Extracellular matrix stiffness affects microbubble-assisted endothelial permeabilization under flow
Bibliographic record
Abstract
Cancer immunotherapy has faced challenges in the treatment of solid cancers due to the complex tumor microenvironment (TME), including physical barriers that prohibit immune cell infiltration. Ultrasound (US) stimulated microbubbles present a novel way to potentiate cancer immunotherapy in tumors by permeabilizing the tumor vasculature, however the effect of individual TME parameters on treatment efficacy has not yet been elucidated. Here, we focus on one biophysical parameter, showing that an increase in matrix stiffness increases US-assisted membrane permeabilization. Using a novel setup that allows for real-time visualization under flow, HUVECs seeded on polyacrylamide hydrogels of different stiffnesses (800 and 1600 Pa) showed an increase in sonoporation rates. Collagen models corroborate this trend at two different flow rates for a range of stiffnesses (5, 25, and 75 Pa): for both 5 ml/min and 30 ml/min, there is a relative increase in sonoporation from the stiffer substrate. For a given collagen substrate, there is a significant increase in sonoporation efficiency with increasing flow rate. These data can be used to fine-tune treatments according to different TMEs; further, our findings have applications in designing US parameters for targeted drug delivery and other clinical contexts that consider a variety of tissue stiffnesses.
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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 | 0.001 |
| 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".