Burnout in South Asian rheumatologists in the COVID-19 pandemic: an online survey.

Physician burnout is recognized as a global crisis and an epidemic. However, burnout in rheumatology is an understudied phenomenon. We explored the prevalence of physician burnout among rheumatologists from South Asia in the peri-pandemic period (2021-2022). Rheumatologists from Asian countries were invited to participate in an anonymized, validated, and pilot-tested e-survey via social media platforms from December 2021 to April 2022. Demographic information, social aspects (marital status, income, vacation time, daily exercise), substance abuse, EHR (electronic health record) use, and years in practice were obtained. In addition, burnout was estimated using Maslach Burnout Inventory-Human Services Survey for Medical Personnel (MBI-HSS) in three domains: emotional exhaustion (EE), depersonalization (DP), and personal accomplishment (PA), and intergroup comparisons were made using independent t test and one-way ANOVA using SPSS v27. Of 146 respondents who participated in the survey, 134 belonged to Asian nations. The highest number of respondents was from Pakistan (56/134, 38.36%), followed by India (49/134, 33.56%). Slightly more respondents were male, 75 (51.4%), than females, 71 (48.6%). Over two-thirds of our respondents (99/134, 67.8%) reported burnout in at least one domain. Notably, we found statistically significant increased depersonalization scores in males. (P < 0.05). Females scored significantly higher on the following items: #5 (I feel I treat some patients as if they were impersonal objects), item# 10 (I've become more insensitive toward people since I took this job and item#22 (I feel patients blame me for some of their problems) (P < 0.05). We found differences in monthly salaries among South Asian (1484 ± 2717 USD) and non-South Asian respondents (5672 ± 8874 USD) (P < 0.01). A substantial proportion of rheumatologists in our survey report burnout, suggesting a felt need to introduce organizational measures to prevent and mitigate burnout and preserve the rheumatology workforce.

Therapeutic strategy targeting host lipolysis limits infection by SARS-CoV-2 and influenza A virus.

The biosynthesis of host lipids and/or lipid droplets (LDs) has been studied extensively as a putative therapeutic target in diverse viral infections. However, directly targeting the LD lipolytic catabolism in virus-infected cells has not been widely investigated. Here, we show the linkage of the LD-associated lipase activation to the breakdown of LDs for the generation of free fatty acids (FFAs) at the late stage of diverse RNA viral infections, which represents a broad-spectrum antiviral target. Dysfunction of membrane transporter systems due to virus-induced cell injury results in intracellular malnutrition at the late stage of infection, thereby making the virus more dependent on the FFAs generated from LD storage for viral morphogenesis and as a source of energy. The replication of SARS-CoV-2 and influenza A virus (IAV), which is suppressed by the treatment with LD-associated lipases inhibitors, is rescued by supplementation with FFAs. The administration of lipase inhibitors, either individually or in a combination with virus-targeting drugs, protects mice from lethal IAV infection and mitigates severe lung lesions in SARS-CoV-2-infected hamsters. Moreover, the lipase inhibitors significantly reduce proinflammatory cytokine levels in the lungs of SARS-CoV-2- and IAV-challenged animals, a cause of a cytokine storm important for the critical infection or mortality of COVID-19 and IAV patients. In conclusion, the results reveal that lipase-mediated intracellular LD lipolysis is commonly exploited to facilitate RNA virus replication and furthermore suggest that pharmacological inhibitors of LD-associated lipases could be used to curb current COVID-19- and future pandemic outbreaks of potentially troublesome RNA virus infection in humans.

Barriers and facilitators to adoption of e-learning in higher education institutions of Pakistan during COVID-19: perspectives from an emerging economy

