Selective Inhibition of Protein Kinase C β2 Attenuates Inducible Nitric Oxide Synthase–Mediated Cardiovascular Abnormalities in Streptozotocin-Induced Diabetic Rats
Bibliographic record
Abstract
OBJECTIVE: Impaired cardiovascular function in diabetes is partially attributed to pathological overexpression of inducible nitric oxide synthase (iNOS) in cardiovascular tissues. We examined whether the hyperglycemia-induced increased expression of iNOS is protein kinase C-beta(2) (PKCbeta(2)) dependent and whether selective inhibition of PKCbeta reduces iNOS expression and corrects abnormal hemodynamic function in streptozotocin (STZ)-induced diabetic rats. RESEARCH DESIGN AND METHODS: Cardiomyocytes and aortic vascular smooth muscle cells (VSMC) from nondiabetic rats were cultured in low (5.5 mmol/l) or high (25 mmol/l) glucose or mannitol (19.5 mmol/l mannitol + 5.5 mmol/l glucose) conditions in the presence of a selective PKCbeta inhibitor, LY333531 (20 nmol/l). Further, the in vivo effects of PKCbeta inhibition on iNOS-mediated cardiovascular abnormalities were tested in STZ-induced diabetic rats. RESULTS: Exposure of cardiomyocytes to high glucose activated PKCbeta(2) and increased iNOS expression that was prevented by LY333531. Similarly, treatment of VSMC with LY333531 prevented high glucose-induced activation of nuclear factor kappaB, extracellular signal-related kinase, and iNOS overexpression. Suppression of PKCbeta(2) expression by small interference RNA decreased high-glucose-induced nuclear factor kappaB and extracellular signal-related kinase activation and iNOS expression in VSMC. Administration of LY333531 (1 mg/kg/day) decreased iNOS expression and formation of peroxynitrite in the heart and superior mesenteric arteries and corrected the cardiovascular abnormalities in STZ-induced diabetic rats, an action that was also observed with a selective iNOS inhibitor, L-NIL. CONCLUSIONS: Collectively, these results suggest that inhibition of PKCbeta(2) may be a useful approach for correcting abnormal hemodynamics in diabetes by preventing iNOS mediated nitrosative stress.
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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) | 0.001 | 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 | 0.000 |
| 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".