10 research outputs found
Genetic Polymorphisms Implicated in Nonalcoholic Liver Disease or Selected Other Disorders Have No Influence on DrugâInduced Liver Injury
With the application of genetic testing to contemporary medical diagnostics and practice, it has become apparent that the phenotypes of many disorders are modulated by host genetic factors. The aim of the current study was to determine whether selected single nucleotide polymorphisms (SNPs) unrelated to the human leukocyte antigen region or other immune pathways, including those associated with nonalcoholic fatty liver disease (NAFLD), may influence development, severity, or outcomes of drug-induced liver injury (DILI). Thirteen variants previously associated with NAFLD and/or selected other liver diseases were tested in 832 Caucasian DILI cases and 10,397 Caucasian population controls. DILI cases were attributed to multiple agents (177 individual drugs), with 56 cases due to herbal/dietary supplement products. Allele frequencies were imputed from recent genome-wide association studies and compared to those for European control samples from the Gnomad database. Significance was tested by linear regression or logistic regression, depending on the nature of the trait. Any variant that passed the Bonferroni threshold of P < 0.0004 (0.05 13) was considered a significant association. None of the variants proved to be significantly associated with DILI as phenotype nor with any of the selected severity traits. Among the variants studied, rs1421085, found in the fat mass and obesity associated (FTO) gene, showed a marginal protective effect (odds ratio, 0.8; 95% confidence interval, 0.77-0.95; P = 0.005). None of the genetic polymorphisms tested were significantly associated with the risk of development, severity, or outcome of DILI. Conclusion: SNPs implicated in common liver diseases, such as NAFLD, do not play a substantial role in DILI pathogenesis across agents. It remains possible that these variants could be involved with DILI due to single agents, but this will require the evaluation of larger numbers of bona fide cases. (Hepatology Communications 2019;3:1032-1035)
HLA-B*14:01 and HLA-B*35:01 are associated with trimethoprim-sulfamethoxazole induced liver injury
Background and AimTrimethoprimâsulfamethoxazole (TMPâSMX) is an important cause of idiosyncratic drug induced liver injury (DILI), but its genetic risk factors are not well understood. We investigated the relationship between variants in the HLA Class I and II genes and well characterized cases of TMPâSMX DILI.MethodsEuropean American and African American persons with TMPâSMX DILI were compared to respective population controls. HLA sequencing was performed by Illumina MiSeq for cases. HLA genotype imputation with attribute bagging (HIBAG) program was used to impute HLA alleles for controls. Allele frequency difference between cases and controls was tested by Fisher exact tests per ethnic group. For European Americans, multivariable logistic regression with Firth penalization was used to test HLA allelic effect after adjusting for age and the top two principal components. Molecular docking was performed to assess the HLA binding with TMP and SMX.ResultsThe European American subset had 51 cases and 12,156 controls, while the African American subset had 10 cases and 5,439 controls. Four HLA alleles were significantly associated in the European American subset, with HLAâB*14:01 ranking at the top (OR: 9.20, 95% CI: 3.16â22.35, p=0.0003) after covariate adjustment. All HLAâB*14:01 carriers with TMPâSMX DILI possessed HLAâC*08:02, another significant allele (p=0.0026). This pattern was supported by HLAâB*14:01âHLAâC*08:02 haplotype association (p=1.33x10â5). For the African Americans, HLAâB*35:01 had 2.8âfold higher frequency in cases than in controls, with five of 10 patients carrying this allele. Molecular docking showed Cys67 in HLAâB*14:01 and Phe67 in HLAâB*35:01 to be the predictive binding sites to SMX metabolites. ConclusionHLAâB*14:01 is associated with TMPâSMX DILI in European Americans, and HLAâB*35:01 may be a potential genetic risk factor for African Americans
A computational and experimental evaluation of the performance of a centrifugal fan volute
The efficacy of peer review in a university-level ESL writing class
Currently, there is a great debate concerning whether peer review is an effective activity in the university-level English-as-a-Second-Language writing classroom. Peer review offers the unique opportunity for second-language writers to share their writing, evaluate others' work, and discuss their observations and opinions about writing in an authentic environment. This study investigated the interactional dynamics of peer review sessions in university-level ESL writing classrooms. The spoken data (transcripts of peer review sessions) were evaluated to determine the types of interactions that occurred and their functions. The written data (rough and final drafts from each participant) were evaluated to determine the quantity and quality of the changes that the participants made. The two data sets were then compared to determine whether (and to what extent) the peer review interactions led to improvements on the drafts. The data indicated that suggestions made during peer review correlated to positive changes if the participants negotiated the suggestion, and if the suggestion pertained to global-level issues in the paper. While the data showed that participants preferred to make changes unilaterally, it also indicated that peer-reviewed suggestions correlated with a higher percentage of positive changes than writer-initiated suggestions. Further, the data indicated that peer review was particularly favored by those participants who had no previous experience with this activity. These results indicated that peer review is an effective activity, especially for students who are new to it. It is best framed as supplementary to the student's existing writing process. Future research should focus on triangulating the data with post-activity student interviews, in order to corroborate the results. (Published By University of Alabama Libraries
Computational and experimental study on the performance improvement of an in-line centrifugal fan by outlet guide vanes
A Missense Variant in PTPN22 is a Risk Factor for Drug-induced Liver Injury.
