Wireless instantaneous neurotransmitter concentration system-based amperometric detection of dopamine, adenosine, and glutamate for intraoperative neurochemical monitoring - laboratory investigation

Object  In a companion study, the authors describe the development of a new instrument named the Wireless Instantaneous Neurotransmitter Concentration System (WINCS), which couples digital telemetry with fast-scan cyclic voltammetry (FSCV) to measure extracellular concentrations of dopamine. In the present study, the authors describe the extended capability of the WINCS to use fixed potential amperometry (FPA) to measure extracellular concentrations of dopamine, as well as glutamate and adenosine. Compared with other electrochemical techniques such as FSCV or high-speed chronoamperometry, FPA offers superior temporal resolution and, in combination with enzyme-linked biosensors, the potential to monitor nonelectroactive analytes in real time.

Methods  The WINCS design incorporated a transimpedance amplifier with associated analog circuitry for FPA; a microprocessor; a Bluetooth transceiver; and a single, battery-powered, multilayer, printed circuit board. The WINCS was tested with 3 distinct recording electrodes: 1) a carbon-fiber microelectrode (CFM) to measure dopamine; 2) a glutamate oxidase enzyme–linked electrode to measure glutamate; and 3) a multiple enzyme–linked electrode (adenosine deaminase, nucleoside phosphorylase, and xanthine oxidase) to measure adenosine. Proof-of-principle analyses included noise assessments and in vitro and in vivo measurements that were compared with similar analyses by using a commercial hardwired electrochemical system (EA161 Picostat, eDAQ; Pty Ltd). In urethane-anesthetized rats, dopamine release was monitored in the striatum following deep brain stimulation (DBS) of ascending dopaminergic fibers in the medial forebrain bundle (MFB). In separate rat experiments, DBS-evoked adenosine release was monitored in the ventrolateral thalamus. To test the WINCS in an operating room setting resembling human neurosurgery, cortical glutamate release in response to motor cortex stimulation (MCS) was monitored using a large-mammal animal model, the pig.

Results   The WINCS, which is designed in compliance with FDA-recognized consensus standards for medical electrical device safety, successfully measured dopamine, glutamate, and adenosine, both in vitro and in vivo. The WINCS detected striatal dopamine release at the implanted CFM during DBS of the MFB. The DBS-evoked adenosine release in the rat thalamus and MCS-evoked glutamate release in the pig cortex were also successfully measured. Overall, in vitro and in vivo testing demonstrated signals comparable to a commercial hardwired electrochemical system for FPA.

Conclusions  By incorporating FPA, the chemical repertoire of WINCS-measurable neurotransmitters is expanded to include glutamate and other nonelectroactive species for which the evolving field of enzyme-linked biosensors exists. Because many neurotransmitters are not electrochemically active, FPA in combination with enzyme-linked microelectrodes represents a powerful intraoperative tool for rapid and selective neurochemical sampling in important anatomical targets during functional neurosurgery.

Fault detection in a cold forging process through feature extraction with a neural network

This paper investigates the application of neural networks to the recognition of lubrication defects typical to an industrial cold forging process employed by fastener manufacturers. The accurate recognition of lubrication errors, such as coating not being applied properly or damaged during material handling, is very important to the quality of the final product in fastener manufacture. Lubrication errors lead to increased forging loads and premature tool failure, as well as to increased defect sorting and the re-processing of the coated rod. The lubrication coating provides a barrier between the work material and the die during the drawing operation; moreover it needs be sufficiently robust to remain on the wire during the transfer to the cold forging operation. In the cold forging operation the wire undergoes multi-stage deformation without the application of any additional lubrication. Four types of lubrication errors, typical to production of fasteners, were introduced to a set of sample rods, which were subsequently drawn under laboratory conditions. The drawing force was measured, from which a limited set of features was extracted. The neural network based model learned from these features is able to recognize all types of lubrication errors to a high accuracy. The overall accuracy of the neural network model is around 98% with almost uniform distribution of errors between all four errors and the normal condition.

Comonitoring of adenosine and dopamine using the wireless instantaneous neurotransmitter concentration system : proof of principle : laboratory investigation

Object

The authors of previous studies have demonstrated that local adenosine efflux may contribute to the therapeutic mechanism of action of thalamic deep brain stimulation (DBS) for essential tremor. Real-time monitoring of the neurochemical output of DBS-targeted regions may thus advance functional neurosurgical procedures by identifying candidate neurotransmitters and neuromodulators involved in the physiological effects of DBS. This would in turn permit the development of a method of chemically guided placement of DBS electrodes in vivo. Designed in compliance with FDA-recognized standards for medical electrical device safety, the authors report on the utility of the Wireless Instantaneous Neurotransmitter Concentration System (WINCS) for real-time comonitoring of electrical stimulation–evoked adenosine and dopamine efflux in vivo, utilizing fast-scan cyclic voltammetry (FSCV) at a polyacrylonitrile-based (T-650) carbon fiber microelectrode (CFM).
Methods

