Capturing the diversity of the human milk microbiota through culture-enriched molecular profiling: a feasibility study
Bibliographic record
Abstract
Previous human milk studies have confirmed the existence of a highly diverse bacterial community using culture-independent and targeted culture-dependent techniques. However, culture-enriched molecular profiling of milk microbiota has not been done. Additionally, the impact of storage conditions and milk fractionation on microbiota composition is not understood. In this feasibility study, we optimized and applied culture-enriched molecular profiling to study culturable milk microbiota in eight milk samples collected from mothers of infants admitted to a neonatal intensive care unit. Fresh samples were immediately plated or stored at -80°C for 2 weeks (short-term frozen). Long-term samples were stored at -20°C for >6 months. Samples were cultured using 10 different culture media and incubated both aerobically and anaerobically. We successfully isolated major milk bacteria, including Streptococcus, Staphylococcus and Bifidobacterium, from fresh milk samples, but were unable to culture any bacteria from the long-term frozen samples. Short-term freezing shifted the composition of viable milk bacteria from the original composition in fresh samples. Nevertheless, the inter-individual variability of milk microbiota composition was observed even after short-term storage. There was no major difference in the overall milk microbiota composition between milk fractions in this feasibility study. This is among the first studies on culture-enriched molecular profiling of the milk microbiota demonstrating the effect of storage and fractionation on milk microbiota composition.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".