scholarly journals The Metabolomic Profile of Lymphoma Subtypes: A Pilot Study

Molecules ◽  
2019 ◽  
Vol 24 (13) ◽  
pp. 2367 ◽  
Author(s):  
Barberini ◽  
Noto ◽  
Fattuoni ◽  
Satta ◽  
Zucca ◽  
...  
Keyword(s):  
Healthy Subjects ◽  
Small Sample Size ◽  
B Cell Lymphoma ◽  
Small Sample ◽  
Partial Least Square ◽  
Lymphocytic Leukemia ◽  
Least Square ◽  
Gas Chromatography Mass Spectrometry ◽  
Metabolomic Analysis ◽  
Population Controls

Lymphoma defines a group of different diseases. This study examined pre-treatment plasma samples from 66 adult patients (aged 20–74) newly diagnosed with any lymphoma subtype, and 96 frequency matched population controls. We used gas chromatography-mass spectrometry (GC-MS) to compare the metabolic profile by case/control status and across the major lymphoma subtypes. We conducted univariate and multivariate analyses, and partial least square discriminant analysis (PLS-DA). When compared to the controls, statistically validated models were obtained for diffuse large B-cell lymphoma (DLBCL), chronic lymphocytic leukemia (CLL), multiple myeloma (MM), and Hodgkin lymphoma (HL), but not follicular lymphoma (FL). The metabolomic analysis highlighted interesting differences between lymphoma patients and population controls, allowing the discrimination between pathologic and healthy subjects: Important metabolites, such as hypoxanthine and elaidic acid, were more abundant in all lymphoma subtypes. The small sample size of the individual lymphoma subtypes prevented obtaining PLS-DA validated models, although specific peculiar features of each subtype were observed; for instance, fatty acids were most represented in MM and HL patients, while 2-aminoadipic acid, 2-aminoheptanedioic acid, erythritol, and threitol characterized DLBCL and CLL. Metabolomic analysis was able to highlight interesting differences between lymphoma patients and population controls, allowing the discrimination between pathologic and healthy subjects. Further studies are warranted to understand whether the peculiar metabolic patterns observed might serve as early biomarkers of lymphoma.

scholarly journals Partial Least Square Analyses of Landscape and Surface Water Biota Associations in the Savannah River Basin

ISRN Ecology ◽  
2011 ◽  
Vol 2011 ◽  
pp. 1-11 ◽  
Author(s):  
Maliha S. Nash ◽  
Deborah J. Chaloud
Keyword(s):  
Surface Water ◽  
Small Sample Size ◽  
Surface Water Quality ◽  
Spatial Location ◽  
Small Sample ◽  
Partial Least Square ◽  
Least Square ◽  
Data Sets ◽  
Savannah River ◽  
Remotely Sensed Data

Ecologists are often faced with problem of small sample size, correlated and large number of predictors, and high noise-to-signal relationships. This necessitates excluding important variables from the model when applying standard multiple or multivariate regression analyses. In this paper, we present the results of applying PLS to explore relationships among biotic indicators of surface water quality and landscape conditions accounting for the above problems. Available field sampling and remotely sensed data sets for the Savannah Basin are used. We were able to develop models and compare results for the whole basin and for each ecoregion (Blue Ridge, Piedmont, and Coastal Plain) in spite of the data constraints. The amount of variability in surface water biota explained by each model reflects the scale, spatial location, and the composition of contributing landscape metrics. The landscape-biota model developed for the whole basin using PLS explains 43% and 80% of the variation in water biota and landscape data sets, respectively. Models developed for each of the three ecoregions indicate dominance of landscape variables which reflect the geophysical characteristics of that ecoregion.

scholarly journals Factors Affecting Whistleblowing Intention: Case Study on the Implementation of Village Funds

2020 ◽  
pp. 18-29
Author(s):  
Ulin Nuha Alfani ◽  
Fajar Gustiawaty Dewi ◽  
Susi Sarumpaet
Keyword(s):  
Locus Of Control ◽  
Latent Variables ◽  
Behavioral Control ◽  
Small Sample Size ◽  
Small Sample ◽  
Partial Least Square ◽  
Least Square ◽  
Path Model ◽  
Whistle Blowing ◽  
Consultative Body

