Healthy ageing and depletion of intracellular glutathione influences T cell membrane thioredoxin-1 levels and cytokine secretion

Background: During ageing an altered redox balance has been observed in both intracellular and extracellular compartments, primarily due to glutathione depletion and metabolic stress. Maintaining redox homeostasis is important for controlling proliferation and apoptosis in response to specific stimuli for a variety of cells. For T cells, the ability to generate specific response to antigen is dependent on the oxidation state of cell surface and cytoplasmic protein-thiols. Intracellular thiols are maintained in their reduced state by a network of redox regulating peptides, proteins and enzymes such as glutathione, thioredoxins and thioredoxin reductase. Here we have investigated whether any relationship exists between age and secreted or cell surface thioredoxin-1, intracellular glutathione concentration and T cell surface thioredoxin 1 (Trx-1) and how this is related to interleukin (IL)-2 production.Results: Healthy older adults have reduced lymphocyte surface expression and lower circulating plasma Trx-1 concentrations. Using buthionine sulfoximine to deplete intracellular glutathione in Jurkat T cells we show that cell surface Trx-1 is lowered, secretion of Trx-1 is decreased and the response to the lectin phytohaemagglutinin measured as IL-2 production is also affected. These effects are recapitulated by another glutathione depleting agent, diethylmaleate.Conclusion: Together these data suggest that a relationship exists between the intracellular redox compartment and Trx-1 proteins. Loss of lymphocyte surface Trx-1 may be a useful biomarker of healthy ageing. © 2013 Carilho Torrao et al.; licensee Chemistry Central Ltd

Magnetic resonance imaging (MRI) contrast agents for tumor diagnosis

10.1260/2040-2295.4.1.23Journal of Healthcare Engineering4123-4

Selenium Effects on Oxidative Stress-Induced Calcium Signaling Pathways in Parkinson�s Disease

Parkinson�s disease (PD) is a neurological disorder in which oxidative stress and reactive oxygen species productions are proposed to be involved in its pathogenesis. Despite considerable advancement in Selenium�s (Se) molecular biology and metabolism, we do not know much about the cell type-specific pattern of Se distribution in the brain of PD humans and experimental animals. Although, there is plenty of evidence around the role of Se deficiency in PD�s pathogenesis impacting lipid peroxidation and reducing glutathione (GSH) and glutathione peroxidase (GPX). It has been suggested that Se has an inducible role in selenium-dependent GPX activity in PD animals and humans. However, calcium as a second messenger regulates the neuron cells� essential activities, but its overloading leads to cellular oxidative stress and apoptosis. Therefore, Se�s antioxidant role can affect calcium signaling and alleviate its complications. There are signs of Se and Selenoproteins incorporation in protecting stress oxidative in various pathways. In conclusion, there is convincing proof for the crucial role of Se and Calcium in PD pathogenesis. © 2022, The Author(s), under exclusive licence to Association of Clinical Biochemists of India

Diagnosis of GLDAS LSM based aridity index and dryland identification

The identification of dryland areas is crucial for guiding policy aimed at intervening in water-stressed areas and addressing the perennial livelihood or food insecurity of these areas. However, the prevailing aridity indices (such as UNEP aridity index) have methodological limitations that restrict their use in delineating drylands and may be insufficient for decision-making frameworks. In this study, we propose a new aridity index based on based on 3 decades of soil moisture time series by accounting for site-specific soil and vegetation that partitions precipitation into the competing demands of evaporation and runoff. Our proposed aridity index is the frequency at which the dominant soil moisture value at a location is not exceeded by the dominant soil moisture values in all of the other locations. To represent the dominant spatial template of the soil moisture conditions, we extract the first eigenfunction from the empirical orthogonal function (EOF) analysis from 3 GLDAS land surface models (LSMs): VIC, MOSAIC and NOAH at 1 × 1 degree spatial resolution. The EOF analysis reveals that the first eigenfunction explains 33%, 43% and 47% of the VIC, NOAH and MOSAIC models, respectively. We compare each LSM aridity indices with the UNEP aridity index, which is created based on LSM data forcings. The VIC aridity index displays a pattern most closely resembling that of UNEP, although all of the LSM-based indices accurately isolate the dominant dryland areas. The UNEP classification identifies portions of south-central Africa, southeastern United States and eastern India as drier than predicted by all of the LSMs. The NOAH and MOSAIC LSMs categorize portions of southwestern Africa as drier than the other two classifications, while all of the LSMs classify portions of central India as wetter than the UNEP classification. We compare all aridity maps with the long-term average NDVI values. Results show that vegetation cover in areas that the UNEP index classifies as drier than the other three LSMs (NDVI values are mostly greater than 0). Finally, the unsupervised clustering of global land surface based on long-term mean temperature and precipitation, soil texture and land slope reveals that areas classified as dry by the UNEP index but not by the LSMs do not have dry region characteristics. The dominant cluster for these areas has high water holding capacity. We conclude that the LSM-based aridity index may identify dryland areas more effectively than the UNEP aridity index because the former incorporates the role of vegetation and soil in the partitioning of precipitation into evaporation, runoff and infiltration. © 2013 Elsevier Ltd

