Microencapsulated<i>Bifidobacterium longum</i>subsp.<i>infantis</i>ATCC 15697 Favorably Modulates Gut Microbiota and Reduces Circulating Endotoxins in F344 Rats
Bibliographic record
Abstract
The gut microbiota is a bacterial bioreactor whose composition is an asset for human health. However, circulating gut microbiota derived endotoxins cause metabolic endotoxemia, promoting metabolic and liver diseases. This study investigates the potential of orally delivered microencapsulated Bifidobacterium infantis ATCC 15697 to modulate the gut microbiota and reduce endotoxemia in F344 rats. The rats were gavaged daily with saline or microencapsulated B. infantis ATCC 15697. Following 38 days of supplementation, the treated rats showed a significant (P < 0.05) increase in fecal Bifidobacteria (4.34 ± 0.46 versus 2.45 ± 0.25% of total) and B. infantis (0.28 ± 0.21 versus 0.52 ± 0.12 % of total) and a significant (P < 0.05) decrease in fecal Enterobacteriaceae (0.80 ± 0.45 versus 2.83 ± 0.63% of total) compared to the saline control. In addition, supplementation with the probiotic formulation reduced fecal (10.52 ± 0.18 versus 11.29 ± 0.16 EU/mg; P = 0.01) and serum (0.33 ± 0.015 versus 0.30 ± 0.015 EU/mL; P = 0.25) endotoxins. Thus, microencapsulated B. infantis ATCC 15697 modulates the gut microbiota and reduces colonic and serum endotoxins. Future preclinical studies should investigate the potential of the novel probiotic formulation in metabolic and liver diseases.
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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) | 0.001 | 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 | 0.001 |
| 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".