Electrical impedance spectroscopy (EIS) measurements on cells is a proven method to assess stem cell proliferation and differentiation. Cell regenerative medicine (CRM) is an emerging field where the need to develop and deploy stem cell assessment techniques is paramount as experimental treatments reach pre-clinical and clinical stages. However, EIS measurements on cells is a method requiring extensive post-processing and analysis. As a contribution to address this concern, we developed three machine learning models for three different stem cell lines able to classify the measured data as proliferation or differentiation laying the stone for future studies on using machine learning to profile EIS measurements on stem cells spectra.
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.
A power efficient, battery powered optogenetic headstage for doing in-vivo experiments with freely moving genetically modified animals is presented. The proposed system is designed with commercial off-the-shelf components, and is based on a Bluetooth Low Energy (BLE) System-on-Chip (SoC) with an integrated antenna and a programmable ARM Cortex-M3 microprocessor core able to control the circuit. The optical signal is generated using a compact laser diode (LD) suitable for a wearable headstage. LD produces light in a highly concentrated way considerably improving the LD-optical fiber coupling efficiency. The proposed optogenetic system is shown to provide 120 mW/mm 2 at the fiber tip with a current consumption of 60mA, considerably lower than LED-based systems. The system is remotely controlled by a smartphone app where the user can define optical stimulations patterns settings (optical power, frequency, duty cycle, etc.). It is also powerful enough to be ready to house additional optogenetics functionalities, like electrochemical sensing of the cell response, without significant modifications, thus being the basis of an integrated optogenetic platform.
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.
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