Wireless Hyperthermia Stent System for Restenosis Treatment and Testing With Swine Model
Bibliographic record
Abstract
This paper reports the first in vivo testing of a resonant-heating stent toward wireless hyperthermia treatment of in-stent restenosis. The stent, made of gold-coated medical-grade stainless steel, is designed to function as an electrical inductor and forms a radiofrequency (RF) resonant circuit with an integrated capacitor microchip. Upon implantation and deployment with the balloon catheter, the stent device serves as a wireless heater as part of the resonant wireless power transfer system, which allows for the device to produce mild heat only when the stent is resonated with a tuned RF electromagnetic field supplied from the external antenna. The wireless power transmitter includes an independent omnidirectional booster antenna that enhances the power delivery to the implanted stent device. The entire stent device is packaged with 40-μm-thick Parylene C film that is shown to be essential for minimizing electrothermal damping in a conductive liquid like blood. The in vitro tests of the prototype system show a temperature increase of 3.3 °C in the stent device couple in a flow loop of saline pumped at a flow rate relevant to the condition of coronary stenosis. In swine models, the system demonstrates RF heating of the stent devices expanded to different diameters, in live blood stream, achieving temperature rises of up to 2.6 °C in a consistent and repeatable manner. These results bring the technology one step closer toward clinical realization of wireless thermal therapy of in-stent restenosis.
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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 | 0.001 | 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".