Browsing by Author "Jiang, Hui"

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    Bariatric Surgery–Induced Cardiac and Lipidomic Changes in Obesity‐Related Heart Failure with Preserved Ejection Fraction
    (Wiley, 2018) Mikhalkova, Deana; Holman, Sujata R.; Jiang, Hui; Sagir, Mohammed; Novak, Eric; Coggan, Andrew R.; O'Connor, Robert; Bashir, Adil; Jamal, Ali; Ory, Daniel S.; Schaffer, Jean E.; Eagon, J. Christopher; Peterson, Linda R.; Kinesiology, School of Physical Education and Tourism Management
    Objective To determine the effects of gastric bypass on myocardial lipid deposition and function and the plasma lipidome in women with obesity and heart failure with preserved ejection fraction (HFpEF). Methods A primary cohort (N = 12) with HFpEF and obesity underwent echocardiography and magnetic resonance spectroscopy both before and 3 months and 6 months after bariatric surgery. Plasma lipidomic analysis was performed before surgery and 3 months after surgery in the primary cohort and were confirmed in a validation cohort (N = 22). Results After surgery‐induced weight loss, Minnesota Living with Heart Failure questionnaire scores, cardiac mass, and liver fat decreased (P < 0.02, P < 0.001, and P = 0.007, respectively); echo‐derived e′ increased (P = 0.03), but cardiac fat was unchanged. Although weight loss was associated with decreases in many plasma ceramide and sphingolipid species, plasma lipid and cardiac function changes did not correlate. Conclusions Surgery‐induced weight loss in women with HFpEF and obesity was associated with improved symptoms, reverse cardiac remodeling, and improved relaxation. Although weight loss was associated with plasma sphingolipidome changes, cardiac function improvement was not associated with lipidomic or myocardial triglyceride changes. The results of this study suggest that gastric bypass ameliorates obesity‐related HFpEF and that cardiac fat deposition and lipidomic changes may not be critical to its pathogenesis.
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    A Unified Model for Joint Normalization and Differential Gene Expression Detection in RNA-Seq data
    (IEEE, 2018-01) Liu, Kefei; Ye, Jieping; Yang, Yang; Shen, Li; Jiang, Hui; Radiology and Imaging Sciences, School of Medicine
    The RNA-sequencing (RNA-seq) is becoming increasingly popular for quantifying gene expression levels. Since the RNA-seq measurements are relative in nature, between-sample normalization of counts is an essential step in differential expression (DE) analysis. The normalization of existing DE detection algorithms is ad hoc and performed once for all prior to DE detection, which may be suboptimal since ideally normalization should be based on non-DE genes only and thus coupled with DE detection. We propose a unified statistical model for joint normalization and DE detection of log-transformed RNA-seq data. Sample-specific normalization factors are modeled as unknown parameters in the gene-wise linear models and jointly estimated with the regression coefficients. By imposing sparsity-inducing L1 penalty (or mixed L1/L2 penalty for multiple treatment conditions) on the regression coefficients, we formulate the problem as a penalized least-squares regression problem and apply the augmented lagrangian method to solve it. Simulation studies show that the proposed model and algorithms perform better than or comparably to existing methods in terms of detection power and false-positive rate. The performance gain increases with increasingly larger sample size or higher signal to noise ratio, and is more significant when a large proportion of genes are differentially expressed in an asymmetric manner.