Role of glucagon-like peptide-1 analogues on insulin receptor regulation in diabetic rat hearts
Bibliographic record
Abstract
This study focused on the regulation and affinity modulation of the insulin receptor of coronary endothelium and cardiomyocytes in nondiabetic and STZ-induced type 1 diabetic rats. Male rats were divided into the following 9 groups: nondiabetic (N), nondiabetic treated with exendin-4 (NE), nondiabetic treated with dipeptidyl peptidase IV (DPP-IV) inhibitor (NDp), diabetic (D), diabetic treated with insulin (DI), diabetic treated with exendin-4 (DE), diabetic co-treated with insulin and exendin-4 (DIE), diabetic treated with DPP-IV inhibitor (DDp), and diabetic co-treated with insulin and DPP-IV inhibitor (DIDp). After the rats were treated for 1 month, a first-order Bessel function was employed to estimate the insulin binding affinity (with time constant tau = 1/k-n) to its receptors on the coronary endothelium and cardiomyocytes using CHAPS-untreated and CHAPS-treated heart perfusion, respectively. The results showed that diabetes (D) decreased the tau value on the coronary endothelium and increased it on cardiomyocytes compared with the nondiabetic group (N). Treatment with insulin and (or) exendin-4, a glucagon-like peptide-1 (GLP-1) analogue, increased tau on the coronary endothelium only. On the coronary endothelium, tau values of DI and DIDp were normalized. Western blots of the insulin receptor showed upregulation in D, downregulation in DI, and normalization in DE and DDp. Immunohistochemistry and RT-PCR findings indicated atrial natriuretic factor (ANF) in all diabetic ventricles, thus ascertaining hypertrophy. Therefore, negative myocardial effects related to the insulin receptor were diminished in diabetic rats treated with DPP-IV inhibitor and, more efficiently, by exendin-4.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".