Objective: This study aimed to validate xenon-enhanced computed tomography (XECT) for the detection of brown adipose tissue (BAT) and to use XECT to assess differences in BAT distribution and perfusion between lean, obese, and diabetic nonhuman primates (NHPs).
Methods: Whole-body XECT imaging was performed in anesthetized rhesus and vervet monkeys during adrenergic stimulation of BAT thermogenesis. In XECT images, BAT was identified as fat tissue that, during xenon inhalation, underwent significant radiodensity enhancement compared with subcutaneous fat. To measure BAT blood flow, BAT radiodensity enhancement was measured over time on the six computed tomography scans acquired during xenon inhalation. Postmortem immunohistochemical staining was used to confirm imaging findings.
Results: XECT was able to correctly identify all BAT depots that were confirmed at necropsy, enabling construction of the first comprehensive anatomical map of BAT in NHPs. A significant decrease in BAT perfusion was found in diabetic animals compared with obese animals and healthy animals, as well as absence of axillary BAT and significant reduction of supraclavicular BAT in diabetic animals compared with obese and lean animals.
Conclusions: The use of XECT in NHP models of obesity and diabetes allows the analysis of the impact of metabolic status on BAT mass and perfusion.
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http://dx.doi.org/10.1002/oby.23519 | DOI Listing |
Acta Radiol
January 2025
Department of Medical Imaging, Dalin Tzu-Chi Hospital, Chiayi, Taiwan.
Background: The wide variability in thresholds on computed tomography (CT) perfusion parametric maps has led to controversy in the stroke imaging community about the most accurate measurement of core infarction.
Purpose: To investigate the feasibility of using U-Net to perform infarct core segmentation in CT perfusion imaging.
Material And Methods: CT perfusion parametric maps were the input of U-Net, while the ground truth segmentation was determined based on diffusion-weighted imaging (DWI).
Acta Radiol
January 2025
Department of Radiology, Changi General Hospital, Singapore, Republic of Singapore.
Background: Computed tomography (CT) is the gold standard imaging modality for the assessment of 3D bony morphology but incurs the cost of ionizing radiation exposure. High-resolution 3D magnetic resonance imaging (MRI) with CT-like bone contrast (CLBC) may provide an alternative to CT in allowing complete evaluation of both bony and soft tissue structures with a single MRI examination.
Purpose: To review the technical aspects of an optimized stack-of-stars 3D gradient recalled echo pulse sequence method (3D-Bone) in generating 3D MR images with CLBC, and to present a pictorial review of the utility of 3D-Bone in the clinical assessment of common musculoskeletal conditions.
Eur Heart J Digit Health
January 2025
Cardiovascular Center, Tufts Medical Center, 800 Washington Street, Boston, MA 02111, USA.
Aims: This study evaluates the performance of OpenAI's latest large language model (LLM), Chat Generative Pre-trained Transformer-4o, on the Adult Clinical Cardiology Self-Assessment Program (ACCSAP).
Methods And Results: Chat Generative Pre-trained Transformer-4o was tested on 639 ACCSAP questions, excluding 45 questions containing video clips, resulting in 594 questions for analysis. The questions included a mix of text-based and static image-based [electrocardiogram (ECG), angiogram, computed tomography (CT) scan, and echocardiogram] formats.
Front Immunol
January 2025
Department of General Surgery, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong, China.
Hepatoid adenocarcinoma of the stomach (HAS) is a rare subtype of gastric cancer characterized by histological features resembling hepatocellular carcinoma. Surgical intervention remains the preferred treatment modality for eligible patients. However, the efficacy of neoadjuvant therapy and alternative treatment regimens has been found to be suboptimal.
View Article and Find Full Text PDFObjectives: The pairing of immunotherapy and radiotherapy in the treatment of locally advanced nonsmall cell lung cancer (NSCLC) has shown promise. By combining radiotherapy with immunotherapy, the synergistic effects of these modalities not only bolster antitumor efficacy but also exacerbate lung injury. Consequently, developing a model capable of accurately predicting radiotherapy- and immunotherapy-related pneumonitis in lung cancer patients is a pressing need.
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