Protein kinase C- inhibition attenuates the progression of nephropathy in non-diabetic kidney disease
Bibliographic record
Abstract
BACKGROUND: Activation of protein kinase C (PKC) has been implicated in the pathogenesis of diabetic nephropathy where therapy targeting the beta isoform of this enzyme is in advanced clinical development. However, PKC-beta is also increased in various forms of human glomerulonephritis with several potentially nephrotoxic factors, other than high glucose, resulting in PKC-beta activation. Accordingly, we sought to examine the effects of PKC-beta inhibition in a non-diabetic model of progressive kidney disease. METHODS: Subtotally nephrectomized (STNx) rats were randomly assigned to receive either the selective PKC-beta inhibitor, ruboxistaurin or vehicle. In addition to functional and structural parameters, gene expression of the podocyte slit-pore diaphragm protein, nephrin, was also assessed. RESULTS: STNx animals developed hypertension, proteinuria and reduced glomerular filtration rate (GFR) in association with marked glomerulosclerosis and tubulointerstitial fibrosis. Glomerular nephrin expression was also reduced. Without affecting blood pressure, ruboxistaurin treatment attenuated the impairment in GFR and reduced the extent of both glomerulosclerosis and tubulointerstitial fibrosis in STNx rats. In contrast, neither proteinuria nor the reduction in nephrin expression was improved by ruboxistaurin. CONCLUSIONS: These findings indicate firstly that PKC-beta inhibition may provide a new therapeutic strategy in non-diabetic kidney disease and secondly that improvement in GFR is not inextricably linked to reduction in proteinuria.
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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) | 0.002 | 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".