Metabolomics for the identification of early biomarkers of nephrotoxicity in a mouse model of cisplatin-induced acute kidney injury
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Cisplatin-induced nephrotoxicity manifests as acute kidney injury (AKI) in approximately one third of patients receiving cisplatin therapy. Current measures of AKI are inadequate in detecting AKI prior to significant renal injury, and better biomarkers are needed for early diagnosis of cisplatin-induced AKI. EXPERIMENTAL APPROACH: ) or saline. Plasma, urine, and kidney samples were collected prior to cisplatin injection and 24-, 48-, 72-, and 96-hours following cisplatin injection. Untargeted metabolomics was employed using liquid chromatography-mass spectrometry to identify early diagnostic biomarkers for cisplatin nephrotoxicity. PRINCIPAL RESULTS: There was clear metabolic discrimination between saline and cisplatin-treated mice at all timepoints (day 1 to day 4). In total, 26 plasma, urine, and kidney metabolites were identified as exhibiting early alterations following cisplatin treatment. Several of the metabolites showing early alterations were associated with mitochondrial function and energetics, including intermediates of the tricarboxylic acid cycle, regulators of mitochondrial function and indicators of fatty acid β-oxidation dysfunction. Furthermore, several metabolites were derived from the gut microbiome. MAJOR CONCLUSIONS: Our results highlight the detrimental effects of cisplatin on mitochondrial function and demonstrate potential involvement of the gut microbiome in the pathophysiology of cisplatin-induced AKI. We provide a panel of metabolites to guide future clinical studies of cisplatin-induced AKI and provide insight into potential mechanisms behind cisplatin nephrotoxicity.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".