Inhibition of Protein Kinase C–β by Ruboxistaurin Preserves Cardiac Function and Reduces Extracellular Matrix Production in Diabetic Cardiomyopathy
Bibliographic record
Abstract
BACKGROUND: Heart failure is a common cause of morbidity and mortality in diabetic patients that frequently manifests in the absence of impaired left ventricular systolic function. In contrast to the strong evidence base for the treatment of systolic heart failure, the treatment of heart failure with preserved left ventricular function is uncertain, and therapeutic targets beyond blockade of the renin-angiotensin-aldosterone and beta-adrenergic systems are being sought. One such target is the beta-isoform of protein kinase C (PKC), implicated in both the complications of diabetes and in cardiac dysfunction in the nondiabetic setting. METHODS AND RESULTS: Using a hemodynamically validated rodent model of diabetic diastolic heart failure, the (mRen-2)27 transgenic rat, we sought to determine whether selective inhibition of PKC-beta would preserve cardiac function and reduce structural injury. Diabetic rats were randomized to receive either vehicle or the PKC-beta inhibitor, ruboxistaurin (20 mg/kg per d) and followed for 6 weeks. Compared with untreated animals, ruboxistaurin-treated diabetic rats demonstrated preserved systolic and diastolic function, as measured by the slope of preload recruitable stroke work relationship (P<0.05) and the slope of the end-diastolic pressure volume relationship (P<0.01). Collagen I deposition and cardiomyocyte hypertrophy were both reduced in diabetic animals treated with ruboxistaurin (P<0.01), as was phosphorylated-Smad2, an index of transforming growth factor-beta activity (P<0.01 for all, versus untreated diabetic rats). CONCLUSIONS: PKC-ss inhibition attenuated diastolic dysfunction, myocyte hypertrophy, and collagen deposition and preserved cardiac contractility. PKC-beta inhibition may represent a novel therapeutic strategy for the prevention of diabetes-associated cardiac dysfunction.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".