Probiotics as Complementary Treatment for Metabolic Disorders
Bibliographic record
Abstract
Recent studies have demonstrated beneficial effects of specific probiotics on alleviating obesity-related disorders.Here we aimed to identify probiotics with potential antiobesity activity among 88 lactic acid bacterial strains via in vitro screening assays, and a Lactobacillus plantarum strain K21 was found to harbor abilities required for hydrolyzing bile salt, reducing cholesterol, and inhibiting the accumulation of lipid in 3T3-L1 preadipocytes.Furthermore, effects of K21 on diet-induced obese (DIO) mice were examined.Male C57Bl/6J mice received a normal diet, high-fat diet (HFD), or HFD with K21 administration (10 9 CFU in 0.2 mL PBS/day) for eight weeks.Supplementation of K21, but not placebo, appeared to alleviate body weight gain and epididymal fat mass accumulation, reduce plasma leptin levels, decrease cholesterol and triglyceride levels, and mitigate liver damage in DIO mice.Moreover, the hepatic expression of peroxisome proliferator-activated receptor-𝛾 (PPAR-𝛾) related to adipogenesis was significantly downregulated in DIO mice by K21 intervention.We also found that K21 supplementation strengthens intestinal permeability and modulates the amount of Lactobacillus spp., Bifidobacterium spp., and Clostridium perfringens in the cecal contents of DIO mice.In conclusion, our results suggest that dietary intake of K21 protects against the onset of HFD-induced obesity through multiple mechanisms of action.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".