An integrative method for COVID-19 patients classification from chest X-ray using deep learning network with image visibility graph as feature extractor

We propose a method by integrating image visibility graph and deep neural network (DL) for classifying COVID-19 patients from their chest X-ray images. The computed assortative coefficient from each image horizonal visibility graph (IHVG) is utilized as a physical parameter feature extractor to improve the accuracy of our image classifier based on Resnet34 convolutional neural network (CNN). We choose the most optimized recently used CNN deep learning model, Resnet34 for training the pre-processed chest X-ray images of COVID-19 and healthy individuals. Independently, the preprocessed X-ray images are passed through a 2D Haar wavelet filter that decomposes the image up to 3 labels and returns the approximation coefficients of the image which is used to obtain the horizontal visibility graph for each X-ray image of both healthy and COVID-19 cases. The corresponding assortative coefficients are computed for each IHVG and was subsequently used in random forest classifier whose output is integrated with Resnet34 output in a multi-layer perceptron to obtain the final improved prediction accuracy. We employed a multilayer perceptron to integrate the feature predictor from image visibility graph with Resnet34 to obtain the final image classification result for our proposed method. Our analysis employed much larger chest X-ray image dataset compared to previous used work. It is demonstrated that compared to Resnet34 alone our integrative method shows negligible false negative conditions along with improved accuracy in the classification of COVID-19 patients. Use of visibility graph in this model enhances its ability to extract various qualitative and quantitative complex network features for each image. Enables the possibility of building disease network model from COVID-19 images which is mostly unexplored. Our proposed method is found to be very effective and accurate in disease classification from images and is computationally faster as compared to the use of multimode CNN deep learning models, reported in recent research works.

Image3C, a multimodal image-based and label independent integrative method for single-cell analysis

Image-based cell classification has become a common tool to identify phenotypic changes in cell populations. However, this methodology is limited to organisms possessing well characterized species-specific reagents (e.g., antibodies) that allow cell identification, clustering and convolutional neural network (CNN) training. In the absence of such reagents, the power of image-based classification has remained mostly off-limits to many research organisms. We have developed an image-based classification methodology we named Image3C (Image-Cytometry Cell Classification) that does not require species-specific reagents nor pre-existing knowledge about the sample. Image3C combines image-based flow cytometry with an unbiased, high-throughput cell cluster pipeline and CNN integration. Image3C exploits intrinsic cellular features and non-species-specific dyes to perform de novo cell composition analysis and to detect changes in cellular composition between different conditions. Therefore, Image3C expands the use of imaged-based analyses of cell population composition to research organisms in which detailed cellular phenotypes are unknown or for which species-specific reagents are not available.

MOGONET integrates multi-omics data using graph convolutional networks allowing patient classification and biomarker identification

To fully utilize the advances in omics technologies and achieve a more comprehensive understanding of human diseases, novel computational methods are required for integrative analysis of multiple types of omics data. Here, we present a novel multi-omics integrative method named Multi-Omics Graph cOnvolutional NETworks (MOGONET) for biomedical classification. MOGONET jointly explores omics-specific learning and cross-omics correlation learning for effective multi-omics data classification. We demonstrate that MOGONET outperforms other state-of-the-art supervised multi-omics integrative analysis approaches from different biomedical classification applications using mRNA expression data, DNA methylation data, and microRNA expression data. Furthermore, MOGONET can identify important biomarkers from different omics data types related to the investigated biomedical problems.

Targeted Molecular Detection of Nosocomial Carbapenemase-Producing Gram-Negative Bacteria—On Near- and Distant-Patient Surfaces

Background: Here, we describe an integrative method to detect carbapenemase-producing Gram-negative bacteria (gn-Cp) on surfaces/fomites in the patient environment. We examined environmental samples from 28 patient rooms occupied with patients who were proven to be colonised with gn-Cp by rectal screening. Methods: We took samples after 24 h, 72 h and one week. For sampling, we divided the patient environment into four parts and took samples from near- and extended patient areas. To obtain a representative bacterial swab from a larger surface, such as the patient cabinet, we used Polywipes. Bacterial DNA was isolated. Carbapenemase was detected with specific qPCR primers. Results: With this culture- and molecular-based approach, we could control the effectiveness of cleaning and disinfection in everyday clinical practice. Therefore, we could track the spread of gn-Cp within the patient room. The number of positive detections fluctuated between 30.5% (mean value positive results after 72 h) and 35.2% (after 24 h and one week). Conclusion: The method used to detect multidrug-resistant bacteria in the environment of patients by using PolywipesTM is reliable and can therefore be used as an effective, new tool in hygiene and infection control.

Model of social protection for war veterans to improve their social well-being and health in the Russian Federation

The relevance of the studied problem is determined by the need to constantly improve the system of social protection of veterans in changing socio-cultural conditions to make it more consistent with the tasks and priorities of social and demographic policy and modern social threats and risks. The objective of the article is to study the Russian experience of social protection of war veterans and to develop practical recommendations for its improvement by updating the model of social protection of veterans to improve their social well-being and health. Research methods: systemic (integrative) method, assuming the need to analyze social, economic, legal, and other measures to support veterans and the elderly; expert survey; modelling. As a result of the study, the authors draw conclusions and offer recommendations on the improvement and implementation of the model of social protection of war veterans to improve their social well-being and health. The practical significance of the conducted study lies in the possibility of using the developed recommendations in the sphere of social policy and social work with veterans and the elderly, social gerontology, and practical activities of social protection institutions for senior citizens.

UNDERSTANDING OF ISLAMIC STUDIES THROUGH SHARIA ECONOMICS PERSPECTIVE IN INDONESIA

The existence of religions now required to be actively involved in solving the problems facing by mankind. One of the problems faced is the interaction with the modern economy. One of the approaches that can be developed for discussion of Islamic studies is Sharia Economics perspective. This research aims to describe the implementation of Sharia Economics perspective in order to understand Islamic studies. Research methods in this research use literature research from both national and international journals and books related to the topic. Ideally, the Sharia Economics approach should be integrative and interdisciplinary. Integrative method seeks to combine the revelation and ra’y in economic studies to understand Islam from the economic aspect. While the method of interdisciplinary attempt to approach it with a wide range of disciplines such as economics, politics, law, history, so that produce a comprehensive study and relevant to the happening facts/phenomena.