scholarly journals Metabolomics profiling of Polygoni Multiflori Radix and Polygoni Multiflori Radix Preparata extracts using UPLC-Q/TOF-MS

2019 ◽  
Vol 14 (1) ◽  
Author(s):  
Zhaoyan Zhang ◽  
Liang Yang ◽  
Xiaoyan Huang ◽  
Yue Gao
Keyword(s):  
High Performance ◽  
Principal Component ◽  
Partial Least Square ◽  
Least Square ◽  
Alpha Linolenic Acid ◽  
Multivariate Statistical ◽  
Biochemical Indicators ◽  
Flight Mass Spectrometry ◽  
Underlying Mechanisms ◽  
Tof Ms

Abstract Background The side effects caused by Polygoni Multiflori Radix (PMR) and Polygoni Multiflori Radix Praeparata (PMRP) have often appeared globally. There is no research on the changes of endogenous metabolites among PMR- and PMRP-treated rats. The aim of this study was to evaluate the varying metabolomic effects between PMR- and PMRP-treated rats. We tried to discover relevant differences in biomarkers and endogenous metabolic pathways. Methods Hematoxylin and eosin staining and immunohistochemistry staining were performed to find pathological changes. Biochemical indicators were also measured, one-way analysis of variance with Dunnett’s multiple comparison test was used for biochemical indicators comparison among various groups. Metabolomics analysis based on ultra-high performance liquid chromatography-quadrupole time of flight mass spectrometry (UPLC-Q/TOF-MS) was performed to find the changes in metabolic biomarkers. Multivariate statistical approaches such as principal component analysis (PCA) and orthogonal partial least square-discriminant analysis (OPLS-DA) were applied to reveal group clustering trend, evaluate and maximize the discrimination between the two groups. MetaboAnalyst 4.0 was performed to find and confirm the pathways. Results PMR extracts exhibited slight hepatotoxic effects on the liver by increasing aspartate and alanine aminotransferase levels. Twenty-nine metabolites were identified as biomarkers, belonging to five pathways, including alpha-linolenic acid metabolism, taurine and hypotaurine metabolism, glycerophospholipid metabolism, arginine and proline metabolism, and primary bile acid biosynthesis. Conclusion This study provided a comprehensive description of metabolomic changes between PMR- and PMRP-treated rats. The underlying mechanisms require further research.

scholarly journals A Novel Validated Injectable Colistimethate Sodium Analysis Combining Advanced Chemometrics and Design of Experiments

Molecules ◽  
2021 ◽  
Vol 26 (6) ◽  
pp. 1546
Author(s):  
Ioanna Dagla ◽  
Anthony Tsarbopoulos ◽  
Evagelos Gikas
Keyword(s):  
Design Of Experiments ◽  
High Performance ◽  
Similarity Index ◽  
Principal Component ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Distance Measures ◽  
Linear Regression Models ◽  
Colistimethate Sodium

Colistimethate sodium (CMS) is widely administrated for the treatment of life-threatening infections caused by multidrug-resistant Gram-negative bacteria. Until now, the quality control of CMS formulations has been based on microbiological assays. Herein, an ultra-high-performance liquid chromatography coupled to ultraviolet detector methodology was developed for the quantitation of CMS in injectable formulations. The design of experiments was performed for the optimization of the chromatographic parameters. The chromatographic separation was achieved using a Waters Acquity BEH C8 column employing gradient elution with a mobile phase consisting of (A) 0.001 M aq. ammonium formate and (B) methanol/acetonitrile 79/21 (v/v). CMS compounds were detected at 214 nm. In all, 23 univariate linear-regression models were constructed to measure CMS compounds separately, and one partial least-square regression (PLSr) model constructed to assess the total CMS amount in formulations. The method was validated over the range 100–220 μg mL−1. The developed methodology was employed to analyze several batches of CMS injectable formulations that were also compared against a reference batch employing a Principal Component Analysis, similarity and distance measures, heatmaps and the structural similarity index. The methodology was based on freely available software in order to be readily available for the pharmaceutical industry.

