Adhesion force imaging in air and liquid by adhesion mode atomic force microscopy

A new imaging mode for the atomic force microscope(AFM), yielding images mapping the adhesion force between tip and sample, is introduced. The adhesion mode AFM takes a force curve at each pixel by ramping a piezoactuator, moving the silicon‐nitride tip up and down towards the sample. During the retrace the tip leaves the sample with an adhesion dip showing up in the force curve. Adhesion force images mapping parameters describing this adhesion dip, such as peak value, width, and area, are acquired on‐line together with the sample topography. Imaging in air gives information on the differences in hydrophobicity of sample features. While imaging a mercaptopentadecane‐gold layer on glass in demineralized water, the adhesion force could be modulated by adding phosphate buffered saline

Diagnostic performance of 320-slice multidetector computed tomography coronary angiography in patients after coronary artery bypass grafting

Cardiovascular Aspects of Radiolog

Cholesteryl Ester Transfer Protein (CETP) Polymorphisms Affect mRNA Splicing, HDL Levels, and Sex-Dependent Cardiovascular Risk

Polymorphisms in and around the Cholesteryl Ester Transfer Protein (CETP) gene have been associated with HDL levels, risk for coronary artery disease (CAD), and response to therapy. The mechanism of action of these polymorphisms has yet to be defined. We used mRNA allelic expression and splice isoform measurements in human liver tissues to identify the genetic variants affecting CETP levels. Allelic CETP mRNA expression ratios in 56 human livers were strongly associated with several variants 2.5–7 kb upstream of the transcription start site (e.g., rs247616 p = 6.4×10−5, allele frequency 33%). In addition, a common alternatively spliced CETP isoform lacking exon 9 (Δ9), has been shown to prevent CETP secretion in a dominant-negative manner. The Δ 9 expression ranged from 10 to 48% of total CETP mRNA in 94 livers. Increased formation of this isoform was exclusively associated with an exon 9 polymorphism rs5883-C>T (p = 6.8×10−10) and intron 8 polymorphism rs9930761-T>C (5.6×10−8) (in high linkage disequilibrium with allele frequencies 6–7%). rs9930761 changes a key splicing branch point nucleotide in intron 8, while rs5883 alters an exonic splicing enhancer sequence in exon 9

Candidate genetic analysis of plasma high-density lipoprotein-cholesterol and severity of coronary atherosclerosis

<p>Abstract</p> <p>Background</p> <p>Plasma level of high-density lipoprotein-cholesterol (HDL-C), a heritable trait, is an important determinant of susceptibility to atherosclerosis. Non-synonymous and regulatory single nucleotide polymorphisms (SNPs) in genes implicated in HDL-C synthesis and metabolism are likely to influence plasma HDL-C, apolipoprotein A-I (apo A-I) levels and severity of coronary atherosclerosis.</p> <p>Methods</p> <p>We genotyped 784 unrelated Caucasian individuals from two sets of populations (Lipoprotein and Coronary Atherosclerosis Study- LCAS, N = 333 and TexGen, N = 451) for 94 SNPs in 42 candidate genes by 5' nuclease assays. We tested the distribution of the phenotypes by the Shapiro-Wilk normality test. We used Box-Cox regression to analyze associations of the non-normally distributed phenotypes (plasma HDL-C and apo A-I levels) with the genotypes. We included sex, age, body mass index (BMI), diabetes mellitus (DM), and cigarette smoking as covariates. We calculated the q values as indicators of the false positive discovery rate (FDR).</p> <p>Results</p> <p>Plasma HDL-C levels were associated with sex (higher in females), BMI (inversely), smoking (lower in smokers), DM (lower in those with DM) and SNPs in <it>APOA5, APOC2</it>, <it>CETP, LPL </it>and <it>LIPC </it>(each q ≤0.01). Likewise, plasma apo A-I levels, available in the LCAS subset, were associated with SNPs in <it>CETP</it>, <it>APOA5</it>, and <it>APOC2 </it>as well as with BMI, sex and age (all q values ≤0.03). The <it>APOA5 </it>variant S19W was also associated with minimal lumen diameter (MLD) of coronary atherosclerotic lesions, a quantitative index of severity of coronary atherosclerosis (q = 0.018); mean number of coronary artery occlusions (p = 0.034) at the baseline and progression of coronary atherosclerosis, as indicated by the loss of MLD.</p> <p>Conclusion</p> <p>Putatively functional variants of <it>APOA2</it>, <it>APOA5, APOC2</it>, <it>CETP, LPL</it>, <it>LIPC </it>and <it>SOAT2 </it>are independent genetic determinants of plasma HDL-C levels. The non-synonymous S19W SNP in <it>APOA5 </it>is also an independent determinant of plasma apo A-I level, severity of coronary atherosclerosis and its progression.</p

Receptor Complementation and Mutagenesis Reveal SR-BI as an Essential HCV Entry Factor and Functionally Imply Its Intra- and Extra-Cellular Domains

