You are presented with a paragraph from a COVID-19 literature. Classify the topics of this piece of text by choosing from the following categories:

- Epidemic Forecasting: The content relates to predictions about the spread of COVID-19.
- Treatment: The paragraph discusses therapeutic methods or medication for COVID-19.
- Prevention: The content covers measures to prevent the spread of COVID-19.
- Mechanism: The paragraph explains the biological mechanisms of the virus.
- Case Report: The content is a report of a COVID-19 case.
- Transmission: The paragraph discusses how COVID-19 is transmitted.
- Diagnosis: The content is about diagnosing COVID-19 in individuals.

Your classification should be provided as an element within a JSON array. The array should be formatted with topics enclosed in double quotes and separated by commas, following the JSON format, for example: ["Topic1", "Topic2", ...]. Include all applicable topics within the array. Your output should be a valid JSON array, and no additional text should be included in your response.

Article:

BACKGROUND: Although there are a growing number of studies on evaluating lymphocyte subset counts as prognostic factors for COVID-19 disease severity, the lymphocyte subsets' analyses of both IgM and IgG responders and non-responders during the periods after onset of symptoms, have not been conducted yet. So, this study aimed to evaluate immune cell profiling of COVID-19 patients with and without antibody responses. METHODS: In this cross-sectional study, the levels of peripheral lymphocyte subsets were measured using flow cytometry in 53 patients with positive SARS-CoV-2 RT-PCR, for whom antibody testing of COVID-19 was performed. RESULTS: The white blood cell, neutrophil, and lymphocyte counts consistently decreased in the IgM and IgG non-responder group, while the differences in the median value between the two study groups were found to be statistically significant only in terms of neutrophil counts (P = 0.024 for IgM response and p-value = 0.046 for IgG response, respectively). Moreover, the level of neutrophil-to-lymphocyte ratio was observed to be significantly lower in the IgM or IgG non-responder group compared to the IgM or IgG responder group (3.6 +/- 3.1 vs. 6.3 +/- 4.2; p-value = 0.021). The patients with IgM antibody response had a significantly lower CD20(+) lymphocytes (11% versus 15% in the groups without IgM antibody response, p-value = 0.031), The percentages of NK cells and CD4(+) T cells significantly increased in the patients with IgG antibody response compared to those without IgG antibody response (13% versus 10%, p-value = 0.028, and 41.5% versus 34%; p-value = 0.03, respectively). Moreover, the patients who produced IgM or IgG antibody had significantly higher percentages of total T lymphocytes (64% versus 54%; p-value = 0.017), CD4(+) T cells (41% versus 34%; p-value = 0.038), and NK cells (13% versus 9%, p-value = 0.023) compared to the group with no serological response. No significant difference was observed in the percentage of other lymphocyte subsets, including CD8(+) T cells, Treg cells, and CD19(+) B cells. CONCLUSION: Our results suggest that the total T cells, CD4(+) T cells, and NK cells percentages are linked to serological response. Moreover, our findings suggested that neutrophil absolute counts and neutrophil-to-lymphocyte ratio may be valuable predictors of IgM or IgG antibody response.
["Diagnosis", "Treatment"]