Effectiveness of parent-training program on children with autism spectrum disorder in China.

Objective: This study intends to explore the effect of parent-training program on the rehabilitation intervention in children with autism spectrum disorder (ASD) in Chinese-speaking areas of China by offering parent skill training and psychology counseling. Methods: From January 2018 to June 2019, a total of 80 children diagnosed with ASD from the Department of Children Healthcare of Wuxi Children's Hospital were randomly grouped into the parent training groups and control groups. Parents in the training group received 12 weeks of skill training, including 8 group and 2 individual training sessions, as well as psychology counseling. This enabled them to give their children >2 h of intervention training daily in a natural environment. Children in the control group were placed on a rehabilitation waiting list or received general community training. Before grouping and after the intervention, all children underwent neuropsychological evaluations with Autism Behavior Checklist (ABC), Childhood Autism Rating Scale (CARS), and Gesell Developmental Schedule (GDS). GDS covers five sectors, namely adaptive behavior, gross motor, fine motor, language, and personal-social behavior. Results: Statistically significant differences were not detected between the two groups in ABC, CARS, and GDS scoring at baseline evaluation. And significant differences were detected between the two groups in ABC, CARS, adaptive behavior, and personal-social behavior scoring at endpoint evaluation. Furthermore, the re-evaluation results of ABC scoring and CARS scoring of the children in the parent training group decreased significantly from the preliminary evaluation results when compared before and after the intervention. Moreover, the intragroup comparison of adaptive behavior scoring, language scoring, and personal-social behavior scoring of the experiment group increased significantly from the preliminary evaluation results, while the difference of the same of the children in the control group between re-evaluation and preliminary evaluation did not differ significantly. Conclusions: In China, the parent-training program enables parents to train ASD children in a natural environment, which would markedly improve behavioral problems, core symptoms, adaptability, language competence, and social development capability.

Deep Learning Analysis Using 18F-FDG PET/CT to Predict Occult Lymph Node Metastasis in Patients With Clinical N0 Lung Adenocarcinoma.

Introduction: The aim of this work was to determine the feasibility of using a deep learning approach to predict occult lymph node metastasis (OLM) based on preoperative FDG-PET/CT images in patients with clinical node-negative (cN0) lung adenocarcinoma. Materials and Methods: Dataset 1 (for training and internal validation) included 376 consecutive patients with cN0 lung adenocarcinoma from our hospital between May 2012 and May 2021. Dataset 2 (for prospective test) used 58 consecutive patients with cN0 lung adenocarcinoma from June 2021 to February 2022 at the same center. Three deep learning models: PET alone, CT alone, and combined model, were developed for the prediction of OLM. The performance of the models was evaluated on internal validation and prospective test in terms of accuracy, sensitivity, specificity, and areas under the receiver operating characteristic curve (AUCs). Results: The combined model incorporating PET and CT showed the best performance, achieved an AUC of 0.81 [95% confidence interval (CI): 0.61, 1.00] in the prediction of OLM in internal validation set (n = 60) and an AUC of 0.87 (95% CI: 0.75, 0.99) in the prospective test set (n = 58). The model achieved 87.50% sensitivity, 80.00% specificity, and 81.00% accuracy in the internal validation set and achieved 75.00% sensitivity, 88.46% specificity, and 86.60% accuracy in the prospective test set. Conclusion: This study presented a deep learning approach to enable the prediction of occult nodal involvement based on the PET/CT images before surgery in cN0 lung adenocarcinoma, which would help clinicians select patients who would be suitable for sublobar resection.

Deep Neural Network to Differentiate Brain Activity Between Patients With First-Episode Schizophrenia and Healthy Individuals: A Multi-Channel Near Infrared Spectroscopy Study.

Backgrounds: Reduced brain cortical activity over the frontotemporal regions measured by near infrared spectroscopy (NIRS) has been reported in patients with first-episode schizophrenia (FES). This study aimed to differentiate between patients with FES and healthy controls (HCs) on basis of the frontotemporal activity measured by NIRS with a support vector machine (SVM) and deep neural network (DNN) classifier. In addition, we compared the accuracy of performance of SVM and DNN. Methods: In total, 33 FES patients and 34 HCs were recruited. Their brain cortical activities were measured using NIRS while performing letter and category versions of verbal fluency tests (VFTs). The integral and centroid values of brain cortical activity in the bilateral frontotemporal regions during the VFTs were selected as features in SVM and DNN classifier. Results: Compared to HCs, FES patients displayed reduced brain cortical activity over the bilateral frontotemporal regions during both types of VFTs. Regarding the classifier performance, SVM reached an accuracy of 68.6%, sensitivity of 70.1%, and specificity of 64.6%, while DNN reached an accuracy of 79.7%, sensitivity of 88.8%, and specificity of 74.9% in the classification of FES patients and HCs. Conclusions: Compared to findings of previous structural neuroimaging studies, we found that using DNN to measure the NIRS signals during the VFTs to differentiate between FES patients and HCs could achieve a higher accuracy, indicating that NIRS can be used as a potential marker to classify FES patients from HCs. Future additional independent datasets are needed to confirm the validity of our model.