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

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

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

Claim: Hydroxychloroquine is an Effective Treatment for COVID-19

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

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


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

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

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

Claim: How long does Coronavirus last on surfaces? And which disinfectants are most effective at cleaning them? Those two questions are important not only for healthcare centrers but any public place with a lot of activity - locations where you'll frequently touch surfaces such as door handles with your hands. You might then potentially infect yourself by touching your face, which happens more often than you think.

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

Answer: {"verdict": "SUPPORT", "evidence": ["RECENT FINDINGS The contaminated surface environment in hospitals plays an important role in the transmission of several key nosocomial pathogens including methicillin-resistant Staphylococcus aureus, vancomycin-resistant Enterococcus spp.", ", Clostridium difficile, Acinetobacter spp., and norovirus."]}


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

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

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

Claim: it's also important to have a strong immune system that can fight back against the germs you may encounter.

Abstract:
Following the outbreak of a novel coronavirus (SARS-CoV-2), studies suggest that the resultant disease (COVID-19) is more severe in individuals with a weakened immune system.
Cytotoxic T-cells (CTLs) and Natural Killer (NK) cells are required to generate an effective immune response against viruses, functional exhaustion of which enables disease progression.
Patients with severe COVID-19 present significantly lower lymphocyte, and higher neutrophil, counts in blood.
Specifically, CD8+ lymphocytes and NK cells were significantly reduced in cases of severe infection compared to patients with mild infection and healthy individuals.
The NK group 2 member A (NKG2A) receptor transduces inhibitory signalling, suppressing NK cytokine secretion and cytotoxicity.
Overexpression of NKG2A has been observed on CD8+ and NK cells of COVID-19 infected patients compared to healthy controls, while NKG2A overexpression also functionally exhausts CD8+ cells and NK cells, resulting in a severely compromised innate immune response.
Blocking NKG2A on CD8+ cells and NK cells in cancers modulated tumor growth, restoring CD8+ T and NK cell function.
A recently proposed mechanism via which SARS-CoV-2 overrides innate immune response of the host is by over-expressing NKG2A on CD+ T and NK cells, culminating in functional exhaustion of the immune response against the viral pathogen.
Monalizumab is an inhibiting antibody against NKG2A which can restore the function of CD8 + T and NK cells in cancers, successfully ceasing tumor progression with no significant side effects in Phase 2 clinical trials.
We hypothesize that patients with severe COVID-19 have a severely compromised innate immune response and could be treated via the use of Monalizumab, interferon α, chloroquine, and other antiviral agents.

Answer: {"verdict": "SUPPORT", "evidence": ["Following the outbreak of a novel coronavirus (SARS-CoV-2), studies suggest that the resultant disease (COVID-19) is more severe in individuals with a weakened immune system."]}


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: De silico design of ace2 protein decoys to neutralize sars-cov-2

Abstract:
There is an urgent need for the ability to rapidly develop effective countermeasures for emerging biological threats, such as the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that causes the ongoing coronavirus disease 2019 (COVID-19) pandemic.
We have developed a generalized computational design strategy to rapidly engineer de novo proteins that precisely recapitulate the protein surface targeted by biological agents, like viruses, to gain entry into cells.
The designed proteins act as decoys that block cellular entry and aim to be resilient to viral mutational escape.
Using our novel platform, in less than ten weeks, we engineered, validated, and optimized de novo protein decoys of human angiotensin-converting enzyme 2 (hACE2), the membrane-associated protein that SARS-CoV-2 exploits to infect cells.
Our optimized designs are hyperstable de novo proteins (∼18-37 kDa), have high affinity for the SARS-CoV-2 receptor binding domain (RBD) and can potently inhibit the virus infection and replication in vitro.
Future refinements to our strategy can enable the rapid development of other therapeutic de novo protein decoys, not limited to neutralizing viruses, but to combat any agent that explicitly interacts with cell surface proteins to cause disease.

