X-RAY-morphological features of the current COVID-19 and HIV infection

Infection of HIV-infected with a new coronavirus infection, due to its high contagiousness, is inevitably growing all over the world. According to estimates of Chinese scientists, their number is currently more than 500 thousand people. Considering that COVID-19 leads to suppression of immunity and the emergence of opportunistic infections in HIV-negative patients, the activation of secondary diseases in patients with HIV is natural. In view of this, the study of the features of the X-ray morphological picture of inflammatory changes in the lungs according to MSCT data in patients with COVID-19/HIV became the goal of this study. 13 patients who underwent treatment at the Surgut Regional Clinical Hospital during the period from March to July 2020 underwent MSCT of the chest organs upon admission, before discharge, as well as in case of deterioration or lack of effect from the therapy — every 3 days. The detection of pathognomonic CT signs of COVID-19 in the lungs in patients with HIV (the nature of the lesion, the stage of the disease, the severity of the inflammatory process) was carried out in parallel with the diagnosis of opportunistic infections. During the examination, atypical clinical and X-ray morphological signs of the course of COVID-19/HIV coinfection were identified. In HIV-infected people, COVID-19 proceeds more easily and in disguise, while opportunistic infections both in clinical and radiological manifestations have priority signs. They are the reasons for the aggravation of the course of the disease, as well as the development of complications in these patients.

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KL-MOB: automated COVID-19 recognition using a novel approach based on image enhancement and a modified MobileNet CNN

PeerJ Computer Science ◽

10.7717/peerj-cs.694 ◽

2021 ◽

Vol 7 ◽

pp. e694

Author(s):

Mundher Mohammed Taresh ◽

Ningbo Zhu ◽

Talal Ahmed Ali Ali ◽

Mohammed Alghaili ◽

Asaad Shakir Hameed ◽

...

Keyword(s):

Neural Network ◽

Loss Function ◽

X Ray ◽

Infected People ◽

Kl Divergence ◽

Novel Approach ◽

Starting Point ◽

The World ◽

Novel Coronavirus ◽

Sensitivity Specificity

The emergence of the novel coronavirus pneumonia (COVID-19) pandemic at the end of 2019 led to worldwide chaos. However, the world breathed a sigh of relief when a few countries announced the development of a vaccine and gradually began to distribute it. Nevertheless, the emergence of another wave of this pandemic returned us to the starting point. At present, early detection of infected people is the paramount concern of both specialists and health researchers. This paper proposes a method to detect infected patients through chest x-ray images by using the large dataset available online for COVID-19 (COVIDx), which consists of 2128 X-ray images of COVID-19 cases, 8,066 normal cases, and 5,575 cases of pneumonia. A hybrid algorithm is applied to improve image quality before undertaking neural network training. This algorithm combines two different noise-reduction filters in the image, followed by a contrast enhancement algorithm. To detect COVID-19, we propose a novel convolution neural network (CNN) architecture called KL-MOB (COVID-19 detection network based on the MobileNet structure). The performance of KL-MOB is boosted by adding the Kullback–Leibler (KL) divergence loss function when trained from scratch. The KL divergence loss function is adopted for content-based image retrieval and fine-grained classification to improve the quality of image representation. The results are impressive: the overall benchmark accuracy, sensitivity, specificity, and precision are 98.7%, 98.32%, 98.82% and 98.37%, respectively. These promising results should help other researchers develop innovative methods to aid specialists. The tremendous potential of the method proposed herein can also be used to detect COVID-19 quickly and safely in patients throughout the world.

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CNN-based Prediction of COVID-19 using Chest CT Images

International Journal of Image and Graphics ◽

10.1142/s0219467822500395 ◽

2021 ◽

pp. 2250039

Author(s):

Tanvi Arora

Keyword(s):

