The tyrosine kinase inhibitor crizotinib influences blood glucose and mRNA expression of GLUT4 and PPARs in the heart of rats with experimental diabetes
Bibliographic record
Abstract
Tyrosine kinases inhibitors (TKIs) may alter glycaemia and may be cardiotoxic with importance in the diabetic heart. We investigated the effect of multi-TKI crizotinib after short-term administration on metabolic modulators of the heart of diabetic rats. Experimental diabetes mellitus (DM) was induced by streptozotocin (STZ; 80 mg·kg–1, i.p.), and controls (C) received vehicle. Three days after STZ, crizotinib (STZ+CRI; 25 mg·kg–1 per day p.o.) or vehicle was administered for 7 days. Blood glucose, C-peptide, and glucagon were assessed in plasma samples. Receptor tyrosine kinases (RTKs), cardiac glucose transporters, and peroxisome proliferator-activated receptors (PPARs) were determined in rat left ventricle by RT-qPCR method. Crizotinib moderately reduced blood glucose (by 25%, P < 0.05) when compared to STZ rats. The drug did not affect levels of C-peptide, an indicator of insulin secretion, suggesting altered tissue glucose utilization. Crizotinib had no impact on cardiac RTKs. However, an mRNA downregulation of insulin-dependent glucose transporter Glut4 in the hearts of STZ rats was attenuated after crizotinib treatment. Moreover, crizotinib normalized Ppard and reduced Pparg mRNA expression in diabetic hearts. Crizotinib decreased blood glucose independently of insulin and glucagon. This could be related to changes in regulators of cardiac metabolism such as GLUT4 and PPARs.
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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 | 0.001 |
| 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 | 0.000 | 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".