BNT162b2 COVID-19 vaccination in children alters cytokine responses to heterologous pathogens and Toll-like receptor agonists
Bibliographic record
Abstract
Background: Vaccines can have beneficial off-target (heterologous) effects that alter immune responses to, and protect against, unrelated infections. The heterologous effects of COVID-19 vaccines have not been investigated in children. Aim: To investigate heterologous and specific immunological effects of BNT162b2 COVID-19 vaccination in children. Methods: cytokine responses to heterologous stimulants (killed pathogens, Toll-like receptor ligands) and SARS-CoV-2 antigens. Samples from 29 children, aged 5-11 years, before and 28 days after a second BNT162b2 vaccination were analysed (V2 + 28). Samples from eight children were analysed six months after BNT162b2 vaccination. Results: , hepatitis B antigen, poly(I:C) and R848 stimulations were decreased compared to pre-vaccination. For most of these heterologous stimulants, IL-6, IL-15 and IL-17 responses were also decreased. There were sustained decreases in cytokine responses to viral, but not bacterial, stimulants six months after BNT162b2 vaccination. Cytokine responses to irradiated SARS-CoV-2, and spike glycoprotein subunits (S1 and S2) were increased at V2 + 28 for most cytokines and remained higher than pre-vaccination responses 6 months after BNT162b2 vaccination for irradiated SARS-CoV-2 and S1. There was no correlation between BNT162b2 vaccination-induced anti-SARS-CoV2-receptor binding domain IgG antibody titre at V2 + 28 and cytokine responses. Conclusions: BNT162b2 vaccination in children alters cytokine responses to heterologous stimulants, particularly one month after vaccination. This study is the first to report the immunological heterologous effects of COVID-19 vaccination in children.
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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".