Deactivation and regeneration dynamics in hierarchical zeolites: Coke characterization and impact on catalytic cracking of vacuum gas oil.

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.

Urinary metabolomic profiling of a cohort of Colombian patients with systemic lupus erythematosus.

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.

Surface-enhanced Raman Spectroscopy in urinalysis of hypertension patients with kidney disease.

Arterial hypertension (AH) is a multifactorial and asymptomatic disease that affects vital organs such as the kidneys and heart. Considering its prevalence and the associated severe health repercussions, hypertension has become a disease of great relevance for public health across the globe. Conventionally, the classification of an individual as hypertensive or non-hypertensive is conducted through ambulatory blood pressure monitoring over a 24-h period. Although this method provides a reliable diagnosis, it has notable limitations, such as additional costs, intolerance experienced by some patients, and interferences derived from physical activities. Moreover, some patients with significant renal impairment may not present proteinuria. Accordingly, alternative methodologies are applied for the classification of individuals as hypertensive or non-hypertensive, such as the detection of metabolites in urine samples through liquid chromatography or mass spectrometry. However, the high cost of these techniques limits their applicability for clinical use. Consequently, an alternative methodology was developed for the detection of molecular patterns in urine collected from hypertension patients. This study generated a direct discrimination model for hypertensive and non-hypertensive individuals through the amplification of Raman signals in urine samples based on gold nanoparticles and supported by chemometric techniques such as partial least squares-discriminant analysis (PLS-DA). Specifically, 162 patient urine samples were used to create a PLS-DA model. These samples included 87 urine samples from patients diagnosed with hypertension and 75 samples from non-hypertensive volunteers. In the AH group, 35 patients were diagnosed with kidney damage and were further classified into a subgroup termed (RAH). The PLS-DA model with 4 latent variables (LV) was used to classify the hypertensive patients with external validation prediction (P) sensitivity of 86.4%, P specificity of 77.8%, and P accuracy of 82.5%. This study demonstrates the ability of surface-enhanced Raman spectroscopy to differentiate between hypertensive and non-hypertensive patients through urine samples, representing a significant advance in the detection and management of AH. Additionally, the same model was then used to discriminate only patients diagnosed with renal damage and controls with a P sensitivity of 100%, P specificity of 77.8%, and P accuracy of 82.5%.

Antioxidants in Photoaging: From Molecular Insights to Clinical Applications.

Photoaging (PA) is considered a silent disease affecting millions of people globally and is defined as skin damage due to prolonged exposure to ultraviolet radiation (UVR) from the sun. Physiologically, the skin is in a state of renewal and synthesis of components of the extracellular matrix (ECM). However, exposure to UVR affects the production of the ECM, and the functioning and response of skin cells to UVR begins to change, thus expressing clinical and phenotypic characteristics of PA. The primary mechanisms involved in PA are direct damage to the DNA of skin cells, increases in oxidative stress, the activation of cell signaling pathways responsible for the loss of skin integrity, and cytotoxicity. The medical and scientific community has been researching new therapeutic tools that counteract PA, considering that the damage caused by UVR exceeds the antioxidant defense mechanisms of the skin. Thus, in recent years, certain nutraceuticals and phytochemicals have been found to exhibit potential antioxidant and photoprotective effects. Therefore, the main objective of this review is to elucidate the molecular bases of PA and the latest pharmaceutical industry findings on antioxidant treatment against the progression of PA.

The dangerous link between coal dust exposure and DNA damage: unraveling the role of some of the chemical agents and oxidative stress.

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.

Photodynamic Antimicrobial Activity of a Novel 5,10,15,20-Tetrakis (4-Ethylphenyl) Porphyrin against Clinically Important Bacteria.

