Male covid-19 patients exceeded the population hazard for males (hazard ratio 1.20, 1.00 to 1.44).
Each additional year of age increased the population hazard in covid-19 patients (hazard ratio 1.04, 1.03 to 1.05).
Limitations: Information about the distribution of relevant comorbidities was not available on population level and the associated risk was not quantified.
Conclusions: Health care professionals, decision makers, and societies are provided with an additional population-adjusted assessment of covid-19 mortality risk.
In combination with absolute measures of risk, the relative risks presented here help to develop a more comprehensive understanding of the actual impact of covid-19.

Answer: {"verdict": "CONTRADICT", "evidence": ["Results: A confirmed SARS-CoV-2 infection substantially increased the probability of death across all patient groups, ranging from nine (6 to 15) times the population mortality in 35-year old infected females to a 53-fold increase (46 to 59) for 95 year old infected males.", "The highest relative risks were observed among males and older patients."]}


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

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

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

Claim: No. Spraying alcohol or chlorine all over your body will not kill viruses that have already entered your body.

Abstract:
Among the basic protective measures against COVID-19, the need to wash hands frequently and in a prolonged way using soap, and to regularly use alcohol-based hand sanitizers is well established for the whole population.
Healthcare workers in general, and particularly those involved in the direct care of COVID-19 patients, have to wear personal protective equipment (PPE) daily for many hours and also accomplish general preventive measurements outside their work.
Cutaneous adverse reactions can develop that need to be prevented, identified and therapeutically managed.
According to the data reported by Lin et al 1 , based in the experience from healthcare workers in Wuhan, adverse skin reactions were reported in 74% of responders (n=376) to a general survey.
The most commonly reported types of eruptions were skin dryness or desquamation (68.6%), papules or erythema (60.4%) and maceration (52,9%).

Answer: {"verdict": "SUPPORT", "evidence": ["Among the basic protective measures against COVID-19, the need to wash hands frequently and in a prolonged way using soap, and to regularly use alcohol-based hand sanitizers is well established for the whole population."]}


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 researchers also ranked face mask material from most to least effective in their testing.

Abstract:
The COVID‐19 pandemic caused by the novel coronavirus SARS‐CoV‐2 has claimed many lives worldwide.
Wearing medical masks or N95 masks (namely N95 respirators) can slow the virus spread and reduce the infection risk.
Reuse of these masks can minimize waste, protect the environment, and help to solve the current imminent shortage of masks.
Disinfection of used masks is needed for reuse of them with safety, but improper decontamination can damage the blocking structure of masks.
In this study, we demonstrated, using avian coronavirus of infectious bronchitis virus to mimic SARS‐CoV‐2, that medical masks and N95 masks remained their blocking efficacy after being steamed on boiling water even for 2 hours.
We also demonstrated that three brands of medical masks blocked over 99% viruses in aerosols.
The avian coronavirus was completely inactivated after being steamed for 5 minutes.
Together, this study suggested that medical masks are adequate for use on most social occasions, and both medical masks and N95 masks can be reused for a few days with steam decontamination between use.
This article is protected by copyright.
All rights reserved.

Answer: {"verdict": "SUPPORT", "evidence": ["Wearing medical masks or N95 masks (namely N95 respirators) can slow the virus spread and reduce the infection risk."]}


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

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

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

Claim: In one study it was found that 100% of ICU COVID-19 patients less than 75 years old had vitamin D insufficiency.

