Peel Test to Assess the Adhesion Strength of the Dermal–Epidermal Junction in Tissue-Engineered Skin
Bibliographic record
Abstract
Innovative therapies combining gene-corrected stem cells and the production of bioengineered tissues to treat epidermolysis bullosa are emerging. However, quantitative tests to measure the adhesion forces between two highly viscoelastic substrates such as those found in bilayered bioengineered skin are needed and are still lacking. The objective of this study was to develop a mechanical test to measure the dermal-epidermal adhesion strength of our bilayered tissue-engineered skin substitute (TES) produced with the self-assembly method. We developed a peel test, which allows the displacement of both skin layers in a T configuration, based on the ASTM International standard. A MATLAB program was written to process and analyze raw data. The experimental setup was tested by measuring the dermal-epidermal adhesion strength in TESs produced with normal or collagen VII-deficient cells. Our peel testing method allowed us to detect the impact of the absence of collagen VII in the dermal-epidermal adhesion strength of TESs and also to examine the progression of the dermal-epidermal adhesion strength in relation to culture time in normal TES. Impact statement This study describes a method for assessing the adhesion strength at the dermal-epidermal junction of individual tissue-engineered skin substitute (TES). An ASTM standardized protocol of peel testing was designed to measure this important mechanical property. Our innovative approach will serve as a quality control in the production, improvement, and application of TESs for the treatment of pathologies affecting the dermal-epidermal adhesion such as epidermolysis bullosa. Data presented contribute to research on the interfaces between biological substrates and provide a reference factor for the characterization of products derived from tissue engineering.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".