Depository effects in slowly metabolised proteins, typically glycation or the estimation of products arising from the reaction of unsaturated long-chain-fatty acid metabolites (possessing aldehydic groups) are very difficult to assess owing to their extremely low concentration in the protein matrix. In order to reveal such alterations we applied deep enzymatic fragmentation resulting in a set of small peptides, which, if modified, are likely to change their electrophoretic properties and can be visualised on the resulting profile. Peptide maps of collagen (a mixture of collagen types I and III digested by bacterial collagenase) were applied as the model protein structure for detecting the nonenzymatic posttranslational changes originating during various physiological conditions like high fructose diet and hypertriglyceridemic state. Capillary electrophoresis in acidic media (sodium phosphate buffer, pH 2.5) was used as the separation method capable of (partial) separation of over 60 peptide peaks. Two to 13 changes were revealed in the profiles obtained reflecting the physiological conditions of the animals tested. Combination of peptide profiling with subsequent t-test evaluation of individual peak areas and principal component analysis based on cumulative peak areas of individual sections of the electropherograms allowed to determine in which section (part) of the electropherogram the physiological state indicating changes occurred. Simultaneously it was possible to reveal the qualitative differences between the four physiological regimes investigated (i.e., which regime affects the collagen molecules most and which affects them least). The approach can be used as guidance for targeted preseparation of the very complex peptide mixture.

• MeSH
• elektroforéza v polyakrylamidovém gelu normy   MeSH
• krysa rodu Rattus MeSH
• peptidové mapování MeSH
• peptidy chemie   MeSH
• potkani Wistar MeSH
• zvířata MeSH
• Check Tag
• krysa rodu Rattus MeSH
• ženské pohlaví MeSH
• zvířata MeSH
• Publikační typ
• časopisecké články MeSH
• hodnotící studie MeSH
• práce podpořená grantem MeSH
• Názvy látek
• peptidy MeSH

The study aimed to apply novel source classification tool for local scale air pollution assessment reducing the total number of organic compounds in the model. Samples of particulate matter (PM) were collected in the town of Napajedla (South-eastern Czech Republic) in 2016. The industrial sector of the town is represented by plastics processing and manufacturing, as well as by mechanical engineering. Analytical technique of pyrolysis chromatography with mass spectroscopy detection was employed to identify organic species in the PM10 fraction. Two datasets (465 determined organic compounds and 50 selected organic markers) were used and compared by multivariate analysis - principal component analysis followed with hierarchical clustering on principal components incorporating compositional data approach. Three resulting clusters were observed in both cases. The cluster representing measurements near plastic processing and manufacturing plants was identical in both the analysed datasets with the same organic compounds that characterized resulting cluster Consequently, leading markers for plastic processing and manufacturing sources were suggested (bumetrizole, bis(tridecyl)phthalate, mono(2-ethylhexyl)phthalate). Other two clusters varied among the analysed datasets, however, dataset with selected markers showed more reliable outcomes. The results imply that concept of using only selected organic marker species with the compositional approach in multivariate statistical methods is sufficient and allows properly distinguishing the main air pollution sources between sampling locations even at a small urban scale.

• Klíčová slova
• Air pollution, Hierarchical clustering on principal components, Organic markers, Plastic plant, Principal component analysis,
• MeSH
• látky znečišťující vzduch * MeSH
• monitorování životního prostředí MeSH
• multivariační analýza MeSH
• pevné částice MeSH
• znečištění ovzduší * MeSH
• Publikační typ
• časopisecké články MeSH
• Geografické názvy
• Česká republika MeSH
• Názvy látek
• látky znečišťující vzduch * MeSH
• pevné částice MeSH

The pharmaceutical industry has to tackle the explosion of high amounts of poorly soluble APIs. This phenomenon leads to numerous sophisticated solutions. These include the use of multifactorial data analysis identifying correlations between the components and dosage form properties, laboratory and production process parameters with respect to the API liberation Example of such API is bicalutamide. Improved liberation is achieved by particle size reduction. Laboratory batches, with different PSD of API, were filled into gelatinous capsules and consequently granulated for tablet compression. Comparative dissolution profiles with Casodex 150 mg (Astra Zeneca) were performed. The component analysis was used for the statistical evaluation of f1 and f2 factors and D(v,0.9) and D[4,3] parameters of PSD to identify optimal PSD values. Suitable PSD limits for API were statistically confirmed in laboratory and in commercial scale with respect to optimized tablet properties. The tablets were bioequivalent with originator (n = 20; 90% CI for ln AUC0-120: 99.8-111.9%; 90% CI for ln cmax: 101.1-112.9%). In conclusion, the micronisation of the API is still an efficient and inexpensive method improving the bioavailability, although there are more complicated and expensive methods available. Statistical multifactorial methods improved the safety and reproducibility of production.

