Using artificial intelligence methods to study the effectiveness of exercise in patients with ADHD.

Attention Deficit Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental disorder that significantly affects children and adults worldwide, characterized by persistent inattention, hyperactivity, and impulsivity. Current research in this field faces challenges, particularly in accurate diagnosis and effective treatment strategies. The analysis of motor information, enriched by artificial intelligence methodologies, plays a vital role in deepening our understanding and improving the management of ADHD. The integration of AI techniques, such as machine learning and data analysis, into the study of ADHD-related motor behaviors, allows for a more nuanced understanding of the disorder. This approach facilitates the identification of patterns and anomalies in motor activity that are often characteristic of ADHD, thereby contributing to more precise diagnostics and tailored treatment strategies. Our approach focuses on utilizing AI techniques to deeply analyze patients' motor information and cognitive processes, aiming to improve ADHD diagnosis and treatment strategies. On the ADHD dataset, the model significantly improved accuracy to 98.21% and recall to 93.86%, especially excelling in EEG data processing with accuracy and recall rates of 96.62 and 95.21%, respectively, demonstrating precise capturing of ADHD characteristic behaviors and physiological responses. These results not only reveal the great potential of our model in improving ADHD diagnostic accuracy and developing personalized treatment plans, but also open up new research perspectives for understanding the complex neurological logic of ADHD. In addition, our study not only suggests innovative perspectives and approaches for ADHD treatment, but also provides a solid foundation for future research exploring similar complex neurological disorders, providing valuable data and insights. This is scientifically important for improving treatment outcomes and patients' quality of life, and points the way for future-oriented medical research and clinical practice.

Acid Water-ground Nano-realgar Is Superior to Crude Realgar in Promoting Apoptosis of MCF-7 Breast Cancer Cells.

OBJECTIVE: Realgar is a traditional mineral Chinese medicine with antitumor effects, but it has high toxicity and low efficacy in its crude form. The purpose of this study was to optimize realgar to increase its efficacy and therapeutic potential. METHODS: Crude realgar (CR) was mechanically ground to obtain nano-realgar (NR), and then nano-realgar processed products (NRPPs) were obtained using three different traditional Chinese medicine processing methods: grinding in water, acid water, and alkali water, respectively. RESULTS: By analyzing the size distribution of nanoparticles and the content of arsenic trioxide (As2O3; ATO), we found that acid water-ground NRPPs had the characteristics of high purity and low toxicity. The effects of CR, NR, and NRPPs on proliferation, cell cycle, and apoptosis of MCF-7 cells were detected, and the ability of NRPPs to induce apoptosis in MCF-7 cells was analyzed. The results showed that the average particle size of acid water-ground NRPPs was 137.7 nm, and the content of ATO was 2.83 mg/g. Acid water-ground NRPPs showed better effects on inhibiting proliferation, cell cycle, and apoptosis of MCF-7 cells than CR and NR. Western blot assays further confirmed that acid water-ground NRPPs upregulated the protein expression of TP53, Bax, cytochrome c, caspase-9, and caspase-3 in MCF-7 cells (P<0.05) and inhibited the expression of Bcl-2 (P<0.05). CONCLUSION: These results suggest that acid water-ground NRPPs can induce apoptosis of MCF-7 cells through regulating mitochondrial-mediated apoptosis, providing evidence for the clinical application of realgar.

Donepezil, a drug for Alzheimer's disease, promotes oligodendrocyte generation and remyelination.

Myelin sheaths play important roles in neuronal functions. In the central nervous system (CNS), the myelin is formed by oligodendrocytes (OLs), which are differentiated from oligodendrocyte precursor cells (OPCs). In CNS demyelinating disorders such as multiple sclerosis (MS), the myelin sheaths are damaged and the remyelination process is hindered. Small molecule drugs that promote OPC to OL differentiation and remyelination may provide a new way to treat these demyelinating diseases. Here we report that donepezil, an acetylcholinesterase inhibitor (AChEI) developed for the treatment of Alzheimer's disease (AD), significantly promotes OPC to OL differentiation. Interestingly, other AChEIs, including huperzine A, rivastigmine, and tacrine, have no such effect, indicating that donepezil's effect in promoting OPC differentiation is not dependent on the inhibition of AChE. Donepezil also facilitates the formation of myelin sheaths in OPC-DRG neuron co-culture. More interestingly, donepezil also promotes the repair of the myelin sheaths in vivo and provides significant therapeutic effect in a cuprizone-mediated demyelination animal model. Donepezil is a drug that has been used to treat AD safely for many years; our findings suggest that it might be repurposed to treat CNS demyelinating diseases such as MS by promoting OPC to OL differentiation and remyelination.