Development of an <i>in vitro</i> Experimental Model for Investigating the Effect of Matrix Stiffness on Epithelial Barrier Permeability
Bibliographic record
Abstract
ABSTRACT Epithelial cells are well-known to be modulated by extracellular mechanical factors including substrate stiffness. However, the effect of substrate stiffness on an epithelial cell’s principal function –creating an effective barrier to protect the underlying tissue – cannot be directly measured using existing experimental techniques. We developed a strategy involving ethylenediamine aminolysis and glutaraldehyde crosslinking to chemically graft polyacrylamide hydrogels with tunable stiffness to PET Transwell membranes. Grafting success was evaluated using spectroscopic methods, scrape tests, and extended incubation in culture. By assessing apical to basolateral transfer of fluorescent tracers, we demonstrated that our model is permeable to biologically relevant molecules and usable for direct measurement of barrier function by calculating paracellular permeability. We found that BEAS-2B epithelial cells form a more effective barrier on stiff substrates, likely attributable to increased cell spreading. We also observed barrier impairment after treatment with transforming growth factor beta, indicating loss of cell-cell junctions, validating our model’s ability to detect biologically relevant stimuli. Thus, we have created an experimental model that allows explicit measurement of epithelial barrier function for cells grown on different substrate stiffnesses. This model will be a valuable tool to study mechanical regulation of epithelial and endothelial barrier function in health and disease.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".