3 research outputs found
The prelimbic cortex neuronal ensembles enconde associative memories between alcohol reward effects and contextual cues.
HFE gene mutations and iron status of Brazilian blood donors
Mutations of the HFE and TFR2 genes have been associated with iron overload. HFE and TFR2 mutations were assessed in blood donors, and the relationship with iron status was evaluated. Subjects (N = 542) were recruited at the Hemocentro da Santa Casa de São Paulo, São Paulo, Brazil. Iron status was not influenced by HFE mutations in women and was independent of blood donation frequency. In contrast, men carrying the HFE 282CY genotype had lower total iron-binding capacity (TIBC) than HFE 282CC genotype carriers. Men who donated blood for the first time and were carriers of the HFE 282CY genotype had higher transferrin saturation values and lower TIBC concentrations than those with the homozygous wild genotype for the HFE C282Y mutation. Moreover, in this group of blood donors, carriers of HFE 63DD plus 63HD genotypes had higher serum ferritin values than those with the homozygous wild genotype for HFE H63D mutation. Multiple linear regression analysis showed that HFE 282CY leads to a 17.21% increase (P = 0.018) and a 83.65% decrease (P = 0.007) in transferrin saturation and TIBC, respectively. In addition, serum ferritin is influenced by age (3.91%, P = 0.001) and the HFE 63HD plus DD genotype (55.84%, P = 0.021). In conclusion, the HFE 282Y and 65C alleles were rare, while the HFE 63D allele was frequent in Brazilian blood donors. The HFE C282Y and H63D mutations were associated with alterations in iron status in blood donors in a gender-dependent manner
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Machine Learning for Prediction of Stable Warfarin Dose in US Latinos and Latin Americans
Populations used to create warfarin dose prediction algorithms largely lacked participants reporting Hispanic or Latino ethnicity. While previous research suggests nonlinear modeling improves warfarin dose prediction, this research has mainly focused on populations with primarily European ancestry. We compare the accuracy of stable warfarin dose prediction using linear and nonlinear machine learning models in a large cohort enriched for US Latinos and Latin Americans (ULLA). Each model was tested using the same variables as published by the International Warfarin Pharmacogenetics Consortium (IWPC) and using an expanded set of variables including ethnicity and warfarin indication. We utilized a multiple linear regression model and three nonlinear regression models: Bayesian Additive Regression Trees, Multivariate Adaptive Regression Splines, and Support Vector Regression. We compared each model’s ability to predict stable warfarin dose within 20% of actual stable dose, confirming trained models in a 30% testing dataset with 100 rounds of resampling. In all patients (n = 7,030), inclusion of additional predictor variables led to a small but significant improvement in prediction of dose relative to the IWPC algorithm (47.8 versus 46.7% in IWPC, p = 1.43 × 10−15). Nonlinear models using IWPC variables did not significantly improve prediction of dose over the linear IWPC algorithm. In ULLA patients alone (n = 1,734), IWPC performed similarly to all other linear and nonlinear pharmacogenetic algorithms. Our results reinforce the validity of IWPC in a large, ethnically diverse population and suggest that additional variables that capture warfarin dose variability may improve warfarin dose prediction algorithms. Copyright © 2021 Steiner, Giles, Patterson, Feng, El Rouby, Claudio, Marcatto, Tavares, Galvez, Calderon-Ospina, Sun, Hutz, Scott, Cavallari, Fonseca-Mendoza, Duconge, Botton, Santos and Karnes.Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]