scholarly journals Authentication of Greek PDO Kalamata Table Olives: A Novel Non-Target High Resolution Mass Spectrometric Approach

Molecules ◽  
2020 ◽  
Vol 25 (12) ◽  
pp. 2919
Author(s):  
Natasa P. Kalogiouri ◽  
Reza Aalizadeh ◽  
Marilena E. Dasenaki ◽  
Nikolaos S. Thomaidis
Keyword(s):  
High Performance ◽  
Model Development ◽  
Principal Component ◽  
Partial Least Square ◽  
Least Square ◽  
Table Olives ◽  
Method Performance ◽  
Validation Parameters ◽  
Targeted Screening ◽  
Mass Spectrometric Approach

Food science continually requires the development of novel analytical methods to prevent fraudulent actions and guarantee food authenticity. Greek table olives, one of the most emblematic and valuable Greek national products, are often subjected to economically motivated fraud. In this work, a novel ultra-high-performance liquid chromatography–quadrupole time of flight tandem mass spectrometry (UHPLC-QTOF-MS) analytical method was developed to detect the mislabeling of Greek PDO Kalamata table olives, and thereby establish their authenticity. A non-targeted screening workflow was applied, coupled to advanced chemometric techniques such as Principal Component Analysis (PCA) and Partial Least Square Discriminant Analysis (PLS-DA) in order to fingerprint and accurately discriminate PDO Greek Kalamata olives from Kalamata (or Kalamon) type olives from Egypt and Chile. The method performance was evaluated using a target set of phenolic compounds and several validation parameters were calculated. Overall, 65 table olive samples from Greece, Egypt, and Chile were analyzed and processed for the model development and its accuracy was validated. The robustness of the chemometric model was tested using 11 Greek Kalamon olive samples that were produced during the following crop year, 2018, and they were successfully classified as Greek Kalamon olives from Kalamata. Twenty-six characteristic authenticity markers were indicated to be responsible for the discrimination of Kalamon olives of different geographical origins.

scholarly journals A Study on Gentiana dahurica Fisch Ethanol Extract Alleviating Alcoholic Liver Disease in Mice: A Metabolomic Analysis of the Liver

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Houkang Cao ◽  
Yanxiu Guo ◽  
Ling Jin
Keyword(s):  
High Performance ◽  
Principal Component ◽  
Ethanol Extract ◽  
Partial Least Square ◽  
Least Square ◽  
Control Group ◽  
Metabolomic Analysis ◽  
Pyrimidine Metabolism ◽  
Alcohol Group ◽  
Glucose And Lipid Metabolism

We clarified the hepatoprotective effect of Gentiana dahurica Fisch ethanol extract (GDEE) in our previous study, and we further revealed the mechanism with the help of metabolomics technology in this study. The livers from Control group, Alcohol group, and Alcohol + GDEE group were analyzed by metabolomics. The metabolites in the liver were separated by ultra-high-performance liquid chromatography (UHPLC) and were tentatively identified using mass spectrometry (MS)/MS analysis. Differential metabolites were defined with VIP > 1 and P < 0.05 . Principal component analysis (PCA) and orthogonal partial least square discriminant analysis (OPLS-DA) were applied to analyze differences among these groups. The results showed that the groups could be clearly distinguished by PCA and OPLS-DA analysis. Alcohol and GDEE could change the overall profile of liver metabolites. Alterations in liver tissues of ALD mice induced by alcohol were mainly involved in the dipeptides, purine and pyrimidine metabolism and glucose and lipid metabolism, which could be partly affected by GDEE. This study revealed that the mechanism of GDEE in alleviating ALD had the characteristics of multitarget and multipathway.

scholarly journals LC–MS based metabolic fingerprinting of apricot pistils after self-compatible and self-incompatible pollinations

