Detection Of Covid19 in Lung X-Rays Using Deep Learning Algorithms

The World Health Organization (WHO) compiled this medical imaging reference guide in response to the emergence of the COVID-19 virus. The Beijing Country Office of the World Health Organization learned on December 31 that there was an epidemic of pneumonia patients in Wuhan, China. The causative agent of the pandemic was quickly identified as a novel coronavirus. In 2019, we should anticipate seeing an increase in the prevalence of coronavirus sickness, also known as the SARS-CoV-2 virus, and the SARS-CoV-1 virus. In order to determine the presence of this virus (COVID-19), we have created two models. Finally, the distorted part of the image was located. Some of the processes that we go through regularly have been the subject of our efforts to automate them. Using Resnet-18 models in combination with Deep Convolutional Neural Network (DenseNet-121 & Resnet-18) models, we were able to successfully detect COVID-19. The Densenet-121 model did well in its training and evaluation on a dataset of 1600 chest X-ray images. Over 2700 CXR pictures may be used for model training and evaluation with Resnet18. We have separated the data into groups according to the suggested models and found widely varying degrees of precision across the board. Data from both sources showed that Densenet-121 was the most reliable model.

Current Artificial Intelligence (AI) Techniques, Challenges, and Approaches in Controlling and Fighting COVID-19: A Review.

SARS-CoV-2 (COVID-19) has been one of the worst global health crises in the 21st century. The currently available rollout vaccines are not 100% effective for COVID-19 due to the evolving nature of the virus. There is a real need for a concerted effort to fight the virus, and research from diverse fields must contribute. Artificial intelligence-based approaches have proven to be significantly effective in every branch of our daily lives, including healthcare and medical domains. During the early days of this pandemic, artificial intelligence (AI) was utilized in the fight against this virus outbreak and it has played a major role in containing the spread of the virus. It provided innovative opportunities to speed up the development of disease interventions. Several methods, models, AI-based devices, robotics, and technologies have been proposed and utilized for diverse tasks such as surveillance, spread prediction, peak time prediction, classification, hospitalization, healthcare management, heath system capacity, etc. This paper attempts to provide a quick, concise, and precise survey of the state-of-the-art AI-based techniques, technologies, and datasets used in fighting COVID-19. Several domains, including forecasting, surveillance, dynamic times series forecasting, spread prediction, genomics, compute vision, peak time prediction, the classification of medical imaging-including CT and X-ray and how they can be processed-and biological data (genome and protein sequences) have been investigated. An overview of the open-access computational resources and platforms is given and their useful tools are pointed out. The paper presents the potential research areas in AI and will thus encourage researchers to contribute to fighting against the virus and aid global health by slowing down the spread of the virus. This will be a significant contribution to help minimize the high death rate across the globe.

COVID-19 Vaccination Acceptance among Healthcare Staff in Sudan, 2021.

Elderly and patients with comorbid conditions have higher risk of infection and complications. Vaccination hesitancy is defined as the refusal of vaccine or the delay in accepting it despite the availability of vaccines and vaccination services. This study was aimed at assessing knowledge, perception, and acceptability of healthcare staff towards different types of COVID-19 vaccination. A multicenter hospital-based descriptive cross-sectional study was implemented to study the knowledge, perception, and acceptability of healthcare staff towards COVID-19 vaccination. Multistage sampling technique was applied. Data were collected through a self-administered questionnaire filled by the participants. 400 participants were studied. 61% of the participants were females, and the most frequent age reported was between 18 and 35 years (67%). A statistically significant association (p = 0.048) was found between knowledge about vaccination and professions. The most common vaccine type known and accepted was AstraZeneca vaccine. On assessing acceptability of COVID-19 vaccination, acceptance rate was high (63.8%) and 22.7% of the participants had already got vaccinated. The rejection rate among our staff was 27.4%. This study was conducted in April, 2021. Majority of our healthcare staff believed that vaccination is the key to combat the pandemic. One of the issues and concerns about vaccination was the safety and the risk of developing acute adverse events (p = 0.001). Encouraging factor for vaccination was the fear of getting infection themselves and their families. The present study revealed the presence of good knowledge and acceptability among medical staff towards COVID-19 vaccinations in Sudan.

The Usage of Antibiotics by COVID-19 Patients with Comorbidities: The Risk of Increased Antimicrobial Resistance.

