Evaluation by indirect immunofluorescent assay and enzyme linked immunosorbent assay of the dynamic changes of serum antibody responses against severe acute respiratory syndrome coronavirus.
Bibliographic record
Abstract
BACKGROUND: Severe acute respiratory syndrome coronavirus (SARS-CoV) is a newly emerging virus that gives rise to SARS patients with high rates of infectivity and fatality. To study the humoral immune responses to SARS-CoV, the authors evaluated IgG and IgM specific antibodies in patients' sera. METHODS: Two methods, enzyme-linked immunosorbent assay (ELISA) and indirect immunofluorescent assay (IFA), were used to detect specific serum IgG and IgM against SARS-CoV in 98 SARS patients and 250 controls consisting of patients with pneumonia, health-care professionals and healthy subjects. The serum antibody profiles were investigated at different times over one and a half years in 18 of the SARS patients. RESULTS: The sensitivity and specificity of ELISA for detecting IgG against SARS-CoV were 100.0% and 97.2% and for IgM 89.8% and 97.6% respectively; the figures using IFA for IgG were 100.0% and 100.0% and for IgM 81.8% and 100.0% respectively. During the first seven days of the antibodies trace test, no IgG and IgM were detected, but on day 15, IgG response increased dramatically, reaching a peak on day 60, remaining high up to day 180 and decreasing gradually until day 540. On day 15, IgM was detected, rapidly reached a peak, then declined gradually until day 180 when IgM was undetectable. CONCLUSION: The detection of antibodies against SARS virus is helpful in the clinical diagnosis of SARS.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".