Controlled drug release from ultrasound-visualized elastic eccentric microcapsules using different resonant modes
Bibliographic record
Abstract
Ultrasound controlled drug delivery and release has attracted increased attention for targeted delivery of drug. In this report, we present a strategy for targeted drug delivery by using ultrasound to image the location of drug carriers, as well as simultaneously controlling the release rate of drug from elastic eccentric microcapsules (EEMs), based on their mode shapes (MSs) and resonant natural frequencies (NFs). We prepared a series of EEMs with various diameters of inner spherical cavities using a microfluidic chip. The EEMs could be visualized by an ultrasound imaging system within a tissue mimic (i.e. phantom). Using theoretical modeling techniques, we investigated the effects of MSs and NFs on the resonant modes of EEMs. Guided by this modelling, we applied external ultrasonic stimuli at various levels of low frequency to regulate the release rate of Rhodamine 6G (R6G, as a model drug) from EEMs. To further demonstrate the control of drug release and evaluate the efficacy of the encapsulated drugs on cancer cells, we released an anticancer drug, doxorubicin hydrochloride (DOX), from the EEMs and tested the viability of cancer cells in vitro. The results show that this novel strategy holds great promise towards development of a controlled drug release system visualized and triggered by ultrasound.
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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) | 0.000 | 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".