Immunization against TGF-β1 reduces collagen deposition but increases sustained inflammation in a murine asthma model
Bibliographic record
Abstract
Transforming growth factor (TGF)-β1 is involved in the processes of airway inflammation and remodeling; however, its reported roles in asthma pathogenesis are controversial. We sought both to investigate the effects of active immunization targeting TGF-β1 on allergen-induced airway inflammatory responses and to evaluate its possible application for asthma treatment. BALB/c mice were immunized with a virus-like-particle (VLP) vaccine presenting a TGF-β1 peptide. For the preventive intervention of acute allergic airway inflammation, immunization was conducted before sensitization and challenges with ovalbumin (OVA), and for the therapeutic treatment of chronic inflammatory responses, immunization was initiated after inflammatory responses were established. Preventive immunization with VLPs led to increased proinflammatory IL-4, IL-13, and IL-33 levels in the bronchoalveolar lavage fluids (BALF) with no significant effects on lung tissue inflammation and airway goblet cell hyperplasia. Therapeutic treatment showed that at 24 h after the fourth 2-day challenge with OVA following 2 intraperitoneal sensitizations, airway subepithelial collagen deposition was significantly ameliorated in vaccinated mice, whereas the lung histology and cytokine profile in the BALF were not changed. In contrast, after a 4-week recovery from the last OVA challenge, the vaccinated mice's collagen deposition remained reduced, but they sustained lung-tissue inflammation and goblet-cell hyperplasia; elevated IL-13, TNF, and IFN-γ levels in the BALF; and increased airway resistance, tissue resistance, and tissue elastance. In a conclusion, the role of TGF-β1 is complicated in allergic airway inflammatory responses. It is important to make a careful assessment in accordance with specific disease conditions when targeting TGF-β1 for a therapeutic purpose.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".