Radiographic Assessment of Bone Union in Proximal Tibia and Distal Femur Osteotomies: A Systematic Review.

Background: Osteotomies around the knee are a well-established treatment option for early and moderate unicompartmental osteoarthritis combined with a lower extremity malalignment. Moreover, osteotomies are often combined with cartilage treatment. Current image-based bone union assessments lack an accepted definition despite widespread use in research and clinical settings.

A 3D printed pulmonary mock loop for hemodynamic studies in congenital heart disease.

. With the increasing survival of the congenital heart disease population, there is a growing need for in-depth understanding of blood circulation in these patients. Mock loops provide the opportunity for comprehensive hemodynamic studies without burden and risks for patients.

Primary care for patients with respiratory tract infection before and early on in the COVID-19 pandemic: an observational study in 16 European countries.

Objective: To describe primary health care (consultation characteristics and management) for patients contacting their general practitioner (GP) with a respiratory tract infection (RTI) early on in the COVID-19 pandemic in contrasting European countries, with comparison to prepandemic findings.

Setting: Primary care in 16 countries (79 practices), when no routine SARS-CoV-2 testing was generally available.

Design And Participants: Before (n=4376) and early in the pandemic (n=3301), patients with RTI symptoms were registered in this prospective audit study.

Opposing effects of S-equol supplementation on metabolic and behavioral parameters in mice fed a high-fat diet.

Phytoestrogens, such as daidzein and genistein, may be used to treat various hormone-dependent disorders. Daidzein can be metabolized by intestinal microbes to S-equol. However, not all individuals possess bacteria producing this metabolite, resulting in categorization of equol vs nonequol producers.

Partition-based and sharp uniform error bounds.

This paper develops probabilistic bounds on out-of-sample error rates for several classifiers using a single set of in-sample data. The bounds are based on probabilities over partitions of the union of in-sample and out-of-sample data into in-sample and out-of-sample data sets. The bounds apply when in-sample and out-of-sample data are drawn from the same distribution.