Adult Skin Wounds Can Be Induced to Regenerate Through Modulation of Cells and Extracellular Matrix Molecules
Bibliographic record
Abstract
Background: Fetal dermal wound healing generally occurs by spontaneous regeneration without scarring. In contrast, adult skin wounds are repaired by replacement of damaged tissue with excess extracellular matrix (ECM) substances and cells that lead to scar formation. The Problem: Hypertrophic scar (HTS) is a fibroproliferative skin disorder characterized by progressive deposition of collagen, which occurs in more than 70% of adult skin wounds such as those following burn or traumatic injury. However, with more investigation the potential exists to induce regeneration without scar formation. Basic/Clinical Science Advances: The process of cutaneous wound healing requires a collaborative interaction of various cell types and ECM molecules. The development of HTS is profoundly influenced by multiple cell types, particularly fibroblasts, cytokines such as transforming growth factor-b1, and the expression of small leucine-rich proteoglycans that regulate collagen fibrillogenesis and the activity of transforming growth factor-b1. Exogenous overexpression of antifibrotic ECM molecules may improve skin wound healing and regeneration, which may ameliorate HTS or tissue fibrosis. Clinical Care Relevance: Emerging evidence in wound healing research describes the multiple roles of ECM molecules in cell function, including signaling, proliferation, migration, and adhesion. By investigating the cellular and molecular mechanisms involved in skin wound healing, we anticipate an increase in our understanding of how the tissue repair process might be adjusted to promote regeneration of injured skin rather than repair with excessive amounts and distorted structure of the ECM. Conclusion: Careful analysis of the cellular and molecular mechanisms involved in HTS formation may contribute to advanced strategies in tissue engineering, skin regeneration, and reduction of scarring and tissue 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 | 0.000 | 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) | 0.003 | 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".