Saliva-Based, COVID-19 RT-PCR Pooled Screening Strategy to Keep Schools Open

Background: As of March 2020, governments throughout the world implemented business closures, work from home policies, and school closures due to exponential increase of coronavirus disease 2019 (COVID-19) cases, leaving only essential workers being able to work on site. For most of the children and adolescent school closures during the first lockdown had significant physical and psychosocial consequences. Here, we describe a comprehensive Return to School program based on a behavior safety protocol combined with the use of saliva-based reverse transcriptase-polymerase chain reaction (RT-PCR) pooled screening technique to keep schools opened. Methods: The program had 2 phases: before school (safety and preparation protocols) and once at school (disease control program: saliva-based RT-PCR pooled screening protocol and contact tracing). Pooling: Aliquots of saliva from 24 individuals were pooled and 1 RT-PCR test was performed. If positive, the initial 24-pool was then retested (12 pools of 2). Individual RT-PCR tests from saliva samples from positive pools of 2 were performed to get an individual diagnosis. Results: From August 31 until December 20, 2020 (16-wk period) a total of 3 pools, and subsequent 3 individual diagnosis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) disease were reported (2 teachers and 1 staff). Conclusion: Until COVID-19 vaccine can be administered broadly to all-age children, saliva-based RT-PCR pooling testing is the missing piece we were searching for to keep schools opened.

An Explainable Machine Learning Approach for COVID-19’s Impact on Mood States of Children and Adolescents during the First Lockdown in Greece

The global spread of COVID-19 led the World Health Organization to declare a pandemic on 11 March 2020. To decelerate this spread, countries have taken strict measures that have affected the lifestyles and economies. Various studies have focused on the identification of COVID-19’s impact on the mental health of children and adolescents via traditional statistical approaches. However, a machine learning methodology must be developed to explain the main factors that contribute to the changes in the mood state of children and adolescents during the first lockdown. Therefore, in this study an explainable machine learning pipeline is presented focusing on children and adolescents in Greece, where a strict lockdown was imposed. The target group consists of children and adolescents, recruited from children and adolescent mental health services, who present mental health problems diagnosed before the pandemic. The proposed methodology imposes: (i) data collection via questionnaires; (ii) a clustering process to identify the groups of subjects with amelioration, deterioration and stability to their mood state; (iii) a feature selection process to identify the most informative features that contribute to mood state prediction; (iv) a decision-making process based on an experimental evaluation among classifiers; (v) calibration of the best-performing model; and (vi) a post hoc interpretation of the features’ impact on the best-performing model. The results showed that a blend of heterogeneous features from almost all feature categories is necessary to increase our understanding regarding the effect of the COVID-19 pandemic on the mood state of children and adolescents.

Profile of Tuberculosis in Children and Adolescent at Dr. Soetomo General Hospital Surabaya

Introduction: Tuberculosis is one of ten leading causes of death worldwide, including Indonesia. Indonesia is one of seven countries that causes 64% deaths due to tuberculosis. Tuberculosis is caused by Mycobacterium tuberculosis through droplet nuclei in the air. It can occur to any group age, including children and adolescent, if there is a contact history of people with tuberculosis infection. In 2016, one million children had tuberculosis and around 250,000 children died because of tuberculosis. This study aimed to know the profile of tuberculosis in children and adolescent at Dr. Soetomo General Hospital Surabaya.Methods: This was a descriptive study using retrospective approach. Sample of this study was collected from electronic medical record provided by Dr. Soetomo General Hospital Surabaya using statistic formula of single sample for estimated population proportions of children and adolescent with tuberculosis from 2013-2017, with total samples of 149 people.Results: There were 149 samples of children and adolescent patients with tuberculosis. Most of the children were mostly 0-4 years old and 57% were female. 84% of the children had been immunized with BCG and classified as moderate, and 35% were under nutritional status. This study showed that 67% of the children in household contacts of adult tuberculosis patients also had tuberculosis. The most frequent symptoms of tuberculosis in children and adolescent were fever (72%) and cough (80%).Conclusion: Tuberculosis in children and adolescent is more likely to occur in children than adolescent, especially children within group age of 0-4 years old. The number of pulmonary tuberculosis in children and adolescent are higher than extrapulmonary tuberculosis.

COVID-19 IN CHILDREN AND ADOLESCENT: CHARACTERISTICS AND SPECIFICITIES IN IMMUNOCOMPETENT AND ONCOHEMATOLOGICAL PATIENTS

SARS-CoV-2 pandemic affected less children and adolescents, morbidity and mortality figures being inferior to that reported for adults. In this review we focused on the clinical course, risk factors for severe COVID-19, mortality, treatment options and prevention measures in the pediatric and adolescent setting with special attention to the pediatric oncohematological patients. In this subgroups of patients, SARS-CoV-2 infection was often asyntomatic but 47 to 68% of patients require hospitalization and 9-10% of those hospitalized needed intensive care with a COVID-19 attributable mortality of about 4%. The multisystem inflammatory syndrome associated to Coronavirus 2019 was less frequent than that reported in the non-oncohematological pediatric population. Noteworthy, the course of COVID-19 was more severe in low-middle income countries. The key measures to prevent SARS-CoV-2 infection are the reduction of patients exposure to the SARS-CoV-2 and vaccination, now available fore care givers and parents and for patients and siblings > 12 years old. The treatment of COVID-19 in pediatric patients was mainly based on supportive care with dexamethasone and heparin prophylaxis for severely ill patients. Other measures, such as convalescent plasma, remdesivir and monoclonal antibodies have been used in limited case or within experimental protocols. Further studies are needed on the risk factors and outcome of SARS-CoV-2 infection in the pediatric immunocompromised patients.

