Background & Aims: Fatty liver disease (FLD) is common in women with polycystic ovary syndrome (PCOS). Here, we use non-invasive tests to quantify liver injury in women with PCOS and analyse whether FLD-associated genetic variants contribute to liver phenotypes in PCOS.Methods: Prospectively, we recruited women with PCOS and controls at two university centres in Germany and Poland. Alcohol abuse was regarded as an exclusion criterion. Genotyping of variants associated with FLD was performed using TaqMan How to cite this article: Smyk W, Papapostoli I, Żorniak M, et al. Liver phenotypes in PCOS: Analysis of exogenous and inherited risk factors for liver injury in two European cohorts.

ABSTRACT

Background and Objectives: Carotid web (CaW) is an intimal variant of fibromuscular dysplasia and may constitute as one of rare causes of acute ischemic stroke (AIS). The objective of this study was to determine the prevalence of CaW in patients with AIS or transient ischemic attack (TIA) based on head/neck CT angiography (CTA) in a Polish cohort study. Materials and Methods: A retrospective study was performed by analyzing 1480 electronic clinical and imaging data regarding patients with AIS or TIA, hospitalized in the years 2018–2020 in the authors’ institution. The final sample consisted of 181 patients who underwent head/neck CTA; aged 67.81 ± 13.51 years (52% were women). All head/neck CTA studies were independently evaluated by two radiologists. The patient’s clinical condition was assessed with the National Institutes of Health Stroke Scale (NIHSS, 5.76 ± 4.05 and 2.88 ± 3.38 at admission and at discharge, respectively). Results: 27 patients were identified with CaW. The prevalence of CaW in the final sample (181 pts with good quality CTA) was 14.9%. In the CaW group, 89% patients had AIS, including 26% diagnosed with recurrent and 11% with cryptogenic strokes. There were no significant differences between the presence of CaW and gender, age, NIHSS score, recurrent or cryptogenic stroke. Conclusions: Our study demonstrated that CaW may be an underrecognized entity leading to cerebrovascular events. The diagnosis of CaW depends on a high level of awareness and a comprehensive analysis of the neuroimaging studies. Our findings support the hypothesis that it is worthwhile to perform CTA to determine the etiology of ischemic stroke, particularly if predicting factors were not identified.

Since its outbreak reported in late 2019 in Wuhan, China, the novel coronavirus disease (COVID-19) has been the major challenge across the globe, affecting virtually all aspects of our lives. To effectively manage the pandemic, we need fast, non-invasive, and precise routines for detecting active COVID-19 cases. Although there exist deep learning approaches for detecting COVID-19 in medical image data, their generalization abilities remain unknown. We tackle this issue and introduce deep ensembles that benefit from a wide range of architectural advances, alongside a new fusing approach to deliver accurate predictions. Also, we evolve their content to not only accelerate the inference but also to boost the classification performance. Our experiments, performed on a number of datasets of chest X-ray images, show that the proposed technique renders high-quality classification and generalizes well over a variety of test scans.

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Odmienną tendencję stwierdzono w przypadku wad wrodzonych, które zdecydowanie częściej pojawiały się u dzieci urodzonych o czasie (p = 0,0012). Odnotowano je u 50% noworodków, podczas gdy w grupie wcześniaków wystąpiły jedynie u 18% badanych.