Discovery of urinary metabolomic biomarkers for early detection of acute kidney injury
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Bibliographic record
Abstract
The discovery of new biomarkers for early detection of drug-induced acute kidney injury (AKI) is clinically important. In this study, sensitive metabolomic biomarkers identified in the urine of rats were used to detect cisplatin-induced AKI. Cisplatin (10 mg kg(-1), i.p.) was administered to Sprague-Dawley rats, which were subsequently euthanized after 1, 3 or 5 days. In cisplatin-treated rats, mild histopathological alterations were noted at day 1, and these changes were severe at days 3 and 5. Blood urea nitrogen (BUN) and serum creatinine (SCr) levels were significantly increased at days 3 and 5. The levels of new urinary protein-based biomarkers, including kidney injury molecule-1 (KIM-1), glutathione S-transferase-α (GST-α), tissue inhibitor of metalloproteinase-1 (TIMP-1), vascular endothelial growth factor (VEGF), calbindin, clusterin, neutrophil, neutrophil gelatinase-associated lipocalin (NGAL), and osteopontin, were significantly elevated at days 3 and 5. Among urinary metabolites, trigonelline and 3-indoxylsulfate (3-IS) levels were significantly decreased in urine collected from cisplatin-treated rats prior to histological kidney damage. However, carbon tetrachloride (CCl4), a hepatotoxicant, did not affect these urinary biomarkers. Trigonelline is closely associated with GSH depletion and results in insufficient antioxidant capacity against cisplatin-induced AKI. The predominant cisplatin-induced AKI marker appeared to be reduced in urinary 3-IS levels. Because 3-IS is predominantly excreted via active secretion in proximal tubules, a decrease is indicative of tubular damage. Further, urinary excretion of 3-IS levels was markedly reduced in patients with AKI compared to normal subjects. The area under the curve receiver operating characteristics (AUC-ROC) for 3-IS was higher than for SCr, BUN, lactate dehydrogenase (LDH), total protein, and glucose. Therefore, low urinary or high serum 3-IS levels may be more useful for early detection of AKI than conventional biomarkers.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 it