Probiotics in early life: a preventative and treatment approach
Bibliographic record
Abstract
Microbial colonization of the infant gut plays a key role in immunological and metabolic pathways impacting human health. Since the maturation of the gut microbiota coincides with early life development, failure to develop a health compatible microbiota composition may result in pathology and disease in later life. Probiotics are live microorganisms that, when administered in adequate amounts, confer a health benefit on the host. Maternal transfer of microorganisms is possible during pregnancy and lactation, and the mother's diet and microbiota can influence that of her offspring. Furthermore, pre-term birth, Caesarean section birth, formula feeding, antibiotic use, and malnutrition have been linked to dysbiosis, which in turn is associated with several pathologies such as necrotizing enterocolitis, inflammatory bowel diseases, antibiotic associated diarrhea, colic, and allergies. Thus, early life should represent a preferred stage of life for probiotic interventions. In this context, they could be regarded as a means to 'program' the individual for health maintenance, in order to prevent pathologies associated with dysbiosis. In order to elucidate the mechanisms underlying the benefits of probiotic administration, pre-clinical studies have been conducted and found an array of positive results such as improved microbial composition, intestinal maturation, decreased pathogenic load and infections, and improved immune response. Moreover, specific probiotic strains administered during the perinatal period have shown promise in attenuating severity of necrotizing enterocolitis. The mechanisms elucidated suggest that probiotic interventions in early life can be envisaged for disease prevention in both healthy offspring and offspring at risk of chronic disease.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".