scholarly journals Urinary Volatomic Expression Pattern: Paving the Way for Identification of Potential Candidate Biosignatures for Lung Cancer

Metabolites ◽  
2022 ◽  
Vol 12 (1) ◽  
pp. 36
Author(s):  
Khushman Taunk ◽  
Priscilla Porto-Figueira ◽  
Jorge A. M. Pereira ◽  
Ravindra Taware ◽  
Nattane Luíza da Costa ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Fatty Acid Biosynthesis ◽  
Solid Phase ◽  
Partial Least Square ◽  
Least Square ◽  
Gas Chromatography Mass Spectrometry ◽  
Support Vector ◽  
Control Group ◽  
Potential Candidate ◽  
Metabolic Pathway Analysis

The urinary volatomic profiling of Indian cohorts composed of 28 lung cancer (LC) patients and 27 healthy subjects (control group, CTRL) was established using headspace solid phase microextraction technique combined with gas chromatography mass spectrometry methodology as a powerful approach to identify urinary volatile organic metabolites (uVOMs) to discriminate among LC patients from CTRL. Overall, 147 VOMs of several chemistries were identified in the intervention groups—including naphthalene derivatives, phenols, and organosulphurs—augmented in the LC group. In contrast, benzene and terpenic derivatives were found to be more prevalent in the CTRL group. The volatomic data obtained were processed using advanced statistical analysis, namely partial least square discriminative analysis (PLS-DA), support vector machine (SVM), random forest (RF), and multilayer perceptron (MLP) methods. This resulted in the identification of nine uVOMs with a higher potential to discriminate LC patients from CTRL subjects. These were furan, o-cymene, furfural, linalool oxide, viridiflorene, 2-bromo-phenol, tricyclazole, 4-methyl-phenol, and 1-(4-hydroxy-3,5-di-tert-butylphenyl)-2-methyl-3-morpholinopropan-1-one. The metabolic pathway analysis of the data obtained identified several altered biochemical pathways in LC mainly affecting glycolysis/gluconeogenesis, pyruvate metabolism, and fatty acid biosynthesis. Moreover, acetate and octanoic, decanoic, and dodecanoic fatty acids were identified as the key metabolites responsible for such deregulation. Furthermore, studies involving larger cohorts of LC patients would allow us to consolidate the data obtained and challenge the potential of the uVOMs as candidate biomarkers for LC.

scholarly journals Comparison of flavour fingerprint, electronic nose and multivariate analysis for discrimination of extra virgin olive oils

2019 ◽  
Vol 6 (3) ◽  
pp. 190002
Author(s):  
Qi Zhou ◽  
Shaomin Liu ◽  
Ye Liu ◽  
Huanlu Song
Keyword(s):  
Multivariate Analysis ◽  
Electronic Nose ◽  
Corn Oil ◽  
Solid Phase ◽  
Partial Least Square ◽  
Least Square ◽  
Gas Chromatography Mass Spectrometry ◽  
Virgin Olive Oils ◽  
Olive Oils ◽  
Coefficient Method

Flavour is a special way to discriminate extra virgin olive oils (EVOOs) from other aroma plant oils. In this study, different ratios (5, 10, 15, 20, 30, 50, 70 and 100%) of peanut oil (PO), corn oil (CO) and sunflower seed oil (SO) were discriminated from raw EVOO using flavour fingerprint, electronic nose and multivariate analysis. Fifteen different samples of EVOO were selected to establish the flavour fingerprint based on eight common peaks in solid-phase microextraction–gas chromatography–mass spectrometry corresponding to 4-methyl-2-pentanol, ( E )-2-hexenal, 1-tridecene, hexyl acetate, ( Z )-3-hexenyl acetate, ( E )-2-heptenal, nonanal and α-farnesene. Partial least square discrimination analysis (PLS-DA) was used to differentiate EVOOs and mixed oils containing more than 20% of PO, CO and SO. Furthermore, better discrimination efficiency was observed in PLS-DA than PCA (70% of CO and SO), which was equivalent to the correlation coefficient method of the fingerprint (20% of PO, CO and SO). The electronic nose was able to differentiate oil samples from samples containing 5% mixture. The discrimination method was selected based on the actual requirements of quality control.

scholarly journals Chemical Markers to Distinguish the Homo- and Heterozygous Bitter Genotype in Sweet Almond Kernels

