Optimization of the elastic modulus for polymeric nanocomposite membranes

The mechanical properties of polymeric membranes are critical factors for a successful and durable application in water treatment technologies. Fabricating membranes with optimal mechanical properties require delicate balancing between material, additives, processing conditions, porosity, and many other variables. Several variables to be optimized demands detailed experimental and computational investigations. The design of experiments (DOEs) technique using a validated framework with a computational model for the prediction of the elastic behavior can lower the number of conducted experiments for optimal membrane fabrication conditions. In this study, optimization of the elastic modulus of polymeric membranes is performed using DOE with computational modeling and validated with experiments. The optimum storage modulus of polymeric nano-filled membranes is determined at an operating temperature of 35°C. DOE is employed with a three-factor–three-level problem. The Taguchi DOE is utilized to obtain the experiments scheme, followed by the prediction of the storage modulus and fabrication of the polymeric nano-filled membrane with the optimum modulus. Predicted results demonstrate that the modulus of polyether sulfone (PES) reinforced with 0.3 wt.% halloysite nanotubes (HNTs) is the optimum combination. The fabricated PES/0.3 wt.% HNT membrane is in good agreement with the predicted modulus with a percentage error of 3%.     

Green tea extract: possible mechanism and antibacterial activity on skin pathogens

Camellia sinensis (tea) is known for its therapeutic properties (anti-inflammatory, anti-microbial, anti-tumour, anti-oxidative and anti-ageing). Although, anti-microbial properties of green tea have been studied, its role against bacterial strains related to skin infections and mechanism of action is not well understood. We focussed on exploring anti-microbial activity and the basic mechanism of aqueous green tea leaf extract on selected bacterial strains. Staphylococcus epidermidis, Micrococcus luteus, Brevibacterium linens, Pseudomonas fluorescens and Bacillus subtilis were found to be sensitive to green tea extract via disc diffusion assay (zone of inhibition ≥7 mm). Minimal inhibitory concentration (MIC) was determined via nitro blue tetrazolium (NBT) assay (0.156-0.313 mg/ml). Moreover, the aqueous extract was found to be not toxic to the Vero cell-line up to a concentration of 500 μg/ml. The effect of aqueous extract on adhesion of different bacteria to Vero cells indicated that it inhibits the adhesion at its MIC value.     

Modeling the mechanical response of polymers and nano-filled polymers: Effects of porosity and fillers content

In this study, models are developed to predict the mechanical behavior of porous polymer-based membranes, since they are exposed to temperature and pressure load under service conditions. To the best of our knowledge, existing studies related to numerical simulations of mechanical behavior of polymer nanocomposites do not explicitly report porosity in the modeling. Hence, the proposed models attempt to explicitly examine the effect of porosity. The first model predicts the elastic modulus of porous polymer nanocomposites which is based on Weibull statistical analysis. The results show the combined effects of fillers content and porosity on the modulus. The second model predicts the yield stress for porous polymer nanocomposites. The coupled effects of fillers content and porosity on the yield stress is examined via our proposed extension of the cooperative model. A parametric study is conducted to show the influence of a single parameter used to model the influence of porosity.     

Effect of manganese doping on conduction in olivine LiFePO<Subscript>4</Subscript>

Lithium ion batteries play a crucial role in terms of good rechargeability, long cycles and higher shelf life. For the fabrication of such a Li ion battery, properties of cathode material can be engineered keeping anode and electrolyte fixed. In the present work, lithium iron phosphate (LiFePO₄) has been utilized as cathode material and the properties of LiFePO₄ have been tuned by doping manganese (Mn). LiFePO₄ and different concentrations of Mn doped LiFePO₄ were prepared by solid state route. X-Ray diffraction and Raman studies were performed to confirm the formation of LiFePO₄ and Mn doped LiFePO₄ in olivine structure. Cyclic voltammetry studies revealed maximum peak oxidation current (2.96 mA) and largest surface coverage (0.066 nanoMoles/cm²) for the LiFePO₄ doped with 15% Mn (LiMn_0.15Fe_0.85PO₄). AC-conductivity study was carried out for different frequencies at room temperature. The conductivity parameters estimated using Almond and West formalism support the cyclic voltammetry results. Ion-hopping rate (ω _p ) and charge carrier concentration (K) maximize for 15% Mn doping (ω _p : 582974.48719 Hz; K: 2.62447?×?10^?6) and drop on either increasing (ω _p : 167134.73521 Hz; K: 1.25647?×?10^?7) or decreasing (ω _p : 130726.49084 Hz; K: 2.52435?×?10^?6) the Mn doping. The increase till 15% Mn doping has been attributed to the increase in unit cell volume with Mn doping while the sudden decrease at 20% Mn doping is due to dominance of back-hopping mechanism. The results clearly indicate that 15% Mn doped LiFePO₄ is the most appropriate for the realization of a cathode for Li-ion battery.     

