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Multi-system inflammatory syndrome in children is a rare complication of corona virus-19 infection characterized by fever, inflammatory changes in body and multi-organ dysfunction or features consistent with incomplete/complete Kawasaki disease. We are presenting two cases of post-covid syndrome (MIS-C) in children. Copyright © 2022, Pakistan Pediatric Journal. All rights reserved.
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Objective: To determine the accuracy of CT chest in diagnosis of COVID-19 taking RT-PCR-testing as gold standard. Materials and Methods: A total of 150 patients of suspicion of COVID-19 who were referred for CT Chest in Radiology Department of Nishtar Medical University Multan from June-2020 to May-2021 were included. In all patients, two RT-PCR test results were obtained with 7 days of admission in hospital. Presence of any of these positive was labelled as COVID-19 infection. CT chest was performed in all patients within 2 days of admission in hospital using 128 slices CT scan machine. The diagnosis of COVID-19 infection was made according to the recommendations by Radiological Society of North America (RSNA) protocol. Results: Mean age was 51.3±14.7 years. 78 (52%) patients were male and 72 (48%) patients were female. RT-PCR test was positive in 89 (59.3%) patients. While the CT chest findings were suggestive of COVID-19 infection in 130 (86.7%) patients. The sensitivity of CT chest was 95.5%, specificity 26.2%, PPV wad 65.4% and NPV was 80.0%. Conclusion: CT chest has a very good sensitivity for detection of COVID-19, it can be used as a rapid diagnostic tool especially in areas of pandemic. However, the specificity of CT chest is low, that can limit its use in low COVID-19 affected areas.
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The declaration of COVID-19 pandemic by the WHO initiated a series of lockdowns globally that varied in stringency and duration; however, the spatiotemporal effects of these lockdowns on air quality remain understudied. This study evaluates the global impact of lockdowns on air pollutants using tropospheric and ground-level indicators over a five-month period. Moreover, the relationship between air pollution and COVID-19 cases and mortalities was examined. Changes in the global tropospheric (NO2, aerosols, and O3) and ground-level (PM2.5, PM10, NO2, and O3) pollutants were observed, and the maximum air quality improvement was observed immediately after lockdown. Except for a few countries, a decline in air pollutants correlated with a reduction in Land Surface Temperature (LST). Notably, regions with higher tropospheric NO2 and aerosol concentrations were also COVID-19 hotspots. Our analysis showed moderate positive correlation for NO2 with COVID-19 cases (R2 = 0.33; r = 0.57, P = 0.006) and mortalities (R2 = 0.40; r = 0.63, P = 0.015), while O3 showed a weak-moderate positive correlation with COVID-19 cases (R2 = 0.22; r = 0.47, P = 0.003) and mortalities (R2 = 0.12; r = 0.35, P = 0.012). However, PM2.5, and PM10 showed no significant correlation with either COVID-19 cases or mortality. This study reveals that humans living under adverse air pollution conditions are at higher risk of COVID-19 infection and mortality.
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Coronavirus (COVID-19) is an intense problem throughout the world. Severe acute respiratory syndrome coronavirus (SARS-CoV-2) is a highly communicable viral infection, which began in Wuhan city of China and then spread around the world. It causes a respiratory infection including pneumonia, cold, sneezing and coughing. In December 2019, a striking outbreak of cryptic disease pneumonia occurred in Wuhan City of Hubei, China. After the outbreak in China, the virus spread through air droplets and when infected persons traveled from one place to another all over the world i.e. India, Italy, Iran, Turkey, USA etc. The World Health Organization (WHO) identified a Novel Coronavirus as the causative agent and subsequently termed it COVID-19. The objective of this review article is to give a preliminary opinion on the disease, its ways of transmission, symptomatology and precautionary measures in this early stage of the COVID-19 outbreak.
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The outbreak of the new coronavirus (COVID19), has caused devastating effects and was declared as a major pandemic by the World Health Organization (WHO). Apart from knowing the main causes, it's very important to timely diagnose the virus in an individual, so that treatment and isolation (if needed) can start as early as possible and spread of the virus is contained among the healthy people. In this research, we discuss various machine learning (ML) and deep learning (DL) approaches that have been proposed for the diagnosis of the virus using different bio-indicators with particular focus on lungs imaging. A detailed analysis of existing techniques is presented with future perspective on the use of new machine learning techniques for the diagnosis of the COVID19 and other similar viruses. © 2021 IEEE.
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Objective: To study the utility of peer support group in a resource limited setting during COVID19 pandemic. Methodology: This observational study was conducted in Pediatric Endocrine Clinic in a teaching hospital over a 3 months period. With the closure of routine outpatients department during the pandemic, a social media group was formed by the treating physician group with parents of children with type 1 diabetes. Consenting parents were enrolled on a common platform where they could discuss concerns regarding their children's diabetes. Results: Of all the type 1 diabetics enrolled in the clinic, about 40% families could not be enrolled as they did not have any internet access. Initially, only 20% of the parents consented to be a part of the group which increased to 80% by the end of 1st month. The mean age of children with diabetes was 8.2 ± 1.4 years. All the children were on basal-bolus insulin with regular SMBG. Most common concern was regarding maintaining uninterrupted supply of insulin;other concerns were relating to diet and decreased physical activity due to ongoing lockdown. After 3 months, it was observed that the number of distress call to the treating team was reduced by about 60%. All the participating families felt more confident and satisfied after being a part of the group. Two children presented to the A&E in diabetic ketoacidosis after being diagnosed by the physician over the phone call. Both families had not consented to be a part of the peer support group and had stopped insulin due to supply related issues. About 30% children reported blood sugars in the hyperglycemic range based on SMBG monitoring, which was addressed through telemedicine. The reasons could be decreased physical activity, binging and increased stress levels. Conclusion: Peer support group in current scenario of limited healthcare access can help in addressing the concerns of patients and their caregivers.
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The COVID-19 pandemic has resulted in more than a million deaths worldwide and wreaked havoc on world economies. SARS-CoV-2, the virus that causes COVID-19, belongs to a family of coronaviruses that have appeared in the past;however, this virus has been proven to be more lethal and have a much higher infection rate than coronaviruses that have previously emerged. Vaccines for COVID-19 are still in development phases, with limited deployment, and the most effective response to the pandemic has been to adopt social distancing and, in extreme cases, complete lockdown. This paper adopts a modified SIRD (Susceptible, Infectious, Recovered, Deaths) disease spread model for COVID-19 and utilizes agent-based simulation to obtain the number of infections in four different scenarios. The simulated scenarios utilized different contact rates in order to identify their effects on disease spread. Our results confirmed that not taking strict precautionary procedures to prohibit human interactions will lead to increased infections and deaths, adversely affecting countries’ healthcare infrastructure. The model is flexible, and other studies can use it to measure other parameters discovered in the future.