Platelets and Cardiac Arrhythmia

Sudden cardiac death (SCD) remains one of the most prevalent modes of death in industrialized countries, and myocardial ischemia due to thrombotic coronary occlusion is its primary cause. The role of platelets in the occurrence of SCD extends beyond coronary flow impairment by clot formation. Here we review the substances released by platelets during clot formation and their arrhythmic properties. Platelet products are released from three types of platelet granules: dense core granules, alpha-granules, and platelet lysosomes. The physiologic properties of dense granule products are of special interest as a potential source of arrhythmic substances. They are released readily upon activation and contain high concentrations of serotonin, histamine, purines, pyrimidines, and ions such as calcium and magnesium. Potential arrhythmic mechanisms of these substances, e.g., serotonin and high energy phosphates, include induction of coronary constriction, calcium overloading, and induction of delayed after-depolarizations. Alpha-granules produce thromboxanes and other arachidonic-acid products with many potential arrhythmic effects mediated by interference with cardiac sodium, calcium, and potassium channels. Alpha-granules also contain hundreds of proteins that could potentially serve as ligands to receptors on cardiomyocytes. Lysosomal products probably do not have an important arrhythmic effect. Platelet products and ischemia can induce coronary permeability, thereby enhancing interaction with surrounding cardiomyocytes. Antiplatelet therapy is known to improve survival after myocardial infarction. Although an important part of this effect results from prevention of coronary clot formation, there is evidence to suggest that antiplatelet therapy also induces anti-arrhythmic effects during ischemia by preventing the release of platelet activation products

Complications in pulmonary vein isolation in the Netherlands Heart Registration differ with sex and ablation technique

Aims Pulmonary vein isolation (PVI) has become a cornerstone of the invasive treatment of atrial fibrillation. Severe complications are reported in 1-3% of patients. This study aims to compare complications and follow-up outcome of PVI in patients with atrial fibrillation. Methods and results The data were extracted from the Netherlands Heart Registration. Procedural and follow-up outcomes in patients treated with conventional radiofrequency (C-RF), multielectrode phased RF (Ph-RF), or cryoballoon (CB) ablation from 2012 to 2017 were compared. Subgroup analysis was performed to identify variables associated with complications and repeat ablations. In total, 13 823 patients (69% male) were included. The reported complication incidence was 3.6%. Patients treated with C-RF developed more cardiac tamponades (C-RF 0.8% vs. Ph-RF 0.3% vs. CB 0.3%, P Conclusion The reported complication rate during PVI was low. Patients treated with C-RF ablation were more likely to develop cardiac tamponades and vascular complications. Female sex was associated with more cardiac tamponade and bleeding complications

Design and rationale of DUTCH-AF:a prospective nationwide registry programme and observational study on long-term oral antithrombotic treatment in patients with atrial fibrillation

Introduction Anticoagulation therapy is pivotal in the management of stroke prevention in atrial fibrillation (AF). Prospective registries, containing longitudinal data are lacking with detailed information on anticoagulant therapy, treatment adherence and AF-related adverse events in practice-based patient cohorts, in particular for non-vitamin K oral anticoagulants (NOAC). With the creation of DUTCH-AF, a nationwide longitudinal AF registry, we aim to provide clinical data and answer questions on the (anticoagulant) management over time and of the clinical course of patients with newly diagnosed AF in routine clinical care. Within DUTCH-AF, our current aim is to assess the effect of non-adherence and non-persistence of anticoagulation therapy on clinical adverse events (eg, bleeding and stroke), to determine predictors for such inadequate anticoagulant treatment, and to validate and refine bleeding prediction models. With DUTCH-AF, we provide the basis for a continuing nationwide AF registry, which will facilitate subsequent research, including future registry-based clinical trials. Methods and analysis The DUTCH-AF registry is a nationwide, prospective registry of patients with newly diagnosed 'non-valvular' AF. Patients will be enrolled from primary, secondary and tertiary care practices across the Netherlands. A target of 6000 patients for this initial cohort will be followed for at least 2 years. Data on thromboembolic and bleeding events, changes in antithrombotic therapy and hospital admissions will be registered. Pharmacy-dispensing data will be obtained to calculate parameters of adherence and persistence to anticoagulant treatment, which will be linked to AF-related outcomes such as ischaemic stroke and major bleeding. In a subset of patients, anticoagulation adherence and beliefs about drugs will be assessed by questionnaire. Ethics and dissemination This study protocol was approved as exempt for formal review according to Dutch law by the Medical Ethics Committee of the Leiden University Medical Centre, Leiden, the Netherlands. Results will be disseminated by publications in peer-reviewed journals and presentations at scientific congresses

Genome-wide association study identifies 30 loci associated with bipolar disorder.

