Investigating the effect of transforming growth factor (TGF)-β isoforms 1, 2, and 3 on lung and dermal fibroblast functions
Bibliographic record
Abstract
Background: Patients with systemic scleroderma (SSc) develop skin fibrosis and are susceptible to developing interstitial lung disease (ILD), which is the most common cause of death in SSc. TGF-β is a pro-fibrotic cytokine upregulated in SSc patients and may cause fibroblast dysfunction, leading to fibrosis. Aim: Assess the effect of the three TGF-β isoforms 1, 2, and 3 on lung and dermal fibroblast functions, including extracellular matrix (ECM) synthesis, pro-fibrotic cytokine release, fibroblast contraction, and myofibroblast differentiation. Methods: Human lung and dermal fibroblast cell lines were treated with TGF-β1, -β2, -β3, or a media control to assess interleukin (IL)-11 and IL-6 release by ELISA. Fibronectin, collagen-I, and α-smooth muscle actin (α-SMA) expression was assessed by western blot. Fibroblast contraction was measured using a collagen gel matrix contraction assay. Results: Lung and dermal fibroblasts treated with TGF-β2 and TGF-β3 had increased fibronectin and collagen-I ECM expression compared to control (P<0.05 and P<0.05). TGF-β2 and TGF-β3 stimulated greater IL-11 and IL-6 release by lung and dermal fibroblasts compared to control (P<0.0001 and P<0.0001). Lung and dermal fibroblasts treated with TGF-β2 and TGF-β3 increased myofibroblast differentiation (α-SMA expression) compared to control (P<0.05). Dermal fibroblasts treated with TGF-β1 and TGF-β2 contracted more than untreated fibroblasts (P<0.05); TGF-β did not affect lung fibroblast contraction. Conclusion: TGF-β2 and TGF-β3 have a more pronounced pro-fibrotic effect than TGF-β1 on lung and dermal fibroblast functions, making them potential targets for treatments in SSc-ILD.
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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 | 0.000 | 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) | 0.002 | 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".