Evaluation of Euroimmun Anti-Zika Virus IgM and IgG Enzyme-Linked Immunosorbent Assays for Zika Virus Serologic Testing
Bibliographic record
Abstract
With the emerging Zika virus (ZIKV) epidemic, serologic diagnosis relies on a labor-intensive IgM antibody capture enzyme-linked immunosorbent assay (MAC-ELISA) and confirmation by a plaque reduction neutralization test (PRNT). To streamline serologic testing, several commercial assays have been developed. Our aim was to compare the commercial Euroimmun anti-ZIKV IgM and IgG assays to the reference MAC-ELISA and PRNT currently in use. Serum specimens submitted to Public Health Ontario Laboratory, Canada, were tested for IgM and IgG using the Euroimmun assays and the results were compared with those from MAC-ELISA. The PRNT was performed on positive or equivocal specimens using either MAC-ELISA or Euroimmun assays, MAC-ELISA-inconclusive specimens, and a convenience sample of specimens negative by both assays (cohort 1). Another set of specimens selected on the basis of PRNT results was subsequently tested by the Euroimmun assays (cohort 2). MAC-ELISA was positive, equivocal, negative, and inconclusive in 57/223, 15/223, 147/223, and 4/223 specimens, respectively. Among the 76 specimens that were MAC-ELISA positive, equivocal, or inconclusive, 30 (39.5%) were Euroimmun IgM and/or IgG positive or equivocal. Among the 147 MAC-ELISA-negative specimens, 136 (92.5%) were Euroimmun IgM and IgG negative. The sensitivity of the combined Euroimmun IgM/IgG against the PRNT was 83% (cohort 1) and 92% (cohort 2), whereas the specificity was 81% (cohort 1) and 65% (cohort 2). The combined Euroimmun IgM/IgG showed good specificity (92.5%) but suboptimal sensitivity (39.5%) compared with that of the MAC-ELISA. However, the sensitivity of the combined Euroimmun IgM/IgG against the PRNT was significantly higher (83 to 92%). More studies are needed before commercial assays are implemented for routine ZIKV serologic diagnosis.
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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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".