screening of potential agonists of a glucagon-like peptide-1 receptor among female sex hormone derivatives.

Glucagon-like peptide-1 (GLP-1) is an intestinal hormone that exerts its pleiotropic effects through a specific GLP-1 receptor (GLP-1R). The hormone-receptor complex might regulate glucose-dependent insulin secretion, and energy homeostasis; moreover, it could decrease inflammation and provide cardio- and neuroprotection. Additionally, the beneficial influence of GLP-1 on obesity in women might lead to improvement of their ovarian function.

Multivariate Exploratory Analysis of the Bulgarian Soil Quality Monitoring Network.

The goal of the present study is to assess the soil quality in Bulgaria using (i) an appropriate set of soil quality indicators, namely primary nutrients (C, N, P), acidity (pH), physical clay content and potentially toxic elements (PTEs: Cu, Zn, Cd, Pb, Ni, Cr, As, Hg) and (ii) respective data mining and modeling using chemometrical and geostatistical methods. It has been shown that five latent factors are responsible for the explanation of nearly 70% of the total variance of the data set available (principal components analysis) and each factor is identified in terms of its contribution to the formation of the overall soil quality-the mountain soil factor, the geogenic factor, the ore deposit factor, the low nutrition factor, and the mercury-specific factor. The obtained soil quality patterns were additionally confirmed via hierarchical cluster analysis.

Collection of Partition Coefficients in Hexadecyltrimethylammonium Bromide, Sodium Cholate, and Lithium Perfluorooctanesulfonate Micellar Solutions: Experimental Determination and Computational Predictions.

This study focuses on determining the partition coefficients (logP) of a diverse set of 63 molecules in three distinct micellar systems: hexadecyltrimethylammonium bromide (HTAB), sodium cholate (SC), and lithium perfluorooctanesulfonate (LPFOS). The experimental log values were obtained through micellar electrokinetic chromatography (MEKC) experiments, conducted under controlled pH conditions. Then, Quantum Mechanics (QM) and machine learning approaches are proposed for the prediction of the partition coefficients in these three micellar systems.

Translational screening platform to evaluate chemotherapy in combination with focal therapy for retinoblastoma.

Retinoblastoma is the most common pediatric eye cancer. It is currently treated with a limited number of drugs, adapted from other pediatric cancer treatments. Drug toxicity and relapse of the disease warrant new therapeutic strategies for these young patients.

Sequence-Based Prediction of Plant Allergenic Proteins: Machine Learning Classification Approach.

This Article proposes a novel chemometric approach to understanding and exploring the allergenic nature of food proteins. Using machine learning methods (supervised and unsupervised), this work aims to predict the allergenicity of plant proteins. The strategy is based on scoring descriptors and testing their classification performance.

Comparison of the Effect of Cold Plasma with Conventional Preservation Methods on Red Wine Quality Using Chemometrics Analysis.

In this study, the effect of cold plasma (CP) on the physicochemical and biological properties of red wine was investigated in comparison with the effects of the conventional preservation method and the combined method. In addition, the effect of storage time after the application of each of the analyzed methods was evaluated. The study examined the effects of the different preservation methods on the pH, color, phenolic content, antioxidant activity and microbiological purity of the red wine.

Multivariate Statistical Analysis for Mutual Dependence Assessment of Selected Polyphenols, Organic Acids and Metals in Cool-Climate Wines.

Polyphenols, organic acids and metal ions are an important group of compounds that affect the human health and quality of food and beverage products, including wines. It is known that a specific correlation between these groups exist. While wines coming from the New World and the Old World countries are extensively studied, wines coming from cool-climate countries are rarely discussed in the literature.

Developing an Intelligent Data Analysis Approach for Marine Sediments.

(1) Background: As the chemical and physicochemical properties of marine sediments are closely related to natural and anthropogenic events, it is a real challenge to use their specific assessment as an indicator of environmental pollution discharges. (2) Methods: It is addressed in this study that collection with intelligent data analysis methods, such as cluster analysis, principal component analysis, and source apportionment modeling, are applied for the assessment of the quality of marine sediment and for the identification of the contribution of pollution sources to the formation of the total concentration of polluting species. A study of sediment samples was carried out on 174 samples from three different areas along the coast of the Varna Gulf, Bulgaria.

