Iohexol Plasma Clearance in Animal Models: The Clear Choice for Measuring Early Renal Dysfunctio
Bibliographic record
Abstract
Glomerular filtration rate is a measure of the kidney’s ability to filter blood. In animal models of early kidney failure, there is no routine method to accurately measure GFR. The expensive gold standard of GFR measurement is exogenous inulin clearance. The commonly used method, endogenous plasma creatinine concentration, is unreliable and insensitive, especially at normal levels of renal function. This study investigates the utility of iohexol, an inexpensive radio-contrast agent as a promising exogenous marker for plasma clearance kidney function evaluation in rats. Early stages of progressive kidney failure were induced with a 0.25% adenine diet in male Sprague Dawley rats (N=8) over 5 weeks. Both plasma clearance of iohexol and inulin and creatinine concentration were evaluated following weekly venous injections and blood sampling. Plasma iohexol clearance and plasma inulin clearance strongly correlate (R2=0.95). However, plasma creatinine concentration correlated weakly with iohexol(R2=0.53) and inulin(R2=0.58). Iohexol plasma clearance accurately measures changes in kidney function, especially in in comparison to creatinine analysis. The data demonstrates creatinine is an inappropriate marker for renal function in early adenine-induced CKD rat models. Ongoing analysis of this data suggests refinement of the protocol will yield a simple method for routine measure of kidney function in murine lab animals. This tool will facilitate advancement in kidney disease onset and allow for more accurate interpretation of kidney function in the various animal models.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".