INTRODUCTION: Asthma is a heterogeneous, multifactorial disease with multiple genetic and environmental risk factors playing a role in pathogenesis and therapeutic response. Understanding of pharmacogenetics can help with matching individualized treatments to specific genotypes of asthma to improve therapeutic outcomes especially in uncontrolled or severe asthma. AREAS COVERED: In this review, we outline novel information about biology, pathways, and mechanisms related to interindividual variability in drug response (corticosteroids, bronchodilators, leukotriene modifiers, and biologics) for childhood asthma. We discuss candidate gene, genome-wide association studies and newer omics studies including epigenomics, transcriptomics, proteomics, and metabolomics as well as integrative genomics and systems biology methods related to childhood asthma. The articles were obtained after a series of searches, last updated November 2022, using database PubMed/CINAHL DB. EXPERT OPINION: Implementation of pharmacogenetic algorithms can improve therapeutic targeting in children with asthma, particularly with severe or uncontrolled asthma who typically have challenges in clinical management and carry considerable financial burden. Future studies focusing on potential biomarkers both clinical and pharmacogenetic can help formulate a prognostic test for asthma treatment response that would represent true bench to bedside clinical implementation.

PURPOSE: Children are at elevated risk for COVID-19 (SARS-CoV-2) infection due to their social behaviors. The purpose of this study was to determine if usage of radiological chest X-rays impressions can help predict whether a young adult has COVID-19 infection or not. METHODS: A total of 2572 chest impressions from 721 individuals under the age of 18 years were considered for this study. An ensemble learning method, Random Forest Classifier (RFC), was used for classification of patients suffering from infection. RESULTS: Five RFC models were implemented with incremental features and the best model achieved an F1-score of 0.79 with Area Under the ROC curve as 0.85 using all input features. Hyper parameter tuning and cross validation was performed using grid search cross validation and SHAP model was used to determine feature importance. The radiological features such as pneumonia, small airways disease, and atelectasis (confounded with catheter) were found to be highly associated with predicting the status of COVID-19 infection. CONCLUSIONS: In this sample, radiological X-ray films can predict the status of COVID-19 infection with good accuracy. The multivariate model including symptoms presented around the time of COVID-19 test yielded good prediction score.