Objective To investigate the proper depth of muscle relaxant without affecting elec-trophysiological monitoring in patients undergoing posterior fossa surgery under total intravenous an-esthesia (TIVA).Methods Forty patients selected for posterior fossa surgery were randomly divided into two groups A and B.Group A:no muscle relaxants were administered after anesthesia induction until the EMG was completed.Group B:muscle relaxants maintained in range of TOF 2%-25% dur-ing electrophysiological monitor.TIVA were used to all patients and BIS was maintained in range of 40-60.Two electrophysiological data were applied during the electrophysiological monitoring:sponta-neity EMG and evoked EMG.EMG amplitude was recorded at different TOF value.Operation time and the dosages of propofol,sufentanil,rocuronium bromide were recorded.Also,the SBP,DBP,HR at perioperational period and facial nerve function before surgery,one week and six months after sur-gery were recorded.Results The electrophysiological monitoring completed successfully in all pa-tients.The total amount of propofol in group A significantly increased than those in group B(P <0.05).Three patients in group A had body movement during the operation.Four cases in group B couldn??t perform electrophysiological monitor when the TOF had three signals.There were no differ-ence between the two groups in SBP,DBP,HR and facial nerve function.Conclusion Maintaining TOF value in range of 2%-25% under non-depolarizing muscle relaxant during CPA surgery doesn??t affect intraoperative nerve electrophysiological monitor and prevent body movement,total dose of sed-ative anesthetics is also decresed.

Objective: To understand weekend screen time of preschool children in Nanjing, Jiangsu and to explore its association with parental factors,aiming to provide effective strategies for screen time intervention. Methods: A questionnaire survey was conducted in 6 424 parents of preschool children in 20 kindergartens selected using convenience samping method in Nanjing to understand the association of screen time during weekends and parental factors. Results: The proportion of preschool children in Nanjing with screen time ≥2 h/d was 13.01%, among which the proportion of boys was 14.61%, and girls was 11.34%. The difference was statistically significant(χ2=15.27, P<0.01). Logistic regression analysis showed that there was a positive correlation between 3.5-6 years old, boys, paternal education lower than junior high school or equal to senior high school, and no exercise habits with children’s screen time ≥2 h/d(OR=1.43, 1.42, 2.21, 1.82, 2.47, 2.05, 1.36, 1.38, 1.39, 1.52, P<0.05). Conclusion The video screen time of 3-6 year old preschool children in Nanjing is at moderate level. Parents’ exercise habits and education shows impact on screen time of preschool children, and should be given attention and support.

In order to ensure normal body function, the human body is dependent on a tight control of its blood glucose levels. This is accomplished by a highly sophisticated network of various hormones and neuropeptides released mainly from the brain, pancreas, liver, intestine as well as adipose and muscle tissue. Within this network, the pancreas represents a key player by secreting the blood sugar-lowering hormone insulin and its opponent glucagon. However, disturbances in the interplay of the hormones and peptides involved may lead to metabolic disorders such as type 2 diabetes mellitus (T2DM) whose prevalence, comorbidities and medical costs take on a dramatic scale. Therefore, it is of utmost importance to uncover and understand the mechanisms underlying the various interactions to improve existing anti-diabetic therapies and drugs on the one hand and to develop new therapeutic approaches on the other. This review summarizes the interplay of the pancreas with various other organs and tissues that maintain glucose homeostasis. Furthermore, anti-diabetic drugs and their impact on signaling pathways underlying the network will be discussed.
Blood Glucose ; Brain ; Comorbidity ; Diabetes Mellitus, Type 2 ; Glucagon ; Glucose* ; Hand ; Homeostasis* ; Human Body ; Insulin ; Intestines ; Liver ; Neuropeptides ; Pancreas ; Peptides ; Prevalence

BACKGROUND: Sarcopenia is an age-related progressive skeletal muscle disorder involving the loss of muscle mass or strength and physiological function. Efficient and precise AI algorithms may play a significant role in the diagnosis of sarcopenia. In this study, we aimed to develop a machine learning model for sarcopenia diagnosis using clinical characteristics and laboratory indicators of aging cohorts. METHODS: We developed models of sarcopenia using the baseline data from the West China Health and Aging Trend (WCHAT) study. For external validation, we used the Xiamen Aging Trend (XMAT) cohort. We compared the support vector machine (SVM), random forest (RF), eXtreme Gradient Boosting (XGB), and Wide and Deep (W&D) models. The area under the receiver operating curve (AUC) and accuracy (ACC) were used to evaluate the diagnostic efficiency of the models. RESULTS: The WCHAT cohort, which included a total of 4057 participants for the training and testing datasets, and the XMAT cohort, which consisted of 553 participants for the external validation dataset, were enrolled in this study. Among the four models, W&D had the best performance (AUC = 0.916 ± 0.006, ACC = 0.882 ± 0.006), followed by SVM (AUC =0.907 ± 0.004, ACC = 0.877 ± 0.006), XGB (AUC = 0.877 ± 0.005, ACC = 0.868 ± 0.005), and RF (AUC = 0.843 ± 0.031, ACC = 0.836 ± 0.024) in the training dataset. Meanwhile, in the testing dataset, the diagnostic efficiency of the models from large to small was W&D (AUC = 0.881, ACC = 0.862), XGB (AUC = 0.858, ACC = 0.861), RF (AUC = 0.843, ACC = 0.836), and SVM (AUC = 0.829, ACC = 0.857). In the external validation dataset, the performance of W&D (AUC = 0.970, ACC = 0.911) was the best among the four models, followed by RF (AUC = 0.830, ACC = 0.769), SVM (AUC = 0.766, ACC = 0.738), and XGB (AUC = 0.722, ACC = 0.749). CONCLUSIONS: The W&D model not only had excellent diagnostic performance for sarcopenia but also showed good economic efficiency and timeliness. It could be widely used in primary health care institutions or developing areas with an aging population. TRIAL REGISTRATION Chictr.org, ChiCTR 1800018895.
Humans ; Aged ; Sarcopenia/diagnosis* ; Deep Learning ; Aging ; Algorithms ; Biomarkers