The growing emergence of microbes resistant to commercially available antibiotic therapies poses a threat to healthcare systems worldwide. Multiple factors have been associated with the increasing incidence of hospital-acquired infections caused by antibiotic-resistant pathogens, including the indiscriminate use of broad-spectrum antibiotics, the massive application of antibiotics in hospitals as a prophylactic measure, self-medication, and nonadherence to pharmacological therapies by patients. In this study, we developed a novel treatment to mitigate the impact of microbial resistance. We synthesized a benzoporphyrin derivative, 5,10,15,20-tetrakis (4-ethylphenyl) porphyrin (TEtPP), with a reaction yield close to 50%. TEtPP exhibited excellent photophysical properties (Φf = 0.12 ± 0.04 and ΦΔ = 0.81 ± 0.23) and was thereby assessed as a potential agent for antibacterial photodynamic therapy. The photophysical properties of the synthesized porphyrin derivative were correlated with the assayed antimicrobial activity. TEtPP showed higher activity against the MRSA strain under irradiation than in the absence of irradiation (minimum inhibitory concentration (MIC) = 69.42 µg/mL vs. MIC = 109.30 µg/mL, p < 0.0001). Similar behavior was observed against P. aeruginosa (irradiated MIC = 54.71 µg/mL vs. nonirradiated MIC = 402.90 µg/mL, p < 0.0001). TEtPP exhibited high activity against S. aureus in both the irradiated and nonirradiated assays (MIC = 67.68 µg/mL vs. MIC = 58.26 µg/mL, p = 0.87).

Analysis of the cytotoxic and genotoxic effects in a population chronically exposed to coal mining residues.

During coal mining activities, many compounds are released into the environment that can negatively impact human health. Particulate matter, polycyclic aromatic hydrocarbons (PAHs), metals, and oxides are part of the complex mixture that can affect nearby populations. Therefore, we designed this study to evaluate the potential cytotoxic and genotoxic effects in individuals chronically exposed to coal residues from peripheral blood lymphocytes and buccal cells. We recruited 150 individuals who lived more than 20 years in La Loma-Colombia and 120 control individuals from the city of Barranquilla without a history of exposure to coal mining. In the cytokinesis-block micronucleus cytome (CBMN-Cyt) assay, significant differences in the frequency of micronucleus (MN), nucleoplasmic bridge (NPB), nuclear bud (NBUD), and apoptotic cells (APOP) were observed between the two groups. In the buccal micronucleus cytome (BM-Cyt) assay, a significant formation of NBUD, karyorrhexis (KRX), karyolysis (KRL), condensed chromatin (CC), and binucleated (BN) cells was observed in the exposed group. Considering the characteristics of the study group, a significant correlation for CBMN-Cyt was found between NBUD and vitamin consumption, between MN or APOP and meat consumption, and between MN and age. Moreover, a significant correlation for BM-Cyt was found between KRL and vitamin consumption or age, and BN versus alcohol consumption. Using Raman spectroscopy, a significant increase in the concentration of DNA/RNA bases, creatinine, polysaccharides, and fatty acids was detected in the urine of individuals exposed to coal mining compared to the control group. These results contribute to the discussion on the effects of coal mining on nearby populations and the development of diseases due to chronic exposure to these residues.

Comparative Analysis of In-House RT-qPCR Detection of SARS-CoV-2 for Resource-Constrained Settings.

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.

Docking and Molecular Dynamic of Microalgae Compounds as Potential Inhibitors of Beta-Lactamase.

Bacterial resistance is responsible for a wide variety of health problems, both in children and adults. The persistence of symptoms and infections are mainly treated with ß-lactam antibiotics. The increasing resistance to those antibiotics by bacterial pathogens generated the emergence of extended-spectrum ß-lactamases (ESBLs), an actual public health problem. This is due to rapid mutations of bacteria when exposed to antibiotics. In this case, ß-lactamases are enzymes used by bacteria to hydrolyze the beta-lactam rings present in the antibiotics. Therefore, it was necessary to explore novel molecules as potential ß-lactamases inhibitors to find antibacterial compounds against infection caused by ESBLs. A computational methodology based on molecular docking and molecular dynamic simulations was used to find new microalgae metabolites inhibitors of ß-lactamase. Six 3D ß-lactamase proteins were selected, and the molecular docking revealed that the metabolites belonging to the same structural families, such as phenylacridine (4-Ph), quercetin (Qn), and cryptophycin (Cryp), exhibit a better binding score and binding energy than commercial clinical medicine ß-lactamase inhibitors, such as clavulanic acid, sulbactam, and tazobactam. These results indicate that 4-Ph, Qn, and Cryp molecules, homologous from microalgae metabolites, could be used, likely as novel ß-lactamase inhibitors or as structural templates for new in-silico pharmaceutical designs, with the possibility of combatting ß-lactam resistance.

Anomaly Identification during Polymerase Chain Reaction for Detecting SARS-CoV-2 Using Artificial Intelligence Trained from Simulated Data.

