Carbon nanotube-mediated high intensity focused ultrasound
Bibliographic record
Abstract
Abstract High intensity focused ultrasound (HIFU) is emerging as a novel therapeutic technique for cancer treatment through a hyperthermal mechanism using ultrasound. However, collateral thermal damages to healthy tissue and skin burns due to the use of high levels of ultrasonic energy during HIFU treatment remain major challenges to clinical application. The main objective of the current study is to evaluate the potential of carbon nanotubes (CNTs) as effective absorption-enhancing agents for HIFU to mediate the heating process at low ultrasonic power levels, and consequently upgrade hyperthermal therapeutic effects of HIFU. An experimental study using in vitro tissue phantoms was conducted to assess the effects of CNTs on HIFU’s heating mechanism. Detailed information was extracted from the experiments for thermal analysis, including rate of absorbed energy density and temperature rise profile at the focal region. Parametric studies were carried out, revealing the effects of ultrasound parameters (ultrasonic power and driving frequency) on the performance of CNTs in various concentrations. The results indicated that CNTs significantly enhanced the thermal effect of HIFU by elevating energy absorption rate and consequential temperature rise. Moreover, it was demonstrated that an increase in ultrasonic power and driving frequency could lead to a better performance of CNTs during HIFU ablation procedures; the effects of CNTs could be further enhanced by increasing their volume concentration inside the medium.
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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".