Poroelastic behavior of skin tissue in response to pressure driven flow
Bibliographic record
Abstract
Better understanding of skin tissue's permeability and poroelasticity could help advance biomedical technologies concerning skin such as intradermal injection and grafting. We applied a pressure driven fluid flow across skin tissue's epidermal and dermal layers in a simple one-dimensional configuration, while measuring the resulting flow rate and capturing cross sectional optical coherence tomography (OCT) images of the tissue's deformation. Digital image correlation was used to analyze the OCT images and via a novel method, image analysis corrected for the change in the tissue's refractive index, which occurred due to flow-induced deformation, thus providing accurate one-dimensional depth-wise deformation profiles. Skin tissue was found to exhibit a nonlinear relationship between pressure and the resulting fluid flow rate, where the increase in flow rate with pressure decreased as pressure increased. The skin tissue was observed to experience compressive strain closest to the supported base, with magnitudes increasing with increasing driving pressure, and the tissue near the free surface experienced relatively little strain. Permeability was found to follow an exponential permeability-volumetric strain relationship with material constants: k0 (initial uniform permeability) of 9.6 × 10−15 m2 and m (extent of nonlinearity for the permeability–strain relationship) of 2.94. Darcy's law and the permeability–strain relationship were used to analyze results with good similarity between observed and calculated flowrates. This work presents a novel and direct method of characterizing soft tissue permeability and provides a fundamental understanding to skin behavior under pressurized driving fluid, which can be generalized to study or model other geometries of induced flow through skin tissue.
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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 | 0.000 | 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 | 0.000 |
| 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 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".