Lactobacilli and Bifidobacteria in Human Breast Milk
Bibliographic record
Abstract
OBJECTIVE: The objective of this work was to study the lactobacilli and bifidobacteria population in human milk of healthy women, and to investigate the influence that several factors (including antibioteraphy during pregnancy and lactation, country and date of birth, delivery mode, or infant age) may exert on such population. METHODS: A total of 160 women living in Germany or Austria provided the breast milk samples. Initially, 66 samples were randomly selected and cultured on MRS-Cys agar plates. Then, the presence of DNA from the genera Lactobacillus and Bifidobacterium, and from most of the Lactobacillus and Bifidobacterium species that were isolated, was assessed by qualitative polymerase chain reaction (PCR) using genus- and species-specific primers. RESULTS: Lactobacilli and bifidobacteria could be isolated from the milk of 27 (40.91%) and 7 (10.61%), respectively, of the 66 cultured samples. On the contrary, Lactobacillus and Bifidobacterium sequences were detected by PCR in 108 (67.50%) and 41 (25.62%), respectively, of the 160 samples analyzed. The Lactobacillus species most frequently isolated and detected was L salivarius (35.00%), followed by L fermentum (25.00%) and L gasseri (21.88%), whereas B breve (13.75%) was the bifidobacterial species most commonly recovered and whose DNA was most regularly found. The number of lactobacilli- or bifidobacteria-positive samples was significantly lower in women who had received antibiotherapy during pregnancy or lactation. CONCLUSIONS: Our results suggest that either the presence of lactobacilli and/or bifidobacteria or their DNA may constitute good markers of a healthy human milk microbiota that has not been altered by the use of antibiotics.
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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 | 0.000 |
| 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) | 0.001 | 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".