Perivascular Cells in Diffuse Cutaneous Systemic Sclerosis Overexpress Activated ADAM12 and Are Involved in Myofibroblast Transdifferentiation and Development of Fibrosis
Bibliographic record
Abstract
OBJECTIVE: Microvascular damage is pivotal in the pathogenesis of systemic sclerosis (SSc), preceding fibrosis, and whose trigger is not still fully understood. Perivascular progenitor cells, with profibrotic activity and function, are identified by the expression of the isoform 12 of ADAM (ADAM12) and this molecule may be upregulated by transforming growth factor-β (TGF-β). The goal of this work was to evaluate whether pericytes in the skin of patients with diffuse cutaneous SSc (dcSSc) expressed ADAM12, suggesting their potential contribution to the fibrotic process, and whether TGF-β might modulate this molecule. METHODS: After ethical approval, mesenchymal stem cells (MSC) and fibroblasts (FB) were isolated from bone marrow and skin samples collected from 20 patients with dcSSc. ADAM12 expression was investigated in the skin and in isolated MSC and FB treated with TGF-β by immunofluorescence, quantitative real-time PCR, and western blot. Further, we silenced ADAM12 expression in both dcSSc-MSC and -FB to confirm the TGF-β modulation. RESULTS: Pericytes and FB of dcSSc skin showed an increased expression of ADAM12 when compared with healthy control skin. TGF-β in vitro treatment induced a significant increase of ADAM12 in both SSc-MSC and -FB, with the higher levels observed in dcSSc cells. After ADAM12 silencing, the TGF-β ability to upregulate α-smooth muscle actin in both SSc-MSC and SSc-FB was inhibited. CONCLUSION: Our results suggest that in SSc, pericytes that transdifferentiate toward activated FB are present in the vascular tree, and TGF-β, while increasing ADAM12 expression, may modulate this transdifferentiation.
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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 | 0.001 |
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".