Using fuzzy clustering algorithms to describe the distribution of trace elements in arable calcareous soils in northwest Iran

Accumulation of trace elements in arable soils is an important global hazard worldwide. In this research, the available content of Zn, Fe, B, Co, Cu, Mn, Mo and other soil parameters (pH, organic carbon content, carbonates and electrical conductivity) were analysed in northwest Iran. Concentration levels of trace elements were relatively low in areas with high pH values and low organic matter content, and only the Mo value exceeded the reference threshold. Based on the correlation among the elements, two datasets were produced. The first consists of Fe and Mn data, while the second contains Zn, B, Co, Cu and Mo data. Two fuzzy clustering approaches, Fuzzy C-means (FCM) and Gustafson-Kessel (GK), were applied for clustering both datasets. Multiple accumulation of trace elements was investigated from the clustering results and then visualized in spatial regionalization maps. The fuzzy clustering evaluating indices showed that the GK method was more appropriate than FCM for clustering datasets. The results revealed that the first and second datasets were divided into seven and six clusters, respectively. Fuzzy clustering analyses combined with geostatistical methods were used to map the spatial variability of each cluster. This method enabled the monitoring of multiple metal accumulation in large agricultural soils. © 2013 Copyright Taylor and Francis Group, LLC

Comparison of machine learning models for predicting groundwater level, case study: Najafabad region

Water resources, consisting of surface water and groundwater, are considered to be among the crucial natural resources in most arid and semiarid regions. Groundwater resources as the sustainable yields can be predicted, whereas this is one of the important stages in water resource management. To this end, several models such as mathematical, statistical, empirical, and conceptual can be employed. In this paper, machine learning and deep learning methods as conceptual ones are applied for the simulations. The selected models are support vector regression (SVR), adaptive neuro-fuzzy inference system (ANFIS), and multilayer perceptron (MLP). Next, these models are optimized with the adaptive moment estimation (ADAM) optimization algorithm which results in hybrid models. The hyper-parameters of the stated models are optimized with the ADAM method. The root mean squared error (RMSE), mean absolute error (MAE), mean squared error (MSE), and coefficient of determination (R2) are used to evaluate the accuracy of the simulated groundwater level. To this end, the aquifer hydrograph is used to compare the results with observations data. So, the RMSE and R2 show that the accuracy of the machine learning and deep learning models is better than the numerical model for the given data. Moreover, the MSE is approximately the same in all three cases (ranging from 0.7113 to 0.6504). Also, the total value of R2 and RMSE for the best hybrid model is 0.9617 and 0.7313, respectively, which are obtained from the model output. The results show that all three techniques are useful tools for modeling hydrological processes in agriculture and their computational capabilities and memory are similar

Electrochemical and Surface Characterisation of Carbon Steel Exposed to Mixed Ce and Iodide Electrolytes

The protection of ferrous metals in acidic environments is important in many industries. Extending the pH range of organic inhibitors to low pH has been achieved with the addition of iodide ions, which facilitate adsorption. It was of interest to see whether similar outcomes could be achieved with inorganic inhibitors. To this end, this paper examines the influence of potassium iodide addition on the level of corrosion protection provided by Ce(NO3)3 in 3.5% NaCl electrolytes over a pH range of 2.5 to 8. Potentiodynamic polarization was used to assess percentage inhibitor efficiency (IE%), and scanning electron microscopy, energy dispersive X-ray spectrometry, and X-ray photoelectron spectroscopy were used to characterize the corrosion product. It was found that KI alone provided only poor corrosion inhibition except at pH 2.5, where nearly 85IE% was achieved. Its addition to the cerium electrolytes was generally in excess of 90% and over 97% for the optimum concentration. The addition of KI seemed to change the mechanism of formation of corrosion products from predominantly Fe2O3 to a mixture of FeOOH, Fe3O4, and Fe2O3, which were more adherent. Corrosion protection was extended to pH 4, but under the conditions explored here, no additional protection was evident at pH 2.5