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.
The GABA molecule is the major inhibitory neurotransmitter in the mammalian central nervous system. Through binding to post-synaptic neurons, GABA reduces the neuronal excitability by hyperpolarization. Correct binding between the GABA molecules and its receptors relies on molecular recognition. Earlier studies suggest that recognition is determined by the geometries of the molecule and its receptor. We employed dielectric relaxation spectroscopy (DRS) to study the conformation and dielectric properties of the GABA molecule under physiologically relevant laboratory conditions. The dielectric properties of GABA investigated have given us new insights about the GABA molecule, such as how they interact with each other and with water molecules at different temperatures (22°C and 37.5°C). Higher temperature leads to lower viscosity, thus lower relaxation time. The change in the GABA relaxation time due to concentration change is more associated with the solution viscosity than with the GABA dipole moment. A resonance behavior was observed with high GABA concentrations at physiological temperature, where there might be a phase transition at a certain temperature for a given GABA concentration that leads to a sudden change of the dielectric properties.
The mineralocorticoid receptor (MR) is a member of the steroid receptor family and acts as a ligand-dependent transcription factor. In addition to its classical effects on water and electrolyte balance, its involvement in the pathogenesis of cardiovascular and renal diseases has been the subject of research for several years. The molecular basis of the latter has not been fully elucidated, but an isolated increase in the concentration of the MR ligand aldosterone or MR expression does not suffice to explain long-term pathologic actions of the receptor. Several studies suggest that MR activity and signal transduction are modulated by the surrounding microenvironment, which therefore plays an important role in MR pathophysiological effects. Local changes in micromilieu, including hypoxia, ischemia/reperfusion, inflammation, radical stress, and aberrant salt or glucose concentrations affect MR activation and therefore may influence the probability of unphysiological MR actions. The surrounding micromilieu may modulate genomic MR activity either by causing changes in MR expression or MR activity; for example, by inducing posttranslational modifications of the MR or novel interaction with coregulators, DNA-binding sites, or non-classical pathways. This should be considered when developing treatment options and strategies for prevention of MR-associated diseases.
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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