• The pathophysiology underlying chronic cardiorenal syndrome is not completely understood. • Chronic cardiorenal syndrome is independent of cardiac output or renal perfusion. • Renal T 1 relaxation appears to be prolonged in HF with renal impairment. • Renal T 1 relaxation is associated with classic cardiovascular risk factors. • Association of renal T 1 relaxation with parenchymal damage should be validated further.

This paper investigates the combined effects of using nanofluid, a porous insert and corrugated walls on heat transfer, pressure drop and entropy generation inside a heat exchanger duct. A series of numerical simulations are conducted for a number of pertinent parameters. It is shown that the waviness of the wall destructively affects the heat transfer process at low wave amplitudes and that it can improve heat convection only after exceeding a certain amplitude. Further, the pressure drop in the duct is found to be strongly influenced by the wave amplitude in a highly non-uniform way. The results, also, show that the second law and heat transfer performances of the system improve considerably by thickening the porous insert and decreasing its permeability. Yet, this is associated with higher pressure drops. It is argued that the hydraulic, thermal and entropic behaviours of the system are closely related to the interactions between a vortex formation near the wavy walls and nanofluid flow through the porous insert. Viscous irreversibilities are shown to be dominant in the core region of duct where the porous insert is placed. However, in the regions closer to the wavy walls, thermal entropy generation is the main source of irreversibility. A number of design recommendations are made on the basis of the findings of this study.

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Particle Swarm Optimization (PSO) is a popular algorithm used extensively in continuous optimization. One of its well-known drawbacks is its propensity for premature convergence. Many techniques have been proposed for alleviating this problem. One of the alternative approaches is hybridization. Genetic Algorithms (GA) are one of the possible techniques used for hybridization. Most often, a mutation scheme is added to the PSO, but some applications of crossover have been added more recently. Some of these schemes use adaptive parameterization when applying the GA operators. In this work, adaptively parameterized mutation and crossover operators are combined with a PSO implementation individually and in combination to test the effectiveness of these additions. The results indicate that an adaptive approach with position factor is more effective for the proposed PSO hybrids. Compared to single PSO with adaptive inertia weight, all the PSO hybrids with adaptive probability have shown satisfactory performance in generating near-optimal solutions for all tested functions.

Background Alzheimer’s disease (AD) is a major neurocognitive disorder identified by memory loss and a significant cognitive decline based on previous level of performance in one or more cognitive domains that interferes in the independence of everyday activities. The accuracy of imaging helps to identify the neuropathological features that differentiate AD from its common precursor, mild cognitive impairment (MCI). Identification of early signs will aid in risk stratification of disease and ensures proper management is instituted to reduce the morbidity and mortality associated with AD. Magnetic resonance imaging (MRI) using structural MRI (sMRI), functional MRI (fMRI), diffusion tensor imaging (DTI), and magnetic resonance spectroscopy (1H-MRS) performed alone is inadequate. Thus, the combination of multiparametric MRI is proposed to increase the accuracy of diagnosing MCI and AD when compared to elderly healthy controls. Methods This protocol describes a non-interventional case control study. The AD and MCI patients and the healthy elderly controls will undergo multi-parametric MRI. The protocol consists of sMRI, fMRI, DTI, and single-voxel proton MRS sequences. An eco-planar imaging (EPI) will be used to perform resting-state fMRI sequence. The structural images will be analysed using Computational Anatomy Toolbox-12, functional images will be analysed using Statistical Parametric Mapping-12, DPABI (Data Processing & Analysis for Brain Imaging), and Conn software, while DTI and 1H-MRS will be analysed using the FSL (FMRIB’s Software Library) and Tarquin respectively. Correlation of the MRI results and the data acquired from the APOE genotyping, neuropsychological evaluations (i.e. Montreal Cognitive Assessment [MoCA], and Mini–Mental State Examination [MMSE] scores) will be performed. The imaging results will also be correlated with the sociodemographic factors. The diagnosis of AD and MCI will be standardized and based on the DSM-5 criteria and the neuropsychological scores. Discussion The combination of sMRI, fMRI, DTI, and MRS sequences can provide information on the anatomical and functional changes in the brain such as regional grey matter volume atrophy, impaired functional connectivity among brain regions, and decreased metabolite levels specifically at the posterior cingulate cortex/precuneus. The combination of multiparametric MRI sequences can be used to stratify the management of MCI and AD patients. Accurate imaging can decide on the frequency of follow-up at memory clinics and select classifiers for machine learning that may aid in the disease identification and prognostication. Reliable and consistent quantification, using standardised protocols, are crucial to establish an optimal diagnostic capability in the early detection of Alzheimer’s disease.

Particle Swarm Optimization (PSO) is a well known technique for solving various kinds of combinatorial optimization problems including scheduling, resource allocation and vehicle routing. However, basic PSO suffers from premature convergence problem. Many techniques have been proposed for alleviating this problem. One of the alternative approaches is hybridization. Genetic Algorithms (GAs) are one of the possible techniques used for hybridization. Most often, a mutation scheme is added to the PSO, but some applications of crossover have been added more recently. Some of these schemes use dynamic parameterization when applying the GA operators. In this work, dynamic parameterized mutation and crossover operators are combined with a PSO implementation individually and in combination to test the effectiveness of these additions. The results indicate that all the PSO hybrids with dynamic probability have shown satisfactory performance in finding the best distance of the Vehicle Routing Problem With Time Windows.

In the recent years, increased understanding of the molecular profiles of non-small cell lung cancer (NSCLC) has allowed for targeted treatment of actionable genetic mutations. The management of NSCLC now requires multiple molecular tests to guide the treatment strategy. In the light of this, there is a need to establish a molecular testing consensus statement for advanced NSCLC patients in Malaysia. This Malaysian consensus statement was developed by a panel of experts, chaired by a pathologist and composed of three other pathologists, four respiratory physicians and three oncologists. It reflects currently available scientific data and adaptations of recommendations from international guidelines to the local landscape. Expert recommendations on different aspects of molecular testing agreed upon by the panel are provided as structured discussions. These recommendations address the appropriate patients and samples to be tested, as well as when and how these tests should be performed. The algorithms for molecular testing in metastatic NSCLC, in the first line setting and upon disease progression beyond first line therapy, were developed.