Purpose Integrating e-learning into higher education institutions (HEIs) is a complex process. Several universities had tried to impart learning online, especially amid the spread of COVID-19. However, they failed miserably due to the many barriers to online learning platforms' delivery and acceptance. This study aims to explore the barriers and facilitators in adopting e-learning in HEIs of Pakistan by taking the perspective of key stakeholders involved in the management and administration of HEIs. Design/methodology/approach The authors recruited participants using purposive and snowball sampling. Interviews were conducted from a variety of participants, including academicians, administrators and information technology (IT) personnel. Data recorded was transcribed into verbatim and then analyzed using thematic analysis. Findings The analysis identified barriers and facilitators to the e-learning implementation. Barriers included lack of resources and training, lack of infrastructure, inadequate e-learning policies, absence of positive mindset among teachers and students and reservations and concerns about e-learning of parents and teachers. By contrast, facilitators included prior training and awareness (provided by HEIs regarding e-learning), the assistance of government and regulatory bodies (in terms of policy and training on e-learning), the role of IT (in development and implementation of online learning system) and good computer knowledge and skills of students and faculty. Moreover, respondents believed that teaching subjects online requires the availability of proper and complete gadgets, but these were hardly available due to high demand. Finally, the academicians and administrators believed that e-learning is indispensable in health emergencies such as COVID-19 and similar events ahead. Originality/value For the HEIs to sustain and grow, the adoption of e-learning is fundamental. Therefore, the government should provide the essential infrastructure for the HEIs to deploy e-learning modules, train faculty and ensure the availability of necessary equipment (e.g. network) and gadgets to faculty and students. From a theoretical perspective, the study provides a framework for similar future studies in other emerging markets, whereas practical implications of the study can assist the governments and HEIs of emerging markets in implementing the e-learning modes of education in times of health emergencies, such as COVID-19.

Quantum Machine Learning Architecture for COVID-19 Classification Based on Synthetic Data Generation Using Conditional Adversarial Neural Network.

Background: COVID-19 is a novel virus that affects the upper respiratory tract, as well as the lungs. The scale of the global COVID-19 pandemic, its spreading rate, and deaths are increasing regularly. Computed tomography (CT) scans can be used carefully to detect and analyze COVID-19 cases. In CT images/scans, ground-glass opacity (GGO) is found in the early stages of infection. While in later stages, there is a superimposed pulmonary consolidation. Methods: This research investigates the quantum machine learning (QML) and classical machine learning (CML) approaches for the analysis of COVID-19 images. The recent developments in quantum computing have led researchers to explore new ideas and approaches using QML. The proposed approach consists of two phases: in phase I, synthetic CT images are generated through the conditional adversarial network (CGAN) to increase the size of the dataset for accurate training and testing. In phase II, the classification of COVID-19/healthy images is performed, in which two models are proposed: CML and QML. Result: The proposed model achieved 0.94 precision (Pn), 0.94 accuracy (Ac), 0.94 recall (Rl), and 0.94 F1-score (Fe) on POF Hospital dataset while 0.96 Pn, 0.96 Ac, 0.95 Rl, and 0.96 Fe on UCSD-AI4H dataset. Conclusion: The proposed method achieved better results when compared to the latest published work in this domain.

Acute Neurological Manifestations of COVID-19 Patients From Three Tertiary Care Hospitals in Qatar.

Introduction Worldwide, there are more than 424 million confirmed cases of COVID-19. Most of the hospitalized critical COVID-19 patients manifested neurological signs and symptoms and higher mortality. The majority of COVID-19 fatalities occurred mostly in patients with advanced age and underlying medical comorbidities. This is the first local retrospective study in Qatar, which reported neurologic manifestations (48.5%) of hospitalized COVID-19 patients. The primary objective of this study is to evaluate acute neurological manifestations in COVID-19 hospitalized patients in the country. Methods This is a retrospective, observational study of 413 hospitalized COVID-19 patients. They were admitted to three different COVID-19 designated hospitals (Hazm Mebaireek, Ras Laffan, and Cuban tertiary care Hospitals) under the Hamad Medical Corporation, Qatar from 1st January 2020, to 31 January 2021. We evaluated electronic medical records of these patients and data were collected while their neurological manifestations were confirmed by two trained neurologists. These neurologic manifestations were categorized into three major groups: central nervous system (CNS), peripheral nervous system (PNS), and neuromuscular system. Results Of 413 patients, 94% (389) were male and 6% (24) were female; the mean age was 52 years. Among all different nationalities of COVID-19 patients, 20.3% (84) were Indian, 12.5% (52) were Bangladeshi, 10.1% (42) were Qatari and 9.2% (38) were Nepali. The most common symptoms at the onset of COVID-19 illness were as follows: 77.5% (321) had a fever, 67.4% (279) experienced cough, 58.7% (243) experienced shortness of breath and 26.1% (108) developed a sore throat. Overall 48.5% (201) patients developed different neurologic manifestations. The most common neurologic symptoms were myalgia (28%; 116), headache (10.4%; 43), dizziness (5.8%; 24) and hemiparesis due to strokes (5.3%; 22). In this study, the most common risk factors were hypertension (47.6%), diabetes (46.9%), obesity (21%), chronic kidney disease (10%), ischemic heart disease (9.7%), and smoking (6.8%). About 45.2% (187) patients were admitted to MICU and 8.5% (35) died due to COVID-19 complications. Significant other extrapulmonary multiorgan system involvement were skeletal muscle injury (39.4%), kidney injury (36.7%), liver injury (27.5%), myocardial injury (23.9%), rhabdomyolysis (15.7%) heart failure (11.4%) and acute pancreatitis (11.1%). Discussion The most common neurologic signs and symptoms were myalgia, headache, dizziness, and strokes, mainly due to large vessel thrombosis, lacunar, and posterior circulation strokes. Conclusions Patients with COVID-19 are at high risk of developing neurological manifestations. The most common COVID-19-related acute neurological manifestations were myalgia, headache, dizziness, and acute ischemic stroke. Prompt recognition, early diagnosis, and appropriate management of these manifestations could potentially lead to better patient outcomes in COVID-19 patients.