Answer: {"verdict": "CONTRADICT", "evidence": ["The designed proteins act as decoys that block cellular entry and aim to be resilient to viral mutational escape.", "Future refinements to our strategy can enable the rapid development of other therapeutic de novo protein decoys, not limited to neutralizing viruses, but to combat any agent that explicitly interacts with cell surface proteins to cause disease."]}


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: Single-dimensional analyses reveal sars-cov-2 interference with intrinsic immune response in the human gut

Abstract:
Objective Exacerbated pro-inflammatory immune response contributes to COVID-19 pathology.
Despite the evidence about SARS-CoV-2 infecting the human gut, little is known about the importance of the enteric phase of SARS-CoV-2 for the viral lifecycle and for the development of COVID-19-associated pathologies.
Similarly, it remains unknown whether the innate immune response triggered in this organ to combat viral infection is similar or distinct compared to the one triggered in other organs.
Design We exploited human ileum-and colon-derived organoids as a non-transformed culture model supporting SARS-CoV-2 infection.
We characterized the replication kinetics of SARS-CoV-2 in intestinal epithelial cells and correlated the expression of the viral receptor ACE2 with infection.
We performed conventional and targeted single-cell transcriptomics and multiplex single-molecule RNA fluorescence in situ hybridization and used IFN-reporter bioassays to characterize the response of primary human intestinal epithelial cells to SARS-CoV-2 infection.
Results We identified a subpopulation of enterocytes as the prime target of SARS-CoV-2.
We found the lack of positive correlation between susceptibility to infection and the expression of ACE2 and revealed that SARS-CoV-2 downregulates ACE2 expression upon infection.
Infected cells activated strong proinflammatory programs and produced interferon, while expression of interferon-stimulated genes was limited to bystander cells due to SARS-CoV-2 suppressing the autocrine action of interferon in infected cells.
Conclusion Our findings reveal that SARS-CoV-2 curtails the immune response in primary human intestinal epithelial cells to promote its replication and spread and this highlights the gut as a proinflammatory reservoir that should be considered to fully understand SARS-CoV-2 pathogenesis.
Significance of the study What is already known about this subject?
COVID-19 patients have gastrointestinal symptoms which likely correlates with SARS-CoV-2 infection of the intestinal epithelium SARS-CoV-2 replicates in human intestinal epithelial cells.
Intestinal organoids are a good model to study SARS-CoV-2 infection of the gastrointestinal tract There is a limited interferon response in human lung epithelial cells upon SARS-CoV-2 infection.
What are the new findings?
A specific subpopulation of enterocytes are the prime targets of SARS-CoV-2 infection of the human gut.
There is a lack of correlation between ACE2 expression and susceptibility to SARS-CoV-2 infection.
SARS-CoV-2 downregulates ACE2 expression upon infection.
Human intestinal epithelium cells produce interferon upon SARS-CoV-2 infection.
Interferon acts in a paracrine manner to induce interferon stimulated genes that control viral infection only in bystander cells.
SARS-CoV-2 actively blocks interferon signaling in infected cells.
How might it impact on clinical practice in the foreseeable future?
The absence of correlation between ACE2 levels and susceptibility suggest that medications influencing ACE2 levels (e.g. high blood pressure drugs) will not make patients more susceptible to SARS-CoV-2 infection.
The restricted cell tropism and the distinct immune response mounted by the GI tract, suggests that specific cellular restriction/replication factors and organ specific intrinsic innate immune pathways can represent unique therapeutic targets to treat COVD-19 patients by considering which organ is most infected/impacted by SARS-CoV-2.
The strong pro-inflammatory signal mounted by the intestinal epithelium can fuel the systemic inflammation observed in COVID-19 patients and is likely participating in the lung specific pathology.

Answer: {"verdict": "CONTRADICT", "evidence": ["Conclusion Our findings reveal that SARS-CoV-2 curtails the immune response in primary human intestinal epithelial cells to promote its replication and spread and this highlights the gut as a proinflammatory reservoir that should be considered to fully understand SARS-CoV-2 pathogenesis.", "Human intestinal epithelium cells produce interferon upon SARS-CoV-2 infection."]}


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

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

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

Claim: Blood pressure drugs may improve COVID-19 survival

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: 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:
Coronaviruses, which were generally considered harmless to humans before 2003, have appeared again with a pandemic threatening the world since December 2019 after the epidemics of SARS and MERS.
It is known that transmission from person to person is the most important way to spread.
However, due to the widespread host diversity, a detailed examination of the role of animals in this pandemic is essential to effectively fight against the outbreak.
Although coronavirus infections in pets are known to be predominantly related to the gastrointestinal tract, it has been observed that there are human-to-animal transmissions in this outbreak and some animals have similar symptoms to humans.
Although animal-to-animal transmission has been shown to be possible, there is no evidence of animal-to-human transmission.

