Antidepressant use and risk of self-harm among people aged 40 years or older: A population-based cohort and self-controlled case series study

Background: Studies on the association between antidepressants and self-harm in adults were mostly conducted over a decade ago and have inconsistent findings. We aimed to compare self-harm risks by antidepressant classes among people aged 40 years or older with depression. Methods: Individuals aged ≥40 years with depression who initiated antidepressant treatment between 2001 and 2015 were retrieved from the Hong Kong Clinical Data Analysis & Reporting system, and were followed up until December 31, 2016. We conducted self-controlled case series (SCCS) analyses to estimate the incidence rate ratio (IRR) of self-harm comparing the pre-exposure (90 days before the first antidepressant use), index exposure (the first antidepressant use), and subsequent exposure (subsequent antidepressant use) periods to nonexposed periods. We applied Cox proportional hazard regressions to estimate the hazard ratio (HR) of self-harm comparing five antidepressant classes (tricyclic and related antidepressant drugs [TCAs], selective serotonin reuptake inhibitors [SSRIs], noradrenergic and specific serotonergic antidepressants [NaSSAs], serotonin–norepinephrine reuptake inhibitors [SNRIs], and others). Findings: A total of 48,724 individuals were identified. SCCS analyses (N = 3,846) found that the increased self-harm risk occurred during the pre-exposure (IRR: 22.24; 95% CI, 20.25-24.42), index exposure (7.03; 6.34-7.80), and subsequent exposure periods (2.47; 2.18-2.79) compared to the unexposed period. Cohort analyses (N = 48,724) found an association of higher self-harm risks in short-term (one year) for NaSSAs vs. TCAs (HR, 2.13; 95% CI, 1.53-2.96), SNRIs vs. TCAs (1.64; 1.01-2.68), and NaSSAs vs. SSRIs (1.75; 1.29-2.36) in the 40-64 years group. The higher risk remained significant in long-term (> one year) for NaSSAs vs. TCAs (1.55; 1.26-1.91) and NaSSAs vs. SSRIs (1.53; 1.26-1.87). In the 65+ group, only short-term differences were observed (SSRIs vs. TCAs [1.31; 1.03-1.66], SNRIs vs. SSRIs [0.44; 0.22-0.87], and SNRIs vs. NaSSAs [0.43; 0.21-0.87]). Interpretation: Within-person comparisons did not suggest that antidepressant exposure is causally associated with an increased risk of self-harm in people with depression. Between-person comparisons revealed differences in self-harm risks between certain pairs of antidepressant classes. These findings may inform clinicians’ benefit-risk assessments when prescribing antidepressants

Bayesian astrostatistics: a backward look to the future

This perspective chapter briefly surveys: (1) past growth in the use of Bayesian methods in astrophysics; (2) current misconceptions about both frequentist and Bayesian statistical inference that hinder wider adoption of Bayesian methods by astronomers; and (3) multilevel (hierarchical) Bayesian modeling as a major future direction for research in Bayesian astrostatistics, exemplified in part by presentations at the first ISI invited session on astrostatistics, commemorated in this volume. It closes with an intentionally provocative recommendation for astronomical survey data reporting, motivated by the multilevel Bayesian perspective on modeling cosmic populations: that astronomers cease producing catalogs of estimated fluxes and other source properties from surveys. Instead, summaries of likelihood functions (or marginal likelihood functions) for source properties should be reported (not posterior probability density functions), including nontrivial summaries (not simply upper limits) for candidate objects that do not pass traditional detection thresholds.Comment: 27 pp, 4 figures. A lightly revised version of a chapter in "Astrostatistical Challenges for the New Astronomy" (Joseph M. Hilbe, ed., Springer, New York, forthcoming in 2012), the inaugural volume for the Springer Series in Astrostatistics. Version 2 has minor clarifications and an additional referenc

Crystallite size-modulated exciton emission in SnO₂ nanocrystalline films grown by sputtering

Author name used in this publication: Pan, Shu Sheng.Author name used in this publication: Yu, Siu Fung.2012-2013 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

Human Bocavirus NS1 and NS1-70 Proteins Inhibit TNF-α-Mediated Activation of NF-κB by targeting p65.

