Ameliorating Effect of <i>Bifidobacterium breve</i> CM02‐09T on High‐Fat Diet‐Induced Nonalcoholic Fatty Liver Disease in Mice
Bibliographic record
Abstract
Evidences have demonstrated that the regulation of gut microbiota by probiotics can be utilized to prevent nonalcoholic fatty liver disease (NAFLD); Bifidobacterium was widely used as a potential therapeutic alternative for metabolic diseases. In this study, the probiotic functions of Bifidobacterium breve CM02-09T and its effects on NAFLD were investigated through genome annotation and high-fat diet (HFD)-induced mouse model. The genome of B. breve CM02-09T consists of a single circular chromosome of 2 372 890 bp. Eighteen male C57BL/6J mice were fed a normal chow diet (NCD), a high-fat diet, and a high-fat diet (HFD) with B. breve CM02-09T separately for 12 weeks. Histopathology, lipid content, biochemical markers in the liver and blood, and metagenomics were assessed and compared. Our results indicate that supplementation with B. breve CM02-09T alleviated liver damage, reduced fat accumulation in the liver and epididymal adipose tissue, decreased TNF-α concentration, and improved NAFLD activity scores. Metagenomic analysis revealed that administration of B. breve CM02-09T promoted a decreased Firmicutes/Bacteroidetes ratio (F/B), an increase in beneficial bacteria, and enhanced lipid metabolism functions. Correlation analysis between the differential species and NAFLD-related indicators suggested that the anti-NAFLD effect of B. breve CM02-09T is related to the gut microbiota. Therefore, B. breve CM02-09T attenuates NAFLD by modulating the gut microbiota and could be used as an alternative therapeutic strategy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 | 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".