Immune responses after two inactivated COVID-19 vaccine doses, a heterologous third dose and subsequent boosting with bivalent mRNA in adults
Bibliographic record
Abstract
To evaluate immune response and safety of a bivalent mRNA booster (ancestral/BA.4/5) vaccine in individuals who had received inactivated COVID-19 vaccine with different heterologous boost regimens. A prospective open-label study of bivalent ancestral/Omicron BA.4/5 was conducted. Healthy participants (age > 18) who completed two doses of inactivated COVID-19 vaccine and received any of these booster vaccines (Ad26.COV2.S, ChAdOx1, or mRNA-based) at least 12 months prior were enrolled. Immunogenicity data (anti-Spike (S) IgG, neutralization antibody titer (NT 50 ) against ancestral and XBB.1.5, and S-specific IFN-γ T- cells) was obtained at baseline, Day 28±7, and Day 90±14. Of 190 participants enrolled; 57 received Ad26.COV2.S, 66 received ChAdOx1, and 67 received mRNA vaccine as the third dose, respectively. Following bivalent mRNA vaccination, anti-S IgG rose at Day 28, and declined at Day 90. In contrast, the NT 50 titers against ancestral peaked at Day90. The NT 50 against XBB.1.5 peaked at Day 28 with the highest fold rise in the mRNA vaccine subgroup (29.16 [19.55–43.49]), followed by the ChAdOx1 (20.94 [14.18–30.92]), and the Ad26.COV2.S subgroups (13.04 [8.61–19.74]). Geometric concentration of T-cells producing IFN-γ rose comparably in all three subgroups. Bivalent mRNA ancestral/BA.4/5 vaccine enhanced humoral immunity against both ancestral and Omicron XBB1.5, and T-cell immunity in inactivated COVID-19 vaccine primed with different heterologous boost participants. The study was registered at WHO platform: Thai Clinical Trial Registry (TCTR20230811004).
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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) | 0.001 | 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".