Protein kinase C ß inhibition ameliorates experimental mesangial proliferative glomerulonephritis
Bibliographic record
Abstract
AIM: Activation of protein kinase C (PKC) has been implicated in the pathogenesis of diabetic nephropathy where therapy targeting the β isoform of this enzyme has been examined. However, PKC-β is also increased in various forms of human glomerulonephritis, including IgA nephropathy. Accordingly, we sought to examine the effects of PKC-β inhibition in the Thy1.1 model of mesangial proliferative glomerulonephritis. METHODS: Following administration of monoclonal OX-7, anti-rat Thy-1.1 antibody, Male Wistar rats were randomized to receive either the PKC-β inhibitor, ruboxistaurin (10 mg/kg per day in chow) or vehicle. Animals were then examined 6 days later. RESULTS: PKC-β inhibition was associated with reductions in mesangial cellularity and extracellular matrix deposition. Proteinuria was, however, unaffected. In vitro, PKC-β inhibition showed modest, dose-dependent reductions in mesangial cell (3) H-thymidine and (3) H-proline incorporations, indices of cell proliferation and collagen synthesis, respectively. CONCLUSION: The amelioration of the pathological findings of experimental mesangial proliferative glomerulonephritis by PKC-β inhibition suggests the potential clinical utility of this approach as a therapeutic strategy in non-diabetic glomerular disease.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".