Pervasive refusal syndrome revisited: a conative disorder

European Child & Adolescent Psychiatry -     

Blood Pressure Variations During a Working Day at Age 28: Effects of Different Types of Work and Blood Pressure Level at Age 18

Abstract

During an ordinary work day blood pressure was self-monitored once every hour in two samples of asymptomatic nonmedicating 28-year-old men. They were selected on the basis of previous compulsory blood pressure recordings made at the age of 18 when they had been drafted for military service. Subjects in the ?original hypertensive sample” with ?strain” occupations (hectic and uncontrollable, such as waiter, driver and cook) had more marked elevations of systolic blood pressure during work hours than other subjects.     

Constrained spherical deconvolution of nonspherically sampled diffusion MRI data

Constrained spherical deconvolution (CSD) of diffusion‐weighted MRI (DW‐MRI) is a popular analysis method that extracts the full white matter (WM) fiber orientation density function (fODF) in the living human brain, noninvasively. It assumes that the DW‐MRI signal on the sphere can be represented as the spherical convolution of a single‐fiber response function (RF) and the fODF, and recovers the fODF through the inverse operation. CSD approaches typically require that the DW‐MRI data is sampled shell‐wise, and estimate the RF in a purely spherical manner using spherical basis functions, such as spherical harmonics (SH), disregarding any radial dependencies. This precludes analysis of data acquired with nonspherical sampling schemes, for example, Cartesian sampling. Additionally, nonspherical sampling can also arise due to technical issues, for example, gradient nonlinearities, resulting in a spatially dependent bias of the apparent tissue densities and connectivity information. Here, we adopt a compact model for the RFs that also describes their radial dependency. We demonstrate that the proposed model can accurately predict the tissue response for a wide range of b‐values. On shell‐wise data, our approach provides fODFs and tissue densities indistinguishable from those estimated using SH. On Cartesian data, fODF estimates and apparent tissue densities are on par with those obtained from shell‐wise data, significantly broadening the range of data sets that can be analyzed using CSD. In addition, gradient nonlinearities can be accounted for using the proposed model, resulting in much more accurate apparent tissue densities and connectivity metrics.     

Przedtransplantacyjne czynniki ryzyka reaktywacji zakażenia wirusem cytomegalii po przeszczepieniach allogenicznych komórek hematopoetycznych u dzieci i młodych dorosłych

BackgroundCytomegalovirus (CMV) reactivation remains one of the most frequent complications after allogeneic hematopoietic stem cell transplantation (HSCT).MethodsAn analysis of the pre-transplant risk factors of CMV reactivation was performed in 98 patients aged 0.5–22 years (median 10.5) undergoing allogeneic HSCT. CMV reactivation was tested by assessing viral load using PCR method. Following factors were analyzed: type of conditioning, graft source, donor type, use of T-depletion and CMV-serostatus of the donor and recipient. Each factor was assigned from 0 to 2 points. Based on total score for each patient, CMV reactivation risk scale was developed, and two groups with low (LR) and high (HR) risk were determined.ResultsCMV reactivation was seen in 25 patients (24.5%). The significant risk factors for CMV reactivation were: CMV-positive recipient (p<0.001), unrelated donor (p<0.002), use of ATG (p<0.002) and PBSC (p<0,01). In the HR group the incidence of reactivation CMV was significantly higher than in LR group (47.8% vs. 5.4%, p<0.001).ConclusionsCMV seropositivity of the recipient was an independent predictor factor of CMV reactivation. The use of risk point scale of CMV reactivation allows for identification of patients with the higher risk of CMV reactivation.     

Visio-spatial skills in athletes: comparison of rugby players and non-athletes

Sport Sciences for Health - Research pertaining to the superiority of athletes’ visio-spatial expertise when compared to non-athletes is conflicting. This discrepancy may arise due to a...     

Complex evolution and epidemiology of Dobrava-Belgrade hantavirus: definition of genotypes and their characteristics

Dobrava-Belgrade virus (DOBV) is a human pathogen that has evolved in, and is hosted by, mice of several species of the genus Apodemus. We propose a subdivision of the species Dobrava-Belgrade virus into four related genotypes – Dobrava, Kurkino, Saaremaa, and Sochi – that show characteristic differences in their phylogeny, specific host reservoirs, geographical distribution, and pathogenicity for humans.     

Combining Automated Organoid Workflows with Artificial Intelligence-Based Analyses: Opportunities to Build a New Generation of Interdisciplinary High-Throughput Screens for Parkinson's Disease and Beyond

Parkinson's disease (PD) is the second most common neurodegenerative disease and primarily characterized by the loss of dopaminergic neurons in the substantia nigra pars compacta of the midbrain. Despite decades of research and the development of various disease model systems, there is no curative treatment. This could be due to current model systems, including cell culture and animal models, not adequately recapitulating human PD etiology. More complex human disease models, including human midbrain organoids, are maturing technologies that increasingly enable the strategic incorporation of the missing components needed to model PD in vitro. The resulting organoid-based biological complexity provides new opportunities and challenges in data analysis of rich multimodal data sets. Emerging artificial intelligence (AI) capabilities can take advantage of large, broad data sets and even correlate results across disciplines. Current organoid technologies no longer lack the prerequisites for large-scale high-throughput screening (HTS) and can generate complex yet reproducible data suitable for AI-based data mining. We have recently developed a fully scalable and HTS-compatible workflow for the generation, maintenance, and analysis of three-dimensional (3D) microtissues mimicking key characteristics of the human midbrain (called “automated midbrain organoids,” AMOs). AMOs build a reproducible, scalable foundation for creating next-generation 3D models of human neural disease that can fuel mechanism-agnostic phenotypic drug discovery in human in vitro PD models and beyond. Here, we explore the opportunities and challenges resulting from the convergence of organoid HTS and AI-driven data analytics and outline potential future avenues toward the discovery of novel mechanisms and drugs in PD research. © 2021 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society