Bipolar disorder is a highly heritable psychiatric disorder. We performed a genome-wide association study (GWAS) including 20,352 cases and 31,358 controls of European descent, with follow-up analysis of 822 variants with P < 1 × 10-4 in an additional 9,412 cases and 137,760 controls. Eight of the 19 variants that were genome-wide significant (P < 5 × 10-8) in the discovery GWAS were not genome-wide significant in the combined analysis, consistent with small effect sizes and limited power but also with genetic heterogeneity. In the combined analysis, 30 loci were genome-wide significant, including 20 newly identified loci. The significant loci contain genes encoding ion channels, neurotransmitter transporters and synaptic components. Pathway analysis revealed nine significantly enriched gene sets, including regulation of insulin secretion and endocannabinoid signaling. Bipolar I disorder is strongly genetically correlated with schizophrenia, driven by psychosis, whereas bipolar II disorder is more strongly correlated with major depressive disorder. These findings address key clinical questions and provide potential biological mechanisms for bipolar disorder

Large-scale gene-centric analysis identifies novel variants for coronary artery disease

Coronary artery disease (CAD) has a significant genetic contribution that is incompletely characterized. To complement genome-wide association (GWA) studies, we conducted a large and systematic candidate gene study of CAD susceptibility, including analysis of many uncommon and functional variants. We examined 49,094 genetic variants in ~2,100 genes of cardiovascular relevance, using a customised gene array in 15,596 CAD cases and 34,992 controls (11,202 cases and 30,733 controls of European descent; 4,394 cases and 4,259 controls of South Asian origin). We attempted to replicate putative novel associations in an additional 17,121 CAD cases and 40,473 controls. Potential mechanisms through which the novel variants could affect CAD risk were explored through association tests with vascular risk factors and gene expression. We confirmed associations of several previously known CAD susceptibility loci (eg, 9p21.3:p&lt;10-33; LPA:p&lt;10-19; 1p13.3:p&lt;10-17) as well as three recently discovered loci (COL4A1/COL4A2, ZC3HC1, CYP17A1:p&lt;5×10-7). However, we found essentially null results for most previously suggested CAD candidate genes. In our replication study of 24 promising common variants, we identified novel associations of variants in or near LIPA, IL5, TRIB1, and ABCG5/ABCG8, with per-allele odds ratios for CAD risk with each of the novel variants ranging from 1.06-1.09. Associations with variants at LIPA, TRIB1, and ABCG5/ABCG8 were supported by gene expression data or effects on lipid levels. Apart from the previously reported variants in LPA, none of the other ~4,500 low frequency and functional variants showed a strong effect. Associations in South Asians did not differ appreciably from those in Europeans, except for 9p21.3 (per-allele odds ratio: 1.14 versus 1.27 respectively; P for heterogeneity = 0.003). This large-scale gene-centric analysis has identified several novel genes for CAD that relate to diverse biochemical and cellular functions and clarified the literature with regard to many previously suggested genes.</p

Advances in mass spectrometry-based post-column bioaffinity profiling of mixtures

In the screening of complex mixtures, for example combinatorial libraries, natural extracts, and metabolic incubations, different approaches are used for integrated bioaffinity screening. Four major strategies can be used for screening of bioactive mixtures for protein targets—pre-column and post-column off-line, at-line, and on-line strategies. The focus of this review is on recent developments in post-column on-line screening, and the role of mass spectrometry (MS) in these systems. On-line screening systems integrate separation sciences, mass spectrometry, and biochemical methodology, enabling screening for active compounds in complex mixtures. There are three main variants of on-line MS based bioassays: the mass spectrometer is used for ligand identification only; the mass spectrometer is used for both ligand identification and bioassay readout; or MS detection is conducted in parallel with at-line microfractionation with off-line bioaffinity analysis. On the basis of the different fields of application of on-line screening, the principles are explained and their usefulness in the different fields of drug research is critically evaluated. Furthermore, off-line screening is discussed briefly with the on-line and at-line approaches

