Genetic and Epigenomic Modifiers of Diabetic Neuropathy

Diabetic neuropathy (DN), the most common chronic and progressive complication of diabetes mellitus (DM), strongly affects patients’ quality of life. DN could be present as peripheral, autonomous or, clinically also relevant, uremic neuropathy. The etiopathogenesis of DN is multifactorial, and genetic components play a role both in its occurrence and clinical course. A number of gene polymorphisms in candidate genes have been assessed as susceptibility factors for DN, and most of them are linked to mechanisms such as reactive oxygen species production, neurovascular impairments and modified protein glycosylation, as well as immunomodulation and inflammation. Different epigenomic mechanisms such as DNA methylation, histone modifications and non-coding RNA action have been studied in DN, which also underline the importance of “metabolic memory” in DN appearance and progression. In this review, we summarize most of the relevant data in the field of genetics and epigenomics of DN, hoping they will become significant for diagnosis, therapy and prevention of DN.     

Phosphorylated chitosan/poly(vinyl alcohol) based proton exchange membranes modified with propylammonium nitrate ionic liquid and silica filler for fuel cell applications

In our previous work, phosphorylated chitosan was modified through polymer blending with poly(vinyl alcohol) (PVA) polymer to produce N-methylene phosphonic chitosan/poly(vinyl alcohol) (NMPC/PVA) composite membranes. The aim of this work is to further investigate the effects of a propylammonium nitrate (PAN) ionic liquid and/or silicon dioxide (SiO₂) filler on the morphology and physical properties of NMPC/PVA composite membranes. The temperature-dependent ionic conductivity of the composite membranes with various ionic liquid and filler compositions was studied by varying the loading of PAN ionic liquid and SiO₂-PAN filler in the range of 5–20 wt%. As the loading of PAN ionic liquid increased in the NMPC/PVA membrane matrix, the ionic conductivity value also increased with the highest value of 0.53 × 10^?3 S cm^?1 at 25 °C and increased to 1.54 × 10^?3 S cm^?1 at 100 °C with 20 wt% PAN. The NMPC/PVA-PAN (20 wt%) composite membrane also exhibited the highest water uptake and ion exchange capacity, with values of 60.5% and 0.60 mequiv g^?1, respectively. In addition, in the single-cell performance test, the NMPC/PVA-PAN (20 wt%) composite membrane displayed a maximum power density, which was increased by approximately 14% compared to the NMPC/PVA composite membrane with 5 wt% SiO₂-PAN. This work demonstrated that modified NMPC/PVA composite membranes with ionic liquid PAN and/or SiO₂ filler showed enhanced performance compared with unmodified NMPC/PVA composite membranes for proton exchange membrane fuel cells.     

Highly stable nanocarbon supported Pt catalyst for fuel cell via a molten salt graphitization strategy

Proton exchange membrane fuel cells (PEMFCs) durability has been severely hindered by carbon support poor stability in the cathodic Pt-based catalyst. Herein, a high-surface-area nitrogen-doped graphitic nanocarbon (N-G-CA) with mesopores is developed as Pt support to address PEMFCs durability challenge. Resorcinol-formaldehyde aerogel pyrolyzed carbon aerogel is selected as N-G-CA raw material. Nitrogen atoms are introduced into carbon aerogel via NH₃ heat treatment. Then, nitrogen-doped carbon aerogel is transferred into N-G-CA via heating together with transition-metal salts (one of FeCl₃, FeCl₂, CoCl₂, or MnCl₂, etc.) at 1200 °C. As ORR catalyst, Pt/N-G-CA half-wave potential only lost 10 mV, after 30, 000 cycles accelerated aging test in the rotating-desk-electrode. Only 12 mV voltage loss at 1.5 A/cm2 is observed, after 5, 000 cycles for membrane electrode. Pt/N-G-CA exhibits superior durability and activity than commercial Pt/C. High durability of Pt/N-G-CA is due to N-G-CA high graphitization extent, as well as the interactions between doping nitrogen and Pt. N-G-CA is promising as stable support for durable Pt-based catalysts in PEMFCs, thanks to enhanced carbon corrosion resistance, uniformly dispersed Pt, and strong support-metals interaction.     

Identifying catalyst layer compositions of proton exchange membrane fuel cells through machine-learning-based approach

Membrane electrode assembly (MEA) is considered a key component of a proton exchange membrane fuel cell (PEMFC). However, developing a new MEA to meet desired properties, such as operation under low-humidity conditions without a humidifier, is a time- and cost-consuming process. This study employs a machine-learning-based approach using K-nearest neighbor (KNN) and neural networks (NN) in the MEA development process by identifying a suitable catalyst layer (CL) recipe in MEA. Minimum redundancy maximum relevance and principal component analysis were implemented to specify the most important predictor and reduce the data dimension. The number of predictors was found to play an essential role in the accuracy of the KNN and NN models although the predictors have self-correlations. The KNN model with a K of 7 was found to minimize the model loss with a loss of 11.9%. The NN model constructed by three corresponding hidden layers with nine, eight, and nine nodes can achieve the lowest error of 0.1293 for the Pt catalyst and 0.031 for PVA as a good additive blending in the CL of the MEA. However, even if the error is low, the prediction of PVA seems to be inaccurate, regardless of the model structure. Therefore, the KNN model is more appropriate for CL recipe prediction.     

