An elasto-plastic damage model for concrete

Constitutive modeling of concrete using continuum damage mechanics and plasticity theory is presented in this work. In order to derive the constitutive equations the strain equivalence hypothesis is adopted. Menetrey-William type yield function (in the effective stress space) with multiple hardening functions is used to define plastic loading of the material. Non-associated plastic flow rule is used to control inelastic dilatancy. DruckerPrager type function is chosen as a plastic potential. Damage is assumed to be isotropic and two damage variables are used to represent tensile and compressive damage independently. Damage parameter is driven based on the plastic strain. Fully implicit integration scheme is employed and the consistent elastic-plastic-damage tangent operator is also derived. The overall performance of the proposed model is verified by comparing the model predictions to various numerical simulations, cyclic uniaxial tensile and compressive tests, monotonic biaxial compression test and reinforced concrete beam test

Atherogenic markers in predicting cardiovascular risk and targeting residual cardiovascular risk

Abstract Low-density lipoprotein (LDL) cholesterol (LDL-C) is the primary target in cardiovascular (CV) disease prevention and is commonly used in estimating CV risk; however, alternative markers may be needed when LDL-C is not an appropriate marker (e.g. in the presence of low LDL-C levels or elevated triglyceride [TG] levels). Non-high-density lipoprotein cholesterol (non-HDL-C) and apolipoprotein B (apoB) are markers of atherogenic lipoproteins with evidenced associations with CV risk and are, therefore, recommended as secondary targets, appropriate for use in the presence of elevated TG levels. The reported strength of the associations of non-HDL-C and apoB in comparison to LDL-C is conflicting between studies, potentially due to discordance of the markers which can alter their predictive pattern. Although LDL-C levels are commonly managed with statin treatment, a residual risk of CV events still remains, and an abnormal lipid profile can persist. Combination therapy to further reduce LDL-C levels can be beneficial; a statin therapy combined with other LDL-C-lowering therapy further reduced the number of CV events. In addition, targeting other markers, including non-HDL-C, apoB, total cholesterol and TGs may also be beneficial, specifically in patients with low HDL-C and elevated TG levels. More clinical evidence is required before definitive recommendations can be made; however, a statin–fenofibrate combination demonstrated favourable reductions in major CV events in these specific patients

Pharmaceutical strategies for reducing LDL-C and risk of cardiovascular disease

Abstract A key strategy in preventing cardiovascular (CV) disease is the reduction of low-density lipoprotein cholesterol (LDL-C). Statins are a crucial therapy for achieving LDL-C reductions, with the highest tolerated dose often prescribed, especially for patients who are at the greatest risk of CV disease. However, statin intolerance, heterogeneous responses to statins and non-adherence make alternative therapies necessary in some cases. Statins can be combined with a multitude of therapies with synergistic mechanisms of action to effectively manage lipid profiles, while improving safety and tolerability profiles. Addition of a cholesterol absorption inhibitor, bile acid sequestrant or fibrate to statin therapy leads to greater numbers of patients achieving and maintaining LDL-C goals. Furthermore, combination therapies can alter the plasma profiles of other molecules involved in hypercholesterolaemia, including triglycerides and high-density lipoprotein cholesterol. An additional strategy is proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibition therapy, for use in patients who are statin intolerant, patients with heterozygous or homozygous familial hypercholesterolaemia, and patients at very high CV risk, as a potential means for achieving large LDL-C reductions and maintaining LDL-C goals. Clinical trials have demonstrated that PCSK9 inhibition therapy is not only effective but can also be combined with statin therapy to ensure greater reductions in LDL-C. Current, ongoing studies are investigating the efficacy of novel therapies, including selective peroxisome proliferator-activated receptor (PPAR) alpha modulators, PCSK9-specific ribonucleic acid (RNA) interference and anti-inflammatory therapies

HIV and maternal mortality: turning the tide

The two top causes of death in women of reproductive age globally are HIV/AIDS and complications related to pregnancy and childbearing, which account for 19% and 15% of all deaths in women aged 15–44 years, respectively. The growing burden of HIV infection in young sexually active women and the maternal health problems that they face have been described as two intersecting epidemics. In settings with a high HIV burden and high maternal mortality ratios, especially in sub-Saharan Africa, many HIV-infected pregnant women are confronted not only with the risk of death associated with advancing HIV disease, but also with an increased risk of pregnancy-related death

Prevalence Of Potential Familial Hypercholesteremia (Fh) In 54,811 Statin-Treated Patients In Clinical Practice

