A leave-p-out cross-validation scheme was used to train and evaluate seven machine learning classifiers logistic regression (LR), k-nearest neighbour (KNN), support vector machine (SVM), multilayer perceptron (MLP), convolutional neural network (CNN), long short-term memory (LSTM) and a residual-based neural network architecture (Resnet50). Our results show that although all classifiers were able to identify COVID-19 coughs, the best performance was exhibited by the Resnet50 classifier, which was best able to discriminate between the COVID-19 positive and the healthy coughs with an area under the ROC curve (AUC) of 0.98. An LSTM classifier was best able to discriminate between the COVID-19 positive and COVID-19 negative coughs, with an AUC of 0.94 after selecting the best 13 features from a sequential forward selection (SFS). Since this type of cough audio classification is cost-effective and easy to deploy, it is potentially a useful and viable means of non-contact COVID-19 screening.Computer Tomography (CT) detection can effectively overcome the problems of traditional detection of Corona Virus Disease 2019 (COVID-19), such as lagging detection results and wrong diagnosis results, which lead to the increase of disease infection rate and prevalence rate. The novel coronavirus pneumonia is a significant difference between the positive and negative patients with asymptomatic infections. To effectively improve the accuracy of doctors’ manual judgment of positive and negative COVID-19, this paper proposes a deep classification network model of the novel coronavirus pneumonia based on convolution and deconvolution local enhancement. Through convolution and deconvolution operation, the contrast between the local lesion region and the abdominal cavity of COVID-19 is enhanced. Besides, the middle-level features that can effectively distinguish the image types are obtained. By transforming the novel coronavirus detection problem into the region of interest (ROI) feature classification problem, it can effectively determine whether the feature vector in each feature channel contains the image features of COVID-19. This paper uses an open-source COVID-CT dataset provided by Petuum researchers from the University of California, San Diego, which is collected from 143 novel coronavirus pneumonia patients and the corresponding features are preserved. The complete dataset (including original image and enhanced image) contains 1460 images. Among them, 1022 (70%) and 438 (30%) are used to train and test the performance of the proposed model, respectively. The proposed model verifies the classification precision in different convolution layers and learning rates. Besides, it is compared with most state-of-the-art models. It is found that the proposed algorithm has good classification performance. The corresponding sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and precision are 0.98, 0.96, 0.98, and 0.97, respectively.Dimensionality reduction or Feature Selection (FS) is a multi-target optimization problem with two goals improving the classification efficiency while simultaneously dropping the characteristics. Harris Hawk Optimization (HHO) is introduced recently to solve different demanding optimization tasks as a metaheuristic tool. The initial HHO is for addressing optimization problems in a continuous environment, but FS is an optimization task in binary space. Therefore, in this article, a Multi-Objective Quadratic Binary HHO (MOQBHHO) technique with K-Nearest Neighbor (KNN) method as wrapper classifier is implemented for extracting the optimal feature subsets. Finally, this study uses the crowding distance (CD) value as a third criterion for picking the best one from the non-dominated solutions. Here, to estimate the performance of the proposed approach, twelve standard medical datasets are considered. The proposed MOQBHHO is compared with MOBHHO-S (using a sigmoid function), multi-objective genetic algorithm (MOGA), multi-objective ant lion optimization (MOALO), and NSGA-II. The experimental findings show that the proposed MOQBHHO finds a set of non-dominated feature subsets effectively in contrast to deep-based FS methods Auto-encoder and Teacher-Student based FS (TSFS). The presented methodology is found superior in obtaining the best trade-off between two fitness assessment criteria compared to the other existing multi-objective techniques for recognizing relevant features.With the goal of establishing a new clinically-relevant bioscaffold format to enable the delivery of high densities of human adipose-derived stromal cells (ASCs) for applications in soft tissue regeneration, a novel “cell-assembly” method was developed to generate robust 3-D scaffolds comprised of fused networks of decellularized adipose tissue (DAT)-derived beads. Tivantinib In vitro studies confirmed that the assembly process was mediated by remodelling of the extracellular matrix by the seeded ASCs, which were well distributed throughout the scaffolds and remained highly viable after 8 days in culture. The ASC density, sulphated glycosaminoglycan content and scaffold stability were enhanced under culture conditions that included growth factor preconditioning. In vivo testing was performed to compare ASCs delivered within the cell-assembled DAT bead foams to an equivalent number of ASCs delivered on a previously-established pre-assembled DAT bead foam platform in a subcutaneous implant model in athymic nude mice. Scaffolds were fabricated with human ASCs engineered to stably co-express firefly luciferase and tdTomato to enable long-term cell tracking. Longitudinal bioluminescence imaging showed a significantly stronger signal associated with viable human ASCs at timepoints up to 7 days in the cell-assembled scaffolds, although both implant groups were found to retain similar densities of human ASCs at 28 days. Notably, the infiltration of CD31+ murine endothelial cells was enhanced in the cell-assembled implants at 28 days. Moreover, microcomputed tomography angiography revealed that there was a marked reduction in vascular permeability in the cell-assembled group, indicating that the developing vascular network was more stable in the new scaffold format. Overall, the novel cell-assembled DAT bead foams represent a promising platform to harness the pro-regenerative paracrine functionality of human ASCs and warrant further investigation as a clinically-translational approach for volume augmentation.