Rheumatoid arthritis (RA) is an autoimmune disease, which attacks human joint system and causes lifelong inflammatory condition. To date, no cure is available for RA and even the ratio of achieving remission is very low. Hence, to enhance the efficacy of RA treatment, it is essential to develop novel approaches specifically targeting pathological tissues. In this study, we discovered that RA synovial fibroblasts exhibited higher reactive oxygen species (ROS) and mitochondrial superoxide level, which were adopted to develop ROS-responsive nano-medicines in inflammatory microenvironment for enhanced RA treatment. A selenocystamine-based polymer was synthesized as a ROS-responsive carrier nanoplatform, and berberine serves as a tool drug. By assembling, ROS-responsive berberine polymeric micelles were fabricated, which remarkably increased the uptake of berberine in RA fibroblast and improved in vitro and in vivo efficacy ten times higher. Mechanistically, the anti-RA effect of micelles was blocked by the co-treatment of AMPK inhibitor or palmitic acid, indicating that the mechanism of micelles was carried out through targeting mitochondrial, suppressing lipogenesis and finally inhibiting cellular proliferation. Taken together, our ROS-responsive nano-medicines represent an effective way of preferentially releasing prodrug at the inflammatory microenvironment and improving RA therapeutic efficacy.

The growing demand for advanced neural interfaces that enable precise brain monitoring and modulation has catalyzed significant research into flexible, biocompatible, and highly conductive materials. PEDOT:PSS-based bioelectronic materials exhibit high conductivity, mechanical flexibility, and biocompatibility, making them particularly suitable for integration into neural devices for brain science research. These materials facilitate high-resolution neural activity monitoring and provide precise electrical stimulation across diverse modalities. This review comprehensively examines recent advances in the development of PEDOT:PSS-based bioelectrodes for brain monitoring and modulation, with a focus on strategies to enhance their conductivity, biocompatibility, and long-term stability. Furthermore, it highlights the integration of multifunctional neural interfaces that enable synchronous stimulation-recording architectures, hybrid electro-optical stimulation modalities, and multimodal brain activity monitoring. These integrations enable fundamentally advancing the precision and clinical translatability of brain–computer interfaces. By addressing critical challenges related to efficacy, integration, safety, and clinical translation, this review identifies key opportunities for advancing next-generation neural devices. The insights presented are vital for guiding future research directions in the field and fostering the development of cutting-edge bioelectronic technologies for neuroscience and clinical applications.

Brain cancer pose significant life-threats by destructively invading normal brain tissues, causing dysneuria, disability and death, and its therapeutics is

Currently, deep learning aided medical imaging is becoming the hot spot of AI frontier application and the future development trend of precision neuroscience. This review aimed to render comprehensive and informative insights into the recent progress of deep learning and its applications in medical imaging for brain monitoring and regulation. The article starts by providing an overview of the current methods for brain imaging, highlighting their limitations and introducing the potential benefits of using deep learning techniques to overcome these limitations. Then, we further delve into the details of deep learning, explaining the basic concepts and providing examples of how it can be used in medical imaging. One of the key strengths is its thorough discussion of the different types of deep learning models that can be used in medical imaging including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial network (GAN) assisted magnetic resonance imaging (MRI), positron emission tomography (PET)/computed tomography (CT), electroencephalography (EEG)/magnetoencephalography (MEG), optical imaging, and other imaging modalities. Overall, our review on deep learning aided medical imaging for brain monitoring and regulation provides a referrable glance for the intersection of deep learning aided neuroimaging and brain regulation.