2020 ◽  
Author(s):  
József Lénárt ◽  
Attila Gere ◽  
Tim Causon ◽  
Stephan Hann ◽  
Mihály Dernovics ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
High Performance ◽  
Differential Expression Analysis ◽  
Principal Component ◽  
Prunus Armeniaca ◽  
Metabolic Fingerprinting ◽  
Negative Ion ◽  
Multivariate Statistical ◽  
Flight Mass Spectrometry ◽  
Incompatibility Reactions

Abstract Key message LC-MS based metabolomics approach revealed that putative metabolites other than flavonoids may significantly contribute to the sexual compatibility reactions in Prunus armeniaca. Possible mechanisms on related microtubule-stabilizing effects are provided. Abstract Identification of metabolites playing crucial roles in sexual incompatibility reactions in apricot (Prunus armeniaca L.) was the aim of the study. Metabolic fingerprints of self-compatible and self-incompatible apricot pistils were created using liquid chromatography coupled to time-of-flight mass spectrometry followed by untargeted compound search. Multivariate statistical analysis revealed 15 significant differential compounds among the total of 4006 and 1005 aligned metabolites in positive and negative ion modes, respectively. Total explained variance of 89.55% in principal component analysis (PCA) indicated high quality of differential expression analysis. The statistical analysis showed significant differences between genotypes and pollination time as well, which demonstrated high performance of the metabolic fingerprinting and revealed the presence of metabolites with significant influence on the self-incompatibility reactions. Finally, polyketide-based macrolides similar to peloruside A and a hydroxy sphingosine derivative are suggested to be significant differential metabolites in the experiment. These results indicate a strategy of pollen tubes to protect microtubules and avoid growth arrest involved in sexual incompatibility reactions of apricot.

scholarly journals A Preliminary Study of Uric Metabolomic Alteration for Postpartum Depression Based on Liquid Chromatography Coupled to Quadrupole Time-of-Flight Mass Spectrometry

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Li Zhang ◽  
Wei Zou ◽  
Yi Huang ◽  
Xiaoke Wen ◽  
Jianxi Huang ◽  
...  
Keyword(s):  
Postpartum Depression ◽  
Permutation Test ◽  
Disease Diagnosis ◽  
Partial Least Square ◽  
Least Square ◽  
Multivariate Statistical ◽  
Flight Mass Spectrometry ◽  
Metabolomic Data ◽  
Statistical Analysis Method ◽  
Metabolomic Study

Postpartum depression affects about 10-20% of newly delivered women, which is harmful for both mothers and infants. However, the current diagnosis of postpartum depression depends on the subjective judgment of a practitioner, which may lead to misdiagnosis. Hence, an appended objective diagnosis index may help the practitioner to improve diagnosis. A metabolomic study can find biomarkers as an objective index to facilitate disease diagnosis. Forty-nine postpartum depressed patients and 50 healthy controls were recruited into this study. The metabolites in urine were scanned with LC-Q-TOF-MS. The metabolomic data were analyzed with a multivariate statistical analysis method. Data from 40 patients and 40 controls were used for partial least square-discriminate analysis (PLS-DA). The urine metabolomic profiles of patients were different from those of controls. The PLS-DA model was validated by a permutation test, and the model could accurately classify the other 9 patients and 10 controls in T-prediction. Ten differentiating metabolites were found as main contributors to this difference, which are involved in amino acid metabolism, neurotransmitter metabolism, bacteria population, etc. Some of these potential biomarkers, such as 4-hydroxyhippuric acid, homocysteine, and tyrosine, showed relatively high sensitivities and specificities. The metabolic profile alteration induced by postpartum depression was found, and some of the differentiating metabolites may serve as biomarkers to facilitate the diagnosis of postpartum depression.

scholarly journals Implementasi Kansei Engineering pada Aplikasi E-learning Untuk Sekolah Menengah Kejuruan

2019 ◽  
Vol 9 (2) ◽  
pp. 165
Author(s):  
Yoga Megasyah
Keyword(s):  
Teaching And Learning ◽  
Logical Consequence ◽  
Principal Component ◽  
Word List ◽  
Partial Least Square ◽  
Least Square ◽  
Kansei Engineering ◽  
Multivariate Statistical ◽  
Coefficient Corrélation ◽  
E Learning

