Monitoring the Modifications of the Vitreous Humor Metabolite Profile after Death: An Animal Model

We applied a metabolomic approach to monitor the modifications occurring in goat vitreous humor (VH) metabolite composition at different times (0, 6, 12, 18, and 24 hours) after death. The1H-NMR analysis of the VH samples was performed for the simultaneous determination of several metabolites (i.e., the metabolite profile) representative of the VHstatusat different times. Spectral data were analyzed by Principal Component Analysis (PCA) and by Orthogonal Projection to Latent Structures (OPLS) regression technique. PCA and OPLS suggested that different spectral regions were involved in time-related changes. The major time-related compositional changes, here detected, were the increase of lactate, hypoxanthine, alanine, total glutathione, choline/phosphocholine, creatine, andmyo-inositol and the decrease of glucose and 3-hydroxybutyrate. We attempted a speculative interpretation of the biological mechanisms underlying these changes. These results show that multivariate statistical approach, based on1H NMR metabolite profiling, is a powerful tool for detecting ongoing differences in VH composition and may be applied to investigate several physiological and pathological conditions.

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DSC, FTIR and Raman Spectroscopy Coupled with Multivariate Analysis in a Study of Co-Crystals of Pharmaceutical Interest

Molecules ◽

10.3390/molecules23092136 ◽

2018 ◽

Vol 23 (9) ◽

pp. 2136 ◽

Cited By ~ 6

Author(s):

Patrycja Garbacz ◽

Marek Wesolowski

Keyword(s):

Principal Component ◽

Water Soluble ◽

Crystal Formation ◽

Multivariate Statistical ◽

Molar Ratios ◽

Pharmaceutical Ingredients ◽

Multivariate Statistical Approach ◽

And Cluster Analysis ◽

Dsc Curves ◽

Physical Mixtures

Co-crystals have garnered increasing interest in recent years as a beneficial approach to improving the solubility of poorly water soluble active pharmaceutical ingredients (APIs). However, their preparation is a challenge that requires a simple approach towards co-crystal detection. The objective of this work was, therefore, to verify to what extent a multivariate statistical approach such as principal component analysis (PCA) and cluster analysis (CA) can be used as a supporting tool for detecting co-crystal formation. As model samples, physical mixtures and co-crystals of indomethacin with saccharin and furosemide with p-aminobenzoic acid were prepared at API/co-former molar ratios 1:1, 2:1 and 1:2. Data acquired from DSC curves and FTIR and Raman spectroscopies were used for CA and PCA calculations. The results obtained revealed that the application of physical mixtures as reference samples allows a deeper insight into co-crystallization than is possible with the use of API and co-former or API and co-former with physical mixtures. Thus, multivariate matrix for PCA and CA calculations consisting of physical mixtures and potential co-crystals could be considered as the most profitable and reliable way to reflect changes in samples after co-crystallization. Moreover, complementary interpretation of results obtained using DSC, FTIR and Raman techniques is most beneficial.

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A Study of Transmission Effects of Sulfonyl and Carbonyl Groups

Collection of Czechoslovak Chemical Communications ◽

10.1135/cccc19950841 ◽

1995 ◽

Vol 60 (5) ◽

pp. 841-850 ◽

Cited By ~ 2

Author(s):

Miroslav Ludwig ◽

Patrik Pařík ◽

Jiří Kulhánek

Keyword(s):

