Neonatal BCG Vaccination Reduces Interferon-γ Responsiveness to Heterologous Pathogens in Infants From a Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: BCG vaccination has beneficial nonspecific (heterologous) effects that protect against nonmycobacterial infections. We have previously reported that BCG vaccination at birth alters in vitro cytokine responses to heterologous stimulants in the neonatal period. This study investigated heterologous responses in 167 infants in the same trial 7 months after randomization. METHODS: A whole-blood assay was used to interrogate in vitro cytokine responses to heterologous stimulants (killed pathogens) and Toll-like receptor (TLR) ligands. RESULTS: Compared to BCG-naive infants, BCG-vaccinated infants had increased production of interferon gamma (IFN-γ) and monokine induced by gamma interferon (MIG) (CXCL9) in response to mycobacterial stimulation and decreased production of IFN-γ in response to heterologous stimulation and TLR ligands. Reduced IFN-γ responses were attributable to a decrease in the proportion of infants who mounted a detectable IFN-γ response. BCG-vaccinated infants also had increased production of MIG (CXCL9) and interleukin-8 (IL-8), and decreased production of IL-10, macrophage inflammatory protein-1α (MIP-1α), and MIP-1β, the pattern of which varied by stimulant. IL-1Ra responses following TLR1/2 (Pam3CYSK4) stimulation were increased in BCG-vaccinated infants. Both sex and maternal BCG vaccination status influenced the effect of neonatal BCG vaccination. CONCLUSIONS: BCG vaccination leads to changes in IFN-γ responsiveness to heterologous stimulation. BCG-induced changes in other cytokine responses to heterologous stimulation vary by pathogen.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".