Human milk microbiota profiles in relation to birthing method, gestation and infant gender
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Human milk is an important source of bacteria for the developing infant and has been shown to influence the bacterial composition of the neonate, which in turn can affect disease risk later in life. Very little is known about what factors shape the human milk microbiome. The goal of the present study was to examine the milk microbiota from a range of women who delivered vaginally or by caesarean (C) section, who gave birth to males or females, at term or preterm. METHODS: Milk was collected from 39 Caucasian Canadian women, and microbial profiles were analyzed by 16S ribosomal RNA (rRNA) sequencing using the Illumina platform. RESULTS: A diverse community of milk bacteria was found with the most dominant phyla being Proteobacteria and Firmicutes and at the genus level, Staphylococcus, Pseudomonas, Streptococcus and Lactobacillus. Comparison of bacterial profiles between preterm and term births, C section (elective and non-elective) and vaginal deliveries, and male and female infants showed no statistically significant differences. CONCLUSIONS: The study revealed the diverse bacterial types transferred to newborns. We postulate that there may be a fail-safe mechanism whereby the mother is "ready" to pass along her bacterial imprint irrespective of when and how the baby is born.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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 it