Ultrasound-mediated drug delivery with real-time cell permeability measurements
Bibliographic record
Abstract
Ultrasound-mediated drug and gene delivery offers a variety of exciting possibilities for improved localized treatment of vascular- and cancer-related diseases. This therapeutic application benefits from the use of the acoustic radiation force that facilitates the exposure for enhanced binding due to ligand-receptor interactions. The main merits of ultrasound lie in the transient increase of cell permeability without exposing the cell corpus to any detrimental and irreversible side-effects. Nonetheless, the related underlying molecular and cellular pathways of ultrasound-induced permeability and the subsequent recovery of cells have not been answered satisfactorily. Real-time studies of cell behavior during and past-ultrasound exposure have been obstructed by the lack of appropriate techniques. The Electric-Cell Impedance Sensing (ECIS) technique is an attractive way of studying cell permeability changes in real-time. Its nanoscale sensitivity and speedy acquisition of data allows for the accurate and timely monitoring of cell behavior. Our preliminary results suggest that cells recover within 24 - 36 hours post-exposure. During this time window the cells undergo drastic changes exhibiting an increased permeability of 2.4 ± 0.6 Ω•cm2 compared to 3.8 ± 0.5 Ω•cm2 that normal untreated cells exhibit.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".