<scp>Prenatal</scp> household size and composition are associated with infant fecal bacterial diversity in <scp>C</scp>ebu, <scp>P</scp>hilippines
Bibliographic record
Abstract
OBJECTIVES: The gut microbiome (GM) connects physical and social environments to infant health. Since the infant GM affects immune system development, there is interest in understanding how infants acquire microbes from mothers and other household members. MATERIALS AND METHODS: As a part of the Cebu Longitudinal Health and Nutrition Survey (CLHNS), we paired fecal samples (proxy for the GM) collected from infants living in Metro Cebu, Philippines at 2 weeks (N = 39) and 6 months (N = 36) with maternal interviews about prenatal household composition. We hypothesized that relationships between prenatal household size and composition and infant GM bacterial diversity (as measured in fecal samples) would vary by infant age, as well as by household member age and sex. We also hypothesized that infant GM bacterial abundances would differ by prenatal household size and composition. RESULTS: Data from 16 S rRNA bacterial gene sequencing show that prenatal household size was the most precise estimator of infant GM bacterial diversity, and that the direction of the association between this variable and infant GM bacterial diversity changed between the two time points. The abundances of bacterial families in the infant GM varied by prenatal household variables. CONCLUSIONS: Results highlight the contributions of various household sources to the bacterial diversity of the infant GM, and suggest that prenatal household size is a useful measure for estimating infant GM bacterial diversity in this cohort. Future research should measure the effect of specific sources of household bacterial exposures, including social interactions with caregivers, on the infant GM.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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) | 0.003 | 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".