Cellular and molecular pathology of HTS: basis for treatment
Bibliographic record
Abstract
Hypertrophic scar and keloids are fibroproliferative disorders of the skin which occur often unpredictably, following trauma and inflammation that compromise cosmesis and function and commonly recur following surgical attempts for improvement. Despite decades of research in these fibrotic conditions, current non-surgical methods of treatment are slow, inconvenient and often only partially effective. Fibroblasts from these conditions are activated to produce extracellular matrix proteins such as collagen I and III, proteoglycans such as versican and biglycan and growth factors, including transforming growth factor-beta and insulin like growth factor I. However, more consistently these cells produce less remodeling enzymes including collagenase and other matrix metalloproteinases, as well as the small proteoglycan decorin which is important for normal collagen fibrillogenesis. Recently, the systemic response to injury appears to influence the local healing process whereby increases in Th2 and possibly Th3 cytokines such as IL-2, IL-4 and IL-10 and TGF-beta are present in the circulating lymphocytes in these fibrotic conditions. Finally, unique bone marrow derived cells including mesenchymal and endothelial stem cells as well as fibrocytes appear to traffic into healing wounds and influence the healing tissue. On this background, clinicians are faced with patients who require treatment and the pathophysiologic basis as currently understood is reviewed for a number of emerging modalities.
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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 | 0.000 |
| 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 | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".