The safety and efficacy of esketamine in comparison to dexmedetomidine during drug-induced sleep endoscopy in children with obstructive sleep apnea hypopnea syndrome: A randomized, controlled and prospective clinical trial.

Background and Purpose: Data and high-quality studies of anesthetic methods for children with obstructive sleep apnea hypopnea syndrome (OSAHS) who undergo drug-induced sleep endoscopy (DISE) are limited. Research on pediatric DISE using esketamine has never been reported before. To test the safety and efficacy of esketamine during DISE in children with OSAHS, we compare esketamine (Group K) with dexmedetomidine (Group D) in this study. Methods: 100 children with ASA Ⅰ∼Ⅱ grade, prepared for an elective adenotonsillectomy under general anesthesia, were enrolled in this study and randomized into two groups. Midazolam 0.1 mg/kg was administered intravenously for both groups. In Group D a 1 µg/kg bolus of dexmedetomidine was given over 10 min followed by the infusion rate 1 µg/kg/hr to the end of DISE. Group K received a 1.0 mg/kg IV bolus of esketamine over 10 s followed by the infusion rate 1 mg/kg/hr to the end of DISE. Results: Group K had a higher percentage of success than Group D (p = 0.008). The onset time of Group K was shorter than that of Group D (p = 0.000). The University of Michigan Sedation Scale (UMSS) score of Group K was higher than that of Group D (p = 0.005). The risk of adverse effects (AEs) was lower in Group K (p = 0.000). In Group D, systolic and diastolic blood pressure (SBP and DBP) and heart rate (HR) all decreased, while in Group K, SBP, DBP, and HR hardly changed. Conclusion: Esketamine in comparison to dexmedetomidine provides more effective and safer depth of anesthesia for OSAHS pediatric DISE by ensuring short onset time, deep sedation, and few AEs. Clinical Trial Registration: ClincalTrials.gov, identifier NCT04877639.

Application of Radiomics in Classification and Prediction of Benign and Malignant Lung Tumors / 中国医疗器械杂志

Aiming at the lack of quantitative evaluation methods in clinical diagnosis of lung cancer, a classification and prediction model of lung cancer based on Support Vector Machine (SVM) was constructed by using radiomics method. Firstly, the definition and processing flow of radiomics were introduced. The experimental samples were selected from 816 lung cancer patients on LIDC. Firstly, ROI was extracted by central pooling convolution neural network segmentation method. Then, Pyradiomics and FSelector feature selection models were used to extract features and reduce dimension. Finally, SVM was used to construct the classification and prediction model of lung tumors. The predictive accuracy of the model is 80.4% for the classification of benign and malignant pulmonary nodules larger than 5 mm, and the value of the area under the curve (AUC) is 0.792. This indicates that the SVM classifier model can accurately distinguish benign and malignant pulmonary nodules larger than 5 mm.