Distinct Metalloproteinase Expression and Functions in Systemic Sclerosis and Fibrosis: What We Know and the Potential for Intervention
Bibliographic record
Abstract
Systemic sclerosis (SSc) is a chronic debilitating idiopathic disorder, characterized by deposition of excessive extracellular matrix (ECM) proteins such as collagen which leads to fibrosis of the skin and other internal organs. During normal tissue repair and remodeling, the accumulation and turnover of ECM proteins are tightly regulated by the interaction of matrix metalloproteinases (MMPs) and endogenous tissue inhibitors of metalloproteinases (TIMPs). SSc is associated with dysregulation of the activity of these proteolytic and inhibitory proteins within the tissue microenvironment, tipping the balance toward fibrosis. The resultant ECM accumulation further perpetuates tissue stiffness and decreased function, contributing to poor clinical outcomes. Understanding the expression and function of these endogenous enzymes and inhibitors within specific tissues is therefore critical to the development of therapies for SSc. This brief review describes recent advances in our understanding of the functions and mechanisms of ECM remodeling by metalloproteinases and their inhibitors in the skin and lungs affected in SSc. It highlights recent progress on potential candidates for intervention and therapeutic approaches for treating SSc fibrosis.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".