The impact of cold plasma on the phenolic composition and biogenic amine content of red wine.

The effect of cold plasma (CP) on phenolic compound (PC) and biogenic amine (BA) contents of red wine was investigated for the first time. The influence of CP was compared with the effects of a wine preservation using potassium metabisulfite and a combined method. The PC profile was determined by UPLC-PDA-MS/MS while BAs using DLLME-GC-MS.

Biomedical Applications of Iron Oxide Nanoparticles: Current Insights Progress and Perspectives.

The enormous development of nanomaterials technology and the immediate response of many areas of science, research, and practice to their possible application has led to the publication of thousands of scientific papers, books, and reports. This vast amount of information requires careful classification and order, especially for specifically targeted practical needs. Therefore, the present review aims to summarize to some extent the role of iron oxide nanoparticles in biomedical research.

Inhibition Ability of Natural Compounds on Receptor-Binding Domain of SARS-CoV2: An In Silico Approach.

The lack of medication to treat COVID-19 is still an obstacle that needs to be addressed by all possible scientific approaches. It is essential to design newer drugs with varied approaches. A receptor-binding domain (RBD) is a key part of SARS-CoV-2 virus, located on its surface, that allows it to dock to ACE2 receptors present on human cells, which is followed by admission of virus into cells, and thus infection is triggered.

Persistent organic pollutants (POPs) - QSPR classification models by means of Machine learning strategies.

Persistent Organic pollutants (POPs) are toxic chemicals with a shallow degradation rate and global negative impact. Their physicochemical is combined with the complex effects of long-term POPs accumulation in the environment and transport function through the food chain. That is why POPs have been linked to adverse effects on human health and animals.

The content of biogenic amines in Rondo and Zweigelt wines and correlations between selected wine parameters.

The purpose of this study was to evaluate the content of biogenic amines (BAs) in wines using dispersive liquid-liquid microextraction-gas chromatography-mass spectrometry (DLLME-GC-MS). An additional objective was to assess the correlations between selected parameters characterizing the samples such as the content of BAs, sugars, and organic acids, pH, and total acidity. Wines produced from the same grape variety in which alcoholic fermentation (AF) was carried out by different yeast strains and in which malolactic fermentation (MLF) was spontaneous, differed in the content of biogenic amines.

Tracing the movement of persistent organic pollutants at a high-mountain sampling site by chemometric assessment.

The main objective of the present study was to determine and differentiate the concentration levels, to define the probable sources of persistent organic pollutants (POPs) pollution in the atmospheric air and their seasonal variations in Bulgaria, on the high mountain peak Moussala, Rila Mountain. The study was based on the obtained results from the passive monitoring of POPs in 2014-2017. During this period, the measurements of POPs were performed with passive samplers, advanced instrumental methods analytically determined the concentrations of PAHs, and the analysis of the obtained data was performed by the multivariate statistical analysis (cluster, factor and time-series analysis).

Multivariate Statistical Approach for Nephrines in Women with Obesity.

Catecholamines are physiological regulators of carbohydrate and lipid metabolism during stress, but their chronic influence on metabolic changes in obese patients is still not clarified. The present study aimed to establish the associations between the catecholamine metabolites and metabolic syndrome (MS) components in obese women as well as to reveal the possible hidden subgroups of patients through hierarchical cluster analysis and principal component analysis. The 24-h urine excretion of metanephrine and normetanephrine was investigated in 150 obese women (54 non diabetic without MS, 70 non-diabetic with MS and 26 with type 2 diabetes).

Combinatorial K-Means Clustering as a Machine Learning Tool Applied to Diabetes Mellitus Type 2.

A new original procedure based on k-means clustering is designed to find the most appropriate clinical variables able to efficiently separate into groups similar patients diagnosed with diabetes mellitus type 2 (DMT2) and underlying diseases (arterial hypertonia (AH), ischemic heart disease (CHD), diabetic polyneuropathy (DPNP), and diabetic microangiopathy (DMA)). Clustering is a machine learning tool for discovering structures in datasets. Clustering has been proven to be efficient for pattern recognition based on clinical records.

Fuzzy partitioning of clinical data for DMT2 patients.

