Evaluating Humoral Immunity against SARS-CoV-2: Validation of a Plaque-Reduction Neutralization Test and a Multilaboratory Comparison of Conventional and Surrogate Neutralization Assays
Bibliographic record
Abstract
SARS-CoV-2, the causal agent of COVID-19, has infected over 246 million people and led to over 5 million deaths as of October 2021. With the approval of several efficacious COVID-19 vaccines, methods to evaluate protective immune responses will be crucial for the understanding of long-term immunity in the rapidly growing vaccinated population. The PRNT, which quantifies SARS-CoV-2-neutralizing antibodies, is used widely as a reference standard to validate new platforms but has not undergone substantial validation to ensure excellent inter- and intraassay precision and specificity. Our work is significant, as it describes the thorough validation of a PRNT, which we then used as a reference standard for the comparison of several alternative serological methods to measure SARS-CoV-2-neutralizing antibodies. These assays demonstrated excellent agreement with the reference-standard PRNT and include high-throughput platforms, which can greatly enhance capacity to assess both natural and vaccine-induced protective immunity against SARS-CoV-2.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".