Earlier in its course, SARS-CoV-2 was primarily identified to cause an acute respiratory illness in adults, the elderly and immunocompromised, while children were known to be afflicted with milder symptoms. However, since mid-April of 2020, latent effects of the virus have begun emerging in children and adolescents, which is characterised by a multisystem hyperinflammatory state; thus, the term Multisystem Inflammatory Syndrome in Children (MIS-C) was introduced by the WHO and CDC. The syndrome manifests itself approximately 4 weeks after COVID-19 infection, with symptoms mimicking Kawasaki Disease and Kawasaki Disease Shock Syndrome. Demographically, MIS-C peaks in children aged 5 to 14 years, with clusters in Europe, North and Latin America seen, later followed by Asia. Although the exact pathophysiology behind the syndrome is unknown, recent studies have proposed a post-infectious immune aetiology, which explains the increased levels of immunoglobulins seen in affected patients. Patient presentation includes, but is not limited to, persistent fever, rash, gastrointestinal symptoms and cardiac complications including myocarditis. These patients also have raised inflammatory markers including C reactive protein, ferritin and interleukin-6. In poorly controlled patients, the syndrome can lead to multiorgan failure and death. The mainstay of treatment includes the use of intravenous immunoglobulins, steroids, immune modulators and aspirin. Adjunct therapy includes the use of low molecular weight heparin or warfarin for long term anticoagulation. Currently very little is known about the syndrome, highlighting the need for awareness amongst healthcare workers and parents. Moreover, with increased cases of COVID-19 as a result of the second wave, it is essential to keep MIS-C in mind when attending patients with a past history of COVID-19 exposure or infection. Additionally, once these patients have been identified and treated, strict follow-up must be done in order carry out long term studies, and to identify possible sequelae and complications.

Background The incidence of obesity has been on the rise worldwide. In Pakistan alone, one in four adults is overweight/obese and thus at risk of developing a number of comorbidities such as cardiovascular disease and diabetes. This research aimed to examine how doctors perceived and managed their obese patients. Methods A standardized questionnaire was filled by 100 doctors working in Pakistan, either by hand or online. The study was conducted from November 2017 to January 2018. Results It was found that only 8% of doctors had completed a training course on obesity. Doctors discussed the links between obesity and diabetes (88%) most often whilst neglecting cancer (30%) and dementia (17%). Only 60% of doctors calculated body mass index (BMI) for adult obese patients, with general practitioners (GPs) being the most confident in discussing their weight issues (p=0.001). In terms of childhood obesity, 54% of doctors were confident in putting in place a weight management program. Doctors who checked their weight more than four times a year were found to calculate the BMI of children and adult patients more often (p=0.000 and p=0.044). Comparably, doctors of normal weight were more confident in managing the complications of adult obesity (p=0.015). Conclusion Training courses regarding obesity should be provided to doctors not only to increase their knowledge but also to increase their confidence levels in managing such patients. Further research needs to be carried out in order to understand the patients’ perception of obesity management.

Autism spectrum disorder is a severe, life-prolonged neurodevelopmental disease typified by disabilities that are chronic or limited in the development of socio-communication skills, thinking abilities, activities, and behavior. In children aged two to three years, the symptoms of autism are more evident and easier to recognize. The major part of the existing literature on autism spectrum disorder is covered by a prediction system based on traditional machine learning algorithms such as support vector machine, random forest, multiple layer perceptron, naive Bayes, convolution neural network, and deep neural network. The proposed models are validated by using performance measurement parameters such as accuracy, precision, and recall. In this research, autism spectrum disorder prediction has been investigated and compared using common parameters such as application type, simulation method, comparison methodology, and input data. The key purpose of this study is to give a centralized framework to use for researchers working on autism spectrum disorder prediction. The best results were obtained by using the random forest algorithm as it performs better than other traditional machine learning algorithms. The achieved accuracy is 89.23%. The workflow representations of the investigated frameworks assist readers in comprehending the fundamental workings and architectures of these frameworks.