Effect of up to 30-days of storage at different temperatures on detection of feline kidney injury molecule-1 in urine
Bibliographic record
Abstract
Abstract Background In humans, kidney injury molecule-1 (KIM-1) is a biomarker of acute kidney injury that can be quantified in urine. Preliminary investigation in cats with experimentally induced acute kidney injury showed that KIM-1 urine concentration correlated with kidney injury histopathology scores. A lateral flow assay (LFA) has recently become available for patient-side feline KIM-1 measurement. In vitro parameters of the assay have not yet been determined. The objectives of this study were to determine detection of KIM-1 in urine stored at different temperatures over time, to establish the linear range of the LFA, and to assess the intra-assay repeatability of measurements. Results Ten urine samples with a range of KIM-1 concentrations were stored at room temperature (22o C), 4o C or -20o C, and tested with the LFA on days 0, 1, 2, 3, 7, 14, and 30. The concentration of KIM-1 in samples was not significantly different from the day 0 value, except one sample that had been stored for 30 days at room temperature yielded a significantly higher value. The assay results had a correlation coefficient of 0.922. The mean coefficient of variation for all samples was 15.7%. The slope of the curve of expected versus measured values in samples diluted two-fold nine times was 0.908, and results were linear over all dilutions. Conclusions The LFA for feline KIM-1 yields consistent results from stored urine samples. These characteristics will allow for KIM-1 to be measured retrospectively if immediate testing is not feasible. Within assay precision was high, and linearity over 9 logs of dilution suggests suitability for a range of subclinical and clinical kidney injuries.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".