BackgroundInsulin resistance (IR) and the consequences of compensatory hyperinsulinemia are pathogenic factors for a set of metabolic abnormalities, which contribute to the development of diabetes mellitus and cardiovascular diseases. We compared traditional lipid levels and ratios and combined them with fasting plasma glucose (FPG) levels or adiposity status for determining their efficiency as independent risk factors for IR.MethodsWe enrolled 511 Taiwanese individuals for the analysis. The clinical usefulness of various parameters—such as traditional lipid levels and ratios; visceral adiposity indicators, visceral adiposity index (VAI), and lipid accumulation product (LAP); the product of triglyceride (TG) and FPG (the TyG index); TyG with adiposity status (TyG-body mass index [BMI]) and TyG-waist circumference index [WC]); and adipokine levels and ratios—was analyzed to identify IR.ResultsFor all lipid ratios, the TG/high-density lipoprotein cholesterol (HDL-C) ratio had the highest additional percentage of variation in the homeostasis model assessment of insulin resistance (HOMA-IR; 7.0% in total); for all variables of interest, TyG-BMI and leptin-adiponectin ratio (LAR) were strongly associated with HOMA-IR, with 16.6% and 23.2% of variability, respectively. A logistic regression analysis revealed similar patterns. A receiver operating characteristic (ROC) curve analysis indicated that TG/HDL-C was a more efficient IR discriminator than other lipid variables or ratios. The area under the ROC curve (AUC) for VAI (0.734) and TyG (0.708) was larger than that for TG/HDL-C (0.707). TyG-BMI and LAR had the largest AUC (0.801 and 0.801, respectively).ConclusionTyG-BMI is a simple, powerful, and clinically useful surrogate marker for early identification of IR.

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While hemodialysis access ligation has been used to manage pacemaker (PM) and implantable cardioverter-defibrillator (ICD) lead-induced central venous stenosis (CVS), percutaneous transluminal balloon angioplasty (PTA) has also been employed to manage this complication. The advantages of PTA include minimal invasiveness and preservation of arteriovenous access for hemodialysis therapy. In this multi-center study we report the patency rates for PTA to manage lead-induced CVS. Consecutive PM/ICD chronic hemodialysis patients with an arteriovenous access referred for signs and symptoms of CVS due to lead-induced CVS were included in this analysis. PTA was performed using the standard technique. Technical and clinical success was examined. Technical success was defined as the ability to successfully perform the procedure. Clinical success was defined as the ability to achieve amelioration of the signs and symptoms of CVS. Both primary and secondary patency rates were also analyzed. Twenty-eight consecutive patients underwent PTA procedure. Technical success was 95%. Postprocedure clinical success was achieved in 100% of the cases where the procedure was successful. The primary patency rates were 18% and 9% at 6 and 12 months, respectively. The secondary patency rates were 95%, 86%, and 73% at 6, 12, and 24 months, respectively. On average, 2.1 procedures/year were required to maintain secondary patency. There were no procedure-related complications. This study finds PTA to be a viable option in the management of PM/ICD lead-induced CVS. Additional studies with appropriate design and sample size are required to conclusively establish the role of PTA in the management of this problem.

non-small cell lung cancer (nScLc) is one of the most common lung cancers worldwide. Accurate prognostic stratification of NSCLC can become an important clinical reference when designing therapeutic strategies for cancer patients. With this clinical application in mind, we developed a deep neural network (Dnn) combining heterogeneous data sources of gene expression and clinical data to accurately predict the overall survival of nScLc patients. Based on microarray data from a cohort set (614 patients), seven well-known NSCLC biomarkers were used to group patients into biomarker-and biomarker+ subgroups. then, by using a systems biology approach, prognosis relevance values (pRV) were then calculated to select eight additional novel prognostic gene biomarkers. finally, the combined 15 biomarkers along with clinical data were then used to develop an integrative DNN via bimodal learning to predict the 5-year survival status of NSCLC patients with tremendously high accuracy (AUC: 0.8163, accuracy: 75.44%). Using the capability of deep learning, we believe that our prediction can be a promising index that helps oncologists and physicians develop personalized therapy and build the foundation of precision medicine in the future.Lung cancer is the worldwide leading cause of cancer-related mortality, with non-small cell lung cancer (NSCLC) accounting for approximately 85% of all lung cancer patients 1 . The most common NSCLC subtypes are adenocarcinoma (ADC), squamous cell carcinoma (SQC), and large cell carcinoma. Although the overall 5-year survival rate of patients diagnosed with stage I ADC was 63%, nearly 35% of patients relapsed after surgery with a poor prognosis 2 . Adjuvant treatments have been considered ideal for ADC patients with the highest risk of recurrence or death to increase survival rates 3 . Therefore, prognostic stratification is crucial for categorizing patients to help doctors make decisions on therapeutic strategies.Recently, researchers have developed predictive methods based on gene expression profiles to classify lung cancer patients with distinct clinical outcomes, including relapse and overall survival 4 . Previous studies have shown the importance of biomarkers for NSCLC, such as EPCAM, HIF1A, PKM, PTK7, ALCAM, CADM1, and SLC2A1, which were used as a single biomarker for predicting prognostic condition or metastasis 5-11 . However, cancer is a systemic disease with complicated and illusive mechanisms that often involves multiple genes and cross-talk between pathways. Therefore, extending our understanding of NSCLC via the single gene biomarkers by studying the interactions between genes is essential for more accurate prognostic prediction.Machine learning algorithms are powerful tools that apply input features (biomarkers) to capture the complicated interdependencies between these features to accurately predict clinical outcomes 12 . In addition, predicting cancer prognosis can be improved by appropriately modeling the interactions between biomarkers compared with the single biomarker approach ...

