21 research outputs found

    Interquantile Shrinkage in Regression Models

    No full text
    Conventional analysis using quantile regression typically focuses on fitting the regression model at different quantiles separately. However, in situations where the quantile coefficients share some common feature, joint modeling of multiple quantiles to accommodate the commonality often leads to more efficient estimation. One example of common features is that a predictor may have a constant effect over one region of quantile levels but varying effects in other regions. To automatically perform estimation and detection of the interquantile commonality, we develop two penalization methods. When the quantile slope coefficients indeed do not change across quantile levels, the proposed methods will shrink the slopes towards constant and thus improve the estimation efficiency. We establish the oracle properties of the two proposed penalization methods. Through numerical investigations, we demonstrate that the proposed methods lead to estimations with competitive or higher efficiency than the standard quantile regression estimation in finite samples. Supplemental materials for the article are available online

    Effect of the DGAT1 Inhibitor Pradigastat on Triglyceride and ApoB48 Levels in Patients with Familial Chylomicronemia Syndrome

    Get PDF
    This pilot study of pradigastat, a diacylglycerol acyltransferase-1 (DGAT1) inhibitor, was conducted to assess its effects on lipid parameters and tolerability after multiple dose administrations in patients with familial chylomicronemia syndrome (FCS), a rare inherited form of severe hypertriglyceridemia. Six patients underwent a 1-week dietary run-in period, followed by an open-label, fixed-sequence treatment with oral pradigastat at 20, 40, and 10 mg once daily for 3 weeks each, with an interlacing ≥4-week wash-out period. Fasting and postprandial triglyceride (TG) and apolipoprotein (apo) B48 levels with lipoprotein fractions were assessed after an FCS-adapted test meal. Pradigastat 20 and 40 mg led to 40 and 70% reduction in fasting TG at Week 3. Postprandial TG drop was significantly greater for both the 20 and 40 mg arms relative to the 10 mg group which did not reduce TG levels (p<0.05 for all parameters at each dose). Favorable effects were noted on lipoprotein fractions including reductions in chylomicron TG content. Mild gastrointestinal events were the most common adverse events. In conclusion, the novel DGAT1 inhibitor pradigastat led to substantial reductions in fasting and postprandial TG in patients with FCS, particularly reducing the number and TG content of chylomicron particles

    Integrative identification of hub genes in development of atrial fibrillation related stroke

    No full text
    Background As the most common arrhythmia, atrial fibrillation (AF) is associated with a significantly increased risk of stroke, which causes high disability and mortality. To date, the underlying mechanism of stroke occurring after AF remains unclear. Herein, we studied hub genes and regulatory pathways involved in AF and secondary stroke and aimed to reveal biomarkers and therapeutic targets of AF-related stroke. Methods The GSE79768 and GSE58294 datasets were used to analyze AF- and stroke-related differentially expressed genes (DEGs) to obtain a DEG1 dataset. Weighted correlation network analysis (WGCNA) was used to identify modules associated with AF-related stroke in GSE66724 (DEG2). DEG1 and DEG2 were merged, and hub genes were identified based on protein–protein interaction networks. Gene Ontology terms were used to analyze the enriched pathways. The GSE129409 and GSE70887 were applied to construct a circRNA-miRNA-mRNA network in AF-related stroke. Hub genes were verified in patients using quantitative real-time polymerase chain reaction (qRT-PCR). Results We identified 3,132 DEGs in blood samples and 253 DEGs in left atrial specimens. Co-expressed hub genes of EIF4E3, ZNF595, ZNF700, MATR3, ACKR4, ANXA3, SEPSECS-AS1, and RNF166 were significantly associated with AF-related stroke. The hsa_circ_0018657/hsa-miR-198/EIF4E3 pathway was explored as the regulating axis in AF-related stroke. The qRT-PCR results were consistent with the bioinformatic analysis. Conclusions Hub genes EIF4E3, ZNF595, ZNF700, MATR3, ACKR4, ANXA3, SEPSECS-AS1, and RNF166 have potential as novel biomarkers and therapeutic targets in AF-related stroke. The hsa_circ_0018657/hsa-miR-198/EIF4E3 axis could play an important role regulating the development of AF-related stroke

    Integrative identification of hub genes in development of atrial fibrillation related stroke.

    No full text
    BackgroundAs the most common arrhythmia, atrial fibrillation (AF) is associated with a significantly increased risk of stroke, which causes high disability and mortality. To date, the underlying mechanism of stroke occurring after AF remains unclear. Herein, we studied hub genes and regulatory pathways involved in AF and secondary stroke and aimed to reveal biomarkers and therapeutic targets of AF-related stroke.MethodsThe GSE79768 and GSE58294 datasets were used to analyze AF- and stroke-related differentially expressed genes (DEGs) to obtain a DEG1 dataset. Weighted correlation network analysis (WGCNA) was used to identify modules associated with AF-related stroke in GSE66724 (DEG2). DEG1 and DEG2 were merged, and hub genes were identified based on protein-protein interaction networks. Gene Ontology terms were used to analyze the enriched pathways. The GSE129409 and GSE70887 were applied to construct a circRNA-miRNA-mRNA network in AF-related stroke. Hub genes were verified in patients using quantitative real-time polymerase chain reaction (qRT-PCR).ResultsWe identified 3,132 DEGs in blood samples and 253 DEGs in left atrial specimens. Co-expressed hub genes of EIF4E3, ZNF595, ZNF700, MATR3, ACKR4, ANXA3, SEPSECS-AS1, and RNF166 were significantly associated with AF-related stroke. The hsa_circ_0018657/hsa-miR-198/EIF4E3 pathway was explored as the regulating axis in AF-related stroke. The qRT-PCR results were consistent with the bioinformatic analysis.ConclusionsHub genes EIF4E3, ZNF595, ZNF700, MATR3, ACKR4, ANXA3, SEPSECS-AS1, and RNF166 have potential as novel biomarkers and therapeutic targets in AF-related stroke. The hsa_circ_0018657/hsa-miR-198/EIF4E3 axis could play an important role regulating the development of AF-related stroke
    corecore