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Molecular and Cellular Mechanisms of Respiratory Syncytial Viral Infection: Using Murine Models to Understand Human Pathology
No full textAbstract: Respiratory syncytial virus (RSV) causes severe pathology of the lower respiratory tract in infants, immunocompromised people, and elderly. Despite decades of research, there is no licensed vaccine against RSV, and many therapeutic drugs are still under development. Detailed understanding of molecular and cellular mechanisms of the RSV infection pathology can accelerate the development of efficacious treatment. Current studies on the RSV pathogenesis are based on the analysis of biopsies from the infected patients; however deeper understanding of molecular and cellular mechanisms of the RSV pathology could be achieved using animal models. Mice are the most often used model for RSV infection because they exhibit manifestations similar to those observed in humans (bronchial obstruction, mucous hypersecretion, and pulmonary inflammation mediated by lymphocytes, macrophages, and neutrophils). Additionally, the use of mice is economically feasible, and many molecular tools are available for studying RSV infection pathogenesis at the molecular and cellular levels. This review summarizes new data on the pathogenesis of RSV infection obtained in mouse models, which demonstrated the role of T cells in both the antiviral defense and the development of lung immunopathology. T cells not only eliminate the infected cells, but also produce significant amounts of the proinflammatory cytokines TNFα and IFNγ. Recently, a new subset of tissue-resident memory T cells (T ) was identified that provide a strong antiviral defense without induction of lung immunopathology. These cells accumulate in the lungs after local rather than systemic administration of RSV antigens, which suggests new approaches to vaccination. The studies in mouse models have revealed a minor role of interferons in the anti-RSV protection, as RSV possesses mechanisms to escape the antiviral action of type I and III interferons, which may explain the low efficacy of interferon-containing drugs. Using knockout mice, a significant breakthrough has been achieved in understanding the role of many pro-inflammatory cytokines in lung immunopathology. It was found that in addition to TNFα and IFNγ, the cytokines IL-4, IL-5, IL-13, IL-17A, IL-33, and TSLP mediate the major manifestations of the RSV pathogenesis, such as bronchial obstruction, mucus hyperproduction, and lung infiltration by pro-inflammatory cells, while IL-6, IL-10, and IL-27 exhibit the anti-inflammatory effect. Despite significant differences between the mouse and human immune systems, mouse models have made a significant contribution to the understanding of molecular and cellular mechanisms of the pathology of human RSV infection. R
Actual directions of individual team training in team sports in context of coronavirus pandemic
No full textSelf-health analysis with two step histogram based procedure using machine learning
No full textMachine learning is the critical tool in the future for prediction in the real-time to analyze the self-health of the person. The self-health is the motivation for the patient who is suffering from different health issues and unaware of those because of not having the accurate backup or motivation. This article presents the two-step histogram-based procedure using machine learning where patient can get the idea on what's their current position in their health. The histogram methodology will be working in the two-stage mechanism which is the proposed methodology. The result of histogram methodology achieved 95% accuracy in identifying the selfhealth of the person. There will a user interface where he can communicate with the model by user inputs and the algorithm behind the submit button can analyse the self-health of the patient. There is a behavior for the patient to give the false inputs to the model and there is a risk analysis in the model which is an in build to analyse the accurate relativity of the inputs given by the patients to the application. The proposed method obtained 95% accuracy and the two-step histogram methodology can help the self to analyse their own health condition using machine learning models
CT-Based COVID-19 triage: Deep multitask learning improves joint identification and severity quantification
