Magnetic nanoparticles-enhanced focused ultrasound heating: size effect, mechanism, and performance analysis
Bibliographic record
Abstract
High intensity focused ultrasound (HIFU) has attracted great interest as a new energy-based technique to treat disordered tissues, such as tumors, through a hyperthermal mechanism using ultrasonic waves. However, long treatment times and collateral damage to healthy tissues due to high acoustic powers are still challenges for the clinical application of HIFU. One possible strategy to enhance the deposition efficiency of HIFU at the tumor site is to employ magnetic nanoparticles (MNPs) as ultrasound absorption agents for the thermal therapy. The objectives of the current study are threefold: (i) to examine the effects of MNP features, including the size and volume concentration, on the thermal mechanism of HIFU (ii) to investigate the performance of MNPs as they were exposed to ultrasound fields at different ranges of power and frequency (iii) and to study the interaction mechanism between MNPs and ultrasonic waves during the MNPs-enhanced HIFU process. To this end, we developed an ultrasound-guided HIFU system to conduct an in vitro experimental study on tissue phantoms containing MNPs of different sizes and volume concentrations. A set of HIFU parameters such as temperature rise and the rate of absorbed energy was monitored to examine the role of MNPs during the NPs-enhanced HIFU thermal procedure. Results showed that the MNPs significantly improved the thermal effect of HIFU by enhancing the rate of energy converted to heat and the temperature rise at the focal region. Moreover, it was demonstrated that the increase of MNP size and volume concentration greatly enhanced the HIFU parameters; the effects of MNPs were further improved by increasing the power and frequency of acoustic field.
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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 | 0.001 |
| 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".