Expression of Kidney Injury Molecule-1 in Healthy and Diseased Feline Kidney Tissue
Bibliographic record
Abstract
Sensitive markers to detect acute kidney injury (AKI) in cats are lacking. Kidney injury molecule-1 (KIM-1) is a promising marker of acute tubular injury in humans, and sequence and structure of feline KIM-1 have been determined. KIM-1 is shed into urine of cats with natural AKI. The objectives of this study were to characterize temporal and cellular expression of KIM-1 in kidneys from cats without and with experimental and natural AKI using histopathology and immunohistochemistry. Tissue sections from 8 cats without kidney disease, 3 to 4 cats with experimentally induced AKI on each day 1, 3, 6, and 12 after unilateral ischemia/reperfusion, and 9 cats with natural AKI were assessed. In sections from cats without kidney disease, patterns of periodic acid-Schiff and aquaporin-1 staining allowed identification of 3 distinct segments of the proximal tubule. KIM-1 staining was absent in segments 1 (S1) and S2, and faint in S3. Injury of S3 in cats with experimental and natural AKI was characterized by cell loss and necrosis, and remaining intact cells had cytoplasmic blebs and reduced brush borders. In experimental AKI, intensity of KIM-1 expression increased in proportion to the severity of injury and was consistently present in S3 but only transiently in other segments. Vimentin was absent in proximal tubules of healthy cats but expressed in injured S3. These findings indicate that S3 is the proximal tubular segment most susceptible to ischemic injury and that KIM-1 is a sensitive tissue indicator of AKI in cats.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".