SG-APSIC1054: Sputnik-V postvaccination immunologic responses in nasal mucosa: A prospective cohort study in Kazakhstan
Bibliographic record
Abstract
Objectives: Sputnik-V (Gam-COVID-Vac) is a recombinant adenoviral (rAdv) vector-based, COVID-19 vaccine now used in >70 countries. Mucosal immunity is thought to be important for protection against COVID-19. We did a prospective cohort study to assess Sputnik-V–elicited mucosal SARS-CoV-2 antibody responses. Methods: We divided 82 COVID-19–free participants into prior COVID-19 and no prior COVID-19 groups and followed them at day 21 after Sputnik-V dose 1′ (rAd5) and dose 2′ (rAd26). Nasopharyngeal swabs and blood were collected to perform SARS-CoV-2 diagnostic and immunologic assays. SARS-CoV-2 spike-specific IgG and IgA ELISAs were performed on both nasal swabs and blood. SARS-CoV-2 real-time RT-PCR testing was performed to exclude infectious influencing. Results: Nasal S-IgG levels increased 25-fold after dose 1′ (P < .001) and remained high after dose 2 in all participants. Prior COVID-19 exposure was associated with both elevated baseline mucosal IgG and IgA and higher postvaccination IgG, but not IgA, boost. Nasal IgA levels increased 16.5-fold after dose 1′ (P < .001) and remained high after dose 2’ in all participants. Compared to dose 1′, Sputnik-V dose 2′ did not further increase either mucosal IgG levels (P = .626) or IgA levels (P = .609). Conclusions: A single dose of Sputnik-V boosted mucosal SARS-CoV-2 immunity. The effects of Sputnik-V dose 2′ on mucosal immunity were minimal. These findings indicate (1) that intramuscularly administered adenoviral vaccines enhance SARS-CoV-2 immunity via both systemic and mucosal routes and (2) that cost-effectiveness and the efficacy of Sputnik-V vaccination could be improved by adjusting the current prime-booster regimen and extending the 21-day interval between the doses. Trial registration: Registered on ClinicalTrials.gov (no. NCT04871841).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".