Profiling of ARDS pulmonary edema fluid identifies a metabolically distinct subset

There is considerable biological and physiological heterogeneity among patients who meet standard clinical criteria for acute respiratory distress syndrome (ARDS). In this study, we tested the hypothesis that there exists a subgroup of ARDS patients who exhibit a metabolically distinct profile. We examined undiluted pulmonary edema fluid obtained at the time of endotracheal intubation from 16 clinically phenotyped ARDS patients and 13 control patients with hydrostatic pulmonary edema. Nontargeted metabolic profiling was carried out on the undiluted edema fluid. Univariate and multivariate statistical analyses including principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were conducted to find discriminant metabolites. Seven-hundred and sixty unique metabolites were identified in the pulmonary edema fluid of these 29 patients. We found that a subset of ARDS patients (6/16, 38%) presented a distinct metabolic profile with the overrepresentation of 235 metabolites compared with edema fluid from the other 10 ARDS patients, whose edema fluid metabolic profile was indistinguishable from those of the 13 control patients with hydrostatic edema. This “high metabolite” endotype was characterized by higher concentrations of metabolites belonging to all of the main metabolic classes including lipids, amino acids, and carbohydrates. This distinct group with high metabolite levels in the edema fluid was also associated with a higher mortality rate. Thus metabolic profiling of the edema fluid of ARDS patients supports the hypothesis that there is considerable biological heterogeneity among ARDS patients who meet standard clinical and physiological criteria for ARDS.

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Statistical Analsysis to Evaluate Heavy Metal Pollution in the Air Obatained by Moss Technique in Hanoi and its Surrounding Region

Communications in Physics ◽

10.15625/0868-3166/29/3si/14336 ◽

2019 ◽

Vol 29 (3SI) ◽

pp. 411

Author(s):

N. H. Quyet ◽

Le Hong Khiem ◽

V. D. Quan ◽

T. T. T. My ◽

M. V. Frontasieva ◽

...

Keyword(s):

Heavy Metal ◽

Statistical Analysis ◽

Metal Pollution ◽

Heavy Metal Pollution ◽

Principal Component ◽

Multivariate Statistical ◽

Five Factors ◽

Heavy Metal Elements ◽

Surrounding Areas ◽

Potential Pollution

The aim of this paper was the application of statistical analysis including principal component analysis to evaluate heavy metal pollution obtained by moss technique in the air of Ha Noi and its surrounding areas and to evaluate potential pollution sources. The concentrations of 33 heavy metal elements in 27 samples of Barbula Indica moss in the investigated region collected in December of 2016 in the investigated area have been examined using multivariate statistical analysis. Five factors explaining 80% of the total variance were identified and their potential sources have been discussed.

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It is better an approximate answer to the right question than the exact answer to the wrong question : the case of the psychometric analysis of the ASQ:SE

10.31234/osf.io/a5tdf ◽

2020 ◽

Author(s):

Luis Anunciacao ◽

janet squires ◽

J. Landeira-Fernandez

Keyword(s):

Internal Structure ◽

Statistical Methods ◽

Principal Component ◽

Psychological Theory ◽

Published Data ◽

Multivariate Statistical ◽

Exact Answer ◽

Wide Range ◽

Ages And Stages Questionnaire ◽

The Right

One of the main activities in psychometrics is to analyze the internal structure of a test. Multivariate statistical methods, including Exploratory Factor analysis (EFA) and Principal Component Analysis (PCA) are frequently used to do this, but the growth of Network Analysis (NA) places this method as a promising candidate. The results obtained by these methods are of valuable interest, as they not only produce evidence to explore if the test is measuring its intended construct, but also to deal with the substantive theory that motivated the test development. However, these different statistical methods come up with different answers, providing the basis for different analytical and theoretical strategies when one needs to choose a solution. In this study, we took advantage of a large volume of published data (n = 22,331) obtained by the Ages and Stages Questionnaire Social-Emotional (ASQ:SE), and formed a subset of 500 children to present and discuss alternative psychometric solutions to its internal structure, and also to its subjacent theory. The analyses were based on a polychoric matrix, the number of factors to retain followed several well-known rules of thumb, and a wide range of exploratory methods was fitted to the data, including EFA, PCA, and NA. The statistical outcomes were divergent, varying from 1 to 6 domains, allowing a flexible interpretation of the results. We argue that the use of statistical methods in the absence of a well-grounded psychological theory has limited applications, despite its appeal. All data and codes are available at https://osf.io/z6gwv/.