Answer: {"verdict": "SUPPORT", "evidence": ["Although animal-to-animal transmission has been shown to be possible, there is no evidence of animal-to-human transmission."]}


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

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

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

Claim: the risk of animals spreading COVID-19 to people is considered to be low.

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

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


Question:
You will be shown a claim 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: Sterilizing immunity against sars-cov-2 infection in rabbits by a single-shot and modified imidazoquinoline tlr7/8 agonist-adjuvanted recombinant spike protein vaccine

Abstract:
The search for vaccines that protect from severe morbidity and mortality as a result of infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus that causes coronavirus disease 2019 (COVID-19) is a race against the clock and the virus.
Several vaccine candidates are currently being tested in the clinic.
Inactivated virus and recombinant protein vaccines can be safe options but may require adjuvants to induce robust immune responses efficiently.
In this work we describe the use of a novel amphiphilic imidazoquinoline (IMDQ-PEG-CHOL) TLR7/8 adjuvant, consisting of an imidazoquinoline conjugated to the chain end of a cholesterol-poly(ethylene glycol) macromolecular amphiphile).
This amphiphile is water soluble and exhibits massive translocation to lymph nodes upon local administration, likely through binding to albumin.
IMDQ-PEG-CHOL is used to induce a protective immune response against SARS-CoV-2 after single vaccination with trimeric recombinant SARS-CoV-2 spike protein in the BALB/c mouse model.
Inclusion of amphiphilic IMDQ-PEG-CHOL in the SARS-CoV-2 spike vaccine formulation resulted in enhanced immune cell recruitment and activation in the draining lymph node.
IMDQ-PEG-CHOL has a better safety profile compared to native soluble IMDQ as the former induces a more localized immune response upon local injection, preventing systemic inflammation.
Moreover, IMDQ-PEG-CHOL adjuvanted vaccine induced enhanced ELISA and in vitro microneutralization titers, and a more balanced IgG2a/IgG1 response.
To correlate vaccine responses with control of virus replication in vivo, vaccinated mice were challenged with SARS-CoV-2 virus after being sensitized by intranasal adenovirus-mediated expression of the human angiotensin converting enzyme 2 (ACE2) gene.
Animals vaccinated with trimeric recombinant spike protein vaccine without adjuvant had lung virus titers comparable to non-vaccinated control mice, whereas animals vaccinated with IMDQ-PEG-CHOL-adjuvanted vaccine controlled viral replication and infectious viruses could not be recovered from their lungs at day 4 post infection.
In order to test whether IMDQ-PEG-CHOL could also be used to adjuvant vaccines currently licensed for use in humans, proof of concept was also provided by using the same IMDQ-PEG-CHOL to adjuvant human quadrivalent inactivated influenza virus split vaccine, which resulted in enhanced hemagglutination inhibition titers and a more balanced IgG2a/IgG1 antibody response.
Enhanced influenza vaccine responses correlated with better virus control when mice were given a lethal influenza virus challenge.
Our results underscore the potential use of IMDQ-PEG-CHOL as an adjuvant to achieve protection after single immunization with recombinant protein and inactivated vaccines against respiratory viruses, such as SARS-CoV-2 and influenza viruses.

Answer: {"verdict": "CONTRADICT", "evidence": ["IMDQ-PEG-CHOL is used to induce a protective immune response against SARS-CoV-2 after single vaccination with trimeric recombinant SARS-CoV-2 spike protein in the BALB/c mouse model."]}


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: Potent neutralizing antibodies from covid-19 patients define multiple targets of vulnerability

Abstract:
The rapid spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has had a large impact on global health, travel, and economy.
Therefore, preventative and therapeutic measures are urgently needed.
Here, we isolated monoclonal antibodies from three convalescent coronavirus disease 2019 (COVID-19) patients using a SARS-CoV-2 stabilized prefusion spike protein.
These antibodies had low levels of somatic hypermutation and showed a strong enrichment in VH1-69, VH3-30-3, and VH1-24 gene usage.
A subset of the antibodies was able to potently inhibit authentic SARS-CoV-2 infection at a concentration as low as 0.007 micrograms per milliliter.
Competition and electron microscopy studies illustrate that the SARS-CoV-2 spike protein contains multiple distinct antigenic sites, including several receptor-binding domain (RBD) epitopes as well as non-RBD epitopes.
In addition to providing guidance for vaccine design, the antibodies described here are promising candidates for COVID-19 treatment and prevention.

