Hydrogen-rich water ameliorates neuropathological impairments in a mouse model of Alzheimer's disease through reducing neuroinflammation and modulating intestinal microbiota

Hydrogen exhibits the potential to treat Alzheimer's disease. Stereotactic injection has been previously used as an invasive method of administering active hydrogen, but this method has limitations in clinical practice. In this study, triple transgenic(3×Tg) Alzheimer's disease mice were treated with hydrogen-rich water for 7 months. The results showed that hydrogen-rich water prevented synaptic loss and neuronal death, inhibited senile plaques, and reduced hyperphosphorylated tau and neurofibrillary tangles in 3×Tg Alzheimer's disease mice. In addition, hydrogen-rich water improved brain energy metabolism disorders and intestinal flora imbalances and reduced inflammatory reactions. These findings suggest that hydrogen-rich water is an effective hydrogen donor that can treat Alzheimer's disease. This study was approved by the Animal Ethics and Welfare Committee of Shenzhen University, China(approval No. AEWC-20140615-002) on June 15, 2014.

Deep Learning in Medical Ultrasound Analysis: A Review

Ultrasound (US) has become one of the most commonly performed imaging modalities in clinical practice. It is a rapidly evolving technology with certain advantages and with unique challenges that include low imaging quality and high variability. From the perspective of image analysis, it is essential to develop advanced automatic US image analysis methods to assist in US diagnosis and/or to make such assessment more objective and accurate. Deep learning has recently emerged as the leading machine learning tool in various research fields, and especially in general imaging analysis and computer vision. Deep learning also shows huge potential for various automatic US image analysis tasks. This review first briefly introduces several popular deep learning architectures, and then summarizes and thoroughly discusses their applications in various specific tasks in US image analysis, such as classification, detection, and segmentation. Finally, the open challenges and potential trends of the future application of deep learning in medical US image analysis are discussed.

Implications of neuroimaging findings in addiction

Addiction is a chronic and often relapsing brain disorder characterized by drug abuse and withdrawal symptoms and compulsive drug seeking(Koob and Volkow,2010)when access to the drug is restricted.Addiction leads to structural and functional brain changes implicated in reward,memory,motivation,and control(Volkow et al.,2019;Lüscher et al.,2020).

Novel photo-theranostic GdB_(6) nanoparticles for fluorescence imaging and NIR-photothermal therapy

The development of multifunctional theranostic nano-agents is an important resolution for personalized treatment of cancer.In this work,we synthesized a new kind of gadolinium boride nanoparticles(GBN)by a microwave-assisted chemical etching method,and discovered their optical characteristics including fluorescence imaging and near-infrared(NIR)photothermal conversion capability.Bright greenishyellow fluorescence enabled for intracellular localization,while effective NIR-photothermal conversion supported photothermal therapy(PTT).In vitro and in vivo results indicated that GBN exhibited a superior antitumor performance and high biocompatibility.This study demonstrated a promising multifunctional theranostic nanoplatform for cancer treatment.

Video-triggered EEG-emotion public databases and current methods: A survey

Emotions,formed in the process of perceiving external environment,directly affect human daily life,such as social interaction,work efficiency,physical wellness,and mental health.In recent decades,emotion recognition has become a promising research direction with significant application values.Taking the advantages of electroencephalogram(EEG)signals(i.e.,high time resolution)and video-based external emotion evoking(i.e.,rich media information),video-triggered emotion recognition with EEG signals has been proven as a useful tool to conduct emotion-related studies in a laboratory environment,which provides constructive technical supports for establishing real-time emotion interaction systems.In this paper,we will focus on video-triggered EEG-based emotion recognition and present a systematical introduction of the current available video-triggered EEG-based emotion databases with the corresponding analysis methods.First,current video-triggered EEG databases for emotion recognition(e.g.,DEAP,MAHNOB-HCI,SEED series databases)will be presented with full details.Then,the commonly used EEG feature extraction,feature selection,and modeling methods in video-triggered EEG-based emotion recognition will be systematically summarized and a brief review of current situation about video-triggered EEG-based emotion studies will be provided.Finally,the limitations and possible prospects of the existing video-triggered EEG-emotion databases will be fully discussed.