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1H-NMR Profiling Shows as Specific Constituents Strongly Affect the International EVOO Blends Characteristics: The Case of the Italian Oil

Molecules ◽

10.3390/molecules26082233 ◽

2021 ◽

Vol 26 (8) ◽

pp. 2233

Author(s):

Francesca Calò ◽

Chiara Roberta Girelli ◽

Federica Angilè ◽

Laura Del Coco ◽

Lucia Mazzi ◽

...

Keyword(s):

1H Nmr ◽

Metabolic Profile ◽

1H Nmr Spectroscopy ◽

Virgin Olive Oil ◽

Extra Virgin Olive Oil ◽

Minor Components ◽

Multivariate Statistical ◽

Olive Oils ◽

The World ◽

Plateau Level

Considering the growing number of extra virgin olive oil (EVOO) producers in the world, knowing the influence of olive oils with different geographical origins on the characteristics of the final blend becomes an interesting goal. The present work is focused on commercial organic EVOO blends obtained by mixing multiple oils from different geographical origins. These blends have been studied by 1H-NMR spectroscopy supported by multivariate statistical analysis. Specific characteristics of commercial organic EVOO blends originated by mixing oils from Italy, Tunisia, Portugal, Spain, and Greece were found to be associated with the increasing content of the Italian component. A linear progression of the metabolic profile defined characteristics for the analysed samples—up to a plateau level—was found in relation to the content of the main constituent of the Italian oil, the monocultivar Coratina. The Italian constituent percentage appears to be correlated with the fatty acids (oleic) and the polyphenols (tyrosol, hydroxytyrosol, and derivatives) content as major and minor components respectively. These results, which highlight important economic aspects, also show the utility of 1H-NMR associated with chemometric analysis as a powerful tool in this field. Mixing oils of different national origins, to obtain blends with specific characteristics, could be profitably controlled by this methodology.

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Characterization of the Volatile Profile of Cultivated and Wild-Type Italian Celery (Apium graveolens L.) Varieties by HS-SPME/GC-MS

Applied Sciences ◽

10.3390/app11135855 ◽

2021 ◽

Vol 11 (13) ◽

pp. 5855

Author(s):

Samantha Reale ◽

Valter Di Cecco ◽

Francesca Di Donato ◽

Luciano Di Martino ◽

Aurelio Manzi ◽

...

Keyword(s):

Solid Phase ◽

Principal Component ◽

Apium Graveolens ◽

Gas Chromatography Mass Spectrometry ◽

Bioactive Metabolites ◽

Chemical Components ◽

Volatile Profile ◽

Wild Type ◽

Multivariate Statistical ◽

Clear Differentiation

Celery (Apium graveolens L.) is a vegetable belonging to the Apiaceae family that is widely used for its distinct flavor and contains a variety of bioactive metabolites with healthy properties. Some celery ecotypes cultivated in specific territories of Italy have recently attracted the attention of consumers and scientists because of their peculiar sensorial and nutritional properties. In this work, the volatile profiles of white celery “Sedano Bianco di Sperlonga” Protected Geographical Indication (PGI) ecotype, black celery “Sedano Nero di Torricella Peligna” and wild-type celery were investigated using head-space solid-phase microextraction combined with gas-chromatography/mass spectrometry (HS-SPME/GC-MS) and compared to that of the common ribbed celery. Exploratory multivariate statistical analyses were conducted using principal component analysis (PCA) on HS-SPME/GC-MS patterns, separately collected from celery leaves and petioles, to assess similarity/dissimilarity in the flavor composition of the investigated varieties. PCA revealed a clear differentiation of wild-type celery from the cultivated varieties. Among the cultivated varieties, black celery “Sedano Nero di Torricella Peligna” exhibited a significantly different composition in volatile profile in both leaves and petioles compared to the white celery and the prevalent commercial variety. The chemical components of aroma, potentially useful for the classification of celery according to the variety/origin, were identified.

