Robust Rat and Mouse Models of Bilateral Renal Ischemia Reperfusion Injury
Bibliographic record
Abstract
BACKGROUND/AIM: Acute and chronic kidney diseases are a major contributor to morbidity and mortality worldwide, with no specific treatments currently available for these. To enable understanding the pathophysiology of and testing novel treatments for acute and chronic kidney disease, a suitable in vivo model of kidney disease is essential. In this article, we describe two reliable rodent models (rats and mice) of efficacious kidney injury displaying acute to chronic kidney injury progression, which is also reversible through novel therapeutic strategies such as ischemic preconditioning (IPC). MATERIALS AND METHODS: We utilized adult male Lewis rats and adult male wildtype (C57BL/6) mice, performed a midline laparotomy, and induced warm ischemia to both kidneys by bilateral clamping of both renal vascular pedicles for a set time, to mimic the hypoxic etiology of disease commonly found in kidney injury. RESULTS: Bilateral ischemia reperfusion injury caused marked structural and functional kidney injury as exemplified by histology damage scores, serum creatinine levels, and kidney injury biomarker levels in both rodents. Furthermore, this effect displayed a dose-dependent response in the mouse model. CONCLUSION: These rodent models of bilateral kidney IRI are reliable, reproducible, and enable detailed mechanistic study of the underlying pathophysiology of both acute and chronic kidney disease. They have been carefully optimised for single operator use with a strong track record of training both surgically trained and surgically naïve operators.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".