Foods ◽  
2020 ◽  
Vol 9 (6) ◽  
pp. 747
Author(s):  
Stefania Vichi ◽  
Morgana N. Mayer ◽  
Maria G. León-Cárdenas ◽  
Beatriz Quintanilla-Casas ◽  
Alba Tres ◽  
...  
Keyword(s):  
Solid Phase ◽  
Single Gene ◽  
Partial Least Square ◽  
Least Square ◽  
Classification Model ◽  
Gas Chromatography Mass Spectrometry ◽  
Chemical Markers ◽  
Volatile Metabolites ◽  
First Time ◽  
Sweet Almond

Bitterness in almonds is controlled by a single gene (Sk dominant for sweet kernel, sk recessive for bitter kernel) and the proportions of the offspring genotypes (SkSk, Sksk, sksk) depend on the progenitors’ genotype. Currently, the latter is deduced after crossing by recording the phenotype of their descendants through kernel tasting. Chemical markers to early identify parental genotypes related to bitter traits can significantly enhance the efficiency of almond breeding programs. On this basis, volatile metabolites related to almond bitterness were investigated by Solid Phase Microextraction-Gas Chromatography-Mass Spectrometry coupled to univariate and multivariate statistics on 244 homo- and heterozygous samples from 42 different cultivars. This study evidenced the association between sweet almonds’ genotype and some volatile metabolites, in particular benzaldehyde, and provided for the first time chemical markers to discriminate between homo- and heterozygous sweet almond genotypes. Furthermore, a multivariate approach based on independent variables was developed to increase the reliability of almond classification. The Partial Least Square-Discriminant Analysis classification model built with selected volatile metabolites that showed discrimination capacity allowed a 98.0% correct classification. The metabolites identified, in particular benzaldehyde, become suitable markers for the early genotype identification in almonds, while a DNA molecular marker is not yet available.

scholarly journals A multi internal standard calibration approach for the automated and robust analysis of wine volatiles by headspace Solid Phase Microextraction and further Gas Chromatography-Mass Spectrometry

1970 ◽  
pp. 34
Author(s):  
P. Herrero ◽  
J. Zapata ◽  
J. Cacho ◽  
Vicente Ferreira
Keyword(s):  
Volatile Compounds ◽  
Solid Phase Microextraction ◽  
Solid Phase ◽  
Relative Weight ◽  
Experimental Parameter ◽  
Internal Standard ◽  
Partial Least Square ◽  
Least Square ◽  
Gas Chromatography Mass Spectrometry ◽  
Volatile Constituents

Head space solid phase microextraction (HS-SPME) is a solvent-free technique that allows an almost complete automatization and getting amazing sensitivities. The hidden risk of SPME lies in the fact that as the amount of analyte extracted is very low; it is extremely sensitive to any experimental parameter that may affect the liquid-gas and gas-solid distribution coefficients. Our aims are to measure the relative weight of these factors on the lack of accuracy, and to design a robust calibration system able to avoid or limit their effects.For the first goal, synthetic but real-like wines containing a fixed amount of selected analytes (70) and variable amounts of ethanol, non-volatile constituents and major volatile constituents were prepared following a 3-Factor complete Factorial design. The study of the relevance of the Factors carried out by analysis of variance (ANOVA) and by Principal Component Analysis revealed that the levels of major volatile constituents affected the extraction of most analytes, while ethanol and matrix affected particularly low volatile compounds. Lipophilic esters are most influenced by major volatile compounds, while acids, phenols and lactones are affected by the non-volatile matrix.13 different internal standard compounds belonging to different chemical classes were used in the calibration experiment. This was similar to the aforementioned experiment, but including as well 5 different concentration levels. In 29 out of 65 cases, a single internal standard provided a robust calibration guaranteeing an accuracy better than 10%, while in others a Partial Least Square Regression analysis was run in order to find a model able to provide maxima accuracy. Satisfactory models in terms of precision, linearity and recovery could be built for 30 other compounds, so that the method can quantify up to 59 relevant wine volatile compounds.

Rapid Determining Contents of the Rhubarb Anthraquinones Compounds by Support Vector Machines Modeling based on Near Infrared Spectra

2020 ◽  
Vol 16 ◽  
Author(s):  
Linqi Liu ◽  
JInhua Luo ◽  
Chenxi Zhao ◽  
Bingxue Zhang ◽  
Wei Fan ◽  
...  
Keyword(s):  
Infrared Spectra ◽  
Near Infrared ◽  
Mean Squared Error ◽  
Rapid Determination ◽  
Partial Least Square ◽  
Least Square ◽  
Support Vector ◽  
Near Infrared Spectra ◽  
Aloe Emodin ◽  
Relative Differences