Background and aims: Familial hypercholesterolemia (FH) is a life-threatening disease, characterized by elevated LDL-C levels and a premature, increased risk of coronary heart disease (CHD) that is globally underdiagnosed. The percentage of patients with possible or probable FH in various countries was examined in the Dyslipidemia International Study (DYSIS). Methods: DYSIS is a multinational, cross-sectional observational study of 54,811 adult outpatients treated with statin therapy. The percentages of patients with high levels of LDL-C, and with possible or probable FH, were assessed using the Dutch scoring method for FH across 29 countries, in age subgroups for the analysis population and among diabetes patients. Results: Despite statin therapy, 16.1% (range 4.4-27.6%) of patients had LDL-C > 3.6 mmol/L (140 mg/dL) across countries and the prevalence of possible FH was 15.0% (range 5.5-27.8%) and 1.1% (range 0.0-5.4%) for probable FH. The highest percentages of probable FH occurred in Egypt (5.4%), the Baltic states (4.2%), Russia (3.2%), and Slovenia (3.1%), with the lowest rates in Israel (0.0%), Canada (0.2%), and Sweden (0.3%). Rates of FH were the highest in younger patients (45-54 years) for secondary prevention, regardless of the presence/absence of diabetes. Conclusions: Despite statin therapy, high LDL-C levels and rates of possible and probable FH were observed in some countries. The prevalence of FH was the highest in younger age patients, and > 60% of patients with probable FH displayed CHD. Earlier diagnosis and treatment of patients with FH are needed to reduce CHD risk in these patients. (C) 2016 The Authors. Published by Elsevier Ireland Ltd

7 T renal MRI: challenges and promises

The progression to 7 Tesla (7 T) magnetic resonance imaging (MRI) yields promises of substantial increase in signal-to-noise (SNR) ratio. This increase can be traded off to increase image spatial resolution or to decrease acquisition time. However, renal 7 T MRI remains challenging due to inhomogeneity of the radiofrequency field and due to specific absorption rate (SAR) constraints. A number of studies has been published in the field of renal 7 T imaging. While the focus initially was on anatomic imaging and renal MR angiography, later studies have explored renal functional imaging. Although anatomic imaging remains somewhat limited by inhomogeneous excitation and SAR constraints, functional imaging results are promising. The increased SNR at 7 T has been particularly advantageous for blood oxygen level-dependent and arterial spin labelling MRI, as well as sodium MR imaging, thanks to changes in field-strength-dependent magnetic properties. Here, we provide an overview of the currently available literature on renal 7 T MRI. In addition, we provide a brief overview of challenges and opportunities in renal 7 T MR imaging

A prognostic model integrating PET-derived metrics and image texture analyses with clinical risk factors from GOYA

Image texture analysis (radiomics) uses radiographic images to quantify characteristics that may identify tumour heterogeneity and associated patient outcomes. Using fluoro‐deoxy‐glucose positron emission tomography/computed tomography (FDG‐PET/CT)‐derived data, including quantitative metrics, image texture analysis and other clinical risk factors, we aimed to develop a prognostic model that predicts survival in patients with previously untreated diffuse large B‐cell lymphoma (DLBCL) from GOYA (NCT01287741). Image texture features and clinical risk factors were combined into a random forest model and compared with the international prognostic index (IPI) for DLBCL based on progression‐free survival (PFS) and overall survival (OS) predictions. Baseline FDG‐PET scans were available for 1263 patients, 832 patients of these were cell‐of‐origin (COO)‐evaluable. Patients were stratified by IPI or radiomics features plus clinical risk factors into low‐, intermediate‐ and high‐risk groups. The random forest model with COO subgroups identified a clearer high‐risk population (45% 2‐year PFS [95% confidence interval (CI) 40%–52%]; 65% 2‐year OS [95% CI 59%–71%]) than the IPI (58% 2‐year PFS [95% CI 50%–67%]; 69% 2‐year OS [95% CI 62%–77%]). This study confirms that standard clinical risk factors can be combined with PET‐derived image texture features to provide an improved prognostic model predicting survival in untreated DLBCL

Is gender encoded in the smile? A computational framework for the analysis of the smile driven dynamic face for gender recognition

YesAutomatic gender classification has become a topic of great interest to the visual computing research community in recent times. This is due to the fact that computer-based automatic gender recognition has multiple applications including, but not limited to, face perception, age, ethnicity, identity analysis, video surveillance and smart human computer interaction. In this paper, we discuss a machine learning approach for efficient identification of gender purely from the dynamics of a person’s smile. Thus, we show that the complex dynamics of a smile on someone’s face bear much relation to the person’s gender. To do this, we first formulate a computational framework that captures the dynamic characteristics of a smile. Our dynamic framework measures changes in the face during a smile using a set of spatial features on the overall face, the area of the mouth, the geometric flow around prominent parts of the face and a set of intrinsic features based on the dynamic geometry of the face. This enables us to extract 210 distinct dynamic smile parameters which form as the contributing features for machine learning. For machine classification, we have utilised both the Support Vector Machine and the k-Nearest Neighbour algorithms. To verify the accuracy of our approach, we have tested our algorithms on two databases, namely the CK+ and the MUG, consisting of a total of 109 subjects. As a result, using the k-NN algorithm, along with tenfold cross validation, for example, we achieve an accurate gender classification rate of over 85%. Hence, through the methodology we present here, we establish proof of the existence of strong indicators of gender dimorphism, purely in the dynamics of a person’s smile