Collagen-Specific HSP47+ Myofibroblasts and CD163+ Macrophages Identify Profibrotic Phenotypes in Deceased Hearts With SARS-CoV-2 Infections.

Background Cardiac fibrosis complicates SARS-CoV-2 infections and has been linked to arrhythmic complications in survivors. Accordingly, we sought evidence of increased HSP47 (heat shock protein 47), a stress-inducible chaperone protein that regulates biosynthesis and secretion of procollagen in heart tissue, with the goal of elucidating molecular mechanisms underlying cardiac fibrosis in subjects with this viral infection. Methods and Results Using human autopsy tissue, immunofluorescence, and immunohistochemistry, we quantified Hsp47+ cells and collagen α 1(l) in hearts from people with SARS-CoV-2 infections. Because macrophages are also linked to inflammation, we measured CD163+ cells in the same tissues. We observed irregular groups of spindle-shaped HSP47+ and CD163+ cells as well as increased collagen α 1(I) deposition, each proximate to one another in "hot spots" of ≈40% of hearts after SARS-CoV-2 infection (HSP47+ P<0.05 versus nonfibrotics and P<0.001 versus controls). Because HSP47+ cells are consistent with myofibroblasts, subjects with hot spots are termed "profibrotic." The remaining 60% of subjects dying with COVID-19 without hot spots are referred to as "nonfibrotic." No control subject exhibited hot spots. Conclusions Colocalization of myofibroblasts, M2(CD163+) macrophages, and collagen α 1(l) may be the first evidence of a COVID-19-related "profibrotic phenotype" in human hearts in situ. The potential public health and diagnostic implications of these observations require follow-up to further define mechanisms of viral-mediated cardiac fibrosis.

Seroprevalence of community-acquired atypical bacterial pneumonia among adult COVID-19 patients from a single center in Al Madinah Al Munawarah, Saudi Arabia: A retrospective cohort study.

OBJECTIVES: To investigate the seroprevalence of the community-acquired bacterial that causes atypical pneumonia among confirmed severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) patients. METHODS: In this cohort study, we retrospectively investigated the seroprevalence of Chlamydia pneumoniae, Mycoplasma pneumoniae, and Legionella pneumophila among randomly selected 189 confirmed COVID-19 patients at their time of hospital presentation via commercial immunoglobulin M (IgM) antibodies against these bacteria. We also carried out quantitative measurements of procalcitonin in patients' serum. RESULTS: The seropositivity for L. pneumophila was 12.6%, with significant distribution among patientsolder than 50 years (χ2 test, p=0.009), while those of M. pneumoniae was 6.3% and C. pneumoniae was 2.1%, indicating an overall co-infection rate of 21% among COVID-19 patients. No significant difference (χ2 test, p=0.628) in the distribution of bacterial co-infections existed between male and female patients. Procalcitonin positivity was confirmed amongst 5% of co-infected patients. CONCLUSION: Our study documented the seroprevalence of community-acquired bacteria co-infection among COVID-19 patients. In this study, procalcitonin was an inconclusive biomarker for non-severe bacterial co-infections among COVID-19 patients. Consideration and proper detection of community-acquired bacterial co-infection may minimize misdiagnosis during the current pandemic and positively reflect disease management and prognosis.

Determinants of Food Expenditure Patterns: Evidence from U.S. Consumers in the Context of the COVID-19 Price Shocks

This study examined the factors that determine U.S. household expenditure patterns for food products in the context of exceptional price shocks due to the COVID-19 pandemic. This research relied on the Consumer Expenditure Diary Survey (CEX) for the year 2020, where households or consumer units represent units of observation. With a sample size of 10,453 observations, the empirical estimation of the Heckman two-step model yields interesting results. Consistent with the inelastic nature of food products, we found conditional expenditure elasticities of income were less than one for all kinds of food, including food away from home (FAFH). The results showed both food and FAFH to be highly price elastic in this special period of higher food prices. For instance, a 1% increase in own price implied a 7.78% decrease in the probability to spend on food and a 20.93% decrease in propensity to purchase FAFH. This study provides business managers and marketing experts with insights on the consumer profile and food product price strategy.

