Mesangial cell protein kinase C isozyme activation in the diabetic milieu
Bibliographic record
Abstract
High-glucose-induced activation of mesangial cell protein kinase C (PKC) contributes significantly to the pathogenesis of diabetic nephropathy. Excess glucose metabolism through the polyol pathway leads to de novo synthesis of both diacylglyerol (DAG) and phosphatidic acid, which may account for increased mesangial cell PKC-alpha, -beta, -delta, -epsilon, and -zeta activation/translocation observed within 48-h exposure to high glucose. Raised intracellular glucose causes generation of reactive oxygen species that may directly activate PKC isozymes and enhance their reactivity to vasoactive peptide signaling. In both diabetic rodent models of diabetes and cultured mesangial cells, PKC-beta appears to be the key isozyme required for the enhanced expression of transforming growth factor-beta(1), initiation of early accumulation of mesangial matrix protein, and increased microalbuminuria. Enhanced collagen IV expression by mesangial cells in response to vasoactive peptide hormone stimulation, e.g., endothelin-1, requires PKC-beta, -delta, -epsilon and -zeta. Loss of mesangial cell contractility to potent vasoactive peptides and coincident F-actin disassembly are due to high-glucose-activation of PKC-zeta. Inhibition of mesangial cell PKC isozyme activation in high glucose may prove to be the next important treatment for diabetic nephropathy.
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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 | 0.001 |
| 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".