Low and transient salivary neutralizing antibodies after COVID vaccination
Bibliographic record
Abstract
Abstract Neutralizing activity found in the blood following intramuscular vaccination protects against systemic disease from SARS-CoV-2 infection. Mild breakthrough mucosal infections in vaccinated individuals can contribute to transmission chains of infection, and as such the contribution of intramuscular vaccination to mucosal immunity is important to understand. We assessed vesicular stomatitis virus (VSV) SARS-CoV-2 SPIKE protein neutralization activities as well as anti-viral spike and RBD IgG and IgA isotype antibody binding, in saliva from individuals who had recovered from SARS-CoV-2 infection and people who received immunization. The various vaccination strategies deployed globally showed key differences in the extent of neutralizing activity that could be measured. Even in subjects with significant peak activity 2–4 weeks following a second dose of mRNA vaccination, this neutralizing activity was transient and significantly reduced within 3–6 months. Among adenoviral vectors, ChAdOx1 elicited significantly more salivary neutralizing activity and antibody levels compared to Ad26.S. A large range of salivary neutralizing activity is induced following different intramuscular SARS-COV-2 vaccination strategies. All were orders of magnitude lower than neutralization levels observed in blood, and it remains to be determined if any of the observed neutralizing activity in the saliva can neutralize virus in the upper respiratory tract upon exposure. Based on studies where high levels of mucosal IgA antibodies were induced in response to stimulation with aerosol intranasal vaccines, such vaccines could be important in lowering transmission of SARS-COV-2. Supported by The Fairbairn Foundation and SPARK
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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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".