Characterizing Bubble Dynamics Created by High-Intensity Focused Ultrasound for the Delivery of Antibacterial Nanoparticles into a Dental Hard Tissue
Bibliographic record
Abstract
Hig hintensity focused ultrasound (HIFU) has been applied for drug delivery in various disease conditions. Delivery of antibacterial-nanoparticles into dental hard tissues may open up new avenues in the treatment of dental infections. However, the basic mechanism of bubble dynamics, its characterization, and working parameters for effective delivery of nanoparticles, warrants further understanding. This study was conducted to highlight the basic concept of HIFU and the associated bubble dynamics for the delivery of nanoparticles. Characterization experiments to deliver micro-scale particles into simulated tubular channels, activity of ultrasonic bubbles, and pressure measurement inside the HIFU system were conducted. Subsequently, experiments were carried out to test the ability of HIFU to deliver nanoparticles into human dentine using field emission scanning electron micrographs (FESEM) and elemental dispersive X-ray analysis (EDX). The characterization experiments showed that the bubbles collapsing at the opening of tubular channels were able to propel particles along their whole length. The pressure measured showed sufficient negative and positive pressure suggesting that the bubble grew to a certain size before collapsing, thus enabling the particles to be pushed. The FESEM and EDX analysis highlighted the ability of HIFU to deliver nanoparticles deep within the dentinal tubules. This study highlighted the characteristics and the mechanism involved of the bubbles generated by the HIFU and their capability to deliver nanoparticles.
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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) | 0.001 | 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".