Human bocavirus (HBoV), a parvovirus, is a single-stranded DNA etiologic agent causing lower respiratory tract infections in young children worldwide. Nuclear factor kappa B (NF-κB) transcription factors play crucial roles in clearance of invading viruses through activation of many physiological processes. Previous investigation showed that HBoV infection could significantly upregulate the level of TNF-α which is a strong NF-κB stimulator. Here we investigated whether HBoV proteins modulate TNF-α-mediated activation of the NF-κB signaling pathway. We showed that HBoV NS1 and NS1-70 proteins blocked NF-κB activation in response to TNF-α. Overexpression of TNF receptor-associated factor 2 (TRAF2)-, IκB kinase alpha (IKKα)-, IκB kinase beta (IKKβ)-, constitutively active mutant of IKKβ (IKKβ SS/EE)-, or p65-induced NF-κB activation was inhibited by NS1 and NS1-70. Furthermore, NS1 and NS1-70 didn't interfere with TNF-α-mediated IκBα phosphorylation and degradation, nor p65 nuclear translocation. Coimmunoprecipitation assays confirmed the interaction of both NS1 and NS1-70 with p65. Of note, NS1 but not NS1-70 inhibited TNF-α-mediated p65 phosphorylation at ser536. Our findings together indicate that HBoV NS1 and NS1-70 inhibit NF-κB activation. This is the first time that HBoV has been shown to inhibit NF-κB activation, revealing a potential immune-evasion mechanism that is likely important for HBoV pathogenesis

New genomic resources for switchgrass: a BAC library and comparative analysis of homoeologous genomic regions harboring bioenergy traits

<p>Abstract</p> <p>Background</p> <p>Switchgrass, a C4 species and a warm-season grass native to the prairies of North America, has been targeted for development into an herbaceous biomass fuel crop. Genetic improvement of switchgrass feedstock traits through marker-assisted breeding and biotechnology approaches calls for genomic tools development. Establishment of integrated physical and genetic maps for switchgrass will accelerate mapping of value added traits useful to breeding programs and to isolate important target genes using map based cloning. The reported polyploidy series in switchgrass ranges from diploid (2X = 18) to duodecaploid (12X = 108). Like in other large, repeat-rich plant genomes, this genomic complexity will hinder whole genome sequencing efforts. An extensive physical map providing enough information to resolve the homoeologous genomes would provide the necessary framework for accurate assembly of the switchgrass genome.</p> <p>Results</p> <p>A switchgrass BAC library constructed by partial digestion of nuclear DNA with <it>Eco</it>RI contains 147,456 clones covering the effective genome approximately 10 times based on a genome size of 3.2 Gigabases (~1.6 Gb effective). Restriction digestion and PFGE analysis of 234 randomly chosen BACs indicated that 95% of the clones contained inserts, ranging from 60 to 180 kb with an average of 120 kb. Comparative sequence analysis of two homoeologous genomic regions harboring orthologs of the rice <it>OsBRI1 </it>locus, a low-copy gene encoding a putative protein kinase and associated with biomass, revealed that orthologous clones from homoeologous chromosomes can be unambiguously distinguished from each other and correctly assembled to respective fingerprint contigs. Thus, the data obtained not only provide genomic resources for further analysis of switchgrass genome, but also improve efforts for an accurate genome sequencing strategy.</p> <p>Conclusions</p> <p>The construction of the first switchgrass BAC library and comparative analysis of homoeologous harboring <it>OsBRI1 </it>orthologs present a glimpse into the switchgrass genome structure and complexity. Data obtained demonstrate the feasibility of using HICF fingerprinting to resolve the homoeologous chromosomes of the two distinct genomes in switchgrass, providing a robust and accurate BAC-based physical platform for this species. The genomic resources and sequence data generated will lay the foundation for deciphering the switchgrass genome and lead the way for an accurate genome sequencing strategy.</p

Gene-environment correlations and causal effects of childhood maltreatment on physical and mental health: a genetically informed approach.