Answer: {"verdict": "SUPPORT", "evidence": ["In addition to providing guidance for vaccine design, the antibodies described here are promising candidates for COVID-19 treatment and prevention."]}


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

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

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

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

Abstract:
The ongoing pandemic coronavirus disease 19 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a matter of global concern.
Environmental factors such as air pollution and smoking and comorbid conditions (hypertension, diabetes mellitus and underlying cardio-respiratory illness) likely increase the severity of COVID-19.
Rheumatic manifestations such as arthralgias and arthritis may be prevalent in about a seventh of individuals.
COVID-19 can result in acute interstitial pneumonia, myocarditis, leucopenia (with lymphopenia) and thrombocytopenia, also seen in rheumatic diseases like lupus and Sjogren's syndrome.
Severe disease in a subset of patients may be driven by cytokine storm, possibly due to secondary hemophagocytic lymphohistiocytosis (HLH), akin to that in systemic onset juvenile idiopathic arthritis or adult-onset Still's disease.
In the absence of high-quality evidence in this emerging disease, understanding of pathogenesis may help postulate potential therapies.
Angiotensin converting enzyme 2 (ACE2) appears important for viral entry into pneumocytes; dysbalance in ACE2 as caused by ACE inhibitors or ibuprofen may predispose to severe disease.
Preliminary evidence suggests potential benefit with chloroquine or hydroxychloroquine.
Antiviral drugs like lopinavir/ritonavir, favipiravir and remdesivir are also being explored.
Cytokine storm and secondary HLH might require heightened immunosuppressive regimens.
Current international society recommendations suggest that patients with rheumatic diseases on immunosuppressive therapy should not stop glucocorticoids during COVID-19 infection, although minimum possible doses may be used.
Disease-modifying drugs should be continued; cessation may be considered during infection episodes as per standard practices.
Development of a vaccine may be the only effective long-term protection against this disease.
Key Pointsâ¢ Patients with coronavirus disease 19 (COVID-19) may have features mimicking rheumatic diseases, such as arthralgias, acute interstitial pneumonia, myocarditis, leucopenia, lymphopenia, thrombocytopenia and cytokine storm with features akin to secondary hemophagocytic lymphohistiocytosis.â¢ Although preliminary results may be encouraging, high-quality clinical trials are needed to better understand the role of drugs commonly used in rheumatology like hydroxychloroquine and tocilizumab in COVID-19.â¢ Until further evidence emerges, it may be cautiously recommended to continue glucocorticoids and other disease-modifying antirheumatic drugs (DMARDs) in patients receiving these therapies, with discontinuation of DMARDs during infections as per standard practice.

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

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

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


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

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

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

Claim: Prospective mapping of viral mutations that escape antibodies used to treat covid-19

Abstract:
Antibodies are becoming a frontline therapy for SARS-CoV-2, but the risk of viral evolutionary escape remains unclear.
Here we map how all mutations to SARS-CoV-2’s receptor-binding domain (RBD) affect binding by the antibodies in Regeneron’s REGN-COV2 cocktail and Eli Lilly’s LY-CoV016.
These complete maps uncover a single amino-acid mutation that fully escapes the REGN-COV2 cocktail, which consists of two antibodies targeting distinct structural epitopes.
The maps also identify viral mutations that are selected in a persistently infected patient treated with REGN-COV2, as well as in lab viral escape selections.
Finally, the maps reveal that mutations escaping each individual antibody are already present in circulating SARS-CoV-2 strains.
Overall, these complete escape maps enable immediate interpretation of the consequences of mutations observed during viral surveillance.

Answer: {"verdict": "SUPPORT", "evidence": ["Finally, the maps reveal that mutations escaping each individual antibody are already present in circulating SARS-CoV-2 strains."]}


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

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

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

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

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

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


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

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

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

Claim: Cross-species transmission of the newly identified coronavirus 2019-ncov

Abstract:
The current outbreak of viral pneumonia in the city of Wuhan, China, was caused by a novel coronavirus designated 2019-nCoV by the World Health Organization, as determined by sequencing the viral RNA genome.
Many initial patients were exposed to wildlife animals at the Huanan seafood wholesale market, where poultry, snake, bats, and other farm animals were also sold.
To investigate possible virus reservoir, we have carried out comprehensive sequence analysis and comparison in conjunction with relative synonymous codon usage (RSCU) bias among different animal species based on the 2019-nCoV sequence.
Results obtained from our analyses suggest that the 2019-nCoV may appear to be a recombinant virus between the bat coronavirus and an origin-unknown coronavirus.
The recombination may occurred within the viral spike glycoprotein, which recognizes a cell surface receptor.
Additionally, our findings suggest that 2019-nCoV has most similar genetic information with bat coronovirus and most similar codon usage bias with snake.
Taken together, our results suggest that homologous recombination may occur and contribute to the 2019-nCoV cross-species transmission.