In Silico Analysis of the Molecular-Level Impact of SMPD1 Variants on Niemann-Pick Disease Severity

Sphingomyelin phosphodiesterase (SMPD1) is a key enzyme in the sphingolipid metabolism. Genetic SMPD1 variants have been related to the Niemann-Pick lysosomal storage disorder, which has different degrees of phenotypic severity ranging from severe symptomatology involving the central nervous system (type A) to milder ones (type B). They have also been linked to neurodegenerative disorders such as Parkinson and Alzheimer. In this paper, we leveraged structural, evolutionary and stability information on SMPD1 to predict and analyze the impact of variants at the molecular level. We developed the SMPD1-ZooM algorithm, which is able to predict with good accuracy whether variants cause Niemann-Pick disease and its phenotypic severity; the predictor is freely available for download. We performed a large-scale analysis of all possible SMPD1 variants, which led us to identify protein regions that are either robust or fragile with respect to amino acid variations, and show the importance of aromatic-involving interactions in SMPD1 function and stability. Our study also revealed a good correlation between SMPD1-ZooM scores and in vitro loss of SMPD1 activity. The understanding of the molecular effects of SMPD1 variants is of crucial importance to improve genetic screening of SMPD1-related disorders and to develop personalized treatments that restore SMPD1 functionality.     

Sputtered Ir–Ru based catalysts for oxygen evolution reaction: Study of iridium effect on stability

Magnetron sputtered low-loading iridium-ruthenium thin films are investigated as catalysts for the Oxygen Evolution Reaction at the anode of the Proton Exchange Membrane Water Electrolyzer. Electrochemical performance of 50 nm thin catalysts (Ir pure, Ir–Ru 1:1, Ir–Ru 1:3, Ru pure) is tested in a Rotating Disk Electrode. Corresponding Tafel slopes are measured before and after the CV-based procedure to compare the activity and stability of prepared compounds. Calculated activities prior to the procedure confirm higher activity of ruthenium-containing catalysts (Ru pure > Ir–Ru 1:3 > Ir–Ru 1:1 > Ir pure). However, after the procedure a higher activity and less degradation of Ir–Ru 1:3 is observed, compared to Ir–Ru 1:1, i.e. the sample with a higher amount of unstable ruthenium performs better. This contradicts the expected behavior of the catalyst. The comprehensive chemical and structural analysis unravels that the stability of Ir–Ru 1:3 sample is connected to RuO₂ chemical state and hcp structure. Obtained results are confirmed by measuring current densities in a single cell.     

基于神经网络和遗传算法的锭子弹性管性能优化

为得到减振弹性管对下锭胆的支承弹性和锭子高速运动下的稳定性等性能的最优匹配效率,依据减振弹性管的等效抗弯刚度及底部等效刚度系数公式,利用MatLab数值分析软件构建弹性管抗弯刚度和底部挠度数学模型。首先,结合Isight优化软件基于径向基神经网络构建其近似模型,且使精度达到可接受水平,并以模型的关键结构参数弹性模量、螺距、槽宽、壁厚为设计变量,结合遗传算法对弹性管抗弯刚度和底部挠度进行多目标优化设计,得到Pareto最优解集和Pareto前沿图,确定出减振弹性管结构工艺参数的优化方案。通过对优化数据进行分析发现,该方案在保证减振弹性管弹性的同时,其底部振幅明显减弱。     

溶液浓差能驱动的逆电渗析反应器制氢实验研究

低品位热能制氢技术首先是将热能转换为溶液浓差能，然后通过逆电渗析（RED）反应器将溶液浓差能转换成氢能。为了验证RED反应器能将溶液浓差能转换为氢能，探索关键运行参数变化对能量转换过程的影响。设计了一个由40个膜对所构成的RED反应器，以NaCl水溶液为工作溶液，NaOH水溶液为电极液的制氢系统。通过改变浓/稀溶液入口浓度，溶液过膜流速以及输出电流来考察对RED反应器产氢率、制氢效率和能量转换效率的影响。实验结果发现，浓/稀溶液入口浓度，过膜流速变化均会影响RED反应器的输出电流。在外电路短接条件下，输出电流越大，反应器产氢率和制氢效率越高，但能量转换效率越低。     

Digital Microfluidics for Manipulation and Analysis of a Single Cell

The basic structural and functional unit of a living organism is a single cell. To understand the variability and to improve the biomedical requirement of a single cell, its analysis has become a key technique in biological and biomedical research. With a physical boundary of microchannels and microstructures, single cells are efficiently captured and analyzed, whereas electric forces sort and position single cells. Various microfluidic techniques have been exploited to manipulate single cells through hydrodynamic and electric forces. Digital microfluidics (DMF), the manipulation of individual droplets holding minute reagents and cells of interest by electric forces, has received more attention recently. Because of ease of fabrication, compactness and prospective automation, DMF has become a powerful approach for biological application. We review recent developments of various microfluidic chips for analysis of a single cell and for efficient genetic screening. In addition, perspectives to develop analysis of single cells based on DMF and emerging functionality with high throughput are discussed.