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

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 ighest 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 rediction of octanol/air partition coefficients by DFT calculations, which has remarkable accuracy and precision

Multivariate analysis for the classification of copper lead and copper zinc glasses

The similarity patterns in the physicochemical properties of copper-lead and copper-zinc borate glasses were identified by means of finding similarity within the objects of study using multivariate statistical analysis. As exploratory methods of multivariate analysis, cluster analysis, principal components analysis, and two-way clustering were applied for a set of copper-lead and copper-zinc borate glasses. Specific correlations among the physicochemical properties of copper glasses were interpreted. In particular, the effect of Pb and Zn doping metal ion in copper glasses in the structural and mechanical properties is identified. Interestingly, the degree of lead content determines two kinds of glasses with specific physicochemical properties

Applying discriminant and cluster analysis to separate allergenic from non-allergenic proteins

As a result of increased healthcare requirements and the introduction of genetically modified foods, the problem of allergies is becoming a growing health problem. The concept of allergies has prompted the use of new methods such as genomics and proteomics to uncover the nature of allergies. In the present study, a selection of 1400 food proteins was analysed by PLS-DA (Partial Least Square-based Discriminant Analysis) after suitable transformation of structural parameters into uniform vectors. Then, the resulting strings of different length were converted into vectors with equal length by Auto and Cross-Covariance (ACC) analysis. Hierarchical and non-hierarchical (K-means) Cluster Analysis (CA) was also performed in order to reach a certain level of separation within a small training set of plant proteins (16 allergenic and 16 non-allergenic) using a new three-dimensional descriptor based on surface protein properties in combination with amino acid hydrophobicity scales. The novelty of the approach in protein differentiation into allergenic and non-allergenic classes is described in the article. The general goal of the present study was to show the effectiveness of a traditional chemometric method for classification (PLS-DA) and the options of Cluster Analysis (CA) to separate by multivariate statistical methods allergenic from non-allergenic proteins

Unveiling the Effect of Low pH on the SARS-CoV-2 Main Protease by Molecular Dynamics Simulations

(1) Background: Main Protease (Mpro) is an attractive therapeutic target that acts in the replication and transcription of the SARS-CoV-2 coronavirus. Mpro is rich in residues exposed to protonation/deprotonation changes which could affect its enzymatic function. This work aimed to explore the effect of the protonation/deprotonation states of Mpro at different pHs using computa- tional techniques. (2) Methods: The different distribution charges were obtained in all the evaluated pHs by the Semi-Grand Canonical Monte Carlo (SGCMC) method. A set of Molecular Dynamics (MD) simulations was performed to consider the different protonation/deprotonation during 250 ns, verifying the structural stability of Mpro at different pHs. (3) Results: The present findings demon- strate that active site residues and residues that allow Mpro dimerisation was not affected by pH changes. However, Mpro substrate-binding residues were altered at low pHs, allowing the increased pocket volume. Additionally, the results of the solvent distribution around Sγ, Hγ, Nδ1 and Hδ1 atoms of the catalytic residues Cys145 and His41 showed a low and high-water affinity at acidic pH, respectively. It which could be crucial in the catalytic mechanism of SARS-CoV-2 Mpro at low pHs. Moreover, we analysed the docking interactions of PF-00835231 from Pfizer in the preclinical phase, which shows excellent affinity with the Mpro at different pHs. (4) Conclusion: Overall, these findings indicate that SARS-CoV-2 Mpro is highly stable at acidic pH conditions, and this inhibitor could have a desirable function at this condition

Insight into Electric field-induced rupture mechanism of water-in-toluene emulsion films from a model system

This paper presents a model, which we have designed to get insight into the development of electro- induced instability of a thin toluene emulsion film in contact with the saline aqueous phase. Molecular dynamics (MD) simulations demonstrate the role of charge accumulation in the toluene-film rupture induced by a DC electric field. Two ensembles¿NVT and NPT ¿are used to determine the critical value of the external field at which the film ruptures, the charge distribution and capacitance of the thin film, number densities, and the film structure. The rupture mechanism as seen from this model is the following: in both NVT and NPT ensembles, condenser plates, where the charge density is maximal, are situated at the very border between the bulk aqueous (water) phase and the mixed layer. No ion penetration is observed within the toluene core, thus leaving all the distribution of charges within the mixed zone and the bulk phase that could be attributed to the formation of hydration shells. When the critical electric field is reached within a certain time after the field application, electric discharge occurs indicating the beginning of the rupturing process. The MD simulations indicate that the NPT ensemble predicts a value of the critical field that is closer to the experimental finding. Published by AIP Publishing. [http://dx.doi.org/10.1063/1.498316

