In silico 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. The links between metabolism and reproduction are tightly connected, and it is not surprising that different estrogen derivatives, estrogen-receptor modulator (SERM) and progestins used for gonadal and oncological disorders might influence carbohydrate and lipid metabolism. However, their possible influence on the GLP-1R has not been studied. The docking scores and top-ranked poses of raloxifene were much higher than those observed for other investigated SERMs and estradiol per se. Among different studied progestins, drospirenone showed slightly higher affinity to GLP-1R. Herein, the same data set of the drugs is evaluated by molecular dynamics (MD) simulations and compared with the obtained docking result. Notably, it is demonstrated that the used docking protocol and the applied MD calculations ranked the same ligand (raloxifene) as the best one. In the present study, raloxifene might exert an allosteric influence on GLP-1R signaling, which might contribute to potential beneficial effects on metabolism and weight regulation. However, further experimental and clinical studies are needed to reveal if the GLP-1R modulation has a real biological impact.Communicated by Ramaswamy H. Sarma.

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 p 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. In the applied QM approach, the experimentally obtained partition coefficients were correlated with the calculated values for the case of the 15 solvent mixtures. Using Density Function Theory (DFT) with the B3LYP functional, we calculated the solvation free energies of 63 molecules in these 16 solvents. The combined data from the experimental partition coefficients in the three micellar formulations showed that the 1-propanol/water combination demonstrated the best agreement with the experimental partition coefficients for the SC and HTAB micelles. Moreover, we employed the SVM approach and k-means clustering based on the generation of the chemical descriptor space. The analysis revealed distinct partitioning patterns associated with specific characteristic features within each identified class. These results indicate the utility of the combined techniques when we want an efficient and quicker model for predicting partition coefficients in diverse micelles.

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. The spatial distribution of the patterns throughout the whole Bulgarian territory was visualized via the mapping of the factor scores for all identified latent factors. The mapping of identified soil quality patterns was used to outline regions where additional measures for the monitoring of the phytoavailability of PTEs were required. The suggested regions are located near to thermoelectric power plants and mining and metal production facilities and are characterized by intensive agricultural activity.

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. Partitioning was based on support vector machines (SVM), and a k-nearest neighbor (KNN) classifier was applied. A fivefold cross-validation approach was used to validate the KNN classifier in the variable selection step as well as the final classifier. To overcome the problem of food allergies, a robust and efficient method for protein classification is needed.

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. This was performed to determine the effects of pollution. As chemical descriptors, 34 indicators (toxic metals, polyaromatic hydrocarbons, polychlorinated biphenyls, nutrient components, humidity, and ignition loss) were used. The major goal of the present study was to assess the sediment quality in three different areas along the Gulf of Varna, Bulgaria by the source apportionment method. (3) Results: There is a general pattern for identifying three types of pollution sources in each area of the coastline with varying degrees of variation between zone A (industrially impacted zones), zone B (recreational areas), and zone C (anthropogenic and industrial wastes). (4) Conclusions: The quantitative apportionment procedure made it possible to determine the contribution of each identified pollution source for each zone in forming the total pollutant concentrations.

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. One of the goals of this study was to determine the elemental composition of the wine samples, which later on, together as polyphenols and organic acids content, was used as input data for chemometric analysis. The multivariate statistical approach was applied in order to find specific correlations between the selected group of compounds in the cool-climate wines and the features that distinguish the most and differ between red and white wines and rosé wines. Moreover, special attention was paid to resveratrol and its correlation with selected wine constituents.

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. Chemometric analysis was used to discover the relationship between the preservation method used and wine quality. In the wine samples tested, a reduction in phenolic compounds and a decrease in antioxidant activity were noted after storage. This effect was mildest for preservation methods with the addition of potassium metabisulphite and those in which a mixture of helium and nitrogen was used as the working gas. On a positive note, the CP treatment did not affect the color of the wine in a way perceptible to the consumer: ∆E*-1.12 (He/N2; 5 min). In addition, the lowest growth of microorganisms was detected in the CP-treated samples. This indicates the potential of cold plasma as an alternative method to the use of potassium metabisulfite in wine production, which may contribute to its wider use in the alcohol industry in the future.

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. Chemometric analysis also was used. The content of PCs was 3.1% higher in the sample preserved by CP treatment (5 min, helium/nitrogen) compared to a sample preserved by the addition of potassium metabisulfite (100 mg/L). On a positive note, CP treatment reduced the concentration of BAs in the wine samples. The lowest BA contents were recorded after 10 min of cold plasma (helium/oxygen) treatment with the addition of potassium metabisulfite (1120.85 µg/L). The results may promote interest in CP as a potential alternative method for the preservation of wine and other alcoholic beverages.

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. Summarizing the fundamental properties of the magnetic iron oxide nanoparticles, the review's next focus was to classify research studies related to applying these particles for cancer diagnostics and therapy (similar to photothermal therapy, hyperthermia), in nano theranostics, multimodal therapy. Special attention is paid to research studies dealing with the opportunities of combining different nanomaterials to achieve optimal systems for biomedical application. In this regard, original data about the synthesis and characterization of nanolipidic magnetic hybrid systems are included as an example. The last section of the review is dedicated to the capacities of magnetite-based magnetic nanoparticles for the management of oncological diseases.

