Loganin regulates glycolipid metabolism by influencing intestinal microbiota and AMPK signaling in obese mice
Bibliographic record
Abstract
We aimed to observe the effects of loganin (Log) on serum glycolipid levels and probe the mechanisms focusing on intestinal flora and AMP-activated protein kinase (AMPK) signaling in obese mice. A high-fat diet was given for 12 consecutive weeks to generate the obesity model in institute of cancer research (ICR) mice. Body weight was measured weekly and fasting blood glucose (FBG) was determined every 2 weeks. Both the oral glucose tolerance test and the intraperitoneal insulin tolerance test were performed. The serum levels of total cholesterol (TC), triglyceride, high-density lipoprotein-cholesterol, low-density lipoprotein-cholesterol (LDL-C), and free fatty acids (FFA) were measured. The expression of key proteins in the AMPK signaling pathway in skeletal muscle tissue was detected by immunoblotting, and gut microbiota were characterized using 16S rDNA sequencing. Log significantly decreased the body weight and the FBG in obese mice (P < .05), and it could restore FBG to normal levels. The total cholesterol, LDL-C, and FFA levels were significantly reduced by Log compared with the obese controls (TC: P = .0020; LDL-C: P = .0233; FFA: P = .0127), and the glucose tolerance of animals was significantly improved (P = .0477). The western blot results showed that Log could upregulate the protein expression of Adenosine 5‘-monophosphate (AMP)-activated protein kinase (AMPKα), Sirtuin 1 (SIRT1), and peroxisome proliferator-activated receptor-gamma coactivator -1alpha (PGC1α) in skeletal muscle tissue of obese mice. 16S rDNA sequencing indicated that Log reduced the diversity of the gut flora in feces and altered the floral composition of obese mice. Log was effective in reducing body weight and improving glucolipid metabolism in obese mice, probably through activating AMPK signaling and regulating intestinal microbial diversity.
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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 | 0.001 |
| 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".