Wirelessly Heating Stents via Radiofrequency Resonance toward Enabling Endovascular Hyperthermia
Bibliographic record
Abstract
Abstract Thermal therapy known as hyperthermia has served as an effective method for cancer treatment. This therapeutic approach has also been attracting attention for treatment of in‐stent restenosis, the most common complication of stenting. Mild heating of stents has been shown to be a possible path to addressing this problem. Despite various studies on stent‐based thermotherapy, this area still lacks a clinically viable method and technology. Here, a radiofrequency‐powered “hot” stent prototype is reported in vitro and in vivo. An implantable stent device based on medical‐grade stainless steel acts as an electrical resonator, or an efficient wireless heater operating only when resonated using tuned external electromagnetic fields. The system architecture uses a custom‐developed power transmitter for wireless resonant powering/heating of the stent. An eight‐shaped antenna is shown to be highly effective for near‐field power transfer to the device and potentially to other smart implants, revealing stent heating efficiencies of up to 120 °C W−1, 206% of the level provided by a conventional loop antenna. Testing with swine models, the prototyped system achieves stent heating in blood flow by powering through air and skin tissue in vivo in a fully controlled manner. The results advance stent hyperthermia technology toward possible future clinical application.
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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".