Leptin and endothelin‐1 mediated increased extracellular matrix protein production and cardiomyocyte hypertrophy in diabetic heart disease
Bibliographic record
Abstract
BACKGROUND: We investigated the role of leptin and its interaction with endothelin 1 (ET-1) in fibronectin (FN) synthesis and cardiomyocyte hypertrophy, two characteristic features of diabetic cardiomyopathy. METHODS: Endothelial cells [human umbilical vein endothelial cells (HUVECs)] were examined for FN production and neonatal rat cardiomyocytes for hypertrophy, following incubation with glucose, ET-1, leptin and specific blockers. FN, ET-1, leptin and leptin receptors mRNA expression and FN protein were measured. Myocytes were also morphometrically examined. Furthermore, hearts from streptozotocin-diabetic rats were analysed. RESULTS: Glucose caused increased FN mRNA and protein expression in HUVECs and cardiomyocytes hypertrophy along with upregulation of ET-1 mRNA, leptin mRNA and protein. Glucosemimetic effects were seen with leptin and ET-1. Leptin receptor antagonist (leptin quadruple mutant) and dual endothelin A endothelin B (ETA/ETB) receptor blocker bosentan normalized such abnormalities. Hearts from the diabetic animals showed hypertrophy and similar mRNA changes. CONCLUSION: These data indicate that in diabetes increased FN production and cardiomyocyte hypertrophy may be mediated through leptin with its interaction with ET-1.
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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 | 0.000 |
| 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".