To access publisher's full text version of this article click on the hyperlink belowWe performed genetic analyses of a multiethnic cohort of patients with idiosyncratic drug-induced liver injury (DILI) to identify variants associated with susceptibility. We performed a genome-wide association study of 2048 individuals with DILI (cases) and 12,429 individuals without (controls). Our analysis included subjects of European (1806 cases and 10,397 controls), African American (133 cases and 1,314 controls), and Hispanic (109 cases and 718 controls) ancestry. We analyzed DNA from 113 Icelandic cases and 239,304 controls to validate our findings. We associated idiosyncratic DILI with rs2476601, a nonsynonymous polymorphism that encodes a substitution of tryptophan with arginine in the protein tyrosine phosphatase, nonreceptor type 22 gene (PTPN22) (odds ratio [OR] 1.44; 95% confidence interval [CI] 1.28-1.62; PÂ = 1.2Â Ă 10 In a genome-wide association study, we identified rs2476601 in PTPN22 as a non-HLA variant that associates with risk of liver injury caused by multiple drugs and validated our finding in a separate cohort. This variant has been associated with increased risk of autoimmune diseases, providing support for the concept that alterations in immune regulation contribute to idiosyncratic DILI.National Institute of Diabetes and Digestive and Kidney Diseases of the National Institutes of Health (NIH)
CTSA
International Serious Adverse Events Consortium
Abbott
Amgen
Daiichi-Sankyo
GlaxoSmithKline
Merck
Novartis
Pfizer
Roche
Sanofi-Aventis
Takeda
Wellcome Trust
National Institute for Health Research (NIHR) Nottingham Digestive Diseases Biomedical Research Unit at the Nottingham University Hospitals NHS Trust
University of Nottingham
EC 5th Framework program
Spanish Medicine Agency
Fondo Europeo de Desarrollo Regional - FEDER
Instituto de Salud Carlos III
Swedish Medical Products Agency
Swedish Society of Medicine
Swedish Research Council
Swedish Heart and Lung Foundation
National Institute for Health Research (NIHR) Biomedical Research Centre at Guy's and St Thomas' NHS Foundation Trust and King's College Londo
The Hexa-X project vision on Artificial Intelligence and Machine Learning-driven communication and computation co-design for 6G
Abstract
This paper provides an overview of the most recent advancements and outcomes of the European 6G flagship project Hexa-X, on the topic of in-network Artificial Intelligence (AI) and Machine Learning (ML). We first present a general introduction to the project and its ambitions in terms of use cases (UCs), key performance indicators (KPIs), and key value indicators (KVIs). Then, we identify the key challenges to realize, implement, and enable the native integration of AI and ML in 6G, both as a means for designing flexible, low-complexity, and reconfigurable networks ( learning to communicate ), and as an intrinsic in-network intelligence feature ( communicating to learn or, 6G as an efficient AI/ML platform). We present a high level description of down selected technical enablers and their implications on the Hexa-X identified UCs, KPIs and KVIs. Our solutions cover lower layer aspects, including channel estimation, transceiver design, power amplifier and distributed MIMO related challenges, and higher layer aspects, including AI/ML workload management and orchestration, as well as distributed AI. The latter entails Federated Learning and explainability as means for privacy preserving and trustworthy AI. To bridge the gap between the technical enablers and the 6G targets, some representative numerical results accompany the high level description. Overall, the methodology of the paper starts from the UCs and KPIs/KVIs, to then focus on the proposed technical solutions able to realize them. Finally, a brief discussion of the ongoing regulation activities related to AI is presented, to close our vision towards an AI and ML-driven communication and computation co-design for 6G