Impact of COVID-19 Pandemic on Vertical Price Transmission in the U.S. Fresh Banana Market

The U.S. imports about two billion dollars of fresh bananas, accounting for over 99 percent of domestic banana consumption annually. The COVID-19 pandemic disrupted the fresh banana supply chain and caused unexpected price movements along the marketing channel. This research investigated the impact of the COVID-19 pandemic on price adjustments in the U.S. fresh banana market. A Vector Error Correction (VEC) model was employed to evaluate the speeds of price adjustments along the U.S. banana marketing channel at the import and retail levels, and historical decomposition graphs were used to investigate the magnitude of price adjustments caused by the COVID-19 shock. The results show that the deviation from the long-run equilibrium caused by the shock was corrected faster for the import prices than retail prices. Hence, the speeds of price adjustments were asymmetric in the period of the COVID-19 shock. Additionally, the magnitudes of price changes caused by the pandemic shock were different, leading to increased price margins. These results point to the inefficiency of the banana marketing channel with welfare, policy, and agribusiness implications.

The COVID-19 Pandemic and Dynamics of Price Adjustment in the U.S. Beef Sector

This research investigates the impact of the COVID-19 pandemic on the dynamics of vertical price transmission in the U.S. beef industry using monthly farm, wholesale, and retail prices for the period 1970–2021. Contemporary time-series techniques and historical decomposition graphs were used to test for possible asymmetries and structural breaks in the price transmission across the beef supply chain. The results show that the impact of COVID-19 has been uneven across the beef marketing channel, with consumers and farmers sharing the burden of the shock. Historical decomposition graphs demonstrated that the COVID-19 pandemic caused consumers paying higher prices, but farmers receiving lower prices than their predicted values. Hence, both consumers and farmers in the U.S. beef supply chain were adversely affected by the COVID-19 pandemic. Furthermore, the results detected asymmetric price adjustments along the U.S. beef supply chain, both in speeds and magnitudes, with the wholesale prices being more flexible, adjusting quicker than farm and retail prices. The results indicated that the U.S. beef markets were resilient enough to absorb the shock and return to their pre-shock patterns in 4 to 6 months. These results have welfare and policy implications for the U.S. beef industry.

Smart and Automated Diagnosis of COVID-19 Using Artificial Intelligence Techniques

Machine Learning (ML) techniques have been combined with modern technologies across medical fields to detect and diagnose many diseases. Mean-while, given the limited and unclear statistics on the Coronavirus Disease 2019 (COVID-19), the greatest challenge for all clinicians is to find effective and accu-rate methods for early diagnosis of the virus at a low cost. Medical imaging has found a role in this critical task utilizing a smart technology through different image modalities for COVID-19 cases, including X-ray imaging, Computed Tomography (CT) and magnetic resonance image (MRI) that can be used for diagnosis by radiologists. This paper combines ML with imaging analysis in an artificial deep learning approach for COVID-19 detection. The proposed methodology is based on convolutional long short term memory (ConvLSTM) to diagnose COVID-19 automatically from X-ray images. The main features are extracted from regions of interest in the medical images, and an intelligent classifier is used for the classification task. The proposed model has been tested on a dataset of X-ray images for COVID-19 and normal cases to evaluate the detection performance. The ConvLSTM model has achieved the desired results with high accuracy of 91.8%, 95.7%, 97.4%, 97.7% and 97.3% at 10, 20, 30, 40 and 50 epochs that will detect COVID-19 patients and reduce the medical diagnosis workload. © 2022, Tech Science Press. All rights reserved.