Ct Scan ◽

Good Accuracy ◽

Ct Images ◽

Chest Ct ◽

X Ray ◽

Infected People ◽

The World ◽

Severity Of The Disease ◽

Application Programs ◽

Chest Ct Scan

The coronavirus disease (COVID-19) pandemic that is caused by the SARS-CoV2 has spread all over the world. It is an infectious disease that can spread from person to person. The severity of the disease can be categorized into five categories namely asymptomatic, mild, moderate, severe, and critical. From the reported cases thus, it has been seen that 80% of the cases that test positive with COVID-19 infection have less than moderate complications, whereas 20% of the positive cases develop severe and critical complications. The virus infects the lungs of an individual, therefore, it has been observed that the X-ray and computed tomography (CT) scan images of the infected people can be used by the machine learning-based application programs to predict the presence of the infection. Therefore, in the proposed work, a Convolutional Neural Network model based upon the DenseNet architecture is being used to predict the presence of COVID-19 infection using the CT scan images of the chest. The proposed work has been carried out using the dataset of the CT images from the COVID CT Dataset. It has 349 images marked as COVID-19 positive and 397 images have been marked as COVID-19 negative. The proposed system can categorize the test set images with an accuracy of 91.4%. The proposed method is capable of detecting the presence of COVID-19 infection with good accuracy using the chest CT scan images of the humans.

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Mechanisms of liver damage in COVID-19

Medical alphabet ◽

10.33667/2078-5631-2020-19-39-46 ◽

2020 ◽

Vol 1 (19) ◽

pp. 39-46

Author(s):

T. V. Pinchuk ◽

N. V. Orlova ◽

T. G. Suranova ◽

T. I. Bonkalo

Keyword(s):

Hepatocellular Carcinoma ◽

Liver Transplantation ◽

Risk Factor ◽

Liver Disease ◽

Liver Function ◽

Liver Damage ◽

The World ◽

Coronavirus Infection ◽

The Impact ◽

Analyze Data

At the end of 2019, a new coronavirus (SARS-CoV-2) was discovered in China, causing the coronavirus infection COVID-19. The ongoing COVID-19 pandemic poses a major challenge to health systems around the world. There is still little information on how infection affects liver function and the significance of pre-existing liver disease as a risk factor for infection and severe COVID-19. In addition, some drugs used to treat the new coronavirus infection are hepatotoxic. In this article, we analyze data on the impact of COVID-19 on liver function, as well as on the course and outcome of COVID-19 in patients with liver disease, including hepatocellular carcinoma, or those on immunosuppressive therapy after liver transplantation.

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ADDRESSING THE ISSUE OF СOVID-19, SARS-COV-2 VIRUS AND THE DISEASE

PEDIATRIA Journal named after G N SPERANSKY ◽

10.24110/0031-403x-2020-99-6-15-31 ◽

2020 ◽

Vol 99 (6) ◽

pp. 15-31

Author(s):

A.A. Korenkova ◽

◽

E.M. Mayorova ◽

V.V. Bahmetjev ◽

M.V. Tretyak ◽

...

Keyword(s):

Public Health ◽

Immune Responses ◽

Molecular Mechanisms ◽

Herd Immunity ◽

Significant Problem ◽

Prevention Measures ◽

Optimal Therapy ◽

The World ◽

Public Health Challenge ◽

Coronavirus Infection

The new coronavirus infection has posed a major public health challenge around the world, but new data on the disease raises more questions than answers. The lack of optimal therapy is a significant problem. The article examines the molecular mechanisms of SARS-CoV-2 infection and the pathogenesis of COVID-19, special attention is paid to features of pathological processes and immune responses in children. COVID-19 leads to a wide diversity of negative outcomes, many of which can persist for at least months. Many of the consequences have yet to be identified. SARS-CoV-2 may provoke autoimmune reactions. Reinfection, herd immunity, vaccines and other prevention measures are also discussed in this review.

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Association and treatment of diabetes in patients affected by Covid-19

International Journal of Research in Pharmaceutical Sciences ◽

10.26452/ijrps.v11ispl1.3591 ◽

2020 ◽

Vol 11 (SPL1) ◽

pp. 1198-1201

Author(s):

Syed Yasir Afaque

Keyword(s):