BACKGROUND: Childhood maltreatment is associated with poor mental and physical health. However, the mechanisms of gene-environment correlations and the potential causal effects of childhood maltreatment on health are unknown. Using genetics, we aimed to delineate the sources of gene-environment correlation for childhood maltreatment and the causal relationship between childhood maltreatment and health. METHODS: We did a genome-wide association study meta-analysis of childhood maltreatment using data from the UK Biobank (n=143 473), Psychiatric Genomics Consortium (n=26 290), Avon Longitudinal Study of Parents and Children (n=8346), Adolescent Brain Cognitive Development Study (n=5400), and Generation R (n=1905). We included individuals who had phenotypic and genetic data available. We investigated single nucleotide polymorphism heritability and genetic correlations among different subtypes, operationalisations, and reports of childhood maltreatment. Family-based and population-based polygenic score analyses were done to elucidate gene-environment correlation mechanisms. We used genetic correlation and Mendelian randomisation analyses to identify shared genetics and test causal relationships between childhood maltreatment and mental and physical health conditions. FINDINGS: Our meta-analysis of genome-wide association studies (N=185 414) identified 14 independent loci associated with childhood maltreatment (13 novel). We identified high genetic overlap (genetic correlations 0·24-1·00) among different maltreatment operationalisations, subtypes, and reporting methods. Within-family analyses provided some support for active and reactive gene-environment correlation but did not show the absence of passive gene-environment correlation. Robust Mendelian randomisation suggested a potential causal role of childhood maltreatment in depression (unidirectional), as well as both schizophrenia and ADHD (bidirectional), but not in physical health conditions (coronary artery disease, type 2 diabetes) or inflammation (C-reactive protein concentration). INTERPRETATION: Childhood maltreatment has a heritable component, with substantial genetic correlations among different operationalisations, subtypes, and retrospective and prospective reports of childhood maltreatment. Family-based analyses point to a role of active and reactive gene-environment correlation, with equivocal support for passive correlation. Mendelian randomisation supports a (primarily bidirectional) causal role of childhood maltreatment on mental health, but not on physical health conditions. Our study identifies research avenues to inform the prevention of childhood maltreatment and its long-term effects. FUNDING: Wellcome Trust, UK Medical Research Council, Horizon 2020, National Institute of Mental Health, and National Institute for Health Research Biomedical Research Centre.This work was supported by the Wellcome Trust (Grant refs: 214322/Z/18/Z, 104036/Z/14/Z, 204623/Z/16/Z, and 217065/Z/19/Z). VW was funded by the Bowring Research Fellowship from St. Catharine’s College, Cambridge and Wellcome Trust Collaborative Award (Grant Ref: 214322/Z/18/Z). ASFK and AM are supported by Wellcome Trust Grant 104036/Z/14/Z. ASFK is also supported by an ESRC Postdoctoral Fellowship (Grant ref: ES/V011650/1). ML is supported by the scholarship from the China Scholarship Council (No. 201706990036). The work of CC has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under the Marie Skłodowska-Curie grant agreement No 707404 and grant agreement No 848158 (EarlyCause Project). MHvIJ is supported by the Dutch Ministry of Education, Culture, and Science and the Netherlands Organization for Scientific Research (NWO grant No. 024.001.003, Consortium on Individual Development) and by a Spinoza Prize of the Netherlands Organization for Scientific Research. HMS and MRM are supported by the Medical Research Council and the University of Bristol (MC_UU_00011/7) and by the National Institute for Health Research (NIHR) Biomedical Research Centre at the University Hospitals Bristol National Health Service Foundation Trust and the University of Bristol. HMS is also supported by the European Research Council (Grant ref: 758813 MHINT). CMN is supported by the National Institute for Mental Health NIMH R01MH106595 and the Center of Excellence for Stress and Mental Health (CESAMH), Veterans Affairs San Diego. AJG and SB are supported by a Sir Henry Dale Fellowship jointly funded by the Wellcome Trust and the Royal Society (grant number 204623/Z/16/Z). TMM and RB are supported by the NIMH (R01MH117014, TMM; K23MH120437, RB).The research was conducted in association with the National Institute for Health Research (NIHR) Cambridge Biomedical Research Centre, and the NIHR Collaboration for Leadership in Applied Health Research and Care East of England at Cambridgeshire and Peterborough NHS Foundation Trust. The views expressed are those of the author(s) and not necessarily those of the National Health Service, the NIHR, or the Department of Health and Social Care. This research was possible due to two applications to the UK Biobank: Projects 20904 and 23787. This research was co-funded by the NIHR Cambridge Biomedical Research Centre and a Marmaduke Sheild grant. The UK Medical Research Council and Wellcome (Grant Ref: 217065/Z/19/Z) and the University of Bristol provide core support for ALSPAC. A comprehensive list of grants funding is available on the ALSPAC website (http://www.bristol.ac.uk/alspac/external/documents/grant-acknowledgements.pdf). We are extremely grateful to all the families who took part in this study, the midwives for their help in recruiting them, and the whole ALSPAC team, which includes interviewers, computer and laboratory technicians, clerical workers, research scientists, volunteers, managers, receptionists and nurses. The study website contains details of all data available through a fully searchable data dictionary (http://www.bristol.ac.uk/alspac/researchers/our-data/). Part of this data was collected using REDCap, see the REDCap website for details https://projectredcap.org/resources/citations/). The first phase of the Generation R Study is made possible by financial support from the Erasmus Medical Centre, Rotterdam; the Erasmus University Rotterdam; ZonMw; the Netherlands Organization for Scientific Research (NWO); and the Ministry of Health, Welfare and Sport. The authors gratefully acknowledge the contribution of all children and parents, general practitioners, hospitals, midwives and pharmacies involved in the Generation R Study. The Generation R Study is conducted by the Erasmus Medical Center in close collaboration with the School of Law and Erasmus School of Social and Behavioural Sciences at Erasmus University Rotterdam; the Municipal Health Service Rotterdam area, Rotterdam; the Rotterdam Homecare Foundation, Rotterdam; and the Stichting Trombosedienst & Artsenlaboratorium Rijnmond (STAR-MDC), Rotterdam

