Kinetics of serum cytokines reflect changes in the severity of chronic hepatitis C presenting minimal fibrosis
Bibliographic record
Abstract
Our aims were to measure the kinetics of serum tumour necrosis alpha (TNF-alpha) and transforming growth factor beta (TGF-beta) levels as markers of progression of disease in nontreated chronic hepatitis C virus (HCV)-infected patients with minimal or no fibrosis and minimal histology activity index (HAI) scores. Our study group consisted of 56 patients diagnosed with minimal (1) or no fibrosis (0) and minimal HAI (0-1) on their first biopsy as defined by Knodell and METAVIR scores. We compared their initial (entry of study) cytokine levels with a group of 103 HCV controls with minimal (0-1) to mild fibrosis (0-3) and mild HAI (5.5). Serum TNF-alpha and TGF-beta levels were measured by enzyme-linked-immunosorbent-assay. A significant difference was seen in TNF-alpha levels at baseline in the study group vs. controls. Regardless of their HAI, there was a correlation between TGF-beta and degree of fibrosis. As shown by their biopsies, during the 3 years (from entry to follow up), many of the patients that initially had minimal fibrosis progressed to higher degree of fibrosis. This progression is paralleled by an increase in TGF-beta levels when comparing initial and follow-up levels. In conclusion, serum TNF-alpha reflects the progression of inflammation as seen in liver biopsies and TGF-beta reflects the degree of fibrosis in HCV patients.
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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 | 0.000 | 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) | 0.001 | 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".