Circulating and inducible IL-32α in chronic hepatitis C virus infection
Bibliographic record
Abstract
Background: Interleukin 32 (IL-32) is a recently described pro-inflammatory cytokine implicated in chronic hepatitis C virus (HCV)-related inflammation and fibrosis. IL-32α is the most abundant IL-32 isoform. Methods: Circulating IL-32α levels were documented in patients with chronic HCV infections ( n = 31) and compared with individuals who spontaneously resolved HCV infection ( n = 14) and HCV-naive controls ( n = 20). In addition, peripheral blood mononuclear cells (PBMC) from the chronic HCV ( n = 12) and HCV-naive ( n = 9) cohorts were investigated for responses to HCV core and non-structural (NS)3 protein induced IL-32α production. Finally, correlations between IL-32α levels, hepatic fibrosis and subsequent responses to interferon-based therapy were documented in patients with chronic HCV. Results: Circulating IL-32α levels in patients with chronic HCV were similar to those of spontaneously resolved and HCV-naive controls. HCV protein induced IL-32α responses were similar in chronic HCV patients and HCV-naive controls. In patients with chronic HCV, serum IL-32α levels correlated with worsening METAVIR fibrosis (F) scores from F0 to F3 ( r = 0.596, P < 0.001) as did NS3 induced IL-32α responses ( r = 0.837, P < 0.05). However, these correlations were not sustained with the inclusion of IL-32α levels at F4 scores, suggesting events at F4 interfere with IL-32α synthesis or release. In chronic HCV patients who underwent treatment ( n = 28), baseline in vivo and in vitro induced IL-32α concentrations were not predictive of therapeutic outcomes. Conclusions: IL-32α activity is associated with worsening fibrosis scores in non-cirrhotic, chronic HCV patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".