Epidemiology of Chronic Pain in Ukraine: Findings from the World Mental Health Survey

Chronic pain can pose a serious challenge in everyday life for many individuals globally, especially in developing countries, but studies explicitly exploring risk factors of chronic pain beyond demographic characteristics using survey data have been scarce. To address this problem, this study analyzed World Health Organization data on chronic pain in Ukraine to explore demographic, psychological, and treatment perception-related risk factors to chronic pain. We replicated previous reports of older age, female sex, married status, inadequate financial resources, and comorbidity of other physical conditions as significant demographic risk factors for chronic pain diagnosis but not necessarily for severe pain. We also found evidence for psychological risk factors and treatment perceptions as significant predictors for chronic pain diagnosis and its severity. These results provide a first step in examining beyond demographic risk factors for chronic pain diagnosis and severity and, instead, assessing potential psychological risk factors

What America means to sixth grade children.

Thesis (Ed.M.)--Boston Universit

Association Between Cytokines and Liver Histology in Children with Nonalcoholic Fatty Liver Disease.

BackgroundReliable non-invasive markers to characterize inflammation, hepatocellular ballooning, and fibrosis in nonalcoholic fatty liver disease (NAFLD) are lacking. We investigated the relationship between plasma cytokine levels and features of NAFLD histology to gain insight into cellular pathways driving NASH and to identify potential non-invasive discriminators of NAFLD severity and pattern.MethodsCytokines were measured from plasma obtained at enrollment in pediatric participants in NASH Clinical Research Network studies with liver biopsy-proven NAFLD. Cytokines were chosen a priori as possible discriminators of NASH and its components. Minimization of Akaike Information Criterion (AIC) was used to determine cytokines retained in multivariable models.ResultsOf 235 subjects, 31% had "Definite NASH" on liver histology, 43% had "Borderline NASH", and 25% had NAFLD but not NASH. Total plasminogen activator inhibitor 1 (PAI1) and activated PAI1 levels were higher in pediatric participants with Definite NASH and with lobular inflammation. Interleukin-8 (IL-8) was higher in those with stage 3-4 fibrosis and lobular inflammation. sIL-2rα was higher in children with stage 3-4 fibrosis and portal inflammation. In multivariable analysis, PAI1 variables were discriminators of Borderline/Definite NASH, definite NASH, lobular inflammation and ballooning. IL-8 increased with steatosis and fibrosis severity; sIL-2rα increased with fibrosis severity and portal inflammation. IL-7 decreased with portal inflammation and fibrosis severity.ConclusionsPlasma cytokines associated with histology varied considerably among NASH features, suggesting promising avenues for investigation. Future, more targeted analysis is needed to identify the role of these markers in NAFLD and to evaluate their potential as non-invasive discriminators of disease severity

Broadband UBVRI Photometry of Horizontal-Branch and Metal-Poor Candidates from the HK and Hamburg/ESO Surveys. I

We report broadband UBV and/or BVRI CCD photometry for a total of 1857 stars in the thick-disk and halo populations of the Galaxy. The majority of our targets were selected as candidate field horizontal-branch or other A-type stars (FHB/A, N = 576), or candidate low-metallicity stars (N = 1221), from the HK and Hamburg/ESO objective-prism surveys. Similar data for a small number of additional stars from other samples are also reported. These data are being used for several purposes. In the case of the FHB/A candidates they are used to accurately separate the lower-gravity FHB stars from various higher-gravity A-type stars, a subsample that includes the so-called Blue Metal Poor stars, halo and thick-disk blue stragglers, main-sequence A-type dwarfs, and Am and Ap stars. These data are also being used to derive photometric distance estimates to high-velocity hydrogen clouds in the Galaxy and for improved measurements of the mass of the Galaxy. Photometric data for the metal-poor candidates are being used to refine estimates of stellar metallicity for objects with available medium-resolution spectroscopy, to obtain distance estimates for kinematic analyses, and to establish initial estimates of effective temperature for analysis of high-resolution spectroscopy of the stars for which this information now exists.Comment: 22 pages, including 3 figures, 5 tables, and two ascii files of full data, accepted for publication in the Astrophysical Journal (Supplements

Broadband UBVR_CI_C Photometry of Horizontal-Branch and Metal-poor Candidates from the HK and Hamburg/ESO Surveys. I.

We report broadband UBV and/or BVR_CI_C CCD photometry for a total of 1857 stars in the thick-disk and halo populations of the Galaxy. The majority of our targets were selected as candidate field horizontal-branch or other A-type stars (FHB/A, N = 576), or candidate low-metallicity stars (N = 1221), from the HK and Hamburg/ESO objective-prism surveys. Similar data for a small number of additional stars from other samples are also reported. These data are being used for several purposes. In the case of the FHB/A candidates they are used to accurately separate the lower gravity FHB stars from various higher gravity A-type stars, a subsample that includes the so-called blue metal poor stars, halo and thick-disk blue stragglers, main-sequence A-type dwarfs, and Am and Ap stars. These data are also being used to derive photometric distance estimates to high-velocity hydrogen clouds in the Galaxy and for improved measurements of the mass of the Galaxy. Photometric data for the metal-poor candidates are being used to refine estimates of stellar metallicity for objects with available medium-resolution spectroscopy, to obtain distance estimates for kinematic analyses, and to establish initial estimates of effective temperature for analysis of high-resolution spectroscopy of the stars for which this information now exists