Post-COVID-19 pneumonia pneumatoceles: a case report.

INTRODUCTION: Pneumatocele formation in COVID-19 pneumonia is arguably a common occurrence. CASE PRESENTATION: We present a case of pneumatoceles, developing as a sequel of COVID-19 infection. We argue that pneumatocele formation in COVID-19 pneumonia is a common occurrence. Importantly pneumothorax, which can lead to a raised morbidity and mortality in these patients, can be a complication of a pneumatocele rupture. CONCLUSION: As pneumatocele in COVID-19 pneumonia patients can lead to life-threatening complications, we emphasize the need to formulate appropriate and standardized monitoring and management guidelines. Our literature review also discusses various plausible mechanisms leading to pneumatocele formation and points to management strategies that may prevent pneumatocele formation and its complications.

Multidimensional impacts of coronavirus pandemic in adolescents in Pakistan: A cross sectional research.

BACKGROUND: COVID-19 has posed unique challenges for adolescents in different dimensions of their life including education, home and social life, mental and physical health. Whether the impact is positive or negative, its significance on the overall shaping of adolescents' lives cannot be overlooked. The aim of the present study was to explore impacts of the pandemic on the adolescents' everyday lives in Pakistan. METHODS: Following ethical approval, this cross-sectional study was conducted through September to December, 2020 via an online survey on 842 adolescents with the mean age of 17.14 ± SD 1.48. Socio-demographic data and Epidemic Pandemic Impact Inventory-Adolescent Adaptation (EPII-A) was used to assess the multi-dimensional effects of the pandemic. RESULTS: Among the 842 participants, 84% were girls. Education emerged as the most negatively affected Pandemic domain (41.6-64.3%). Most of the adolescents (62.0-65.8%) had reported changes in responsibilities at home including increased time spent in helping family members. Besides, increase in workload of participants and their parents was prominent (41.8% & 47.6%). Social activities were mostly halted for approximately half (41-51%) of the participants. Increased screen time, decreased physical activity and sedentary lifestyle were reported by 52.7%, 46.3% and 40.7% respectively. 22.2-62.4% of the adolescents had a direct experience with quarantine, while 15.7% experienced death of a close friend or relative. Positive changes in their lives were endorsed by 30.5-62.4% respondents. Being male and older adolescents had significant association with negative impact across most domains (p<0.05). CONCLUSIONS: Results have shown that COVID-19 exert significant multidimensional impacts on the physical, psycho-social, and home related domains of adolescents that are certainly more than what the previous researches has suggested.

Complete heart block in patients infected with SARS-CoV-2: A case series from a developing country.

Coronavirus Disease 19 (COVID-19) has led to a global pandemic and has been the center of attention across the entire medical community. This novel virus was initially thought to affect primarily the respiratory system, but now it is evident that it has a multitude of effects on the human body. Our point of interest is to establish the effect of COVID-19 infection on the conducting system of the heart. We present a case series of four patients who developed complete heart block (CHB) shortly after being infected with COVID-19 without any previous known risk factors of complete heart block. There have only been a few previous case reports on the occurrence of CHB in COVID-19 patients highlighting the importance and the need of our case series to the literature of cardiovascular outcomes in COVID-19 patients. Our case series highlight that COVID-19 can indeed affect the conduction system of the heart and cause CHB in patients who then recovered spontaneously further elucidating the transient nature of cardiovascular effects caused by the novel virus.