Treatment of hepatitis c with direct acting antiviral drugs can protect against severe acute respiratory syndrome coronavirus 2 infection

Introduction: Owing to the similarity between SARS-CoV-2 and hepatitis C virus (both SARS-CoV-2 Mpro protease and HCV NS3/4A protease are double B-barrel folded with similar orientation), and based on molecular docking models, many researchers suggested using hepatitis C direct acting antiviral drugs (DAAs) for the treatment of SARS-CoV-2 infection. Aims & Methods: This study aimed to estimate the prevalence of SARSCoV- 2 infection among chronic hepatitis C patients receiving treatment with sofosbuvir plus daclatasvir in comparison to chronic hepatitis C patients who finished treatment course one year before COVID-19 pandemic (control group). A retrospective case-control study was designed including 500 chronic hepatitis C patients receiving treatment with sofosbuvir plus daclatasvir (study group) during COVID-19 pandemic (March to September 2020) in comparison to matched 500 individuals who finished treatment course for hepatitis C one year (March to September 2019) before COVID-19 pan demic (control group). Both groups were followed up for 6 months starting from March 2020 up to September 2020. Baseline demographic data, comorbidities, history of confirmed diagnosis of with SARS-CoV-2 infection, residence in an area endemic with SARS-CoV-2 infection or close contact with confirmed or suspected cases were compared in both groups. Results: Our study included 1000 participants (500 in each group), mean age (± standard deviation) was 48.45 (± 7.68) in the study group and 47.67 (± 10.56) in the control group (p value=0.18). Most of participants in the study were males, 400 (80%) in the study group and 380 (76%) in the control group. No significant differences were present in baseline characteristics including area of residence (rural versus urban), level of education, work in medical field, smoking, presence of liver cirrhosis or other comorbidities (Diabetes mellitus, Hypertension, Chest diseases, Cardiac disease, Autoimmune disease or Obesity). In the study group 22 (4.4%) patients had contact with SARS-CoV-2 infected patient while in the control group 24 (4.8%) individuals had contact with SARS-CoV-2 infected patient (p value= 0.88). Patient receiving chronic hepatitis C treatment with sofosbuvir plus daclatasvir had a lower rate of SARS-CoV-2 infection (2.2%, 11 SARSCoV- 2 infections) than individuals in the control group (6%, 30 SARS-CoV-2 infections). Conclusion: Chronic hepatitis C treatment (sofosbuvir plus daclatasvir) can protect against SARS-CoV-2 infection. Larger randomized controlled studies are urgently required to explore the efficacy of sofosbuvir plus daclatasvir combination as a potential therapy for SARS-CoV-2 infection.

Utilization of Deep Learning-Based Crowd Analysis for Safety Surveillance and Spread Control of COVID-19 Pandemic

Crowd monitoring analysis has become an important challenge in academic researches ranging from surveillance equipment to people behavior using different algorithms. The crowd counting schemes can be typically processed in two steps, the images ground truth density maps which are obtained from ground truth density map creation and the deep learning to estimate density map from density map estimation. The pandemic of COVID-19 has changed our world in few months and has put the normal human life to a halt due to its rapid spread and high danger. Therefore, several precautions are taken into account during COVID-19 to slowdown the new cases rate like maintaining social distancing via crowd estimation. This manuscript presents an efficient detection model for the crowd counting and social distancing between visitors in the two holy mosques, Al Masjid Al Haram in Mecca and the Prophet's Mosque in Medina. Also, the manuscript develops a secure crowd monitoring structure based on the convolutional neural network (CNN) model using real datasets of images for the two holy mosques. The proposed framework is divided into two procedures, crowd counting and crowd recognition using datasets of different densities. To confirm the effectiveness of the proposed model, some metrics are employed for crowd analysis, which proves the monitoring efficiency of the proposed model with superior accuracy. Also, it is very adaptive to different crowd density levels and robust to scale changes in several places.