Answer: {"verdict": "SUPPORT", "evidence": ["Results obtained from our analyses suggest that the 2019-nCoV may appear to be a recombinant virus between the bat coronavirus and an origin-unknown coronavirus."]}


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: Infection affects global patterns of covid-19 early outbreak dynamics

Abstract:
Environmental factors are well known to affect spatio-temporal patterns of infectious disease outbreaks, but whether the rapid spread of COVID-19 across the globe is related to local environmental conditions is highly debated.
We assessed the impact of environmental factors (temperature, humidity and air pollution) on the global patterns of COVID-19 early outbreak dynamics during January-May 2020, controlling for several key socio-economic factors and airport connections.
We showed that during the earliest phase of the global outbreak (January-March), COVID-19 growth rates were non-linearly related to climate, with fastest spread in regions with a mean temperature of ca.
5 °C, and in the most polluted regions.
However, environmental effects faded almost completely when considering later outbreaks, in keeping with the progressive enforcement of containment actions.
Accordingly, COVID-19 growth rates consistently decreased with stringent containment actions during both early and late outbreaks.
Our findings indicate that environmental drivers may have played a role in explaining the early variation among regions in disease spread.
With limited policy interventions, seasonal patterns of disease spread might emerge, with temperate regions of both hemispheres being most at risk of severe outbreaks during colder months.
Nevertheless, containment measures play a much stronger role and overwhelm impacts of environmental variation, highlighting the key role for policy interventions in curbing COVID-19 diffusion within a given region.
If the disease will become seasonal in the next years, information on environmental drivers of COVID-19 can be integrated with epidemiological models to inform forecasting of future outbreak risks and improve management plans.

Answer: {"verdict": "CONTRADICT", "evidence": ["We showed that during the earliest phase of the global outbreak (January-March), COVID-19 growth rates were non-linearly related to climate, with fastest spread in regions with a mean temperature of ca."]}


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

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

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

Claim: The CORONAVIRUS did not emerge in Wuhan

Abstract:
Coronavirus disease 2019 (COVID-19) originated in the city of Wuhan, Hubei Province, Central China, and has spread quickly to 72 countries to date.
COVID-19 is caused by a novel coronavirus, named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) [previously provisionally known as 2019 novel coronavirus (2019-nCoV)].
At present, the newly identified SARS-CoV-2 has caused a large number of deaths with tens of thousands of confirmed cases worldwide, posing a serious threat to public health.
However, there are no clinically approved vaccines or specific therapeutic drugs available for COVID-19.
Intensive research on the newly emerged SARS-CoV-2 is urgently needed to elucidate the pathogenic mechanisms and epidemiological characteristics and to identify potential drug targets, which will contribute to the development of effective prevention and treatment strategies.
Hence, this review will focus on recent progress regarding the structure of SARS-CoV-2 and the characteristics of COVID-19, such as the aetiology, pathogenesis and epidemiological characteristics.

Answer: {"verdict": "CONTRADICT", "evidence": ["Coronavirus disease 2019 (COVID-19) originated in the city of Wuhan, Hubei Province, Central China, and has spread quickly to 72 countries to date."]}


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 study of COVID-19 patients in the Netherlands did not find the disease to be associated with cytokine storm, as previously suggested.

Abstract:
Cytokine storm is an excessive immune response to external stimuli.
The pathogenesis of the cytokine storm is complex.
The disease progresses rapidly, and the mortality is high.
Certain evidence shows that, during the coronavirus disease 2019 (COVID-19) epidemic, the severe deterioration of some patients has been closely related to the cytokine storm in their bodies.
This article reviews the occurrence mechanism and treatment strategies of the COVID-19 virus-induced inflammatory storm in attempt to provide valuable medication guidance for clinical treatment.

