Rosiglitazone Induces Cardiac Protein Kinase C beta2 Over‐expression And Increases Ventricular Mass in Type 2 Diabetic db/db Mice
Bibliographic record
Abstract
Rosiglitazone (RSG) treatment in type 2 diabetes has been associated with increased incidence and worsening of heart failure, but the mechanism is unknown. It has been shown that protein kinase C (PKC) β2 is over‐expressed in failing human hearts (Circulation 1999;99:384‐91) and that RSG‐induced weight gain is attributable to increased activation of PKCβ in adipose tissue (FASEB J 2006;20:1203‐5). Further, left ventricular mass is reported to be a predictor of heart failure (Eur Heart J 2008;29:741‐747). We, therefore, hypothesized that RSG may induce PKCβ2 over‐expression and increase ventricular mass of the hearts from db/db (D) mice, a mongenic model of obesity and features of type 2 diabetes. Diabetic mice were either untreated (D), treated with RSG (D+RSG) at 26 mg/kg/day or with RSG plus N‐acetylcysteine (NAC, 1.4 g/kg/day) (D+RN), for 3 weeks; NAC can inhibit PKCβ via mechanisms independent of its antioxidant property. Both RSG or RSG+NAC reduced body weight (BW) relative to D group. However, RSG increased ventricular mass and heart weight/BW ratio, accompanied by 1.5‐fold increase of myocardial PKCβ2 protein expression and activation as well as increased protein kinase B/AKT phosphorylation at serine‐473 relative to D (all P<0.05). NAC prevented all these changes. RGS may have increased ventricular mass by inducing PKCβ2 over‐expression. Supported by a CIHR grant (DLS) and an AHFMR postdoctoral fellowship (ZX)
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".