Crocetin improves the insulin resistance induced by high‐fat diet in rats
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The amelioration of insulin resistance by treatment with crocetin is closely related to the hypolipidaemic effect. The present study is designed to clarify the insulin-sensitizing mechanism of crocetin by elucidating the mechanism of regulation of lipid metabolism by crocetin. EXPERIMENTAL APPROACH: Rats given a high-fat diet were treated with crocetin for 6 weeks before hyperinsulinaemic-euglycaemic clamp. 14C-palmitate was used as tracer to track the fate of non-esterified fatty acids or as substrate to measure beta-oxidation rate. Triglyceride clearance in plasma and lipoprotein lipase activity in tissues were tested. Content of lipids in plasma and tissues was determined. Real-time PCR was used to assay the level of mRNA from genes involved in non-esterified fatty acid and triglyceride uptake and oxidation. KEY RESULTS: Crocetin prevented high-fat-diet induced insulin resistance (increased clamp glucose infusion rate), raised hepatic non-esterified fatty acid uptake and oxidation, accelerated triglyceride clearance in plasma, enhanced lipoprotein lipase activity in liver, and reduced the accumulation of detrimental lipids (DAG and long-chain acyl CoA) in liver and muscle. Genes involved in hepatic lipid metabolism which are regulated by peroxisome proliferator-activated receptor-alpha, were modulated to accelerate lipid uptake and oxidation. CONCLUSIONS AND IMPLICATIONS: Through regulating genes involved in lipid metabolism, crocetin accelerated hepatic uptake and oxidation of non-esterified fatty acid and triglyceride, and reduced lipid availability to muscle, thus decreasing lipid accumulation in muscle and liver, and consequently improving sensitivity to insulin.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".