Birth and household exposures are associated with changes to skin bacterial communities during infancy
Bibliographic record
Abstract
Background and objectives: Microbial exposures during infancy shape the development of the microbiome, the collection of microbes living in and on the body, which in turn directs immune system training. Newborns acquire a substantial quantity of microbes during birth and throughout infancy via exposure to microbes in the physical and social environment. Alterations to early life microbial environments may give rise to mismatches, where environmental, cultural and behavioral changes that outpace the body's adaptive responses can lead to adverse health outcomes, particularly those related to microbiome development and immune system regulation. Methods: This study explored the development of the skin microbiome among infants born in Chicago, USA. We collected skin swab microbiome samples from 22 mother-infant dyads during the first 48 h of life and again at 6 weeks postpartum. Mothers provided information about social environments and hygiene behaviors that may impact infants' microbial exposures. Results: Analysis of amplicon bacterial gene sequencing data revealed correlations between infant skin bacterial abundances shortly after birth and factors such as antibiotic exposure and receiving a bath in the hospital. The composition of the infant microbiome at 6 weeks of age was associated with interactions with caregivers and infant feeding practices. We also found shifts in maternal skin microbiomes that may reflect increased hygiene practices in the hospital. Conclusions and implications: Our data suggest that factors related to the birth and household environment can impact the development of infant skin microbiomes and point to practices that may produce mismatches for the infant microbiome and immune system.
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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 | 0.002 |
| 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 | 0.000 |
| 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".