Latent Variables ◽

Dissociation Constants ◽

Substituent Effects ◽

Principal Component ◽

Carbonyl Groups ◽

H Nmr ◽

Deviation Analysis ◽

Substituent Constants ◽

The Difference ◽

Latent Structures

Seventeen p-substituted N-phenylsulfonylbenzamides of general formulas XC6H4SO2NHCOC6H5 and C6H5SO2NHCOC6H4X have been synthesized and their structure has been confirmed by elemental analysis and 1H NMR spectra. The dissociation constants of all the compounds have been measured by potentiometric titration in methanol, acetonitrile, dimethylformamide, dimethyl sulfoxide, and pyridine. The obtained pKHA values have been correlated with three sets of Hammett substituent constants using simple or double linear regression. The solvent and substituent effects are discussed on the basis of experimental results, and the difference between the substituent effects from sulfonamide and benzamide sections is evaluated. It has been found that due to the extensive delocalization of negative charge in the conjugated base the transmission effects of carbonyl and sulfonyl groups on the transmission of substituent effect are roughly the same. The experimental data have been interpreted by the methods with latent variables: the principal component analysis (PCA), the conjugated deviation analysis (CDA), and the method of projection to latent structures (PLS). The results obtained by these procedures were similar.

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Reconstructing Herbivore Diets: A Multivariate Statistical Approach To Interpreting Compound-Specific Isotope Values

10.21203/rs.3.rs-836199/v1 ◽

2021 ◽

Author(s):

Mickey Hong Yi Chen ◽

Iain P. Kendall ◽

Richard P. Evershed ◽

Amy Bogaard ◽

Amy K. Styring

Keyword(s):

Case Studies ◽

Isotope Analysis ◽

Statistical Treatment ◽

Principal Component ◽

Nitrogen Isotope ◽

Multivariate Statistical ◽

Sharp Separation ◽

Statistical Framework ◽

Multivariate Statistical Approach ◽

Isotope Analyses

Abstract Stable nitrogen (N) isotope analysis of bulk tissues is a technique for reconstructing the diets of organisms. However, bulk nitrogen isotope (δ15N) values can be influenced by a variety of metabolic and environmental factors that can confound accurate dietary reconstruction. Compound-specific isotope analyses of amino acids (CSIA-AA) have demonstrated the power of the approach in understanding how the δ15N values of bulk collagen are assembled from the constituent AAs. Furthermore, by connecting these AA δ15N values within a robust biochemical framework interpretation of diet and environment are greatly enhanced. Several new proxies have emerged, built around selected AAs; however, the interconnectedness of AA biosynthetic pathways means that patterning of δ15N values across a wider suite of collagen AAs will occur under different environmental or dietary influences. This work seeks to test this idea by situating CSIA-AA within a robust statistical framework using principal component analysis (PCA) and Bayesian statistics to increase the interpretability of a wider range of AA δ15N values in terms of reconstructing herbivore diet. The model was tested using wild and domestic herbivores from the Neolithic settlements of Çatalhöyük (Turkey), Makriyalos (Greece), and Vaihingen (Germany) as case studies. It was found that at Makriyalos there was a sharp separation between domesticated and wild herbivores, which was present to a lesser extent at Çatalhöyük and not observed at Vaihingen. The case studies presented in this work demonstrate that multivariate statistical treatment of CSIA-AA data can deliver new insights into herbivore diet, exceeding those achievable with the Bayesian model.

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Lithofacies Identification and Multivariate Analysis of Groundwater Chemistry in Coastal Aquifers in Koko Area of the Western Niger Delta

Hydrology ◽

10.3390/hydrology6020031 ◽

2019 ◽

Vol 6 (2) ◽

pp. 31 ◽

Cited By ~ 1

Author(s):

Oghenero Ohwoghere-Asuma ◽

Kizito Aweto ◽

Chukwuma Ugbe

Keyword(s):