Structure and catalytic regulatory function of ubiquitin specific protease 11 N-terminal and ubiquitin-like domains

The ubiquitin specific protease 11 (USP11) is implicated in DNA repair, viral RNA replication, and TGFβ signaling. We report the first characterization of the USP11 domain architecture and its role in regulating the enzymatic activity. USP11 consists of an N-terminal "domain present in USPs" (DUSP) and "ubiquitin-like" (UBL) domain, together referred to as DU domains, and the catalytic domain harboring a second UBL domain. Crystal structures of the DU domains show a tandem arrangement with a shortened β-hairpin at the two-domain interface and altered surface characteristics compared to the homologues USP4 and USP15. A conserved VEVY motif is a signature feature at the two-domain interface that shapes a potential protein interaction site. Small angle X-ray scattering and gel filtration experiments are consistent with the USP11DU domains and full-length USP11 being monomeric. Unexpectedly, we reveal, through kinetic assays of a series of deletion mutants, that the catalytic activity of USP11 is not regulated through intramolecular autoinhibition or activation by the N-terminal DU or UBL domains. Moreover, ubiquitin chain cleavage assays with all eight linkages reveal a preference for Lys(63)-, Lys(6)-, Lys(33)-, and Lys(11)-linked chains over Lys(27)-, Lys(29)-, and Lys(48)-linked and linear chains consistent with USP11's function in DNA repair pathways that is mediated by the protease domain. Our data support a model whereby USP11 domains outside the catalytic core domain serve as protein interaction or trafficking modules rather than a direct regulatory function of the proteolytic activity. This highlights the diversity of USPs in substrate recognition and regulation of ubiquitin deconjugation

Green qualities in the neighbourhood and mental health - results from a longitudinal cohort study in Southern Sweden

Background: Poor mental health is a major issue worldwide and causality is complex. For diseases with multifactorial background synergistic effects of person-and place-factors can potentially be preventive. Nature is suggested as one such positive place-factor. In this cohort study we tested the effect of defined green qualities (Serene, Space, Wild, Culture, Lush) in the environment at baseline on mental health at follow-up. We also studied interaction effects on mental health of those place factors and varied person factors (financial stress, living conditions, and physical activity). Methods: Data on person factors were extracted from a longitudinal (years 1999/2000 and 2005) population health survey (n = 24945). The participants were geocoded and linked to data on green qualities from landscape assessments, and stored in the Geographical Information System (GIS). Crude odds ratios (OR) and 95% confidence intervals (CI) were calculated, and multivariate logistic analyses were performed. Results: Mental health was not affected by access to the chosen green qualities, neither in terms of amount nor in terms of any specific quality. However, we found a reduced risk for poor mental health at follow-up among women, through a significant interaction effect between physical activity and access to the qualities Serene or Space. For men the tendencies were similar, though not significant. Regarding the other three green qualities, as well as amount of qualities, no statistically certain synergistic effects were found. Likewise, no significant synergies were detected between green qualities and the other person-factors. Only advanced exercise significantly reduced the risk for poor mental health among women, but not for men, compared to physical inactivity. Conclusions: The results do not directly support the hypothesis of a preventive mental health effect by access to the green qualities. However, the additive effect of serene nature to physical activity contributed to better mental health at follow-up. This tendency was equal for both sexes, but statistically significant only for women. Objective landscape assessments may be important in detangling geographic determinants of health. This study stresses the importance of considering interaction effects when dealing with disorders of multifactorial background

Report on the sixth blind test of organic crystal structure prediction methods.