BACKGROUND: Measuring medicinal compounds to evaluate their quality and efficacy has been recognized as a useful approach in treatment. Rhubarb anthraquinones compounds (mainly including aloe-emodin, rhein, emodin, chrysophanol and physcion) are its main effective components as purgating drug. In the current Chinese Pharmacopoeia, the total anthraquinones content is designated as its quantitative quality and control index while the content of each compound has not been specified. METHODS: On the basis of forty rhubarb samples, the correlation models between the near infrared spectra and UPLC analysis data were constructed using support vector machine (SVM) and partial least square (PLS) methods according to Kennard and Stone algorithm for dividing the calibration/prediction datasets. Good models mean they have high correlation coefficients (R2) and low root mean squared error of prediction (RMSEP) values. RESULTS: The models constructed by SVM have much better performance than those by PLS methods. The SVM models have high R2 of 0.8951, 0.9738, 0.9849, 0.9779, 0.9411 and 0.9862 that correspond to aloe-emodin, rhein, emodin, chrysophanol, physcion and total anthraquinones contents, respectively. The corresponding RMSEPs are 0.3592, 0.4182, 0.4508, 0.7121, 0.8365 and 1.7910, respectively. 75% of the predicted results have relative differences being lower than 10%. As for rhein and total anthraquinones, all of the predicted results have relative differences being lower than 10%. CONCLUSION: The nonlinear models constructed by SVM showed good performances with predicted values close to the experimental values. This can perform the rapid determination of the main medicinal ingredients in rhubarb medicinal materials.

scholarly journals Monitoring the Foliar Nutrients Status of Mango Using Spectroscopy-Based Spectral Indices and PLSR-Combined Machine Learning Models

2021 ◽  
Vol 13 (4) ◽  
pp. 641
Author(s):  
Gopal Ramdas Mahajan ◽  
Bappa Das ◽  
Dayesh Murgaokar ◽  
Ittai Herrmann ◽  
Katja Berger ◽  
...  
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Support Vector ◽  
Spectral Indices ◽  
Learning Models ◽  
Leaf Nutrients ◽  
Machine Learning Models

Conventional methods of plant nutrient estimation for nutrient management need a huge number of leaf or tissue samples and extensive chemical analysis, which is time-consuming and expensive. Remote sensing is a viable tool to estimate the plant’s nutritional status to determine the appropriate amounts of fertilizer inputs. The aim of the study was to use remote sensing to characterize the foliar nutrient status of mango through the development of spectral indices, multivariate analysis, chemometrics, and machine learning modeling of the spectral data. A spectral database within the 350–1050 nm wavelength range of the leaf samples and leaf nutrients were analyzed for the development of spectral indices and multivariate model development. The normalized difference and ratio spectral indices and multivariate models–partial least square regression (PLSR), principal component regression, and support vector regression (SVR) were ineffective in predicting any of the leaf nutrients. An approach of using PLSR-combined machine learning models was found to be the best to predict most of the nutrients. Based on the independent validation performance and summed ranks, the best performing models were cubist (R2 ≥ 0.91, the ratio of performance to deviation (RPD) ≥ 3.3, and the ratio of performance to interquartile distance (RPIQ) ≥ 3.71) for nitrogen, phosphorus, potassium, and zinc, SVR (R2 ≥ 0.88, RPD ≥ 2.73, RPIQ ≥ 3.31) for calcium, iron, copper, boron, and elastic net (R2 ≥ 0.95, RPD ≥ 4.47, RPIQ ≥ 6.11) for magnesium and sulfur. The results of the study revealed the potential of using hyperspectral remote sensing data for non-destructive estimation of mango leaf macro- and micro-nutrients. The developed approach is suggested to be employed within operational retrieval workflows for precision management of mango orchard nutrients.

scholarly journals Blood Glucose Level Regression for Smartphone PPG Signals Using Machine Learning

2021 ◽  
Vol 11 (2) ◽  
pp. 618
Author(s):  
Tanvir Tazul Islam ◽  
Md Sajid Ahmed ◽  
Md Hassanuzzaman ◽  
Syed Athar Bin Amir ◽  
Tanzilur Rahman
Keyword(s):  
Blood Glucose ◽  
Blood Glucose Level ◽  
Glucose Level ◽  
Quantitative Estimation ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Support Vector ◽  
Frequency Noise ◽  
Optical Noise

