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-cell transcriptome of bronchoalveolar lavage fluid reveals dynamic change of macrophages during sars-cov-2 infection in ferrets

Abstract:
Although the profile of immune cells changes during the natural course of SARS-CoV-2 inflection in human patients, few studies have used a longitudinal approach to reveal their dynamic features.
Here, we performed single-cell RNA sequencing of bronchoalveolar lavage fluid cells longitudinally obtained from SARS-CoV-2-infected ferrets.
Landscape analysis of the lung immune microenvironment showed dynamic changes in cell proportions and characteristics in uninfected control, at 2 days post-infection (dpi) (early stage of SARS-CoV-2 infection with peak viral titer), and 5 dpi (resolution phase).
NK cells and CD8+ T cells exhibited activated subclusters with interferon-stimulated features, which were peaked at 2 dpi.
Intriguingly, macrophages were classified into 10 distinct subpopulations, and their relative proportions changed over the time.
We observed prominent transcriptome changes among monocyte-derived infiltrating macrophages and differentiated M1/M2 macrophages, especially at 2 dpi.
Moreover, trajectory analysis revealed gene expression changes from monocyte-derived infiltrating macrophages toward M1 or M2 macrophages and identified the distinct macrophage subpopulation that had rapidly undergone SARS-CoV-2-mediated activation of inflammatory responses.
Finally, we found that different spectrums of M1 or M2 macrophages showed distinct patterns of gene modules downregulated by immune-modulatory drugs.
Overall, these results elucidate fundamental aspects of the immune response dynamics provoked by SARS-CoV-2 infection.
{"verdict": "SUPPORT", "evidence": ["Landscape analysis of the lung immune microenvironment showed dynamic changes in cell proportions and characteristics in uninfected control, at 2 days post-infection (dpi) (early stage of SARS-CoV-2 infection with peak viral titer), and 5 dpi (resolution phase).", "Moreover, trajectory analysis revealed gene expression changes from monocyte-derived infiltrating macrophages toward M1 or M2 macrophages and identified the distinct macrophage subpopulation that had rapidly undergone SARS-CoV-2-mediated activation of inflammatory responses."]}