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Oleanolic acid (OA) is a pentacyclic triterpene with reported protective effects against various diseases, including diabetes, hepatitis, and different cancers. However, the effects of OA on obesity‐induced muscle atrophy remain largely unknown. This study investigated the effects of OA on skeletal muscle production and proliferation of C2C12 cells. We report that OA significantly increased skeletal muscle mass and improved glucose intolerance and insulin resistance. OA inhibited dexamethasone (Dex)‐induced muscle atrophy in C2C12 myoblasts by regulating the PI3K/Akt signaling pathway. In addition, it also inhibited expression of MuRF1 and Atrogin1 genes in skeletal muscle of obese mice suffering from muscle atrophy, and increased the activation of PI3K and Akt, thereby promoting protein synthesis, and eventually alleviating muscle atrophy. Taken together, these findings suggest OA may have potential for the prevention and treatment of muscle atrophy. [ABSTRACT FROM AUTHOR]

Obesity is a common chronic metabolic disease that induces chronic systemic inflammation in the body, eventually leading to related complications such as insulin resistance (IR), type 2 diabetes mellitus, and metabolic syndromes such as cardiovascular disease. Exosomes transfer bioactive substances to neighboring or distal cells through autosomal, paracrine, or distant secretion, regulating the gene and protein expression levels of receptor cells. In this study, we investigated the effect of mouse bone marrow mesenchymal stem cell‐derived exosomes (BMSC‐Exos) on high‐fat diet obese mice and mature 3T3‐L1 adipocyte models of IR. BMSC‐Exo treatment of obese mice promoted their metabolic homeostasis, including reduction of obesity, inhibition of M1‐type proinflammatory factor expression, and improvement of insulin sensitivity. In vitro analysis revealed that BMSC‐Exos improved IR and lipid droplet accumulation in mature 3T3‐L1 adipocytes treated with palmitate (PA). Mechanistically, BMSC‐Exos cause increased glucose uptake and improved IR in high‐fat chow‐fed mice and PA‐acting 3T3‐L1 adipocytes by activating the phosphoinositide 3‐kinases/protein kinase B (PI3K/AKT) signaling pathway and upregulating glucose transporter protein 4 (GLUT4) expression. This study offers a new perspective for the development of treatments for IR in obese and diabetic patients. [ABSTRACT FROM AUTHOR]

Background Artificial intelligence (AI) models have improved US assessment of thyroid nodules; however, the lack of generalizability limits the application of these models. Purpose To develop AI models for segmentation and classification of thyroid nodules in US using diverse data sets from nationwide hospitals and multiple vendors, and to measure the impact of the AI models on diagnostic performance. Materials and Methods This retrospective study included consecutive patients with pathologically confirmed thyroid nodules who underwent US using equipment from 12 vendors at 208 hospitals across China from November 2017 to January 2019. The detection, segmentation, and classification models were developed based on the subset or complete set of images. Model performance was evaluated by precision and recall, Dice coefficient, and area under the receiver operating characteristic curve (AUC) analyses. Three scenarios (diagnosis without AI assistance, with freestyle AI assistance, and with rule-based AI assistance) were compared with three senior and three junior radiologists to optimize incorporation of AI into clinical practice. Results A total of 10 023 patients (median age, 46 years [IQR 37-55 years]; 7669 female) were included. The detection, segmentation, and classification models had an average precision, Dice coefficient, and AUC of 0.98 (95% CI: 0.96, 0.99), 0.86 (95% CI: 0.86, 0.87), and 0.90 (95% CI: 0.88, 0.92), respectively. The segmentation model trained on the nationwide data and classification model trained on the mixed vendor data exhibited the best performance, with a Dice coefficient of 0.91 (95% CI: 0.90, 0.91) and AUC of 0.98 (95% CI: 0.97, 1.00), respectively. The AI model outperformed all senior and junior radiologists ( P < .05 for all comparisons), and the diagnostic accuracies of all radiologists were improved ( P < .05 for all comparisons) with rule-based AI assistance. Conclusion Thyroid US AI models developed from diverse data sets had high diagnostic performance among the Chinese population. Rule-based AI assistance improved the performance of radiologists in thyroid cancer diagnosis. © RSNA, 2023 Supplemental material is available for this article.