Delayed interval BNT162b2 mRNA COVID-19 vaccination provides robust immunity
Bibliographic record
Abstract
Abstract Shortages of COVID-19 vaccines have results in delayed dosing intervals as a strategy to immunize a greater proportion of the population. The effect of this strategy on vaccine immunogenicity is not well studied. Humoral (anti-RBD levels and neutralization) and cellular immune responses were compared in health care workers receiving two doses of BNT162b2 (Pfizer-BioNTech) vaccines at standard (3-6 week) and delayed (8-12 week) intervals. In the delayed group, anti-RBD antibody titres were significantly enhanced compared to the standard interval group. Neutralizing antibody responses were excellent and comparable in both groups. A slight decrease in Spike-specific polyfunctional CD4+ T-cells expressing interferon-γ and IL-2 as well as monofunctional CD4+ T-cells was seen in the delayed group. Both polyfunctional and monofunctional CD8+ T-cell responses were comparable. Our data suggest that the strategy of delayed second dose mRNA vaccination is not overtly detrimental, and specifically may lead to an enhanced humoral immune response.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.003 | 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".