Department of Electrical and Electronics Engineering, Bilkent University, Ankara 06800, Turkey; National Magnetic Resonance Research Center (UMRAM), Bilkent University, Ankara 06800, Turkey; Neuroscience Program, Bilkent University, Ankara 06800, Turkey. Electronic address:
Published: May 2022
AI Article Synopsis
Article Abstract
Magnetic resonance imaging (MRI) offers the flexibility to image a given anatomic volume under a multitude of tissue contrasts. Yet, scan time considerations put stringent limits on the quality and diversity of MRI data. The gold-standard approach to alleviate this limitation is to recover high-quality images from data undersampled across various dimensions, most commonly the Fourier domain or contrast sets. A primary distinction among recovery methods is whether the anatomy is processed per volume or per cross-section. Volumetric models offer enhanced capture of global contextual information, but they can suffer from suboptimal learning due to elevated model complexity. Cross-sectional models with lower complexity offer improved learning behavior, yet they ignore contextual information across the longitudinal dimension of the volume. Here, we introduce a novel progressive volumetrization strategy for generative models (ProvoGAN) that serially decomposes complex volumetric image recovery tasks into successive cross-sectional mappings task-optimally ordered across individual rectilinear dimensions. ProvoGAN effectively captures global context and recovers fine-structural details across all dimensions, while maintaining low model complexity and improved learning behavior. Comprehensive demonstrations on mainstream MRI reconstruction and synthesis tasks show that ProvoGAN yields superior performance to state-of-the-art volumetric and cross-sectional models.
Gastric cancer remains a leading cause of cancer-related mortality worldwide. The prognosis often depends on early detection and understanding the molecular mechanisms involved in its progression. Periodic tryptophan protein 1 (PWP1) has emerged as a novel diagnostic marker, potentially linked to gastric cancer progression.
Intraepithelial type 1 innate lymphoid cells (ieILC1s) are tissue-resident lymphocytes in the microenvironment of head and neck squamous cell carcinoma. Here, we evaluate how these cells influence T-cell trafficking to tumors. We generated cytotoxic ieILC1-like cells from natural killer (NK) cells in vitro.
The evolution of orthopaedics has witnessed a pronounced shift from generalist practice to subspecialisation, a trend that has fundamentally reshaped the field over the past century. Originally, orthopaedic surgeons were trained to address a broad spectrum of musculoskeletal conditions. However, increasing complexity and specialization in knowledge have driven a progressive narrowing of focus within orthopaedics.
Department of Laboratory Medicine, The Affiliated Hospital of Southwest Medical University, Sichuan Province Engineering Technology Research Center of Molecular Diagnosis of Clinical Diseases, Molecular Diagnosis of Clinical Diseases Key Laboratory of Luzhou, Sichuan, China.
Breast cancer (BC) is a prevalent malignant tumor that poses a significant health risk to women. The complexity of basic BC research and clinical treatment is influenced by multiple factors, including age, fertility, hormone metabolism, molecular subtypes, and tumor grading and staging. Traditional in vitro models often fall short of meeting modern research demands, whereas organoids-an emerging 3D primary culture technology-offer a unique platform that better replicates the tumor microenvironment (TME).
This study investigates the relationship between out-of-pocket (OOP) healthcare spending, economic growth, population growth, and government health expenditure as a proportion of general government expenditure using National Health Accounts (NHA) estimates. Out-of-Pocket (OOP) healthcare spending imposes a substantial financial burden on households, especially in developing economies such as India. Understanding the factors that influence OOP payments is crucial for policymakers seeking to enhance healthcare systems and achieve Universal Health Coverage (UHC).