The WINCS was used for FSCV, which consisted of a triangle wave scanned between −0.4 and +1.5 V at a rate of 400 V/second and applied at 10 Hz. All voltages applied to the CFM were with respect to an Ag/AgCl reference electrode. The CFM was constructed by aspirating a single T-650 carbon fiber (r = 2.5 μm) into a glass capillary and pulling to a microscopic tip using a pipette puller. The exposed carbon fiber (the sensing region) extended beyond the glass insulation by ~ 50 μm. Proof of principle tests included in vitro measurements of adenosine and dopamine, as well as in vivo measurements in urethane-anesthetized rats by monitoring adenosine and dopamine efflux in the dorsomedial caudate putamen evoked by high-frequency electrical stimulation of the ventral tegmental area and substantia nigra.
Results

The WINCS provided reliable, high-fidelity measurements of adenosine efflux. Peak oxidative currents appeared at +1.5 V and at +1.0 V for adenosine, separate from the peak oxidative current at +0.6 V for dopamine. The WINCS detected subsecond adenosine and dopamine efflux in the caudate putamen at an implanted CFM during high-frequency stimulation of the ventral tegmental area and substantia nigra. Both in vitro and in vivo testing demonstrated that WINCS can detect adenosine in the presence of other easily oxidizable neurochemicals such as dopamine comparable to the detection abilities of a conventional hardwired electrochemical system for FSCV.
Conclusions

Altogether, these results demonstrate that WINCS is well suited for wireless monitoring of high-frequency stimulation-evoked changes in brain extracellular concentrations of adenosine. Clinical applications of selective adenosine measurements may prove important to the future development of DBS technology.

Online motor fault detection and diagnosis using a hybrid FMM-CART model

In this brief, a hybrid model combining the fuzzy min-max (FMM) neural network and the classification and regression tree (CART) for online motor detection and diagnosis tasks is described. The hybrid model, known as FMM-CART, exploits the advantages of both FMM and CART for undertaking data classification and rule extraction problems. To evaluate the applicability of the proposed FMM-CART model, an evaluation with a benchmark data set pertaining to electrical motor bearing faults is first conducted. The results obtained are equivalent to those reported in the literature. Then, a laboratory experiment for detecting and diagnosing eccentricity faults in an induction motor is performed. In addition to producing accurate results, useful rules in the form of a decision tree are extracted to provide explanation and justification for the predictions from FMM-CART. The experimental outcome positively shows the potential of FMM-CART in undertaking online motor fault detection and diagnosis tasks.

Motor fault detection and diagnosis using a hybrid FMM-CART model with online learning

In this paper, a hybrid online learning model that combines the fuzzy min-max (FMM) neural network and the Classification and Regression Tree (CART) for motor fault detection and diagnosis tasks is described. The hybrid model, known as FMM-CART, incorporates the advantages of both FMM and CART for undertaking data classification (with FMM) and rule extraction (with CART) problems. In particular, the CART model is enhanced with an importance predictor-based feature selection measure. To evaluate the effectiveness of the proposed online FMM-CART model, a series of experiments using publicly available data sets containing motor bearing faults is first conducted. The results (primarily prediction accuracy and model complexity) are analyzed and compared with those reported in the literature. Then, an experimental study on detecting imbalanced voltage supply of an induction motor using a laboratory-scale test rig is performed. In addition to producing accurate results, a set of rules in the form of a decision tree is extracted from FMM-CART to provide explanations for its predictions. The results positively demonstrate the usefulness of FMM-CART with online learning capabilities in tackling real-world motor fault detection and diagnosis tasks. © 2014 Springer Science+Business Media New York.

Noncontact detection and analysis of respiratory function using microwave Doppler Radar

Real-time respiratory measurement with Doppler Radar has an important advantage in the monitoring of certain conditions such as sleep apnoea, sudden infant death syndrome (SIDS), and many other general clinical uses requiring fast nonwearable and non-contact measurement of the respiratory function. In this paper, we demonstrate the feasibility of using Doppler Radar in measuring the basic respiratory frequencies (via fast Fourier transform) for four different types of breathing scenarios: normal breathing, rapid breathing, slow inhalation-fast exhalation, and fast inhalation-slow exhalation conducted in a laboratory environment. A high correlation factor was achieved between the Doppler Radar-based measurements and the conventional measurement device, a respiration strap. We also extended this work from basic signal acquisition to extracting detailed features of breathing function (I: E ratio). This facilitated additional insights into breathing activity and is likely to trigger a number of new applications in respiratory medicine.

Considering the chemical energy requirements of the tri-n-propylamine co-reactant pathways for the judicious design of new electrogenerated chemiluminescence detection systems

The introduction of a 'co-reactant' was a critical step in the evolution of electrogenerated chemiluminescence (ECL) from a laboratory curiosity to a widely utilised detection system. In conjunction with a suitable electrochemiluminophore, the co-reactant enables generation of both the oxidised and reduced precursors to the emitting species at a single electrode potential, under the aqueous conditions required for most analytical applications. The most commonly used co-reactant is tri-n-propylamine (TPrA), which was developed for the classic tris(2,2'-bipyridine)ruthenium(ii) ECL reagent. New electrochemiluminophores such as cyclometalated iridium(iii) complexes are also evaluated with this co-reactant. However, attaining the excited states in these systems can require much greater energy than that of tris(2,2'-bipyridine)ruthenium(ii), which has implications for the co-reactant reaction pathways. In this tutorial review, we describe a simple graphical approach to characterise the energetically feasible ECL pathways with TPrA, as a useful tool for the development of new ECL detection systems.