Real-time reverse transcription (RT) PCR is the gold standard for detecting Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), owing to its sensitivity and specificity, thereby meeting the demand for the rising number of cases. The scarcity of trained molecular biologists for analyzing PCR results makes data verification a challenge. Artificial intelligence (AI) was designed to ease verification, by detecting atypical profiles in PCR curves caused by contamination or artifacts. Four classes of simulated real-time RT-PCR curves were generated, namely, positive, early, no, and abnormal amplifications. Machine learning (ML) models were generated and tested using small amounts of data from each class. The best model was used for classifying the big data obtained by the Virology Laboratory of Simon Bolivar University from real-time RT-PCR curves for SARS-CoV-2, and the model was retrained and implemented in a software that correlated patient data with test and AI diagnoses. The best strategy for AI included a binary classification model, which was generated from simulated data, where data analyzed by the first model were classified as either positive or negative and abnormal. To differentiate between negative and abnormal, the data were reevaluated using the second model. In the first model, the data required preanalysis through a combination of prepossessing. The early amplification class was eliminated from the models because the numbers of cases in big data was negligible. ML models can be created from simulated data using minimum available information. During analysis, changes or variations can be incorporated by generating simulated data, avoiding the incorporation of large amounts of experimental data encompassing all possible changes. For diagnosing SARS-CoV-2, this type of AI is critical for optimizing PCR tests because it enables rapid diagnosis and reduces false positives. Our method can also be used for other types of molecular analyses.

Applications of Quantum Cascade Laser Spectroscopy in the Analysis of Pharmaceutical Formulations.

Quantum cascade laser spectroscopy was used to quantify active pharmaceutical ingredient content in a model formulation. The analyses were conducted in non-contact mode by mid-infrared diffuse reflectance. Measurements were carried out at a distance of 15 cm, covering the spectral range 1000-1600 cm(-1) Calibrations were generated by applying multivariate analysis using partial least squares models. Among the figures of merit of the proposed methodology are the high analytical sensitivity equivalent to 0.05% active pharmaceutical ingredient in the formulation, high repeatability (2.7%), high reproducibility (5.4%), and low limit of detection (1%). The relatively high power of the quantum-cascade-laser-based spectroscopic system resulted in the design of detection and quantification methodologies for pharmaceutical applications with high accuracy and precision that are comparable to those of methodologies based on near-infrared spectroscopy, attenuated total reflection mid-infrared Fourier transform infrared spectroscopy, and Raman spectroscopy.

TNT removal from culture media by three commonly available wild plants growing in the Caribbean.

Plants growing in the Caribbean, Rubia tinctorum, Lippia dulcis and Spermacoce remota, were used in vitro to remove TNT from culture media. Plants were found to be resistant to high TNT levels. S. remota was able to remove TNT in less than 48 h. Part of the TNT was physically removed from the culture media by evaporation.

Vibrational spectra and structure of RDX and its 13C- and 15N-labeled derivatives: a theoretical and experimental study.

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.

Vibrational spectroscopy standoff detection of explosives.

Standoff infrared and Raman spectroscopy (SIRS and SRS) detection systems were designed from commercial instrumentation and successfully tested in remote detection of high explosives (HE). The SIRS system was configured by coupling a Fourier-transform infrared interferometer to a gold mirror and detector. The SRS instrument was built by fiber coupling a spectrograph to a reflective telescope. HE samples were detected on stainless steel surfaces as thin films (2-30 microg/cm(2)) for SIRS experiments and as particles (3-85 mg) for SRS measurements. Nitroaromatic HEs: TNT, DNT, RDX, C4, and Semtex-H and TATP cyclic peroxide homemade explosive were used as targets. For the SIRS experiments, samples were placed at increasing distances and an infrared beam was reflected from the stainless steel surfaces coated with the target chemicals at an angle of approximately 180 degrees from surface normal. Stainless steel plates containing TNT and RDX were first characterized for coverage distribution and surface concentration by reflection-absorption infrared spectroscopy. Targets were then placed at the standoff distance and SIRS spectra were collected in active reflectance mode. Limits of detection (LOD) were determined for all distances measured for the target HE. LOD values of 18 and 20 microg/cm(2) were obtained for TNT and RDX, respectively, for the SIR longest standoff distance measured. For SRS experiments, as low as 3 mg of TNT and RDX were detected at 7 m source-target distance employing 488 and 514.5 nm excitation wavelengths. The first detection and quantification study of the important formulation C4 is reported. Detection limits as function of laser powers and acquisition times and at a standoff distance of 7 m were obtained.