Developing Secure Healthcare Video Consultations for Corona Virus (COVID-19) Pandemic

Many health networks became increasingly interactive in implementing a consulting approach to telemedicine before the COVID-19 pandemic. To mitigate patient trafficking and reduce the virus exposure in health centers, several GPs, physicians and people in the video were consulted during the pandemic at the start. Video and smartphone consultations will allow well-insulated and high-risk medical practitioners to maintain their patient care security. Video appointments include diabetes, obesity, hypertension, stroke, mental health, chemotherapy and chronic pain. Many urgent diseases, including an emergency triage for the eye, may also be used for online consultations and triages. The COVID-19 pandemic shows that healthcare option for healthy healthcare and the potential to increase to a minimum, such as video consultations, have grown quickly. The dissemination of COVID-19 viruses now aims at extending the use of Video-Health Consultations by exchanging insights and simulations of health consultations and saving costs and healthcare practices as a consequence of the COVID-19 pandemic. Our paper focuses on video consulting privacy. This essay further presents the advantages and inconveniences of video consultation and its implementation. This paper suggests the most recent video encryption method known as high efficiency video coding selective encryption (HEVC SE). Our video consultation schema has been improved to secure video streaming on a low calculation overhead, with the same bit rate and to ensure compatibility with the video format. The contribution is made with RC5, a low complexity computer, to encrypt subsets of bin-strings binarized in the HEVC sense using the context adaptive binary arithmetic coding (CABAC) method through the bypass binary arithmetic coding. This sequence of binstrings consists of a non-zero differential transforming cosine (DCT) coefficient bit, MVD sign bits, remainder absolute DCT suffixes and absolute MVD suffixes. This paper also examines the efficiency assessment of the use of the RC5 with its modes of operations in the HEVC CABAC SE proposed. This study chooses the best operating mode for RC5 to be used for the healthcare video consultation application. Security analysis, such as histogram analysis, correlation coefficient testing and key sensitivity testing, is presented to protect against brute force and statistical attacks for the proposed schema.

Visual Protection Using RC5 Selective Encryption in Telemedicine

The dissemination of the COVID-19 viruses now extends the usage of video consultations to share perspectives and virtual medical consultations, save expenses and health procedures, track the success of care proposals with detail, consistency, and ease from moment to time. The research aims to study the secur-ity of video consultations. We will also present the advantages and disadvantages of video consultations and the complications of their implementation. This paper mainly proposes a practical, high-efficiency video encoding technique for the new video encoding technique (HEVC) used in video consultations. The technology offered uses the RC5 block encryption algorithm for encrypting the sensitive bits of HEVC with low complexity, short encoding times in real-time implementa-tions, format-compliant HEVC, and constant bitrate. In addition, this paper pre-sents experimental results comparing the proposed HEVC RC5-based selective encryption (SE) algorithm with the other algorithms used in various operating modes using the Advanced Encryption Standard (AES). Also, this paper provides the security analysis of the proposed HEVC RC5-based SE algorithm for video consultation, including;the correlation analysis between the ciphered video and the original one, the histogram of the ciphered video, and the cipher cycle analysis. The security analysis results ensured that the proposed HEVC RC5-based SE algorithm for video consultation is safe and stable.

Utilization of HEVC ChaCha20-based selective encryption for secure telehealth video conferencing

