Cytokine profiles in acute liver injury—Results from the US Drug-Induced Liver Injury Network (DILIN) and the Acute Liver Failure Study Group
Bibliographic record
Abstract
Changes in levels of cytokines and chemokines have been proposed as possible biomarkers of tissue injury, including liver injury due to drugs. Recently, in acute drug-induced liver injury (DILI), we showed that 19 of 27 immune analytes were differentially expressed and that disparate patterns of immune responses were evident. Lower values of serum albumin (< 2.8 g/dL) and lower levels of only four analytes, namely, IL-9, IL-17, PDGF-bb, and RANTES, were highly predictive of early death [accuracy = 96%]. The goals of this study were to assess levels of the same 27 immune analytes in larger numbers of subjects to learn whether the earlier findings would be confirmed in new and larger cohorts of subjects, compared with a new cohort of healthy controls. We studied 127 subjects with acute DILI enrolled into the US DILIN. We also studied 118 subjects with severe acute liver injury of diverse etiologies, enrolled into the ALF SG registry of subjects. Controls comprised 63 de-identified subjects with no history of liver disease and normal liver tests. Analytes associated with poor outcomes [death before 6 months, n = 32 of the total of 232 non-acetaminophen (Apap) subjects], were lower serum albumin [2.6 vs 3.0 g/dL] and RANTES [6,458 vs 8,999 pg/mL] but higher levels of IL-6 [41 vs 18], IL-8 [78 vs 48], and MELD scores [30 vs 24]. Similar patterns were observed for outcome of death/liver transplant within 6 months. A model that included only serum albumin < 2.8 g/dL and RANTES below its median value of 11,349 had 83% (or 81%) accuracy for predicting early death (or early death/liver transplant) in 127 subjects from DILIN. No patterns of serum immune analytes were reflective of the etiologies of acute liver failure, but there were cytokine patterns that predicted prognosis in both acute DILI and ALF.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| 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".