A New SARS-CoV-2 Dual-Purpose Serology Test: Highly Accurate Infection Tracing and Neutralizing Antibody Response Detection
Bibliographic record
Abstract
Many severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) serology tests have proven to be less accurate than expected and do not assess antibody function as neutralizing, correlating with protection from reinfection. A new assay technology measuring the interaction of the purified SARS-CoV-2 spike protein receptor binding domain (RBD) with the extracellular domain of the human angiotensin-converting enzyme 2 (hACE2) receptor detects these important antibodies. The cPass surrogate virus neutralization test (sVNT), compared directly with eight SARS-CoV-2 IgG serology and two live-cell neutralization tests, gives similar or improved accuracy for qualitative delineation between positive and negative individuals in a fast, scalable, and high-throughput assay. The combined data support the cPass sVNT as a tool for highly accurate SARS-CoV-2 immunity surveillance of infected/recovered and/or vaccinated individuals as well as drug and convalescent-phase donor screening. The data also preview a novel application for the cPass sVNT in calibrating the stringency of live-cell neutralization tests and its use in longitudinal testing of recovered and/or vaccinated patients.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".