Tunable Stiffness of Matrix-Derived Membranes Enables Independent and Coupled Analysis of Pressure and Strain Effects in Barrier Tissue-On-Chip Models
Bibliographic record
Abstract
Organ-on-a-chip (OOC) devices modeling thin barrier tissues often incorporate features of the mechanical microenvironment which play regulatory roles in maintaining tissue function. In contexts where tissues are exposed to complex combinations of forces including stretch, hydrostatic pressure, and shear stress, different forces can interact synergistically. As such, modeling mechanical complexity is important to accurately recapitulate tissue behavior. Various combinations of these three mechanical forces have been investigated using OOC or other complex in vitro models, but the combination of hydrostatic pressure and stretch has received relatively little focus. This work presents the development of a platform capable of simultaneously applying controlled pressure and stretch to suspended thin model tissues for investigating the effects of complex mechanical cues. This is accomplished through direct application of variable pressure to one side of the suspended tissue, resulting in pressure-induced stretch. Independent control of the two mechanical cues is established through detailed mechanical characterization and development of stiffness tuning strategies for the nonlinearly elastic extra-cellular matrix (ECM)-based thin membrane scaffolds used to support the model tissue. Mechanical characterization is accomplished through the development of a bulge-test method relying on imaging the curvature of membranes under pressure loading. Application of the developed platform is demonstrated through functional characterization of pulmonary endothelial monolayers exposed to various combinations of pressure and stretch corresponding to healthy and pathological levels found in pulmonary hypertension. It was found that exposure of monolayers to both elevated pressure and elevated stretch results in increased permeability and altered cytoskeleton and junctional morphology, and that these changes are not observed in response to only one elevated mechanical cue. The presented work has the potential to advance the use of biomaterial-based OOC platforms with complex mechanical properties in modeling barrier tissue responses to complex mechanical cues.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".