Impact of the maternal microbiome on neonatal immune development
Bibliographic record
Abstract
Historically, multigenerational health and disease transmission have primarily focused on genetic inheritance. However, the discovery that beneficial microorganisms known as commensal microbiota outnumber human genes tenfold has reshaped this perspective, highlighting their critical role in maintaining homeostasis and protecting against pathogens. Unlike the human genome, commensal microbiota is not genetically inherited but is acquired anew with each generation. with initial gut colonization playing a pivotal role in shaping an infant's immune system, neurodevelopment, and long-term health, all heavily influenced by maternal factors. In this review, we examine emerging research on maternal microbial influences on the fetus beginning in utero. We provide an updated overview of the current insights into the impact of the vaginal microbiome during parturition on offspring immunity and discuss the potential long-term health implications for infants born via cesarean section. We explore the advantages and limitations of techniques designed to mitigate these effects, such as vaginal seeding and emphasize that the development of the neonatal immune system is a dynamic process influenced by maternal factors beyond birth, including the transfer of microbiota through breast milk and skin contact. Finally, we present gaps in current research and propose future research directions to deepen our understanding of the impacts of the maternal microbiome on her child. Together, these insights demonstrate how maternal influence on offspring health and immunity extends beyond genetic factors, encompassing the transmission of microbiota, which, in turn, has profound long-term implications for health and disease resilience, offering a novel perspective on intergenerational health dynamics.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".