Bioimpedance measurements on human neural stem cells as a benchmark for the development of smart mobile biomedical applications

EasyChair Preprint no. 991

Abstract

Over the past 30 years, stem cell technologies matured from an attractive option to investigate neurodegenerative diseases to a possible paradigm shift in their treatment through the development of cell-based regenerative medicine (CRM). Implantable cell replacement therapies promise to completely restore function of neural structures possibly changing how we currently perceive the onset of these conditions. One of the major clinical hurdles facing the routine implementation of stem cell therapy is the limited and inconsistent benefit observed thus far. While unclear, numerous pre-clinical and a handful of clinical cell fate imaging studies point to poor cell retention and survival. Coupling the need to better understand these mechanisms while providing scalable approaches to monitor these treatments in both pre-clinical and clinical scenarios, we show a proof of concept bioimpedance electronic platform for the Agile development of smart and mobile biomedical applications like neural implants or highly portable monitoring devices.

Keyphrases: Bioimpedance, cell based regenerative medicine, Electrical Impedance Spectroscopy (EIS), embedded system, human neural stem cell, mfia impedance analyzer, mobile biomedical application, mobile technologies, neural stem cell, neural stem cell line, Neural Stem Cells (NSCs), pluripotent stem cell, proliferation, psoc based instrument, stem cell, stem cell therapy, ventral mesencephalic neural stem cell, zurich instrument mfia impedance

@Booklet{EasyChair:991,
  author = {André B. Cunha and Christin Schuelke and Arto Heiskanen and Afia Asif and Yasmin Hassan and Stephan S. Keller and Håvard Kalvøy and Alberto Martínez-Serrano and Jenny Emnéus and Ørjan Grøttem Martinsen},
  title = {Bioimpedance measurements on human neural stem cells as a benchmark for the development of smart mobile biomedical applications},
  howpublished = {EasyChair Preprint no. 991},
  doi = {10.29007/4b1g},
  year = {EasyChair, 2019}}