Ultrasound-assisted membrane permeabilization of endothelial cells under flow conditions
Bibliographic record
Abstract
Ultrasound-stimulated microbubbles have been shown a feasible approach for localized therapeutic delivery. As applications of this technique span many anatomical sites, so too do the local fluid dynamics experienced by the circulating microbubbles and the adjacent endothelial cells. Our objective was to assess the relative effectiveness of endothelial cell sonoporation as a function of flow conditions. Human umbilical vein (HUVECs) or human brain endothelial cells (HBECs) were cultured as a monolayer in flow chamber slides connected to a fluidic system and placedupon an acoustically-coupled microscope. A suspension of diluted lipid-encapsulated microbubbles and propidium iodide (PI), used as a sonoporation marker, was constantly perfused over the monolayer at either 5 or 30 ml/min. Cells were treated with 1 MHz ultrasound (PRI= 1 ms, 20 cycles, duration = 2 s), and the video-microscopy data were quantified offline to assess the number of PI-positive cells. Our results demonstrate a marked increase in sonoporation efficiency at 30 ml/min as compared to 5 ml/min in both endothelial cell lines under identical acoustic conditions (9.7-fold increase and 2.3-fold increase for HUVECs and HBECs respectively, p < 0.001). Our results suggest the local fluid flow environment plays a role in US-mediated endothelial perforation efficiency and can modulate treatment strategies.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".