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: Spatial profiling of lung sars-cov-2 and influenza virus infection dissects virus-specific host responses and gene signatures

Abstract:
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that emerged in late 2019 has spread globally, causing a pandemic of respiratory illness designated coronavirus disease 2019 (COVID-19).
Robust blood biomarkers that reflect tissue damage are urgently needed to better stratify and triage infected patients.
Here, we use spatial transcriptomics to generate an in-depth picture of the pulmonary transcriptional landscape of COVID-19 (10 patients), pandemic H1N1 (pH1N1) influenza (5) and uninfected control patients (4).
Host transcriptomics showed a significant upregulation of genes associated with inflammation, type I interferon production, coagulation and angiogenesis in the lungs of COVID-19 patients compared to non-infected controls.
SARS-CoV-2 was non-uniformly distributed in lungs with few areas of high viral load and these were largely only associated with an increased type I interferon response.
A very limited number of genes were differentially expressed between the lungs of influenza and COVID-19 patients.
Specific interferon-associated genes (including IFI27) were identified as candidate novel biomarkers for COVID-19 differentiating this COVID-19 from influenza.
Collectively, these data demonstrate that spatial transcriptomics is a powerful tool to identify novel gene signatures within tissues, offering new insights into the pathogenesis of SARS-COV-2 to aid in patient triage and treatment.
{"verdict": "SUPPORT", "evidence": ["Here, we use spatial transcriptomics to generate an in-depth picture of the pulmonary transcriptional landscape of COVID-19 (10 patients), pandemic H1N1 (pH1N1) influenza (5) and uninfected control patients (4).", "Collectively, these data demonstrate that spatial transcriptomics is a powerful tool to identify novel gene signatures within tissues, offering new insights into the pathogenesis of SARS-COV-2 to aid in patient triage and treatment."]}