Effects of Macitentan and Its Active Metabolite on Cultured Human Systemic Sclerosis and Control Skin Fibroblasts
Bibliographic record
Abstract
OBJECTIVE: To investigate the effects of the endothelin 1 (ET-1) receptor antagonists (ETRA) macitentan, its active metabolite ACT-132577, and bosentan on myofibroblast activation and extracellular matrix production induced by ET-1 in cultured systemic sclerosis (SSc) and control skin fibroblasts. METHODS: Fibroblasts were obtained from skin biopsies of 6 patients with SSc and 5 healthy subjects. Some cultured cells were untreated or treated with macitentan, ACT-132577, or bosentan alone (10 μM). Other cultured cells were treated with ET-1 alone (100 nM) or with ETRA, and after 1 h, also with ET-1. After 48 h of treatment, myofibroblast activation was investigated to evaluate the α-smooth muscle actin (α-SMA) expression by immunofluorescence; type I collagen (COL-1) and fibronectin (FN) were investigated by immunocytochemistry, Western blotting, and quantitative real-time PCR (qRT-PCR). Statistical analysis was performed by the nonparametric Mann-Whitney U test. RESULTS: In cultured SSc skin fibroblasts, only the treatment with macitentan significantly reduced the basal level of α-SMA expression (p = 0.03 vs untreated cells). Macitentan also significantly reduced the basal level of COL-1 synthesis, similarly to bosentan (p < 0.05 vs untreated cells). Macitentan or ACT-132577 antagonized the ability of ET-1 to further induce α-SMA expression (p = 0.03), COL-1, and FN synthesis (p = 0.03, p = 0.005); bosentan showed similar effects. These results obtained by immunofluorescence and immunocytochemistry were confirmed by Western blotting and qRT-PCR. The downregulatory effects exerted by ETRA were observed also in cultured human control skin fibroblasts. CONCLUSION: Macitentan and ACT-132577 seem to downregulate in vitro the profibrotic myofibroblast phenotype induced by ET-1 in cultured human SSc skin fibroblasts.
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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".