• Klíčová slova
• Bicalutamide, bioequivalence, dissolution, multivariate statistics, particle size,
• MeSH
• anilidy chemická syntéza   metabolismus   MeSH
• biologická dostupnost MeSH
• farmaceutická chemie metody   MeSH
• multivariační analýza MeSH
• nitrily chemická syntéza   metabolismus   MeSH
• tablety MeSH
• terapeutická ekvivalence MeSH
• tosylové sloučeniny chemická syntéza   metabolismus   MeSH
• Publikační typ
• časopisecké články MeSH
• Názvy látek
• anilidy MeSH
• bicalutamide MeSH Prohlížeč
• nitrily MeSH
• tablety MeSH
• tosylové sloučeniny MeSH

Feature selection is a significant part of many machine learning applications dealing with small-sample and high-dimensional data. Choosing the most important features is an essential step for knowledge discovery in many areas of biomedical informatics. The increased popularity of feature selection methods and their frequent utilisation raise challenging new questions about the interpretability and stability of feature selection techniques. In this study, we compared the behaviour of ten state-of-the-art filter methods for feature selection in terms of their stability, similarity, and influence on prediction performance. All of the experiments were conducted on eight two-class datasets from biomedical areas. While entropy-based feature selection appears to be the most stable, the feature selection techniques yielding the highest prediction performance are minimum redundance maximum relevance method and feature selection based on Bhattacharyya distance. In general, univariate feature selection techniques perform similarly to or even better than more complex multivariate feature selection techniques with high-dimensional datasets. However, with more complex and smaller datasets multivariate methods slightly outperform univariate techniques.

• Klíčová slova
• Classification performance, Feature selection, Multivariate FS, Stability, Univariate FS,
• MeSH
• algoritmy MeSH
• databáze faktografické MeSH
• lidé MeSH
• multivariační analýza MeSH
• Parkinsonova nemoc diagnóza   MeSH
• sekvenční analýza hybridizací s uspořádaným souborem oligonukleotidů metody   MeSH
• software MeSH
• statistické modely MeSH
• výpočetní biologie metody   MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• srovnávací studie MeSH

The visual evaluation of data derived from screening and optimization experiments in the development of new analytical methods poses a considerable time investment and introduces the risk of subjectivity. This study presents a novel approach to processing such data, based on factor analysis of mixed data and hierarchical clustering - multivariate techniques implemented in the R programming language. The methodology is demonstrated in the early-stage screening and optimization of the chromatographic separation of 15 structurally diverse drugs that affect the central nervous system, using a custom R Language script. The presented explorative approach enabled the identification of key parameters affecting the separation and significantly reduced the time required to evaluate the comprehensive dataset from the screening experiments. Based on the data analysis results, the optimal combination of stationary phase and mobile phase composition was selected, considering retention, overall resolution, and peak shape of compounds. Additionally, compounds vulnerable to changes in selected chromatographic conditions were identified. As a complement to the presented R Language script, a web-based application ChromaFAMDeX has been developed to offer an intuitive interface that enhances the accessibility of the used statistical methods. Accompanying the publication, the R script and the link to the standalone application are provided, enabling replication and adaptation of the methodology.

• Klíčová slova
• Factor analysis of mixed data, Hierarchical clustering, Liquid chromatography, Optimization, R Language,
• MeSH
• chromatografie kapalinová metody   MeSH
• multivariační analýza MeSH
• programovací jazyk * MeSH
• software * MeSH
• vysokoúčinná kapalinová chromatografie metody   MeSH
• Publikační typ
• časopisecké články MeSH

A test-statistic typically employed in the gene set enrichment analysis (GSEA) prevents this method from being genuinely multivariate. In particular, this statistic is insensitive to changes in the correlation structure of the gene sets of interest. The present paper considers the utility of an alternative test-statistic in designing the confirmatory component of the GSEA. This statistic is based on a pertinent distance between joint distributions of expression levels of genes included in the set of interest. The null distribution of the proposed test-statistic, known as the multivariate N-statistic, is obtained by permuting group labels. Our simulation studies and analysis of biological data confirm the conjecture that the N-statistic is a much better choice for multivariate significance testing within the framework of the GSEA. We also discuss some other aspects of the GSEA paradigm and suggest new avenues for future research.

