Platelet and immune signature associated with a rapid response to the BNT162b2 mRNA COVID‐19 vaccine
Bibliographic record
Abstract
BACKGROUND: A rapid immune response is critical to ensure effective protection against COVID-19. Platelets are first-line sentinels of the vascular system able to rapidly alert and stimulate the immune system. However, their role in the immune response to vaccines is not known. OBJECTIVE: To identify features of the platelet-immune crosstalk that would provide an early readout of vaccine efficacy in adults who received the mRNA-based COVID-19 vaccine (BNT162b2). METHODS: We prospectively enrolled 11 young healthy volunteers (54% females, median age: 28 years) who received two doses of BNT162b2, 21 days apart, and we studied their platelet and immune response before and after each dose of the vaccine (3 and 10 ± 2 days post-injection), in relation to the kinetics of the humoral response. RESULTS: Participants achieving an effective level of neutralizing antibodies before the second dose of the vaccine (fast responders) had a higher leukocyte count, mounted a rapid cytokine response that incremented further after the second dose, and an elevated platelet turnover that ensured platelet count stability. Their circulating platelets were not more reactive but expressed lower surface levels of the immunoreceptor tyrosine-based inhibitory motif (ITIM)-coupled receptor CD31 (PECAM-1) compared to slow responders, and formed specific platelet-leukocyte aggregates, with B cells, just 3 days after the first dose, and with non-classical monocytes and eosinophils. CONCLUSION: We identified features of the platelet-immune crosstalk that are associated with the development of a rapid humoral response to an mRNA-based vaccine (BNT162b2) and that could be exploited as early biomarkers of vaccine efficacy.
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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 | 0.000 |
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".