Stratified analyses of genome wide association study data reveal haplotypes for a candidate gene on chromosome 2 (KIAA1211L) is associated with opioid use in patients of Arabian descent

Background: Genome Wide Association Studies (GWAS) have been conducted to identify genes and pathways involved in development of opioid use disorder. This study extends the first GWAS of substance use disorder (SUD) patients from the United Arab Emirates (UAE) by stratifying the study group based on opioid use, which is the most common substance of use in this cohort. Methods: The GWAS cohort consisted of 512 (262 case, 250 controls) male participants from the UAE. The samples were genotyped using the Illumina Omni5 Exome system. Data was stratified according to opioid use using PLINK. Haplotype analysis was conducted using Haploview 4.2. Results: Two main associations were identified in this study. Firstly, two SNPs on chromosome 7 were associated with opioid use disorder, rs118129027 (p-value = 1.23 × 10 -8) and rs74477937 (p-value = 1.48 × 10 -8). This has been reported in Alblooshi et al. (Am J Med Genet B Neuropsychiatr Genet 180(1):68-79, 2019). Secondly, haplotypes on chromosome 2 which mapped to the KIAA1211L locus were identified in association with opioid use. Five SNPs in high linkage disequilibrium (LD) (rs2280142, rs6542837, rs12712037, rs10175560, rs11900524) were arranged into haplotypes. Two haplotypes GAGCG and AGTTA were associated with opioid use disorders (p-value 3.26 × 10-8 and 7.16 × 10-7, respectively). Conclusion: This is the first GWAS to identify candidate genes associated with opioid use disorder in participants from the UAE. The lack of other genetic data of Arabian descent opioid use patients has hindered replication of the findings. Nevertheless, the outcomes implicate new pathways in opioid use disorder that requires further research to assess the role of the identified genes in the development of opioid use disorder

MtNramp1 mediates iron import in rhizobia-infected Medicago truncatula cells.

Symbiotic nitrogen fixation is a process that requires relatively high quantities of iron provided by the host legume. Using synchrotron-based X-ray fluorescence, we have determined that this iron is released from the vasculature into the apoplast of zone II of M. truncatula nodules. This overlaps with the distribution of MtNramp1, a plasma membrane iron importer. The importance of MtNramp1 in iron transport for nitrogen fixation is indicated by the 60% reduction of nitrogenase activity observed in knock-down lines, most likely due to deficient incorporation of this essential metal cofactor at the necessary levels

Big data and credit risk assessment: a bibliometric review, current streams, and directions for future research

This study aims to track the structural development of academic research on credit risk assessment and big data using bibliometric analysis. The bibliography is obtained from the Scopus database and contains all studies with citations published between 2012 and 2021. The study’s findings suggest that credit risk assessment and big data are vast fields that have increased significantly in the last nine years. Chinese researchers and organizations contributed the most to the documents. The current study concludes that several possibilities exist to improve the knowledge of credit risk assessment and big data

Utilizing machine learning for survival analysis to identify risk factors for COVID-19 intensive care unit admission: A retrospective cohort study from the United Arab Emirates.

BackgroundThe current situation of the unprecedented COVID-19 pandemic leverages Artificial Intelligence (AI) as an innovative tool for addressing the evolving clinical challenges. An example is utilizing Machine Learning (ML) models-a subfield of AI that take advantage of observational data/Electronic Health Records (EHRs) to support clinical decision-making for COVID-19 cases. This study aimed to evaluate the clinical characteristics and risk factors for COVID-19 patients in the United Arab Emirates utilizing EHRs and ML for survival analysis models.MethodsWe tested various ML models for survival analysis in this work we trained those models using a different subset of features extracted by several feature selection methods. Finally, the best model was evaluated and interpreted using goodness-of-fit based on calibration curves,Partial Dependence Plots and concordance index.ResultsThe risk of severe disease increases with elevated levels of C-reactive protein, ferritin, lactate dehydrogenase, Modified Early Warning Score, respiratory rate and troponin. The risk also increases with hypokalemia, oxygen desaturation and lower estimated glomerular filtration rate and hypocalcemia and lymphopenia.ConclusionAnalyzing clinical data using AI models can provide vital information for clinician to measure the risk of morbidity and mortality of COVID-19 patients. Further validation is crucial to implement the model in real clinical settings

Missing data patterns in multivariate data.

Explore patterns of missingness between levels of included variables. The pairs plots show relationships between missing values (gray) and observed values (Blue) for all the features. The distributions are used to visualize the continuous features, and the proportions are shown for categorical variables (continue). (PDF)</p

Missing data patterns in multivariate data.

Explore patterns of missingness between levels of included variables. The pairs plots show relationships between missing values (gray) and observed values (Blue) for all the features. The distributions are used to visualize the continuous features, and the proportions are shown for categorical variables (continue). (PDF)</p

Missing data patterns in multivariate data.

Explore patterns of missingness between levels of included variables. The pairs plots show relationships between missing values (gray) and observed values (Blue) for all the features. The distributions are used to visualize the continuous features, and the proportions are shown for categorical variables (continue). (PDF)</p

Missing value imputation using random forest.

The figure compare the distribution of the original and imputed data. The magenta points represent the imputed points, and the blue ones show the observed ones. The plots infer that the imputed values are plausible values for the missing points. (PDF)</p

Missing data patterns in multivariate data.

Explore patterns of missingness between levels of included variables. The pairs plots show relationships between missing values (gray) and observed values (Blue) for all the features. The distributions are used to visualize the continuous features, and the proportions are shown for categorical variables (continue). (PDF)</p