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:

We intend to evaluate the differences of the clinical characteristics, cytokine profiles and immunological features in patients with different severity of COVID-19, and to develop novel nomograms based on inflammatory cytokines or lymphocyte subsets for the differential diagnostics for severe or critical and non-severe COVID-19 patients. We retrospectively studied 254 COVID-19 patients, 90 of whom were severe or critical patients and 164 were non-severe patients. Severe or critical patients had significantly higher levels of inflammatory cytokines than non-severe patients as well as lower levels of lymphocyte subsets. Significantly positive correlations between cytokine profiles were observed, while they were all significantly negatively correlated with lymphocyte subsets. Two effective nomograms were developed according to two multivariable logistic regression cox models based on inflammatory cytokine profiles and lymphocyte subsets separately. The areas under the receiver operating characteristics of two nomograms were 0.834 (95% CI: 0.779-0.888) and 0.841 (95% CI: 0.756-0.925). The bootstrapped-concordance indexes of two nomograms were 0.834 and 0.841 in training set, and 0.860 and 0.852 in validation set. Calibration curves and decision curve analyses demonstrated that the nomograms were well calibrated and had significantly more clinical net benefits. Our novel nomograms can accurately predict disease severity of COVID-19, which may facilitate the identification of severe or critical patients and assist physicians in making optimized treatment suggestions.
["Diagnosis", "Treatment"]