Migraine: Calcium Channels and Glia

Migraine is a common neurological disease that affects about 11% of the adult population. The disease is divided into two main clinical subtypes: migraine with aura and migraine without aura. According to the neurovascular theory of migraine, the activation of the trigeminovascular system (TGVS) and the release of numerous neuropeptides, including calcitonin gene-related peptide (CGRP) are involved in headache pathogenesis. TGVS can be activated by cortical spreading depression (CSD), a phenomenon responsible for the aura. The mechanism of CSD, stemming in part from aberrant interactions between neurons and glia have been studied in models of familial hemiplegic migraine (FHM), a rare monogenic form of migraine with aura. The present review focuses on those interactions, especially as seen in FHM type 1, a variant of the disease caused by a mutation in CACNA1A, which encodes the α1A subunit of the P/Q-type voltage-gated calcium channel.     

Salivary Proteome Changes in Response to Acute Psychological Stress Due to an Oral Exam Simulation in University Students: Effect of an Olfactory Stimulus

The autonomic nervous system (ANS) plays a crucial role both in acute and chronic psychological stress eliciting changes in many local and systemic physiological and biochemical processes. Salivary secretion is also regulated by ANS. In this study, we explored salivary proteome changes produced in thirty-eight University students by a test stress, which simulated an oral exam. Students underwent a relaxation phase followed by the stress test during which an electrocardiogram was recorded. To evaluate the effect of an olfactory stimulus, half of the students were exposed to a pleasant odor diffused in the room throughout the whole session. Saliva samples were collected after the relaxation phase (T0) and the stress test (T1). State anxiety was also evaluated at T0 and T1. Salivary proteins were separated by two-dimensional electrophoresis, and patterns at different times were compared. Spots differentially expressed were trypsin digested and identified by mass spectrometry. Western blot analysis was used to validate proteomic results. Anxiety scores and heart rate changes indicated that the fake exam induced anxiety. Significant changes of α-amylase, polymeric immunoglobulin receptor (PIGR), and immunoglobulin α chain (IGHA) secretion were observed after the stress test was performed in the two conditions. Moreover, the presence of pleasant odor reduced the acute social stress affecting salivary proteome changes. Therefore, saliva proteomic analysis was a useful approach to evaluate the rapid responses associated to an acute stress test also highlighting known biomarkers.     

Deep Learning Based Process Analytics Model for Predicting Type 2 Diabetes Mellitus

Process analytics is one of the popular research domains that advanced in the recent years. Process analytics encompasses identification, monitoring, and improvement of the processes through knowledge extraction from historical data. The evolution of Artificial Intelligence (AI)-enabled Electronic Health Records (EHRs) revolutionized the medical practice. Type 2 Diabetes Mellitus (T2DM) is a syndrome characterized by the lack of insulin secretion. If not diagnosed and managed at early stages, it may produce severe outcomes and at times, death too. Chronic Kidney Disease (CKD) and Coronary Heart Disease (CHD) are the most common, long-term and life-threatening diseases caused by T2DM. Therefore, it becomes inevitable to predict the risks of CKD and CHD in T2DM patients. The current research article presents automated Deep Learning (DL)-based Deep Neural Network (DNN) with Adagrad Optimization Algorithm i.e., DNN-AGOA model to predict CKD and CHD risks in T2DM patients. The paper proposes a risk prediction model for T2DM patients who may develop CKD or CHD. This model helps in alarming both T2DM patients and clinicians in advance. At first, the proposed DNN-AGOA model performs data preprocessing to improve the quality of data and make it compatible for further processing. Besides, a Deep Neural Network (DNN) is employed for feature extraction, after which sigmoid function is used for classification. Further, Adagrad optimizer is applied to improve the performance of DNN model. For experimental validation, benchmark medical datasets were used and the results were validated under several dimensions. The proposed model achieved a maximum precision of 93.99%, recall of 94.63%, specificity of 73.34%, accuracy of 92.58%, and F-score of 94.22%. The results attained through experimentation established that the proposed DNN-AGOA model has good prediction capability over other methods.     

Seismic behavior of geosynthetic-reinforced retaining walls backfilled with cohesive soil

This study investigates the seismic performance of geosynthetic-reinforced modular block retaining walls backfilled with cohesive, fine grained clay-sand soil mixture. Shaking table tests were performed for three ½ scaled (wall height 190 cm) and ¼ scaled model walls to investigate the effects of backfill type, the influence of reinforcement length and reinforcement stiffness effects. The El Centro and Kobe earthquake records of varying amplitudes were used as base acceleration. Displacement of the front wall, accelerations at different locations, strains on the reinforcements, and the visual observations of the facing and the backfill surface were used to evaluate the seismic performance of model walls. The model walls were subjected to rigorous shaking and the walls did not exhibit any stability problems or signs of impending failure. The maximum deformations observed on the models with cohesive backfill was less than half of the deformation of the sand model. The load transfers between the geogrid and cohesive soil was comparable to that of sand and hence the needed reinforcement length was similar as well. As a result; the model walls with cohesive backfills performed within acceptable limits under seismic loading conditions when compared with granular backfilled counterparts.     

