Review and Perspectives on Bifidobacterium lactis for Infants’ and Children’s Health
Bibliographic record
Abstract
The influence of microbiota dysbiosis in early life is increasingly recognized as a risk factor for the development of several chronic diseases later in life, including an increased risk of asthma, eczema, allergies, obesity, and neurodevelopmental disorders. The question whether the potential lifelong consequences of early life dysbiosis could be mitigated by restoring microbiota composition remains unresolved. However, the current evidence base suggests that protecting the normal development of the microbiome during this critical developmental window could represent a valuable public health strategy to curb the incidence of chronic and lifestyle-related diseases. Probiotic Bifidobacteria are likely candidates for this purpose in newborns and infants considering the natural dominance of this genus on microbiota composition in early life. Moreover, the most frequently reported microbiota composition alteration in association with newborn and infant diseases, including necrotizing enterocolitis and diarrhea, is a reduction in Bifidobacteria levels. Several studies have assessed the effects of B. animalis subsp. lactis strains in newborns and infants, but recent expert opinions recommend analyzing their efficacy at the strain-specific level. Hence, using the B94 strain as an example, this review summarizes the clinical evidence available in infants and children in various indications, discussing the safety and potential modes of actions while providing perspectives on the concept of “non-infant-type” probiotics for infants’ health.
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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 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| 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) | 0.007 | 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".