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1
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Chemometrics as an option to assess clinical data from diabetes mellitus type 2 patients

100%
Nikolov M., Simeonova P., Simeonov V.
Open Medicine
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2009
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vol. 4
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issue 4
433-443
EN
The present study deals with the application of two major multivariate statistical approaches - Cluster Analysis (CA) and Principal Components Analysis (PCA) as an option for assessment of clinical data from diabetes mellitus type 2 patients. One hundred clinical cases of patients are considered as object of the statistical classification and modeling, each one of them characterized by 34 various clinical parameters. The goal of the study was to find patterns of similarity, both between the patients and the clinical tests. Each group of similarity is interpreted revealing at least five clusters of correlated parameters or five latent factors, which determine the data structure. Relevant explanation of the clustering is found based on the pattern of similarity like glucose level, anthropometric data, enzyme level, liver function, kidney function etc. It is assumed that this classification could be of help in optimizing the performance of clinical test for this type of patients and for designing a pattern for the role of the different groups of test in determining the metabolic syndrome of the patients.
2
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Interpretation of data in patients with enteral and parenteral nutrition

100%
Tomova R., Tzaneva P., Nikolov M., Simeonov V.
Open Medicine
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2012
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vol. 7
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issue 6
720-728
EN
Multivariate statistical analysis is performed using clinical data characterizing the state of patients subject to early enteral (EEN) and pareneteral (PN) nutrition after major gastrointestinal surgery. Several patterns of linkage, between the clinical parameters for both groups of observed patients (with mixed (EEN+PN) and with parenteral nutrition only (TPN)), were found and interpreted. Discriminating indices for the internal grouping of patients were found related to the type of nutrition and the clinical status of the patients. It was found that the mixed (enteral and parenteral) nutrition offers better options for the overcoming of the metabolic stress after the surgery.
3
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Multivariate statistical assessment of polluted soils

100%
Simeonov V., Einax J., Tsakovski S., Kraft J.
Open Chemistry
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2005
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vol. 3
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issue 1
1-9
EN
This study deals with the application of several multivariate statistical methods (cluster analysis, principal components analysis, multiple regression on absolute principal components scores) for assessment of soil pollution by heavy metals. The sampling was performed in a heavily polluted region and the chemometric analysis revealed four latent factors, which describe 84.5 % of the total variance of the system, responsible for the data structure. These factors, whose identity was proved also by cluster analysis, were conditionally named “ore specific”, “metal industrial”, “cement industrial”, and “steel production” factors. Further, the contribution of each identified factor to the total pollution of the soil by each metal pollutant in consideration was determined.
4
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An advanced multivariate statistical approach to study coastal sediment data

88%
Simeonova P., Sarbu C., Spanos T., Simeonov V., Tsakovski S.
Open Chemistry
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2006
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vol. 4
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issue 1
68-80
EN
The present paper deals with the application of classical and fuzzy principal components analysis to a large data set from coastal sediment analysis. Altogether 126 sampling sites from the Atlantic Coast of the USA are considered and at each site 16 chemical parameters are measured. It is found that four latent factors are responsible for the data structure (“natural”, “anthropogenic”, “bioorganic”, and “organic anthropogenic”). Additionally, estimating the scatter plots for factor scores revealed the similarity between the sampling sites. Geographical and urban factors are found to contribute to the sediment chemical composition. It is shown that the use of fuzzy PCA helps for better data interpretation especially in case of outliers.
5
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Chemometrics in the assessment of the sustainable development rule implementation

76%
Astel A., Głosińska G., Sobczyński T., Boszke L., Simeonov V., Siepak J.
Open Chemistry
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2006
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vol. 4
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issue 3
543-564
EN
The sustainable development rule implementation is tested by the application of chemometrics in the field of environmental pollution. A data set consisting of Cd, Pb, Cr, Zn, Cu, Mn, Ni, and Fe content in bottom sediment samples collected in the Odra River (Germany/Poland) is treated using cluster analysis (CA), principal component analysis (PCA), and source apportionment techniques. Cluster analysis clearly shows that pollution on the German bank is higher than on the Polish bank. Two latent factors extracted by PCA explain over 88 % of the total variance of the system, allowing identification of the dominant “semi-natural” and “anthropogenic” pollution sources in the river ecosystem. The complexity of the system is proved by MLR analysis of the absolute principal component scores (APCS). The apportioning clearly shows that Cd, Pb, Cr, Zn and Cu participate in an “anthropogenic” source profile, whereas Fe and Mn are “semi-natural”. Multiple regression analysis indicates that for particular elements not described by the model, the amounts vary from 4.2 % (Mn) to 13.1 % (Cr). The element Ni participates to some extent to each source and, in this way, is neither pure “semi-natural” nor pure “anthropogenic”. Apportioning indicates that the whole heavy metal pollution in the investigated river reach is 12510.45 mg·kg−1. The contribution of pollutants originating from “anthropogenic sources” is 9.04 % and from “semi-natural” sources is 86.53 %.
6
Content available remote

Statistical interpretation of medical data of patients with alcohol abuse

76%
Bardarov V., Simeonova P., Neikova L., Bardarov K., Simeonov V., Tsakovski S., Kanev K.
Open Medicine
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2012
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vol. 7
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issue 4
465-474
EN
An attempt is made to assess a set of biochemical, kinetic and anthropometric data for patients suffering from alcohol abuse (alcoholics) and healthy patients (non-alcoholics). The main goal is to identify the data set structure, finding groups of similarity among the clinical parameters or among the patients. Multivariate statistical methods (cluster analysis and principal components analysis) were used to assess the data collection. Several significant patterns of related parameters were found to be representative of the role of the liver function, kinetic and anthropometric indicators (conditionally named “liver function factor”, “ethanol metabolism factor”, “body weight factor”, and “acetaldehyde metabolic factor”). An effort is made to connect the role of kinetic parameters for acetaldehyde metabolism with biochemical, ethanol kinetic and anthropometric data in parallel.
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