Serum markers for mitochondrial dysfunction and cell death are possible predictive indicators for drug‐induced liver injury by direct acting antivirals
Bibliographic record
Abstract
AIM: We prospectively screened patients treated with direct-acting antivirals (DAA) in order to detect and analyze serum markers that are present prior to the development of drug-induced liver injury (DILI). METHODS: The levels of various serum markers among DILI, non-DILI and control groups were compared. The DILI group consisted of eight patients whose alanine aminotransferase (ALT) levels exceeded 32 IU/L during the DAA treatment. Eight patients without DILI were selected for the non-DILI group via a matched-group design based on age, sex and disease severity. Additionally, eight healthy volunteers were employed as the controls. Serum measurements of cytokines/chemokines, cytokeratin-18 fragment (CK-18F) and super oxidase dismutase-2 (SOD2) were evaluated on the date at which hepatitis C virus RNA was absent (baseline). For patients with DILI, serum measurements taken before treatment, 1 week before pronounced transaminase elevation (prominence-1 W) and on the date at which pronounced elevation of transaminase occurred (prominence) were also evaluated. RESULTS: All patients treated with DAA had normalized transaminase levels at baseline. In patients with DILI, interferon-inducible protein-10 (IP-10) levels were higher at prominence-1 W than at baseline. Those patients also had significantly higher levels of SOD2 and CK-18F at prominence-1 W than at baseline. CONCLUSION: Elevated IP-10 may be a preconditioning chemokine for DAA-induced liver injury, and damage markers associated with cell death and mitochondrial dysfunction are potential predictive serum markers for DILI.
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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 | 0.001 | 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) | 0.001 | 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".