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. They circulate globally via different environmental pathways, and could be detected in regions far from their source of origin. The primary goal of the present study is to carry out classification of various representatives of POPs using different theoretical descriptors (molecular, structural) to develop quantitative structure-properties relationship (QSPR) models for predicting important properties POPs. Multivariate statistical methods such as hierarchical cluster analysis, principal components analysis and self-organizing maps were applied to reach excellent partitioning of 149 representatives of POPs into 4 classes using ten most appropriate descriptors (out of 63) defined by variable reduction procedure. The predictive capabilities of the defined classes could be applied as a pattern recognition for new and unidentified POPs, based only on structural properties that similar molecules may have. The additional self-organizing maps technique made it possible to visualize the feature-space and investigate possible patterns and similarities between POPs molecules. It contributes to confirmation of the proper classification into four classes. Based on SOM results, the effect of each variable and pattern formation has been presented.

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. The concentrations of putrescine, cadaverine and tryptamine were higher in the Rondo wines (237-405, 34.04-61.11,

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. Specific receptor-binding domains on the spike protein play a pivotal role in binding to the receptor. In this regard, the in silico method plays an important role, as it is more rapid and cost effective than the trial and error methods using experimental studies. A combination of virtual screening, molecular docking, molecular simulations and machine learning techniques are applied on a library of natural compounds to identify ligands that show significant binding affinity at the hydrophobic pocket of the RBD. A list of ligands with high binding affinity was obtained using molecular docking and molecular dynamics (MD) simulations for protein-ligand complexes. Machine learning (ML) classification schemes have been applied to obtain features of ligands and important descriptors, which help in identification of better binding ligands. A plethora of descriptors were used for training the self-organizing map algorithm. The model brings out descriptors important for protein-ligand interactions.

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). It is shown that the POPs species could be correctly classified according to their chemical nature into several patterns of similarity and their concentration profile depends on the annual season.

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). The interrelations between carbohydrate disturbances, metabolic syndrome components and stress response hormones were studied. Exploratory data analysis was used to determine different patterns of similarities among the patients. Normetanephrine concentrations were significantly increased in postmenopausal patients and in women with morbid obesity, type 2 diabetes, and hypertension but not with prediabetes. Both metanephrine and normetanephrine levels were positively associated with glucose concentrations one hour after glucose load irrespectively of the insulin levels. The exploratory data analysis showed different risk subgroups among the investigated obese women. The development of predictive tools that include not only traditional metabolic risk factors, but also markers of stress response systems might help for specific risk estimation in obesity patients.

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. The considered combinatorial k-means procedure explores all possible k-means clustering with a determined number of descriptors and groups. The predetermined conditions for the partitioning were as follows: every single group of patients included patients with DMT2 and one of the underlying diseases; each subgroup formed in such a way was subject to partitioning into three patterns (good health status, medium health status, and degenerated health status); optimal descriptors for each disease and groups. The selection of the best clustering is obtained through the parameter called global variance, defined as the sum of all variance values of all clinical variables of all the clusters. The best clinical parameters are found by minimizing this global variance. This methodology has to identify a set of variables that are assumed to separate each underlying disease efficiently in three different subgroups of patients. The hierarchical clustering obtained for these four underlying diseases could be used to build groups of patients with correlated clinical data. The proposed methodology gives surmised results from complex data based on a relationship with the health status of the group and draws a picture of the prediction rate of the ongoing health status.

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. The proposed computational scheme was also explored for the case of the isolated-molecule model. The goals of the study were to access the accuracy of the proposed procedure for solid-state calculations along with electron calculations for the isolated molecules and to reveal the similarities within the groups of objects by the cluster analysis (CA) techniques and two-way CA for the data. The presented simulation procedure should be very valuable for exploring and to classify other oxalate compounds.

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 CO2 capture, a process that is quite well described. The solubility of CO2 in ILs can be used as a model to investigate gas absorption processes. The aim is to find the relationships between the solubility of CO2 and other variables-physicochemical properties and parameters related to greenness. In this study, 12 variables are used to describe a dataset consisting of 26 ILs and 16 molecular solvents. We used a cluster analysis, a principal component analysis, and a K-means hierarchical clustering to find the patterns in the dataset and the discriminators between the clusters of compounds. The results showed that ILs and molecular solvents form two well-separated groups, and the variables were well separated into greenness-related and physicochemical properties. Such patterns suggest that the modeling of greenness properties and of the solubility of CO2 on physicochemical properties can be difficult.

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. The octanol/water and octanol/air partition coefficients are predicted for a group of polar solvents using density functional theory (DFT) calculations in combination with a solvation model based on density (SMD) and are in excellent agreement with experimental data. Thus, the use of quantum-chemical calculations to predict partition coefficients from free energies should be a valuable alternative for unknown solvents. The obtained results indicate that the SMD continuum model in conjunction with any of the three DFT functionals (B3LYP, M06-2X, and M11) agrees with the observed experimental values. The highest correlation to experimental data for the octanol/water partition coefficients was reached by the M11 functional; for the octanol/air partition coefficient, the M06-2X functional yielded the best performance. To the best of our knowledge, this is the first computational approach for the prediction of octanol/air partition coefficients by DFT calculations, which has remarkable accuracy and precision.

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. Five latent factors are found to explain over 80% of the total variance of the system being conditionally named "organic", "non-ferrous smelter", "acidic", "secondary anthropogenic contribution" and "natural" factor. The apportionment models designate the contribution of the identified sources quantitatively and help in the interpretation of risk assessment and management actions. Since the study takes into account pollution uptake from soil to a cabbage plant, the data interpretation could help in introducing biomonitoring aspects of the assessment. The chemometric expertise helps in revealing hidden relationships between the objects and the variables involved to achieve a better understanding of specific pollution events in the soil of a severely industrially impacted region.