Answer: {"verdict": "CONTRADICT", "evidence": ["The disease progresses rapidly, and the mortality is high.", "Certain evidence shows that, during the coronavirus disease 2019 (COVID-19) epidemic, the severe deterioration of some patients has been closely related to the cytokine storm in their bodies."]}


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: Infections occur mainly through exposure to respiratory droplets when a person is in close contact with someone who has COVID-19.

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

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


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

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

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

Claim: Severe COVID-19 outcomes decreases as the pandemic progressed from winter to the warmer months

Abstract:
The undefendable outbreak of novel coronavirus (SARS-COV-2) lead to a global health emergency due to its higher transmission rate and longer symptomatic duration, created a health surge in a short time.
Since Nov 2019 the outbreak in China, the virus is spreading exponentially everywhere.
The current study focuses on the relationship between environmental parameters and the growth rate of COVID-19.
The statistical analysis suggests that the temperature changes retarded the growth rate and found that -6.28{degrees}C and +14.51{degrees}C temperature is the favorable range for COVID-19 growth.
Gutenberg- Richter's relationship is used to estimate the mean daily rate of exceedance of confirmed cases concerning the change in temperature.
Temperature is the most influential parameter that reduces the growth at the rate of 13-16 cases/day with a 1{degrees}C rise in temperature.

Answer: {"verdict": "SUPPORT", "evidence": ["The statistical analysis suggests that the temperature changes retarded the growth rate and found that -6.28{degrees}C and +14.51{degrees}C temperature is the favorable range for COVID-19 growth.", "Temperature is the most influential parameter that reduces the growth at the rate of 13-16 cases/day with a 1{degrees}C rise in temperature."]}


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

Answer: {"verdict": "SUPPORT", "evidence": ["In conclusion, current evidence did not confirm the concern that ACEI/ARB exposure is harmful in patientswith COVID-19 infection.", "This study supports the current guidelines that discourage discontinuation of ACEIs or ARBs in COVID-19 patients and the setting of the COVID-19 pandemic."]}


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: Antiviral drugs that have been used to treat other viral infections including HIV are also being tried for COVID-19, so far without proven success.

Abstract:
The SARS-CoV-2 virus emerged in December 2019 and then spread rapidly worldwide, particularly to China, Japan, and South Korea.
Scientists are endeavoring to find antivirals specific to the virus.
Several drugs such as chloroquine, arbidol, remdesivir, and favipiravir are currently undergoing clinical studies to test their efficacy and safety in the treatment of coronavirus disease 2019 (COVID-19) in China; some promising results have been achieved thus far.
This article summarizes agents with potential efficacy against SARS-CoV-2.

Answer: {"verdict": "SUPPORT", "evidence": ["Several drugs such as chloroquine, arbidol, remdesivir, and favipiravir are currently undergoing clinical studies to test their efficacy and safety in the treatment of coronavirus disease 2019 (COVID-19) in China; some promising results have been achieved thus far."]}


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

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

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

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

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

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


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

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

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

Claim: the virus spreads mainly through small airborne droplets when an infected person coughs or sneezes.

Abstract:
BACKGROUND: The Australian and New Zealand governments both initiated strict social distancing measures in response to the COVID-19 pandemic in late March.
It remains difficult to quantify the impact this had in reducing the spread of the virus.
METHODS: Bayesian structural time series model provide a model to quantify the scenario in which these government-level interventions were not placed.
Our models predict these strict social distancing measures caused a 79% and 61% reduction in the daily cases of COVID-19 across Australia and New Zealand respectively.
CONCLUSION: This provides both evidence and impetus for governments considering similar measures in response to COVID-19 and other pandemics.

