Automated Algorithm for Detection of Transient Adenosine Release.

ABSTRACT: Spontaneous adenosine release events have been discovered in the brain that last only a few seconds. The identification of these adenosine events from fast-scan cyclic voltammetry (FSCV) data is difficult due to the random nature of adenosine release. In this study, we develop an algorithm that automatically identifies and characterizes adenosine transient features, including event time, concentration, and duration. Automating the data analysis reduces analysis time from 10 to 18 h to about 40 min per experiment. The algorithm identifies adenosine based on its two oxidation peaks, the time delay between them, and their current vs time peak ratios. In order to validate the program, four data sets from three independent researchers were analyzed by the algorithm and then compared to manual i

SUBMITTER: Borman RP 

PROVIDER: S-EPMC5312768 | biostudies-literature | 2017 Feb

REPOSITORIES: biostudies-literature