Type 2 diabetes mellitus (T2DM) is characterized by hyperglycemia and dyslipidemia. Carvacrol (CAR) has demonstrated the potential to mitigate dyslipidemia. This study aims to investigate whether CAR can modulate blood glucose and lipid levels in a T2DM rat model by regulating short-chain fatty acids (SCFAs) and the GPR41/43 pathway. The T2DM rat model was induced by a high-fat diet combined with low-dose streptozocin injection and treated with oral CAR and/or mixed antibiotics. Fasting blood glucose, oral glucose tolerance, and insulin tolerance tests were assessed. Serum lipid parameters, hepatic and renal function indicators, tissue morphology, and SCFAs were measured. In vitro, high glucose (HG)-induced IEC-6 cells were treated with CAR, and optimal CAR concentration was determined. HG-induced IEC-6 cells were treated with SCFAs or/and GPR41/43 agonists. CAR significantly reduced blood lipid and glucose levels, improved tissue damage, and increased SCFA levels in feces and GPR41/43 expression in colonic tissues of T2DM rats. CAR also attenuated HG-induced apoptosis of IEC-6 cells and enhanced GPR41/43 expression.Overall, these findings suggest that CAR alleviates blood lipid and glucose abnormalities in T2DM rats by modulating SCFAs and the GPR41/43 pathway.

OBJECTIVETo explore the relationship between the time after thrombosis and the efficacy of combined ultrasound and microbubble treatment for rescuing the ischemic tissues.

METHODSRat models of thrombosis in the right common iliac artery were established and received combined ultrasound and microbubble treatment at 3, 6 and 12 h after thrombosis. The recanalization rate of the right common iliac artery was assessed using both 2-dimensional and Doppler ultrasound. The plateau acoustic intensity (AI) was quantified for estimating the skeletal microvascular blood volume, and skeletal muscle injury markers including myoglobin (Mb) and creatinine kinase (CK) were measured using ELISA. Postmortem TUNEL staining was used to detect the apoptotic rate of skeletal muscle cells in the hind limb of the rats.

RESULTSCompared with those in 3 h group, the recanalization rate and AI were significantly lower, and the levels of Mb and CK and the apoptotic rate of the skeletal muscle cells were significantly higher in both 6 h group and 12 h group ( < 0.05). Compared with those in 6 h group, the rats receiving treatment at 12 h after thrombosis showed significantly lowered AI and increased Mb, CK and apoptotic rate of the skeletal muscle cells ( < 0.05).

CONCLUSIONSThe efficacy of combined ultrasound and microbubble treatment for rescuing ischemic tissues tends to be attenuated as the time after thrombosis prolongs in rats.

Objective:To evaluate artificial intelligence constructed by deep convolutional neural network (DCNN) for the site identification in upper gastrointestinal endoscopy.Methods:A total of 21 310 images of esophagogastroduodenoscopy from the Cancer Hospital of Chinese Academy of Medical Sciences from January 2019 to June 2021 were collected. A total of 19 191 images of them were used to construct site identification model, and the remaining 2 119 images were used for verification. The performance differences of two models constructed by DCCN in the identification of 30 sites of the upper digestive tract were compared. One model was the traditional ResNetV2 model constructed by Inception-ResNetV2 (ResNetV2), the other was a hybrid neural network RESENet model constructed by Inception-ResNetV2 and Squeeze-Excitation Networks (RESENet). The main indices were the accuracy, the sensitivity, the specificity, positive predictive value (PPV) and negative predictive value (NPV).Results:The accuracy, the sensitivity, the specificity, PPV and NPV of ResNetV2 model in the identification of 30 sites of the upper digestive tract were 94.62%-99.10%, 30.61%-100.00%, 96.07%-99.56%, 42.26%-86.44% and 97.13%-99.75%, respectively. The corresponding values of RESENet model were 98.08%-99.95%, 92.86%-100.00%, 98.51%-100.00%, 74.51%-100.00% and 98.85%-100.00%, respectively. The mean accuracy, mean sensitivity, mean specificity, mean PPV and mean NPV of ResNetV2 model were 97.60%, 75.58%, 98.75%, 63.44% and 98.76%, respectively. The corresponding values of RESENet model were 99.34% ( P<0.001), 99.57% ( P<0.001), 99.66% ( P<0.001), 90.20% ( P<0.001) and 99.66% ( P<0.001). Conclusion:Compared with the traditional ResNetV2 model, the artificial intelligence-assisted site identification model constructed by RESENNet, a hybrid neural network, shows significantly improved performance. This model can be used to monitor the integrity of the esophagogastroduodenoscopic procedures and is expected to become an important assistant for standardizing and improving quality of the procedures, as well as an significant tool for quality control of esophagogastroduodenoscopy.