P04-09 Changes in miRNA levels as biomarkers for drug-induced liver injury in human plasma
Bibliographic record
Abstract
Purpose: We have previously reported changes in the expression of microRNAs (miRNAs) under the administration of drugs with relatively high incidence rates of drug-induced liver injury (hTOX) and drugs with negligible risk (non-hTOX) using chimeric mice with highly humanized livers as markers for assessing the risk of DILI. In this study, we aimed to determine whether the target miRNAs showed similar changes in a clinical setting using residual blood samples obtained after therapeutic drug monitoring (TDM). Methods: This study analyzed 250 samples from 34 patients who underwent TDM for methotrexate, tacrolimus, or cyclosporine at Nagasaki University Hospital between August 3, 2021, and December 3, 2023. Quantitative real-time PCR was employed to measure the expression levels of miR-1237, miR-4306, and miR-340, with miR-320 used as an endogenous control. The expression levels of these miRNAs were compared with alanine aminotransferase (ALT) and aspartate aminotransferase (AST) levels measured during TDM to analyze the behavior of each marker. Results and Conclusion: Variations in the miRNA biomarkers for DILI candidates identified in chimeric mouse experiments – miR-1237, miR-4306, and miR-340 – were also observed in human plasma. In some cases, miR-1237 and miR-340 exhibited changes preceding elevations in ALT and AST levels, whereas miR-4306 tended to vary after ALT and AST increased. To validate these findings further, additional sample analysis with a larger cohort is necessary.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.002 | 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".