This study aims to analyze the factors that influence the individual's intention to do whistle blowing. This study uses a questionnaire to gather the information needed. The variables used in this study are Subjective Norms, Attitudes Towards Behavior, Perceptions About Behavioral Control, Locus of Control, and Reward as independent variables and Intentions as dependent variables. The total samples in this study were 112 samples and using random sampling techniques in data collection. Respondents in this study were the Village Consultative Body in 7 Sub-districts in South Lampung District. Data were analyzed using Partial Least Square (PLS). The Partial Least Squares (PLS) technique was chosen because this tool is widely used to estimate the path model with a small sample size [1] then it is used for a very complex model (consisting of many latent variables and manifests) without problems [2]. The results of this study indicate that the subjective norm, attitudes toward behavior and the reward variable does not affect the individual's intention to do whistle blowing. Then, the behavioral control and locus of control variables indicate that the two variables affect the individual's intention to do whistleblowing.

scholarly journals Metabolomic Analysis of Plasma from GABAB(1) Knock-Out Mice Reveals Decreased Levels of Elaidic Trans-Fatty Acid

Metabolites ◽  
2020 ◽  
Vol 10 (12) ◽  
pp. 484
Author(s):  
Claudia Fattuoni ◽  
Luigi Barberini ◽  
Antonio Noto ◽  
Paolo Follesa
Keyword(s):  
Partial Least Square ◽  
Least Square ◽  
Gas Chromatography Mass Spectrometry ◽  
Gamma Aminobutyric Acid ◽  
Metabolomic Analysis ◽  
Wild Type ◽  
Metabolic Pattern ◽  
Knock Out ◽  
Spontaneous Seizures ◽  
Knock Out Mice

Mice lacking the GABAB(1) subunit of gamma-aminobutyric acid (GABA) type B receptors exhibit spontaneous seizures, hyperalgesia, hyperlocomotor activity, and memory impairment. Although mice lacking the GABAB(1) subunit are viable, they are sterile, and to generate knockout (KO) mice, it is necessary to cross heterozygous (HZ) mice. The aim of our study was to detect the metabolic differences between the three genotypes of GABAB(1) KO mice in order to further characterize this experimental animal model. Plasma samples were collected from wild-type (WT), HZ, and KO mice. Samples were analyzed by means of a gas chromatography-mass spectrometry (GC-MS) platform. Univariate t-test, and partial least square discriminant analysis (PLS-DA) were performed to compare the metabolic pattern of different genotypes. The metabolomic analysis highlighted differences between the three genotypes and identified some metabolites less abundant in KO mice, namely elaidic acid and other fatty acids, and chiro-inositol.

scholarly journals KAJIAN SIMULASI PERBANDINGAN METODE REGRESI KUADRAT TERKECIL PARSIAL, SUPPORT VECTOR MACHINE, DAN RANDOM FOREST

2020 ◽  
Vol 4 (1) ◽  
pp. 203-215
Author(s):  
Asep Andri Fauzi ◽  
Agus M. Soleh ◽  
Anik Djuraidah
Keyword(s):  
Random Forest ◽  
Small Sample Size ◽  
Small Sample ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Support Vector ◽  
Ordinary Least Square ◽  
Highly Correlated ◽  
Correlated Predictors

Highly correlated predictors and nonlinear relationships between response and predictors potentially affected the performance of predictive modeling, especially when using the ordinary least square (OLS) method. The simple technique to solve this problem is by using another method such as Partial Least Square Regression (PLSR), Support Vector Regression with kernel Radial Basis Function (SVR-RBF), and Random Forest Regression (RFR). The purpose of this study is to compare OLS, PLSR, SVR-RBF, and RFR using simulation data. The methods were evaluated by the root mean square error prediction (RMSEP). The result showed that in the linear model, SVR-RBF and RFR have large RMSEP; OLS and PLSR are better than SVR-RBF and RFR, and PLSR provides much more stable prediction than OLS in case of highly correlated predictors and small sample size. In nonlinear data, RFR produced the smallest RMSEP when data contains high correlated predictors.

