Active immunization against transforming growth factor beta1 prevents hepatic fibrosis in a rat model of liver disease
Bibliographic record
Abstract
Transforming growth factor beta1 (TGF-β1) plays an important role in hepatic fibrogenesis. In this study, we documented the effects of active immunization against TGF-β1 on hepatic fibrosis in an animal model of chronic liver disease. BALB/c mice were immunized against 3 different peptides of TGF-β1 ligated into hepatitis B virus core protein (HBVc). Titers of TGF-β1 antibodies were documented by enzyme linked immunoassays and antibody activity by cell membrane receptor binding and proliferation assays. The most immunogenic recombinant HBVc + TGF-β1 peptide (HBVc + C) then served as a vaccine in Sprague-Dawley rats with dimethylnitrosamine-induced chronic liver disease. Hepatic fibrosis was documented by serum hyaluronic acid levels, liver histology, and reverse transcriptase polymerase chain reaction for hepatic collagen I (α1) and smooth muscle alpha actin mRNA expression. Relative to control rats vaccinated with HBVc alone, recombinant HBVc + C vaccinated animals had significantly lower serum hyaluronic acid levels, less histologic evidence of hepatic fibrosis, and reduced expression of collagen I (α1) and smooth muscle alpha actin mRNA in the liver. The results of this proof-of-concept study suggest that active immunization against TGF-β1 is a worthwhile strategy to pursue in efforts to prevent hepatic fibrosis associated with chronic liver disease.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".