AIMC Topic: Neurotransmitter Agents

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Machine Learning for Neurotransmitter Monitoring by Fast Voltammetry: Current and Future Prospects.

ACS chemical neuroscience
Chemical neuroscience wields tools to uncover the molecular mysteries of the brain. Sensors can be fabricated with properties tailored to the scales needed to decode neurochemical information. Current instrumentation is capable of measurement rates t...

Machine learning-powered plasmonic pattern recognition: etch-suppressed gold nanorods for multiplex urinary analysis of catecholamine neurotransmitters.

Analytical methods : advancing methods and applications
Simultaneous monitoring of catecholamine neurotransmitters (CNTs)-including epinephrine (Epi), norepinephrine (NE), levodopa (L-DOPA), and dopamine (DA)-is essential for the accurate diagnosis and effective management of various neurological disorder...

Graph Convolutional Neural Network-Enabled Frontier Molecular Orbital Prediction: A Case Study with Neurotransmitters and Antidepressants.

Journal of chemical information and modeling
With the advancement of artificial intelligence-embedded methodologies, their application to predict fundamental molecular properties has become increasingly prevalent. In this study, a graph convolutional neural network fingerprint-enabled artificia...

Single-Component Double-Emissive Ratiometric Probe: Toward Machine Learning Driven Detection and Discrimination of Neurological Biomarkers.

Analytical chemistry
This study presents an attractive single-component ratiometric fluorescent sensor that utilizes the oxidation of BSA-protected Au nanoclusters (BSA-Au NCs) by -Bromosuccinimide (NBS) to detect catecholamine neurotransmitters and their metabolites, wh...

A comprehensive review of neurotransmitter modulation via artificial intelligence: A new frontier in personalized neurobiochemistry.

Computers in biology and medicine
The deployment of artificial intelligence (AI) is revolutionizing neuropharmacology and drug development, allowing the modulation of neurotransmitter systems at the personal level. This review focuses on the neuropharmacology and regulation of neurot...

Predicting Response to Neuromodulators or Prokinetics in Patients With Suspected Gastroparesis Using Machine Learning: The "BMI, Infectious Prodrome, Delayed GES, and No Diabetes" Model.

Clinical and translational gastroenterology
INTRODUCTION: Pharmacologic therapies for symptoms of gastroparesis (GP) have limited efficacy, and it is difficult to predict which patients will respond. In this study, we implemented a machine learning model to predict the response to prokinetics ...

An organic brain-inspired platform with neurotransmitter closed-loop control, actuation and reinforcement learning.

Materials horizons
Organic neuromorphic platforms have recently received growing interest for the implementation and integration of artificial and hybrid neuronal networks. Here, achieving closed-loop and learning/training processes as in the human brain is still a maj...

Resolution of tonic concentrations of highly similar neurotransmitters using voltammetry and deep learning.

Molecular psychiatry
With advances in our understanding regarding the neurochemical underpinnings of neurological and psychiatric diseases, there is an increased demand for advanced computational methods for neurochemical analysis. Despite having a variety of techniques ...

Deep learning-assisted mass spectrometry imaging for preliminary screening and pre-classification of psychoactive substances.

Talanta
Currently, it is of great urgency to develop a rapid pre-classification and screening method for suspected drugs as the constantly springing up of new psychoactive substances. In most researches, psychoactive substances classification approaches depe...

Highly Bionic Neurotransmitter-Communicated Neurons Following Integrate-and-Fire Dynamics.

Nano letters
In biological neural networks, chemical communication follows the reversible integrate-and-fire (I&F) dynamics model, enabling efficient, anti-interference signal transport. However, existing artificial neurons fail to follow the I&F model in chemica...