@article{192fd7d344ca4f33b724d4af33adcc1e,
title = "Predicting Cardiovascular Stent Complications using self-reporting biosensors for non-invasive detection of disease",
abstract = "Self-reporting implantable medical devices are the future of cardiovascular healthcare. Cardiovascular complications such as blocked arteries that lead to the majority of heart attacks and strokes are frequently treated with inert metal stents that reopen affected vessels. Stents frequently re-block after deployment due to a wound response called in-stent restenosis (ISR). Herein, an implantable miniaturized sensor and telemetry system are developed that can detect this process, discern the different cell types associated with ISR, distinguish sub plaque components as demonstrated with ex vivo samples, and differentiate blood from blood clot, all on a silicon substrate making it suitable for integration onto a vascular stent. This work shows that microfabricated sensors can provide clinically relevant information in settings closer to physiological conditions than previous work with cultured cells.",
keywords = "blood clot, Cardiovascular disease, restenosis, stent, wireless impedance sensor, cardiovascular disease, Coronary Restenosis/etiology, Humans, Myocardial Infarction/complications, Biosensing Techniques, Stents/adverse effects, Plaque, Atherosclerotic/complications",
author = "Daniel Hoare and Andreas Tsiamis and Jamie Marland and Jakub Czyzewski and Kirimi, {Mahmut Talha} and Michael Holsgrove and Ewan Russell and Steve Neale and Nosrat Mirzai and Srinjoy Mitra and John Mercer",
note = "Funding Information: The authors would also like to thank the contribution of statistician Dr. John McClure, and the University of Glasgow Computing Science teams involved in software development, and clinical input from Mr. David Kingsmore, Prof. Paddy Mark and Dr. Pete Thompson, Consultants at The Queen Elizabeth University Hospital, Glasgow. This work was supported under the BHF Centre of Excellence research award (BHF RE/13/5/30177); the Engineering and Physical Sciences Research Council (EP/S515401/1; EP/R020892/1); the University of Glasgow, College of Medical Veterinary and Life Sciences; Chief Scientist Office Grant Number: CGA/17/29; Welcome IAA funding (219390/Z/19/Z), and a Medical Research Council Confidence in Concept (MRC‐CiC) award. Funding Information: The authors would also like to thank the contribution of statistician Dr. John McClure, and the University of Glasgow Computing Science teams involved in software development, and clinical input from Mr. David Kingsmore, Prof. Paddy Mark and Dr. Pete Thompson, Consultants at The Queen Elizabeth University Hospital, Glasgow. This work was supported under the BHF Centre of Excellence research award (BHF RE/13/5/30177); the Engineering and Physical Sciences Research Council (EP/S515401/1; EP/R020892/1); the University of Glasgow, College of Medical Veterinary and Life Sciences; Chief Scientist Office Grant Number: CGA/17/29; Welcome IAA funding (219390/Z/19/Z), and a Medical Research Council Confidence in Concept (MRC-CiC) award. Publisher Copyright: {\textcopyright} 2022 The Authors. Advanced Science published by Wiley-VCH GmbH.",
year = "2022",
month = may,
day = "25",
doi = "10.1002/advs.202105285",
language = "English",
volume = "9",
journal = "Advanced Science",
issn = "2198-3844",
publisher = "Wiley Open Access",
number = "15",
}