The role of fluid flow patterns in microbubble-mediated endothelial cell membrane permeabilization
Bibliographic record
Abstract
Blood flow dynamics vary throughout the circulatory system, influencing the pathophysiology of vascular endothelium. We investigated endothelial cell response to ultrasound-stimulated microbubbles across diverse anatomical sites by mimicking assorted blood flow patterns. First, we examined the effect of culture condition on cell sensitivity to sonication by culturing HUVECs either statically or under pulsatile flow (8 or 16 dyn/cm2) for two days. Flow chambers were then co-perfused with microbubbles and propidium iodide under pulsatile flow (8 or 16 dyn/cm2) and sonicated (1MHz, 20 cycles, 1ms PRI, 300kPa) using an acoustically coupled microscope. Additionally, in a subset of studies we investigated ultrasound-assisted endothelial permeability under both pulsatile and oscillatory flow patterns. Compared to static, cells cultured under pulsatile flow resulted in 1.2 to 2.0-fold increase in the percentage of permeabilized endothelial cells (p < 0.0001). Next, compared to sonication under laminar flow (15–30 ml/min), endothelial permeability was significantly augmented under pulsatile flow, ranging from 1.3 to 2.1-fold. Additionally, compared to laminar flow, oscillatory flow at ∼16 ml/min with 0.5 Hz oscillation resulted in a 1.76-fold enhancement in cell perforation, yet yielded no observable permeability at slower flow rates (∼8 ml/min). These findings highlight the influence of local blood flow dynamics on ultrasound-mediated cell permeabilization.
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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 | 0.001 | 0.001 |
| 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".