Diabetes is a chronic illness that affects millions of people worldwide and requires regular monitoring of a patient’s blood glucose level. Currently, blood glucose is monitored by a minimally invasive process where a small droplet of blood is extracted and passed to a glucometer—however, this process is uncomfortable for the patient. In this paper, a smartphone video-based noninvasive technique is proposed for the quantitative estimation of glucose levels in the blood. The videos are collected steadily from the tip of the subject’s finger using smartphone cameras and subsequently converted into a Photoplethysmography (PPG) signal. A Gaussian filter is applied on top of the Asymmetric Least Square (ALS) method to remove high-frequency noise, optical noise, and motion interference from the raw PPG signal. These preprocessed signals are then used for extracting signal features such as systolic and diastolic peaks, the time differences between consecutive peaks (DelT), first derivative, and second derivative peaks. Finally, the features are fed into Principal Component Regression (PCR), Partial Least Square Regression (PLS), Support Vector Regression (SVR) and Random Forest Regression (RFR) models for the prediction of glucose level. Out of the four statistical learning techniques used, the PLS model, when applied to an unbiased dataset, has the lowest standard error of prediction (SEP) at 17.02 mg/dL.

scholarly journals Impacts of multicollinearity on CAPT modalities: An heterogeneous machine learning framework for computer-assisted French phoneme pronunciation training

PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0257901
Author(s):  
Yanjing Bi ◽  
Chao Li ◽  
Yannick Benezeth ◽  
Fan Yang
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Support Vector Machines ◽  
Partial Least Square ◽  
Least Square ◽  
Support Vector ◽  
Computer Assisted ◽  
Long Distance ◽  
Relationship Analysis ◽  
Vector Machines

Phoneme pronunciations are usually considered as basic skills for learning a foreign language. Practicing the pronunciations in a computer-assisted way is helpful in a self-directed or long-distance learning environment. Recent researches indicate that machine learning is a promising method to build high-performance computer-assisted pronunciation training modalities. Many data-driven classifying models, such as support vector machines, back-propagation networks, deep neural networks and convolutional neural networks, are increasingly widely used for it. Yet, the acoustic waveforms of phoneme are essentially modulated from the base vibrations of vocal cords, and this fact somehow makes the predictors collinear, distorting the classifying models. A commonly-used solution to address this issue is to suppressing the collinearity of predictors via partial least square regressing algorithm. It allows to obtain high-quality predictor weighting results via predictor relationship analysis. However, as a linear regressor, the classifiers of this type possess very simple topology structures, constraining the universality of the regressors. For this issue, this paper presents an heterogeneous phoneme recognition framework which can further benefit the phoneme pronunciation diagnostic tasks by combining the partial least square with support vector machines. A French phoneme data set containing 4830 samples is established for the evaluation experiments. The experiments of this paper demonstrates that the new method improves the accuracy performance of the phoneme classifiers by 0.21 − 8.47% comparing to state-of-the-arts with different data training data density.

scholarly journals Characterization of α-Glucosidase Inhibitors from Clinacanthus nutans Lindau Leaves by Gas Chromatography-Mass Spectrometry-Based Metabolomics and Molecular Docking Simulation

Molecules ◽  
2018 ◽  
Vol 23 (9) ◽  
pp. 2402 ◽  
Author(s):  
Suganya Murugesu ◽  
Zalikha Ibrahim ◽  
Qamar-Uddin Ahmed ◽  
Nik-Idris Nik Yusoff ◽  
Bisha-Fathamah Uzir ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Gas Chromatography ◽  
Molecular Docking ◽  
Acid Methyl Ester ◽  
Docking Simulation ◽  
Partial Least Square ◽  
Least Square ◽  
Gas Chromatography Mass Spectrometry ◽  
Glucosidase Inhibitors ◽  
Clinacanthus Nutans

Background: Clinacanthus nutans (C. nutans) is an Acanthaceae herbal shrub traditionally consumed to treat various diseases including diabetes in Malaysia. This study was designed to evaluate the α-glucosidase inhibitory activity of C. nutans leaves extracts, and to identify the metabolites responsible for the bioactivity. Methods: Crude extract obtained from the dried leaves using 80% methanolic solution was further partitioned using different polarity solvents. The resultant extracts were investigated for their α-glucosidase inhibitory potential followed by metabolites profiling using the gas chromatography tandem with mass spectrometry (GC-MS). Results: Multivariate data analysis was developed by correlating the bioactivity, and GC-MS data generated a suitable partial least square (PLS) model resulting in 11 bioactive compounds, namely, palmitic acid, phytol, hexadecanoic acid (methyl ester), 1-monopalmitin, stigmast-5-ene, pentadecanoic acid, heptadecanoic acid, 1-linolenoylglycerol, glycerol monostearate, alpha-tocospiro B, and stigmasterol. In-silico study via molecular docking was carried out using the crystal structure Saccharomyces cerevisiae isomaltase (PDB code: 3A4A). Interactions between the inhibitors and the protein were predicted involving residues, namely LYS156, THR310, PRO312, LEU313, GLU411, and ASN415 with hydrogen bond, while PHE314 and ARG315 with hydrophobic bonding. Conclusion: The study provides informative data on the potential α-glucosidase inhibitors identified in C. nutans leaves, indicating the plant’s therapeutic effect to manage hyperglycemia.

