Astaxanthin prevents loss of insulin signaling and improves glucose metabolism in liver of insulin resistant mice
Bibliographic record
Abstract
This study investigates the effects of astaxanthin (ASX) on insulin signaling and glucose metabolism in the liver of mice fed a high fat and high fructose diet (HFFD). Adult male Mus musculus mice of body mass 25-30 g were fed either normal chow or the HFFD. After 15 days, mice in each group were subdivided among 2 smaller groups and treated with ASX (2 mg·(kg body mass)⁻¹) in olive oil for 45 days. At the end of 60 days, HFFD-fed mice displayed insulin resistance while ASX-treated HFFD animals showed marked improvement in insulin sensitivity parameters. ASX treatment normalized the activities of hexokinase, pyruvate kinase, glucose-6-phosphatase, fructose-1,6-bisphosphatase, glycogen phosphorylase, and increased glycogen reserves in the liver. Liver tissue from ASX-treated HFFD-fed animals showed increased tyrosine phosphorylation and decreased serine phosphorylation of insulin receptor substrates (IRS)-1 and -2. ASX increased IRS 1/2 and phosphatidylinositol 3-kinase (PI3K) association and serine phosphorylation of Akt. In addition, ASX decreased HFFD-induced serine kinases (c-jun N-terminal kinase-1 and extracellular signal-regulated kinase-1). The results suggest that ASX treatment promotes the IRS-PI3K-Akt pathway of insulin signaling by decreasing serine phosphorylation of IRS proteins, and improves glucose metabolism by modulating metabolic enzymes.
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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) | 0.000 | 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".