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: High-throughput assays that can infer neutralizing activity against SARS-CoV-2 are of great importance for assessing the immunity induced by natural infection and COVID-19 vaccines. We aimed to evaluate the performance and degree of correlation of three fully automated anti-SARS-CoV-2 immunoassays with neutralization activity using a surrogate virus-neutralizing test (sVNT) from GenScript, targeting the receptor-binding domain. Methods: 110 sera collected from PCR-confirmed asymptomatic COVID-19 individuals were tested for neutralizing antibodies (nAbs) using the sVNT. Positive samples were tested on three automated immunoassays targeting different viral antigens: Mindray CL-900i(R), Abbott Architect, and Ortho VITROS(R). The diagnostic sensitivity, specificity, agreement, and correlation with the sVNT were assessed. Receiver operating characteristic (ROC) curve analysis was performed to determine optimal thresholds for predicting the presence of neutralizing activity by each assay. Results: All three assays showed 100% specificities. The highest sensitivity was 99.0%, demonstrated by VITROS(R), followed by 94.3%, for CL-900i(R), and 81.0%, for Architect. Both VITROS(R) and CL-900i(R) had the strongest correlation with the sVNT (rho = 0.718 and rho = 0.712, respectively), while Architect showed a moderate correlation (rho = 0.618). ROC curve analysis indicated that the manufacturer's recommended cutoff values are adequate for predicting the presence of nAbs and providing a strong correlation with the sVNT. Conclusion: VITROS(R) and CL-900i(R) serological assays, which detect antibodies against SARS-CoV-2 spike protein, could serve as reliable assays to predict neutralization activity after infection or vaccination.
["Diagnosis", "Treatment"]