Rapid decline of SARS-CoV-2–specific salivary IgA antibody levels in people with hybrid immunity—data from the STOPCoV study
Bibliographic record
Abstract
Hybrid immune individuals who experience an infection after a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) mRNA vaccine series show evidence of salivary anti-spike and anti-receptor binding domain (RBD) IgA antibodies with broad specificity against different variants. It is unclear how long these antibodies persist and whether they offer protection against new SARS-CoV-2 infections. We compared salivary IgA levels to full-length spike protein and its RBD of the ancestral Wuhan SARS-CoV-2 virus and the Omicron BA.1 variant in a subset of persons participating in a longitudinal study of binding antibody responses to vaccination. We assessed the decay rate of the salivary IgA antibodies in those with hybrid immunity. In our heavily vaccinated population, low levels of salivary IgA to RBD and spike was variably detected in vaccine-only immunity to both Wuhan and Omicron BA.1, but antibody levels were an order of magnitude higher in those with hybrid immunity. In hybrid immune individuals, anti-spike/RBD salivary IgA rapidly decayed over a 4-month observation period. In a multivariate analysis, salivary IgA antibody to Omicron BA.1 was not associated with protection from a new SARS-CoV-2 infection over the subsequent 10 months during the Omicron XBB.1.5, EG.5, and JN waves of infection. In contrast, receipt of a new vaccine dose was significantly associated with protection.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".