| 1. |
- Oh, Jiyeon, et al.
(författare)
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Global burden of vaccine-associated rheumatic diseases and their related vaccines, 1967-2023: A comprehensive analysis of the international pharmacovigilance database
- 2024
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Ingår i: INTERNATIONAL JOURNAL OF RHEUMATIC DISEASES. - : WILEY. - 1756-1841. ; 27:8
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Tidskriftsartikel (refereegranskat)abstract
- Vaccine-associated rheumatic diseases are rare but one of the most feared adverse drug reactions (ADRs). However, this topic has been investigated less with large-scale data in the literature. With the rapid progress in the development and approval of vaccines during the pandemic, public concerns regarding their safety have been raised. To assess the global and regional burden, long-term trends, and potential risk factors of vaccines-associated six types of rheumatic diseases (ankylosing spondylitis [AS], polymyalgia rheumatica [PMR], rheumatoid arthritis [RA], Sj & ouml;gren's syndrome, Systemic lupus erythematosus [SLE], Systemic scleroderma), this study conducted disproportionality analysis based on the reports from the World Health Organization International Pharmacovigilance Database documented between 1967 and 2023 (n for total reports = 131 255 418) across 156 countries and territories. We estimated the reporting odds ratio (ROR) and information component (IC) to determine the disproportionality signal for rheumatic diseases. Of 198 046 reports of all-cause rheumatic diseases, 14 703 reports of vaccine-associated rheumatic diseases were identified. While the reporting counts have gradually increased over time globally, we observed a dramatic increase in reporting counts after 2020, potentially due to a large portion of reports of COVID-19 mRNA vaccine-associated rheumatic diseases. The disproportionality signal for rheumatic diseases was most pronounced in HBV vaccines (ROR, 4.11; IC025, 1.90), followed by COVID-19 mRNA (ROR, 2.79; IC025, 1.25), anthrax (ROR, 2.52; IC025, 0.76), papillomavirus (ROR, 2.16; IC025, 0.95), encephalitis (ROR, 2.01; IC025, 0.58), typhoid (ROR, 1.91; IC025, 0.44), influenza (ROR, 1.49; IC025, 0.46), and HAV vaccines (ROR, 1.41; IC025, 0.20). From age- and sex-specific perspective, young females and old males are likely to have vaccine-associated rheumatic disease reports. Furthermore, overall vaccines showed a disproportionality signal for PMR (IC025, 3.13) and Sj & ouml;gren's syndrome (IC025, 0.70), systemic scleroderma (IC025, 0.64), specifically while the COVID-19 mRNA vaccines are associated with all six types of diseases. Although multiple vaccines are associated with rheumatic disease reports, healthcare providers should be aware of the potential of autoimmune manifestations following vaccination, particularly the COVID-19 mRNA and HBV vaccines, and take into account for risk factors associated with these ADRs. Most ADRs exhibited an average time to onset of 11 days, underscoring the significance of monitoring and timely management by clinicians.
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| 2. |
- Smith, Daniel G. A., et al.
(författare)
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Quantum Chemistry Common Driver and Databases (QCDB) and Quantum Chemistry Engine (QCEngine) : Automation and interoperability among computational chemistry programs
- 2021
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Ingår i: Journal of Chemical Physics. - : American Institute of Physics (AIP). - 0021-9606 .- 1089-7690. ; 155:20
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Tidskriftsartikel (refereegranskat)abstract
- Community efforts in the computational molecular sciences (CMS) are evolving toward modular, open, and interoperable interfaces that work with existing community codes to provide more functionality and composability than could be achieved with a single program. The Quantum Chemistry Common Driver and Databases (QCDB) project provides such capability through an application programming interface (API) that facilitates interoperability across multiple quantum chemistry software packages. In tandem with the Molecular Sciences Software Institute and their Quantum Chemistry Archive ecosystem, the unique functionalities of several CMS programs are integrated, including CFOUR, GAMESS, NWChem, OpenMM, Psi4, Qcore, TeraChem, and Turbomole, to provide common computational functions, i.e., energy, gradient, and Hessian computations as well as molecular properties such as atomic charges and vibrational frequency analysis. Both standard users and power users benefit from adopting these APIs as they lower the language barrier of input styles and enable a standard layout of variables and data. These designs allow end-to-end interoperable programming of complex computations and provide best practices options by default.
