Enhancing Thermal Effect of Focused Ultrasound Therapy Using Gold Nanoparticles
Bibliographic record
Abstract
High intensity focused ultrasound (HIFU) has gained increasing attention as a noninvasive therapeutic method for wide range of biomedical applications from drug delivery to cancer treatment. However, high level of ultrasonic power required for efficient HIFU treatment can cause adverse effects such as damage to surrounding healthy tissues and skin burns. One of the strategies to improve the therapeutic mechanism of HIFU is to use ultrasound absorption agents during the treatment. The objectives of current study are to investigate the feasibility of adopting gold nanoparticles (AuNPs) as ultrasound absorption agents to enhance the HIFU thermal ablation when the NPs were injected locally to the focal region; and to examine the dose effects of AuNPs on both heating and cooling mechanisms of HIFU. To this end, we conducted an experimental study on tissue-mimicking phantoms where AuNPs were injected to the focal region under the guidance of ultrasound imaging. A set of thermal parameters including temperature, specific absorption rate of acoustic energy, and cooling rate were measured to monitor the mechanism of AuNPs-mediated HIFU. The results suggest that both heating and cooling rates of HIFU procedure could be greatly improved by injecting AuNPs, which demonstrates the feasibility of using AuNPs to reduce the level of ultrasonic power from extracorporeal source for HIFU treatment.
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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".