Antimicrobial resistance (AMR) is a global health issue that plays a significant role in morbidity and mortality, especially in immunocompromised patients. It also becomes a serious threat to the successful treatment of many bacterial infections. The widespread and irrelevant use of antibiotics in hospitals and local clinics is the leading cause of AMR. Under this scenario, the study was conducted in a tertiary care hospital in Lahore, Pakistan, from 2 August 2021 to 31 October 2021 to discover the prevalence of bacterial infections and AMR rates in COVID-19 patients admitted in surgical intensive care units (SICUs). Clinical samples were collected from the patients and we proceeded to identify bacterial isolates, followed by antibiotic susceptibility testing (AST) using the Kirby Bauer disk diffusion method and minimum inhibitory concentration (MIC). The data of other comorbidities were also collected from the patient's medical record. The current study showed that the most common pathogens were E. coli (32%) and Klebsiella pneumoniae (17%). Most E. coli were resistant to ciprofloxacin (16.8%) and ampicillin (19.8%). Klebsiella pneumoniae were more resistant to ampicillin (13.3%) and amoxycillin (12.0%). The most common comorbidity was chronic kidney disease (CKD) and urinary tract infections (UTIs). Around 17 different types of antibiotic, the carbapenem, fluoroquinolones, aminoglycoside, and quinolones, were highly prevalent in ICU patients. The current study provides valuable data on the clinical implication of antibiotics consumed by COVID-19 patients in SICUs and the AMR rates, especially with different comorbidities.

Incidence, Risk Factors, and Outcomes of Rhabdomyolysis in Hospitalized Patients With COVID-19 Infection.

INTRODUCTION: There is a paucity of studies examining the prevalence and clinical characteristics of rhabdomyolysis in hospitalized patients with COVID-19 infection. The purpose of this study is to examine the incidence, clinical characteristics, and outcome of hospitalized patients with COVID-19 infection who develop rhabdomyolysis. METHODOLOGY: This is a single-center retrospective analysis of all hospitalized patients with COVID-19 admitted between March 8, 2020, and January 11, 2021. All patients with creatinine kinase (CK) levels available during the hospital admission were included. Rhabdomyolysis was defined as an elevation in CK level higher than five times the upper limit of normal (i.e., 1125 U/L). We compared clinical characteristics and outcomes of patients who developed rhabdomyolysis with patients who did not develop rhabdomyolysis. RESULTS: The incidence of rhabdomyolysis in hospitalized patients with COVID-19 infection was 9.2%. There was no significant difference noted in comorbidities and clinical characteristics between the two groups. Moreover, there was no significant difference noted in the presence of severe COVID-19 infection (72.7% vs 54.6%, p = 0.1), mortality (27.3% vs 23.9%, p = 0.72), acute kidney injury (59.1% vs 42.7%, p = 0.14), or need for intensive care unit (ICU) care (72.7% vs 51.4%, p = 0.051). However, a higher percentage of patients in the rhabdomyolysis group required physical rehabilitation after discharge (40.9% vs 19.3%, p = 0.02). CONCLUSION: The overall incidence of rhabdomyolysis in hospitalized patients with COVID-19 infection was high (9.2%). The presence of rhabdomyolysis was not associated with the increased severity of the disease. Patients with rhabdomyolysis more frequently required physical rehabilitation compared to those without rhabdomyolysis.

Occupational Stress and Stress Busters Used Among Saudi Dental Practitioners During the COVID-19 Pandemic Outbreak.

OBJECTIVE: This study aims to assess the stress levels, stress busters (stress relievers), and coping mechanisms among Saudi dental practitioners (SDPs) during the coronavirus disease (COVID-19) pandemic outbreak. METHOD: A self-administered questionnaire was sent to SDPs via Google Forms. Cohen's stress score scale was used for stress evaluation, and the mean scores were compared based on age, gender, qualification, and occupation. In addition, comparisons of the utilization of stress coping mechanisms and stress busters based on gender, age, and occupation were evaluated. Descriptive statistics were carried out using SPSS Version 21.0 (IBM Corp, Armonk, NY). RESULTS: A total of 206 SDPs (69% males and 31% females) participated in the study. Male SDPs showed a higher score than females (P > 0.05). SDPs around age 50 years and above obtained high stress scores (25 ± 7.4) as compared with other age groups (P < 0.05). The occupational level showed higher stress scores (22.6 ± 4.6 than the other occupation groups (P < 0.05). The majority of the SDPs used watching TV/mobile/computer (80%) as a stress buster, followed by binge eating (64%), exercise (44%), smoking (32%), do-it-yourself (DIY; 23%), and meditation (17%). CONCLUSION: SDPs are experiencing stress levels during the COVID-19 pandemic. Male SDPs above age 50 years and private practitioners showed higher levels of stress scores. An overall commonly used stress buster was smoking in males and meditation in females.

Remote Management of Prosthodontic Emergencies in the Geriatric Population During the Pandemic Outbreak of COVID-19.

The global pandemic outbreak of coronavirus disease 2019 (COVID-19) has put the world in a panic situation. It has been evident that the geriatric population is more susceptible to acquire this infection. Thus, due to this ongoing crisis, geriatric individuals cannot visit a dental operatory, and there is the possibility of their dental needs and emergencies to remain unattended. Partial or complete edentulism is very prevalent among the geriatric population, and prosthodontic management of these patients is essential for their well-being. However, the prosthesis can lead to various emergencies of the oral and its supporting structures. There are currently no available recommendations to address these prosthodontic emergencies in geriatric individuals during this pandemic period. Hence, the aims of this minireview were to discuss the common prosthodontic emergencies in the geriatric population and provide recommendations to manage these issues during the COVID-19 pandemic outbreak.