Genomic variants within chromosome 14q32.32 regulate bone mass through MARK3 signaling in osteoblasts

Bone mineral density (BMD) is a highly heritable predictor of osteoporotic fracture. GWAS have identified hundreds of loci influencing BMD, but few have been functionally analyzed. In this study, we show that SNPs within a BMD locus on chromosome 14q32.32 alter splicing and expression of PAR-1a/microtubule affinity regulating kinase 3 (MARK3), a conserved serine/threonine kinase known to regulate bioenergetics, cell division, and polarity. Mice lacking Mark3 either globally or selectively in osteoblasts have increased bone mass at maturity. RNA profiling from Mark3-deficient osteoblasts suggested changes in the expression of components of the Notch signaling pathway. Mark3-deficient osteoblasts exhibited greater matrix mineralization compared with controls that was accompanied by reduced Jag1/Hes1 expression and diminished downstream JNK signaling. Overexpression of Jag1 in Mark3-deficient osteoblasts both in vitro and in vivo normalized mineralization capacity and bone mass, respectively. Together, these findings reveal a mechanism whereby genetically regulated alterations in Mark3 expression perturb cell signaling in osteoblasts to influence bone mass.     

COVID-19 Gender Disparities and Mitigation Recommendations: A Narrative Review

The coronavirus disease 2019 (COVID-19) pandemic has rapidly created widespread impacts on global health and the economy. Data suggest that women are less susceptible to severe illness. However, sex-disaggregated data are incomplete, leaving room for misinterpretation, and focusing only on biologic sex underestimates the gendered impact of the pandemic on women. This narrative review summarizes what is known about gender disparities during the COVID-19 pandemic and the economic, domestic, and health burdens along with overlapping vulnerabilities related to the pandemic. In addition, this review outlines recommended strategies that advocacy groups, community leaders, and policymakers should implement to mitigate the widening gender disparities related to COVID-19.     

Emergency Care EMTALA Alterations During the COVID-19 Pandemic in the United States

The coronavirus 2019 pandemic has affected almost every aspect of health care delivery in the United States, and the emergency medicine system has been hit particularly hard while dealing with this public health crisis. In an unprecedented time in our history, medical systems and clinicians have been asked to be creative, flexible, and innovative, all while continuing to uphold the important standards in the US health care system. To continue providing quality services to patients during this extraordinary time, care providers, organizations, administrators, and insurers have needed to alter longstanding models and procedures to respond to the dynamics of a pandemic. The Emergency Medicine Treatment and Active Labor Act of 1986, or EMTALA, is 1 example of where these alterations have allowed health care facilities and clinicians to continue their work of caring for patients while protecting both the patients and the clinicians themselves from infectious exposures at the same time.     

Clinical Evaluation of Deep Learning and Atlas-Based Auto-Contouring of Bladder and Rectum for Prostate Radiation Therapy

PurposeAuto-contouring may reduce workload, interobserver variation, and time associated with manual contouring of organs at risk. Manual contouring remains the standard due in part to uncertainty around the time and workload savings after accounting for the review and editing of auto-contours. This preliminary study compares a standard manual contouring workflow with 2 auto-contouring workflows (atlas and deep learning) for contouring the bladder and rectum in patients with prostate cancer.Methods and MaterialsThree contouring workflows were defined based on the initial contour-generation method including manual (MAN), atlas-based auto-contour (ATLAS), and deep-learning auto-contour (DEEP). For each workflow, initial contour generation was retrospectively performed on 15 patients with prostate cancer. Then, radiation oncologists (ROs) edited each contour while blinded to the manner in which the initial contour was generated. Workflows were compared by time (both in initial contour generation and in RO editing), contour similarity, and dosimetric evaluation.ResultsMean durations for initial contour generation were 10.9 min, 1.4 min, and 1.2 min for MAN, DEEP, and ATLAS, respectively. Initial DEEP contours were more geometrically similar to initial MAN contours. Mean durations of the RO editing steps for MAN, DEEP, and ATLAS contours were 4.1 min, 4.7 min, and 10.2 min, respectively. The geometric extent of RO edits was consistently larger for ATLAS contours compared with MAN and DEEP. No differences in clinically relevant dose-volume metrics were observed between workflows.ConclusionAuto-contouring software affords time savings for initial contour generation; however, it is important to also quantify workload changes at the RO editing step. Using deep-learning auto-contouring for bladder and rectum contour generation reduced contouring time without negatively affecting RO editing times, contour geometry, or clinically relevant dose–volume metrics. This work contributes to growing evidence that deep-learning methods are a clinically viable solution for organ-at-risk contouring in radiation therapy.