The sixth blind test of organic crystal structure prediction (CSP) methods has been held, with five target systems: a small nearly rigid molecule, a polymorphic former drug candidate, a chloride salt hydrate, a co-crystal and a bulky flexible molecule. This blind test has seen substantial growth in the number of participants, with the broad range of prediction methods giving a unique insight into the state of the art in the field. Significant progress has been seen in treating flexible molecules, usage of hierarchical approaches to ranking structures, the application of density-functional approximations, and the establishment of new workflows and `best practices' for performing CSP calculations. All of the targets, apart from a single potentially disordered Z' = 2 polymorph of the drug candidate, were predicted by at least one submission. Despite many remaining challenges, it is clear that CSP methods are becoming more applicable to a wider range of real systems, including salts, hydrates and larger flexible molecules. The results also highlight the potential for CSP calculations to complement and augment experimental studies of organic solid forms.The organisers and participants are very grateful to the crystallographers who supplied the candidate structures: Dr. Peter Horton (XXII), Dr. Brian Samas (XXIII), Prof. Bruce Foxman (XXIV), and Prof. Kraig Wheeler (XXV and XXVI). We are also grateful to Dr. Emma Sharp and colleagues at Johnson Matthey (Pharmorphix) for the polymorph screening of XXVI, as well as numerous colleagues at the CCDC for assistance in organising the blind test. Submission 2: We acknowledge Dr. Oliver Korb for numerous useful discussions. Submission 3: The Day group acknowledge the use of the IRIDIS High Performance Computing Facility, and associated support services at the University of Southampton, in the completion of this work. We acknowledge funding from the EPSRC (grants EP/J01110X/1 and EP/K018132/1) and the European Research Council under the European Union’s Seventh Framework Programme (FP/2007-2013)/ERC through grant agreements n. 307358 (ERC-stG- 2012-ANGLE) and n. 321156 (ERC-AG-PE5-ROBOT). Submission 4: I am grateful to Mikhail Kuzminskii for calculations of molecular structures on Gaussian 98 program in the Institute of Organic Chemistry RAS. The Russian Foundation for Basic Research is acknowledged for financial support (14-03-01091). Submission 5: Toine Schreurs provided computer facilities and assistance. I am grateful to Matthew Habgood at AWE company for providing a travel grant. Submission 6: We would like to acknowledge support of this work by GlaxoSmithKline, Merck, and Vertex. Submission 7: The research was financially supported by the VIDI Research Program 700.10.427, which is financed by The Netherlands Organisation for Scientific Research (NWO), and the European Research Council (ERC-2010-StG, grant agreement n. 259510-KISMOL). We acknowledge the support of the Foundation for Fundamental Research on Matter (FOM). Supercomputer facilities were provided by the National Computing Facilities Foundation (NCF). Submission 8: Computer resources were provided by the Center for High Performance Computing at the University of Utah and the Extreme Science and Engineering Discovery Environment (XSEDE), supported by NSF grant number ACI-1053575. MBF and GIP acknowledge the support from the University of Buenos Aires and the Argentinian Research Council. Submission 9: We thank Dr. Bouke van Eijck for his valuable advice on our predicted structure of XXV. We thank the promotion office for TUT programs on advanced simulation engineering (ADSIM), the leading program for training brain information architects (BRAIN), and the information and media center (IMC) at Toyohashi University of Technology for the use of the TUT supercomputer systems and application software. We also thank the ACCMS at Kyoto University for the use of their supercomputer. In addition, we wish to thank financial supports from Conflex Corp. and Ministry of Education, Culture, Sports, Science and Technology. Submission 12: We thank Leslie Leiserowitz from the Weizmann Institute of Science and Geoffrey Hutchinson from the University of Pittsburgh for helpful discussions. We thank Adam Scovel at the Argonne Leadership Computing Facility (ALCF) for technical support. Work at Tulane University was funded by the Louisiana Board of Regents Award # LEQSF(2014-17)-RD-A-10 “Toward Crystal Engineering from First Principles”, by the NSF award # EPS-1003897 “The Louisiana Alliance for Simulation-Guided Materials Applications (LA-SiGMA)”, and by the Tulane Committee on Research Summer Fellowship. Work at the Technical University of Munich was supported by the Solar Technologies Go Hybrid initiative of the State of Bavaria, Germany. Computer time was provided by the Argonne Leadership Computing Facility (ALCF), which is supported by the Office of Science of the U.S. Department of Energy under contract DE-AC02-06CH11357. Submission 