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: Immune phenotyping based on neutrophil-to-lymphocyte ratio and igg predicts disease severity and outcome for patients with covid-19

Abstract:
Background: A recently emerging respiratory disease named coronavirus disease 2019 (COVID-19) has quickly spread across the world.
This disease is initiated by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) and uncontrolled cytokine storm, but it remains unknown as to whether a robust antibody response is related to clinical deterioration and poor outcome in laboratory-confirmed COVID-19 patients.
Methods: Anti-SARS-CoV-2 IgG and IgM antibodies were determined by chemiluminescence analysis (CLIA) in COVID-19 patients from a single center in Wuhan.
Median IgG and IgM levels in acute and convalescent-phase sera (within 35 days) for all included patients were calculated and compared among severe and nonsevere patients.
Immune response phenotyping based on late IgG levels and neutrophil-to-lymphocyte ratio (NLR) was characterized to stratify patients with different disease severities and outcome.
Laboratory parameters in patients with different immune response phenotypes and disease severities were analyzed.
Findings: A total of 222 patients were included in this study.
IgG was first detected on day 4 of illness, and its peak levels occurred in the fourth week.
Severe cases were more frequently found in patients with high IgG levels, compared to those who with low IgG levels (51.8% versus 32.3%; p=0.008).
Severity rates for patients with NLRhiIgGhi, NLRhiIgGlo, NLRloIgGhi, and NLRloIgGlo phenotype was 72.3%, 48.5%, 33.3%, and 15.6%, respectively (p<0.0001).
Furthermore, severe patients with NLRhiIgGhi, NLRhiIgGlo had higher proinflammatory cytokines levels including IL-2, IL-6 and IL-10, and decreased CD4+ T cell count compared to those with NLRloIgGlo phenotype (p<0.05).
Recovery rate for severe patients with NLRhiIgGhi, NLRhiIgGlo, NLRloIgGhi, and NLRloIgGlo phenotype was 58.8% (20/34), 68.8% (11/16), 80.0% (4/5), and 100% (12/12), respectively (p=0.0592).
Dead cases only occurred in NLRhiIgGhi and NLRhiIgGlo phenotypes.
Interpretation: COVID-19 severity is associated with increased IgG response, and an immune response phenotyping based on late IgG response and NLR could act as a simple complementary tool to discriminate between severe and nonsevere COVID-19 patients, and further predict their clinical outcome.
{"verdict": "SUPPORT", "evidence": ["Immune response phenotyping based on late IgG levels and neutrophil-to-lymphocyte ratio (NLR) was characterized to stratify patients with different disease severities and outcome."]}