Synergistic Effects of Heat-Killed Kefir Paraprobiotics and Flavonoid-Rich Prebiotics on Western Diet-Induced Obesity
Bibliographic record
Abstract
The synergistic anti-obesity effect of paraprobiotic heat-killed lactic acid bacteria (HLAB) and prebiotics has not been studied. To determine the anti-obesity properties of prebiotic polyphenol-rich wine grape seed flour (GSF) and paraprobiotic HLAB, C57BL/6J mice were administered a high-fat and high-fructose diet (HFFrD) with 5% microcrystalline cellulose (CON), HFFrD supplemented with 2.5% GSF, HFFrD with orally administered HLAB, or HFFrD with a combination of GSF and orally administered HLAB (GSF+HLAB) for 8 weeks. Compared with the CON group, the GSF and HLAB groups both showed significant reductions in HFFrD-induced body weight gain and adipose tissue weights (p < 0.05). Interestingly, combined supplementation with GSF and HLAB revealed statistically significant synergistic effects on body weight gain, visceral adiposity, and plasma triacylglycerol concentrations (p < 0.05). The synergistic action was significantly related to a decreased adipocyte gene expression in fatty acid synthesis and inflammation metabolism. In conclusion, the combination of prebiotic GSF and paraprobiotic kefir HLAB is potentially useful, as natural food ingredients, in the prevention of obesity and obesity-related diseases, especially for immunocompromised individuals.
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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) | 0.000 | 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 | 0.000 |
| 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".