Experiences of sexual and reproductive health screening and counseling in the clinical setting among adolescents and young adults with rheumatic disease.

Background: Our objective was to describe differences among adolescents and young adults (AYAs) with rheumatic disease using teratogens compared to non-users in receipt of sexual and reproductive health (SRH) counseling, teratogenicity knowledge, perceived importance of SRH topics, and preferences around counseling.

Methods: AYAs ages 14-23 years and assigned female at birth were recruited from pediatric rheumatology clinics at a Midwest tertiary care program. Participants completed a one-time online survey assessing SRH.

Mixed method study of feasibility and acceptability of electronic screening for measurement-based symptom monitoring of veterans accessing mental health treatment in VA community care program settings.

Background: 2022 survey data showed 29% of Veterans utilized Veterans Affairs (VA) paid health care at a non-VA facility, 6% higher than in 2021. Despite an increase in the number of Veterans accessing care in the community via the MISSION Act Community Care Program (CCP), there is limited information on the quality of mental health care delivered to Veterans in these settings. Further, Veterans report barriers to quality care, including poor communication between CCP and VA providers, which can result in negative patient outcomes.

Classical Reaction Barriers in DFT: An Adiabatic-Connection Perspective.

Classical reaction barriers in density-functional theory are considered from the perspective of the density-fixed adiabatic connection. A 'reaction adiabatic-connection integrand', , is introduced, where λ is the electron-electron interaction strength, for which equals the barrier, meaning the barrier can be easily visualized as the area under a plot of vs λ. For five chemical reactions, plots of reference , calculated from Lieb maximizations at the coupled-cluster level of theory, are compared with approximate , calculated from common exchange-correlation functionals using coordinate scaling, for coupled-cluster densities.

Deep learning can detect elbow disease in dogs screened for elbow dysplasia.

Medical image analysis based on deep learning is a rapidly advancing field in veterinary diagnostics. The aim of this retrospective diagnostic accuracy study was to develop and assess a convolutional neural network (CNN, EfficientNet) to evaluate elbow radiographs from dogs screened for elbow dysplasia. An auto-cropping tool based on the deep learning model RetinaNet was developed for radiograph preprocessing to crop the radiographs to the region of interest around the elbow joint.