Nuclear Magnetic Resonance Permeability Models by Partial Least Square Regression

2011 ◽  
Author(s):  
Edmilson Helton Rios ◽  
Giovanni Chaves Stael ◽  
Paulo Frederico de Oliveira Ramos ◽  
Vinicius de França Machado ◽  
Rodrigo Bagueira de Vasconcellos Azeredo
Keyword(s):  
Nuclear Magnetic Resonance ◽  
Magnetic Resonance ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Least Square Regression ◽  
Nuclear Magnetic

scholarly journals Changes in the Salivary Metabolic Profile of Generalized Periodontitis Patients after Non-Surgical Periodontal Therapy. A Metabolomic Analysis Using Nuclear Magnetic Resonance Spectroscopy

2020 ◽  
Author(s):  
Filippo Citterio ◽  
Federica Romano ◽  
Gaia Meoni ◽  
Giovanni Iaderosa ◽  
Silvia Grossi ◽  
...  
Keyword(s):  
Nuclear Magnetic Resonance ◽  
Magnetic Resonance ◽  
Metabolic Profile ◽  
Periodontal Therapy ◽  
Partial Least Square ◽  
Least Square ◽  
Resonance Spectroscopy ◽  
Metabolomic Profile ◽  
Whole Saliva ◽  
Nuclear Magnetic

Pattern analysis of salivary metabolic profile has been proven accurate to discriminate generalized periodontitis (GP) patients from healthy individuals (HI) as disease modifies the salivary concentrations of specific metabolites. Due to the scarcity of data in the literature, the aim of this study was to determine whether non-surgical periodontal therapy (NST) could change salivary metabolomic profile in GP to one more similar to HI. Unstimulated whole saliva of 11 HI and 12 GP patients were obtained prior to and 3 months after NST. Metabolic profiling was performed using Nuclear Magnetic Resonance (NMR) spectroscopy, followed by supervised multivariate statistical approach on entire saliva spectra and partial least square (PLS) discriminant analysis. In GP group, periodontal treatment improved all clinical parameters, but not all the diseased sites were eradicated. PLS revealed an accuracy of 100% in discriminating the metabolomic profile of each GP patient before and after NST. OPLS was able to discriminate the 3 groups of subjects with an accuracy of 85.6%. However the post-NST metabolic profile of GP patients could not be completely assimilated to that of HS. Although NST may produce significant changes in the metabolic profile, GP patients maintained a distinctive fingerprint compared to HI.

scholarly journals Changes in the Salivary Metabolic Profile of Generalized Periodontitis Patients after Non-surgical Periodontal Therapy: A Metabolomic Analysis Using Nuclear Magnetic Resonance Spectroscopy

2020 ◽  
Vol 9 (12) ◽  
pp. 3977
Author(s):  
Filippo Citterio ◽  
Federica Romano ◽  
Gaia Meoni ◽  
Giovanni Iaderosa ◽  
Silvia Grossi ◽  
...  
Keyword(s):  
Nuclear Magnetic Resonance ◽  
Magnetic Resonance ◽  
Metabolic Profile ◽  
Periodontal Therapy ◽  
Partial Least Square ◽  
Least Square ◽  
Resonance Spectroscopy ◽  
Metabolic Profiles ◽  
Multivariate Statistical ◽  
Nuclear Magnetic

Pattern analysis of the salivary metabolic profile has been proven accurate in discriminating between generalized periodontitis (GP) patients and healthy individuals (HI), as this disease modifies the salivary concentrations of specific metabolites. Due to the scarcity of data from previous studies, this study aimed to evaluate if non-surgical periodontal therapy (NST) could affect the metabolomic profile in GP patients’ saliva and if it compares to that of HI. Unstimulated salivary samples were collected from 11 HI and 12 GP patients before and 3 months after NST. Nuclear Magnetic Resonance (NMR) spectroscopy, followed by a supervised multivariate statistical approach on entire saliva spectra and partial least square (PLS) discriminant analysis, were performed to obtain metabolic profiles. In the GP group, periodontal treatment improved all clinical parameters, but not all the diseased sites were eradicated. PLS revealed an accuracy of 100% in distinguishing between metabolic profiles of GP patients before and after NST. Orthogonal projection to latent structure was able to discriminate between the three groups of subjects with an accuracy of 85.6%. However, the post-NST metabolic profile of GP patients could not be completely assimilated to that of HI. Although NST may produce significant changes in the metabolic profile, GP patients maintained a distinctive fingerprint compared to HI.

scholarly journals An Evaluation of 1-Deoxynojirimycin Oral Administration in Eri Silkworm through Fat Body Metabolomics Based on1H Nuclear Magnetic Resonance

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Chao-wei Wen ◽  
Xiao-dong Lin ◽  
Min-jian Dong ◽  
Ming-jie Deng
Keyword(s):  
Nuclear Magnetic Resonance ◽  
Energy Metabolism ◽  
Magnetic Resonance ◽  
Fat Body ◽  
Biological Effects ◽  
Partial Least Square ◽  
Least Square ◽  
Control Group ◽  
Metabolic Pattern ◽  
Nuclear Magnetic

