Pleural effusion or mediastinal lymphadenopathy is rarely seen. In CT imaging, COVID-19 manifests differently in its various stages including the early stage, the progression (consolidation) stage, and the absorption stage. In its early stage, it manifests as scattered flaky GGOs in various sizes, dominated by peripheral pulmonary zone/subpleural distributions. In the progression state, GGOs increase in number and/or size, and lung consolidations may become visible. The main manifestation in the absorption stage is interstitial change of both lungs, such as fibrous cords and reticular opacities. Differentiation between COVID-19 pneumonia and other viral pneumonias are also analyzed. Thus, CT examination can help reduce false negatives of nucleic acid tests.Meningioma is among the most common primary tumors of the brain. The firmness of Meningioma is a critical factor that influences operative strategy and patient counseling. Conventional methods to predict the tumor firmness rely on the correlation between the consistency of Meningioma and their preoperative MRI findings such as the signal intensity ratio between the tumor and the normal grey matter of the brain. Machine learning techniques have not been investigated yet to address the Meningioma firmness detection problem. The main purpose of this research is to couple supervised learning algorithms with typical descriptors for developing a computer-aided detection (CAD) of the Meningioma tumor firmness in MRI images. Specifically, Local Binary Patterns (LBP), Gray Level Co-occurrence Matrix (GLCM) and Discrete Wavelet Transform (DWT) are extracted from real labeled MRI-T2 weighted images and fed into classifiers, namely support vector machine (SVM) and k-nearest neighbor (KNN) algorithm to learn association between the visual properties of the region of interest and the pre-defined firm and soft classes. The learned model is then used to classify unlabeled MRI-T2 weighted images. This paper represents a baseline comparison of different features used in CAD system that intends to accurately recognize the Meningioma tumor firmness. The proposed system was implemented and assessed using a clinical dataset. Using LBP feature yielded the best performance with 95% of F-score, 87% of balanced accuracy and 0.87 of the area under ROC curve (AUC) when coupled with KNN classifier, respectively.Objective To evaluate the utility of radiomics analysis for differentiating benign and malignant epithelial salivary gland tumors on diffusion-weighted imaging (DWI). Methods A retrospective dataset involving 218 and 51 patients with histology-confirmed benign and malignant epithelial salivary gland tumors was used in this study. A total of 396 radiomic features were extracted from the DW images. Analysis of variance (ANOVA) and least-absolute shrinkage and selection operator regression (LASSO) were used to select optimal radiomic features. The selected features were used to build three classification models namely, logistic regression method (LR), support vector machine (SVM), and K-nearest neighbor (KNN) by using a five-fold cross validation strategy on the training dataset. The diagnostic performance of each classification model was quantified by receiver operating characteristic (ROC) curve and area under the ROC curve (AUC) in the training and validation datasets.  https://www.selleckchem.com/products/CP-690550.html  Results Eight most valuable features were selected by LASSO. LR and SVM models yielded optimally diagnostic performance. In the training dataset, LR and SVM yielded AUC values of 0.886 and 0.893 via five-fold cross validation, respectively, while KNN model showed relatively lower AUC (0.796). In the testing dataset, a similar result was found, where AUC values for LR, SVM, and KNN were 0.876, 0.870, and 0.791, respectively. Conclusions Classification models based on optimally selected radiomics features computed from DW images present a promising predictive value in distinguishing benign and malignant epithelial salivary gland tumors and thus have potential to be used for preoperative auxiliary diagnosis.Background Over time, scholars have invented various types of feeding robots to help patients with hand disabilities. However, most commercially available feeding robots are functionally simple or expensive. Objective The purpose of this study is to develop a cheap, multi-functional feeding robot with excellent performance to help disabled elderly eat independently. Methods Our feeding robot (called 'I-feed') uses human-computer interaction based on voice recognition. The feeding system we developed with a four-degree-of-freedom robotic arm is capable of completing the two tasks of food selection and feeding through speech recognition, but also simultaneously meets users' diverse needs with three bowls. We also designed a U-shaped table to adjust the height of the feeding robot. Results This newly developed feeding robot can not only select bowls with different foods by efficient voice commands, but also adapts to users of different heights through a U-shaped table with an adjustable height. Conclusions The experimental results show that the accuracy of speech recognition is excellent, and the robot arm can perform the corresponding tasks successfully.Background Flutter is a device used in removing excess lung secretions. The conventional flutter lacks a biofeedback component to facilitate optimal use by the patients. Objective The current research aims to compare the effects of biofeedback flutter devices with the conventional flutter in managing the symptoms of patients with chronic obstructive pulmonary diseases. Methods One hundred and sixty-eight participants were randomly allocated into four groups Group A (conventional), Group B (visual biofeedback), Group C (auditory biofeedback) and Group D (visual and auditory biofeedback). All groups were treated five days for 20 minutes. Outcome measures included wet sputum weight [during intervention (T1) and 1 hour after intervention (T2)], oxygen saturation and dyspnea score (before and after intervention) on all days. Results The wet sputum expectorated (T2) by Group B was significantly higher than Group A (P less then 0.001), Group C (P less then 0.001) and Group D (P less then 0.05). The dyspnea score for Group B (P less then 0.