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Programmable chemical actuator control of soluble and membrane-bound enzymatic catalysis. Methods Enzymol 2022; 676:159-194. [DOI: 10.1016/bs.mie.2022.07.022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Gupta S, Ghatak S, Hery T, Khanna S, El Masry M, Sundaresan VB, Sen CK. Ad hoc hybrid synaptic junctions to detect nerve stimulation and its application to detect onset of diabetic polyneuropathy. Biosens Bioelectron 2020; 169:112618. [PMID: 33007616 DOI: 10.1016/j.bios.2020.112618] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2020] [Revised: 08/21/2020] [Accepted: 09/14/2020] [Indexed: 01/05/2023]
Abstract
We report a minimally invasive, synaptic transistor-based construct to monitor in vivo neuronal activity via a longitudinal study in mice and use depolarization time from measured data to predict the onset of polyneuropathy. The synaptic transistor is a three-terminal device in which ionic coupling between pre- and post-synaptic electrodes provides a framework for sensing low-power (sub μW) and high-bandwidth (0.1-0.5 kHz) ionic currents. A validated first principles-based approach is discussed to demonstrate the significance of this sensing framework and we introduce a metric, referred to as synaptic efficiency to quantify structural and functional properties of the electrodes in sensing. The application of this framework for in vivo neuronal sensing requires a post-synaptic electrode and its reference electrode and the tissue becomes the pre-synaptic signal. The ionic coupling resembles axo-axonic junction and hence we refer to this framework as an ad hoc synaptic junction. We demonstrate that this arrangement can be applied to measure excitability of sciatic nerves due to a stimulation of the footpad in cohorts of m+/db and db/db mice for detecting loss in sensitivity and onset of polyneuropathy. The signal attributes were subsequently integrated with machine learning-based framework to identify the probability of polyneuropathy and to detect the onset of diabetic polyneuropathy.
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Affiliation(s)
- Sujasha Gupta
- Department of Mechanical and Aerospace Engineering, The Ohio State University, Columbus, OH, 43220, USA
| | - Subhadip Ghatak
- Indiana Center for Regenerative Medicine and Engineering, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Travis Hery
- Department of Mechanical and Aerospace Engineering, The Ohio State University, Columbus, OH, 43220, USA
| | - Savita Khanna
- Indiana Center for Regenerative Medicine and Engineering, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Mohamed El Masry
- Indiana Center for Regenerative Medicine and Engineering, Indiana University School of Medicine, Indianapolis, IN, 46202, USA; Plastic and Reconstructive Surgery Department, Zagazig University, 44519, Egypt
| | - Vishnu Baba Sundaresan
- Department of Mechanical and Aerospace Engineering, The Ohio State University, Columbus, OH, 43220, USA.
| | - Chandan K Sen
- Indiana Center for Regenerative Medicine and Engineering, Indiana University School of Medicine, Indianapolis, IN, 46202, USA.
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