Groundwater Quality ◽

Principal Component ◽

Organic Pollution ◽

Clayey Sand ◽

Multivariate Statistical ◽

Subsurface Geology ◽

Dissolved Ions ◽

Considerable Impact ◽

And Cluster Analysis

Understanding aquifer lithofacies and depth of occurrence, and what factors influence its quality and chemistry are of paramount importance to the management of groundwater resource. Subsurface lithofacies distribution was characterized by resistivity and validated with available subsurface geology. Resistivity values varied from less than 100 Ωm to above 1000 Ωm. Lithofacies identified includes clay, clayey sand, sand and peat. Shallow unconfined and confined aquifers occurred at depths ranging from 0 to 12 m and 18 to 63 m, respectively. Geochemistry and multivariate statistical analysis consisting of principal component analysis (PCA) and cluster analysis (CA) were used for the determination of quality and groundwater evolution. Groundwater types depicted by Piper plots were Ca3+, Cl− and Na+, Cl−, which was characterized by low dissolved ions, slightly acidic and Fe2+. The dominant variables influencing groundwater quality as returned by PCA were organic pollution resulting from swampy depositional environment, anthropogenic effects resulting from septic and leachates from haphazard dumpsites mixing with groundwater from diffuse sources. In addition, the weathering and dissolution of aquifer sediments rich in feldspar and clay minerals have considerable impact on groundwater quality. CA depicted two distinct types of groundwater that are significantly comparable to those obtained from Piper plots.

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THE INFLUENCE OF CHECK DAMS ON FLUVIAL PROCESSES AND RIPARIAN VEGETATION IN MOUNTAIN REACHES OF TORRENTS

Journal of Agricultural Engineering ◽

10.4081/jae.2010.3.37 ◽

2010 ◽

Vol 41 (3) ◽

pp. 37 ◽

Cited By ~ 5

Author(s):

Giuseppe Bombino ◽

Vincenzo Tamburino ◽

Demetrio Antonio Zema ◽

Santo Marcello Zimbone

Keyword(s):

Riparian Vegetation ◽

Principal Component ◽

Multivariate Statistical ◽

Vegetation Development ◽

Multivariate Statistic ◽

Check Dams ◽

Multivariate Statistic Analysis ◽

Multivariate Statistical Approach ◽

Specific Discharge ◽

Active Channel

The complex hydrogeomorphological processes within the active channel of rivers strongly influence riparian vegetation development and organization, particularly in mountain streams where such processes can be remarkably impacted by engineering control works. In four mountain reaches of Calabrian fiumaras we analyze, through previously arranged methods (integrated by a multivariate statistic analysis), the relationships among hydrogeomorphological river characteristics and structure and the development of riparian vegetation within the active channel in transects located in proximity of check dams and in less disturbed sites. The results of this study demonstrate clear and relevant contrasts, due to the presence of check dams, in the physical and vegetation properties of upstream, downstream and intermediate sites around check dams. The multivariate statistical approach through the Principal Component Analysis (PCA) highlighted evident relationships in all transects between groups of physical and vegetation properties. The regression analysis performed between the vegetation properties and the width:depth ratio or the specific discharge showed very different relationships between groups of transects, due to evident changes in channel morphology and in flow regime locally induced by check dams. Overall we have shown that check dams have far reaching effects in the extent and development of riparian vegetation of mountain torrent reaches, which extend far beyond physical adjustments to changed morphological, hydraulic and sedimentary conditions.

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The 3Rs: Reduction and refinement through a multivariate statistical analysis approach in a behavioural study to unveil anxiolytic effects of natural extracts of Tilia tomentosa

Biomedical Science and Engineering ◽

10.4081/bse.2019.116 ◽

2020 ◽

Cited By ~ 1

Author(s):

Guendalina Olivero ◽

Federica Turrini ◽

Matteo Vergassola ◽

Raffaella Boggia ◽

Paola Zunin ◽

...

Keyword(s):

Statistical Approach ◽

Principal Component ◽

Experimental Protocol ◽

Multivariate Statistical ◽

Data Set ◽

Natural Extracts ◽

Multivariate Statistical Approach ◽

Behavioural Tests ◽

Tilia Tomentosa

We propose a multivariate statistical approach based on Principal Component Analysis (PCA) as an useful instrument to improve the Rules of Refinement and Reduction in in vivo animal experimentation. We analysed with PCA the preliminary data from a study on the effects of the oral administration of Tilia tomentosa bud extracts (TTBEs) on the behavioural skills of adult and aged male and female mice. PCA allows to rationalize the data set information and to dissect the results, showing connections among variables under study (behavioural parameters) and different trends in the experimental groups (control and TTBEs-administered animals). Our results show that PCA can give some important information that can be useful for the refinement of the experimental protocol, in order to reduce the number of the animals used in the experiments and/or the behavioural tests to get reliable information.

