creased rate of very preterm delivery, preterm PROM or reduced neonatal survival when compared with surgery after 18 weeks. This article is protected by copyright. All rights reserved.Laser surgery prior to 18 weeks of gestation is not associated with an increased rate of very preterm delivery, preterm PROM or reduced neonatal survival when compared with surgery after 18 weeks. This article is protected by copyright. All rights reserved.Complex human diseases are affected by genetic and environmental risk factors and their interactions. Gene-environment interaction (GEI) tests for aggregate genetic variant sets have been developed in recent years. However, existing statistical methods become rate limiting for large biobank-scale sequencing studies with correlated samples. We propose efficient Mixed-model Association tests for GEne-Environment interactions (MAGEE), for testing GEI between an aggregate variant set and environmental exposures on quantitative and binary traits in large-scale sequencing studies with related individuals. Joint tests for the aggregate genetic main effects and GEI effects are also developed. A null generalized linear mixed model adjusting for covariates but without any genetic effects is fit only once in a whole genome GEI analysis, thereby vastly reducing the overall computational burden. Score tests for variant sets are performed as a combination of genetic burden and variance component tests by accounting for the genetic main effects using matrix projections. The computational complexity is dramatically reduced in a whole genome GEI analysis, which makes MAGEE scalable to hundreds of thousands of individuals. We applied MAGEE to the exome sequencing data of 41,144 related individuals from the UK Biobank, and the analysis of 18,970 protein coding genes finished within 10.4 CPU hours.The coronavirus disease 2019 (COVID-19) pandemic has affected the daily lives of millions of people worldwide and had caused significant mortality; hence, the assessment of therapeutic options is of great interest. The leading cause of death among COVID-19 patients is acute respiratory distress syndrome caused by hyperinflammation secondary to cytokine release syndrome (CRS). Cytokines, such as tumor necrosis factor-α, interleukin-6, interferon-γ and interleukin-10, are the main mediators of CRS. Based on recent evidence, the angiotensin-converting enzyme (ACE) II is known to be the target of the COVID-19 spike protein, which enables the virus to penetrate human cells. ACE II also possesses an anti-inflammatory role in many pathologies such as cardiovascular disease, hypertension, diabetes mellitus and other conditions, which are the main risk factors of poor prognosis in COVID-19 infection. Changes in tissue ACE II levels are associated with many diseases and hyperinflammatory states, and it is assumed that elevated levels of ACE II could aggravate the course of COVID-19 infection. Therefore, the use of renin-angiotensin-aldosterone system inhibitors (RASis) in COVID-19 patients could be hypothetically considered, though sufficient evidence is not presented by the scientific community. In this work, based on the most recent pieces of evidence, the roles of RAS and RASi in immunologic interactions are addressed. Furthermore, the molecular and immunologic aspects of RASi and their potential significance in COVID-19 are discussed.The quantum theory of atoms in molecules (QTAIM) provides a theoretical foundation to determine the properties of functional groups through additive atomic contributions. Many studies have used QTAIM in their analyses with a variety of electronic structure methods, but it is unknown if the properties measured using one model chemistry, the combination of the electronic structure method and basis set, can be compared to those measured by another. Here, we evaluate the sensitivity of QTAIM functional group and bond critical point properties using six functionals and seven basis sets. High-level B2PLYPD3-BJ/aug-cc-pV5Z reference values are provided for 116 functional groups and the property sensitivity with respect to these values are evaluated based on absolute deviations and by assessing linear relationships. Functional group properties, including charges, dipoles, quadrupoles and volumes, were found to be mostly insensitive to choice of computational model chemistry. However, due to structural and topological inconsistencies, the 6-31G(d) basis set is not recommended for use. Bond critical point properties varied with choice of model chemistry, but models incorporating hybrid functionals and triple-ζ basis sets provided values suitable for use in regression studies.Computer-aided diagnosis (CAD) systems have become an important tool in the assessment of breast tumors with magnetic resonance imaging (MRI). CAD systems can be used for the detection and diagnosis of breast tumors as a "second opinion" review complementing the radiologist's review. CAD systems have many common parts, such as image preprocessing, tumor feature extraction, and data classification that are mostly based on machine-learning (ML) techniques. AACOCF3 In this review article, we describe applications of ML-based CAD systems in MRI covering the detection of diagnostically challenging lesions of the breast such as nonmass enhancing (NME) lesions, and furthermore discuss how multiparametric MRI and radiomics can be applied to the study of NME, including prediction of response to neoadjuvant chemotherapy (NAC). Since ML has been widely used in the medical imaging community, we provide an overview about the state-of-the-art and novel techniques applied as classifiers to CAD systems. The differences in the CAD systems in MRI of the breast for several standard and novel applications for NME are explained in detail to provide important examples, illustrating 1) CAD for detection and diagnosis, 2) CAD in multiparametric imaging, 3) CAD in NAC, and 4) breast cancer radiomics. We aim to provide a comparison between these CAD applications and to illustrate a global view on intelligent CAD systems based on machine and deep learning in MRI of the breast. LEVEL OF EVIDENCE 2 TECHNICAL EFFICACY STAGE 2.