Intramuscular SARS-CoV-2 vaccines elicit varying degrees of plasma and salivary antibody responses as compared to natural infection
Bibliographic record
Abstract
ABSTRACT Vaccination induced antibody and T-cell immune responses are important for systemic protection from COVID-19. Because SARS-CoV-2 infects and is transmitted by oral-pharyngeal mucosa, we wished to test mucosal antibodies elicited by natural infection or intramuscular vaccine injection. In a non-randomized observational study, we measured antibodies against the SARS-CoV-2 RBD in plasma and saliva from convalescent or vaccinated individuals and tested their neutralizing potential using a replication competent rVSV-eGFP-SARS-CoV-2. We found IgG and IgA anti-RBD antibodies as well as neutralizing activity in convalescent plasma and saliva. Two doses of mRNA vaccination (BNT162b2 or mRNA-1273) induced high levels of IgG anti-RBD in saliva, a subset of whom also had IgA, and significant neutralizing activity. We detected anti-RBD IgG and IgA with significant neutralizing potential in the plasma of single dose Ad26.COV2.S vaccinated individuals, and we detected slight amounts of anti-RBD antibodies in matched saliva. The role of salivary antibodies in protection against SARS-CoV-2 infection is unknown and merits further investigation. This study was not designed to, nor did it study the full kinetics of the antibody response or protection from infection, nor did it address variants of SARS-CoV-2.
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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 | 0.001 |
| 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 | 0.001 |
| 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".