Development of a Model of Chronic Kidney Disease in the C57BL/6 Mouse with Properties of Progressive Human CKD
Bibliographic record
Abstract
Chronic kidney disease (CKD) is a major healthcare problem with increasing prevalence in the population. CKD leads to end stage renal disease and increases the risk of cardiovascular disease. As such, it is important to study the mechanisms underlying CKD progression. To this end, an animal model was developed to allow the testing of new treatment strategies or molecular targets for CKD prevention. Many underlying risk factors result in CKD but the disease itself has common features, including renal interstitial fibrosis, tubular epithelial cell loss through apoptosis, glomerular damage, and renal inflammation. Further, CKD shows differences in prevalence between the genders with premenopausal women being relatively resistant to CKD. We sought to develop and characterize an animal model with these common features of human CKD in the C57BL/6 mouse. Mice of this genetic background have been used to produce transgenic strains that are commercially available. Thus, a CKD model in this strain would allow the testing of the effects of numerous genes on the severity or progression of CKD with minimal cost. This paper describes such a mouse model of CKD utilizing angiotensin II and deoxycorticosterone acetate as inducers.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".