Both sexes develop DKD in the CD1 uninephrectomized streptozotocin mouse model
Bibliographic record
Abstract
Diabetic kidney disease (DKD) is characterized by a progressive increase in albuminuria and typical pathologic features. Recent studies have shown that sex is an important factor to consider in the pathogenesis of DKD. Presently, the hallmarks of this disease have primarily been studied in male rodent models. Here we explored the influence of sex in a murine model of DKD. CD1 mice underwent a right nephrectomy followed by intraperitoneal injection with 200 mg/kg streptozotocin to induce type 1 diabetes. Due to a high mortality rate, females required a reduction in streptozotocin to 150 mg/kg. Mice were followed for 12 weeks. Both sexes developed comparable hyperglycemia, while albuminuria and glomerular volume were increased to a greater degree in females and kidney hypertrophy was only seen in females. Males had a greater increase in blood pressure and glomerular basement membrane thickening, and a greater decrease in endpoint weight. Serum TGFβ1 levels were increased only in females. However, both sexes showed a similar increase in induction of kidney fibrosis. T cell and macrophage infiltration were also increased in both sexes. While some differences were observed, overall, both sexes developed clinical and pathologic characteristics of early DKD. Future studies evaluating therapeutic interventions can thus be assessed in both sexes of this DKD model.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".