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Automated Algorithm for Detection of Transient Adenosine Release.

Ryan P Borman1, Ying Wang1, Michael D Nguyen1

  • 1Department of Chemistry, University of Virginia , Charlottesville, Virginia 22904, United States.

ACS Chemical Neuroscience
|February 16, 2017
PubMed
Summary

Researchers developed an algorithm to automatically detect brain adenosine release events from fast-scan cyclic voltammetry (FSCV) data, significantly reducing analysis time and improving accuracy.

Keywords:
Adenosineautomated analysisbrain slicecaudatehippocampusin vivo voltammetry

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Area of Science:

  • Neuroscience
  • Analytical Chemistry
  • Biochemistry

Background:

  • Spontaneous adenosine release in the brain occurs in brief, seconds-long events.
  • Identifying these events in fast-scan cyclic voltammetry (FSCV) data is challenging due to their random nature.

Purpose of the Study:

  • To develop and validate an algorithm for automated identification and characterization of adenosine transient events in FSCV data.
  • To significantly reduce the time required for analyzing FSCV data of adenosine release.

Main Methods:

  • Developed a novel algorithm to identify adenosine based on its characteristic oxidation peaks, time delay, and current vs. time peak ratios.
  • Validated the algorithm using datasets from multiple researchers, comparing its performance to manual analysis.
  • Tested algorithm specificity using calibration data for adenosine triphosphate (ATP), histamine, hydrogen peroxide, and pH changes, as well as in vivo stimulated histamine release.

Main Results:

  • The algorithm automates data analysis, reducing processing time from 10–18 hours to approximately 40 minutes per experiment.
  • Achieved high accuracy with 10 ± 4% false negatives and 9 ± 3% false positives.
  • Demonstrated specificity, correctly distinguishing adenosine from other analytes like ATP, histamine, and pH variations, and not misidentifying stimulated histamine release.

Conclusions:

  • The developed algorithm effectively and efficiently identifies spontaneous adenosine release events in FSCV data.
  • This automated approach offers a significant improvement in analysis speed and accuracy for neurochemical monitoring.
  • The modular design allows for potential adaptation to detect other neurochemical transients, such as dopamine, in FSCV recordings.