Background Adults infected with severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) have had high rates of thrombosis. A novel condition in children infected with SARS‐CoV‐2, multisystem inflammatory syndrome in children (MIS‐C), has limited data on their prothrombotic state or need for thromboprophylaxis. Objectives We aimed to analyze the prothrombotic state using coagulation profiles, rotational thromboelastometry (ROTEM) parameters and clinical outcomes, to determine if this could aid in risk stratification for thromboprophylaxis. Methods This analysis included patients (<21 years of age) with a diagnosis of MIS‐C (n = 40) and controls (presenting with suspicion of MIS‐C but later ruled out; n = 26). Results MIS‐C patients had higher levels of inflammatory markers including D‐dimer (p < .0001), compared with controls, along with evidence of hypercoagulability on ROTEM with elevated evaluation of fibrinogen activity (FIBTEM) maximum clot firmness (MCF) (p < .05). For MIS‐C patients with D‐dimers >1000 ng/ml, there was a significant correlation of FIBTEM MCF (p < .0001) with a mean value of 37.4 (standard deviation 5.1). D‐dimer >2144 ng/ml was predictive of intensive care unit admission (area under the curve [AUC] 0.80; 95% confidence interval, 0.60–0.99; p < .01; sensitivity: 82%, specificity: 75%), and elevated FIBTEM MCF (AUC 1 for >2500 ng/ml). MIS‐C patients (50%) received enoxaparin thromboprophylaxis (in addition to aspirin) with significant improvement in their inflammatory and ROTEM parameters upon outpatient follow‐up; none developed symptomatic thrombosis. Conclusions Despite an observed prothrombotic state, none of the MIS‐C patients (on aspirin alone or in combination with enoxaparin) developed symptomatic thrombosis. ROTEM, in addition to coagulation profiles, may be helpful to tailor thromboprophylaxis in critically ill MIS‐C patients.

Coronavirus disease , also known as Severe acute respiratory syndrome (SARS-COV2) and it has imposed deep concern on public health globally. Based on its fast-spreading breakout among the people exposed to the wet animal market in Wuhan city of China, the city was indicated as its origin. The symptoms, reactions, and the rate of recovery shown in the coronavirus cases worldwide have been varied . The number of patients is still rising exponentially, and some countries are now battling the third wave. Since the most effective treatment of this disease has not been discovered so far, early detection of potential COVID-19 patients can help isolate them socially to decrease the spread and flatten the curve. In this study, we explore state-of-the-art research on coronavirus disease to determine the impact of this illness among various age groups. Moreover, we analyze the performance of the Decision tree (DT), K-nearest neighbors (KNN), Naïve bayes (NB), Support vector machine (SVM), and Logistic regression (LR) to determine COVID-19 in the patients based on their symptoms. A dataset obtained from a public repository was collected and pre-processed, before applying the selected Machine learning (ML) algorithms on them. The results demonstrate that all the ML algorithms incorporated perform well in determining COVID-19 in potential patients. NB and DT classifiers show the best performance with an accuracy of 93.70%, whereas other algorithms, such as SVM, KNN, and LR, demonstrate an accuracy of 93.60%, 93.50%, and 92.80% respectively. Hence, we determine that ML models have a significant role in detecting COVID-19 in patients based on their symptoms.

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Information and communication technology (ICT) and World Wide Web (WWW) are increasingly being used in daily life and becoming important in community, business, personal performance, and improvement of livelihood. people with disabilities (PWDs) can easily perform many tasks using WWW which might be difficult or impossible for them. However, many websites applications such as e-learning, e-commerce, and e-government are not specifically designed keeping in view PWD users. Through the web accessibility guidelines, web developers can build a web program accessible to PWDs. In this paper, we have investigated the issues related to website design that make it unavailable for PWDs. Keeping in view these issues, we have built a framework to make the web easier for PWDs. In addition, these issues are assessed using the GTmetrix, Netcraft, and WAVE accessibility tools and the results are generated using Google Analytics. Based on these results, we have proposed a simplified web version to improve website access for people with disabilities. The proposed prototype is also implemented on a website called Easywebcare by incorporating our recommendations for resolving the investigated issues. Analytics shows that the proposed type surpasses all existing activities in improving website accessibility for people with disabilities.

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