The Roles of SDF-1/CXCR4, MCP-1/CCR2 and CCL5/CCR5 Chemokine Pathway in Hypertrophic Scarring
Bibliographic record
Abstract
Hypertrophic scars are a dermal form of fibroproliferative diseases that develop after skin trauma to the deep dermis.Dysfunction of the chemokine network can result in prolonged inflammation, abnormal blood vessel development, and a fibrotic wound-healing environment.To understand the role of the chemokine signaling in pathological scar formation, the inhibitory effects of scar formation by chemokine inhibitors of AMD3100, Maraviroc, and CAS445479-97-0 were investigated using a mouse model of dermal fibrosis, in which human split-thickness skin grafts were transplanted into full-thickness excision wounds in the dorsal of athymic nude mice, and the human skin grafts developed scars morphologically and histologically similar to human hypertrophic scars.The mice were treated with one of chemokine inhibitors from 5 days after grafting daily for 1 week and thereafter once a week.Mouse wounds were monitored, and scar tissues were collected at 2, 6, and 12 weeks after grafting for further histological and biochemical analysis.AMD3100 and Maraviroc reduced wound contraction.AMD3100 improved collagen orientation and up-regulated mRNA level of decorin in the early phase of wound healing.AMD3100, CAS445479-97-0 and Maraviroc reduced scar thickness, macrophage recruitment and myofibroblast formation, and down-regulated gene expressions of type I collagen in the early phase, heat shock protein 47 in the late phase, alpha-smooth muscle actin, and connective tissue growth factor throughout the whole phase of wound healing.Blocking chemokine pathways significantly improved scars in this dermal fibrotic model, which can be potential therapeutic targets for hypertrophic scars.
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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".