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This study investigated the deactivation and regeneration of hierarchical zeolites in vacuum gas oil conversion, aiming to reach the equilibrium state seen in fluidized bed catalytic cracking (FCC). The research utilized various characterization techniques to analyze the properties of zeolites before and after coking and regeneration. Zeolite Y-0.20-S was found to have the highest gasoline selectivity and quality, mirroring industrial yields, and displayed notable stability across deactivation/regeneration cycles. Higher mesopore concentration in zeolites led to increased coke selectivity and better resistance to deactivation. The study observed a dominance of aromatic coke with a higher degree of condensation in these zeolites. Despite coke deposition affecting acid and textural properties, the regeneration process effectively restored these characteristics, proving its efficiency. The zeolites with greater mesoporosity retained their fundamental properties responsible for activity and selectivity, highlighting the importance of selecting materials that provide high conversions and maintain stability and product selectivity over multiple cycles. The Y-0.20-S zeolite, in particular, was identified as a promising candidate for commercial catalyst development for gasoline production, contributing to the FCC process's energy efficiency., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors. Published by Elsevier Ltd.)

Systemic lupus erythematosus (SLE) is an autoimmune and multisystem disease with a high public health impact. Lupus nephritis (LN), commonly known as renal involvement in SLE, is associated with a poorer prognosis and increased rates of morbidity and mortality in patients with SLE. Identifying new urinary biomarkers that can be used for LN prognosis or diagnosis is essential and is part of current active research. In this study, we applied an untargeted metabolomics approach involving liquid and gas chromatography coupled with mass spectrometry to urine samples collected from 17 individuals with SLE and no kidney damage, 23 individuals with LN, and 10 clinically healthy controls (HCs) to identify differential metabolic profiles for SLE and LN. The data analysis revealed a differentially abundant metabolite expression profile for each study group, and those metabolites may act as potential differential biomarkers of SLE and LN. The differential metabolic pathways found between the LN and SLE patients with no kidney involvement included primary bile acid biosynthesis, branched-chain amino acid synthesis and degradation, pantothenate and coenzyme A biosynthesis, lysine degradation, and tryptophan metabolism. Receiver operating characteristic curve analysis revealed that monopalmitin, glycolic acid, and glutamic acid allowed for the differentiation of individuals with SLE and no kidney involvement and individuals with LN considering high confidence levels. While the results offer promise, it is important to recognize the significant influence of medications and other external factors on metabolomics studies. This impact has the potential to obscure differences in metabolic profiles, presenting a considerable challenge in the identification of disease biomarkers. Therefore, experimental validation should be conducted with a larger sample size to explore the diagnostic potential of the metabolites found as well as to examine how treatment and disease activity influence the identified chemical compounds. This will be crucial for refining the accuracy and effectiveness of using urine metabolomics for diagnosing and monitoring lupus and lupus nephritis., (© 2024. The Author(s).)

Exposure to coal mining dust poses a substantial health hazard to individuals due to the complex mixture of components released during the extraction process. This study aimed to assess the oxidative potential of residual coal mining dust on human lymphocyte DNA and telomeres and to perform a chemical characterization of coal dust and urine samples. The study included 150 individuals exposed to coal dust for over ten years, along with 120 control individuals. The results revealed significantly higher levels of DNA damage in the exposed group, as indicated by the standard comet assay, and oxidative damage, as determined by the FPG-modified comet assay. Moreover, the exposed individuals exhibited significantly shorter telomeres compared to the control group, and a significant correlation was found between telomere length and oxidative DNA damage. Using the PIXE method on urine samples, significantly higher concentrations of sodium (Na), phosphorus (P), sulfur (S), chlorine (Cl), potassium (K), iron (Fe), zinc (Zn), and bromine (Br) were observed in the exposed group compared to the control group. Furthermore, men showed shorter telomeres, greater DNA damage, and higher concentrations of nickel (Ni), calcium (Ca), and chromium (Cr) compared to exposed women. Additionally, the study characterized the particles released into the environment through GC-MS analysis, identifying several compounds, including polycyclic aromatic hydrocarbons (PAHs) such as fluoranthene, naphthalene, anthracene, 7H-benzo[c]fluorene, phenanthrene, pyrene, benz[a]anthracene, chrysene, and some alkyl derivatives. These findings underscore the significant health risks associated with exposure to coal mining dust, emphasizing the importance of further research and the implementation of regulatory measures to safeguard the health of individuals in affected populations., (© 2023. The Author(s).)

Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2) infection triggers various events from molecular to tissue level, which in turn is given by the intrinsic characteristics of each patient. Given the molecular diversity characteristic of each cellular phenotype, the possible cytopathic, tissue and clinical effects are difficult to predict, which determines the heterogeneity of COVID-19 symptoms. The purpose of this article is to provide a comprehensive review of the cytopathic effects of SARS-CoV-2 on various cell types, focusing on the development of COVID-19, which in turn may lead, in some patients, to a persistence of symptoms after recovery from the disease, a condition known as long COVID. We describe the molecular mechanisms underlying virus-host interactions, including alterations in protein expression, intracellular signaling pathways, and immune responses. In particular, the article highlights the potential impact of these cytopathies on cellular function and clinical outcomes, such as immune dysregulation, neuropsychiatric disorders, and organ damage. The article concludes by discussing future directions for research and implications for the management and treatment of COVID-19 and long COVID.

We developed and standardized an efficient and cost-effective in-house RT-PCR method to detect severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We evaluated sensitivity, specificity, and other statistical parameters by different RT-qPCR methods including triplex, duplex, and simplex assays adapted from the initial World Health Organization- (WHO) recommended protocol. This protocol included the identification of the E envelope gene (E gene; specific to the Sarvecovirus genus), RdRp gene of the RNA-dependent RNA polymerase (specific for SARS-CoV-2), and RNase P gene as endogenous control. The detection limit of the E and the RdRp genes were 3.8 copies and 33.8 copies per 1 µL of RNA, respectively, in both triplex and duplex reactions. The sensitivity for the RdRp gene in the triplex and duplex RT-qPCR tests were 98.3% and 83.1%, respectively. We showed a decrease in sensitivity for the RdRp gene by 60% when the E gene acquired Ct values > 31 in the diagnostic tests. This is associated with the specific detection limit of each gene and possible interferences in the protocol. Hence, developing efficient and cost-effective methodologies that can be adapted to various health emergency scenarios is important, especially in developing countries or settings where resources are limited.

Unambiguous vibrational band assignments have been made to cyclic nitramine hexahydro-1,3,5-trinitro-s-triazine, commonly known as the alpha-phase of RDX or alpha-RDX, with the use of (13)C and (15)N (on ring) enriched isotopic RDX analogues. Vibrational spectra were collected using Raman and IR spectroscopy in solid state and ab initio normal mode calculations were performed using density functional theory (DFT) and a 6-311G++** basis set. The calculated isotopic frequency shifts, induced by (13)C and (15)N labeling, are in very good accordance with measures ones. The changes in vibrational modes associated with the isotopic substitutions are well modeled by the calculation and previous assignments of the vibrational spectra have been revised, especially where the exact nature of the vibrational modes had been either vague or contradictory., (Copyright 2010 Elsevier B.V. All rights reserved.)

Quantitative structure-property relationships (QSPR) have been determined to predict the melting point temperatures of 1,2,3-diazaborine compounds (n = 72). Electronic and topological descriptors were computed from molecular structures, and a QSPR model was generated by linear multiple regression using reported melting point temperatures as the dependent variable. The most important molecular descriptors describing this physicochemical property were the sum of the atomic charges for the heteroatoms, the sum of Randić connectivity indexes (0)Chi(0), (0)Chi(1), and (0)Chi(2), the total number of atoms in the molecule, and the volume of the box in which the molecule fits. The multiple determination coefficient (R(2)) and standard error of estimation for the model were 0.856 and 16.787 degrees C, respectively. In addition to regression techniques, a back-propagation neural network was used to include nonlinear relationships between molecular structure and melting point temperatures. It is concluded that melting point temperatures for 1,2,3-diazaborine compounds can be described by electrostatic interactions mediated by atomic charges and steric properties. The results of this study demonstrate that multiple linear regression analysis and back-propagation neural network are techniques that can be used to successfully predict the melting point temperatures of 1,2,3-diazaborine compounds. The most accurate prediction results were obtained using the Levenberg-Marquardt neural network algorithm.

Quantitative structure-activity relationship (QSAR) models are useful in providing a biochemical understanding of the biological activity of natural and synthetic chemicals based solely on molecular structure. One- to three-parameter multiregression equations were generated for three different groups of flavonoids to model experimental flavonoid-induced cytotoxicity and inhibition of human immunodeficiency virus I replication in lymphocyte-infected cells, using quantum chemical and geometrical parameters derived from optimized molecular structures. Both biological properties were basically dependent on electronic parameters describing charge distribution on the two fused rings of the flavonoid molecule. Atomic charges in C3 and the carbonyl carbon as well as the dipolar moment were important electronic descriptors to define the studied biological properties of flavonoids.