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Chemical Fingerprinting of Cryptic Species and Genetic Lineages of Aneura pinguis (L.) Dumort. (Marchantiophyta, Metzgeriidae)

Molecules ◽

10.3390/molecules26041180 ◽

2021 ◽

Vol 26 (4) ◽

pp. 1180

Author(s):

Rafał Wawrzyniak ◽

Wiesław Wasiak ◽

Beata Jasiewicz ◽

Alina Bączkiewicz ◽

Katarzyna Buczkowska

Keyword(s):

Cluster Analysis ◽

Cryptic Species ◽

Plant Material ◽

Chemical Constituents ◽

Principal Component ◽

Molecular Data ◽

Rapid Identification ◽

Multivariate Statistical ◽

Genetic Lineages ◽

Marker Compounds

Aneura pinguis (L.) Dumort. is a representative of the simple thalloid liverworts, one of the three main types of liverwort gametophytes. According to classical taxonomy, A. pinguis represents one morphologically variable species; however, genetic data reveal that this species is a complex consisting of 10 cryptic species (named by letters from A to J), of which four are further subdivided into two or three evolutionary lineages. The objective of this work was to develop an efficient method for the characterisation of plant material using marker compounds. The volatile chemical constituents of cryptic species within the liverwort A. pinguis were analysed by GC-MS. The compounds were isolated from plant material using the HS-SPME technique. Of the 66 compounds examined, 40 were identified. Of these 40 compounds, nine were selected for use as marker compounds of individual cryptic species of A. pinguis. A guide was then developed that clarified how these markers could be used for the rapid identification of the genetic lineages of A. pinguis. Multivariate statistical analyses (principal component and cluster analysis) revealed that the chemical compounds in A. pinguis made it possible to distinguish individual cryptic species (including genetic lineages), with the exception of cryptic species G and H. The classification of samples based on the volatile compounds by cluster analysis reflected phylogenetic relationships between cryptic species and genetic lineages of A. pinguis revealed based on molecular data.

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Metabolic Profiling of Pitaya (Hylocereus polyrhizus) during Fruit Development and Maturation

Molecules ◽

10.3390/molecules24061114 ◽

2019 ◽

Vol 24 (6) ◽

pp. 1114 ◽

Cited By ~ 12

Author(s):

Yawei Wu ◽

Juan Xu ◽

Yizhong He ◽

Meiyan Shi ◽

Xiumei Han ◽

...

Keyword(s):

Mass Spectrometry ◽

Metabolic Profiling ◽

Developmental Stages ◽

Principal Component ◽

Nutrient Content ◽

Flight Mass Spectrometry ◽

Potential Biomarker ◽

Fruit Samples ◽

Liquid Chromatography Tandem Mass ◽

Hylocereus Polyrhizus

Pitaya (Hylocereus polyrhizus) has attracted much interest from consumers as it is a novelty fruit with high nutrient content and a tolerance to drought stress. As a group of attractive pigment- and health-promoting natural compounds, betalains represent a visual feature for pitaya fruit quality. However, little information on the correlation between betalains and relevant metabolites exists so far. Currently, color (Commission International del’Eclairage, CIE) parameters, betalain contents, and untargeted metabolic profiling (gas chromatography-time-of-flight-mass spectrometry, GC–MS and liquid chromatography tandem mass spectrometry, LC–MS) have been examined on ‘Zihonglong’ fruits at nine different developmental stages, and the variation character of the metabolite contents was simultaneously investigated between peel and pulp. Furthermore, principal component analysis (PCA) and partial least-squares discriminant analysis (PLS-DA) were used to explore metabolite profiles from the fruit samples. Our results demonstrated that the decrease of amino acid, accompanied by the increase of sugars and organic acid, might contribute to the formation of betalains. Notably, as one of four potential biomarker metabolites, citramalic acid might be related to betalain formation.