Metabolomic Signatures for the Effects of Weight Loss Interventions on Severe Obesity in Children and Adolescents

Childhood obesity has increased worldwide, and many clinical and public interventions have attempted to reduce morbidity. We aimed to determine the metabolomic signatures associated with weight control interventions in children with obesity. Forty children from the “Intervention for Children and Adolescent Obesity via Activity and Nutrition (ICAAN)” cohort were selected according to intervention responses. Based on changes in body mass index z-scores, 20 were responders and the remaining non-responders. Their serum metabolites were quantitatively analyzed using capillary electrophoresis time-of-flight mass spectrometry at baseline and after 6 and 18 months of intervention. After 18 months of intervention, the metabolite cluster changes in the responders and non-responders showed a difference on the heatmap, but significant metabolites were not clear. However, regardless of the responses, 13 and 49 metabolites were significant in the group of children with obesity intervention at 6 months and 18 months post-intervention compared to baseline. In addition, the top five metabolic pathways (D-glutamine and D-glutamate metabolism; arginine biosynthesis; alanine, aspartate, and glutamate metabolism; TCA cycle; valine, leucine, and isoleucine biosynthesis) including several amino acids in the metabolites of obese children after 18 months were significantly changed. Our study showed significantly different metabolomic profiles based on time post obesity-related intervention. Through this study, we can better understand and predict childhood obesity through metabolite analysis and monitoring.

Morbidity, mortality and clinico-laboratory profile of COVID-19 related illness in children: a retrospective observational study from a tertiary care centre in India

Background: As India is poised for a third wave of SARS Co-V2 infection with a large unvaccinated pediatric population, it becomes imperative and pertinent for a study to find out its demographic, clinico-laboratory profile, and outcome in children with COVID-19 disease and its related illness.Methods: This is a retrospective observational study undertaken for Children and Adolescent admitted in the department of pediatrics of a teaching and tertiary care referral hospital, Delhi.Results: The median age of admitted children with COVID-19 disease was 11 years with an interquartile range 3 to 16 years. The median duration of hospital stay was 10 days (mean: 18±14 days). Mortality was 9/62 (14%). Recovery in non-severe (asymptomatic, mild, moderate) was 41/41 (100%), and in severe and critical illness including MISC was 42.8% (9/21). Mortality in severe and critical patients managed in SARI and COVID ward was 44% (8/18). Death among MISC patient in PICU was 33% (1/3). Difference in CRP rise was significant in severe and non-severe group of COVID-19 (p=0.017).Conclusions: Even though the morbidity and mortality associated with COVID-19 infection and related illness seems to be miniscule, the infection causes significant illness in the subgroup of children who requires hospitalization and can be fatal in those with comorbidity.

An Explainable Machine Learning Approach for COVID-19 Impact on Mood State of Children and Adolescents during the 1st Lockdown in Greece

The global spread of COVID-19 led the World Health Organization to declare a pandemic on 11 March 2020. To decelerate this spread, countries have taken strict measures that affected the lifestyle and economy. Various studies have been focused on the identification of COVID-19 impact to mental health of children and adolescents via traditional statistical approaches. However, a machine learning methodology must be developed to explain the main factors that contribute to the change of mood state of children and adolescents during the first lockdown. Therefore, to this study an explainable machine learning pipeline is presented focusing on children and adolescents in Greece, where a strict lockdown was imposed. The target group consists of children and adolescents, recruited from children and adolescent mental health services, who present mental health problems diagnosed before the pandemic. The proposed methodology imposes: (i) data collection via questionnaires; (ii) a clustering process to identify the groups of subjects with amelioration, deterioration and stability to their mood state; (iii) a feature selection process to identify the most informative features that contribute to mood state prediction; (iv) a decision-making process based on an experimental evaluation among classifiers; (v) calibration of the best performing model and (v) a post-hoc interpretation of the features’ impact on the best performing model. The results showed that a blend of heterogeneous features from almost all feature categories is necessary to increase our understanding regarding the effect of COVID-19 pandemic on the mood state of children and adolescents.

Association of Psychiatric Disorders in Children and Adolescents with Epilepsy in a Tertiary Care Hospital in Bangladesh

Background Psychiatric disorders are important aspects of epilepsy and have received increasing attention in the last several years. Although a significant number of children are afflicted with epilepsy with psychiatric comorbidities, the actual burden was not evaluated sufficiently. Objective To determine the types and frequency of psychiatric disorders in children with epilepsy. Materials and Methods This hospital-based case-control study was conducted at the outpatient department of a tertiary care center in Dhaka, Bangladesh, from September 2018 to August 2019. In total, 68 epileptic children, ranging from 5 to 17 years of age, were enrolled as cases. A similar number of nonepileptic children of age, sex, and sociodemographic status matched were enrolled as control. Parent, teacher, and self-version of Bengali Development and Well-Being Assessment (DAWBA) were used to assess the psychiatric disorders, and the diagnosis was assigned as Diagnostic and Statistical Manual (DSM)-V of Mental Disorders. Results Higher proportion of psychiatric illness were found significantly among the cases (83.8% vs. 16.2%; p < 0.001) and broad categories of disorders, namely, neurodevelopmental (30.9% vs. 1.5%, p < 0.001), emotional (48.5% vs. 7.4%, p < 0.001) and behavioral disorder (19.1% vs. 7.4%, p = 0.043) compared with controls. There was a significant relationship between psychiatric disorders with the duration of epilepsy of the respondents (p = 0.032). Conclusions This study result showed the significant association of psychiatric disorders with epilepsy among children and adolescent population. Thus, psychiatric disorders should be properly addressed during treatment of epilepsy.