Multiscale brain modeling: bridging microscopic and macroscopic brain dynamics for clinical and technological applications

Frontiers in Cellular Neuroscience, Journal Year: 2025, Volume and Issue: 19

Published: Feb. 19, 2025

The brain's complex organization spans from molecular-level processes within neurons to large-scale networks, making it essential understand this multiscale structure uncover brain functions and address neurological disorders. Multiscale modeling has emerged as a transformative approach, integrating computational models, advanced imaging, big data bridge these levels of organization. This review explores the challenges opportunities in linking microscopic phenomena macroscopic functions, emphasizing methodologies driving progress field. It also highlights clinical potential including their role advancing artificial intelligence (AI) applications improving healthcare technologies. By examining current research proposing future directions for interdisciplinary collaboration, work demonstrates how can revolutionize both scientific understanding practice.

Language: Английский

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Factors responsible for alpha-Synuclein aggregation

Progress in molecular biology and translational science, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

Language: Английский

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SUMO-LMNet: Lossless Mapping Network for Predicting SUMOylation Sites in SUMO1 and SUMO2 using High-Dimensional Features

Computational and Structural Biotechnology Journal, Journal Year: 2025, Volume and Issue: 27, P. 1048 - 1059

Published: Jan. 1, 2025

Accurate SUMOylation site prediction is crucial for deciphering gene regulation and disease mechanisms. However, distinguishing SUMO1 SUMO2 modifications remains a major challenge due to their structural similarities. Conventional models often struggle differentiate between these paralogues, limiting applicability in biological research. To address this, we introduce SUMO-LMNet, deep learning-based framework the precise of sites. Unlike previous models, SUMO-LMNet integrates lossless mapping strategy learning architectures enhance both accuracy interpretability. Our model extracts high-dimensional features from sequences transforms them into two-dimensional feature maps, enabling convolutional neural networks (CNNs) effectively capture local global dependencies within data. By leveraging Lossless Mapping Network (LM-Net), this approach preserves original space, ensuring that integrity retained without loss spatial information. While Grad-CAM highlights key individual predictions, it lacks consistency across samples does not provide dataset-wide evaluation importance. Combined Heatmap Feature Analysis (CHFA), which systematically aggregates importance multiple samples, providing more reliable interpretable assessment. Experimental results reveal distinct SUMO2, underscoring necessity paralogue-specific predictive models. Through systematic comparison network architectures, demonstrate our achieves over 80 % modification prioritizing candidate sites further study, aids experimental design accelerates discovery biologically relevant targets. publicly available at https://predictor.isu.edu.tw/sumo-lmnet.

Language: Английский

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Neurofilament light chain as a biomarker for neurodegenerative changes in COVID-19 and clinical implications

Future Virology, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 15

Published: March 21, 2025

Language: Английский

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