Is Nanofat the Long-Awaited Treatment for Hypertensive Ischemic Leg Ulcers?
Bibliographic record
Abstract
BACKGROUND: Martorell hypertensive ischemic leg ulcer (HYTILU) is a chronic, hard-to-heal wound linked to hypertension. This study explores nanofat grafting as a regenerative alternative to traditional skin grafting for improved healing and patient outcomes. OBJECTIVE: To explore the efficacy of nanofat grafting in the management of HYTILU and compare it with skin grafting. MATERIALS AND METHODS: This was a retrospective single-center pilot study involving 23 patients with HYTILU treated with adipose-derived stromal cells (adipose-derived stromal cells/nanofat). The primary outcomes were ulcer healing rate, pain reduction, and improvements in quality of life, as measured by SCAR-Q (a scar quality of life questionnaire) and the Vancouver Scar Scale, over a period of 6 months posttreatment. RESULTS: This study revealed a significant decrease in ulcer size from an initial mean of 39.69 cm² to complete healing, with an average healing time of 4.65 months. The mean visual analog scale pain scores significantly decreased from an initial score of 5.87 to 0.39 at 3 months postinjection (P < .0001). Quality of life was significantly improved after treatment, as underscored by higher SCAR-Q scores and lower Vancouver scale scores, indicating better scar quality and minimal adverse effects. CONCLUSION: These study results underscore nanofat grafting as a superior alternative to traditional skin grafting for HYTILU, offering advantages in terms of healing time, pain management, and patient quality of life. Further research is needed to confirm these findings and assess the use of nanofat in the management of other chronic wounds.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".