Accurate detection of Zika virus IgG using a novel immune complex binding ELISA
Bibliographic record
Abstract
OBJECTIVES: Accurate serological assays are urgently needed to support public health responses to Zika virus (ZIKV) infection with its potential to cause foetal damage during pregnancy. Current flavivirus serology for ZIKV infections lacks specificity due to cross-reacting antibodies from closely related other flaviviruses. In this study, we evaluated novel serological tests for accurate ZIKV IgG detection. METHODS: Our ELISAs are based on immune complex binding. The high specificity is achieved by the simultaneous incubation of labelled ZIKV antigen and unlabelled flavivirus homolog protein competitors. Two assays were validated with a panel of 406 human samples from PCR-confirmed ZIKV patients collected in Brazil (n = 154), healthy blood donors and other infections from Brazil, Europe, Canada and Colombia (n = 252). RESULTS: The highest specificity (100% [252/252, 95% confidence interval (CI) 98.5-100.0]) was shown by the ZIKV ED3 ICB ELISA using the ED3 antigen of the ZIKV envelope. A similar test using the NS1 antigen (ZIKV NS1 ICB ELISA) was slightly less specific (92.1% [232/252, 95% CI 88.0-95.1]). The commercial Euroimmun ZIKV ELISA had a specificity of only 82.1% (207/252, 95% CI 76.8-86.7). Sensitivity was high (93-100%) from day 12 after onset of symptoms in all three tests. Seroprevalence of ZIKV IgG was analysed in 87 samples from Laos (Asia) confirming that the ED3 ELISA showed specific reactions in other populations. CONCLUSIONS: The novel ED3 ICB ELISA will be useful for ZIKV-specific IgG detection for seroepidemiological studies and serological diagnosis for case management in travellers and in countries where other flavivirus infections are co-circulating.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".