Coronavirus (COVID-19) is a contagious disease that causes exceptional effect on healthcare organizations worldwide with dangerous impact on medical services within the hospitals. Because of the fast spread of COVID-19, the healthcare facilities could be a big source of disease infection. So, healthcare video consultations should be used to decrease face-to-face communication between clinician and patients. Healthcare video consultations may be beneficial for some COVID-19 conditions and reduce the need for face-to-face contact with a potentially positive patient without symptoms. These conditions are like top clinicians who provide remote consultations to develop treatment methodology and follow-up remotely, patients who consult about COVID-19, and those who have mild symptoms suggestive of the COVID-19 virus. Video consultations are a supplement to, and not a substitute for, telephone consultations. It may also form part of a broader COVID-19 distance care strategy that contains computerized screening, separation of possibly infectious patients within medical services, and computerized video-intensive observing of their intensive care that helps reduce mixing. Nowadays, the spread of the COVID-19 virus helps to expand the use of video healthcare consultations because it helps to exchange experiences and remote medical consultations, save costs and health procedures used to cope with the pandemic of the COVID-19 virus, and monitor the progress of treatment plans, moment by moment from a distance with precision, clarity and ease. From this perspective, this paper introduces a high-efficiency video coding (HEVC) ChaCha20-based selective encryption (SE) scheme for secure healthcare video Consultations. The proposed HEVC ChaCha20-based SE scheme uses the ChaCha20 for encrypting the sign bits of the Discrete Cosine Transform (DCT) and Motion Vector Difference (MVD) in the HEVC entropy phase. The main achievement of HEVC ChaCha20-based SE scheme is encrypting the most sensitive video bits with keeping low delay time, fixed bit rate of the HEVC, and format compliance. Experimental tests guarantee that the proposed HEVC ChaCha20-based SE scheme can ensure the confidentiality of the healthcare video consultations which has become easy to transmit through the internet. © 2021 Tech Science Press. All rights reserved.

Utilization of Artificial Intelligence in Medical Image Analysis for COVID-19 Patients Detection

In the era of medical technology, automatic scan detection can be con-sidered a charming tool in medical diagnosis, especially with rapidly spreading diseases. In light of the prevalence of the current Coronavirus disease (COVID-19), which is characterized as highly contagious and very complicated, it is urgent and necessary to find a quick way that can be practically implemented for diagnosing COVID-19. The danger of the virus lies in the fact that patients can spread the disease without showing any symptoms. Moreover, several vaccines have been produced and vaccinated in large numbers but, the outbreak does not stop. Therefore, it is urgent and necessary to find a quick way that can be prac-tically implemented for diagnosing COVID-19 cases. One of the most important ways to combat this disease is the early detection of the virus by using technology to identify and isolate patients. The combined results of recent researches showed that both CT and CXR scan correctly diagnosed COVID-19 in 87% and 80% of infected people. From these perspectives, this research paper aims to employ a deep learning model using the convolutional neural network (CNN) to detect and diagnose COVID-19 from both CT and CXR scans. The CNN is being used for features extraction and then detect COVID-19 cases in a little bit of time. Dataset collections of CT and CXR scans are being applied for examining the pro-posed CNN-based COVID-19 detection model. The results show that the pro-posed C NN-based COVID-19 detection model can achieve an accuracy of 99%, effectively speeding up the diagnosis and treatment of COVID-19 patients from CT and CXR scans.

Effect of COVID-19 stressors on healthcare workers’ performance and attitude at Suez Canal university hospitals

Background: Coronavirus disease 2019 is an emerging respiratory disease caused by a novel coronavirus effect on 10-20% of total healthcare workers and was first detected in December 2019 in Wuhan, China. This study was designed to assess effect of COVID-19 stressors on healthcare workers’ performance and attitude. A descriptive cross sectional research design was used. A convenient sample (all available healthcare workers) physicians “112,”, nurses “183,” pharmacists “31,” and laboratory technicians “38” was participated to conduct aim of the study. Utilize the study with two tools;online self-administrated questionnaire to assess level of knowledge, attitude, and infection control measures regarding coronavirus disease 2019 and COVID-19 stress scales to assess the varied stressors among healthcare workers. Results: More than three quarter of the studied participants had satisfactory level of knowledge and infection control measures. Approximately all of the studied participants had positive attitude regarding COVID-19. A total of 57.4% of the studied medical participants had moderate COVID-19 psychological stress levels, while 49.1% of the studied paramedical participants had moderate COVID-19 psychological stress levels. But less than one quarter had severe COVID-19 psychological stress levels. There is a significant correlation between COVID-19 psychological stressor levels and satisfactory level of knowledge among medical participants. Conclusion/implications for practice: Most of healthcare workers had satisfactory level of knowledge, infection control measures, and positive attitude regarding COVID-19. Most of them had moderate COVID-19 psychological stress levels. © 2021, The Author(s).