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

Abstract:
The ocular surface has been suggested as a site of infection with Coronavirus-2 (SARS-CoV-2) responsible for the coronavirus disease-19 (COVID-19).
This review examines the evidence for this hypothesis, and its implications for clinical practice.
Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2), responsible for the COVID-19 pandemic, is transmitted by person-to-person contact, via airborne droplets, or through contact with contaminated surfaces.
SARS-CoV-2 binds to angiotensin converting enzyme-2 (ACE2) to facilitate infection in humans.
This review sets out to evaluate evidence for the ocular surface as a route of infection.
A literature search in this area was conducted on 15 April 2020 using the Scopus database.
In total, 287 results were returned and reviewed.
There is preliminary evidence for ACE2 expression on corneal and conjunctival cells, but most of the other receptors to which coronaviruses bind appear to be found under epithelia of the ocular surface.
Evidence from animal studies is limited, with a single study suggesting viral particles on the eye can travel to the lung, resulting in very mild infection.
Coronavirus infection is rarely associated with conjunctivitis, with occasional cases reported in patients with confirmed COVID-19, along with isolated cases of conjunctivitis as a presenting sign.
Coronaviruses have been rarely isolated from tears or conjunctival swabs.
The evidence suggests coronaviruses are unlikely to bind to ocular surface cells to initiate infection.
Additionally, hypotheses that the virus could travel from the nasopharynx or through the conjunctival capillaries to the ocular surface during infection are probably incorrect.
Conjunctivitis and isolation of the virus from the ocular surface occur only rarely, and overwhelmingly in patients with confirmed COVID-19.
Necessary precautions to prevent person-to-person transmission should be employed in clinical practice throughout the pandemic, and patients should be reminded to maintain good hygiene practices.

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: Based on currently available information, WHO does not recommend against the use of of ibuprofen.

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

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


Question:
You will be shown a claim 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: Virus transmission affected early covid-19 spread

Abstract:
As the SARS-Cov-2 virus spreads around the world afflicting millions of people, it has undergone divergent genetic mutations.
Although most of these mutations are expected to be inconsequential, some mutations in the spike protein structure have been hypothesized to affect the critical stage at which the virus invades human cells, which could affect transmission probability and disease expression.
If true, then we expect an increased growth rate of reported COVID-19 cases in regions dominated by viruses with these altered proteins.
We modeled early global infection dynamics based on clade assignment along with other demographic and meteorological factors previously found to be important.
Clade, but not variant D614G which has been associated with increased viral load, enhanced our ability to describe early COVID-19 growth dynamics.
Including clade identity in models significantly improved predictions over earlier work based only on weather and demographic variables.
In particular, higher proportions of clade 19A and 19B were negatively correlated with COVID-19 growth rate, whereas higher proportions of 20A and 20C were positively correlated with growth rate.
A strong interaction between the prevalence of clade 20C and relative humidity suggests that the impact of clade identity might be more important when coupled with certain weather conditions.
In particular, 20C an 20A generate the highest growth rates when coupled with low humidity.
Projections based on data through April 2020 suggest that, without intervention, COVID-19 has the potential to grow more quickly in regions dominated by the 20A and 20C clades, including most of South and North America.

Answer: {"verdict": "CONTRADICT", "evidence": ["In particular, higher proportions of clade 19A and 19B were negatively correlated with COVID-19 growth rate, whereas higher proportions of 20A and 20C were positively correlated with growth rate."]}


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 severe cases of COVID-19, activation patterns of B cells resemble those seen in systemic lupus erythematosus, an autoimmune disease. Emory researchers want to see how far that resemblance extends.

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

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


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

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

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

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

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

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


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: CBD help prevent or treat the Covid-19 coronavirus

Abstract:
The recently discovered novel coronavirus, SARS-CoV-2 (COVID-19 virus), has brought the whole world to standstill with critical challenges, affecting both health and economic sectors worldwide.
Although initially, this pandemic was associated with causing severe pulmonary and respiratory disorders, recent case studies reported the association of cerebrovascular-neurological dysfunction in COVID-19 patients, which is also life-threatening.
Several SARS-CoV-2 positive case studies have been reported where there are mild or no symptoms of this virus.
However, a selection of patients are suffering from large artery ischemic strokes.
Although the pathophysiology of the SARS-CoV-2 virus affecting the cerebrovascular system has not been elucidated yet, researchers have identified several pathogenic mechanisms, including a role for the ACE2 receptor.
Therefore, it is extremely crucial to identify the risk factors related to the progression and adverse outcome of cerebrovascular-neurological dysfunction in COVID-19 patients.
Since many articles have reported the effect of smoking (tobacco and cannabis) and vaping in cerebrovascular and neurological systems, and considering that smokers are more prone to viral and bacterial infection compared to non-smokers, it is high time to explore the probable correlation of smoking in COVID-19 patients.
Herein, we have reviewed the possible role of smoking and vaping on cerebrovascular and neurological dysfunction in COVID-19 patients, along with potential pathogenic mechanisms associated with it.

