Evaluating the Effect of Drug Loading on the Acoustic Response of Nanobubbles in Stable and Inertial Cavitation Regimes
Bibliographic record
Abstract
Nanobubbles (NBs) have been investigated as ultrasound contrast agents and drug delivery carriers in medical applications. Previous studies have reported the efficacy of NBs in imaging and therapy. However, ultrasound (US) exposure parameters for stable and inertial cavitation of NBs need to be investigated. This study examines passive cavitation detection of ultrasound signatures from two types of NBs across three pressure ranges using passive cavitation detection (PCD), aiming to investigate their cavitation behaviour with and without the inclusion of the chemotherapeutic drug, Doxorubicin (HDox). We compared the power spectral density (PSD) of non-drug loaded Propylene-Glycol Glycerol (PGG) NBs and HDox NBs under 300 kPa, 600 kPa, and 900 kPa pressures at 1 MHz fundamental frequency and 1% duty cycle. Our results indicate that NBs exhibit stable cavitation at 300 kPa, while a mix of inertial and stable cavitation occur at 600 and 900 kPa. The loading of NBs with HDox affects their acoustic activity. HDox NBs exhibit higher acoustic activity at 300 kPa, but their PCD signal profile becomes similar to PGG NBs at 600 kPa and 900 kPa. A numerical model was used to gain additional insight into some of the characteristics of the NB power spectra observed. Our simulations indicate that nonlinear bubble oscillations are characterized by distinctly pronounced harmonics, particularly the third harmonic, with this effect being even more prominent in smaller bubbles and lower ultrasound amplitudes. These findings will help determine ultrasound pulse parameters in imaging and therapeutic applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.003 |
| 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".