Role of Endocrine-Disrupting Chemicals in the Pathogenesis of Non-Alcoholic Fatty Liver Disease: A Comprehensive Review

Non-alcoholic fatty liver disease (NAFLD) is considered the most common liver disorder, affecting around 25% of the population worldwide. It is a complex disease spectrum, closely linked with other conditions such as obesity, insulin resistance, type 2 diabetes mellitus, and metabolic syndrome, which may increase liver-related mortality. In light of this, numerous efforts have been carried out in recent years in order to clarify its pathogenesis and create new prevention strategies. Currently, the essential role of environmental pollutants in NAFLD development is recognized. Particularly, endocrine-disrupting chemicals (EDCs) have a notable influence. EDCs can be classified as natural (phytoestrogens, genistein, and coumestrol) or synthetic, and the latter ones can be further subdivided into industrial (dioxins, polychlorinated biphenyls, and alkylphenols), agricultural (pesticides, insecticides, herbicides, and fungicides), residential (phthalates, polybrominated biphenyls, and bisphenol A), and pharmaceutical (parabens). Several experimental models have proposed a mechanism involving this group of substances with the disruption of hepatic metabolism, which promotes NAFLD. These include an imbalance between lipid influx/efflux in the liver, mitochondrial dysfunction, liver inflammation, and epigenetic reprogramming. It can be concluded that exposure to EDCs might play a crucial role in NAFLD initiation and evolution. However, further investigations supporting these effects in humans are required.     

Analysis of ZTE MRI application to sandstone and carbonate

Microtomography (μCT) and nuclear magnetic resonance (NMR) have been used to characterize porous media for decades. Magnetic resonance imaging (MRI) enables direct visualization of pore architecture and many pulse sequences exist. In this work, we tested the MRI pulse sequence Zero Echo Time (ZTE) to study sandstone and carbonate for its ability to address short relaxation times. We aimed at resolving two fluid conduit scales, that is, pores and fractures. In this research, we study tighter porous systems than those previously reported using ZTE. Additionally, pore cluster analysis (PCA), combined with ZTE, can be used to analyze pore-fracture connectivity of relatively large core plugs. We show that ZTE can resolve two-scale pore systems simultaneously, that is, fractures and pores. By combining time-domain NMR pore-size analysis and PCA, we show that careful selection of resolution is necessary to understand transport in porous media.     

Development of vibration damping materials based on butyl rubber: A study of the phase equilibrium,rheological, and dynamic properties of compositions

A detailed study of butyl rubber-based vibration damping formulations linking their composition, morphology, phase structure, viscosity, mechanical loss factor, and other characteristics is presented for the first time. High performance of the compositions including aromatic petroleum oil is explained by limited solubility of the plasticizer that leads to the formation of a highly-viscous emulsion (η_20°C ≈ 1000 Pa·s) consisting of a swollen butyl rubber matrix and dispersed oil droplets in the broad composition range. Chalk is found to be the best inorganic filler as its spherical particles provide strong adhesion to the reinforcing layer of aluminum foil. Aiming to eliminate ecologically unfriendly aromatic compounds, a new low-cost binding agent formulation based on butyl rubber mixed with polyisobutylene and highly refined mineral oil is suggested. Being environmentally safe, it possesses high viscosity of 1000–3000 Pa·s, cohesion strength of 3.5–5.0 N/cm, penetration of 4.5–6.0 mm, and mechanical loss factor up to 0.34 at room temperature, which are as good as, or even better than, the properties of currently produced vibration damping materials containing aromatic compounds. New materials can be used in car and aircraft parts for effective vibration isolation.     

Some Molecular and Cellular Stress Mechanisms Associated with Neurodegenerative Diseases and Atherosclerosis

Chronic stress is a combination of nonspecific adaptive reactions of the body to the influence of various adverse stress factors which disrupt its homeostasis, and it is also a corresponding state of the organism’s nervous system (or the body in general). We hypothesized that chronic stress may be one of the causes occurence of several molecular and cellular types of stress. We analyzed literary sources and considered most of these types of stress in our review article. We examined genes and mutations of nuclear and mitochondrial genomes and also molecular variants which lead to various types of stress. The end result of chronic stress can be metabolic disturbance in humans and animals, leading to accumulation of reactive oxygen species (ROS), oxidative stress, energy deficiency in cells (due to a decrease in ATP synthesis) and mitochondrial dysfunction. These changes can last for the lifetime and lead to severe pathologies, including neurodegenerative diseases and atherosclerosis. The analysis of literature allowed us to conclude that under the influence of chronic stress, metabolism in the human body can be disrupted, mutations of the mitochondrial and nuclear genome and dysfunction of cells and their compartments can occur. As a result of these processes, oxidative, genotoxic, and cellular stress can occur. Therefore, chronic stress can be one of the causes forthe occurrence and development of neurodegenerative diseases and atherosclerosis. In particular, chronic stress can play a large role in the occurrence and development of oxidative, genotoxic, and cellular types of stress.     

On the Size Effect in MOS Structures under Ionizing Irradiation

Semiconductors - A quantitative model of the size effect, i.e., the dependence of surface states on the gate size, in metal—oxide—semiconductor (MOS) structures subjected to ionizing...     

Building up PtII−Thiosemicarbazone−Lysine−sC18 Conjugates

Three chiral tridentate N^N^S coordinating pyridine-carbaldehyde (S)-N4-(α-methylbenzyl)thiosemicarbazones (HTSCmB) were synthesised along with lysine-modified derivatives. One of them was selected and covalently conjugated to the cell-penetrating peptide sC18 by solid-phase peptide synthesis. The HTSCmB model ligands, the HTSCLp derivatives and the peptide conjugate rapidly and quantitatively form very stable Pt^II chlorido complexes [Pt(TSC)Cl] when treated with K₂PtCl₄ in solution. The Pt(CN) derivatives were obtained from one TSCmB model complex and the peptide conjugate complex through Cl⁻→CN⁻ exchange. Ligands and complexes were characterised by NMR, IR spectroscopy, HR-ESI-MS and single-crystal XRD. Intriguingly, no decrease in cell viability was observed when testing the biological activity of the lysine-tagged HdpyTSCLp, its sC18 conjugate HdpyTSCL-sC18 or the PtCl and Pt(CN) conjugate complexes in three different cell lines. Thus, given the facile and effective preparation of such Pt-TSC-peptide conjugates, these systems might pave the way for future use in late-stage labelling with Pt radionuclides and application in nuclear medicine.     

Feature selection methods for root-cause analysis among top-level product attributes

Manufacturing companies not only strive to deliver flawless products but also monitor product failures in the field to identify potential quality issues. When product failures occur, quality engineers must identify the root cause to improve any affected product and process. This root-cause analysis can be supported by feature selection methods that identify relevant product attributes, such as manufacturing dates with an increased number of product failures. In this paper, we present different methods for feature selection and evaluate their ability to identify relevant product attributes in a root-cause analysis. First, we compile a list of feature selection methods. Then, we summarize the properties of product attributes in warranty case data and discuss these properties regarding the challenges they pose for machine learning algorithms. Next, we simulate datasets of warranty cases, which emulate these product properties. Finally, we compare the feature selection methods based on these simulated datasets. In the end, the univariate filter information gain is determined to be a suitable method for a wide range of applications. The comparison based on simulated data provides a more general result than other publications, which only focus on a single use case. Due to the generic nature of the simulated datasets, the results can be applied to various root-cause analysis processes in different quality management applications and provide a guideline for readers who wish to explore machine learning methods for their analysis of quality data.