A new, easily generated mouse model of diabetic kidney fibrosis
Bibliographic record
Abstract
Abstract Our understanding of diabetic kidney disease pathogenesis has been hampered by the lack of easily generated pre-clinical animal models that faithfully recapitulate critical features of human disease. While most standard animal models develop manifestations of early stage diabetic injury such as hyperfiltration and mesangial matrix expansion, only a select few develop key late stage features such as interstitial fibrosis and reduced glomerular filtration rate. An underlying theme in these late stage disease models has been the addition of renin-angiotensin system hyperactivation, an important contributor to human disease pathogenesis. Widespread use of these models has been limited, however, as they are either labour intensive to generate, or have been developed in the rat, preventing the use of the many powerful genetic tools developed for mice. Here we describe the Akita+/− Ren+/− mouse, a new, easily generated murine model of diabetic kidney disease that develops many features of late stage human injury, including not only hyperglycemia, hypertension, and albuminuria, but also reduced glomerular filtration rate, glomerulosclerosis, and interstitial fibrosis.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".