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Multivariate Analysis as a Tool for Quantification of Conformational Transitions in DNA Thin Films

Applied Sciences ◽

10.3390/app11135895 ◽

2021 ◽

Vol 11 (13) ◽

pp. 5895

Author(s):

Kristina Serec ◽

Sanja Dolanski Babić

Keyword(s):

Thin Films ◽

Learning Algorithm ◽

Principal Component Regression ◽

Principal Component ◽

Conformational Transitions ◽

Cancer Diagnostics ◽

Dna Conformation ◽

Support Vector ◽

Multivariate Statistical ◽

The Impact

The double-stranded B-form and A-form have long been considered the two most important native forms of DNA, each with its own distinct biological roles and hence the focus of many areas of study, from cellular functions to cancer diagnostics and drug treatment. Due to the heterogeneity and sensitivity of the secondary structure of DNA, there is a need for tools capable of a rapid and reliable quantification of DNA conformation in diverse environments. In this work, the second paper in the series that addresses conformational transitions in DNA thin films utilizing FTIR spectroscopy, we exploit popular chemometric methods: the principal component analysis (PCA), support vector machine (SVM) learning algorithm, and principal component regression (PCR), in order to quantify and categorize DNA conformation in thin films of different hydrated states. By complementing FTIR technique with multivariate statistical methods, we demonstrate the ability of our sample preparation and automated spectral analysis protocol to rapidly and efficiently determine conformation in DNA thin films based on the vibrational signatures in the 1800–935 cm−1 range. Furthermore, we assess the impact of small hydration-related changes in FTIR spectra on automated DNA conformation detection and how to avoid discrepancies by careful sampling.

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STATISTICAL MULTIVARIATE ANALYSIS APPLIED TO ENVIRONMENTAL CHARACTERIZATION OF SOIL IN SEMIARID REGION1

Revista Caatinga ◽

10.1590/1983-21252019v32n120rc ◽

2019 ◽

Vol 32 (1) ◽

pp. 200-210

Author(s):

Antônio Italcy de Oliveira Júnior ◽

Luiz Alberto Ribeiro Mendonça ◽

Sávio de Brito Fontenele ◽

Adriana Oliveira Araújo ◽

Maria Gorethe de Sousa Lima Brito

Keyword(s):

Organic Matter Content ◽

Principal Component ◽

Matter Content ◽

Multivariate Statistical ◽

Statistical Tool ◽

Environmental Characterization ◽

Hierarchical Grouping ◽

Two Factors ◽

Total Data

ABSTRACT Soil is a dynamic and complex system that requires a considerable number of samples for analysis and research purposes. Using multivariate statistical methods, favorable conditions can be created by analyzing the samples, i.e., structural reduction and simplification of the data. The objective of this study was to use multivariate statistical analysis, including factorial analysis (FA) and hierarchical groupings, for the environmental characterization of soils in semiarid regions, considering anthropic (land use and occupation) and topographic aspects (altitude, moisture, granulometry, PR, and organic-matter content). As a case study, the São José Hydrographic Microbasin, which is located in the Cariri region of Ceará, was considered. An FA was performed using the principal component method, with normalized varimax rotation. In hierarchical grouping analysis, the “farthest neighbor” method was used as the hierarchical criterion for grouping, with the measure of dissimilarity given by the “square Euclidean distance.” The FA indicated that two factors explain 75.76% of the total data variance. In the analysis of hierarchical groupings, the samples were agglomerated in three groups with similar characteristics: one with samples collected in an area of the preserved forest and two with samples collected in areas with more anthropized soils. This indicates that the statistical tool used showed sensitivity to distinguish the most conserved soils and soils with different levels of anthropization.

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