No abstract

Purpose: To identify racial/ethnic disparities in utilization rates, in-hospital outcomes and health care resource use among Non-Hispanic Whites (NHW), African Americans (AA) and Hispanics undergoing transcatheter aortic valve replacement (TAVR) in the United States (US).

Cancer genomes accumulate nucleotide sequence variations that number in the tens of thousands per genome. A prominent fraction of these mutations is thought to arise as a consequence of the off-target activity of DNA/RNA editing cytosine deaminases. These enzymes, collectively called activation induced deaminase (AID)/APOBECs, deaminate cytosines located within defined DNA sequence contexts. The resulting changes of the original C:G pair in these contexts (mutational signatures) provide indirect evidence for the participation of specific cytosine deaminases in a given cancer type. The conventional method used for the analysis of mutable motifs is the consensus approach. Here, for the first time, we have adopted the frequently used weight matrix (sequence profile) approach for the analysis of mutagenesis and provide evidence for this method being a more precise descriptor of mutations than the sequence consensus approach. We confirm that while mutational footprints of APOBEC1, APOBEC3A, APOBEC3B, and APOBEC3G are prominent in many cancers, mutable motifs characteristic of the action of the humoral immune response somatic hypermutation enzyme, AID, are the most widespread feature of somatic mutation spectra attributable to deaminases in cancer genomes. Overall, the weight matrix approach reveals that somatic mutations are significantly associated with at least one AID/APOBEC mutable motif in all studied cancers.

Despite some previous examples of successful application to the field of pharmacogenomics, the utility of machine learning (ML) techniques for warfarin dose predictions in Caribbean Hispanic patients has yet to be fully evaluated. This study compares seven ML methods to predict warfarin dosing in Caribbean Hispanics. This is a secondary analysis of genetic and non-genetic clinical data from 190 cardiovascular Hispanic patients. Seven ML algorithms were applied to the data. Data was divided into 80 and 20% to be used as training and test sets. ML algorithms were trained with the training set to obtain the models. Model performance was determined by computing the corresponding mean absolute error (MAE) and % patients whose predicted optimal dose were within ±20% of the actual stabilization dose, and then compared between groups of patients with "normal" (i.e., > 21 but <49 mg/week), low (i.e., ≤21 mg/week, "sensitive"), and high (i.e., ≥49 mg/week, "resistant") dose requirements. Random forest regression (RFR) significantly outperform all other methods, with a MAE of 4.73 mg/week and 80.56% of cases within ±20% of the actual stabilization dose. Among those with "normal" dose requirements, RFR performance is also better than the rest of models (MAE = 2.91 mg/week). In the "sensitive" group, support vector regression (SVR) shows superiority over the others with lower MAE of 4.79 mg/week. Finally, multivariate adaptive splines (MARS) shows the best performance in the resistant group (MAE = 7.22 mg/week) and 66.7% of predictions within ±20%. Models generated by using RFR, MARS, and SVR algorithms showed significantly better predictions of weekly warfarin dosing in the studied cohorts than other algorithms. Better performance of the ML models for patients with "normal," "sensitive," and "resistant" to warfarin were obtained when compared to other populations and previous statistical models.

Zika virus (ZIKV) infection has been associated with fetal abnormalities by compromising placental integrity, but the mechanisms by which this occurs are unknown. Flavivirus can deregulate the host proteome, especially extracellular matrix (ECM) proteins. We hypothesize that a deregulation of specific ECM proteins by ZIKV, affects placental integrity. Using twelve different placental samples collected during the 2016 ZIKV Puerto Rico epidemic, we compared the proteome of five ZIKV infected samples with four uninfected controls followed by validation of most significant proteins by immunohistochemistry. Quantitative proteomics was performed using tandem mass tag TMT10plex™ Isobaric Label Reagent Set followed by Q Exactive™ Hybrid Quadrupole Orbitrap Mass Spectrometry. Identification of proteins was performed using Proteome Discoverer 2.1. Proteins were compared based on the fold change and p value using Limma software. Significant proteins pathways were analyzed using Ingenuity Pathway (IPA). TMT analysis showed that ZIKV infected placentas had 94 reviewed differentially abundant proteins, 32 more abundant, and 62 less abundant. IPA analysis results indicate that 45 of the deregulated proteins are cellular components of the ECM and 16 play a role in its structure and organization. Among the most significant proteins in ZIKV positive placenta were fibronectin, bone marrow proteoglycan, and fibrinogen. Of these, fibrinogen was further validated by immunohistochemistry in 12 additional placenta samples and found significantly increased in ZIKV infected placentas. The upregulation of this protein in the placental tissue suggests that ZIKV infection is promoting the coagulation of placental tissue and restructuration of ECM potentially affecting the integrity of the tissue and facilitating dissemination of the virus from mother to the fetus.