scholarly journals Matching organizational frame of reference and business strategy with contemporary marketing practices

2016 ◽  
Vol 11 (4) ◽  
pp. 533-549 ◽  
Author(s):  
Allam Abu Farha
Keyword(s):  
Business Strategy ◽  
Survey Methodology ◽  
Assessment Tool ◽  
Small Sample Size ◽  
Small Sample ◽  
Frame Of Reference ◽  
Partial Least Square ◽  
Least Square ◽  
Marketing Practice ◽  
Content Type

Purpose The purpose of this paper is to investigate diversity of marketing practices of firms operating in the same environment, by identifying how management perception and business strategy (BS) fits with the choice of the marketing practice. Design/methodology/approach A model was developed and tested using survey methodology based on three well-validated research instruments. Data were analyzed using the partial least square approach. Findings The results showed that different marketing practice were coupled with different frame of reference, as well as different BS. These forces were found to be inter related, and internally coherent, resulting in viable configurational profiles. Research limitations/implications The research is unique and exploratory, and was conducted in three Arabic countries with a small sample size. For these reasons, generalizability is somewhat constrained. Practical implications The findings would help managers to carefully examine the internal logic of their marketing-related profiling; it can be used as an assessment tool, where performance should be enhanced if the variables are coherent. Originality/value To author’s knowledge this is the first study that inspect three variables that had been associated with decision making, but not integrated together in a holistic framework to explain marketing diversity. Additionally it identified four viable types of marketing practices with its corresponding frame of reference and BS. Therefore, the paper reports a work in an area not previously researched.

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 Hodgkin lymphoma arising in patients with chronic lymphocytic leukemia: outcomes from a large multi-center collaboration

Haematologica ◽  
2020 ◽  
pp. 0-0
Author(s):  
Deborah M. Stephens ◽  
Ken Boucher ◽  
Elizabeth Kander ◽  
Sameer A. Parikh ◽  
Erin M. Parry ◽  
...  
Keyword(s):  
Chronic Lymphocytic Leukemia ◽  
Hodgkin Lymphoma ◽  
Hematopoietic Cell Transplantation ◽  
Small Sample Size ◽  
Case Series ◽  
Standard Of Care ◽  
Median Number ◽  
Small Sample ◽  
Lymphocytic Leukemia ◽  
Treatment Type

Chronic lymphocytic leukemia (CLL) patients who develop Hodgkin lymphoma (HL) have limited survival. No current therapeutic standard of care exists. We conducted a multi-center retrospective study of patients with Hodgkin Transformation (HT) of CLL. Clinicobiologic characteristics, treatment type, and survival outcomes were analyzed and compared with historic case series. Ninety-four patients were identified. Median age at HT was 67 years (range, 38-85). Median time from CLL diagnosis to HT was 5.5 years (range, 0-20.2). Prior to HT, patients received a median of 2 therapies for CLL (range, 0-12). As initial therapy for HT, 61% (n=62) received ABVD-based regimens (adriamycin, bleomycin, vinblastine, and dacarbazine). Seven (7%) patients received hematopoietic cell transplantation (HCT) while in first complete remission (CR1). The median number of treatments for HT per patient was 1 (range, 0-5) with 59 (61%) patients only receiving one line of therapy. After HT, patients had a median follow-up of 1.6 years (range, 0-15.1). Two-year overall survival (OS) after HT diagnosis was 72% (95%CI 62-83%). The patients who received standard ABVD-based therapy had a median OS of 13.2 years. Although limited by small sample size, the patients who underwent HCT for HT in CR1 had a similar 2-year OS (n=7; 67%) compared to patients who did not undergo HCT for HT in CR1 (n=87; 72%; p=0.46). In this multi-center study, HT patients treated with ABVD-based regimens had prolonged survival supporting the use of these regimens as standard of care for these patients.