Microscopic segmentation and classification of COVID-19 infection with ensemble convolutional neural network.

The detection of biological RNA from sputum has a comparatively poor positive rate in the initial/early stages of discovering COVID-19, as per the World Health Organization. It has a different morphological structure as compared to healthy images, manifested by computer tomography (CT). COVID-19 diagnosis at an early stage can aid in the timely cure of patients, lowering the mortality rate. In this reported research, three-phase model is proposed for COVID-19 detection. In Phase I, noise is removed from CT images using a denoise convolutional neural network (DnCNN). In the Phase II, the actual lesion region is segmented from the enhanced CT images by using deeplabv3 and ResNet-18. In Phase III, segmented images are passed to the stack sparse autoencoder (SSAE) deep learning model having two stack auto-encoders (SAE) with the selected hidden layers. The designed SSAE model is based on both SAE and softmax layers for COVID19 classification. The proposed method is evaluated on actual patient data of Pakistan Ordinance Factories and other public benchmark data sets with different scanners/mediums. The proposed method achieved global segmentation accuracy of 0.96 and 0.97 for classification.

An integrated framework for COVID-19 classification based on classical and quantum transfer learning from a chest radiograph.

COVID-19 is a quickly spreading over 10 million persons globally. The overall number of infected patients worldwide is estimated to be around 133,381,413 people. Infection rate is being increased on daily basis. It has also caused a devastating effect on the world economy and public health. Early stage detection of this disease is mandatory to reduce the mortality rate. Artificial intelligence performs a vital role for COVID-19 detection at an initial stage using chest radiographs. The proposed methods comprise of the two phases. Deep features (DFs) are derived from its last fully connected layers of pre-trained models like AlexNet and MobileNet in phase-I. Later these feature vectors are fused serially. Best features are selected through feature selection method of PCA and passed to the SVM and KNN for classification. In phase-II, quantum transfer learning model is utilized, in which a pre-trained ResNet-18 model is applied for DF collection and then these features are supplied as an input to the 4-qubit quantum circuit for model training with the tuned hyperparameters. The proposed technique is evaluated on two publicly available x-ray imaging datasets. The proposed methodology achieved an accuracy index of 99.0% with three classes including corona virus-positive images, normal images, and pneumonia radiographs. In comparison to other recently published work, the experimental findings show that the proposed approach outperforms it.

An intelligence design for detection and classification of COVID19 using fusion of classical and convolutional neural network and improved microscopic features selection approach.

Coronavirus19 is caused due to infection in the respiratory system. It is the type of RNA virus that might infect animal and human species. In the severe stage, it causes pneumonia in human beings. In this research, hand-crafted and deep microscopic features are used to classify lung infection. The proposed work consists of two phases; in phase I, infected lung region is segmented using proposed U-Net deep learning model. The hand-crafted features are extracted such as histogram orientation gradient (HOG), noise to the harmonic ratio (NHr), and segmentation based fractal texture analysis (SFTA) from the segmented image, and optimum features are selected from each feature vector using entropy. In phase II, local binary patterns (LBPs), speeded up robust feature (Surf), and deep learning features are extracted using a pretrained network such as inceptionv3, ResNet101 from the input CT images, and select optimum features based on entropy. Finally, the optimum selected features using entropy are fused in two ways, (i) The hand-crafted features (HOG, NHr, SFTA, LBP, SURF) are horizontally concatenated/fused (ii) The hand-crafted features (HOG, NHr, SFTA, LBP, SURF) are combined/fused with deep features. The fused optimum features vector is passed to the ensemble models (Boosted tree, bagged tree, and RUSBoosted tree) in two ways for the COVID19 classification, (i) classification using fused hand-crafted features (ii) classification using fusion of hand-crafted features and deep features. The proposed methodology is tested /evaluated on three benchmark datasets. Two datasets employed for experiments and results show that hand-crafted & deep microscopic feature's fusion provide better results compared to only hand-crafted fused features.

Association of PPE Availability, Training, and Practices with COVID-19 Sero-Prevalence in Nurses and Paramedics in Tertiary Care Hospitals of Peshawar, Pakistan.

