Evaluation of a Commercial Culture-free Neutralization Antibody Detection Kit for Severe Acute Respiratory Syndrome-Related Coronavirus-2 and Comparison with an Anti-RBD ELISA Assay
Bibliographic record
Abstract
ABSTRACT Background SARS-CoV-2 surrogate neutralization assays that obviate the need for viral culture offer substantial advantages regarding throughput and cost. The cPass SARS-CoV-2 Neutralization Antibody Detection Kit (Genscript) is the first such commercially available assay, detecting antibodies that block RBD/ACE-2 interaction. We aimed to evaluate cPass to inform its use and assess its added value compared to anti-RBD ELISA assays. Methods Serum reference panels comprising 205 specimens were used to compare cPass to plaque-reduction neutralization test (PRNT) and a pseudotyped lentiviral neutralization (PLV) assay for detection of neutralizing antibodies. We assessed the correlation of cPass with an ELISA detecting anti-RBD IgG, IgM, and IgA antibodies at a single timepoint and across intervals from onset of symptoms of SARS-CoV-2 infection. Results Compared to PRNT-50, cPass sensitivity ranged from 77% - 100% and specificity was 95% - 100%. Sensitivity was also high compared to the pseudotyped lentiviral neutralization assay (93% [95%CI 85-97]), but specificity was lower (58% [95%CI 48-67]). Highest agreement between cPass and ELISA was for anti-RBD IgG ( r =0.823). Against the pseudotyped lentiviral neutralization assay, anti-RBD IgG sensitivity (99% [95%CI 94-100]) was very similar to that of cPass, but overall specificity was lower (37% [95%CI 28-47]). Against PRNT-50, results of cPass and anti-RBD IgG were nearly identical. Conclusions The added value of cPass compared to an IgG anti-RBD ELISA was modest.
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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 | 0.013 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".