Diabetes Mellitus ◽

Prognostic Value ◽

Heart Diseases ◽

The Novel ◽

The World ◽

Treatment Of Diabetes ◽

Coronavirus Infection ◽

Composite Outcomes ◽

Novel Coronavirus ◽

The Impact

In December 2019, a unique coronavirus infection, SARS-CoV-2, was first identified in the province of Wuhan in China. Since then, it spread rapidly all over the world and has been responsible for a large number of morbidity and mortality among humans. According to a latest study, Diabetes mellitus, heart diseases, Hypertension etc. are being considered important risk factors for the development of this infection and is also associated with unfavorable outcomes in these patients. There is little evidence concerning the trail back of these patients possibly because of a small number of participants and people who experienced primary composite outcomes (such as admission in the ICU, usage of machine-driven ventilation or even fatality of these patients). Until now, there are no academic findings that have proven independent prognostic value of diabetes on death in the novel Coronavirus patients. However, there are several conjectures linking Diabetes with the impact as well as progression of COVID-19 in these patients. The aim of this review is to acknowledge about the association amongst Diabetes and the novel Coronavirus and the result of the infection in such patients.

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Ibuprofen safety at the golden anniversary. A commentary and recent developments

10.22514/sv.2020.16.0097 ◽

2020 ◽

Keyword(s):

The World ◽

Recent Developments ◽

Inflammatory Drugs ◽

Coronavirus Infection ◽

Interesting Story ◽

Anti Inflammatory ◽

Short Comment ◽

Published Paper

Ibuprofen is a long lasting non-steroidal anti-inflammatory drugs (NSAIDs) and still represents one of the most diffused analgesics around the world. It has an interesting story started over 50 years ago. In this short comment to an already published paper, the authors try to focus some specific important point. On top, they illustrate the recent, confusing and fake assertion on the potentially dangerous influence that ibuprofen could have, increasing the risk of Coronavirus infection. This is also better illustrated in a previously published paper, where the readers could find more clear responses to eventual doubts.

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First Report on the Geologic Occurrence of Natural Na–A Zeolite and Associated Minerals in Cretaceous Mudstones of the Paja Formation of Vélez (Santander), Colombia

Crystals ◽

10.3390/cryst11020218 ◽

2021 ◽

Vol 11 (2) ◽

pp. 218

Author(s):

Carlos Alberto Ríos-Reyes ◽

German Alfonso Reyes-Mendoza ◽

José Antonio Henao-Martínez ◽

Craig Williams ◽

Alan Dyer

Keyword(s):

Natural Zeolite ◽

Scanning Electron Micrographs ◽

Total Sulfur ◽

Zeolite A ◽

X Ray Diffraction ◽

X Ray ◽

Mineral Phases ◽

The World ◽

Porosity And Permeability ◽

First Time

This study reports for the first time the geologic occurrence of natural zeolite A and associated minerals in mudstones from the Cretaceous Paja Formation in the urban area of the municipality of Vélez (Santander), Colombia. These rocks are mainly composed of quartz, muscovite, pyrophyllite, kaolinite and chlorite group minerals, framboidal and cubic pyrite, as well as marcasite, with minor feldspar, sulphates, and phosphates. Total organic carbon (TOC), total sulfur (TS), and millimeter fragments of algae are high, whereas few centimeters and not biodiverse small ammonite fossils, and other allochemical components are subordinated. Na–A zeolite and associated mineral phases as sodalite occur just beside the interparticle micropores (honeycomb from framboidal, cube molds, and amorphous cavities). It is facilitated by petrophysical properties alterations, due to processes of high diagenesis, temperatures up to 80–100 °C, with weathering contributions, which increase the porosity and permeability, as well as the transmissivity (fluid flow), allowing the geochemistry remobilization and/or recrystallization of pre-existing silica, muscovite, kaolinite minerals group, salts, carbonates, oxides and peroxides. X-ray diffraction analyses reveal the mineral composition of the mudstones and scanning electron micrographs show the typical cubic morphology of Na–A zeolite of approximately 0.45 mμ in particle size. Our data show that the sequence of the transformation of phases is: Poorly crystalline aluminosilicate → sodalite → Na–A zeolite. A literature review shows that this is an unusual example of the occurrence of natural zeolites in sedimentary marine rocks recognized around the world.

