Viral antibody response predicts morbidity and mortality in alcohol-associated hepatitis
Bibliographic record
Abstract
BACKGROUND AND AIMS: Alcohol-associated hepatitis (AH) is associated with very high mortality despite abstinence from alcohol; up to 40% of patients die within 6 months of diagnosis. Patients with AH are especially prone to infections, which can lead to multiorgan dysfunction and poorer prognosis. APPROACH AND RESULTS: We performed comprehensive serological profiling of the viral and bacterial infection history of 36 healthy controls, 48 patients with alcohol use disorder, and 224 patients with AH from 2 multicenter observational studies. We used systematic viral and bacterial epitope scanning by VirScan, a phage-display immunoprecipitation and sequencing technology that detects the peptides recognized by antibodies in patient sera, to comprehensively analyze antiviral and antibacterial antibodies and identify serologic biomarkers to predict patient outcomes. We found significant differences in the serological profiles of the 3 populations. The number of serum antibody epitopes in patients with alcohol use disorder during abstinence was increased compared with during active alcohol use. A decreased number and diversity of viral and bacterial antibody targets were detected in the sera of patients with AH, particularly those with a higher Child-Pugh score. In patients with AH, a decrease in the serum antiviral, but not antibacterial, antibody repertoire was associated with decompensation and mortality. Ninety-day mortality in AH could be predicted using a serum viral epitope signature. CONCLUSIONS: Abstinence from alcohol is associated with a significant increase in serum viral and bacterial antibody response. Decreased serum antiviral antibody repertoire is predictive of decompensation of liver disease and mortality in patients with AH.
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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 | 0.002 |
| 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".