Orphan G-Protein Coupled Receptor GPRC5B Is Critical for Lymphatic Development

Numerous studies have focused on the molecular signaling pathways that govern the development and growth of lymphatics in the hopes of elucidating promising druggable targets. G protein-coupled receptors (GPCRs) are currently the largest family of membrane receptors targeted by FDA-approved drugs, but there remain many unexplored receptors, including orphan GPCRs with no known biological ligand or physiological function. Thus, we sought to illuminate the cadre of GPCRs expressed at high levels in lymphatic endothelial cells and identified four orphan receptors: GPRC5B, AGDRF5/GPR116, FZD8 and GPR61. Compared to blood endothelial cells, GPRC5B is the most abundant GPCR expressed in cultured human lymphatic endothelial cells (LECs), and in situ RNAscope shows high mRNA levels in lymphatics of mice. Using genetic engineering approaches in both zebrafish and mice, we characterized the function of GPRC5B in lymphatic development. Morphant gprc5b zebrafish exhibited failure of thoracic duct formation, and Gprc5b^−/− mice suffered from embryonic hydrops fetalis and hemorrhage associated with subcutaneous edema and blood-filled lymphatic vessels. Compared to Gprc5^+/+ littermate controls, Gprc5b^−/− embryos exhibited attenuated developmental lymphangiogenesis. During the postnatal period, ~30% of Gprc5b^−/− mice were growth-restricted or died prior to weaning, with associated attenuation of postnatal cardiac lymphatic growth. In cultured human primary LECs, expression of GPRC5B is required to maintain cell proliferation and viability. Collectively, we identify a novel role for the lymphatic-enriched orphan GPRC5B receptor in lymphangiogenesis of fish, mice and human cells. Elucidating the roles of orphan GPCRs in lymphatics provides new avenues for discovery of druggable targets to treat lymphatic-related conditions such as lymphedema and cancer.     

Cell isolation via spiral microfluidics and the secondary anchor targeted cell release system

Precision medicine requires high throughput cell isolation and measurement that maintains physiology. Unfortunately, many techniques are slow or alter cell biomarkers cells. This necessitates new approaches, which we achieve by integrating affinity-based cell isolation with spiral microfluidics. We characterize the device via computational simulations, predicting wall shear stress within an order of magnitude of arterial wall shear stress (~0.2 Pa). We identify that poly-l -lysine supplementation preserves cell geometry and improves cell release. We demonstrate preservation of angiogenic biomarker concentrations, measuring 1,000–2,000 vascular endothelial growth factor receptor-1 per human umbilical vein endothelial cell, which is in line with the previously reported measurements. We attain 76.7 ± 9.0% release of captured cells by integrating thermophoresis and optimizing buffer residence time. Ultimately, we find that combining affinity-based cell isolation (secondary anchor targeted cell release) with spiral microfluidics offers a fast, biomarker preserving approach needed to individualize medicine.     

Automatic diagnosis of diabetic retinopathy with the aid of adaptive average filtering with optimized deep convolutional neural network

The most effective treatment for diabetic retinopathy (DR) is the early detection through regular screening, which is critical for a better prognosis. Automatic screening of the images would assist the physicians in diagnosing the condition of patients easily and accurately. This condition searches out for special importance of image processing technology in the way of processing the retinal fundus images. Accordingly, this article plans to develop an automatic DR detection model with the aid of three main stages like (a) image preprocessing, (b) blood vessel segmentation, and (c) classification. The preprocessing phase includes two steps: conversion of RGB to Lab, and contrast enhancement. The Histogram equalization process is done using the contrast enhancement of an image. To the next of preprocessing, the segmentation phase starts with a valuable procedure. It includes (a), thresholding the contrast-enhanced and filtered images, (b) thresholding the keypoints of contrast-enhanced and filtered images, and (c) adding both thresholded binary images. Here, the filtering process is performed by proposed adaptive average filtering, where the filter coefficients are tuned or optimized by an improved meta-heuristic algorithm called fitness probability-based CSO (FP-CSO). Finally, the classification part uses Deep CNN, where the improvement is exploited on the convolutional layer, which is optimized by the same improved FP-CSO. Since the conventional CSO depends on a fitness probability in the improved algorithm, the proposed algorithm termed as FP-CSO. Finally, valuable comparative and performance analysis has confirmed the effectiveness of the proposed model.