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Longitudinal evolution of the transdiagnostic prodrome to severe mental disorders: a dynamic temporal network analysis informed by natural language processing and electronic health records.
Mol Psychiatry
January 2025
Early Psychosis: Interventions and Clinical-Detection (EPIC) Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK.
Modelling the prodrome to severe mental disorders (SMD), including unipolar mood disorders (UMD), bipolar mood disorders (BMD) and psychotic disorders (PSY), should consider both the evolution and interactions of symptoms and substance use (prodromal features) over time. Temporal network analysis can detect causal dependence between and within prodromal features by representing prodromal features as nodes, with their connections (edges) indicating the likelihood of one feature preceding the other. In SMD, node centrality could reveal insights into important prodromal features and potential intervention targets.
View Article and Find Full Text PDFRandom resistive memory-based deep extreme point learning machine for unified visual processing.
Nat Commun
January 2025
Key Lab of Fabrication Technologies for Integrated Circuits Institute of Microelectronics, Chinese Academy of Sciences, 100029, Beijing, China.
Visual sensors, including 3D light detection and ranging, neuromorphic dynamic vision sensor, and conventional frame cameras, are increasingly integrated into edge-side intelligent machines. However, their data are heterogeneous, causing complexity in system development. Moreover, conventional digital hardware is constrained by von Neumann bottleneck and the physical limit of transistor scaling.
View Article and Find Full Text PDFDevelopment of a droplet digital PCR method for the detection of .
Pract Lab Med
January 2025
Department of Clinical Laboratory Medicine, Guangdong Provincial Key Laboratory of Major Obstetric Diseases, Guangdong Provincial Clinical Research Center for Obstetrics and Gynecology, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510150, China.
Background: Human infection with is mainly manifested as non-gonococcal urethritis, where it can lead to cervicitis, premature rupture of membranes and abortion in women, as well as infertility in males, which becomes a major problem in clinical diagnosis and treatment. At present, real-time fluorescence quantitative PCR and culture are the two main methods for detecting UU. The real-time fluorescence quantitative PCR method is cumbersome and cannot accomplish absolute quantification on nucleic acids, while the cultivation method has limitations such as low sensitivity and being time-consuming.
View Article and Find Full Text PDFPerformance of the automated digital cell image analyzer UIMD PBIA in white blood cell classification: a comparative study with sysmex DI-60.
Clin Chem Lab Med
January 2025
Department of Laboratory Medicine, Soonchunhyang University Cheonan Hospital, Soonchunhyang University College of Medicine, Cheonan, Republic of Korea.
Objectives: This study aimed to evaluate the performance of PBIA (UIMD, Seoul, Republic of Korea), an automated digital morphology analyzer using deep learning, for white blood cell (WBC) classification in peripheral blood smears and compare it with the widely used DI-60 (Sysmex, Kobe, Japan).
Methods: A total of 461 slides were analyzed using PBIA and DI-60. For each instrument, pre-classification performance was evaluated on the basis of post-classification results verified by users.
Distribution-based sub-population selection (DSPS): A method for in-silico reproduction of clinical trials outcomes.
Comput Biol Med
January 2025
University of Virginia, Center for Diabetes Technology, Charlottesville, VA, 22903, USA. Electronic address:
Diabetes presents a significant challenge to healthcare due to the short- and long-term complications associated with poor blood sugar control. Computer simulation platforms have emerged as promising tools for advancing diabetes therapy by simulating patient responses to treatments in a virtual environment. The University of Virginia Virtual Lab (UVLab) is a new simulation platform engineered to mimic the metabolic behavior of individuals with type 2 diabetes (T2D) using a mathematical model of glucose homeostasis in T2D and a large population of 6062 virtual subjects.
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