E-learning is the basis and logical consequence of the development of information and communication technology. With E-learning, teaching and learning methods in schools that use technology through electronic media such as computers, laptops, netbooks, or smartphones with internet networks or others. This study uses Kansei Word to detect the feelings of users of E-learning applications. The Kansei Word list is used as many 15 words related to the appearance of the E-learning application. E-learning application specimens used 8 specimens. This study involved 80 participants consisting of 40 students from SMK PGRI 3 Cimahi, 40 students from SMK 4 Padalarang. The questionnaire results from participants were then processed using multivariate statistical analysis, namely Cronbach's Alpha, Coefficient Correlation Analysis (CCA), Principal Component Analysis (PCA), Factor Analysis (FA) and Partial Least Square (PLS) analysis. This study produced 3 recommendations for the design of E-learning application. This recommendation is the result of the Kansei Engineering process which comes from 3 groups of participant data, namely groups of data of all participants, participants of SMK PGRI 3 Cimahi students and participants of SMK 4 Padalarang students.

scholarly journals Multiple Chromatographic Analysis of Urine in the Detection of Bladder Cancer

Diagnostics ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 1793
Author(s):  
Daniela Džubinská ◽  
Milan Zvarík ◽  
Boris Kollárik ◽  
Libuša Šikurová
Keyword(s):  
Bladder Cancer ◽  
Discriminant Analysis ◽  
High Performance ◽  
Partial Least Square ◽  
Least Square ◽  
Fluorescence Detector ◽  
Multivariate Statistical ◽  
Non Invasive ◽  
Invasive Method ◽  
Chromatographic Peaks

Bladder cancer (BC) is the most common type of carcinoma of the urological system. Recently, there has been an increasing interest in non-invasive diagnostic tumor markers due to the invasive attribute of cystoscopy, which is still considered the gold standard diagnostic method. However, markers published in the literature so far do not meet expectations for replacing cystoscopy due to their low specificity and excessively high false-positive results, which can be mainly caused by frequently occurring hematuria also in benign cases. No reliable non-invasive method has yet been identified that can distinguish patients with bladder cancer and non-malignant hematuria patients. Our work examined the possibilities of non-targeted biomarkers of urine to distinguish patients with malignant and non-malignant diseases of the bladder using 3D HPLC in combination with computer processing of multiple datasets. Urine samples from 47 patients, 23 patients with bladder cancer (BC) and 24 patients with non-malignant hematuria (NMHU), were enrolled in clinical trials. For the separation and subsequent analysis of a large number of urine components, 3D HPLC (high-performance liquid chromatography) with an absorption and fluorescence detector was used. The obtained dataset was further subjected to various uni- and multi-dimensional statistical analyses and mathematical modeling. We found 334 chromatographic peaks, of which 18 peaks were identified as significantly different for BC and NMHU patients. Using receiver operating characteristic (ROC) analysis, we assessed the informative ability of significant chromatographic peaks (90% sensitivity and 74% specificity). By logistic regression, we identified the optimal and simplified set of seven chromatographic peaks (5 absorptions plus 2 fluorescence) with strong classification power (100% sensitivity and 100% specificity) for distinguishing patients with bladder cancer and those with non-malignant hematuria. Partial least square discriminant analysis (PLS-DA) model and orthogonal projection to latent structure discriminant analysis (OPLS-DA) with 100% sensitivity and 96% specificity were used to distinguish BC and NMHU patients. Multivariate statistical analysis of urinary metabolomic profiles of patients revealed that BC patients can be discriminated from NMHU patients and the results can likely contribute to an early and non-invasive diagnosis of BC.

scholarly journals Different Metabolites in the Roots, Seeds, and Leaves of Acanthopanax senticosus and Their Role in Alleviating Oxidative Stress

