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Deep Proteome Coverage of Microglia Using a Streamlined Data-Independent Acquisition-Based Proteomic Workflow: Method Consideration for a Phenotypically Diverse Cell Type.
Proteomes
November 2024
Department of Molecular Biosciences, University of South Florida, Tampa, FL 33620, USA.
As the primary innate immune cells of the brain, microglia play a key role in various homeostatic and disease-related processes. To carry out their numerous functions, microglia adopt a wide range of phenotypic states. The proteomic landscape represents a more accurate molecular representation of these phenotypes; however, microglia present unique challenges for proteomic analysis.
View Article and Find Full Text PDFNoise Reduction in Brain CT: A Comparative Study of Deep Learning and Hybrid Iterative Reconstruction Using Multiple Parameters.
Tomography
December 2024
Department of Diagnostic Radiology, Kitasato University School of Medicine, Sagamihara 252-0374, Japan.
Objectives: We evaluated the noise reduction effects of deep learning reconstruction (DLR) and hybrid iterative reconstruction (HIR) in brain computed tomography (CT).
Methods: CT images of a 16 cm dosimetry phantom, a head phantom, and the brains of 11 patients were reconstructed using filtered backprojection (FBP) and various levels of DLR and HIR. The slice thickness was 5, 2.
Deep Learning-Based Diagnosis Algorithm for Alzheimer's Disease.
J Imaging
December 2024
College of Electrical and Information, Northeast Agricultural University, 600 Changjiang Road, Harbin 150038, China.
Alzheimer's disease (AD), a degenerative condition affecting the central nervous system, has witnessed a notable rise in prevalence along with the increasing aging population. In recent years, the integration of cutting-edge medical imaging technologies with forefront theories in artificial intelligence has dramatically enhanced the efficiency of identifying and diagnosing brain diseases such as AD. This paper presents an innovative two-stage automatic auxiliary diagnosis algorithm for AD, based on an improved 3D DenseNet segmentation model and an improved MobileNetV3 classification model applied to brain MR images.
View Article and Find Full Text PDFEvaluating Brain Tumor Detection with Deep Learning Convolutional Neural Networks Across Multiple MRI Modalities.
J Imaging
November 2024
2nd Department of Radiology, Medical School, Attikon University Hospital, National and Kapodistrian University of Athens, 11527 Athens, Greece.
Central Nervous System (CNS) tumors represent a significant public health concern due to their high morbidity and mortality rates. Magnetic Resonance Imaging (MRI) has emerged as a critical non-invasive modality for the detection, diagnosis, and management of brain tumors, offering high-resolution visualization of anatomical structures. Recent advancements in deep learning, particularly convolutional neural networks (CNNs), have shown potential in augmenting MRI-based diagnostic accuracy for brain tumor detection.
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