Boron oxide glasses and nanocomposites: synthetic, structural and statistical approach

Three different precursors of boron-aqua and glycerol solutions of boric acid and ethanol solution of trimethyl borate were used for the preparation of organic-inorganic advanced materials. The films and bulk materials samples were heat treated at 100, 400, 800 °C for 2 h. The hybrid samples were stable and transparent until 100 °C. The further increase of temperature to 400 °C led to destruction of samples, and at 800 °C they were molten. The structural changes during the pyrolysis were studied by Fourier transform infrared spectroscopy, differential thermal analysis, and X-ray diffraction. Details of surface morphology were observed by scanning electron microscopy. The obtained BO 3 and BO 4 groups were identified in the molten materials after pyrolysis. The quantities and order of borate structural units as well as residual carbon in the networks depended on boron precursor type. PVA/PEG/B 2 O 3 hybrid materials were proved to be appropriate precursors for synthesizing borate and carboborate glass and carbon/borate glass anocomposites. To access the impact of the experimental conditions on the structural changes of the nanocomposites, cluster analysis of the IR-spectral data was used as a classification method

Antimicrobial Activity of Rhenium Di- and Tricarbonyl Diimine Complexes: Insights on Membrane-Bound S. aureus Protein Binding

Antimicrobial resistance is one of the major human health threats, with significant impacts on the global economy. Antibiotics are becoming increasingly ineffective as drug-resistance spreads, imposing an urgent need for new and innovative antimicrobial agents. Metal complexes are an untapped source of antimicrobial potential. Rhenium complexes, amongst others, are particularly attractive due to their low in vivo toxicity and high antimicrobial activity, but little is known about their targets and mechanism of action. In this study, a series of rhenium di- and tricarbonyl diimine complexes were prepared and evaluated for their antimicrobial potential against eight different microorganisms comprising Gram-negative and -positive bacteria. Our data showed that none of the Re dicarbonyl or neutral tricarbonyl species have either bactericidal or bacteriostatic potential. In order to identify possible targets of the molecules, and thus possibly understand the observed differences in the antimicrobial efficacy of the molecules, we computationally evaluated the binding affinity of active and inactive complexes against structurally characterized membrane-bound S. aureus proteins. The computational analysis indicates two possible major targets for this class of compounds, namely lipoteichoic acids flippase (LtaA) and lipoprotein signal peptidase II (LspA). Our results, consistent with the published in vitro studies, will be useful for the future design of rhenium tricarbonyl diimine-based antibiotics

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

Diabetes mellitus type 2: Exploratory data analysis based on clinical reading

Diabetes mellitus type 2 (DMT2) is a severe and complex health problem. It is the most common type of diabetes. DMT2 is a chronic metabolic disorder that affects the way your body metabolizes sugar. With DMT2, your body either resists the effects of insulin or does not produce sufficient insulin to continue normal glucose levels. DMT2 is a disease that requires a multifactorial approach of controlling that includes lifestyle change and pharmacotherapy. Less than ideal management increases the risk of developing complications and comorbidities such as cardiovascular disease and numerous social and economic penalties. That is why the studies dedicated to the pathophysiological mechanisms and the treatment of DMT2 are extremely numerous and diverse. In this study, exploratory data analysis approaches are applied for the treatment of clinical and anthropometric readings of patients with DMT2. Since multivariate statistics is a well-known method for classification, modeling and interpretation of large collections of data, the major aim of the present study was to reveal latent relations between the objects of the investigation (group of patients and control group) and the variables describing the objects (clinical and anthropometric parameters). In the proposed method by the application of hierarchical cluster analysis and principal component analysis it is possible to identify reduced number of parameters which appear to be the most significant discriminant parameters to distinguish between four patterns of patients with DMT2. However, there is still lack of multivariate statistical studies using DMT2 data sets to assess different aspects of the problem like optimal rapid monitoring of the patients or specific separation of patients into patterns of similarity related to their health status which could be of help in preparation of data bases for DMT2 patients. The outcome from the study could be of custom for the selection of significant tests for rapid monitoring of patients and more detailed approach to the health status of DMT2 patients