Tissue-remodelling M2 Macrophages Recruits Matrix Metalloproteinase-9 for Cryotherapy-induced Fibrotic Resolution during Keloid Treatment
Bibliographic record
Abstract
Cryotherapy is used to treat keloid scars; however, the molecular and pathological mechanisms are not clearly understood. This study retrospectively evaluated the efficacy of combined treatment with cryotherapy and intralesional triamcinolone injection (Cryo+TA) or intralesional TA monotherapy (TA) in 40 Asian patients with keloid scars. Scar improvement was assessed using the Vancouver Scar Scale and Global Improvement Scale. Clinical improvement in scars, especially reduced vascularity and redness, was significantly greater in the Cryo+TA group than in the TA group. Cryotherapy-treated and untreated keloid tissue was collected from six patients for analysis. Histo-logically, collagen bundles from cryotherapy-treated keloid tissue were more fibrillar and abnormal thickness was reduced. Immunohistochemical staining showed a reduced number of dermal vessels after cryotherapy. Moreover, CD163+ M2 macrophages and matrix metalloproteinase-9 (MMP-9) were significantly increased in cryotherapy-treated tissue. Double immunofluorescence staining revealed co-expression of CD163 and MMP-9. These data indicate that cryotherapy recruits tissue-remodelling M2 macrophages with accompanying MMP-9, suggesting that cryotherapy-recruited M2 macrophages function in fibrotic resolution during keloid treatment.
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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) | 0.001 | 0.000 |
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".