Using Implantable Artificial Dermis-PELNAC as a Functional Material to Guide Reconstruction of Finger Body Defect
Bibliographic record
Abstract
AIM: Managing partial defects of the finger is crucial for both function and aesthetics, especially when bone or tendon is exposed. Permacol Enhanced Layer for Neodermis and Coverage (PELNAC), an artificial dermis, serves as a promising scaffold in surgical procedures, providing wound protection and promoting tissue healing. This study assesses the effectiveness of PELNAC in treating a range of partial finger defects. METHODS: We assessed PELNAC's morphology and microstructure using scanning electron microscopy, characterized its degradation profile over six weeks in simulated body fluid, and confirmed its cytocompatibility with L929 cell cultures. In the clinical setting, 47 patients with 56 partial finger defects (both superficial and deep) were treated using PELNAC alone. Outcome measures included wound closure time, range of motion (ROM), sensory recovery (two-point discrimination), Vancouver Scar Scale (VSS) scores, and patient satisfaction. RESULTS: Scanning electron microscopy revealed interconnected micropores in PELNAC, with a porosity of 81.3 ± 2.1% and aperture sizes of 40-70 µm (top view) and 60-100 µm (section view). After six weeks in simulated body fluid, PELNAC retained 86.4 ± 1.5% of its weight, and cells proliferated well on its surface. All treated wounds healed without the need for split-thickness skin grafts, with an average closure time of 58.7 ± 12.8 days (range: 30-84 days). Age showed weak positive correlation with healing time (r = 0.152, p < 0.01) and weak negative correlation with two-point discrimination (r = -0.55, p < 0.01). Longer healing times correlated with reduced ROM (r = -0.143, p < 0.01), while higher VSS scores were linked to poorer functional outcomes (r = -0.22, p < 0.01). The average ROM in patients with distal interphalangeal joint (DIPJ) defects was 49° (IQR: 45-56.25°). Sensory recovery averaged 5.95 mm (IQR: 5.175-6.7 mm). The mean VSS score was 2 (IQR: 1-3), indicating minimal scarring. Patient satisfaction was high (functional score: 9 (IQR: 8-9.25)), with no severe complications reported. CONCLUSIONS: This study evaluates the clinical and biomechanical effectiveness of PELNAC as a single-stage reconstructive material for partial finger defects. PELNAC facilitates wound healing without secondary skin grafts, preserving joint mobility, promoting sensory recovery, and minimizing scarring. The results highlight PELNAC as a simple, safe, and effective alternative to traditional approaches, reducing donor site morbidity and eliminating the need for multiple surgeries.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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 teacher head, 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".