Efficacy and Safety of Ablative Fractional CO <sub>2</sub> Laser Therapy for Localized Scleroderma: A Comprehensive Bench‐to‐Bedside Approach
Bibliographic record
Abstract
Background Localized scleroderma (LS) is a disfiguring chronic inflammatory disease characterized by fibrosis of the skin and subcutaneous tissue. As the available therapeutic options are limited, developing effective and safe treatment protocols is crucial. Methods This study included both animal experiments and a single‐arm, open‐label clinical trial. Results In the animal experiments, ablative fractional carbon dioxide laser (CO 2 ‐AFL) treatment (reaching the deep dermis) improved skin fibrosis, reduced dermal thickness, and induced collagen restructuring, promoting hair follicle proliferation. MMP‐1, Krt15, and PCNA clearly increased after treatment. The subsequent clinical trial demonstrated that CO 2 ‐AFL treatment significantly improved the appearance and key parameters of skin lesions in LS patients. The tested therapy was associated with reduced skin hardening, restoration of the adipose tissue structure, and increased hair follicle growth. Following laser treatment, VAS scores decreased from 5.61 (1.09) to 3.90 (1.03), clinical ratings from 5.06 (1.32) to 3.68 (1.49), and ultrasound‐based lesion activity scores from 4.79 (1.34) to 2.67 (1.85), all with p < 0.001. No severe adverse effects were observed. Conclusions This study underscores CO 2 ‐AFL as a promising therapeutic option for LS patients, elucidating underlying mechanisms and establishing a foundation for advancing future therapeutic strategies. Trial Registration Chinese Clinical Trial Register (ChiCTR2200065939).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".