Metabolomic signature of esophageal cancer.

21 Background: Esophageal cancer is a pervasive malignancy, and early detection combined with newer therapeutic targets could alter the landscape of this condition. Metabolomic profiling offers one such innovative opportunity. We applied metabolomic techniques to identify urinary metabolites uniquely associated with this condition. Methods: Urine samples from patients with histologically confirmed esophageal cancer (n=66) and healthy volunteers (n=25) were collected and examined using 1H-NMR spectroscopy. Targeted profiling of spectra using Chenomx NMR Suite 7.0 software permitted detection and quantification of 66 distinct metabolites. Unsupervised (principal component analysis, PCA) and supervised (partial least-squares discriminant analysis, PLS-DA) multivariate pattern recognition techniques were applied to discriminate between sample spectra of esophageal cancer patients and healthy volunteers using SIMCA-P (version 11, Umetrics, Umeå, Sweden). Results: Significant differences were found when comparing concentrations of 59 metabolites in urines of healthy volunteers and esophageal cancer patients. Those metabolites contributing most class discriminating information included choline, urea, 2-aminobutyrate, and 3-hydoxybutyrate. Clear distinctions between patients with esophageal cancer and healthy controls were noted when PLS-DA was applied to the data set. Model parameters for both goodness of fit R2, and predictive capability Q2, were high (R2 = 0.867; Q2 = 0.732). Model validity was tested using response permutation and results were suggestive of excellent predictive power (see Figure). Conclusions: Urinary metabolomics identified a discrete signature associated with esophageal cancer compared to healthy controls. This profile has the potential to aid in diagnosis and development of new therapeutic targets.

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The urinary metabolomic signature of Barrett's esophagus.

Journal of Clinical Oncology ◽

10.1200/jco.2012.30.4_suppl.36 ◽

2012 ◽

Vol 30 (4_suppl) ◽

pp. 36-36

Author(s):

Vanessa Wylie Davis ◽

Dan E. Schiller ◽

Michael B. Sawyer

Keyword(s):

Healthy Volunteers ◽

Barrett’S Esophagus ◽

Barrett's Esophagus ◽

Goodness Of Fit ◽

Principal Component ◽

Urinary Metabolites ◽

Model Parameters ◽

Healthy Controls ◽

Data Set ◽

Screening And Surveillance

36 Background: Current screening and surveillance strategies for Barrett’s esophagus are inadequate. More reliable tools are needed. A unique urinary metabolomic signature could fill this niche. We applied metabolomic techniques to identify urinary metabolites capable of facilitating in the diagnosis of Barrett’s esophagus. Methods: Urine samples from patients with histologically confirmed Barrett’s esophagus (n=32) and normal, healthy volunteers (n=25) were collected and examined using 1H-NMR spectroscopy. Targeted profiling of spectra using Chenomx NMR Suite 7.0 software permitted the detection and quantification of 66 distinct metabolites. Unsupervised (principal component analysis, PCA) and supervised (partial-least squares discriminant analysis, PLS-DA) multivariate pattern recognition techniques were applied to discriminate between sample spectra of patients with Barrett’s esophagus and healthy volunteers using SIMCA-P (version 11, Umetrics, Umeå, Sweden). Results: Significant differences were found when comparing the concentrations of 59 metabolites in the urine of healthy volunteers and patients with Barrett’s esophagus. Those metabolites contributing the most class discriminating information included 3-hydoxybutyrate, adipate and choline. Clear distinction between patients with Barrett’s esophagus and healthy controls was noted when PLS-DA was applied to the data set. Model parameters for both the goodness of fit R2, and the predictive capability Q2, were high (R2 = 0.96; Q2 = 0.90). Model validity was tested using response permutation and results were suggestive of excellent predictive power. Conclusions: Urinary metabolomics identified a discrete signature associated with Barrett’s esophagus compared to healthy controls. This profile has the potential to aid in diagnosis and the development of new therapeutic targets.

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The metabolomic signature of pancreatic cancer in urine.