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: As the novel coronavirus sweeps the globe, those with high blood pressure are at heightened risk for more severe complications should they contract Covid-19

Abstract:
Since its recognition in December 2019, covid-19 has rapidly spread globally causing a pandemic.
Pre-existing comorbidities such as hypertension, diabetes, and cardiovascular disease are associated with a greater severity and higher fatality rate of covid-19.
Furthermore, covid-19 contributes to cardiovascular complications, including acute myocardial injury as a result of acute coronary syndrome, myocarditis, stress-cardiomyopathy, arrhythmias, cardiogenic shock, and cardiac arrest.
The cardiovascular interactions of covid-19 have similarities to that of severe acute respiratory syndrome, Middle East respiratory syndrome and influenza.
Specific cardiovascular considerations are also necessary in supportive treatment with anticoagulation, the continued use of renin-angiotensin-aldosterone system inhibitors, arrhythmia monitoring, immunosuppression or modulation, and mechanical circulatory support.

Answer: {"verdict": "SUPPORT", "evidence": ["Pre-existing comorbidities such as hypertension, diabetes, and cardiovascular disease are associated with a greater severity and higher fatality rate 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: Infections occur mainly through exposure to respiratory droplets when a person is in close contact with someone who has COVID-19.

Abstract:
OBJECTIVE.
To analyze the effectiveness of social distancing in the United States (U.S.).
METHODS.
A novel cell-phone ping data was used to quantify the measures of social distancing by all U.S. counties.
RESULTS.
Using a difference-in-difference approach results show that social distancing has been effective in slowing the spread of COVID-19.
CONCLUSIONS.
As policymakers face the very difficult question of the necessity and effectiveness of social distancing across the U.S., counties where the policies have been imposed have effectively increased social distancing and have seen slowing the spread of COVID-19.
These results might help policymakers to make the public understand the risks and benefits of the lockdown.

Answer: {"verdict": "SUPPORT", "evidence": ["As policymakers face the very difficult question of the necessity and effectiveness of social distancing across the U.S., counties where the policies have been imposed have effectively increased social distancing and have seen slowing the spread of 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: Virus evolution affected early covid-19 spread

Abstract:
As the SARS-Cov-2 virus spreads around the world afflicting millions of people, it has undergone divergent genetic mutations.
Although most of these mutations are expected to be inconsequential, some mutations in the spike protein structure have been hypothesized to affect the critical stage at which the virus invades human cells, which could affect transmission probability and disease expression.
If true, then we expect an increased growth rate of reported COVID-19 cases in regions dominated by viruses with these altered proteins.
We modeled early global infection dynamics based on clade assignment along with other demographic and meteorological factors previously found to be important.
Clade, but not variant D614G which has been associated with increased viral load, enhanced our ability to describe early COVID-19 growth dynamics.
Including clade identity in models significantly improved predictions over earlier work based only on weather and demographic variables.
In particular, higher proportions of clade 19A and 19B were negatively correlated with COVID-19 growth rate, whereas higher proportions of 20A and 20C were positively correlated with growth rate.
A strong interaction between the prevalence of clade 20C and relative humidity suggests that the impact of clade identity might be more important when coupled with certain weather conditions.
In particular, 20C an 20A generate the highest growth rates when coupled with low humidity.
Projections based on data through April 2020 suggest that, without intervention, COVID-19 has the potential to grow more quickly in regions dominated by the 20A and 20C clades, including most of South and North America.

Answer: {"verdict": "SUPPORT", "evidence": ["In particular, higher proportions of clade 19A and 19B were negatively correlated with COVID-19 growth rate, whereas higher proportions of 20A and 20C were positively correlated with growth rate."]}


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

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

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

Claim: vitamin D might help protect against becoming infected with, and developing serious symptoms of, COVID-19

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

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


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

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

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

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

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

Answer: {"verdict": "CONTRADICT", "evidence": ["Cannabis smoking is linked with poor respiratory health, immunosuppression and multiple contaminants.", "Potential synergism between the two epidemics would represent a major public health convergence.", "Cigarettes were implicated with disease severity in Wuhan, China.", "Data indicate CVIR demonstrates significant trends across cannabis use intensity quintiles and with relaxed cannabis legislation.", "Recent cannabis use is independently predictive of CVIR in bivariate and multivariable adjusted models and intensity of use is interactively significant.", "Cannabis thus joins tobacco as a SARS2-CoV-2 risk factor."]}