A 23-year-old man presented for evaluation of multiple dense asymptomatic papules on the entire glans. Histologically, the lesions resembled acral angiofibroma. A diagnosis of profound pearly penile papules was made. This is the third reported case and more serious and typical than described in previous reports.
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http://dx.doi.org/10.2147/CCID.S421272 | DOI Listing |
BMC Public Health
January 2025
Department of Public Health, Woldia University, Woldia, Ethiopia.
Background: Despite advancements in Human Immunodeficiency Virus (HIV) treatment and care, undernutrition remains a significant concern, accelerating disease progression and risk of Acquired Immune Deficiency Syndrome (AIDS)-related deaths. The nutritional status of second-line antiretroviral treatment (SLART) users in Ethiopia has not been thoroughly investigated. So, this study aimed to assess the nutritional status of HIV/AIDS patients who were on SLART and its associated factors in Northern Ethiopia.
View Article and Find Full Text PDFDigit Health
January 2025
Department of Computer Science and Engineering, Hajee Mohammad Danesh Science and Technology University, Dinajpur, Bangladesh.
Objective: Breast cancer detection is critical for timely and effective treatment, and automatic detection systems can significantly reduce human error and improve diagnosis speed. This study aims to develop an accurate and robust framework for classifying breast cancer into benign and malignant categories using a novel machine learning architecture.
Methods: We propose a dense-ResNet attention integration (DRAI) architecture that combines DenseNet and ResNet models with three attention mechanisms to enhance feature extraction from the BreakHis dataset.
BMC Med Imaging
January 2025
Department of Information, Third Affiliated Hospital of Naval Medical University, No. 225 Changhai Road, Yangpu District, 200438, Shanghai, China.
Purpose: To develop an end-to-end convolutional neural network model for analyzing hematoxylin and eosin(H&E)-stained histological images, enhancing the performance and efficiency of nuclear segmentation and classification within the digital pathology workflow.
Methods: We propose a dual-mechanism feature pyramid fusion technique that integrates nuclear segmentation and classification tasks to construct the HistoNeXt network model. HistoNeXt utilizes an encoder-decoder architecture, where the encoder, based on the advanced ConvNeXt convolutional framework, efficiently and accurately extracts multi-level abstract features from tissue images.
Diagnostics (Basel)
December 2024
Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia.
Background: Cardiac magnetic resonance imaging (MRI) plays a crucial role in monitoring disease progression and evaluating the effectiveness of treatment interventions. Cardiac MRI allows medical practitioners to assess cardiac function accurately by providing comprehensive and quantitative information about the structure and function, hence making it an indispensable tool for monitoring the disease and treatment response. Deep learning-based segmentation enables the precise delineation of cardiac structures including the myocardium, right ventricle, and left ventricle.
View Article and Find Full Text PDFEntropy (Basel)
December 2024
Henan International Joint Laboratory of Intelligent Network Theory and Key Technology, Henan University, Kaifeng 475001, China.
Federated learning enables devices to train models collaboratively while protecting data privacy. However, the computing power, memory, and communication capabilities of IoT devices are limited, making it difficult to train large-scale models on these devices. To train large models on resource-constrained devices, federated split learning allows for parallel training of multiple devices by dividing the model into different devices.
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