Journal of Clinical Oncology ◽

10.1200/jco.2012.30.4_suppl.180 ◽

2012 ◽

Vol 30 (4_suppl) ◽

pp. 180-180

Author(s):

Vanessa Wylie Davis ◽

Dan E. Schiller ◽

Michael B. Sawyer

Keyword(s):

Pancreatic Cancer ◽

Healthy Volunteers ◽

Cancer Patients ◽

Goodness Of Fit ◽

Stage Iv ◽

Urinary Metabolites ◽

Screening Methods ◽

Model Parameters ◽

Data Set ◽

Early Results

180 Background: Pancreatic cancer is one of the leading causes of cancer-related death, due partly to the lack of early detection and screening methods. Metabolomics, the newest of the “omics” sciences, provides a means for non-invasive screening of early tumor associated perturbations in cellular metabolism. We applied metabolomic techniques to identify urinary metabolites capable of facilitating diagnosis of pancreatic cancer. Methods: Urine samples from pancreatic cancer patients (n=55) and healthy volunteers (n=25) were collected and examined using 1H-NMR spectroscopy. Targeted profiling of spectra using Chenomx NMR Suite 7.0 software permitted quantification of 66 metabolites. Unsupervised (PCA) and supervised (PLS-DA) multivariate pattern recognition techniques were applied to discriminate between sample spectra of pancreatic cancer patients and healthy volunteers using SIMCA-P (version 11, Umetrics, Umeå, Sweden). Results: Significant differences were found when comparing concentrations of 66 metabolites in urines of healthy volunteers and pancreatic cancer patients. Those metabolites contributing the most class discriminating information included choline, 2-aminobutyrate, urea and 2-oxoglutarate. Clear distinctions between pancreatic cancer patients and healthy controls were noted when PLS-DA was applied to the data set. Model parameters for both goodness of fit R2, and predictive capability Q2, were high (R2 = 0.829; Q2 = 0.76). Model validity was tested using response permutation and results were suggestive of excellent predictive power. Application of PLS-DA to the data set also revealed clear discrimination of Stage I-III and Stage IV disease states, with the following model parameters, R2 = 0.62; Q2 =0.45. Conclusions: Urinary metabolomics detected clear differences in metabolic profiles of pancreatic cancer patients and healthy volunteers. Early results presented here suggest that metabolomic approaches may facilitate discovery of novel biomarkers capable of early disease detection.

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A Three-parameter New Exponentiated Distribution for Life-time Data

International Journal of Scientific Research in Science Engineering and Technology ◽

10.32628/ijsrset207633 ◽

2020 ◽

pp. 194-203

Author(s):

Arun Kumar Chaudhary ◽

Vijay Kumar

Keyword(s):

Goodness Of Fit ◽

Survival Function ◽

Continuous Distribution ◽

Life Time ◽

Least Square ◽

Cumulative Distribution ◽

Model Parameters ◽

Time Data ◽

Data Set ◽

Exponentiated Distribution

In the presented work, a continuous distribution consisting of three-parameters is proposed for life-time data called new exponentiated distribution. The discussion of some of the distribution’s statistical as well as mathematical properties, including the Cumulative Distribution Function (CDF), Probability Density function (PDF), quantile function, survival function, hazard rate function, kurtosis measures and skewness, is conducted. The estimation of the presented distribution’s model parameters is performed using the techniques of Cramer-Von-Mises estimation (CVME), least-square estimation (LSE), and maximum likelihood estimation (MLE). The evaluation of the proposed distribution’s goodness of fit is performed through its fitting in comparison with some of the other existing life-time models with the help of a real data set.

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Norms for Automatic Estimation of Hippocampal Atrophy and a Step Forward for Applicability to the Italian Population

Frontiers in Neuroscience ◽

10.3389/fnins.2021.656808 ◽

2021 ◽

Vol 15 ◽

Author(s):

Silvia De Francesco ◽

Samantha Galluzzi ◽

Nicola Vanacore ◽

Cristina Festari ◽

Paolo Maria Rossini ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Goodness Of Fit ◽

