Surfactant dysfunction during overexpression of TGF-β1 precedes profibrotic lung remodeling in vivo
Bibliographic record
Abstract
Transforming growth factor-β1 (TGF-β1) is involved in regulation of cellular proliferation, differentiation, and fibrogenesis, inducing myofibroblast migration and increasing extracellular matrix synthesis. Here, TGF-β1 effects on pulmonary structure and function were analyzed. Adenovirus-mediated gene transfer of TGF-β1 in mice lungs was performed and evaluated by design-based stereology, invasive pulmonary function testing, and detailed analyses of the surfactant system 1 and 2 wk after gene transfer. After 1 wk decreased static compliance was linked with a dramatic alveolar derecruitment without edema formation or increase in the volume of septal wall tissue or collagen fibrils. Abnormally high surface tension correlated with downregulation of surfactant proteins B and C. TTF-1 expression was reduced, and, using PLA (proximity ligand assay) technology, we found Smad3 and TTF-1 forming complexes in vivo, which are normally translocated into the nucleus of the alveolar epithelial type II cells (AE2C) but in the presence of TGF-β1 remain in the cytoplasm. AE2C show altered morphology, resulting in loss of total apical surface area per lung and polarity. These changes of AE2C were progressive 2 wk after gene transfer and correlated with lung compliance. Although static lung compliance remained low, the volume of septal wall tissue and collagen fibrils increased 2 wk after gene transfer. In this animal model, the primary effect of TGF-β1 signaling in the lung is downregulation of surfactant proteins, high surface tension, alveolar derecruitment, and mechanical stress, which precede fibrotic tissue remodeling and progressive loss of AE2C polarity. Initial TTF-1 dysfunction is potentially linked to downregulation of surfactant proteins.
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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) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".