Clinical Features and Burden of Post-Acute Sequelae of SARS-CoV-2 Infection in Children and Adolescents

Background. The post-acute sequelae of SARS-CoV-2 (PASC) has emerged as a long-term complication in adults, but current understanding of the clinical presentation of PASC in children is limited. Our study objectives were to identify symptoms, health conditions, and medications associated with PASC in children. Methods. We conducted a retrospective cohort study using electronic health records from 9 US children's hospitals for individuals < 21 years who underwent polymerase chain reaction (PCR) testing for SARS-CoV-2 between March 1, 2020 - October 31, 2021 and had at least 1 encounter in the 3 years before testing. Our exposure of interest was SARS-CoV-2 PCR positivity. We identified syndromic (symptoms), systemic (conditions), and medication PASC features in the 28-179 days following the initial test date. Adjusted hazard ratios (aHRs) were obtained for 151 clinically predicted PASC features by contrasting PCR-positive with PCR-negative groups using proportional hazards models, adjusting for site, age, sex, testing location, race/ethnicity, and time-period of cohort entrance. We estimated the incidence proportion for any syndromic, systemic or medication PASC feature in the two groups to estimate PASC burden. Results. Among 659,286 children in the study sample, 59,893 (9.1%) tested positive by PCR for SARS-CoV-2. Most were tested in outpatient testing facility (50.3%) or office (24.6%) settings (Table 1). The most common syndromic, systemic, and medication features were loss of taste or smell (aHR 1.96 [95% CI 1.16-3.32), myocarditis (aHR 3.10 [95% CI 1.94-4.96]) (Figures 1 and 2), and cough and cold preparations (aHR 1.52 [95% CI 1.18-1.96]). The incidence of at least one systemic/syndromic/ medication feature of PASC was 42.0% among PCR-positive children versus 38.2% among PCR-negative children, with an incidence proportion difference of 3.8% (95% CI 3.3-4.3%). A higher strength of association for PASC was identified in those cared for in the ICU during the acute illness phase, children less than 5 years-old, and individuals with complex chronic conditions. Adjusted hazard ratios (aHR) with associated 95% CI among patients who tested positive for SARS-CoV-2 infection versus those who tested negative for the risk of each syndromic feature (symptom) using Cox proportional hazards models. Models were adjusted for age at cohort entrance, sex, race/ethnicity, institution, testing place location, presence of a complex medical condition and date of cohort entrance. Adjusted hazard ratios (aHR) with associated 95% CI among patients who tested positive for SARS-CoV-2 infection versus those who tested negative for the risk of each systemic feature using Cox proportional hazards models. Models were adjusted for age at cohort entrance, sex, race/ethnicity, institution, testing place location, and date of cohort entrance. For each health condition evaluated, patients with evidence of that condition 18 months before cohort entrance were excluded from the denominator in order to identify incident cases. Each ratio compares the risk of the outcome in children who tested positive for SARS-CoV-2 infection versus those who tested negative. Footnote: The diagnostic cluster for COVID-19 indicates children receiving care for the illness in the post-acute period. Conclusion. In this large-scale, exploratory study, the burden of PASC in children appeared to be lower than earlier reports. Acute illness severity, young age, and comorbid complex chronic disease increased the risk of PASC. (Figure Presented).

NNPCov19: Artificial Neural Network-Based Propaganda Identification on Social Media in COVID-19 Era

The latest trend of sharing information has evolved many concerns for the current researchers, which are working on computational social sciences. Online social network platforms have become a tool for sharing propagandistic information. This is being used as a lethal weapon in modern days to destabilize democracies and other political or religious events. The COVID-19 affected almost every corner of the world. Various propagandistic tweets were shared on Twitter during the peak time of COVID-19. In this paper, improved artificial neural network algorithm is proposed to classify tweets into propagandistic and nonpropagandistic class. The data are extracted using multiple ambiguous hashtags and are manually annotated into binary class. Hybrid feature engineering is being performed by combining "Term Frequency (TF)/Inverse Document Frequency (IDF)," "Bag of Words," and Tweet Length. The proposed algorithm is compared with logistic regression, support vector machine, and multinomial Naive Bayes. Results showed that improved artificial neural network algorithm outperforms other machine learning algorithms by having 77.15% accuracy, 77% of recall, and 79% precision. In future, deep learning approaches like LSTM may be used for this classification task.

