ArticleACS chemical neuroscience2017
Automated Algorithm for Detection of Transient Adenosine Release.
Article in ACS chemical neuroscience, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
21 citing papers in PubMed, 29 citations in OpenAlex.
- Machine Learning for Separating Dopamine and Octopamine Electrochemical Signals in Drosophila.Analytical chemistry · 2026Article
- Machine Learning for Neurotransmitter Monitoring by Fast Voltammetry: Current and Future Prospects.ACS chemical neuroscience · 2026Review
- Biological and Mechanical Limitations for Chronic Fast-Scan Cyclic Voltammetry Sensor Design.Advanced materials technologies · 2025Article
- Glassy Carbon Fiber-like Multielectrode Arrays for Neurochemical Sensing and Electrophysiology Recording.Advanced materials technologies · 2025Article
- Novel, User-Friendly Experimental and Analysis Strategies for Fast Voltammetry: Next Generation FSCAV with Artificial Neural Networks.ACS measurement science au · 2022Article
- Pannexin1 channels regulate mechanically stimulated but not spontaneous adenosine release.Analytical and bioanalytical chemistry · 2022Article
- Spontaneous, transient adenosine release is not enhanced in the CA1 region of hippocampus during severe ischemia models.Journal of neurochemistry · 2021Article
- AACS chemical neuroscience · 2020Article
- Structural Similarity Image Analysis for Detection of Adenosine and Dopamine in Fast-Scan Cyclic Voltammetry Color Plots.Analytical chemistry · 2020Article
- Complex sex and estrous cycle differences in spontaneous transient adenosine.Journal of neurochemistry · 2020Article
- CD73 or CD39 Deletion Reveals Different Mechanisms of Formation for Spontaneous and Mechanically Stimulated Adenosine and Sex Specific Compensations in ATP Degradation.ACS chemical neuroscience · 2020Article
- Fundamentals of fast-scan cyclic voltammetry for dopamine detection.The Analyst · 2020Review
- Recent advances in fast-scan cyclic voltammetry.The Analyst · 2020Review
- Perspective on the Future Role of Aptamers in Analytical Chemistry.Analytical chemistry · 2019Article
- Drift Subtraction for Fast-Scan Cyclic Voltammetry Using Double-Waveform Partial-Least-Squares Regression.Analytical chemistry · 2019Article
- Polymer Modified Carbon Fiber-Microelectrodes and Waveform Modifications Enhance Neurotransmitter Metabolite Detection.Analytical methods : advancing methods and applications · 2019Article
- Comparison of spontaneous and mechanically-stimulated adenosine release in mice.Neurochemistry international · 2019Article
- Regional Variations of Spontaneous, Transient Adenosine Release in Brain Slices.ACS chemical neuroscience · 2018Article
- Fast-Scan Cyclic Voltammetry: Chemical Sensing in the Brain and Beyond.Analytical chemistry · 2018Review
- Early changes in transient adenosine during cerebral ischemia and reperfusion injury.PloS one · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 1 institution in 1 country.
Funding
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 identification by an analyst. The algorithm resulted in 10 ± 4% false negatives and 9 ± 3% false positives. The specificity of the algorithm was verified by comparing calibration data for adenosine triphosphate (ATP), histamine, hydrogen peroxide, and pH changes and these analytes were not identified as adenosine. Stimulated histamine release in vivo was also not identified as adenosine. The code is modular in design and could be easily adjusted to detect features of spontaneous dopamine or other neurochemical transients in FSCV data.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.