Abstract:
Background: Following emerge of a novel coronavirus from Wuhan, China, in December 2019, it has affected the whole world and after months of efforts by the medical communities, there is still no specific approach for prevention and treatment against the Coronavirus Disease 2019 (COVID-19).
Evidence recommends that vitamin D might be an important supportive agent for the immune system, mainly in cytokine response regulation against COVID-19.
Hence, we carried out a rapid systematic review and meta-analysis along with an ecological investigation in order to maximize the use of everything that exists about the role of vitamin D in the COVID-19.
Methods: A systematic search was performed in PubMed, Scopus, Embase, Cochrane Library, Web of Science and Google Scholar (intitle) as well as preprint database of medRxiv, bioRxiv, Research Square, preprints.org, search engine of ScienceDirect and a rapid search through famous journals up to May 26, 2020.
Studies focused on the role of vitamin D in confirmed COVID-19 patients were entered into the systematic review.
Along with our main aim, to find the second objective: correlation of global vitamin D status and COVID-19 recovery and mortality we carried out a literature search in PubMed database to identify the national or regional studies reported the vitamin D status globally.
CMA v. 2.2.064 and SPSS v.16 were used for data analysis.
Results: Out of nine studies entered into our systematic review, six studies containing 3,822 participants entered into the meta-analysis.
The meta-analysis indicated that 46.5% of COVID-19 patients were suffering from vitamin D deficiency (95% CI, 28.2%-65.8%) and in 43.3% of patients, levels of vitamin D were insufficient (95% CI, 27.4%-60.8%).
In regard to our ecological investigation on 51 countries including 408,748 participants, analyses indicated no correlation between vitamin D levels and recovery rate (r= 0.041) as well as mortality rate (r=-0.073) globally.
However, given latitude, a small reverse correlation between mortality rate and vitamin D status was observed throughout the globe (r= -0.177).
In Asia, a medium direct correlation was observed for recovery rate (r= 0.317) and a significant reveres correlation for mortality rate (r= -0.700) with vitamin D status in such patients.
In Europe, there were no correlations for both recovery (r= 0.040) and mortality rate (r= -0.035).
In Middle East, the recovery rate (r= 0.267) and mortality rate (r= -0.217) showed a medium correlation.
In North and Sought America, surprisingly, both recovery and mortality rate demonstrated a direct correlation respectively (r= 1.000, r=0.500).
In Oceania, unexpectedly, recovery (r= -1.000) and mortality (r= -1.000) rates were in considerable reverse correlation with vitamin D levels.
Conclusion: In this systematic review and meta-analysis with an ecological approach, we found a high percentage of COVID-19 patients who suffer from vitamin D deficiency or insufficiency.
Much more important, our ecological investigation resulted in substantial direct and reverse correlations between recovery and mortality rates of COVID-19 patients with vitamin D status in different countries.
Considering latitudes, a small reverse correlation between vitamin D status and mortality rate was found globally.
It seems that populations with lower levels of vitamin D might be more susceptible to the novel coronavirus infection.
Nevertheless, due to multiple limitations, if this study does not allow to quantify a value of the Vitamin D with full confidence, it allows at least to know what the Vitamin D might be and that it would be prudent to invest in this direction through comprehensive large randomized clinical trials.

Answer: {"verdict": "SUPPORT", "evidence": ["In Middle East, the recovery rate (r= 0.267) and mortality rate (r= -0.217) showed a medium correlation."]}


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

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

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

Claim: Hydroxychloroquine safety outcome within approved therapeutic protocol included covid-19 outpatients in saudi arabia

Abstract:
Background: Healthcare systems globally has been challenged following the COVID-19 pandemic, since late 2019.
Multiple approaches and strategies have been performed to relieve the pressure and support existing healthcare systems.
The Saudi Arabian Ministry of Health (MOH) launched an initiative to support the National Healthcare System.
Since the 5th of June 2020, 238 outpatient fever clinics were established across Saudi Arabia.
Methods: A cross-sectional study included 2,733 eligible patients subjected to MOH treatment protocol (hydroxychloroquine and zinc) and revisited the clinics within 3-7 days after treatment initiation.
This study aimed to assess the safety outcome and reported adverse events from hydroxychloroquine use among suspected COVID-19 patients.
The data was collected through an electronic link and cross-checked with that of the national database (Health Electronic Surveillance Network, HESN) and reports from the MOH Morbidity and Mortality (M&M) Committee.
Results: Majority of the cases were males (70.4%).
Upon reassessing the studied participants within 3-7 days, 240 patients (8.8%) discontinued the treatment protocol because of the development of side effects (4.1%) and for non-clinical reasons in the remaining (4.7%).
Medication side effects overall were reported among (6.7%) of all studied participants, including mainly cardiovascular adverse events (2.5%), followed by gastrointestinal (GI) symptoms (2.4%).
No Intensive Care Unit (ICU) admission or death were reported among these patients.
Conclusion: In our study, results show that the use of hydroxychloroquine for COVID-19 patients in mild to moderate cases in an outpatient setting, within the protocol recommendation and inclusion/exclusion criteria, is safe, highly tolerable, and with minimum side effects.

Answer: {"verdict": "CONTRADICT", "evidence": ["Conclusion: In our study, results show that the use of hydroxychloroquine for COVID-19 patients in mild to moderate cases in an outpatient setting, within the protocol recommendation and inclusion/exclusion criteria, is safe, highly tolerable, and with minimum side effects."]}


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

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

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

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

Abstract:
Intravenous infusions of angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin receptor blockers (ARBs) in experimental animals increase the numbers of angiotensin-converting enzyme 2 (ACE2) receptors in the cardiopulmonary circulation.
ACE2 receptors serve as binding sites for SARS-CoV-2 virions in the lungs.
Patients who take ACEIs and ARBS may be at increased risk of severe disease outcomes due to SARS-CoV-2 infections.

