Imaging fluid injections into soft biological tissue to extract permeability model parameters
Bibliographic record
Abstract
One of the most common health care procedures is injecting fluids, in the form of drugs and vaccines, into our bodies, and hollow microneedles are emerging medical devices that deliver such fluids into the skin. Fluid injection into the skin through microneedles is advantageous because of improved patient compliance and the dose sparing effect for vaccines. Since skin tissue is a deformable porous medium, injecting fluid into the skin involves a coupled interaction between the injected fluid flow and the deformation of the soft porous matrix of skin tissue. Here, we introduce a semiempirical model that describes the fluid transport through skin tissue based on experimental data and constitutive equations of flow through biological tissue. Our model assumes that fluid flows radially outward and tissue deformation varies spherically from the microneedle tip. The permeability of tissue, assumed to be initially homogeneous, varies as a function of volumetric strain in the tissue based on a two-parameter exponential relationship. The model is optimized to extract two macroscopic parameters, k0 and m, for each of the seven experiments on excised porcine skin, using a radial form of Darcy’s law, the two-parameter exponential dependence of permeability on strain, and the experimental data on fluid flow recorded by a flow sensor and tissue deformation captured in real time using optical coherence tomography. The fluid flow estimated by the permeability model with optimized macroscopic parameters matches closely with the recorded flow rate, thus validating our semiempirical model.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".