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Brown and beige adipocytes dissipate energy as heat. Thus, the activation of brown adipocytes and the emergence of beige adipocytes in white adipose tissue (WAT) are suggested to be useful for preventing and treating obesity. Although β -adrenergic receptor activation is known to stimulate lipolysis and activation of brown and beige adipocytes, fat depot-dependent changes in metabolite concentrations are not fully elucidated. The current study examined the effect of treatment with CL-316,243, a β -adrenergic receptor agonist, on the relative abundance of metabolites in interscapular brown adipose tissue (iBAT), inguinal WAT (ingWAT), and epididymal WAT (epiWAT). Intraperitoneal injection of CL-316,243 (1 mg/kg) for 3 consecutive days increased the relative abundance of several glycolysis-related metabolites in all examined fat depots. The cellular concentrations of metabolites involved in the citric acid cycle and of free amino acids were also increased in epiWAT by CL-316,243. CL-316,243 increased the expression levels of several enzymes and transporters related to glucose metabolism and amino acid catabolism in ingWAT and iBAT but not in epiWAT. CL-316,243 also induced the emergence of more beige adipocytes in ingWAT than in epiWAT. Furthermore, adipocytes surrounded by macrophages were detected in the epiWAT of mice given CL-316,243. The current study reveals the fat depot-dependent modulation of cellular metabolites in CL-316,243-treated mice, presumably resulting from differential regulation of cell metabolism in different cell populations.

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Background This study aimed to (1) develop a fully residual deep convolutional neural network (CNN)-based segmentation software for computed tomography image segmentation of the male pelvic region and (2) demonstrate its efficiency in the male pelvic region. Methods A total of 470 prostate cancer patients who had undergone intensity-modulated radiotherapy or volumetric-modulated arc therapy were enrolled. Our model was based on FusionNet, a fully residual deep CNN developed to semantically segment biological images. To develop the CNN-based segmentation software, 450 patients were randomly selected and separated into the training, validation and testing groups (270, 90, and 90 patients, respectively). In Experiment 1, to determine the optimal model, we first assessed the segmentation accuracy according to the size of the training dataset (90, 180, and 270 patients). In Experiment 2, the effect of varying the number of training labels on segmentation accuracy was evaluated. After determining the optimal model, in Experiment 3, the developed software was used on the remaining 20 datasets to assess the segmentation accuracy. The volumetric dice similarity coefficient (DSC) and the 95th-percentile Hausdorff distance (95%HD) were calculated to evaluate the segmentation accuracy for each organ in Experiment 3. Results In Experiment 1, the median DSC for the prostate were 0.61 for dataset 1 (90 patients), 0.86 for dataset 2 (180 patients), and 0.86 for dataset 3 (270 patients), respectively. The median DSCs for all the organs increased significantly when the number of training cases increased from 90 to 180 but did not improve upon further increase from 180 to 270. The number of labels applied during training had a little effect on the DSCs in Experiment 2. The optimal model was built by 270 patients and four organs. In Experiment 3, the median of the DSC and the 95%HD values were 0.82 and 3.23 mm for prostate; 0.71 and 3.82 mm for seminal vesicles; 0.89 and 2.65 mm for the rectum; 0.95 and 4.18 mm for the bladder, respectively. Conclusions We have developed a CNN-based segmentation software for the male pelvic region and demonstrated that the CNN-based segmentation software is efficient for the male pelvic region.

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