RESUMO
Neuronal assemblies within the nervous system produce electrical activity that can be recorded in terms of action potential patterns. Such patterns provide a sensitive endpoint to detect effects of a variety of chemical and physical perturbations. They are a function of synaptic changes and do not necessarily involve structural alterations. In vitro neuronal networks (NNs) grown on micro-electrode arrays (MEAs) respond to neuroactive substances as well as the in vivo brain. As such, they constitute a valuable tool for investigating changes in the electrophysiological activity of the neurons in response to chemical exposures. However, the reproducibility of NN responses to chemical exposure has not been systematically documented. To this purpose six independent laboratories (in Europe and in USA) evaluated the response to the same pharmacological compounds (Fluoxetine, Muscimol, and Verapamil) in primary neuronal cultures. Common standardization principles and acceptance criteria for the quality of the cultures have been established to compare the obtained results. These studies involved more than 100 experiments before the final conclusions have been drawn that MEA technology has a potential for standard in vitro neurotoxicity/neuropharmacology evaluation. The obtained results show good intra- and inter-laboratory reproducibility of the responses. The consistent inhibitory effects of the compounds were observed in all the laboratories with the 50% Inhibiting Concentrations (IC(50)s) ranging from: (mean ± SEM, in µM) 1.53 ± 0.17 to 5.4 ± 0.7 (n = 35) for Fluoxetine, 0.16 ± 0.03 to 0.38 ± 0.16 µM (n = 35) for Muscimol, and 2.68 ± 0.32 to 5.23 ± 1.7 (n = 32) for Verapamil. The outcome of this study indicates that the MEA approach is a robust tool leading to reproducible results. The future direction will be to extend the set of testing compounds and to propose the MEA approach as a standard screen for identification and prioritization of chemicals with neurotoxicity potential.
RESUMO
AIM: This paper reports studies of mathematical algorithms for intra-cardiac atrial bipolar electrogram compression suitable with implementation on implantable devices. PATIENTS AND METHODS: Bipolar intra-cardiac electrograms (IEGMs) of high right atrium were obtained from 20 patients who underwent electrophysiological studies for arrhythmias. Four thousand seven hundred and eighty-two seconds of IEGM were collected and divided into three rhythm groups: sinus rhythm (SR), atrial fibrillation (AF) and atrial flutter (AFL). Since mathematical algorithms suitable for use with implantable devices demand low computational cost, we employed piecemeal linear approximation methods (ZOP--Zero Order Prediction and SAPA--Scan Along Polygonal Approximation), and beat detection method (Peak) both or which need small numbers of operations to perform electrogram compression. Compression ratio (CR) and percent root mean square difference (PRD) were used to compare the three methods, with statistical analyses performed using paired t-test. RESULTS AND CONCLUSION: The best performance was obtained using the Peak method which reaches an average CR of 10.6 in the case of SR group, 2.8 for AF, and 3.6 for AFL groups, respectively, while PRD lies below 2% for SR and AFL groups and 6% for the AF group. Results show that, for bipolar electrograms, the Peak method reaches statistically significant better performance (P<0.001) in all cases except for Peak vs SAPA applied to AF (P=0.2). The number of operations necessary to compress the data indicate that time consumption can be reduced to be suitable for real time compression in implantable devices. The Peak method, which was assumed to receive the instant of occurrence of each recognized beat, from the hardware of the device, requires fewer operations than ZOP and SAPA. Increasing the length of electrograms recorded in pacemakers will enhance the amount of information provided by the implantable device, allowing more detailed characterization of the intra-cardiac activity and leading to new perspectives in arrhythmia diagnosis and therapy.