Answer: {"verdict": "CONTRADICT", "evidence": ["Patients who take ACEIs and ARBS may be at increased risk of severe disease outcomes due to SARS-CoV-2 infections."]}


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: No, garlic won't prevent COVID-19. These 'home remedies' and 'cures' just don't work

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

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


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

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

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

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

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

Answer: {"verdict": "CONTRADICT", "evidence": ["CONCLUSION The present study has found that the use of ACE inh/ARBs therapy might be associated with an increased in-hospital mortality in patients who were diagnosed with Covid-19 pneumonia."]}


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: drugs (Anti-inflammatory drugs, which includes steroids such as prednisone and non-steroidal drugs like ibuprofen) are helpful for symptomatic treatment only and are not a cure. By decreasing inflammation, they can decrease pain, swelling, redness and other symptoms that may be associated with inflammation.

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

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


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

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

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

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

Abstract:
Patients with pre-existing cardiovascular disease and risk factors are more likely to experience adverse outcomes associated with the novel coronavirus disease-2019 (COVID-19).
Additionally, consistent reports of cardiac injury and de novo cardiac complications, including possible myocarditis, arrhythmia, and heart failure in patients without prior cardiovascular disease or significant risk factors, are emerging, possibly due to an accentuated host immune response and cytokine release syndrome.
As the spread of the virus increases exponentially, many patients will require medical care either for COVID-19 related or traditional cardiovascular issues.
While the COVID-19 pandemic is dominating the attention of the healthcare system, there is an unmet need for a standardized approach to deal with COVID-19 associated and other traditional cardiovascular issues during this period.
We provide consensus guidance for the management of various cardiovascular conditions during the ongoing COVID-19 pandemic with the goal of providing the best care to all patients and minimizing the risk of exposure to frontline healthcare workers.

Answer: {"verdict": "SUPPORT", "evidence": ["Additionally, consistent reports of cardiac injury and de novo cardiac complications, including possible myocarditis, arrhythmia, and heart failure in patients without prior cardiovascular disease or significant risk factors, are emerging, possibly due to an accentuated host immune response and cytokine release syndrome."]}


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

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

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

Claim: Young people as diligent about Covid measures as older people

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

Answer: {"verdict": "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: Is there an airborne component to the transmission of covid-19 ?

Abstract:
Objectives While COVID-19 is known to be spread by respiratory droplets (which travel <2m horizontally), much less is known about its transmission via aerosols, which can become airborne and be widely distributed throughout room spaces.
In order to quantify the risk posed by COVID-19 infectors exhaling respiratory aerosols in enclosed spaces, we undertook a computer modelling study to simulate transmission in an office building.
Methods Respiratory droplet data from four published datasets were analysed to quantify the number and volume of droplets <100m diameter produced by a typical cough and speaking event (i.e. counting from 1 to 100).
This was used in a stochastic model to simulate (10,000 simulations) the number of respiratory particles, originating from a COVID-19 infector, that would be inhaled in one hour by a susceptible individual practicing socially distancing in a 5 x 5 x 2.75m office space.
Several scenarios were simulated that mimicked the presence of both symptomatic and asymptomatic COVID-19 infectors.
Results On average, each cough and speaking event produced similar numbers of droplets <100 m diameter (median range = 955-1010).
Computer simulations (at ventilation rate = 2AC/h) revealed that sharing the office space with a symptomatic COVID-19 infector (4 coughs per hour) for one hour resulted in the inhalation of 187.3 (median value) respiratory droplets, whereas sharing with an asymptomatic COVID-19 positive person (10 speaking events per hour) resulted in the inhalation of 482.9 droplets.
Increasing the ventilation rate resulted in only modest reductions in particle numbers inhaled.
Conclusions Given that live SARS-CoV-2 virions are known to be shed in high concentrations from the nasal cavity of both symptomatic and asymptomatic COVID-19 patients, the results suggest that individuals who share enclosed spaces with an infector may be at risk of contracting COVID-19 by the aerosol route, even when practicing social distancing.

Answer: {"verdict": "SUPPORT", "evidence": ["In order to quantify the risk posed by COVID-19 infectors exhaling respiratory aerosols in enclosed spaces, we undertook a computer modelling study to simulate transmission in an office building."]}


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

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

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

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

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

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


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

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

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

Claim: Can apple cider vinegar prevent the coronavirus? Nothing can really prevent the corona virus (COVid-19) all that authorities can do is slow it down which is why such tough measures are being enforced in some countries.