1-Deoxynojirimycin (DNJ), the main hypoglycemic constituent in mulberry (Morus alba) latex, has been extensively researched. Although there is considerable interest in the biological effects of DNJ, the roles of 1-deoxynojirimycin (DNJ) in glycometabolism and energy metabolism in insects have received little attention. In this paper,1H nuclear magnetic resonance (1H NMR) based metabonomic was performed to study the effects of the oral supplementation of 0.25% DNJ, 0.5% DNJ, latex, and the mixture of 0.5% DNJ and latex (1 : 1) on the fat body glycometabolism and energy metabolism of the fourth-instar larvae of Eri silkworms,Samia cynthia ricini. Metabolic pattern recognition analysis (partial least square-discriminant analysis, PLS-DA) of fat body extracts indicated that the groups of 0.25% DNJ, 0.5% DNJ, latex, and the mixture of 0.5% DNJ and latex (1 : 1) were significantly different from the control group. Further, compared to the control group, the metabolites levels of lactate, trehalose, succinate, malate, and fumarate were remarkably changed in experimental groups, which were involved in glycolysis, hydrolysis of trehalose, and tricarboxylic acid (TCA) cycle. Our results indicate that DNJ has a positive impact on the reverse energy metabolism of Eri silkworms and metabonomic analysis based on NMR can be used as a tool to identify potential biomarkers.

DISCRIMINATION OF DENGUE DISEASE FROM HEALTHY BASED ON THE CHEMOMETRY OF 1H NUCLEAR MAGNETIC RESONANCE SPECTROSCOPY

2015 ◽  
Vol 77 (33) ◽  
Author(s):  
Nurul Shahfizaa ◽  
Maulidiani Maulidiani ◽  
Hasnah Osman ◽  
Tang T. Hock ◽  
Khozirah Shaari ◽  
...  
Keyword(s):  
Nuclear Magnetic Resonance ◽  
Nmr Spectroscopy ◽  
Magnetic Resonance ◽  
Specific Treatment ◽  
Partial Least Square ◽  
Least Square ◽  
Resonance Spectroscopy ◽  
Healthy Individuals ◽  
1H Nuclear Magnetic Resonance ◽  
Nuclear Magnetic

Dengue is the most important human viral disease transmitted by arthropod vectors and over half of the world's populations live in areas at risk of infection. The severity of the infection depends on the form of the disease, which can be symptomatic or asymptomatic. Currently there is neither specific treatment nor vaccine to tackle this emerging disease. Metabolomics applied in this study, aimed to provide a global snapshot of all small-molecule metabolites in urine as biological sample of choice to more focused studies of metabolism to distinguish between healthy and dengue infected subjects. Fifty-two patients diagnosed with dengue fever at Penang General Hospital and fourty-three healthy individuals were recruited in this study. 1H-nuclear magnetic resonance (NMR) spectroscopy combined with multivariate analysis (MVA) methods such as principal component analysis (PCA), partial least square discriminant analysis (PLS-DA) and orthogonal PLS-DA (OPLS-DA) were employed for statistical data exploration. The model score plot results showed that all three MVAs showed very good spatial distributions with clear clusters/grouping between healthy individuals and dengue infected individuals. Also, statistically, the PLS-DA and OPLS-DA models had high reproducibility and predictivity values, > 0.5. In conclusion, this study established the potential of using a combination of 1H NMR spectroscopy and multivariate data analyses in differentiating healthy and non-healthy individuals, based on obtained score plots reflecting the metabolites pertubation, where spectral features contributing most to variation or separation are identified for further analysis.

Modeling CO2 exchange and meteorological factors of an apple orchard using partial least square regression

2020 ◽  
Vol 27 (35) ◽  
pp. 43439-43451 ◽  
Author(s):  
Jianfeng Yang ◽  
Yumin Duan ◽  
Xiaoni Yang ◽  
Mukesh Kumar Awasthi ◽  
Huike Li ◽  
...  
Keyword(s):  
Meteorological Factors ◽  
Co2 Exchange ◽  
Apple Orchard ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Least Square Regression

QSPR MODELLING OF STABILITY CONSTANTS OF METAL-THIOSEMICARBAZONE COMPLEXES USING MULTIVARIATE REGRESSION METHODS AND ARTIFICIAL NEURAL NETWORK

2021 ◽  
Vol 36 (06) ◽  
Author(s):  
NGUYEN MINH QUANG ◽  
TRAN NGUYEN MINH AN ◽  
NGUYEN HOANG MINH ◽  
TRAN XUAN MAU ◽  
PHAM VAN TAT
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Stability Constants ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Structure Property ◽  
Least Square Regression ◽  
Thiosemicarbazone Complexes ◽  
Artificial Neural

In this study, the stability constants of metal-thiosemicarbazone complexes, logb11 were determined by using the quantitative structure property relationship (QSPR) models. The molecular descriptors, physicochemical and quantum descriptors of complexes were generated from molecular geometric structure and semi-empirical quantum calculation PM7 and PM7/sparkle. The QSPR models were built by using the ordinary least square regression (QSPROLS), partial least square regression (QSPRPLS), primary component regression (QSPRPCR) and artificial neural network (QSPRANN). The best linear model QSPROLS (with k of 9) involves descriptors C5, xp9, electric energy, cosmo volume, N4, SsssN, cosmo area, xp10 and core-core repulsion. The QSPRPLS, QSPR PCR and QSPRANN models were developed basing on 9 varibles of the QSPROLS model. The quality of the QSPR models were validated by the statistical values; The QSPROLS: R2train = 0.944, Q2LOO = 0.903 and MSE = 1.035; The QSPRPLS: R2train = 0.929, R2CV = 0.938 and MSE = 1.115; The QSPRPCR: R2train = 0.934, R2CV = 0.9485 and MSE = 1.147. The neural network model QSPRANN with architecture I(9)-HL(12)-O(1) was presented also with the statistical values: R2train = 0.9723, and R2CV = 0.9731. The QSPR models also were evaluated externally and got good performance results with those from the experimental literature.

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