GOLPH3 Regulates EGFR in T98G Glioblastoma Cells by Modulating Its Glycosylation and Ubiquitylation

Protein trafficking is altered when normal cells acquire a tumor phenotype. A key subcellular compartment in regulating protein trafficking is the Golgi apparatus, but its role in carcinogenesis is still not well defined. Golgi phosphoprotein 3 (GOLPH3), a peripheral membrane protein mostly localized at the trans-Golgi network, is overexpressed in several tumor types including glioblastoma multiforme (GBM), the most lethal primary brain tumor. Moreover, GOLPH3 is currently considered an oncoprotein, however its precise function in GBM is not fully understood. Here, we analyzed in T98G cells of GBM, which express high levels of epidermal growth factor receptor (EGFR), the effect of stable RNAi-mediated knockdown of GOLPH3. We found that silencing GOLPH3 caused a significant reduction in the proliferation of T98G cells and an unexpected increase in total EGFR levels, even at the cell surface, which was however less prone to ligand-induced autophosphorylation. Furthermore, silencing GOLPH3 decreased EGFR sialylation and fucosylation, which correlated with delayed ligand-induced EGFR downregulation and its accumulation at endo-lysosomal compartments. Finally, we found that EGF failed at promoting EGFR ubiquitylation when the levels of GOLPH3 were reduced. Altogether, our results show that GOLPH3 in T98G cells regulates the endocytic trafficking and activation of EGFR likely by affecting its extent of glycosylation and ubiquitylation.     

樟芝多糖通过降低NLRP3 Caspase1炎性小体表达改善帕金森小鼠神经行为学的机制研究

目的:研究樟芝多糖通过降低NLRP3-Caspase1炎性小体表达改善6-OHDA构建的帕金森小鼠模型的行为学机制。方法:利用6-OHDA脑内注射构建帕金森小鼠模型，通过酪氨酸羟化酶（TH）免疫组化染色和行为学判定小鼠模型的构建成功。利用樟芝多糖进行干预，分别在干预前、干预后的第1、3、7天4个时间点进行神经行为学实验，分别采用转棒实验、爬杆实验检测小鼠自主行为能力以及协调能力，4个时间点取小鼠尾静脉外周血采用ELISA法检测外周血中Caspase1和IL-1β的表达，樟芝多糖干预第7天时待进行完行为学实验后小鼠断颈处死，取小鼠脑组织-纹状体，Western blot法检测纹状体中Caspase1、proCaspase1、NLRP3的表达，高效液相色谱检测纹状体中单胺类神经递质的表达，RT-QPCR检测Caspase1、NLRP3、IL-1β、IL-4、IL-6的mRNA表达。NISSl染色检测小鼠脑组织神经细胞凋亡情况。 结果:6-OHDA脑内注射可以造成小鼠帕金森样病变，且TH蛋白表达显著下调，樟芝多糖干预后小鼠的行为学得到显著改善（P<0.05），纹状体中Caspase1、proCaspase1、NLRP3的表达显著下调，与模型小鼠相比具有统计学差异（P<0.05），且相关炎症因子Caspase1、NLRP3、IL-1β、IL-4、IL-6的mRNA表达下调（P<0.05），纹状体中单胺类神经递质表达上升（P<0.05）。结论:樟芝多糖可以通过下调NLRP3-Caspase1炎性小体表达来改善6-OHDA构建的帕金森小鼠模型行为改善，这可能是樟芝多糖治疗帕金森的机制之一。     

Restructuring bank networks after mergers and acquisitions: A capacitated delocation model for closing and resizing branches

During restructuring processes, due to mergers and acquisitions, banks frequently face the problem of having redundant branches competing in the same market. In this work, we introduce a new Capacitated Branch Restructuring Model which extends the available literature in delocation models. It considers both closing down and long term operations׳ costs, and addresses the problem of resizing open branches in order to maintain a constant service level. We consider, as well, the presence of competitors and allow for ceding market share whenever the restructuring costs are prohibitively expensive.We test our model in a real life scenario, obtaining a reduction of about 40% of the network size, and annual savings over 45% in operation costs from the second year on. We finally perform a sensitivity analysis on critical parameters. This analysis shows that the final design of the network depends on certain strategic decisions concerning the redundancy of the branches, as well as their proximity to the demand nodes and to the competitor׳s branches. At the same time, this design is quite robust to changes in the parameters associated with the adjustments on service capacity and with the market reaction.     

Combination of nuclear magnetic resonance spectroscopy and nonlinear methods to analyze the copolymerization of phosphonic acid derivatives

We demonstrate in this study that the combination of modern inline monitoring methods [here: inline nuclear magnetic resonance (NMR)] with simulations gains more exact and profound kinetic results than previously used methods like linearization without that combination. The ¹H-NMR spectroscopic data (more than 100 data points) are used to construct the copolymerization diagram. The reactivity ratios are obtained applying the van Herks nonlinear least square method. The examination of the radical copolymerization of 2-hydroxyethyl methacrylate (HEMA) with (2-{[2-(ethoxycarbonyl)prop-2-en-1-yl]oxy}ethyl) phosphonic acid (ECPPA) as important adhesive monomer used in dentistry yields reactivity ratios of r_HEMA = 1.83; r_ECPPA = 0.42. The copolymerization diagram reflects nonideal, non-azeotropic copolymerization. The sequence distribution of the obtained by Monte Carlo simulation indicates the generation of statistical copolymers. As an important finding, it is demonstrated that the repeating units responsible for etching and adhesion are arranged over the whole polymer chain, which is necessary to achieve proper functionality. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2019 , 136, 48256.