Objective-Scavenger receptor class B type I (SR-BI) is a multiligand cell-surface receptor that mediates the selective uptake of lipid from HDL cholesterol (HDL-C) into cells. This study hypothesized an association between functional variants in the promoter region of SR-BI gene and HDL-C levels. Methods and Results-We identified 2 novel mutations in the SR-BI gene promoter region by using single-strand conformation polymorphism. One mutation was an 11-bp CCCCGCCCCGT deletion mutation from positions Ϫ140 to Ϫ150 relative to the transcription start site, corresponding to an Sp1 binding site; the other was a C3T substitution at position Ϫ142. Twenty-six of 690 unrelated subjects were heterozygous for the Ϫ140 to Ϫ150 deletion mutation, and the allele frequency in this population was 0.02. This study showed that the deletion variant prevented binding of Sp1 to this region of the SR-BI promoter and effectively reduced transcriptional activities in HepG2 cells. Notably, the Ϫ140 to Ϫ150 deletion mutation was significantly associated with increased HDL-C levels and explained Ϸ0.5% of the variation in HDL-C levels in this population. Conclusions-A genetic variant at the SR-BI gene promoter region might explain a significant proportion of individual differences in HDL-C levels among Taiwanese Key Words: deletion Ⅲ mutation Ⅲ scavenger receptor class B type I Ⅲ HDL cholesterol E pidemiologic investigations have demonstrated an inverse relation between the plasma HDL cholesterol (HDL-C) level and the incidence of coronary heart disease (CHD). 1 The main mechanism of CHD protection of HDL-C is believed to be through reverse transport of cholesterol from arterial cells to the liver. 2 In addition, HDL-C uptake by cells involves the selective transfer of cholesterol ester to the cell without HDL protein uptake and degradation, a process termed selective lipid uptake. 3 Acton et al 4 have demonstrated that scavenger receptor class B type I (SR-BI), a multiligand cell-surface receptor isolated from Chinese hamster ovary cells by expression cloning, 5 binds closely with HDL and mediates selective cholesterol uptake in transfected cells. This receptor is mainly expressed in tissues that display selective lipid uptake in vivo, namely, the liver, adrenal gland, and testis. [4][5][6][7] Further in vivo analyses in mice and rats suggested that SR-BI is crucial in HDL metabolism. For example, SR-BI expression is upregulated in the adrenal gland, where HDL-C is used for steroid hormone synthesis, in response to depleted plasma HDL-C levels in apolipoprotein AI-knockout (KO) mice. 6 Adenovirus-mediated overexpression of SR-BI in mouse livers causes a significant reduction in plasma HDL-C levels and a corresponding increase in biliary cholesterol. 8 Targeted disruption of the SR-BI gene in mice reduced selective uptake of cholesterol ester from HDL into the liver 9 and significantly increased plasma HDL-C. 9,10 Furthermore, Acton et al 11 also found an association between several single-nucleotide polymorphisms (SNPs) in the coding r...

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Key findings of the present study are the demonstration of a distinct anatomical border between transitional and atrial cells, connection between transitional cells and both lower nodal cells and posterior nodal extension, and distinctive connexin expression patterns in different compartments of the rabbit atrioventricular conduction axis. These features, synthesized in a novel 3D model, provide a structural framework for the interpretation of nodal function.

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