Vaccine Interactions With the Infant Microbiome: Do They Define Health and Disease?
Bibliographic record
Abstract
Over the past decade, there has been a growing awareness of the vital role of the microbiome in the function of the immune system. Recently, several studies have demonstrated a relationship between the composition of the microbiome and the vaccine-specific immune response. As a result of these findings, the administration of probiotics has been proposed as a means of boosting vaccine-specific immunity. Early results have so far been highly inconsistent, with little evidence of sustained benefit. To date, a precise determination of the aspects of the microbiome that impact immunity is still lacking, and the mechanisms of action are also unknown. Further investigations into these questions are necessary to effectively manipulate the microbiome for the purpose of boosting immunity and enhancing vaccine-specific responses in infants. In this review, we summarize recent studies aimed at altering the neonatal gut microbiome to enhance vaccine responses and highlight gaps in knowledge and understanding. We also discuss research strategies aimed at filling these gaps and developing potential therapeutic interventions.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".