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Methionine and choline both are essential nutrients which are needed for methyl group metabolism. A methionine-choline-deficient (MCD) diet leads to pathological changes in the kidney. The mechanism of the MCD diet is complex, and fundamental research is still required to provide a better understanding of the driving forces behind it. We evaluated the regional effects of the MCD diet on the metabolites of mouse kidney tissue using desorption electrospray ionization mass spectrometry imaging technology. A total of 20, 17, and 13 metabolites were significantly changed in the cortex, outer medulla, and inner medulla, respectively, of the mouse kidney tissue after the administration of the MCD diet. Among the discriminating metabolites, only three metabolites (guanidoacetic acid, serine, and nicotinamide riboside) were significantly increased, and all the other metabolites showed a significant decrease. The results showed that there were significant region-specific changes in the serine metabolism, carnitine metabolism, choline metabolism, and arginine metabolism. This study presents unique regional metabolic data, providing a more comprehensive understanding of the molecular characteristics of the MCD diet in the kidney. [ABSTRACT FROM AUTHOR]

Objectives: This study aims to decrease the scan time and enhance image quality in pediatric total-body PET imaging by utilizing multimodal artificial intelligence techniques. Methods: A total of 270 pediatric patients who underwent total-body PET/CT scans with a uEXPLORER at the Sun Yat-sen University Cancer Center were retrospectively enrolled. 18F-fluorodeoxyglucose (18F-FDG) was administered at a dose of 3.7 MBq/kg with an acquisition time of 600 s. Short-term scan PET images (acquired within 6, 15, 30, 60 and 150 s) were obtained by truncating the list-mode data. A three-dimensional (3D) neural network was developed with a residual network as the basic structure, fusing low-dose CT images as prior information, which were fed to the network at different scales. The short-term PET images and low-dose CT images were processed by the multimodal 3D network to generate full-length, high-dose PET images. The nonlocal means method and the same 3D network without the fused CT information were used as reference methods. The performance of the network model was evaluated by quantitative and qualitative analyses. Results: Multimodal artificial intelligence techniques can significantly improve PET image quality. When fused with prior CT information, the anatomical information of the images was enhanced, and 60 s of scan data produced images of quality comparable to that of the full-time data. Conclusion: Multimodal artificial intelligence techniques can effectively improve the quality of pediatric total-body PET/CT images acquired using ultrashort scan times. This has the potential to decrease the use of sedation, enhance guardian confidence, and reduce the probability of motion artifacts. [ABSTRACT FROM AUTHOR]