A Mix of Probiotic Strains Prevents Hepatic Steatosis, and Improves Oxidative Stress Status and Gut Microbiota Composition in Obese Mice
Bibliographic record
Abstract
SCOPE: The gut microbiota plays a role in fat accumulation and energy homeostasis. Therefore, probiotic supplementation may improve metabolic parameters and control body weight. METHODS AND RESULTS: In this study, mice are fed either a high-fat diet (HFD) or an HFD supplemented with oral gavage of a mixture of three probiotic strains, Bifidobacterium lactis Lafti B94, Lactobacillus plantarum HA-119, and Lactobacillus helveticus Lafti L10 for 7 weeks. It finds that probiotic supplementation modulates body weight gain, food energy efficiency, and fat accumulation caused by the HFD. This probiotic mix prevents liver damage and lipid metabolic disorders in HFD-fed obese mice. The probiotic supplementation significantly downregulates the expression of the proinflammatory cytokines interleukin-1β, tumor necrosis factor-α, and malondialdehyde (MDA) in the liver and upregulated catalase (CAT), superoxide dismutase (SOD), and nuclear respiratory factor 1 (Nrf1) expression. Mice supplemented with the probiotic mix also show different microbiota compositions, with an increase in Clostridia_UCG-014 and Lachnospiraceae_nk4a136_group and a decrease in the Dubosiella genus compared with those in mice fed only an HFD. Finally, the amounts of fecal pentanoic acid and the three bile acid species increase in mice with probiotic supplementation. CONCLUSION: Treatment with a combination of a mixture of three probiotic strains, B. lactis Lafti B94, L. plantarum HA-119, and L. helveticus Lafti L10 for 7 weeks, ameliorates the effects of HFD induced obesity in mice.
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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 | 0.001 | 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".