MO005HIGH LEVELS OF FIVE SPECIFIC FECAL METABOLITES IN IGA NEPHROPATHY PATIENTS SUPPORT THE HYPOTHESIS OF THE INTESTINAL-RENAL AXIS CONNECTION

2020 ◽  
Vol 35 (Supplement_3) ◽  
Author(s):  
Claudia Curci ◽  
Fabio Sallustio ◽  
Nada Chaoul ◽  
Angela Picerno ◽  
Gabriella Lauriero ◽  
...  
Keyword(s):  
B Cells ◽  
Iga Nephropathy ◽  
Solid Phase ◽  
Serum Levels ◽  
Intestinal Lumen ◽  
Gas Chromatography Mass Spectrometry ◽  
Control Group ◽  
Intestinal Immunity ◽  
Adaptive Immune Cells

Abstract Background and Aims The IgA nephropathy (IgAN) is the most frequent primitive glomerulonephritis. In the last years, the role of mucosal immunity in IgAN, together with that of the gut microbiota in the activation of innate and adaptive immune cells, has gained importance. Particularly interesting is the role of the microbiota and intestinal immunity in IgAN. BAFF and APRIL can be produced by the intestinal epithelium, in response to signals triggered by TLRs once activated by the commensal bacteria present in the intestinal lumen, representing the link between microbiota and intestinal immunity. To date, even if hypothesized, this relationship in IgAN patients has not been investigated. Here, we studied the intestinal-renal axis connections analyzing levels of BAFF, April and intestinal-activated B cells in IgAN patients. Method Serum and fecal samples were collected from 44 IgAN patients, 22 non-IgA glomerulonephritides (controls) and 22 healthy subjects (HS) with similar clinical features. BAFF and APRIL serum levels were measured by ELISA assay. Metabolomic analysis of fecal microbiome was performed using Biochrom 30 series amino acid analyzer and gas-chromatography mass spectrometry/solid-phase microextraction (GC-MS/SPME) analysis. B cell subsets were investigated by FACS. Results IgAN patients had increased serum levels of BAFF cytokine compared to the control group of patients with non-IgA glomerulonephritis and compared with HS (p<0.0001and p=0.012, respectively). We found that serum BAFF levels positively correlated with the levels of 24h-proteinuria in IgAN patients (r2 = 0.2269, p <0.001). We correlated serum BAFF levels with fecal concentration of 5 different metabolites of 30 IgAN patients, which were previously investigated for the fecal microbiota. These organic compounds had been found at significantly higher levels in the feces of IgAN patients compared to HS. Serum BAFF levels positively correlated with the levels of fecal metabolites: 4-(1,1,3,3-tetramethylbutyl) phenol (r2 = 0.2882, p = 0.0027), p-tert-butyl-phenol (r2 = 0.386, p = 0.0003), methyl neopentyl phthalic acid (r2 = 0.3491, p =0.0007), hexadecyl ester benzoic acid (r2 = 0.2832, p =0.003) and furanone A (r2 = 0.1743, p = 0.024). Serum levels of APRIL were significantly increased in IgAN patients respect to control groups (4.49 ± 0.54 vs 2.27 ± 1 ng/ml, p=0.0014). We found a correlation between APRIL and serum creatinine (r2 = 0.159, p =0.04) and eGFR (r2 = 0.2395, p =0.0082), while no correlation was found between APRIL and fecal metabolite levels in IgAN patients. In addition, we found that subjects with IgAN have a significantly higher proportion of circulating Bregs, Memory B cells and IgA secreting-plasmablasts activated at the intestinal level (CCR9+INTB7+) compared to HS. Conclusion The results of our study showed for the first time an important correlation of serum levels of BAFF with intestinal microbiota in patients with IgAN, confirming the hypothesis of the pathogenic role of intestinal mucosal hyperresponsiveness in the IgAN patients. The intestinal-renal axis plays a crucial role in Berger's glomerulonephritis, whose complex pathogenesis may contribute several factors as genetics, pathogens and food.

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