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

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


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

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

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

Claim: However, some children can get severely ill from COVID-19. They might require hospitalization, intensive care, or a ventilator to help them breathe.

Abstract:
BACKGROUND The new Coronavirus identified in Whuan at the end of 2019 (SARS-CoV-2) belongs to the Beta Coronavirus genus and is responsible for the new Coronavirus 2019 pandemia (COVID-19).
Infected children may be asymptomatic or present fever, dry cough, fatigue or gastrointestinal symptoms.
The CDC recommends that clinicians should decide to test patients based on the presence of signs and symptoms compatible with COVID-19.
MATERIAL AND METHODS 42 children (the majority < 5 years of age) were referred, to our Pediatric Department, as possible cases of COVID-19 infection.
Blood analysis, chest X-ray, and naso-oropharyngeal swab specimens for viral identification of COVID-19 were requested.
RESULTS None of the screened children resulted positive for COVID-19 infection.
At first presentation, the most frequent signs and symptoms were: fever (71.4%), fatigue (35.7%) and cough (30.9%).
An high C-reactive protein value and abnormalities of chest X-ray (bronchial wall thickening) were detected in 26.2% and 19% of patients, respectively.
Almost half of patients (45.2%) required hospitalization in our Pediatric Unit and one patient in Intensive Care Unit.
CONCLUSIONS Testing people who meet the COVID-19 suspected case definition criteria is essential for clinical management and outbreak control.
Children of all ages can get COVID-19, although they appear to be affected less frequently than adults, as reported in our preliminary survey.
Further studies are needed to confirm our observations.

Answer: {"verdict": "SUPPORT", "evidence": ["RESULTS None of the screened children resulted positive for COVID-19 infection.", "At first presentation, the most frequent signs and symptoms were: fever (71.4%), fatigue (35.7%) and cough (30.9%).", "An high C-reactive protein value and abnormalities of chest X-ray (bronchial wall thickening) were detected in 26.2% and 19% of patients, respectively.", "Almost half of patients (45.2%) required hospitalization in our Pediatric Unit and one patient in Intensive Care Unit.", "Children of all ages can get COVID-19, although they appear to be affected less frequently than adults, as reported in our preliminary survey."]}


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

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


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

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

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

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

Abstract:
The COVID‐19 pandemic caused by the novel coronavirus SARS‐CoV‐2 has claimed many lives worldwide.
Wearing medical masks or N95 masks (namely N95 respirators) can slow the virus spread and reduce the infection risk.
Reuse of these masks can minimize waste, protect the environment, and help to solve the current imminent shortage of masks.
Disinfection of used masks is needed for reuse of them with safety, but improper decontamination can damage the blocking structure of masks.
In this study, we demonstrated, using avian coronavirus of infectious bronchitis virus to mimic SARS‐CoV‐2, that medical masks and N95 masks remained their blocking efficacy after being steamed on boiling water even for 2 hours.
We also demonstrated that three brands of medical masks blocked over 99% viruses in aerosols.
The avian coronavirus was completely inactivated after being steamed for 5 minutes.
Together, this study suggested that medical masks are adequate for use on most social occasions, and both medical masks and N95 masks can be reused for a few days with steam decontamination between use.
This article is protected by copyright.
All rights reserved.

Answer: {"verdict": "CONTRADICT", "evidence": ["Wearing medical masks or N95 masks (namely N95 respirators) can slow the virus spread and reduce the infection risk.", "We also demonstrated that three brands of medical masks blocked over 99% viruses in aerosols.", "The avian coronavirus was completely inactivated after being steamed for 5 minutes.", "Together, this study suggested that medical masks are adequate for use on most social occasions, and both medical masks and N95 masks can be reused for a few days with steam decontamination between use."]}


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

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

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

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

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

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


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

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

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

Claim: The overall risk of dying in the hospital from COVID-19 among people with T1D is very low, however, one study found that this risk is higher for people with T1D (3.5 times higher) compared to people without diabetes.

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

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


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

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

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

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

Abstract:
Face masks are an avenue to curb the spread of coronavirus, but few people in Western societies wear face masks.
Social scientists have rarely studied face mask wearing, leaving little guidance for methods to encourage these behaviours.
In the current article, we provide an approach to address this issue by developing the 32-item and 8-dimension Face Mask Perceptions Scale (FMPS).
We begin by developing an over-representative item list in a qualitative study, wherein participants' responses are used to develop items to ensure content relevance.
This item list is then reduced via exploratory factor analysis in a second study, and the eight dimensions of the scale are supported.
We also support the validity of the FMPS, as the scale significantly relates to both face mask wearing and health perceptions.
We lastly confirm the factor structure of the FMPS in a third study via confirmatory factor analysis.
From these efforts, we identify an avenue that social scientists can aid in preventing coronavirus and illness more broadly - by studying face mask perceptions and behaviours.