No full textThe current COVID-19 pandemic overloads healthcare systems, including radiology departments. Though several deep learning approaches were developed to assist in CT analysis, nobody considered study triage directly as a computer science problem. We describe two basic setups: Identification of COVID-19 to prioritize studies of potentially infected patients to isolate them as early as possible; Severity quantification to highlight patients with severe COVID-19, thus direct them to a hospital or provide emergency medical care. We formalize these tasks as binary classification and estimation of affected lung percentage. Though similar problems were well-studied separately, we show that existing methods could provide reasonable quality only for one of these setups. We employ a multitask approach to consolidate both triage approaches and propose a convolutional neural network to leverage all available labels within a single model. In contrast with the related multitask approaches, we show the benefit from applying the classification layers to the most spatially detailed feature map at the upper part of U-Net instead of the less detailed latent representation at the bottom. We train our model on approximately 1500 publicly available CT studies and test it on the holdout dataset that consists of 123 chest CT studies of patients drawn from the same healthcare system, specifically 32 COVID-19 and 30 bacterial pneumonia cases, 30 cases with cancerous nodules, and 31 healthy controls. The proposed multitask model outperforms the other approaches and achieves ROC AUC scores of 0.87±0.01 vs. bacterial pneumonia, 0.93±0.01 vs. cancerous nodules, and 0.97±0.01 vs. healthy controls in Identification of COVID-19, and achieves 0.97±0.01 Spearman Correlation in Severity quantification. We have released our code and shared the annotated lesions masks for 32 CT images of patients with COVID-19 from the test dataset
The Novel Coronavirus Infection (COVID-19) and Nervous System Involvement: Mechanisms of Neurological Disorders, Clinical Manifestations, and the Organization of Neurological Care
No full textThe new coronavirus SARS-CoV-2 and the disease it causes COVID-19 involves not only respiratory system damage, but can also lead to disorders of the central and peripheral nervous system, as well as the muscular system. This article presents published data and our own observations on the course of neurological disorders in COVID-19 patients. There is a relationship between the severity of COVID-19 and the severity and frequency of neurological manifestations. Severe neurological disorders are mostly seen in severe cases of COVID-19 and include acute cerebrovascular accidents (aCVA), acute necrotizing encephalopathy, and Guillain–Barré syndrome. Factors potentially complicating the course of COVID-19 and increasing the development of neurological complications include arterial hypertension, diabetes mellitus, and chronic cardiac and respiratory system diseases. Questions of the possible effects of human coronaviruses on the course of chronic progressive neurological diseases are addressed using multiple sclerosis (MS) as an example. We discuss the management of patients with aCVA and MS depending on the risk of developing coronavirus infection
Research of the Efficiency of Gender and Age Recognition According to an X-ray Image Based on a Neural Network
No full textThis article examines the effectiveness of gender and age recognition using a neural network. The search and analysis of existing methods of image recognition and selection of characteristics from them are performed, and similar studies are described. The description of the used model, the principle of its operation, training parameters, and the reason for choosing this model is produced. An experiment is being conducted on the effectiveness of gender and age recognition based on chest x-rays. To conduct the experiment, a neural network model is created based on the existing one. The obtained results are analyzed. Conclusions are drawn on the experiment and the possibility of further research
Scarred Lung. An Update on Radiation-Induced Pulmonary Fibrosis
No full textRadiation-induced pulmonary fibrosis is a common severe long-time complication of radiation therapy for tumors of the thorax. Current therapeutic options used in the clinic include only supportive managements strategies, such as anti-inflammatory treatment using steroids, their efficacy, however, is far from being satisfactory. Recent studies have demonstrated that the development of lung fibrosis is a dynamic and complex process, involving the release of reactive oxygen species, activation of Toll-like receptors, recruitment of inflammatory cells, excessive production of nitric oxide and production of collagen by activated myofibroblasts. In this review we summarized the current state of knowledge on the pathophysiological processes leading to the development of lung fibrosis and we also discussed the possible treatment options
Sustainable development of recreational areas in Nizhnevartovsk
