Detection of Anti-SARS-CoV-2 Nucleocapsid and Spike Antibodies in Patients with Coronavirus Disease 2019 in Japan
Bibliographic record
Abstract
OBJECTIVES: Coronavirus Disease 2019 (COVID-19) is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Serological testing for anti-SARS-CoV-2 nucleocapsid (N) antibodies (Abs) and anti-SARS-CoV-2 spike (S) Abs is performed to detect prior COVID-19 infection. It is still controversial which antibodies are the most sensitive and specific, and which can be detected earliest after infection. Here, we evaluated the results of serological tests of anti-SARS-CoV-2 N and S Abs in Japan. METHODS: Symptomatic COVID-19 patients (n = 84) and control patients with rheumatoid arthritis (n = 93) were recruited at Tokyo National Hospital. Anti-SARS-CoV-2 N and S Abs were measured by commercial electrochemiluminescence immunoassays. RESULTS: The fraction of patients positive for anti-SARS-CoV-2 N and S Abs was highest >14 days after symptom onset. The frequency of anti-SARS-CoV-2 S Ab positivity at this time (80.4%) tended to be slightly but not significantly lower than anti-SARS-CoV-2 N Ab positivity (84.8%). Optimized cut-off levels for anti-SARS-CoV-2 N and S Ab positivity were lower than the manufacturer's recommended cut-off levels. Using multiple linear regression analyzes with anti-SARS-CoV-2 N and S Abs, we created an Ab-index with high sensitivity. CONCLUSION: To increase the sensitivity of serological diagnostic tests for COVID-19, it is suggested that both anti-SARS-CoV-2 N and S Abs should be measured and cut-off levels decreased.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".