Anti-obesity effects of<i>Grifola frondosa</i>through the modulation of lipid metabolism<i>via</i>ceramide in mice fed a high-fat diet
Bibliographic record
Abstract
Obesity is characterized by massive fat deposition and is related to a series of metabolic complications, such as insulin resistance (IR) and steatohepatitis. Grifola frondosa (GF) is a basidiomycete fungus and a source of various nutritional ingredients related to human health. In this study, after a systematic analysis of its nutritional ingredients, GF was administered to mice fed a high-fat diet (HFD) to investigate its effects on lipid metabolism. In HFD-fed mice, GF significantly controlled the body weight, blood glucose and related organ indices, and effectively counteracted hyperlipidemia and IR triggered by the HFD. GF administration efficiently alleviated hepatic steatosis and adipocyte hypertrophy, and regulated alanine aminotransferase and aspartate aminotransferase in the liver. An analysis of the intestinal microflora showed that GF reversed obesity-induced dysbiosis by affecting the relative abundance of certain bacteria, reducing lipopolysaccharide production and regulating the superpathway of heme biosynthesis associated with inflammation. According to the results of lipidomics, ceramide, a metabolite related to inflammation and IR, was found to be dysregulated in HFD-fed mice. However, GF regulated the ceramide levels and restored lipid metabolism via the suppression of Toll-like receptor 4/nuclear factor kappa-B signaling, which is involved in inflammation and IR. This study provides the experimental basis for the application of GF as an agent for obesity.
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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 | 0.001 |
| 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".