13: This work would not have been possible without funding from Khalifa University’s College of Engineering. I would like to acknowledge Prof. Robert Bennell and Prof. Bayan Sharif for supporting me in acquiring the resources needed to carry out this research. Dr. Louise Price is thanked for her guidance on the use of DMACRYS and NEIGHCRYS during the course of this research. She is also thanked for useful discussions and numerous e-mail exchanges concerning the blind test. Prof. Sarah Price is acknowledged for her support and guidance over many years and for providing access to DMACRYS and NEIGHCRYS. Submission 15: The work was supported by the United Kingdom’s Engineering and Physical Sciences Research Council (EPSRC) (EP/J003840/1, EP/J014958/1) and was made possible through access to computational resources and support from the High Performance Computing Cluster at Imperial College London. We are grateful to Professor Sarah L. Price for supplying the DMACRYS code for use within CrystalOptimizer, and to her and her research group for support with DMACRYS and feedback on CrystalPredictor and CrystalOptimizer. Submission 16: R. J. N. acknowledges financial support from the Engineering and Physical Sciences Research Council (EPSRC) of the U.K. [EP/J017639/1]. R. J. N. and C. J. P. acknowledge use of the Archer facilities of the U.K.’s national high-performance computing service (for which access was obtained via the UKCP consortium [EP/K014560/1]). C. J. P. also acknowledges a Leadership Fellowship Grant [EP/K013688/1]. B. M. acknowledges Robinson College, Cambridge, and the Cambridge Philosophical Society for a Henslow Research Fellowship. Submission 17: The work at the University of Delaware was supported by the Army Research Office under Grant W911NF-13-1- 0387 and by the National Science Foundation Grant CHE-1152899. The work at the University of Silesia was supported by the Polish National Science Centre Grant No. DEC-2012/05/B/ST4/00086. Submission 18: We would like to thank Constantinos Pantelides, Claire Adjiman and Isaac Sugden of Imperial College for their support of our use of CrystalPredictor and CrystalOptimizer in this and Submission 19. The CSP work of the group is supported by EPSRC, though grant ESPRC EP/K039229/1, and Eli Lilly. The PhD students support: RKH by a joint UCL Max-Planck Society Magdeburg Impact studentship, REW by a UCL Impact studentship; LI by the Cambridge Crystallographic Data Centre and the M3S Centre for Doctoral Training (EPSRC EP/G036675/1). Submission 19: The potential generation work at the University of Delaware was supported by the Army Research Office under Grant W911NF-13-1-0387 and by the National Science Foundation Grant CHE-1152899. Submission 20: The work at New York University was supported, in part, by the U.S. Army Research Laboratory and the U.S. Army Research Office under contract/grant number W911NF-13-1-0387 (MET and LV) and, in part, by the Materials Research Science and Engineering Center (MRSEC) program of the National Science Foundation under Award Number DMR-1420073 (MET and ES). The work at the University of Delaware was supported by the U.S. Army Research Laboratory and the U.S. Army Research Office under contract/grant number W911NF-13-1- 0387 and by the National Science Foundation Grant CHE-1152899. Submission 21: We thank the National Science Foundation (DMR-1231586), the Government of Russian Federation (Grant No. 14.A12.31.0003), the Foreign Talents Introduction and Academic Exchange Program (No. B08040) and the Russian Science Foundation, project no. 14-43-00052, base organization Photochemistry Center of the Russian Academy of Sciences. Calculations were performed on the Rurik supercomputer at Moscow Institute of Physics and Technology. Submission 22: The computational results presented have been achieved in part using the Vienna Scientific Cluster (VSC). Submission 24: The potential generation work at the University of Delaware was supported by the Army Research Office under Grant W911NF-13-1-0387 and by the National Science Foundation Grant CHE-1152899. Submission 25: J.H. and A.T. acknowledge the support from the Deutsche Forschungsgemeinschaft under the program DFG-SPP 1807. H-Y.K., R.A.D., and R.C. acknowledge support from the Department of Energy (DOE) under Grant Nos. DE-SC0008626. This research used resources of the Argonne Leadership Computing Facility at Argonne National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-06CH11357. This research used resources of the National Energy Research Scientific Computing Center, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DEAC02-05CH11231. Additional computational resources were provided by the Terascale Infrastructure for Groundbreaking Research in Science and Engineering (TIGRESS) High Performance Computing Center and Visualization Laboratory at Princeton University.This is the final version of the article. It first appeared from Wiley via http://dx.doi.org/10.1107/S2052520616007447