The present study represents an original approach to data interpretation of clinical data for patients with diagnosis diabetes mellitus type 2 (DMT2) using fuzzy clustering as a tool for intelligent data analysis. Fuzzy clustering is often used in classification and interpretation of medical data (including in medical diagnosis studies) but in this study it is applied with a different goal: to separate a group of 100 patients with DMT2 from a control group of healthy volunteers and, further, to reveal three different patterns of similarity between the patients. Each pattern is described by specific descriptors (variables), which ensure pattern interpretation by appearance of underling disease to DMT2.

Vibrational Analysis of Manganese(II) Oxalates Hydrates: An In Silico Statistical Approach.

The experimental and computational vibrational study for three different manganese(II) oxalates hydrates was explored. The elucidation of IR and Raman spectra were discussed based on their structural singularity; in the same way, they establish some interesting relations between them in the field of computational and statistical approaches. The density functional theory (DFT) computational approach was conducted for accurate prediction and interpretation of the intermolecular effects based on experimental and calculated IR and Raman spectra in the solid-state data in combination with multivariate statistical technique.

Searching for Solvents with an Increased Carbon Dioxide Solubility Using Multivariate Statistics.

Ionic liquids (ILs) are used in various fields of chemistry. One of them is CO capture, a process that is quite well described. The solubility of CO in ILs can be used as a model to investigate gas absorption processes.

Calculating the Partition Coefficients of Organic Solvents in Octanol/Water and Octanol/Air.

Partition coefficients define how a solute is distributed between two immiscible phases at equilibrium. The experimental estimation of partition coefficients in a complex system can be an expensive, difficult, and time-consuming process. Here a computational strategy to predict the distributions of a set of solutes in two relevant phase equilibria is presented.

Chemometric Assessment of Soil Pollution and Pollution Source Apportionment for an Industrially Impacted Region around a Non-Ferrous Metal Smelter in Bulgaria.

The present study deals with the assessment of pollution caused by a large industrial facility using multivariate statistical methods. The primary goal is to classify specific pollution sources and to apportion their involvement in the formation of the total concentration of the chemical parameters being monitored. This aim is accomplished by intelligent data analysis based on cluster analysis, principal component analysis and principal component regression analysis.

Binary Mixtures of Selected Bisphenols in the Environment: Their Toxicity in Relationship to Individual Constituents.

Bisphenol A (BPA) is one of the most popular and commonly used plasticizer in the industry. Over the past decade, new chemicals that belong to the bisphenol group have increasingly been used in industrial applications as alternatives to BPA. Nevertheless, information on the combined effects of bisphenol (BP) analogues is insufficient.

Chemometric Assessment and Best-Fit Function Modelling of the Toxic Potential of Selected Food Packaging Extracts.

Food packaging materials constitute an ever more threatening environmental pollutant. This study examined options to specifically assess the ecotoxicity of packaged wastes, such as cans, subjected to various experimental treatments (in terms of extraction media, time of exposure, and temperature) that imitate several basic conditions of the process of food production. The extracts were studied for their ecotoxicity with bioluminescent bacteria.

Determination of Metals Content in Wine Samples by Inductively Coupled Plasma-Mass Spectrometry.

Knowledge about the metal content of wine is very important, for many reasons. Depending on the element, its quantity varies in wine from ng/L to mg/L. Despite the fact that metals are not directly connected to the taste and aroma of the wine, their content should be determined and controlled, because excess is undesirable, and in some cases prohibited, due to potential toxicity.

Phytoavailability of potentially toxic elements from industrially contaminated soils to wild grass.

Topsoil and grass samples from 14 sites located in different distances from three copper mining factories and a copper smelter were collected in Srednogorie, Bulgaria. The paper discusses results of studies on the mobility of potentially toxic elements (As, Cd, Cr, Cu, Mn, Ni, Pb, Zn) from contaminated soils to wild grass Cynodon dactylon in terms of transfer (TF: soil to plant) and phytoavailability (PF: EDTA (ethylenediaminetetraacetic acid)-soluble to plant) factors. Soils located close to mine and smelter factories were heavily contaminated by As and Cu reaching levels up to 500 mg/kg and 2400 mg/kg, respectively for As and Cu.