Search

Alcoholic liver disease (ALD) poses a significant health challenge, so comprehensive research efforts to improve our understanding and treatment strategies are needed. However, the development of effective treatments is hindered by the limitation of existing liver disease models. Liver organoids, characterized by their cellular complexity and three-dimensional (3D) tissue structure closely resembling the human liver, hold promise as ideal models for liver disease research. In this study, we use a meticulously designed protocol involving the differentiation of human induced pluripotent stem cells (hiPSCs) into liver organoids. This process incorporates a precise combination of cytokines and small molecule compounds within a 3D culture system to guide the differentiation process. Subsequently, these differentiated liver organoids are subject to ethanol treatment to induce ALD, thus establishing a disease model. A rigorous assessment through a series of experiments reveals that this model partially recapitulates key pathological features observed in clinical ALD, including cellular mitochondrial damage, elevated cellular reactive oxygen species (ROS) levels, fatty liver, and hepatocyte necrosis. In addition, this model offers potential use in screening drugs for ALD treatment. Overall, the liver organoid model of ALD, which is derived from hiPSC differentiation, has emerged as an invaluable platform for advancing our understanding and management of ALD in clinical settings.

Autism Spectrum Disorders (ASD) remain a healthcare challenge and gain considerable attention due to the increasing prevalence rates and insupportable burden on families and society. It is noted that the recognition of children's emotional states plays an important role in the evaluation and intervention process of ASD. In this paper, we aim to address the problem of automatic recognition of the emotional states of ASD children in social interactive scenarios. Since the child can be unconstrained in realistic scenarios, the face occlusion under pose variations and uncertain backgrounds become challenges of this task. To tackle this problem, we employ both facial expressions as well as body poses as cues to recognize the emotional states while most traditional methods only leverage the former. Firstly for the facial information, spatial features are extracted through convolutional neural networks followed by a temporal transformer to extract temporal information. Then for the body pose information, graph convolutional networks combined with the self-attention part are used to represent spatial features and temporal convolutional layers for temporal counterparts. Finally, different multimodal fusion ways are explored to generate final recognition results. We evaluate this method on a challenging database collected by us in real-world child-clinician interactive scenarios and the proposed method achieved significantly better results than baselines using only facial information. Thus it is suggested that there is a potential to assist in clinical practice by providing the recognized emotion as feedback. [ABSTRACT FROM AUTHOR]