OBJECTIVE: Nurses and paramedics by being the frontline workers of the health-care profession need to be equipped with the relevant knowledge, skills, and protective gears against different forms of infection, including coronavirus disease 2019 (COVID-19). Although the governments and concerned stakeholders have provided personal protective equipment (PPE), training and information to protect the health-care professionals; however, until now the scientific literature has virtually not reported the impact of PPE availability, training, and practices on the COVID-19 sero-prevalence among the nurses and paramedics. This study aimed to assess the impact of PPE availability, training, and practices on COVID-19 sero-prevalence among nurses and paramedics in teaching hospitals of Peshawar, Pakistan. METHODS: A cross-sectional survey was conducted with a total of 133 nurses and paramedics as subjects of the study. RESULTS: A univariate analysis was done for 4 variables. The findings indicate that the health-care professionals (nurses and paramedics) who have received PPE on time at the start of COVID-19 emergence have fewer chances of contracting the COVID-19 infection (odds ratio = 0.96); while the odds for PPE supplies was 0.73, and the odds of hand hygiene training was 0.95. CONCLUSIONS: The study concluded that the availability of the PPE, COVID-19-related training, and compliance with World Health Organization recommended practices against COVID-19 were instrumental in protection against the infection and its spread.

Demographic, psychological, and environmental factors affecting student's health during the COVID-19 pandemic: on the rocks.

The novel coronavirus (COVID-19) is spreading exponentially, increasing fear, depression, and other mental health disorders in the general public. Pakistan's economy is suffered mainly by the novel coronavirus. The massive healthcare expenditures bring inadequacy to manage COVID-19. The study explored the effects of coronavirus fear among the students who remain in their homes due to educational institutions' closure. The study results show that female students mostly fear the coronavirus pandemic compared to their male counterparts that negatively impact their health. The "age" of the students and "household size" positively impact students' health, while the student's existing "healthcare profile" is not competitive enough to escape from the deadly coronavirus. The "knowledge" for the coronavirus pandemic and its prevention guidelines is the only solution to contain coronavirus. Simultaneously, "ignorance" is the foremost factor that could be more dangerous to spread coronavirus among the students; besides the COVID-19 pandemic, students and general public health mainly suffered from environmental pollution. The current epidemic also exacerbated environmental concerns among students isolated in their homes, and their outdoor activities are primarily limited. Hence, the student's quality of life is exposed mainly to environmental pollution over time. The "healthcare expenditures" and "government support" both are not competitive enough to control novel coronavirus. Thus, it required more sustainable strategic policies and national unity to controlled coronavirus with firm conviction and provincial synchronization.

Prediction of COVID-19 - Pneumonia based on Selected Deep Features and One Class Kernel Extreme Learning Machine.

In this work, we propose a deep learning framework for the classification of COVID-19 pneumonia infection from normal chest CT scans. In this regard, a 15-layered convolutional neural network architecture is developed which extracts deep features from the selected image samples - collected from the Radiopeadia. Deep features are collected from two different layers, global average pool and fully connected layers, which are later combined using the max-layer detail (MLD) approach. Subsequently, a Correntropy technique is embedded in the main design to select the most discriminant features from the pool of features. One-class kernel extreme learning machine classifier is utilized for the final classification to achieving an average accuracy of 95.1%, and the sensitivity, specificity & precision rate of 95.1%, 95%, & 94% respectively. To further verify our claims, detailed statistical analyses based on standard error mean (SEM) is also provided, which proves the effectiveness of our proposed prediction design.

Psychological burden of quarantine in children and adolescents: A rapid systematic review and proposed solutions.

As COVID-19 grips the world, many people are quarantined or isolated resulting in adverse consequences for the mental health of youth. This rapid review takes into account the impact of quarantine on mental health of children and adolescents, and proposes measures to improve psychological outcomes of isolation. Three electronic databases including PubMed, Scopus, and ISI Web of Science were searched. Two independent reviewers performed title and abstract screening followed by full-text screening. This review article included 10 studies. The seven studies before onset of COVID 19 about psychological impact of quarantine in children have reported isolation, social exclusion stigma and fear among the children. The most common diagnoses were acute stress disorder, adjustment disorder, grief, and post-traumatic stress disorder. Three studies during the COVID-19 pandemic reported restlessness, irritability, anxiety, clinginess and inattention with increased screen time in children during quarantine. These adverse consequences can be tackled through carefully formulated multilevel interventions.