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The impact of common and rare variants in COVID-19 host genetics has been widely studied. In particular, in Fallerini et al. (Human genetics, 2022, 141, 147–173), common and rare variants were used to define an interpretable machine learning model for predicting COVID-19 severity. First, variants were converted into sets of Boolean features, depending on the absence or the presence of variants in each gene. An ensemble of LASSO logistic regression models was used to identify the most informative Boolean features with respect to the genetic bases of severity. After that, the Boolean features, selected by these logistic models, were combined into an Integrated PolyGenic Score (IPGS), which offers a very simple description of the contribution of host genetics in COVID-19 severity.. IPGS leads to an accuracy of 55%–60% on different cohorts, and, after a logistic regression with both IPGS and age as inputs, it leads to an accuracy of 75%. The goal of this paper is to improve the previous results, using not only the most informative Boolean features with respect to the genetic bases of severity but also the information on host organs involved in the disease. In this study, we generalize the IPGS adding a statistical weight for each organ, through the transformation of Boolean features into “Boolean quantum features,” inspired by quantum mechanics. The organ coefficients were set via the application of the genetic algorithm PyGAD, and, after that, we defined two new integrated polygenic scores (IPGSph1 and IPGSph2). By applying a logistic regression with both IPGS, (IPGSph2 (or indifferently IPGSph1) and age as inputs, we reached an accuracy of 84%–86%, thus improving the results previously shown in Fallerini et al. (Human genetics, 2022, 141, 147–173) by a factor of 10%.

BackgroundIn recent years, lung ultrasound (LUS) has spread to emergency departments and clinical practise gaining great support, especially in time of pandemic, but only a few studies have been done on children. The aim of the present study is to compare the diagnostic accuracy of LUS (using Soldati LUS score) and that of chest X-ray (CXR) in CAP and COVID-19 pneumonia in paediatric patients. Secondary objective of the study is to examine the association between LUS score and disease severity. Finally, we describe the local epidemiology of paediatric CAP during the study period in the era of COVID-19 by comparing it with the previous 2 years.MethodsThis is an observational retrospective single-centre study carried out on patients aged 18 or younger and over the month of age admitted to the Paediatric Unit of our Foundation for suspected community-acquired pneumonia or SARS-CoV-2 pneumonia during the third pandemic wave of COVID-19. Quantitative variables were elaborated with Shapiro–Wilks test or median and interquartile range (IQR). Student's t-test was used for independent data. Association between quantitative data was evaluated with Pearson correlation. ROC curve analysis was used to calculate best cut-off of LUS score in paediatric patients. Area under the ROC curve (AUC), sensibility, and specificity are also reported with 95% confidence interval (CI).ResultsThe diagnostic accuracy of the LUS score in pneumonia, the area underlying the ROC curve (AUC) was 0.67 (95% CI: 0.27–1) thus showing a discrete discriminatory power, with a sensitivity of 89.66% and specificity 50% setting a LUS score greater than or equal to 1 as the best cut-off. Nine patients required oxygen support and a significant statistical correlation (p = 0.0033) emerged between LUS score and oxygen therapy. The mean LUS score in patients requiring oxygen therapy was 12. RCP was positively correlated to the patient's LUS score (p = 0.0024).ConclusionsOur study has shown that LUS is a valid alternative to CXR. Our results show how LUS score can be applied effectively for the diagnosis and stratification of paediatric pneumonia.