Detection of SARS-CoV-2 antibodies formed in response to the BNT162b2 and mRNA-1237 mRNA vaccine by commercial antibody tests
Bibliographic record
Abstract
ABSTRACT PURPOSE With rapid approval of SARS-CoV-2 vaccines, the ability of clinical laboratories to detect vaccine-induced antibodies with available high-throughput commercial assays is unknown. We aimed to determine if commercial serology assays can detect vaccine-induced antibodies (VIAs) and understand the vaccination response. METHODS This cohort study recruited healthcare workers and residents of long-term care facilities (receiving the BNT162b2 and mRNA-1273 products, respectively) who underwent serum collection pre-vaccination (BNT162b2 group), 2-weeks post vaccination (both groups), and pre-2 nd dose (both groups). Sera were tested for the presence of SARS-CoV-2 IgG using four commercial assays (Abbott Architect SARS-CoV-2 IgG, Abbott Architect SARS-CoV-2 IgG II Quant, DiaSorin Liaison Trimeric S IgG, and GenScript cPASS) to detect VIAs. Secondary outcomes included description of post-vaccination antibody response and correlation with neutralising titers. RESULTS 225 participants (177 receiving BNT162b2 and 48 receiving mRNA-1273) were included (median age 41 years,; 66-78% female). Nucleocapsid IgG was found in 4.1% and 21.9% of the BNT162b2 (baseline) and mRNA-1273 (2-weeks post first dose). All anti-spike assays detected antibodies post-vaccination, with an average increase of 87.2% (range 73.8-94.3%; BNT162b2), and 25.2% (range 23.8-26.7%; mRNA-1273) between the first and last sampling time points (all p<0.05). Neutralising antibodies were detected at all post-vaccine timepoints for both vaccine arms, with increasing titers over time (all p<0.05). CONCLUSION Anti-spike vaccine-induced SARS-CoV-2 IgG are detectable by commercially available high-throughput assays and increases over time. Prior to second dose of vaccination, neutralising antibodies are detectable in 73-89% of individuals, suggesting the majority of individuals would have some degree of protection from subsequent infection.
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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 | 0.001 |
| 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) | 0.003 | 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".