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X-Ray Microanalysis of Art Glass Surfaces

Microscopy Today ◽

10.1017/s1551929510000891 ◽

2010 ◽

Vol 18 (5) ◽

pp. 28-31

Author(s):

R.B. Simmons

Keyword(s):

Heavy Metals ◽

Low Cost ◽

Metallic Surfaces ◽

High Quality ◽

X Ray ◽

Skin Contact ◽

Work Area ◽

The World ◽

Glass Surfaces ◽

Glass Workers

In recent years there has been a virtual explosion in the world of art glass. New glass formulations have brought a host of new colors into the marketplace, and the availability of low-cost, high-quality torches and other tools has brought art glass to the hobbyist. In addition to burn risks and possible cutting injury, there are a number of less obvious hazards that should be known to novice glass workers. One of these is the presence of heavy metals in or on glass surfaces and possibly in the atmosphere immediately surrounding the work area, presenting both potential skin contact and inhalation hazards. This study examines the metallic surfaces generated on five glass colors commonly used in art glass jewelry.

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HCV Detection, Discrimination, and Genotyping Technologies

Sensors ◽

10.3390/s18103423 ◽

2018 ◽

Vol 18 (10) ◽

pp. 3423 ◽

Cited By ~ 7

Author(s):

Shrikant Warkad ◽

Satish Nimse ◽

Keum-Soo Song ◽

Taisun Kim

Keyword(s):

Hepatitis C ◽

Hcv Infection ◽

World Health ◽

Clinical Samples ◽

Infected People ◽

Advantages And Disadvantages ◽

Highly Sensitive ◽

The World ◽

Health Organization

According to the World Health Organization (WHO), 71 million people were living with Hepatitis C virus (HCV) infection worldwide in 2015. Each year, about 399,000 HCV-infected people succumb to cirrhosis, hepatocellular carcinoma, and liver failure. Therefore, screening of HCV infection with simple, rapid, but highly sensitive and specific methods can help to curb the global burden on HCV healthcare. Apart from the determination of viral load/viral clearance, the identification of specific HCV genotype is also critical for successful treatment of hepatitis C. This critical review focuses on the technologies used for the detection, discrimination, and genotyping of HCV in clinical samples. This article also focuses on advantages and disadvantages of the reported methods used for HCV detection, quantification, and genotyping.

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Meta-Transfer Learning Driven Tensor-Shot Detector for the Autonomous Localization and Recognition of Concealed Baggage Threats

Sensors ◽

10.3390/s20226450 ◽

2020 ◽

Vol 20 (22) ◽

pp. 6450

Author(s):

Taimur Hassan ◽

Muhammad Shafay ◽

Samet Akçay ◽

Salman Khan ◽

Mohammed Bennamoun ◽

...

Keyword(s):

Transfer Learning ◽

State Of The Art ◽

Autonomous Systems ◽

Aviation Security ◽

Dual Energy ◽

Average Precision ◽

X Ray ◽

Shot Classification ◽

Object Proposals ◽

The World

Screening baggage against potential threats has become one of the prime aviation security concerns all over the world, where manual detection of prohibited items is a time-consuming and hectic process. Many researchers have developed autonomous systems to recognize baggage threats using security X-ray scans. However, all of these frameworks are vulnerable against screening cluttered and concealed contraband items. Furthermore, to the best of our knowledge, no framework possesses the capacity to recognize baggage threats across multiple scanner specifications without an explicit retraining process. To overcome this, we present a novel meta-transfer learning-driven tensor-shot detector that decomposes the candidate scan into dual-energy tensors and employs a meta-one-shot classification backbone to recognize and localize the cluttered baggage threats. In addition, the proposed detection framework can be well-generalized to multiple scanner specifications due to its capacity to generate object proposals from the unified tensor maps rather than diversified raw scans. We have rigorously evaluated the proposed tensor-shot detector on the publicly available SIXray and GDXray datasets (containing a cumulative of 1,067,381 grayscale and colored baggage X-ray scans). On the SIXray dataset, the proposed framework achieved a mean average precision (mAP) of 0.6457, and on the GDXray dataset, it achieved the precision and F1 score of 0.9441 and 0.9598, respectively. Furthermore, it outperforms state-of-the-art frameworks by 8.03% in terms of mAP, 1.49% in terms of precision, and 0.573% in terms of F1 on the SIXray and GDXray dataset, respectively.

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