Exploration of novel therapeutic targets for keloids based on clinical and molecular data: the role of the IL-4/IL-13 signaling pathway
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Keloids are problematic fibrotic lesions with excessive collagen and chronic inflammation; their underlying causes are not fully understood. Type 2 helper T (Th2) cytokines IL-4 and IL-13 are implicated in various fibrotic disorders, including but not limited to pulmonary fibrosis and systemic sclerosis. This study aimed to investigate the IL-4/IL-13 signaling pathway's contribution to keloid pathophysiology and evaluate its potential as a novel therapeutic target. METHODS: We retrospectively analyzed keloid patients, collecting demographic data and grading scar severity with the Vancouver Scar Scale (VSS). Keloid tissue specimens underwent immunofluorescence, Western blotting, and real-time PCR to quantify IL-4, IL-13, Type I Collagen alpha 1 (Collagen I A1), and phosphorylated STAT6 (p-STAT6) expression. RESULTS: Patients had an average total VSS score of 11.0 ± 2.9, indicating active fibrotic inflammation. Molecular assays showed significant upregulation of IL-4 and IL-13, alongside increased p-STAT6 and Collagen I A1 in keloid tissues. Notably, IL-4, IL-13, and p-STAT6 expression positively correlated with VSS thickness and pliability scores. CONCLUSIONS: Our findings demonstrate that the IL-4/IL-13 signaling axis is markedly activated in keloid tissues, promoting collagen synthesis via STAT6 phosphorylation. Therefore, targeting the IL-4/IL-13/STAT6 pathway may represent a promising therapeutic strategy for managing keloids.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".