Answer: {"verdict": "SUPPORT", "evidence": ["Face masks are an avenue to curb the spread of coronavirus, but few people in Western societies 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: the warmer weather will slow the spread of SARS-CoV-2, the novel coronavirus that causes COVID-19

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

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


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

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

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

Claim: The sars-cov-2 cytopathic effect is achieved with autophagy modulators

Abstract:
SARS-CoV-02 is a new type of coronavirus capable of rapid transmission and causing severe clinical symptoms; much of which has unknown biological etiology.
It has prompted researchers to rapidly mobilize their efforts towards identifying and developing anti-viral therapeutics and vaccines.
Discovering and understanding the virus’ pathways of infection, host-protein interactions, and cytopathic effects will greatly aid in the design of new therapeutics to treat COVID-19.
While it is known that chloroquine and hydroxychloroquine, extensively explored as clinical agents for COVID-19, have multiple cellular effects including inhibiting autophagy, there are also dose-limiting toxicities in patients that make clearly establishing their potential mechanisms-of-action problematic.
Therefore, we evaluated a range of other autophagy modulators to identify an alternative autophagy-based drug repurposing opportunity.
In this work, we found that 6 of these compounds blocked the cytopathic effect of SARS-CoV-2 in Vero-E6 cells with EC(50) values ranging from 2.0 to 13 μM and selectivity indices ranging from 1.5 to >10-fold.
Immunofluorescence staining for LC3B and LysoTracker dye staining assays in several cell lines indicated their potency and efficacy for inhibiting autophagy correlated with the measurements in the SARS-CoV-2 cytopathic effect assay.
Our data suggest that autophagy pathways could be targeted to combat SARS-CoV-2 infections and become an important component of drug combination therapies to improve the treatment outcomes for COVID-19.

Answer: {"verdict": "CONTRADICT", "evidence": ["While it is known that chloroquine and hydroxychloroquine, extensively explored as clinical agents for COVID-19, have multiple cellular effects including inhibiting autophagy, there are also dose-limiting toxicities in patients that make clearly establishing their potential mechanisms-of-action problematic.", "Immunofluorescence staining for LC3B and LysoTracker dye staining assays in several cell lines indicated their potency and efficacy for inhibiting autophagy correlated with the measurements in the SARS-CoV-2 cytopathic effect assay."]}


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

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

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

Claim: high doses of vitamins and natural remedies can stave off COVID-19 — but evidence to support these claims is lacking.

Abstract:
As viral infections are an increasing threat to human societies, the need for new therapeutic strategies is becoming even more obvious.
As no vaccine is available for COVID-19, the development of directly acting antiviral agents and preventive strategies have to be considered.
Nature provides a huge reservoir of anti-infectious compounds, from which we can deduce innovative ideas, therapies, and products.
Anti-adhesive natural products interact with the receptor-mediated recognition and early interaction of viruses with the host cells, leading to a reduced internalisation of the virus and reduced infections (e.g., procyanidin-B-2-di-O-gallate against influenza and herpes virus).
Lignans like podophyllotoxin and bicyclol show strong antiviral activities against different viruses, and essential oils can directly interact with viral membranes and reduce the host's inflammatory responses (e.g., 1,8-cineol).
Echinacea extracts stimulate the immune system, and bioavailable alkamides are key players by interacting with immunomodulating cannabinoid receptors.
COVID-19 and SARS-CoV-2 infections have, in part, successfully been treated in China by preparations from traditional Chinese medicine and, while it is too early to draw conclusions, some promising data are available.
There is huge potential, but intensified research is needed to develop evidence-based medicines with a clearly defined chemical profile.
Intensified research and development, and therefore funding, are needed for exploiting nature's reservoir against viral infections.
Combined action for basic research, chemistry, pharmacognosy, virology, and clinical studies, but also supply chain, sustainable sourcing, and economic aspects have to be considered.
This review calls for intensified innovative science on natural products for the patients and for a healthier world!

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: dexamethasone reduced death rates

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

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


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

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

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

Claim: Sars-cov2 enables anaerobic bacteria to colonize the intestinal disrupting homeostasis

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

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