Microbiota at Multiple Body Sites during Pregnancy in a Rural Tanzanian Population and Effects of Moringa-Supplemented Probiotic Yogurt
Bibliographic record
Abstract
The nutritional status of pregnant women is vital for healthy outcomes and is a concern for a large proportion of the world's population. The role of the microbiota in pregnancy and nutrition is a promising new area of study with potential health ramifications. In many African countries, maternal and infant death and morbidity are associated with malnutrition. Here, we assess the influence of probiotic yogurt containing Lactobacillus rhamnosus GR-1, supplemented with Moringa plant as a source of micronutrients, on the health and oral, gut, vaginal, and milk microbiotas of 56 pregnant women in Tanzania. In an open-label study design, 26 subjects received yogurt daily, and 30 were untreated during the last two trimesters and for 1 month after birth. Samples were analyzed using 16S rRNA gene sequencing, and dietary recalls were recorded. Women initially categorized as nourished or undernourished consumed similar calories and macronutrients, which may explain why there was no difference in the microbiota at any body site. Consumption of yogurt increased the relative abundance of Bifidobacterium and decreased Enterobacteriaceae in the newborn feces but had no effect on the mother's microbiota at any body site. The microbiota of the oral cavity and GI tract remained stable over pregnancy, but the vaginal microbiota showed a significant increase in diversity leading up to and after birth. In summary, daily micronutrient-supplemented probiotic yogurt provides a safe, affordable food for pregnant women in rural Tanzania, and the resultant improvement in the gut microbial profile of infants is worthy of further study.
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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".