Plug and Pop: A 3D-Printed, Modular Platform for Drug Delivery Using Clinical Ultrasound and Microbubbles
Bibliographic record
Abstract
Targeted drug and gene delivery using ultrasound and microbubbles (USMB) has the potential to treat several diseases. In vitro investigation of USMB-mediated delivery is of prime importance prior to in vivo studies because it is cost-efficient and allows for the rapid optimization of experimental parameters. Most in vitro USMB studies are carried out with non-clinical, research-grade ultrasound systems, which are not approved for clinical use and are difficult to replicate by other labs. A standardized, low-cost, and easy-to-use in vitro experimental setup using a clinical ultrasound system would facilitate the eventual translation of the technology to the bedside. In this paper, we report a modular 3D-printed experimental setup using a clinical ultrasound transducer that can be used to study USMB-mediated drug delivery. We demonstrate its utility for optimizing various cargo delivery parameters in the HEK293 cell line, as well as for the CMT167 lung carcinoma cell line, using dextran as a model drug. We found that the proportion of dextran-positive cells increases with increasing mechanical index and ultrasound treatment time and decreases with increasing pulse interval (PI). We also observed that dextran delivery is most efficient for a narrow range of microbubble concentrations.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".