Experimental and theoretical investigation of ligand effects on the synthesis of ZnO nanoparticles

ZnO nanoparticles with highly controllable particle sizes(less than 10 nm) were synthesized using organic capping ligands in Zn(Ac)2 ethanolic solution. The molecular structure of the ligands was found to have significant influence on the particle size. The multi-functional molecule tris(hydroxymethyl)-aminomethane (THMA) favoured smaller particle distributions compared with ligands possessing long hydrocarbon chains that are more frequently employed. The adsorption of capping ligands on ZnnOn crystal nuclei (where n = 4 or 18 molecular clusters of(0001) ZnO surfaces) was modelled by ab initio methods at the density functional theory (DFT) level. For the molecules examined, chemisorption proceeded via the formation of Zn...O, Zn...N, or Zn...S chemical bonds between the ligands and active Zn2+ sites on ZnO surfaces. The DFT results indicated that THMA binds more strongly to the ZnO surface than other ligands, suggesting that this molecule is very effective at stabilizing ZnO nanoparticle surfaces. This study, therefore, provides new insight into the correlation between the molecular structure of capping ligands and the morphology of metal oxide nanostructures formed in their presence

Discovering patterns in drug-protein interactions based on their fingerprints

<p>Abstract</p> <p>Background</p> <p>The discovering of interesting patterns in drug-protein interaction data at molecular level can reveal hidden relationship among drugs and proteins and can therefore be of paramount importance for such application as drug design. To discover such patterns, we propose here a computational approach to analyze the molecular data of drugs and proteins that are known to have interactions with each other. Specifically, we propose to use a data mining technique called <it>Drug-Protein Interaction Analysis </it>(<it>D-PIA</it>) to determine if there are any commonalities in the fingerprints of the substructures of interacting drug and protein molecules and if so, whether or not any patterns can be generalized from them.</p> <p>Method</p> <p>Given a database of drug-protein interactions, <it>D-PIA </it>performs its tasks in several steps. First, for each drug in the database, the fingerprints of its molecular substructures are first obtained. Second, for each protein in the database, the fingerprints of its protein domains are obtained. Third, based on known interactions between drugs and proteins, an interdependency measure between the fingerprint of each drug substructure and protein domain is then computed. Fourth, based on the interdependency measure, drug substructures and protein domains that are significantly interdependent are identified. Fifth, the existence of interaction relationship between a previously unknown drug-protein pairs is then predicted based on their constituent substructures that are significantly interdependent.</p> <p>Results</p> <p>To evaluate the effectiveness of <it>D-PIA</it>, we have tested it with real drug-protein interaction data. <it>D-PIA </it>has been tested with real drug-protein interaction data including enzymes, ion channels, and protein-coupled receptors. Experimental results show that there are indeed patterns that one can discover in the interdependency relationship between drug substructures and protein domains of interacting drugs and proteins. Based on these relationships, a testing set of drug-protein data are used to see if <it>D-PIA </it>can correctly predict the existence of interaction between drug-protein pairs. The results show that the prediction accuracy can be very high. An AUC score of a ROC plot could reach as high as 75% which shows the effectiveness of this classifier.</p> <p>Conclusions</p> <p><it>D-PIA </it>has the advantage that it is able to perform its tasks effectively based on the fingerprints of drug and protein molecules without requiring any 3D information about their structures and <it>D-PIA </it>is therefore very fast to compute. <it>D-PIA </it>has been tested with real drug-protein interaction data and experimental results show that it can be very useful for predicting previously unknown drug-protein as well as protein-ligand interactions. It can also be used to tackle problems such as ligand specificity which is related directly and indirectly to drug design and discovery.</p