Deep learning model for binary classification of COVID-19 based on Chest X-Ray

COVID-19 is a novel virus infecting the upper respiratory tract and lungs. On a scale of the global pandemic, the number of cases and deaths had been increasing each day. Chest X-ray (CXR) images proved effective in monitoring a variety of lung illnesses, including the COVID-19 disease. In recent years, deep learning (DL) has become one of the most significant topics in the computing world and has been extensively applied in several medical applications. In terms of automatic diagnosis of COVID-19, those approaches had proven to be very effective. In this research, a DL technology based on convolution neural networks (CNN) models had been implemented with less number of layers with tuning parameters that will take less time for training for binary classification of COVID-19 based on CXR images. Experimental results had shown that the proposed model for training had achieved an accuracy of 96.68%, Recall of 94.12%, Precision of 93.49%, Specificity of 97.61%, and F1 Score of 93.8%. Those results had shown the high value of utilizing DL for early COVID-19 diagnosis, which can be utilized as a useful tool for COVID-19 screening. © 2023 IEEE.

Covid-19 Variants Detection and Classification Using Self Proposed Two stage MNN-2: Robust Comparison with Yolo V5 and Faster R-CNN

Covid-19 is unpredictable evolutionary discipline which requires continuous advancements for its appropriate Detection and Classifications which can be helpful for bio-medical stream. In this research, two dimensions are covered that is detection and classification using self-proposed 2 stage learning detector. Detection of different variants of Covid-19 are performed using images of CT-Scan and X-Rays of effected lungs. Furthermore, classification of different variants is carried out. Dataset of 27000 indigenous images were used for detection and classification purposed. Moreover, in depth survey and comparison is carried out with state-of-the-art Yolo v5 single state detector and Faster R-CNN 2 stage detector. Accuracy analysis of self-proposed 2 stage detector was 91.66% and 87.9% for detection and classification in comparison with YOLOv5 which had accuracy of 92.8% and 87.175% for detection and classification. Moreover, in comparison with Faster R-CNN which had accuracy of 94.8% and 87% The training analysis was performed on Nvidia T4 (16GB GDDR6). Self-proposed MNN-2 superseded Yolov5 and faster R-CNN in real time video analysis with least real time rate at FPS 30 at duration of 72 min video. © 2022 IEEE.

Medical Application of Substances Derived from Non-Pathogenic Fungi Aspergillus oryzae and A. luchuensis-Containing Koji.

Although most fungi cause pathogenicity toward human beings, dynasties of the East Asian region have domesticated and utilized specific fungi for medical applications. The Japanese dynasty and nation have domesticated and utilized koji fermented with non-pathogenic fungus Aspergillus oryzae for more than 1300 years. Recent research has elucidated that koji contains medicinal substances such as Taka-diastase, acid protease, koji glycosylceramide, kojic acid, oligosaccharides, ethyl-α-d-glucoside, ferulic acid, ergothioneine, pyroglutamyl leucine, pyranonigrin A, resistant proteins, deferriferrichrysin, polyamines, Bifidobacterium-stimulating peptides, angiotensin I-converting enzyme inhibitor peptides, 14-dehydroergosterol, beta-glucan, biotin, and citric acid. This review introduces potential medical applications of such medicinal substances to hyperlipidemia, diabetes, hypertension, cardiovascular and cognitive diseases, chronic inflammation, epidermal permeability barrier disruption, coronavirus disease 2019 (COVID-19), and anti-cancer therapy.

Potential Therapeutic Applications of Hydrogen in Chronic Inflammatory Diseases: Possible Inhibiting Role on Mitochondrial Stress.

Mitochondria are the largest source of reactive oxygen species (ROS) and are intracellular organelles that produce large amounts of the most potent hydroxyl radical (·OH). Molecular hydrogen (H2) can selectively eliminate ·OH generated inside of the mitochondria. Inflammation is induced by the release of proinflammatory cytokines produced by macrophages and neutrophils. However, an uncontrolled or exaggerated response often occurs, resulting in severe inflammation that can lead to acute or chronic inflammatory diseases. Recent studies have reported that ROS activate NLRP3 inflammasomes, and that this stimulation triggers the production of proinflammatory cytokines. It has been shown in literature that H2 can be based on the mechanisms that inhibit mitochondrial ROS. However, the ability for H2 to inhibit NLRP3 inflammasome activation via mitochondrial oxidation is poorly understood. In this review, we hypothesize a possible mechanism by which H2 inhibits mitochondrial oxidation. Medical applications of H2 may solve the problem of many chronic inflammation-based diseases, including coronavirus disease 2019 (COVID-19).

Developing a Training Web Application for Improving the COVID-19 Diagnostic Accuracy on Chest X-ray.

In December 2019, a new coronavirus known as 2019-nCoV emerged in Wuhan, China. The virus has spread globally and the infection was declared pandemic in March 2020. Although most cases of coronavirus disease 2019 (COVID-19) are mild, some of them rapidly develop acute respiratory distress syndrome. In the clinical management, chest X-rays (CXR) are essential, but the evaluation of COVID-19 CXR could be a challenge. In this context, we developed COVID-19 TRAINING, a free Web application for training on the evaluation of COVID-19 CXR. The application included 196 CXR belonging to three categories: non-pathological, pathological compatible with COVID-19, and pathological non-compatible with COVID-19. On the training screen, images were shown to the users and they chose a diagnosis among those three possibilities. At any time, users could finish the training session and be evaluated through the estimation of their diagnostic accuracy values: sensitivity, specificity, predictive values, and global accuracy. Images were hand-labeled by four thoracic radiologists. Average values for sensitivity, specificity, and global accuracy were .72, .64, and .68. Users who achieved better sensitivity registered less specificity (p < .0001) and those with higher specificity decreased their sensitivity (p < .0001). Users who sent more answers achieved better accuracy (p = .0002). The application COVID-19 TRAINING provides a revolutionary tool to learn the necessary skills to evaluate COVID-19 on CXR. Diagnosis training applications could provide a new original manner of evaluation for medical professionals based on their diagnostic accuracy values, and an efficient method to collect valuable data for research purposes.

Ultraviolet Irradiation Enhances the Microbicidal Activity of Silver Nanoparticles by Hydroxyl Radicals.

It is known that silver has microbicidal qualities; even at a low concentration, silver is active against many kinds of bacteria. Silver nanoparticles (AgNPs) have been extensively studied for a wide range of applications. Alternately, the toxicity of silver to human cells is considerably lower than that to bacteria. Recent studies have shown that AgNPs also have antiviral activity. We found that large amounts of hydroxyl radicals-highly reactive molecular species-are generated when AgNPs are irradiated with ultraviolet (UV) radiation with a wavelength of 365 nm, classified as ultraviolet A (UVA). In this study, we used electron spin resonance direct detection to confirm that UV irradiation of AgNPs produced rapid generation of hydroxyl radicals. As hydroxyl radicals are known to degrade bacteria, viruses, and some chemicals, the enhancement of the microbicidal activity of AgNPs by UV radiation could be valuable for the protection of healthcare workers and the prevention of the spread of infectious diseases.