FPGA-Based Deep Learning Models for Analysing Corona Using Chest X-Ray Images

Coronavirus is a large family of viruses that affects humans and damages respiratory functions ranging from cold to more serious diseases such as ARDS and SARS. But the most recently discovered virus causes COVID-19. Isolation at home or hospital depends on one's health history and conditions. The prevailing disease that might get instigated due to the existence of the virus might lead to deterioration in health. Therefore, there is a need for early detection of the virus. Recently, many works are found to be observed with the deployment of techniques for the detection based on chest X-rays. In this work, a solution has been proposed that consists of a sample prototype of an AI-based Flask-driven web application framework that predicts the six different diseases including ARDS, bacteria, COVID-19, SARS, Streptococcus, and virus. Here, each category of X-ray images was placed under scrutiny and conducted training and testing using deep learning algorithms such as CNN, ResNet (with and without dropout), VGG16, and AlexNet to detect the status of X-rays. Recent FPGA design tools are compatible with software models in deep learning methods. FPGAs are suitable for deep learning algorithms to make the design as flexible, innovative, and hardware acceleration perspective. High-performance FPGA hardware is advantageous over GPUs. Looking forward, the device can efficiently integrate with the deep learning modules. FPGAs act as a challenging substitute podium where it bridges the gap between the architectures and power-related designs. FPGA is a better option for the implementation of algorithms. The design attains 121μW power and 89 ms delay. This was implemented in the FPGA environment and observed that it attains a reduced number of gate counts and low power. © 2022 Anupama Namburu et al.

Increased Referrals for New PFAPA (Aphthous Stomatitis, Pharyngitis, Adenitis) Diagnosis during the COVID-19 Pandemic

Background. COVID-19 pandemic caused by SARS-CoV-2 resulted in a global health crisis in 2020. Quarantining, wearing masks and physical distancing- key infection prevention strategies implemented to stop the spread of COVID-19, also led to dramatic decreases in rates of common respiratory viral infection seen in young children. Due to lack of school and daycare exposure, we evaluated a larger than usual number of patients with periodic fevers without any known infectious contacts. Based on this observation, we conducted an analysis of all suspected cases of periodic fevers seen at our institution during the COVID-19 lockdown compared to prior seasons. Methods. The clinical charts were queried for all patients presenting to any Lurie Children's Hospital outpatient specialty clinic or laboratory with ICD diagnosis code of MO4.1 and MO4.8 (all recurrent and periodic fever syndromes) from June 1, 2020 through September 30, 2020, and compared to similar months the previous 2 years (2018 and 2019). Each patient chart was reviewed by the lead investigator to verify all new diagnoses of PFAPA. The number of new patients with PFAPA diagnosis were tallied and analyzed. Statistical comparisons were made using Kruskal-Wallis tests for monthly distributions in different years. Results. We noted a significant increase in patients with new PFAPA diagnosis between June through August 2020 compared to similar months in 2018 and 2019 (Figure1). Experienced pediatric infectious disease physicians and rheumatologists diagnosed majority of the cases. During these months, a monthly median (IQR) of 13 (11.5, 14.5) patients were diagnosed among different Lurie specialty clinics, which is more than 2.5 folds increase in new PFAPA patients from the previous two years which were about 5 (3.5, 6) (Figure 2). Conclusion. We observed a significant increase in PFAPA patients referred to our institution soon after introduction of public health measures to slow spread of COVID-19. Given that most children were not in daycare, schools, or camps, we suspect that parents and pediatricians were able to recognize patterns of periodic fevers in children much quicker than preceding years, when fevers would typically be attributed to an infectious process.

To determine the critical factors for the adoption of cloud computing in the educational sector in developing countries - a fuzzy DEMATEL approach

Purpose The paper focuses on reviewing and theorizing the factors that affect the adoption of cloud computing in the education sector narrowing the focus to developing countries such as India. Design/methodology/approach Through an extensive literature survey, critical factors of cloud computing for education were identified. Further, the fuzzy DEMATEL approach was used to define their interrelationship and its cause and effect. Findings A total of 17 factors were identified for the study based on the literature survey and experts' input. These factors were classified as causes and effects and ranked and interrelated. "Required Learning Skills and Attitude," "Lack of Infrastructure," "Learners' Ability" and "Increased Investment" are found to be the most influential factors. Practical implications The resultant ranking factors can be used as a basis for managing the process of cloud adoption in several institutions. The study could guide academicians, policymakers and government authorities for the effective adoption of cloud computing in education. Originality/value The study investigates interdependency amongst the factors of cloud computing for education in context with developing economy. This is one of first study in higher education institutes of India.