Cordyceps guangdongensis lipid-lowering formula alleviates fat and lipid accumulation by modulating gut microbiota and short-chain fatty acids in high-fat diet mice
Bibliographic record
Abstract
Obesity has caused serious health and economic problems in the world. Cordyceps guangdongensis is a high-value macrofungus with broad application potential in the food and bio-medicine industry. This current study aimed to estimate the role of C. guangdongensis lipid-lowering compound formula (CGLC) in regulating fat and lipid accumulation, gut microbiota balance, short-chain fatty acid (SCFA) contents, and expression levels of genes involved in fat and lipid metabolism in high-fat diet (HFD) mice. The results showed that CGLC intervention markedly reduced body weights and fat accumulation in HFD mice, improved glucose tolerance and blood lipid levels, and decreased lipid droplet accumulation and fat vacuole levels in the liver. CGLC decreased the ratio of Firmicutes and Bacteroidetes and increased the relative abundances of Bacteroides (B. acidifaciens) and Bifidobacterium (B. pseudolongum). In addition, CGLC treatment significantly promoted the production of SCFAs and regulated the relative expression levels of genes involved in fat and lipid metabolism in liver. Association analysis showed that several species of Bacteroides and most of SCFAs were significantly associated with serum lipid indicators. These results suggested that CGLC is a novel candidate formulation for treating obesity and non-alcohol fatty liver by regulating gut microbiota, SCFAs, and genes involved in fat and lipid metabolism.
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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 | 0.000 | 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".