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.

scholarly journals Detecting Pulmonary Oxygen Toxicity Using eNose Technology and Associations between Electronic Nose and Gas Chromatography–Mass Spectrometry Data

Metabolites ◽  
2019 ◽  
Vol 9 (12) ◽  
pp. 286
Author(s):  
Thijs T. Wingelaar ◽  
Paul Brinkman ◽  
Rianne de Vries ◽  
Pieter-Jan A.M. van Ooij ◽  
Rigo Hoencamp ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Gas Chromatography ◽  
Electronic Nose ◽  
Oxygen Toxicity ◽  
Partial Least Square ◽  
Least Square ◽  
Sensor Data ◽  
Gas Chromatography Mass Spectrometry ◽  
Exhaled Breath ◽  
Chromatography Mass Spectrometry

Exposure to oxygen under increased atmospheric pressures can induce pulmonary oxygen toxicity (POT). Exhaled breath analysis using gas chromatography–mass spectrometry (GC–MS) has revealed that volatile organic compounds (VOCs) are associated with inflammation and lipoperoxidation after hyperbaric–hyperoxic exposure. Electronic nose (eNose) technology would be more suited for the detection of POT, since it is less time and resource consuming. However, it is unknown whether eNose technology can detect POT and whether eNose sensor data can be associated with VOCs of interest. In this randomized cross-over trial, the exhaled breath from divers who had made two dives of 1 h to 192.5 kPa (a depth of 9 m) with either 100% oxygen or compressed air was analyzed, at several time points, using GC–MS and eNose. We used a partial least square discriminant analysis, eNose discriminated oxygen and air dives at 30 min post dive with an area under the receiver operating characteristics curve of 79.9% (95%CI: 61.1–98.6; p = 0.003). A two-way orthogonal partial least square regression (O2PLS) model analysis revealed an R² of 0.50 between targeted VOCs obtained by GC–MS and eNose sensor data. The contribution of each sensor to the detection of targeted VOCs was also assessed using O2PLS. When all GC–MS fragments were included in the O2PLS model, this resulted in an R² of 0.08. Thus, eNose could detect POT 30 min post dive, and the correlation between targeted VOCs and eNose data could be assessed using O2PLS.

scholarly journals Analysis of protein glycation in human fingernail clippings with near-infrared (NIR) spectroscopy as an alternative technique for the diagnosis of diabetes mellitus

2018 ◽  
Vol 56 (9) ◽  
pp. 1551-1558 ◽  
Author(s):  
Tinne Monteyne ◽  
Renaat Coopman ◽  
Antoine S. Kishabongo ◽  
Jonas Himpe ◽  
Bruno Lapauw ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Healthy Subjects ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Partial Least Square ◽  
Least Square ◽  
Protein Glycation ◽  
Patients With Diabetes ◽  
Diagnosis Of Diabetes Mellitus

Abstract Background: Glycated keratin allows the monitoring of average tissue glucose exposure over previous weeks. In the present study, we wanted to explore if near-infrared (NIR) spectroscopy could be used as a non-invasive diagnostic tool for assessing glycation in diabetes mellitus. Methods: A total of 52 patients with diabetes mellitus and 107 healthy subjects were enrolled in this study. A limited number (n=21) of nails of healthy subjects were glycated in vitro with 0.278 mol/L, 0.556 mol/L and 0.833 mol/L glucose solution to study the effect of glucose on the nail spectrum. Consequently, the nail clippings of the patients were analyzed using a Thermo Fisher Antaris II Near-IR Analyzer Spectrometer and near infrared (NIR) chemical imaging. Spectral classification (patients with diabetes mellitus vs. healthy subjects) was performed using partial least square discriminant analysis (PLS-DA). Results: In vitro glycation resulted in peak sharpening between 4300 and 4400 cm−1 and spectral variations at 5270 cm−1 and between 6600 and 7500 cm−1. Similar regions encountered spectral deviations during analysis of the patients’ nails. Optimization of the spectral collection parameters was necessary in order to distinguish a large dataset. Spectra had to be collected at 16 cm−1, 128 scans, region 4000–7500 cm−1. Using standard normal variate, Savitsky-Golay smoothing (7 points) and first derivative preprocessing allowed for the prediction of the test set with 100% correct assignments utilizing a PLS-DA model. Conclusions: Analysis of protein glycation in human fingernail clippings with NIR spectroscopy could be an alternative affordable technique for the diagnosis of diabetes mellitus.

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