Automatic Segmentation ◽

Hippocampal Volume ◽

Hippocampal Atrophy ◽

Italian Population ◽

Healthy Controls ◽

Goodness Of Fit Test ◽

Data Set

IntroductionHippocampal volume is one of the main biomarkers of Alzheimer’s Dementia (AD). Over the years, advanced tools that performed automatic segmentation of Magnetic Resonance Imaging (MRI) T13D scans have been developed, such as FreeSurfer (FS) and ACM-Adaboost (AA). Hippocampal volume is considered abnormal when it is below the 5th percentile of the normative population. The aim of this study was to set norms, established from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) population, for hippocampal volume measured with FS v.6.0 and AA tools in the neuGRID platform (www.neugrid2.eu) and demonstrate their applicability for the Italian population.MethodsNorms were set from a large group of 545 healthy controls belonging to ADNI. For each pipeline, subjects with segmentation errors were discarded, resulting in 532 valid segmentations for FS and 421 for AA (age range 56–90 years). The comparability of ADNI and the Italian Brain Normative Archive (IBNA), representative of the Italian general population, was assessed testing clinical variables, neuropsychological scores and normalized hippocampal volumes. Finally, percentiles were validated using the Italian Alzheimer’s disease Repository Without Borders (ARWiBo) as external independent data set to evaluate FS and AA generalizability.ResultsHippocampal percentiles were checked with the chi-square goodness of fit test. P-values were not significant, showing that FS and AA algorithm distributions fitted the data well. Clinical, neuropsychological and volumetric features were similar in ADNI and IBNA (p > 0.01). Hippocampal volumes measured with both FS and AA were associated with age (p < 0.001). The 5th percentile thresholds, indicating left/right hippocampal atrophy were respectively: (i) below 3,223/3,456 mm3 at 56 years and 2,506/2,415 mm3 at 90 years for FS; (ii) below 4,583/4,873 mm3 at 56 years and 3,831/3,870 mm3 at 90 years for AA. The average volumes computed on 100 cognitively intact healthy controls (CN) selected from ARWiBo were close to the 50th percentiles, while those for 100 AD patients were close to the abnormal percentiles.DiscussionNorms generated from ADNI through the automatic FS and AA segmentation tools may be used as normative references for Italian patients with suspected AD.

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Cancer genomics-based screening of new therapeutic targets and biomarkers for esophageal cancer.

Journal of Clinical Oncology ◽

10.1200/jco.2020.38.15_suppl.e16514 ◽

2020 ◽

Vol 38 (15_suppl) ◽

pp. e16514-e16514

Author(s):

Yataro Daigo ◽

Atsushi Takano ◽

Yusuke Nakamura

Keyword(s):

Esophageal Cancer ◽

Cancer Patients ◽

Mammalian Cells ◽

Cancer Genomics ◽

Profile Analysis ◽

Gene Transfection ◽

Objective Response ◽

Therapeutic Targets ◽

Immunohistochemical Analysis ◽

Esophageal Cancers

e16514 Background: Since the number of esophageal cancer patients who show objective response to standard therapies is still small, development of new anti-cancer agents with minimum risk of adverse events and highly precise molecular biomarkers is eagerly awaited. Methods: We have been screening novel therapeutic targets and their companion diagnostics for esophageal cancers as follows; i) To identify up-regulated genes in esophageal cancers by the gene expression profile analysis, ii) To verify the candidate genes for their low expression in normal tissues, iii) To validate the clinicopathological significance of their protein expression by tissue microarray covering 265 esophageal cancers, and iv) To verify their function for the growth of the esophageal cancer cells by siRNAs and gene transfection assays. Results: We identified dozens of candidate oncoproteins and selected a metyltransferase ESOC1 (esophageal cancer-associated oncoprotein 1). Immunohistochemical analysis revealed that ESOC1 positivity was observed in 68.5% of esophageal cancers and associated with tumor size. Moreover ESOC1 expression was an independent prognostic factor for esophageal cancer patients. Suppression of ESOC1 expression by its siRNAs inhibited growth of esophageal cancer cell lines. Introduction of ESOC1 increased the growth activity of mammalian cells, suggesting that ESOC1 is likely to be a prognostic biomarker and therapeutic target for esophageal cancers. Conclusions: Cancer genomics-based approach could be useful for the development of new cancer biomarkers as well as therapeutic targets for small molecules, antibodies, nucleic acid drugs, and immunotherapies.

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THE LOGISTIC INVERSE LOMAX DISTRIBUTION WITH PROPERTIES AND APPLICATIONS

INTERNATIONAL JOURNAL OF MATHEMATICS AND COMPUTER RESEARCH ◽

10.47191/ijmcr/v9i1.02 ◽

2021 ◽

Vol 09 (01) ◽

Author(s):

Ramesh Kumar Joshi ◽

Keyword(s):

Goodness Of Fit ◽

Survival Function ◽

Continuous Distribution ◽

Least Square ◽

Cumulative Distribution ◽

Estimation Methods ◽

Model Parameters ◽

Data Set ◽

Lomax Distribution ◽

Inverse Lomax Distribution

In this article, a three-parameter continuous distribution is introduced called Logistic inverse Lomax distribution. We have discussed some mathematical and statistical properties of the distribution such as the probability density function, cumulative distribution function and hazard rate function, survival function, quantile function, the skewness, and kurtosis measures. The model parameters of the proposed distribution are estimated using three well-known estimation methods namely maximum likelihood estimation (MLE), least-square estimation (LSE), and Cramer-Von-Mises estimation (CVME) methods. The goodness of fit of the proposed distribution is also evaluated by fitting it in comparison with some other existing distributions using a real data set.

