Objective Using maestro software to analyze the application of electrically elicited stapedius reflex and auditory nerve response in assessing acoustic function intraoperatively .Methods 20 SONATATI100cochlear im-plant patients participated in this study .Both ESRT and ECAP were recorded intraoperatively by using MED -EL Maestro software and analyzed .Results 96 .67% typical ESR and 95 .0% typical ECAP were recorded .Certain properties of ECAP recordings varied depending on the stimulation sites in the cochlea .There was strong relation-ship between ESRT and ECAP thresholds .Conclusion ESR and ART were proved to be most beneficial in assessing the functions of the implanted as well as proving that the auditory pathway is stimulated during the cochlear implan-tation surgery .

OBJECTIVE:To study the effects of Xinghuang tablets on tumor growth and immunologic function in S 180 sarcoma-bearing mice.METHODS:The model of mice with S 180 sarcoma were given Xinhuang tablets intragastrically to ob?serve the effects on the ratio of tumor to body,the inhibition rate of tumor,the indices of the spleen and thymus,and the rate of micronuclei in PCE in bone marrow of the mice.RESULTS:The body weight of the mice with S 180 sarcoma was decreased;the growth of the tumor was inhibited;the weight of spleen was increased;the number of leucocyte of peripheral blood was raised(P

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