Citations

Introduction: The population of patients with cirrhosis is growing and shifting toward a more elderly demographic and thus are at risk of developing orthopedic complications. There is lack of data on safety of orthopedic procedures in this population. Methods: We performed an analysis of the Nationwide Inpatient Sample from 2005 to 2011 for patients undergoing hip arthroplasty, knee arthroplasty, and spinal laminectomy/fusion, stratified by presence of cirrhosis. The primary endpoint was in-hospital mortality and secondary endpoints included length of stay (LOS) and costs. Results: There were 693,610 inpatient stays for orthopedic procedures conducted during the study period, with 3014 (0.43%) patients coded as having cirrhosis. Patients with cirrhosis had a lower median age (62 vs. 66 y; P<0.001) and were more likely to be male (52.3% vs. 41.1%; P<0.001). The inpatient mortality rate was significantly higher in patients with cirrhosis (2.4% vs. 0.4%; P<0.001) as was median LOS (4 vs. 3 d; P<0.001) and mean costs ($19,321 vs. $18,833; P<0.001). Patients with decompensated cirrhosis (vs. compensated cirrhosis) had significantly higher inpatient mortality rates (5.8% vs. 1.1%; P<0.001) with higher LOS and costs (P<0.001). On multivariable analysis, cirrhosis was associated with an increased risk of mortality (odds ratio, 4.22; 95% confidence interval, 2.92-6.10). Hospital cirrhosis volume was inversely associated with mortality, while hospital orthopedic procedure volumes had an inconsistent impact on outcomes. Conclusions: Inpatient orthopedic procedures in patients with cirrhosis result in high postoperative mortality, LOS, and costs. Careful patient selection is warranted to optimize cirrhosis patient postoperative outcomes.

DPP (dipeptidyl peptidase)-4 inhibitors are antidiabetic drugs that may increase heart failure in high-risk patients. NPY (neuropeptide Y) is coreleased with norepinephrine, causes vasoconstriction via the Y1 receptor, and is degraded by DPP4 to NPY (3–36) in vitro. NPY (3–36) decreases release of norepinephrine via the Y2 receptor. We tested the hypothesis that DPP4 inhibition would potentiate the vasoconstrictor effect of NPY. Eighteen nonsmokers (12 healthy controls and 6 with type 2 diabetes mellitus) participated in 1 of 2 randomized, double-blind, placebo-controlled crossover studies. First, subjects were randomized to order of treatment with sitagliptin 100 mg/d versus placebo for 7 days separated by 4-week washout. On the last day of treatment, NPY was infused by brachial artery and forearm blood flow was measured using plethysmography. Blood samples were collected after each dose. NPY infusions were repeated after 90-minute washout and intra-arterial enalaprilat. Second, 5 healthy subjects were randomized to crossover treatment with sitagliptin 100 mg/d plus valsartan 160 mg/d versus placebo plus valsartan. NPY infusions were performed on the seventh day of treatment. NPY caused dose-dependent vasoconstriction. During enalaprilat, sitagliptin significantly potentiated NPY-induced vasoconstriction in controls and diabetics ( P ≤0.02 for forearm blood flow in either group). Baseline norepinephrine release was increased during sitagliptin and enalaprilat, but not further by NPY. Sitagliptin increased the ratio of NPY to NPY (3–36). During valsartan, sitagliptin also significantly potentiated NPY-induced vasoconstriction ( P =0.009 for forearm blood flow). Potentiation of endogenous NPY could contribute to cardiovascular effects of DPP4 inhibitors in patients taking an angiotensin-converting enzyme inhibitor or angiotensin receptor blocker.

Expression of histone H3.3K27M mutant proteins in human diffuse intrinsic pontine glioma (DIPG) results in a global reduction of tri-methylation of H3K27 (H3K27me3), and paradoxically, H3K27me3 peaks remain at hundreds of genomic loci, a dichotomous change that lacks mechanistic insights. Here, we show that the PRC2 complex is sequestered at poised enhancers, but not at active promoters with high levels of H3.3K27M proteins, thereby contributing to the global reduction of H3K27me3. Moreover, the levels of H3.3K27M proteins are low at the retained H3K27me3 peaks and consequently having minimal effects on the PRC2 activity at these loci. H3K27me3-mediated silencing at specific tumor suppressor genes, including Wilms Tumor 1, promotes proliferation of DIPG cells. These results support a model in which the PRC2 complex is redistributed to poised enhancers in H3.3K27M mutant cells and contributes to tumorigenesis in part by locally enhancing H3K27me3, and hence silencing of tumor suppressor genes.

AbstractMotivationThe use of single nucleotide polymorphism (SNP) interactions to predict complex diseases is getting more attention during the past decade, but related statistical methods are still immature. We previously proposed the SNP Interaction Pattern Identifier (SIPI) approach to evaluate 45 SNP interaction patterns/patterns. SIPI is statistically powerful but suffers from a large computation burden. For large-scale studies, it is necessary to use a powerful and computation-efficient method. The objective of this study is to develop an evidence-based mini-version of SIPI as the screening tool or solitary use and to evaluate the impact of inheritance mode and model structure on detecting SNP–SNP interactions.ResultsWe tested two candidate approaches: the ‘Five-Full’ and ‘AA9int’ method. The Five-Full approach is composed of the five full interaction models considering three inheritance modes (additive, dominant and recessive). The AA9int approach is composed of nine interaction models by considering non-hierarchical model structure and the additive mode. Our simulation results show that AA9int has similar statistical power compared to SIPI and is superior to the Five-Full approach, and the impact of the non-hierarchical model structure is greater than that of the inheritance mode in detecting SNP–SNP interactions. In summary, it is recommended that AA9int is a powerful tool to be used either alone or as the screening stage of a two-stage approach (AA9int+SIPI) for detecting SNP–SNP interactions in large-scale studies.Availability and implementationThe ‘AA9int’ and ‘parAA9int’ functions (standard and parallel computing version) are added in the SIPI R package, which is freely available at https://linhuiyi.github.io/LinHY_Software/.Supplementary informationSupplementary data are available at Bioinformatics online.