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Jie Su ◽  
Qi Wang ◽  
Zhifeng Li ◽  
Yan Feng ◽  
Yan Li ◽  
...  
Keyword(s):  
Oxidative Stress ◽  
Phenolic Acid ◽  
Principal Component ◽  
Partial Least Square ◽  
Least Square ◽  
Chemical Components ◽  
Leaf Extracts ◽  
Multivariate Statistical ◽  
Acanthopanax Senticosus ◽  
Different Parts

In this study, we examined the metabolites from different parts of Acanthopanax senticosus and their role in alleviating damage caused by oxidative stress. We used UHPLC-QTOF-MS to analyze the chemical components in the root, seed, and leaf extracts of A. senticosus. Two multivariate statistical analysis methods—namely, principal component analysis and partial least square discriminant analysis—were used to distinguish the samples obtained from different parts of the plant. Using univariate statistics, 130 different metabolites were screened out. Among these, the relative content of flavonoids and terpenoids was found to be highest in the leaves, the lignin and phenolic acid content was highest in the roots, and the amino acid and phenolic acid levels were highest in seeds. An MTT assay was used to test the anti-H2O2 oxidative damage to PC12 cells in different parts of the sample. Lastly, using Pearson’s correlation analysis, various metabolites from different parts of A. senticosus were correlated with their antioxidant effects from the corresponding parts. Fifty-two related different metabolites were found, of which 20 metabolites that were positively correlated to oxidative stress were present at a relatively higher level in the roots, whereas 32 metabolites that were negatively correlated were present at relatively higher levels in the seeds and leaves. The results of this study reveal the distribution characteristics and the antioxidant activity of different metabolites of A. senticosus and provide a reference for the rational development of its medicinal parts.

scholarly journals Feed forensic strategy: Incorporating multivariate data analysis with high-performance liquid chromatography refractive index detector and differential scanning calorimeter for authentication of fish feed sources

2021 ◽  
Author(s):  
Mohamed Haniff Hanafy Idris ◽  
Muhamad Shirwan Abdullah Sani ◽  
Amalia Mohd Hashim ◽  
Nor Nadiha Mohd Zaki ◽  
Yanty Noorzianna Abdul Manaf ◽  
...  
Keyword(s):  
Mean Square Error ◽  
High Performance ◽  
Principal Component ◽  
Factor Loading ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
High Loading ◽  
Fish Feed ◽  
Mean Square

Abstract This study authenticated fish feed sources and determined lard adulteration using dataset pre-processing, principal component analysis (PCA), discriminant analysis (DA) and partial least square regression (PLSR) on 19 triacylglycerols (TAGs) and 16 thermal properties (TPs). At cumulative variability (90.625%) and Keiser-Meyer Olkin (KMO) value (0.811), the PCA identified strong factor loading variables, i.e., OLL, PLL, OOL, POL, PPL, POO, PPO, PSO, ICT and FHT in PC1 and LLLn, OOO and CT2 in PC2. These variables were significantly (p < 0.05) contributing to lard-palm-oil (L-PO) clusters: (1) POO, PPO and PPL (high loading) and OLL, PLL, OOL, ICT, POL, PSO and FHT (low loading) in 0:100 and 25:75 L-PO clusters; (2) CT2, OOO and LLLn (high loading) in 50:50 L-PO cluster; and (3) OLL, PLL, OOL, ICT, POL, PSO and FHT (high loading) and POO, PPO and PPL (low loading) in 72:25 and 100:0 L-PO clusters. Training, validation and testing datasets had 100%, 84.44% and 100% correct-classification, respectively at p < 0.0001 of Wilks' lambda and p < 0.0001 Fisher distance. The DA selected PLL, OOL, POL, PPL, PSO, ICT and FHT as the significantly authenticating biomarkers (p < 0.05). With determination coefficient (R²) (0.9693), mean square error (MSE) (38.382) and root mean square error (RMSE) (6.195), the PLSR's variable importance in the projection (VIP) identified the most influential biomarkers, i.e., PPL, POL, PPO, OOL, ICT, PLL, FHT, POO and OLL. The Z-test result (p > 0.05) indicated that the PLSR could determine the lard adulteration percentage in fish feed.

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