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Deep learning system for the differential diagnosis of oral mucosal lesions through clinical photographic imaging.
J Dent Sci
January 2025
School of Dentistry, College of Medicine, National Cheng Kung University, Tainan, Taiwan.
Background/purpose: Oral mucosal lesions are associated with a variety of pathological conditions. Most deep-learning-based convolutional neural network (CNN) systems for computer-aided diagnosis of oral lesions have typically concentrated on determining limited aspects of differential diagnosis. This study aimed to develop a CNN-based diagnostic model capable of classifying clinical photographs of oral ulcerative and associated lesions into five different diagnoses, thereby assisting clinicians in making accurate differential diagnoses.
View Article and Find Full Text PDFManipulating autophagic degradation in human diseases: from mechanisms to interventions.
Life Med
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Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education, Department of Pathophysiology, Shanghai Jiao Tong University School of Medicine (SJTU-SM), Shanghai 20025, China.
Targeted degradation, having emerged as a powerful and promising strategy in drug discovery in the past two decades, has provided a solution for many once undruggable targets involved in various diseases. While earlier targeted degradation tools, as exemplified by PROteolysis-TArgeting Chimera (PROTAC), focused on harnessing the ubiquitin-proteasome system, novel approaches that aim to utilize autophagy, a potent, lysosome-dependent degradation pathway, have also surfaced recently as promising modalities. In this review, we first introduce the mechanisms that establish selectivity in autophagy, which provides the rationales for autophagy-based targeted degradation; we also provide an overview on the panoply of cellular machinery involved in this process, an arsenal that could be potentially harnessed.
View Article and Find Full Text PDFMulti scale multi attention network for blood vessel segmentation in fundus images.
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Department of Data Science and Artificial Intelligence, Sunway University, 47500, Petaling Jaya, Selangor Darul Ehsan, Malaysia.
Precise segmentation of retinal vasculature is crucial for the early detection, diagnosis, and treatment of vision-threatening ailments. However, this task is challenging due to limited contextual information, variations in vessel thicknesses, the complexity of vessel structures, and the potential for confusion with lesions. In this paper, we introduce a novel approach, the MSMA Net model, which overcomes these challenges by replacing traditional convolution blocks and skip connections with an improved multi-scale squeeze and excitation block (MSSE Block) and Bottleneck residual paths (B-Res paths) with spatial attention blocks (SAB).
View Article and Find Full Text PDFNavigating Dementia and Delirium: Balancing Identity and Interests in Advance Directives.
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Department of Medical Psychology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
The moral authority of advance directives (ADs) in the context of persons living with dementia (PLWD) has sparked a multifaceted debate, encompassing concerns such as authenticity and the appropriate involvement of caregivers. Dresser critiques ADs based on Parfit's account of numeric personal identity, using the often-discussed case of a PLWD called Margo. She claims that dementia leads to a new manifestation of Margo emerging, which then contracts pneumonia.
View Article and Find Full Text PDFMachine Learning-Based predictive model for adolescent metabolic syndrome: Utilizing data from NHANES 2007-2016.
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Department of Anesthesiology and Surgical Intensive Care Unit, Kunming Children's Hospital, Kunming, Yunnan, China.
Metabolic syndrome (Mets) in adolescents is a growing public health issue linked to obesity, hypertension, and insulin resistance, increasing risks of cardiovascular disease and mental health problems. Early detection and intervention are crucial but often hindered by complex diagnostic requirements. This study aims to develop a predictive model using NHANES data, excluding biochemical indicators, to provide a simple, cost-effective tool for large-scale, non-medical screening and early prevention of adolescent MetS.
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