Heparanase Stimulation of Physiologic Cardiac Hypertrophy Is Suppressed After Chronic Diabetes, Resulting in Cardiac Remodeling and Dysfunction
Bibliographic record
Abstract
In addition to controlling smooth muscle tone in coronary vessels, endothelial cells also influence subjacent cardiomyocyte growth. Because heparanase, with exclusive expression in endothelial cells, enables extracellular matrix remodeling, angiogenesis, metabolic reprogramming, and cell survival, it is conceivable that it could also encourage development of cardiac hypertrophy. Global heparanase overexpression resulted in physiologic cardiac hypertrophy, likely an outcome of HSPG clustering and activation of hypertrophic signaling. The heparanase autocrine effect of releasing neuregulin-1 could have also contributed to this overexpression. Hyperglycemia induced by streptozotocin-induced diabetes sensitized the heart to flow-induced release of heparanase and neuregulin-1. Despite this excess secretion, progression of diabetes caused significant gene expression changes related to mitochondrial metabolism and cell death that led to development of pathologic hypertrophy and heart dysfunction. Physiologic cardiac hypertrophy was also observed in rats with cardiomyocyte-specific vascular endothelial growth factor B overexpression. When perfused, hearts from these animals released significantly higher amounts of both heparanase and neuregulin-1. However, subjecting these animals to diabetes triggered robust transcriptome changes related to metabolism and a transition to pathologic hypertrophy. Our data suggest that in the absence of mechanisms that support cardiac energy generation and prevention of cell death, as seen after diabetes, there is a transition from physiologic to pathologic cardiac hypertrophy and a decline in cardiac function.
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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) | 0.002 | 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".