Late intervention in the remnant kidney model attenuates proteinuria but not glomerular filtration rate decline
Bibliographic record
Abstract
AIM: The use of animal models to predict the response to new therapies in humans is a vexing issue in nephrology. Unlike patients with chronic kidney disease (CKD), few rodent models develop a progressive decline in glomerular filtration rate (GFR) so that experimental studies frequently report a reduction in proteinuria as the primary efficacy outcome. Moreover, while humans present with established kidney disease that continues to progress, many experimental studies investigate therapies in the prevention rather than in a therapeutic setting. METHODS: We used the remnant kidney (subtotal nephrectomy [SNX]) rat model that develops a decline in GFR in conjunction with heavy proteinuria and hypertension along with the histological hallmarks of CKD in humans, glomerulosclerosis and tubulointerstitial fibrosis. Using agents that had been shown to improve GFR as well as proteinuria in the prevention setting, angiotensin-converting enzyme (ACE) inhibition with enalapril and SIRT1 activation with SRT3025, treatment was initiated 6 weeks after SNX. RESULTS: While enalapril reduced blood pressure, proteinuria and histological injury, it did not improve GFR, as measured by inulin clearance. SRT3025 improved neither GFR nor structural damage despite a reduction in proteinuria. CONCLUSION: These findings demonstrate that neither a reduction in proteinuria nor a reversal of structural damage in the kidney will necessarily translate to a restoration of kidney function.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".