Alanine aminotransferase, HCV RNA levels and pro-inflammatory and pro-fibrogenic cytokines/chemokines during acute hepatitis C virus infection
Bibliographic record
Abstract
BACKGROUND: This study assessed the association of alanine-aminotransferase (ALT) and hepatitis C virus (HCV) RNA levels with pro-inflammatory and pro-fibrogenic cytokines and chemokines during acute HCV infection to provide further insight into the potential HCV immunopathogenesis. METHODS: Participants in the ATAHC study, a prospective study of recent HCV infection, with detectable HCV RNA at the time of HCV detection were included. Plasma levels of 27 cytokines and chemokines were measured and their correlation with ALT and HCV RNA levels were assessed. Log10 transformed cytokines and ALT values were used in the analysis. RESULTS: Among 117 individuals, the plasma levels of interferon-gamma inducible protein-10 (IP-10) and macrophage inflammatory protein-1beta (MIP-1β) were positively correlated with ALT levels (IP-10: r = 0.42, P < 0.001; MIP-1β: r = 0.29, P = 0.001) and HCV RNA levels (IP-10: rs = 0.44, P < 0.001; MIP-1β: rs = 0.43, P < 0.001). Using linear regression, after adjusting for sex, age, infection duration, symptomatic infection, HIV co-infection, interferon-lambda rs12979860 genotype, HCV genotype, and assay run, higher ALT levels (β = 0.20; 95 % CI: 0.07, 0.32; P = 0.002) and HCV RNA levels >400,000 IU/mL (vs. <8,500 IU/mL; β = 0.16; 95 % CI: 0.03, 0.28; P = 0.014) were independently associated with higher IP-10 levels. HCV RNA levels >400,000 IU/mL (vs. <8,500 IU/mL; β = 0.16; 95 % CI: 0.01, 0.31; P = 0.036) were associated with higher MIP-1β levels. CONCLUSIONS: During acute HCV infection, high ALT and HCV RNA levels were associated with increased IP-10 levels, while high HCV RNA levels were also associated with increased MIP-1β levels. These data suggest that IP-10 and MIP-1β may have a role in HCV immuno-pathogenesis starting early in acute HCV infection.
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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 | 0.000 | 0.000 |
| 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 | 0.001 |
| 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".