Persistent pro‐inflammatory cytokines following the initiation of pegylated IFN therapy in hepatitis C infection is associated with treatment‐induced depression
Bibliographic record
Abstract
Pegylated interferon (IFN), the basis for chronic hepatitis C virus (HCV) treatment, causes depression in 30-40% of patients. The potential for cytokine mRNA patterns from baseline into early treatment to associate with the onset of treatment-induced depression (TID) was examined. Depression was measured by the Beck Depression Inventory at baseline and weeks 2, 4, 8 and 12 of treatment (n = 38). At baseline and weeks 2 and 4, peripheral blood mononuclear cell (PMBC, n = 28), isolated ex vivo, were examined for tumour neurosis factor (TNF)-alpha, interleukin (IL)-1beta and IL-10 mRNA expression. In patients that developed treatment-induced depression, pro-inflammatory TNF-alpha mRNA levels from baseline into week 4 of therapy remained constant (1.1-fold increase); whereas IL-1beta transcripts decreased 3.5 fold. However, corresponding TNF-alpha (3-fold, P < 0.05) and IL-1beta (7.5-fold) transcript expression diminished to a greater extent in the absence of TID. Changes in TNF-alpha mRNA values correlated to the average change in BDI scores over the 12 weeks (r = 0.56, P < 0.05). Concomitantly, anti-inflammatory IL-10 transcript levels decreased in (TID), relative to increased expression in the absence of TID (P < 0.05). The potential influence of IL-10 was observed upon calculation of individual pro- verses anti-inflammatory mRNA ratios. Stable in the presence of depression, TNF-alpha/IL-10 and IL-1beta/IL-10 mRNA ratios declined significantly over time in its absence (P < 0.05). This study suggests that in chronic HCV infection, upon pegylated IFN administration persistent pro-inflammatory cytokine MRNA expression associates with TID. In contrast, therapeutic activation of mechanisms that decrease pro-inflammatory immunity may protect against depression during therapy.
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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 | 0.000 |
| 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".