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| 3. |
- Wessel, Jennifer, et al.
(författare)
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Low-frequency and rare exome chip variants associate with fasting glucose and type 2 diabetes susceptibility
- 2015
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Ingår i: Nature Communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 6
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Tidskriftsartikel (refereegranskat)abstract
- Fasting glucose and insulin are intermediate traits for type 2 diabetes. Here we explore the role of coding variation on these traits by analysis of variants on the HumanExome BeadChip in 60,564 non-diabetic individuals and in 16,491 T2D cases and 81,877 controls. We identify a novel association of a low-frequency nonsynonymous SNV in GLP1R (A316T; rs10305492; MAF = 1.4%) with lower FG (beta = -0.09 +/- 0.01 mmol l(-1), P = 3.4 x 10(-12)), T2D risk (OR[95% CI] = 0.86[0.76-0.96], P = 0.010), early insulin secretion (beta = -0.07 +/- 0.035 pmol(insulin) mmol(glucose)(-1), P = 0.048), but higher 2-h glucose (beta = 0.16 +/- 0.05 mmol l(-1), P = 4.3 x 10(-4)). We identify a gene-based association with FG at G6PC2 (p(SKAT) = 6.8 x 10(-6)) driven by four rare protein-coding SNVs (H177Y, Y207S, R283X and S324P). We identify rs651007 (MAF = 20%) in the first intron of ABO at the putative promoter of an antisense lncRNA, associating with higher FG (beta = 0.02 +/- 0.004 mmol l(-1), P = 1.3 x 10(-8)). Our approach identifies novel coding variant associations and extends the allelic spectrum of variation underlying diabetes-related quantitative traits and T2D susceptibility.
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| 4. |
- Sui, Xin, et al.
(författare)
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Parallel clustered low-rank approximation of graphs and its application to link prediction
- 2012
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Ingår i: Proceedings of the International Workshop on Languages and Compilers for Parallel Computing. - Berlin, Heidelberg : Springer Berlin Heidelberg. - 9783642376573 - 9783642376580 ; , s. 76-95
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Social network analysis has become a major research area that has impact in diverse applications ranging from search engines to product recommendation systems. A major problem in implementing social network analysis algorithms is the sheer size of many social networks, for example, the Facebook graph has more than 900 million vertices and even small networks may have tens of millions of vertices. One solution to dealing with these large graphs is dimensionality reduction using spectral or SVD analysis of the adjacency matrix of the network, but these global techniques do not necessarily take into account local structures or clusters of the network that are critical in network analysis. A more promising approach is clustered low-rank approximation: instead of computing a global low-rank approximation, the adjacency matrix is first clustered, and then a low-rank approximation of each cluster (i.e., diagonal block) is computed. The resulting algorithm is challenging to parallelize not only because of the large size of the data sets in social network analysis, but also because it requires computing with very diverse data structures ranging from extremely sparse matrices to dense matrices. In this paper, we describe the first parallel implementation of a clustered low-rank approximation algorithm for large social network graphs, and use it to perform link prediction in parallel. Experimental results show that this implementation scales well on large distributed-memory machines; for example, on a Twitter graph with roughly 11 million vertices and 63 million edges, our implementation scales by a factor of 86 on 128 processes and takes less than 2300 seconds, while on a much larger Twitter graph with 41 million vertices and 1.2 billion edges, our implementation scales by a factor of 203 on 256 processes with a running time about 4800 seconds.
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