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Liver Metabolic Alterations and Changes in Host Intercompartmental Metabolic Correlation during Progression of Malaria

Journal of Parasitology Research ◽

10.1155/2011/901854 ◽

2011 ◽

Vol 2011 ◽

pp. 1-14 ◽

Cited By ~ 9

Author(s):

Arjun Sengupta ◽

Angika Basant ◽

Soumita Ghosh ◽

Shobhona Sharma ◽

Haripalsingh M. Sonawat

Keyword(s):

Sexual Dimorphism ◽

Metabolic Profile ◽

Kynurenic Acid ◽

Early Stage ◽

Global Analysis ◽

Principal Component ◽

Degradation Process ◽

Metabolite Profile ◽

Malarial Parasite ◽

H Nmr

1H NMR-based metabonomics was used to investigate the multimodal response of mice to malarial parasite infection byP. bergheiANKA. Liver metabolism was followed by NMR spectroscopy through the course of the disease in both male and female mice. Our results showed alterations in the level of several metabolites as a result of the infection. Metabolites like kynurenic acid, alanine, carnitine, andβ-alanine showed significant alteration in the liver, suggesting altered kynurenic acid, glucose, fatty acid and amino acid pathways. Distinct sexual dimorphism was also observed in the global analysis of the liver metabolic profiles. Multiway principal component analysis (MPCA) was carried out on the liver, brain, and serum metabolic profile in order to explore the correlation of liver and brain metabolic profile to the metabolite profile of serum. Changes in such correlation profile also indicated distinct sexual dimorphism at the early stage of the disease. Indications are that the females are able to regulate their metabolism in the liver in such a way to maintain homeostasis in the blood. In males, however, choline in liver showed anticorrelation to choline content of serum indicating a higher phospholipid degradation process. The brain-serum correlation profile showed an altered energy metabolism in both the sexes. The differential organellar responses during disease progression have implications in malaria management.

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Multivariate statistical approach to comparison of the nutrient status of Norway spruce (Picea abies [L.] Karst.) and top-soil properties in differently managed forest stands

Journal of Forest Science ◽

10.17221/2173-jfs ◽

2008 ◽

Vol 53 (No. 3) ◽

pp. 101-112 ◽

Cited By ~ 3

Author(s):

P. Samec ◽

D. Vavříček ◽

P. Šimková ◽

J. Pňáček

Keyword(s):

Picea Abies ◽

Soil Properties ◽

Norway Spruce ◽

Principal Component ◽

Nutrient Status ◽

Element Cycling ◽

Multivariate Statistical ◽

Plant Soil ◽

Multivariate Statistical Approach ◽

Altitudinal Zone

The soil is an irreplaceable component of forest ecosystems. Soil-forming processes directly influence element cycling (EC). Plant-soil interaction is a specific part of EC. Plant-soil interactions were observed on an example of natural spruce stand (NSS), semi-natural spruce stand (SNSS) and allochthonous spruce stand (ASS) in conditions of the spruce forest altitudinal zone (1,140−1,260 m a.s.l.; +3.0°C; 1,200 mm) of the Hrubý Jeseník Mts. (Czech Republic, Central Europe), where Norway spruce (<i>Picea abies</i> [L.] Karst.) is the main edificator and stand-forming tree species. We evaluated the soil properties of H- and Ep-horizons at selected sites with Haplic and Skeletic Podzols and they were compared with the nutrient status of spruce. A method of the principal component analysis was used for definition of the basic hypotheses: (1) each forest stand is in specific and topically individual interactions with soil and these interactions influence its state, (2) the influence of forest management reflects in humification and in the nutrient status in plant assimilatory tissues. Cluster analysis calculated results comparable with the multivariate analysis of variance. The results show that the continuity of linear and multivariate statistical methods gives the approach to detection of the forest stage based on soil and plant tissue data.

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