Metabolomics for the identification of early biomarkers of cisplatin-induced 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: C57BL/6 and FVB/N mice were treated with a single intraperitoneal injection of cisplatin (15 mg kg-1) 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-induced AKI. Key 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. Conclusion and Implications: 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. Here 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 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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".