Tumor necrosis factor‐<b>α</b> and transforming growth factor‐<b>β</b> reflect severity of liver damage in primary biliary cirrhosis
Bibliographic record
Abstract
BACKGROUND AND AIMS: The pathogenesis of primary biliary cirrhosis (PBC) is unknown. The role of cytokines such as tumor necrosis factor-alpha (TNF-alpha) and transforming growth factor-beta (TGF-beta), and the effect of ursodeoxycholic acid (UDCA) in modifying the cytokine environment in patients with PBC has remained largely unstudied. Our aims were to determine: (i) the relationship between serum levels of TNF-alpha and TGF-beta and the severity of PBC; and (ii) the effects of UDCA therapy on TNF-alpha and TGF-beta levels in patients with PBC. METHODS: We studied 90 patients who had been treated with UDCA (53 patients) or placebo (37 patients) for 2 years as part of a randomized, double-blind, controlled trial. Patients were divided into histological stage I/II or stage III/IV disease. Serum TNF-alpha and TGF-beta levels were quantified by enzyme-linked immunoabsorbent assay. RESULTS: Baseline levels of TNF-alpha were significantly greater in patients with stage III/IV compared to stage I/II disease. After 2 years of treatment with UDCA, patients showed a significantly greater decrease in TNF-alpha levels and progression risk score compared to placebo-treated patients. TNF-alpha and TGF-beta levels were significantly reduced compared to baseline levels in the UDCA-treated group after 2 years, while there was no significant change in the levels of placebo-treated patients. CONCLUSIONS: Serum TNF-alpha and TGF-beta levels may reflect severity of disease in patients with PBC. The beneficial effects of UDCA therapy may be explained by lowering serum levels of these two cytokines.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".