• MeSH
• fenotyp MeSH
• modely genetické MeSH
• multivariační analýza MeSH
• sekvenční analýza hybridizací s uspořádaným souborem oligonukleotidů statistika a číselné údaje   MeSH
• stanovení celkové genové exprese statistika a číselné údaje   MeSH
• výpočetní biologie MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• Research Support, N.I.H., Extramural MeSH

INTRODUCTION: It is often questioned whether to perform replantation or revision amputation for amputation injuries in elderly patients and smokers. According to the current indication criteria, neither old age nor smoking in the absence of other risk factors are considered to be risk factors for replantation failure. However, many microsurgeons still may make the decision not to perform digital replantation based solely on these factors. MATERIAL AND METHODS: In order to evaluate the influence of both factors, we provided univariate and multivariate analyses of patients who underwent replantation at our centre during a 10-year period. We divided patients in two groups according to age (< and ≥ 60 years) and smoking status. RESULTS: In the univariate analysis, there were no differences in immediate results between the two age groups. In the multivariate analysis, no statistical difference was found in neither long-term nor short-term results between the two age groups and between smokers and non-smokers. CONCLUSION: Smoking and age should not be considered the only risk factors when deciding whether to perform digital replantation.

• MeSH
• amputace MeSH
• kouření tabáku * MeSH
• kouření * škodlivé účinky   epidemiologie   MeSH
• lidé středního věku MeSH
• lidé MeSH
• multivariační analýza MeSH
• replantace MeSH
• senioři MeSH
• Check Tag
• lidé středního věku MeSH
• lidé MeSH
• senioři MeSH
• Publikační typ
• časopisecké články MeSH

Biorelevant dissolution instruments represent an important tool for pharmaceutical research and development. These instruments are designed to simulate the dissolution of drug formulations in conditions most closely mimicking the gastrointestinal tract. In this work, we focused on the optimization of dissolution compartments/vessels for an updated version of the biorelevant dissolution apparatus-Golem v2. We designed eight compartments of uniform size but different inner geometry. The dissolution performance of the compartments was tested using immediate release caffeine tablets and evaluated by standard statistical methods and principal component analysis. Based on two phases of dissolution testing (using 250 and 100 mL of dissolution medium), we selected two compartment types yielding the highest measurement reproducibility. We also confirmed a statistically ssignificant effect of agitation rate and dissolution volume on the extent of drug dissolved and measurement reproducibility.

• Klíčová slova
• Golem, biorelevant, caffeine, dissolution, multivariate data analysis,
• MeSH
• biologické modely * MeSH
• design vybavení MeSH
• farmaceutická chemie * MeSH
• farmakokinetika * MeSH
• gastrointestinální absorpce MeSH
• gastrointestinální trakt metabolismus   MeSH
• multivariační analýza MeSH
• počítačová simulace MeSH
• rozpustnost * MeSH
• Publikační typ
• časopisecké články MeSH

Enhancing plant productivity and mitigating the impact of environmental stressors require a thorough understanding of phytomonitoring and physiological features indicative of plant health. This study delves into the response of cucumber plants to phosphorus deficiency employing diverse tools to identify key indicators and unravel the underlying mechanisms. Under phosphorus deficiency, a rapid response in older leaves was observed through the analysis of chlorophyll and carotenoid content. Molecular-level changes in photosynthetic performance were found to be age-dependent, as revealed by multidimensional statistical methods, highlighting the interconnectedness of examined features with the experimental setup timing. This can assist in understanding the long-term fluctuations in traits linked to phosphorus deficiency, facilitating early detection of stress.

• Klíčová slova
• chlorophyll fluorescence, confocal microscopy, greenhouse cucumber, leaf area index, multivariate statistical analyses, photosynthetic pigment,
• Publikační typ
• časopisecké články MeSH

The paper presents a brief overview of statistical methods used in clinical and experimental medicine, ranging from basic indicators and parameters of descriptive statistics and hypotheses testing (parametric as well as non-parametric methods) to a description of the most frequently used multivariate methods in medical scientific publications, to logistic regression. The paper also describes Principle Component Analysis (PCA), which is one of the methods used to decrease a data dimensionality. The proper use of statistical methods is demonstrated on specific clinical cases.

• MeSH
• analýza hlavních komponent MeSH
• analýza rozptylu MeSH
• interpretace statistických dat MeSH
• logistické modely MeSH
• multivariační analýza MeSH
• neparametrická statistika MeSH
• regresní analýza MeSH
• statistika jako téma * MeSH
• Publikační typ
• anglický abstrakt MeSH
• časopisecké články MeSH
• přehledy MeSH