Hyperglycemia and Renal Mass Ablation Synergistically Augment Albuminuria in the Diabetic Subtotally Nephrectomized Rat: Implications for Modeling Diabetic Nephropathy
Bibliographic record
Abstract
UNLABELLED: BACKGROUND/AIMSBACKGROUND/AIMS: While experimental models that emulate diabetic nephropathy are valuable tools for elucidating pathogenetic mechanisms and developing novel therapies, existing models imperfectly recapitulate human disease. In diabetes, hyperglycemia and hemodynamic forces act in concert to induce renal injury. Accordingly, in the present study, we combined streptozotocin-induced diabetes with surgical ablation of 5/6 of the kidney mass with the aim of evaluating their additive effects on renal function and glomerular morphology. METHODS: Female F344 rats were randomized to undergo subtotal nephrectomy (SNx) either at baseline or following 4 weeks of diabetes. RESULTS: In comparison to sham rats, rats with diabetes or rats after SNx surgery, diabetic subtotally nephrectomized (DM-SNx) rats demonstrated an increase in systolic blood pressure, glomerular volume and mesangial matrix. Albuminuria was synergistically increased by hyperglycemia and renal mass ablation associated with decreased nephrin expression. In contrast, glomerular capillary rarefaction and glomerular filtration rate were similarly reduced in SNx and DM-SNx rats. CONCLUSION: The DM-SNx rat recapitulates some of the features of human disease, most notably augmented albuminuria. Since this model avoids the deletion or overexpression of gene(s) linked to the pathogenesis of nephropathy, the DM-SNx rat model represents a complementary tool for the trial of novel therapies.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".