Regulation of IRS-2 signaling by IGF-1 receptor in the diabetic rat heart
Bibliographic record
Abstract
Cardiovascular disease involves changes in inflammatory markers. Since insulin/insulin-like growth factor 1 receptor (IGF-1R) can activate vascular endothelial growth factor to promote vascular growth, reduced IGF-1R signaling in the type I diabetic heart could be detrimental, leading to reduced, collateral blood vessel growth. This study assessed whether diabetes can induce an inflammatory phenotype to regulate molecules in the IGF-1 signaling cascade, thus mediating apoptosis. Rats were made diabetic using streptozotocin (to render them type I diabetic) for 2 months with no insulin treatment. At 2 months, rats were sacrificed under anesthesia, and the left ventricle was immediately removed and placed into cold lysis buffer for protein analyses. Western blotting, immunoprecipitation, and enzyme-linked immunosorbent assay analyses were completed to evaluate protein levels. Diabetes increased TNF-alpha, interleukin-6 (IL-6), and IL-1alpha levels in the heart. JNK and p42/p44 activity was significantly increased in the diabetic heart, while IGF-1R phosphorylation, IRS-2 tyrosine phosphorylation, and Akt activities were reduced. A significant increase in Bad protein levels and the cleavage of caspase 3 was observed in the diabetic heart. These results suggest that diabetes activates multiple inflammatory markers in the heart, which then signal a decrease in the activities of key players in the insulin-signaling cascade, namely IGF-1R, IRS-2, and Akt, to regulate apoptosis.
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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) | 0.002 | 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".