human lungs
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2022 ◽  
Author(s):  
Sertaç Yaman ◽  
Barış Karakaya ◽  
yavuz erol

Abstract COVID-19 is still a fatal disease, which has threatened all people by affecting the human lungs. Chest X-Ray or computed tomography (CT) imaging is commonly used to make a fast and reliable medical investigation to detect the COVID-19 virus from these medical images is remarkably challenging because it is a full-time job and prone to human errors. In this paper, a new normalization algorithm that consists of Mean-Variance-Softmax-Rescale (MVSR) processes respectively is proposed to provide facilitation pre-assessment and diagnosis Covid-19 disease. In order to show the effect of MVSR normalization technique on image processing, the algorithm is applied to chest X-ray images. Therefore, the normalized X-ray images with MVSR are used to recognize via one of the neural network models as known Convolutional Neural Networks (CNNs). At the implementation stage, the MVSR algorithm is executed on MATLAB environment, then it is implemented on FPGA platform. All the arithmetic operations of the MVSR normalization are coded in VHDL with the help of fixed-point fractional number representation format. The experimental platform consists of Zynq-7000 Development Board and VGA monitor to display the both original X-ray and MVSR normalized image. The CNN model is constructed and executed using Anaconda Navigator interface with python language. Based on the results of this study, infections of Covid-19 disease can be easily diagnosed for MVSR normalized image. The proposed MVSR normalization makes the accuracy of CNN model increase from 83.01%, to 96.16% for binary class of chest X-ray images.


Author(s):  
V.V. Krivosheev ◽  
◽  
A.I. Stoliarov ◽  

Hydrometeorological Research and Forecasting, 2021, no. 4 (382), pp. 112-133. The results of analytical studies are presented, which show that restrictive measures for reduction of SARS-CoV-2 propagation speed and the incidence of the COVID-19 pandemic on the territory of Western Europe and the Russian Federation have led to a significant reduction of anthropogenic load on the natural environment and a considerable improvement of environmental conditions for the main types of contaminants. At the same time there is a dramatic growth of total ozone in the troposphere during the period of restrictions almost for all studied territories. It is revealed that after finishing the restrictive measures the level of air contamination reached its initial point: by September in Western Europe and by October in the European part of Russia. The calculations demonstrated that poor air quality aggravates the consequences of COVID-19, and a significant contribution is made by the PM2.5 concentration of fine solid particles, which can penetrate deeper into the human lungs and exacerbate the course of respiratory diseases. Keywords: COVID-19, ecology, tropospheric conditions, satellite information, morbidity level and air quality


2021 ◽  
Vol 4 ◽  
pp. 112-133
Author(s):  
V.V. Krivosheev ◽  
◽  
A.I. Stoliarov ◽  

The results of analytical studies are presented, which show that restrictive measures for reduction of SARS-CoV-2 propagation speed and the incidence of the COVID-19 pandemic on the territory of Western Europe and the Russian Federation have led to a significant reduction of anthropogenic load on the natural environment and a considerable improvement of environmental conditions for the main types of contaminants. At the same time there is a dramatic growth of total ozone in the troposphere during the period of restrictions almost for all studied territories. It is revealed that after finishing the restrictive measures the level of air contamination reached its initial point: by September in Western Europe and by October in the European part of Russia. The calculations demonstrated that poor air quality aggravates the consequences of COVID-19, and a significant contribution is made by the PM2.5 concentration of fine solid particles, which can penetrate deeper into the human lungs and exacerbate the course of respiratory diseases. Keywords: COVID-19, ecology, tropospheric conditions, satellite information, morbidity level and air quality


Author(s):  
A.E. Medvedev ◽  
P.S. Golysheva

The paper deals with numerical simulation of the air flow in the full human bronchial tree. In their previous studies, the authors developed an analytical model of the full human bronchial tree and a method of stage-by-stage computation of the respiratory tract. A possibility of using the proposed method for a wide range of problems of numerical simulations of the air flow in human lungs is analyzed. The following situations are considered: 1) steady inspiration (with different flow rates of air) for circular and “starry” cross sections of bronchi (“starry” cross sections models some lung pathology); 2) steady expiration; 3) unsteady inspiration; 4) precipitation of medical drug aerosol droplets in human bronchi. The results predicted by the proposed method are compared with results of other researchers and found to be in good agreement. In contrast to previous investigations, the air flow in the full (down to alveoli) bronchial tree is studied for the first time. It is shown that expiration requires a greater pressure difference (approximately by 30%) than inspiration. Numerical simulations of precipitation of medical drug aerosol droplets in the human respiratory tract show that aerosol droplets generated by a standard nebulizer do not reach the alveoli (the droplets settle down in the lower regions of the bronchi).


