An Exploratory Study of PN HPT for Treating Postsurgical Atrophic and Depressed Scars
Bibliographic record
Abstract
BACKGROUND: Postsurgical atrophic scars tend to respond poorly to treatments, especially non-energy-based ones. Hydrophilic PN HPT (Polynucleotides High Purification Technology) injected intradermally is a non-energy-based option with an immediate volume-enhancing effect that indirectly improves the fibroblast synthesis of collagen and extracellular matrix. The PN HPT ingredient has the further benefit of a dermal "priming" effect that enhances the efficacy of other scar treatments. OBJECTIVES: Verify retrospectively, with advanced techniques, the efficacy of PN HPT monotherapy as postsurgical scar treatment. METHODS: Retrospective data collection in 18- to 65-year-old women with moderate-to-severe atrophic scars after mammary surgery undergoing a five-session intradermal treatment course with 0.75% PN HPT gel formulation in single-use syringes starting 6 months after surgery. Primary retrospective efficacy parameter: changes in scar morphology and symptom severity after three and 6 months (modified Vancouver Scar Scale, mVSS). Secondary efficacy parameters: roughness score 6 months after baseline (Antera 3D CS tridimensional skin analysis system) and Global Aesthetic Improvement Scale (GAIS, Investigator and Patient subscales) after three and 6 months. RESULTS: Total mean mVSS highly significantly improved from 11.2 ± 1.92 at baseline to 7.0 ± 1.68 and 6.9 ± 1.55 after three and 6 months, respectively; the mean Antera 3D CS roughness score improved from 13.5 ± 4.14 to 10.0 ± 3.49 after 6 months. After three and 6 months, the GAIS subscores for investigators and cohort subjects were identical (3.0 ± 0.81 and 3.0 ± 0.72, respectively). The photographic documentation supported the previous results. CONCLUSIONS: In monotherapy, the intradermal PN HPT ingredient seems to quickly and safely relieve the burden of postsurgical atrophic scars. However, the lack of a formal parallel control group is a severe limitation. The objective quantitative measurements confirmed the long-lasting benefits.
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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) | 0.000 | 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) | 0.002 | 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".