Towards an anti-fibrotic therapy for scleroderma: targeting myofibroblast differentiation and recruitment
Bibliographic record
Abstract
BACKGROUND: In response to normal tissue injury, fibroblasts migrate into the wound where they synthesize and remodel new extracellular matrix. The fibroblast responsible for this process is called the myofibroblast, which expresses the highly contractile protein alpha-smooth muscle actin (alpha-SMA). In normal tissue repair, the myofibroblast disappears. Conversely, abnormal myofibroblast persistence is a key feature of fibrotic dieases, including scleroderma (systemic sclerosis, SSc). Myofibroblasts can be derived from differentiation of local resident fibroblasts or by recruitment of microvascular pericytes. CLINICAL PROBLEM ADDRESSED: Controlling myofibroblast differentiation and persistence is crucial for developing anti-fibrotic therapies targeting SSc. BASIC SCIENCE ADVANCES: Insights have been recently generated into how the proteins transforming growth factor beta (TGFbeta), endothelin-1 (ET-1), connective tissue growth factor (CCN2/CTGF) and platelet derived growth factor (PDGF) contribute to myofibroblast differentiation and pericyte recruitment in general and to the persistent myofibroblast phenotype of lesional SSc fibroblast, specifically. RELEVANCE TO CLINICAL CARE: This minireview summarizes recent findings pertinent to the origin of myofibroblasts in SSc and how this knowledge might be used to control the fibrosis in this disease. CONCLUSIONS: TGFbeta, ET-1, CCN2 and PDGF are likely to cooperate in driving tissue repair and fibrogenic responses in fibroblasts. TGFbeta, ET-1 and CCN2 appear to contribute to myofibroblast differentiation; PDGF appears to be involved with pericyte recruitment. Thus, different therapeutic strategies may exist for targeting the multisystem fibrotic disorder SSc.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".