HCV entry into cells is a multi-step and slow process. It is believed that the initial capture of HCV particles by glycosaminoglycans and/or lipoprotein receptors is followed by coordinated interactions with the scavenger receptor class B type I (SR-BI), a major receptor of high-density lipoprotein (HDL), the CD81 tetraspanin, and the tight junction protein Claudin-1, ultimately leading to uptake and cellular penetration of HCV via low-pH endosomes. Several reports have indicated that HDL promotes HCV entry through interaction with SR-BI. This pathway remains largely elusive, although it was shown that HDL neither associates with HCV particles nor modulates HCV binding to SR-BI. In contrast to CD81 and Claudin-1, the importance of SR-BI has only been addressed indirectly because of lack of cells in which functional complementation assays with mutant receptors could be performed. Here we identified for the first time two cell types that supported HCVpp and HCVcc entry upon ectopic SR-BI expression. Remarkably, the undetectable expression of SR-BI in rat hepatoma cells allowed unambiguous investigation of human SR-BI functions during HCV entry. By expressing different SR-BI mutants in either cell line, our results revealed features of SR-BI intracellular domains that influence HCV infectivity without affecting receptor binding and stimulation of HCV entry induced by HDL/SR-BI interaction. Conversely, we identified positions of SR-BI ectodomain that, by altering HCV binding, inhibit entry. Finally, we characterized alternative ectodomain determinants that, by reducing SR-BI cholesterol uptake and efflux functions, abolish HDL-mediated infection-enhancement. Altogether, we demonstrate that SR-BI is an essential HCV entry factor. Moreover, our results highlight specific SR-BI determinants required during HCV entry and physiological lipid transfer functions hijacked by HCV to favor infection

Co-infection and ICU-acquired infection in COIVD-19 ICU patients: a secondary analysis of the UNITE-COVID data set

Background: The COVID-19 pandemic presented major challenges for critical care facilities worldwide. Infections which develop alongside or subsequent to viral pneumonitis are a challenge under sporadic and pandemic conditions; however, data have suggested that patterns of these differ between COVID-19 and other viral pneumonitides. This secondary analysis aimed to explore patterns of co-infection and intensive care unit-acquired infections (ICU-AI) and the relationship to use of corticosteroids in a large, international cohort of critically ill COVID-19 patients.Methods: This is a multicenter, international, observational study, including adult patients with PCR-confirmed COVID-19 diagnosis admitted to ICUs at the peak of wave one of COVID-19 (February 15th to May 15th, 2020). Data collected included investigator-assessed co-infection at ICU admission, infection acquired in ICU, infection with multi-drug resistant organisms (MDRO) and antibiotic use. Frequencies were compared by Pearson's Chi-squared and continuous variables by Mann-Whitney U test. Propensity score matching for variables associated with ICU-acquired infection was undertaken using R library MatchIT using the "full" matching method.Results: Data were available from 4994 patients. Bacterial co-infection at admission was detected in 716 patients (14%), whilst 85% of patients received antibiotics at that stage. ICU-AI developed in 2715 (54%). The most common ICU-AI was bacterial pneumonia (44% of infections), whilst 9% of patients developed fungal pneumonia; 25% of infections involved MDRO. Patients developing infections in ICU had greater antimicrobial exposure than those without such infections. Incident density (ICU-AI per 1000 ICU days) was in considerable excess of reports from pre-pandemic surveillance. Corticosteroid use was heterogenous between ICUs. In univariate analysis, 58% of patients receiving corticosteroids and 43% of those not receiving steroids developed ICU-AI. Adjusting for potential confounders in the propensity-matched cohort, 71% of patients receiving corticosteroids developed ICU-AI vs 52% of those not receiving corticosteroids. Duration of corticosteroid therapy was also associated with development of ICU-AI and infection with an MDRO.Conclusions: In patients with severe COVID-19 in the first wave, co-infection at admission to ICU was relatively rare but antibiotic use was in substantial excess to that indication. ICU-AI were common and were significantly associated with use of corticosteroids

Drug Off-Target Effects Predicted Using Structural Analysis in the Context of a Metabolic Network Model

Recent advances in structural bioinformatics have enabled the prediction of protein-drug off-targets based on their ligand binding sites. Concurrent developments in systems biology allow for prediction of the functional effects of system perturbations using large-scale network models. Integration of these two capabilities provides a framework for evaluating metabolic drug response phenotypes in silico. This combined approach was applied to investigate the hypertensive side effect of the cholesteryl ester transfer protein inhibitor torcetrapib in the context of human renal function. A metabolic kidney model was generated in which to simulate drug treatment. Causal drug off-targets were predicted that have previously been observed to impact renal function in gene-deficient patients and may play a role in the adverse side effects observed in clinical trials. Genetic risk factors for drug treatment were also predicted that correspond to both characterized and unknown renal metabolic disorders as well as cryptic genetic deficiencies that are not expected to exhibit a renal disorder phenotype except under drug treatment. This study represents a novel integration of structural and systems biology and a first step towards computational systems medicine. The methodology introduced herein has important implications for drug development and personalized medicine