Human Milk Microbiota in an Indigenous Population Is Associated with Maternal Factors, Stage of Lactation, and Breastfeeding Practices
Bibliographic record
Abstract
BACKGROUND: Human milk contains a diverse community of bacteria that are modified by maternal factors, but whether these or other factors are similar in developing countries has not been explored. Our objective was to determine whether the milk microbiota was modified by maternal age, BMI, parity, lactation stage, subclinical mastitis (SCM), and breastfeeding practices in the first 6 mo of lactation in an indigenous population from Guatemala. METHODS: = 86) and processed for 16S rRNA sequencing at the genus level. Microbial diversity and relative abundance were compared with maternal factors [age, BMI, parity, stage of lactation, SCM, and 3 breastfeeding practices (exclusive, predominant, mixed)] obtained through questionnaires. RESULTS: was found in mothers with a healthy BMI. Finally, distinct microbial communities differed by stage of lactation and by exclusive, predominant, or mixed breastfeeding practices. CONCLUSION: Milk bacterial communities in an indigenous community were associated with maternal factors. Higher microbial diversity was supported by having a healthy BMI, the absence of SCM, and by breastfeeding. Interestingly, breastfeeding practices when assessed by lactation stage were associated with distinct microbiota profiles.
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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 | 0.001 |
| 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) | 0.001 | 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".