Pathogens ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1572
Author(s):  
Robert E. Brown ◽  
Robert L. Hunter

The characteristic lesion of primary tuberculosis is the granuloma as is widely studied in human tissues and animal models. Post-primary tuberculosis is different. It develops only in human lungs and begins as a prolonged subclinical obstructive lobular pneumonia that slowly accumulates mycobacterial antigens and host lipids in alveolar macrophages with nearby highly sensitized T cells. After several months, the lesions undergo necrosis to produce a mass of caseous pneumonia large enough to fragment and be coughed out to produce a cavity or be retained as the focus of a post-primary granuloma. Bacteria grow massively on the cavity wall where they can be coughed out to infect new people. Here we extend these findings with the demonstration of secreted mycobacterial antigens, but not acid fast bacilli (AFB) of M. tuberculosis in the cytoplasm of ciliated bronchiolar epithelium and alveolar pneumocytes in association with elements of the programmed death ligand 1 (PD-L1), cyclo-oxygenase (COX)-2, and fatty acid synthase (FAS) pathways in the early lesion. This suggests that M. tuberculosis uses its secreted antigens to coordinate prolonged subclinical development of the early lesions in preparation for a necrotizing reaction sufficient to produce a cavity, post-primary granulomas, and fibrocaseous disease.


Author(s):  
Aleena Syed

Abstract: Pneumonia is a form of a respiration contamination that impacts the lungs. In those acute breathing sicknesses, human lungs which can be made from small sacs referred to as alveoli which can be in air in everyday and wholesome people however in pneumonia those alveoli get filled with fluid or "pus” one of the fundamental step of phenomena detection and treatment is getting the chest X-ray of the (CXR). So Chest X-ray is a first-rate tool in treating pneumonia, similarly to many alternatives taken with the aid of the usage of doctor are dependent on the chest X-ray. Our venture is ready detection of Pneumonia by means of chest X-ray using Convolutional Neural network. on this undertaking, we are able to look at the abilties of 2nd medical imaging to investigate records from the NIH Chest X-ray Dataset and educate a CNN to classify a given chest x-ray for the presence or absence of pneumonia. Keywords: alveoli, CNN, NIH


Author(s):  
Helena Obernolte ◽  
Monika Niehof ◽  
Peter Braubach ◽  
Hans-Gerd Fieguth ◽  
Danny Jonigk ◽  
...  

AbstractChronic obstructive pulmonary disease (COPD) is a complex chronic respiratory disorder often caused by cigarette smoke. Cigarette smoke contains hundreds of toxic substances. In our study, we wanted to identify initial mechanisms of cigarette smoke induced changes in the distal lung. Viable slices of human lungs were exposed 24 h to cigarette smoke condensate, and the dose–response profile was analyzed. Non-toxic condensate concentrations and lipopolysaccharide were used for further experiments. COPD-related protein and gene expression was measured. Cigarette smoke condensate did not induce pro-inflammatory cytokines and most inflammation-associated genes. In contrast, lipopolysaccharide significantly induced IL-1α, IL-1β, TNF-α and IL-8 (proteins) and IL1B, IL6, and TNF (genes). Interestingly, cigarette smoke condensate induced metabolism- and extracellular matrix–associated proteins and genes, which were not influenced by lipopolysaccharide. Also, a significant regulation of CYP1A1 and CYP1B1, as well as MMP9 and MMP9/TIMP1 ratio, was observed which resembles typical findings in COPD. In conclusion, our data show that cigarette smoke and lipopolysaccharide induce significant responses in human lung tissue ex vivo, giving first hints that COPD starts early in smoking history.


2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Rahib H. Abiyev ◽  
Abdullahi Ismail

This paper proposes a Convolutional Neural Networks (CNN) based model for the diagnosis of COVID-19 and non-COVID-19 viral pneumonia diseases. These diseases affect and damage the human lungs. Early diagnosis of patients infected by the virus can help save the patient’s life and prevent the further spread of the virus. The CNN model is used to help in the early diagnosis of the virus using chest X-ray images, as it is one of the fastest and most cost-effective ways of diagnosing the disease. We proposed two convolutional neural networks (CNN) models, which were trained using two different datasets. The first model was trained for binary classification with one of the datasets that only included pneumonia cases and normal chest X-ray images. The second model made use of the knowledge learned by the first model using transfer learning and trained for 3 class classifications on COVID-19, pneumonia, and normal cases based on the second dataset that included chest X-ray (CXR) images. The effect of transfer learning on model constriction has been demonstrated. The model gave promising results in terms of accuracy, recall, precision, and F1_score with values of 98.3%, 97.9%, 98.3%, and 98.0%, respectively, on the test data. The proposed model can diagnose the presence of COVID-19 in CXR images; hence, it will help radiologists make diagnoses easily and more accurately.


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