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A New Parametric Life Distribution with Modified Bagdonavičius–Nikulin Goodness-of-Fit Test for Censored Validation, Properties, Applications, and Different Estimation Methods

Entropy ◽

10.3390/e22050592 ◽

2020 ◽

Vol 22 (5) ◽

pp. 592 ◽

Cited By ~ 6

Author(s):

Mahmoud Mansour ◽

Mahdi Rasekhi ◽

Mohamed Ibrahim ◽

Khaoula Aidi ◽

Haitham M. Yousof ◽

...

Keyword(s):

Maximum Likelihood ◽

Goodness Of Fit ◽

Real Data ◽

Weibull Model ◽

Estimation Methods ◽

Model Parameters ◽

Goodness Of Fit Test ◽

Chi Square ◽

Data Set ◽

The Right

In this paper, we first study a new two parameter lifetime distribution. This distribution includes “monotone” and “non-monotone” hazard rate functions which are useful in lifetime data analysis and reliability. Some of its mathematical properties including explicit expressions for the ordinary and incomplete moments, generating function, Renyi entropy, δ-entropy, order statistics and probability weighted moments are derived. Non-Bayesian estimation methods such as the maximum likelihood, Cramer-Von-Mises, percentile estimation, and L-moments are used for estimating the model parameters. The importance and flexibility of the new distribution are illustrated by means of two applications to real data sets. Using the approach of the Bagdonavicius–Nikulin goodness-of-fit test for the right censored validation, we then propose and apply a modified chi-square goodness-of-fit test for the Burr X Weibull model. The modified goodness-of-fit statistics test is applied for the right censored real data set. Based on the censored maximum likelihood estimators on initial data, the modified goodness-of-fit test recovers the loss in information while the grouped data follows the chi-square distribution. The elements of the modified criteria tests are derived. A real data application is for validation under the uncensored scheme.

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Semiparametric Inference for Proportional Mean Past Life Model

The International Journal of Biostatistics ◽

10.1515/ijb-2018-0020 ◽

2019 ◽

Vol 15 (1) ◽

Author(s):

Z. Mansourvar ◽

M. Asadi

Keyword(s):

Goodness Of Fit ◽

Asymptotic Properties ◽

Model Parameters ◽

Goodness Of Fit Test ◽

Data Set ◽

Semiparametric Inference ◽

Expected Time ◽

Life Model ◽

Past Life ◽

The Mean

Abstract The mean past lifetime provides the expected time elapsed since the failure of a subject given that he/she has failed before the time of observation. In this paper, we propose the proportional mean past lifetime model to study the association between the mean past lifetime function and potential regression covariates. In the presence of left censoring, martingale estimating equations are developed to estimate the model parameters, and the asymptotic properties of the resulting estimators are studied. To assess the adequacy of the model, a goodness of fit test is also investigated. The proposed method is evaluated via simulation studies and further applied to a data set.

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Customer-Related Social Stressors

Journal of Personnel Psychology ◽

10.1027/1866-5888/a000132 ◽

2015 ◽

Vol 14 (4) ◽

pp. 165-181 ◽

Cited By ~ 14

Author(s):

Sarah Dudenhöffer ◽

Christian Dormann

Keyword(s):

Service Providers ◽

Principal Component ◽

Well Being ◽

Emotional Dissonance ◽

Social Stressors ◽

Data Set ◽

Confirmatory Factor Analyses ◽

Confirmatory Factor ◽

Service Jobs ◽

Single Data

Abstract. The purpose of this study was to replicate the dimensions of the customer-related social stressors (CSS) concept across service jobs, to investigate their consequences for service providers’ well-being, and to examine emotional dissonance as mediator. Data of 20 studies comprising of different service jobs (N = 4,199) were integrated into a single data set and meta-analyzed. Confirmatory factor analyses and explorative principal component analysis confirmed four CSS scales: disproportionate expectations, verbal aggression, ambiguous expectations, disliked customers. These CSS scales were associated with burnout and job satisfaction. Most of the effects were partially mediated by emotional dissonance. Further analyses revealed that differences among jobs exist with regard to the factor solution. However, associations between CSS and outcomes are mainly invariant across service jobs.

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