No full textThis paper reflects an analysis of recreational areas in Nizhnevartovsk with an account of the recreational activities of the local population. The territory of Nizhnevartovsk is situated in hypocomfortable climatic conditions. The local environment is significantly affected by anthropogenic activities, and technogenic landscapes are prevailing. Therefore, it is required to analyze the recreational potential of the territory for the well-planned design of recreational areas and the preservation of outdoor recreational resources. The arrangement of the infrastructure of recreational areas in Nizhnevartovsk is considered to be satisfactory; however, there are some drawbacks to the quality of the road network and street lighting. Furthermore, the organization of leisure activities and services does not fully meet the demands of the city residents. Underdevelopment of recreational areas requires the implementation of certain measures aimed at increasing the number of recreational areas, diversifying the types of recreation, improving the infrastructure of recreational areas, improving hydrographic resources and cleaning water bodies, as well as expanding the number of historical and natural monuments, memorials, places of cultural and historical significance, sports and recreation centers. Seven types of sociocultural recreational resources of urban areas have been identified: sports centers, entertainment centers, cultural and educational centers, public eating places, Russian bathhouses and saunas, shopping and recreation centers, monuments, and historical sites. Sustainable development of recreational areas requires a monitoring program based on GIS technologies that involve the participation of the local community, university students, and students of secondary schools to provide long-term care for green areas
Improvements in Gene Editing Technology Boost Its Applications in Livestock
No full textAccelerated development of novel CRISPR/Cas9-based genome editing techniques provides a feasible approach to introduce a variety of precise modifications in the mammalian genome, including introduction of multiple edits simultaneously, efficient insertion of long DNA sequences into specific targeted loci as well as performing nucleotide transitions and transversions. Thus, the CRISPR/Cas9 tool has become the method of choice for introducing genome alterations in livestock species. The list of new CRISPR/Cas9-based genome editing tools is constantly expanding. Here, we discuss the methods developed to improve efficiency and specificity of gene editing tools as well as approaches that can be employed for gene regulation, base editing, and epigenetic modifications. Additionally, advantages and disadvantages of two primary methods used for the production of gene-edited farm animals: somatic cell nuclear transfer (SCNT or cloning) and zygote manipulations will be discussed. Furthermore, we will review agricultural and biomedical applications of gene editing technology
Dark-field/hyperspectral microscopy for detecting nanoscale particles in environmental nanotoxicology research
No full textNanoscale contaminants (including engineered nanoparticles and nanoplastics) pose a significant threat to organisms and environment. Rapid and non-destructive detection and identification of nanosized materials in cells, tissues and organisms is still challenging, although a number of conventional methods exist. These approaches for nanoparticles imaging and characterisation both inside the cytoplasm and on the cell or tissue outer surfaces, such as electron or scanning probe microscopies, are unquestionably potent tools, having excellent resolution and supplemented with chemical analysis capabilities. However, imaging and detection of nanomaterials in situ, in wet unfixed and even live samples, such as living isolated cells, microorganisms, protozoans and miniature invertebrates using electron microscopy is practically impossible, because of the elaborate sample preparation requiring chemical fixation, contrast staining, matrix embedding and exposure into vacuum. Atomic force microscopy, in several cases, can be used for imaging and mechanical analysis of live cells and organisms under ambient conditions, however this technique allows for investigation of surfaces. Therefore, a different approach allowing for imaging and differentiation of nanoscale particles in wet samples is required. Dark-field microscopy as an optical microscopy technique has been popular among researchers, mostly for imaging relatively large specimens. In recent years, the so-called “enhanced dark field” microscopy based on using higher numerical aperture light condensers and variable numerical aperture objectives has emegred, which allows for imaging of nanoscale particles (starting from 5 nm nanospheres) using almost conventional optical microscopy methodology. Hyperspectral imaging can turn a dark-field optical microscope into a powerful chemical characterisation tool. As a result, this technique is becoming popular in environmental nanotoxicology studies. In this Review Article we introduce the reader into the methodology of enhanced dark-field and dark-field-based hyperspectral microscopy, covering the most important advances in this rapidly-expanding area of environmental nanotoxicology