Biomechanical behavior of scar tissue and uninjured skin in a porcine model
Bibliographic record
Abstract
A new method to test axial and transverse tensile properties of skin was developed to improve our understanding of skin mechanical behavior, and how it changes following injury and formation of a scar. Skin tissue was evaluated at 70 days following full-thickness wounding in juvenile female pigs (N=14). Samples were taken in the axial (cranial-caudal) and transverse (dorsal-ventral) directions, for both scar tissue and uninjured skin, and were evaluated mechanically in vitro using a protocol of stress relaxation followed by tensile failure. Uninjured skin was more compliant, with a larger toe-in region, and faster load relaxation, in the axial direction than the transverse. Such directional differences were not present in high-load responses, such as linear stiffness or failure properties. When compared with uninjured skin, scars displayed a similar linear stiffness, with considerably reduced failure properties, and reduced low-load compliance. Scars showed no directional differences in low-load behavior, viscous response, or failure properties. These findings suggest morphological changes that may occur with injury that are consistent with the viscoelastic and directional changes observed experimentally. This improved understanding of how injury affects skin biomechanical function provides valuable information necessary for the design of successful grafting procedures and tissue-engineered skin replacements.
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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 | 0.000 |
| 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 | 0.001 |
| 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".