AIMC Topic: Magnetic Resonance Spectroscopy
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Preventing mislabeling of organic white button mushrooms (Agaricus bisporus) combining NMR-based foodomics, statistical, and machine learning approach.
Food research international (Ottawa, Ont.)
Nov 19, 2024
Organic foods are among the most susceptible to fraud and mislabeling since the differentiation between organic and conventionally grown food relies on a paper-trail-based system. This study aimed to develop a differentiation model that combines nucl...
CBMAFF-Net: An Intelligent NMR-Based Nontargeted Screening Method for New Psychoactive Substances.
Analytical chemistry
Nov 13, 2024
With the proliferation and rapid evolution of new psychoactive substances (NPSs), traditional database-based search methods face increasing challenges in identifying NPS seizures with complex compositions, thereby complicating their regulation and ea...
Autonomous mobile robots for exploratory synthetic chemistry.
Nature
Nov 6, 2024
Autonomous laboratories can accelerate discoveries in chemical synthesis, but this requires automated measurements coupled with reliable decision-making. Most autonomous laboratories involve bespoke automated equipment, and reaction outcomes are ofte...
A Flow-based Truncated Denoising Diffusion Model for super-resolution Magnetic Resonance Spectroscopic Imaging.
Medical image analysis
Sep 27, 2024
Magnetic Resonance Spectroscopic Imaging (MRSI) is a non-invasive imaging technique for studying metabolism and has become a crucial tool for understanding neurological diseases, cancers and diabetes. High spatial resolution MRSI is needed to charact...
Integrating HRMAS-NMR Data and Machine Learning-Assisted Profiling of Metabolite Fluxes to Classify Low- and High-Grade Gliomas.
Interdisciplinary sciences, computational life sciences
Sep 27, 2024
Diagnosing and classifying central nervous system tumors such as gliomas or glioblastomas pose a significant challenge due to their aggressive and infiltrative nature. However, recent advancements in metabolomics and magnetic resonance spectroscopy (...
Nuclear magnetic resonance-based metabolomics with machine learning for predicting progression from prediabetes to diabetes.
eLife
Sep 20, 2024
BACKGROUND: Identification of individuals with prediabetes who are at high risk of developing diabetes allows for precise interventions. We aimed to determine the role of nuclear magnetic resonance (NMR)-based metabolomic signature in predicting the ...
Utilizing Molecular Dynamics Simulations, Machine Learning, Cryo-EM, and NMR Spectroscopy to Predict and Validate Protein Dynamics.
International journal of molecular sciences
Sep 8, 2024
Protein dynamics play a crucial role in biological function, encompassing motions ranging from atomic vibrations to large-scale conformational changes. Recent advancements in experimental techniques, computational methods, and artificial intelligence...
The development of machine learning approaches in two-dimensional NMR data interpretation for metabolomics applications.
Analytical biochemistry
Aug 24, 2024
Metabolomics has been widely applied in human diseases and environmental science to study the systematic changes of metabolites over diverse types of stimuli. NMR-based metabolomics has been widely used, but the peak overlap problems in the one-dimen...
Metabolic signatures derived from whole-brain MR-spectroscopy identify early tumor progression in high-grade gliomas using machine learning.
Journal of neuro-oncology
Aug 24, 2024
PURPOSE: Recurrence for high-grade gliomas is inevitable despite maximal safe resection and adjuvant chemoradiation, and current imaging techniques fall short in predicting future progression. However, we introduce a novel whole-brain magnetic resona...
The combination of HSI and NMR techniques with deep learning for identification of geographical origin and GI markers of Lycium barbarum L.
Food chemistry
Aug 21, 2024
Lycium barbarum L. (L. barbarum) is renowned worldwide for its nutritional and medicinal benefits. Rapid and accurate identification of L.barbarum's geographic origin is essential because its nutritional content, medicinal efficacy, and market price ...
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