Bovine Lactoferrin Supplementation Does Not Disrupt Microbiota Development in Preterm Infants Receiving Probiotics
Bibliographic record
Abstract
OBJECTIVE: The aim of the study was to assess whether bovine lactoferrin (bLf) supplementation disrupts intestinal microbiota development in preterm infants less than 31 weeks gestational age receiving prophylactic probiotic administration. METHODS: Subjects were recruited from the LACUNA trial (ISRCTN66482337), designed to assess bLf safety. These subjects were randomized to daily receive either probiotic supplements or probiotics supplemented with 100 mg bLf mixed with their feeds (human milk or formula). Stools were collected weekly from enrolled infants for 1 month and the microbiota characterized using V6-16S rRNA gene amplicon profiling. RESULTS: Infants' microbiomes did not increase in alpha diversity over time in both feeding interventions. Infants receiving bLf supplementation had overall higher species richness as compared with those not receiving these supplements and lactoferrin supplementation had differing effects on infant microbiota species richness depending on the infant's gestational age. Principal co-ordinate analysis revealed that the infant microbiotas did not separate by intervention group, gestational age bracket at birth or sampling time and the main factor dictating sample clustering was infant identity. There were very few detectable differences in taxa relative abundance or functional gene content between the microbiotas in the 2 study groups. CONCLUSIONS: Bovine lactoferrin supplementation has minimal impact on microbiota composition/function in preterm infants receiving probiotics, and therefore, is unlikely to disrupt microbiota development.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".