A prognostic neural epigenetic signature in high-grade glioma

Study of Neuroepithelial Genetic Markers and Prognosis in High-Grade Gliomas Background and Research Motivation High-grade gliomas are highly malignant brain tumors with generally poor patient prognosis. Previous preclinical model studies have suggested that interactions between neural and tumor cells drive tumor growth, but clinical validation of ...

Beta to Theta Power Ratio in EEG Periodic Components as a Potential Biomarker in Mild Cognitive Impairment and Alzheimer's Dementia

Alzheimer’s Disease Research and Treatment Frontiers: Beta/Theta Power Ratio in EEG Periodic Components as a Potential Biomarker Background Introduction Alzheimer’s dementia (AD) is a progressively developing disease, accounting for 60% to 80% of all dementia cases [1]. In the early stages of AD, mild cognitive impairment (MCI) typically occurs, du...

Theta Oscillations Support Prefrontal-Hippocampal Interactions in Sequential Working Memory

Study on Theta Oscillations in Hippocampus-Prefrontal Interaction Supporting Sequential Working Memory Academic Background The dorsolateral prefrontal cortex (DLPFC) and the hippocampus play crucial roles in sequential working memory, but the specific interaction mechanisms are not yet clear. Previous studies have shown that these two brain regions...

Increases in Pre-Stimulus Theta and Alpha Oscillations Precede Successful Encoding of Crossmodal Associations

Enhancement of Theta and Alpha Oscillations Prior to Crossmodal Memory Encoding Background Episodic memory is a crucial component of human memory, with one of its core mechanisms being the formation of associations through stimuli from different sensory channels. Current theories suggest that during crossmodal associative encoding, the phase and po...

Hierarchical Negative Sampling Based Graph Contrastive Learning Approach for Drug-Disease Association Prediction

Research on Drug-Disease Association Prediction Using Graph Contrastive Learning Based on Layered Negative Sampling The prediction of drug-disease associations (RDAs) plays a critical role in unveiling disease treatment strategies and promoting drug repurposing. However, existing methods mainly rely on limited domain-specific knowledge when predict...

Predicting Drug-Target Affinity by Learning Protein Knowledge from Biological Networks

Predicting Drug-Target Affinity Based on Learning Protein Knowledge from Biological Networks Background The prediction of drug-target affinity (DTA) plays a crucial role in drug discovery. Efficient and accurate DTA prediction can significantly reduce the time and economic costs of new drug development. In recent years, the explosive development of...