The <scp>JAK–STAT</scp> pathway in keloid pathogenesis: a systematic review with qualitative synthesis
Bibliographic record
Abstract
Keloid tissues contain inflammatory cells and upregulated pro-inflammatory cytokines. The Janus kinase (JAK)-signal transducer and activator of transcription (STAT) pathway mediate cellular responses to these cytokines. We performed a systematic review on the role of the JAK-STAT pathway in keloid pathogenesis and the evidence for JAK-STAT inhibitors in keloid treatment. The search combined the terms (1) keloid and (2) JAK or TYK or STAT and included MeSH terms and synonyms. Two reviewers screened the articles and assessed the full texts on eligibility. Data were collected on the tested drugs and molecules, the type of cells and tissues used in the experiments, and study findings on the association between the JAK-STAT pathway and keloid cells and tissues. A total of twenty preclinical studies were included. Eleven preclinical studies proved that STAT3 expression and phosphorylation are enhanced in keloid tissue and keloid fibroblasts. Thirteen different JAK and/or STAT inhibitors were investigated. Tested drugs inhibited keloid progression as demonstrated by different processes, including reduced collagen production, cell proliferation and migration, increased cell cycle arrest and apoptosis, enhanced antioxidant responses, decreased (paracrine) signalling, and decreased profibrotic gene expression. No clinical studies have been published to date. Preclinical studies indicate a role for the JAK-STAT pathway in keloid pathogenesis and a potential role for JAK-STAT inhibitors in keloid treatment. The effect of these drugs should be further investigated on relevant biomarkers in a human keloid skin model, preferably including immune cells besides keloid fibroblasts and keratinocytes and in clinical studies.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".