Sonoprinting nanoparticles on cellular spheroids <i>via</i> surface acoustic waves for enhanced nanotherapeutics delivery
Bibliographic record
Abstract
Nanotherapeutics, on their path to the target tissues, face numerous physicochemical hindrances that affect their therapeutic efficacy. Physical barriers become more pronounced in pathological tissues, such as solid tumors, where they limit the penetration of nanocarriers into deeper regions, thereby preventing the efficient delivery of drug cargo. To address this challenge, we introduce a novel approach that employs surface acoustic wave (SAW) technology to sonoprint and enhance the delivery of nanoparticles onto and into cell spheroids. Our SAW platform is designed to generate focused and unidirectional acoustic waves for creating vigorous acoustic streaming while promoting Bjerknes forces. The effect of SAW excitation on cell viability, as well as the accumulation and penetration of nanoparticles on human breast cancer (MCF 7) and mouse melanoma (YUMM 1.7) cell spheroids were investigated. The high frequency, low input voltage, and contact-free nature of the proposed SAW system ensured over 92% cell viability for both cell lines after SAW exposure. SAW sonoprinting enhanced the accumulation of 100 nm polystyrene particles on the periphery of the spheroids to near four-fold, while the penetration of nanoparticles into the core regions of the spheroids was improved up to three times. To demonstrate the effectiveness of our SAW platform on the efficacy of nanotherapeutics, the platform was used to deliver nanoliposomes encapsulated with the anti-cancer metal compound copper diethyldithiocarbamate (CuET) to MCF 7 and YUMM 1.7 cell spheroids. A three-fold increase in the cytotoxic activity of the drug was observed in spheroids under the effect of SAW, compared to controls. The capacity of SAW-based devices to be manufactured as minuscule wearable patches can offer highly controllable, localized, and continuous acoustic waves to enhance drug delivery efficiency to target tissues.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".