An Open-Label, Uncontrolled, Single-Arm Clinical Trial of Tofacitinib, an Oral JAK1 and JAK3 Kinase Inhibitor, in Chinese Patients with Keloid
Bibliographic record
Abstract
BACKGROUND: The keloid treatment is still a thorny and complicated clinical problem, especially in multiple keloids induced by wound, severe burn, ethnic background or cultural behaviors, or unexplained skin healing. Mainstream treatments have limited efficacy in treating multiple keloids. As no oral treatment with painlessness and convenience is available, oral treatment strategies should be formulated. OBJECTIVES: This study aimed to investigate the efficacy and therapeutic mechanism of oral tofacitinib in keloid patients. METHODS: We recruited the 7 patients with keloid scars and prescribed 5 mg of tofacitinib twice a day orally with a maximum follow-up of 12 weeks. The Patient and Observer Scar Assessment Scale (POSAS), the Vancouver scar scale (VSS), ANTERA 3D camera, and the DUB Skin Scanner 75 were used to assess the characteristics of the lesion. Immunohistochemistry was performed to evaluate collagen synthesis, proliferation, and relative molecular pathways. Moreover, the effects of tofacitinib were assessed on keloid fibroblast in vitro. RESULTS: After 12 weeks of oral tofacitinib, significant improvement in POSAS, VSS, and Dermatology Life Quality Index (DLQI) scores was observed (p < 0.05). The volume, lesion height, and dermis thickness of the keloid decreased (p < 0.05). Moreover, significant decreases in the expression of collagen I, Ki67, p-STAT 3, and p-SMAD2 were observed after 12 weeks of administration. In vitro experiments suggested that tofacitinib treatment inhibits fibroblast proliferation and collagen I synthesis via suppression of STAT3 and SMAD2 pathway. CONCLUSION: Tofacitinib, a new candidate oral drug for keloid, could reduce keloid lesion volume by inhibiting collagen synthesis and inhibiting fibroblast proliferation, and alleviate itch and pain to obtain a better life quality.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".