Joint morphogenetic cells in the adult mammalian synovium

The authors thank all members of the Arthritis & Regenerative Medicine Laboratory, particularly Dr Ana Sergijenko; Drs David Kingsley, Grigori Enikolopov, Fernando Camargo and Lora Heisler for sharing transgenic mice; Drs Henning Wackerhage, Neil Vargesson, Lynda Erskine, Chris Buckley, Francesco Dell’Accio and Frank Luyten for support and helpful discussions; Staff at the University of Aberdeen’s Animal Facility, Microscopy & Histology Facility and Iain Fraser Cytometry Centre for their support. C.D.B. is grateful to Dr Frank Luyten’s support for the experiment in Fig. 8, performed in his laboratory at KU Leuven, Belgium. We are grateful for the following funding: Arthritis Research UK (Grants No. 20050, 19429 and 20775), Medical Research Council (Grant No. MR/L020211/1) and Tenovus Scotland (Grant No. G13/14). A.H.K.R. is supported by the Wellcome Trust through the Scottish Translational Medicine and Therapeutics Initiative (Grant No. WT 085664).Peer reviewedPublisher PD

Electron and hole g-factors and spin dynamics of negatively charged excitons in CdSe/CdS colloidal nanoplatelets with thick shells

We address spin properties and spin dynamics of carriers and charged excitons in CdSe/CdS colloidal nanoplatelets with thick shells. Magneto-optical studies are performed by time-resolved and polarization-resolved photoluminescence, spin-flip Raman scattering and picosecond pump-probe Faraday rotation in magnetic fields up to 30 T. We show that at low temperatures the nanoplatelets are negatively charged so that their photoluminescence is dominated by radiative recombination of negatively charged excitons (trions). Electron g-factor of 1.68 is measured and heavy-hole g-factor varying with increasing magnetic field from -0.4 to -0.7 is evaluated. Hole g-factors for two-dimensional structures are calculated for various hole confining potentials for cubic- and wurtzite lattice in CdSe core. These calculations are extended for various quantum dots and nanoplatelets based on II-VI semiconductors. We developed a magneto-optical technique for the quantitative evaluation of the nanoplatelets orientation in ensemble

Genomic analysis of expressed sequence tags in American black bear Ursus americanus

<p>Abstract</p> <p>Background</p> <p>Species of the bear family (<it>Ursidae</it>) are important organisms for research in molecular evolution, comparative physiology and conservation biology, but relatively little genetic sequence information is available for this group. Here we report the development and analyses of the first large scale Expressed Sequence Tag (EST) resource for the American black bear (<it>Ursus americanus</it>).</p> <p>Results</p> <p>Comprehensive analyses of molecular functions, alternative splicing, and tissue-specific expression of 38,757 black bear EST sequences were conducted using the dog genome as a reference. We identified 18 genes, involved in functions such as lipid catabolism, cell cycle, and vesicle-mediated transport, that are showing rapid evolution in the bear lineage Three genes, Phospholamban (<it>PLN</it>), cysteine glycine-rich protein 3 (<it>CSRP3</it>) and Troponin I type 3 (<it>TNNI3</it>), are related to heart contraction, and defects in these genes in humans lead to heart disease. Two genes, biphenyl hydrolase-like (<it>BPHL</it>) and <it>CSRP3</it>, contain positively selected sites in bear. Global analysis of evolution rates of hibernation-related genes in bear showed that they are largely conserved and slowly evolving genes, rather than novel and fast-evolving genes.</p> <p>Conclusion</p> <p>We provide a genomic resource for an important mammalian organism and our study sheds new light on the possible functions and evolution of bear genes.</p

A small step toward generalizability: training a machine learning scoring function for structure-based virtual screening

Over the past few years, many machine learning-based scoring functions for predicting the binding of small molecules to proteins have been developed. Their objective is to approximate the distribution which takes two molecules as input and outputs the energy of their interaction. Only a scoring function that accounts for the interatomic interactions involved in binding can accurately predict binding affinity on unseen molecules. However, many scoring functions make predictions based on data set biases rather than an understanding of the physics of binding. These scoring functions perform well when tested on similar targets to those in the training set but fail to generalize to dissimilar targets. To test what a machine learning-based scoring function has learned, input attribution, a technique for learning which features are important to a model when making a prediction on a particular data point, can be applied. If a model successfully learns something beyond data set biases, attribution should give insight into the important binding interactions that are taking place. We built a machine learning-based scoring function that aimed to avoid the influence of bias via thorough train and test data set filtering and show that it achieves comparable performance on the Comparative Assessment of Scoring Functions, 2016 (CASF-2016) benchmark to other leading methods. We then use the CASF-2016 test set to perform attribution and find that the bonds identified as important by PointVS, unlike those extracted from other scoring functions, have a high correlation with those found by a distance-based interaction profiler. We then show that attribution can be used to extract important binding pharmacophores from a given protein target when supplied with a number of bound structures. We use this information to perform fragment elaboration and see improvements in docking scores compared to using structural information from a traditional, data-based approach. This not only provides definitive proof that the scoring function has learned to identify some important binding interactions but also constitutes the first deep learning-based method for extracting structural information from a target for molecule design