Licochalcone A, a flavonoid from licorice, promotes TFEB's nuclear translocation through a non-canonical mTORC1 pathway involving Rag C and exerts neuroprotective effects in a mouse model of Parkinson's disease.

This protocol comprises seven steps: problem definition, data quality control, image preprocessing, feature extraction, feature filtering, modeling, and performance analysis. We present a standardized, handcrafted feature–based protocol for processing and analyzing whole-slide histology images, guiding readers through a detailed example of how to leverage digitized WSIs for biomarker discovery and validating the model's generalization performance in an external validation cohort.

This perspective examines the synergy between DeepSeek, a leader in efficient, open-source artificial intelligence models, and next-generation brain–computer interface (BCI) technologies. Based on the advanced technology of DeepSeek, this study explores the fusion of large language models (LLMs) in the fields of BCI devices, electroencephalogram signal processing, and individual service, and deeply discusses the data safety and ethical issues that are inevitable with LLMs, providing unique insights into the path to democratizing neurotechnology.

Parkinson's disease ranks as the second most prevalent neurodegenerative disorder, and there is an urgent need for proper experimental models to enhance our understanding of this complex disease. Non-human primates exhibit various similarities to humans, particularly in terms of motor skills, cognitive functions, and the complexity of their neural architecture. Models of Parkinson's disease using non-human primates can help us to understand the pathogenesis of Parkinson's disease, advance current therapies, and develop new methods in neuroscience.

Early diagnosis of Alzheimer's disease (AD) is a challenge because of the invasive nature of the lumbar puncture for cerebrospinal fluid and the high cost of positron emission tomography imaging barriers for their common use as biomarkers. This study aimed to evaluate the diagnostic potential of blood-based neuro-markers in this advanced age group using an ultra-sensitive Single Molecule Array (SiMoA) platform. This simplified diagnostic model may offers a feasible approach for AD screening in both clinical and community-based settings.

This study aims to address the challenges in brain tumor classification by leveraging magnetic resonance imaging (MRI) to optimize diagnostic accuracy. Five classification models, namely machine learning, pre-trained deep learning, convolutional neural network (CNN), hyperparameter-tuned deep CNN, and deep siamese CNN (DeepSCNN), were evaluated. The high precision and robustness establish this model as a new benchmark in neuro-oncology diagnostics, facilitating more timely and accurate brain tumor identification for improved patient outcomes.