Effects of resveratrol on the amelioration of insulin resistance in KKA<sup>y</sup> mice
Bibliographic record
Abstract
Resveratrol (Res) has attracted great interest regarding its effects related to metabolic syndrome, especially for lipid metabolic disorder or insulin resistance; however, the underlying mechanisms remain elusive. To explore the effects of Res on insulin sensitivity and the underlying mechanism, insulin-resistant KKA(y) mice were treated with 2 and 4 g/kg diets of Res for 12 weeks. After the treatment, blood glucose, serum insulin, glucose tolerance, and insulin tolerance, as well as other indices such as adiponectin mRNA in epididymal adipose tissues, silent information regulator 1 (Sirt1), AMP-activated protein kinase (AMPK), insulin receptor substrate 1 (IRS1), and phosphorylated protein kinase B (PKB/AKT) proteins in liver and soleus muscles, were investigated. The results indicate that Res intervention reduces blood glucose and serum insulin levels, improves insulin and glucose tolerance, increases serum adiponectin and adiponectin mRNA levels in epididymal adipose tissues, and more importantly, elevates Sirt1, p-AMPK, p-IRS1, and p-AKT levels in liver and soleus muscles. In conclusion, Res could improve insulin sensitivity and ameliorate insulin resistance in KKA(y) mice, which may be associated with the upregulation of Sirt1 protein in liver and soleus muscles and consequent AMPK activation, as well as insulin-signaling related proteins.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".