Validation and Establishment of a SARS-CoV-2 Lentivirus Surrogate Neutralization Assay as a pre-screening tool for the Plaque Reduction Neutralization Test
Bibliographic record
Abstract
Abstract Neutralization assays are important in understanding and quantifying neutralizing antibody responses towards SARS-CoV-2. The SARS-CoV-2 Lentivirus Surrogate Neutralization Assay (SCLSNA) can be used in biosafety level 2 (BSL-2) laboratories and has been shown to be a reliable, alternative approach to the plaque reduction neutralization test (PRNT). In this study, we optimized and validated the SCLSNA to assess its ability as a comparator and pre-screening method to support the PRNT. Comparability between the PRNT and SCLSNA was determined through clinical sensitivity and specificity evaluations. Clinical sensitivity and specificity produced acceptable results with 100% (95% CI: 94-100) specificity and 100% (95% CI: 94-100) sensitivity against ancestral Wuhan spike pseudotyped lentivirus. The sensitivity and specificity against B.1.1.7 spike pseudotyped lentivirus resulted in 88.3% (95% CI: 77.8 to 94.2) and 100% (95% CI: 94-100), respectively. Assay precision measuring intra-assay variability produced acceptable results for High (1:≥ 640 PRNT 50 ), Mid (1:160 PRNT 50 ) and Low (1:40 PRNT 50 ) antibody titer concentration ranges based on the PRNT 50 , with %CV of 14.21, 12.47, and 13.28 respectively. Intermediate precision indicated acceptable ranges for the High and Mid concentrations, with %CV of 15.52 and 16.09, respectively. However, the Low concentration did not meet the acceptance criteria with a %CV of 26.42. Acceptable ranges were found in the robustness evaluation for both intra-assay and inter-assay variability. In summary, the validation parameters tested met the acceptance criteria, making the SCLSNA method fit for its intended purpose, which can be used to support the PRNT.
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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 | 0.014 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".