Gadolinium-based contrast agents (GBCAs) have been used to improve image quality of MRI examinations for decades and have an excellent overall safety record. However, there are well-documented risks associated with GBCAs and our understanding and management of these risks continue to evolve. The purpose of this review is to discuss the safety of GBCAs used in MRI in adult and pediatric populations. We focus particular attention on acute adverse reactions, nephrogenic systemic fibrosis and gadolinium deposition. We also discuss the non-GBCA MRI contrast agent ferumoxytol, which is increasing in use and has its own risk profile. Finally, we identify special populations at higher risk of harm from GBCA administration.
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http://dx.doi.org/10.1007/s00247-020-04896-7 | DOI Listing |
Adv Clin Exp Med
January 2025
Department of Dermatology, The Affiliated Hospital to Changchun University of Chinese Medicine, China.
Background: The skin, with its robust structural integrity and advanced immune defense system, serves as a critical protective barrier against environmental toxins and carcinogenic compounds. Despite this, it remains vulnerable to the harmful effects of certain hazardous agents.
Objectives: This study aimed to investigate the chemopreventive potential of β-caryophyllene (BCP) in mitigating 7,12-dimethylbenz[a]anthracene (DMBA)-induced skin carcinogenesis, focusing on the modulation of apoptosis and PI3K/AKT signaling pathways.
CNS Neurosci Ther
January 2025
Department of Radiology, Affiliated Hangzhou First People's Hospital, Westlake University School of Medicine, Hangzhou, China.
Aims: To develop a transformer-based generative adversarial network (trans-GAN) that can generate synthetic material decomposition images from single-energy CT (SECT) for real-time detection of intracranial hemorrhage (ICH) after endovascular thrombectomy.
Materials: We retrospectively collected data from two hospitals, consisting of 237 dual-energy CT (DECT) scans, including matched iodine overlay maps, virtual noncontrast, and simulated SECT images. These scans were randomly divided into a training set (n = 190) and an internal validation set (n = 47) in a 4:1 ratio based on the proportion of ICH.
J Phys Chem B
January 2025
College of Chemistry, Beijing Normal University, Beijing 100875, P. R. China.
Under conditions that are close to the real cellular environment, the human telomeric single-stranded overhang (∼200 nt) consisting of tens of TTAGGG repeats tends to form higher order structures of multiple G-quadruplex (G4) blocks. On account of the higher biological relevance of higher order G4 structures, ligand compounds binding to higher order G4 are significant for the drug design toward inhibiting telomerase activity. Here, we study the interaction between a cationic porphyrin derivative, 5,10,15,20-tetra{4-[2-(1-methyl-1-piperidinyl)propoxy]phenyl}porphyrin (T4), and a human telomeric G4-dimer (AG(TAG)) in the mimic intracellular molecularly crowded environment (PEG as a crowding agent) and K or Na solution (i.
View Article and Find Full Text PDFTomography
January 2025
Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea.
Objectives: To evaluate the effectiveness of breast MRI, including diffusion-weighted imaging (DWI), in detecting residual lesions in patients with malignancy after excisional biopsy.
Methods: From January 2018 to December 2023, 3T breast MRI was performed to assess lesion morphology, residual size, and enhancement kinetics. The apparent diffusion coefficient (ADC) values were measured, and the diagnostic outcomes of CE-MRI, CE-MRI with DWI, mammography (MG), and ultrasound (US) were compared with clinical and histopathological data.
Tomography
January 2025
Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA.
Objectives: Accurate kidney and tumor segmentation of computed tomography (CT) scans is vital for diagnosis and treatment, but manual methods are time-consuming and inconsistent, highlighting the value of AI automation. This study develops a fully automated AI model using vision transformers (ViTs) and convolutional neural networks (CNNs) to detect and segment kidneys and kidney tumors in Contrast-Enhanced (CECT) scans, with a focus on improving sensitivity for small, indistinct tumors.
Methods: The segmentation framework employs a ViT-based model for the kidney organ, followed by a 3D UNet model with enhanced connections and attention mechanisms for tumor detection and segmentation.
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