Aliskiren, exendin-4, and insulin: their impact on endothelin receptor subtype(s) regulation/binding in type 1 diabetic rat hearts
Bibliographic record
Abstract
This study focuses on the impact of aliskiren and (or) glucagon-like peptide-1 analogue on the binding affinity/regulation of endothelin-1 (ET-1) to its receptor subtypes A (ETAR) and B (ETBR) at the level of the coronary endothelium and the cardiomyocytes in a type-1 diabetic rat model. Seven groups were used: (i) normal rats, (ii) rats with induced diabetes, (iii) rats with induced diabetes that were treated with insulin, (iv) rats with induced diabetes that were treated with exendin-4, (v) rats with induced diabetes that were treated with aliskiren, (vi) rats with induced diabetes that were co-treated with insulin plus aliskiren, and (vii) rats with induced diabetes that were co-treated with exendin-4 plus aliskiren. Heart perfusion with [(125)I]-ET-1 was employed to estimate ET-1 binding affinity (τ = 1/K-n) to ETAR and ETBR at the level of the coronary endothelium and the cardiomyocytes. Plasma ET-1 levels were measured using enzyme immunoassay, whereas densities of ETAR and ETBR were detected using Western blot. No significance differences were detected in the τ of ETAR and ETBR between normal and diabetic in cardiomyocytes and the coronary endothelium. Exendin-4 normalized the τ value for ETAR and ETBR on coronary endothelium, while aliskiren normalized it on cardiomyocytes. Furthermore, ETAR and ETBR densities were normalized with monotreatments of aliskiren and exendin-4, compared with up-regulated ETAR and down-regulated ETBR band densities in the diabetic animals. Our data indicate that aliskiren alleviates diabetes-associated hypertrophy in type 1 diabetes mellitus.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".