scholarly journals Altered Glycosylation of Human Alpha-1-Acid Glycoprotein as a Biomarker for Malignant Melanoma

Molecules ◽  
2021 ◽  
Vol 26 (19) ◽  
pp. 6003
Author(s):  
Dávid Virág ◽  
Tibor Kremmer ◽  
Kende Lőrincz ◽  
Norbert Kiss ◽  
Antal Jobbágy ◽  
...  
Keyword(s):  
Malignant Melanoma ◽  
Predictive Power ◽  
Cross Validation ◽  
Comparative Assessment ◽  
Healthy Individuals ◽  
Glycosylation Pattern ◽  
Linear Discriminant ◽  
Acid Glycoprotein ◽  
Derivatives Of ◽  
Anthranilic Acid Derivatives

A high-resolution HILIC-MS/MS method was developed to analyze anthranilic acid derivatives of N-glycans released from human serum alpha-1-acid glycoprotein (AGP). The method was applied to samples obtained from 18 patients suffering from high-risk malignant melanoma as well as 19 healthy individuals. It enabled the identification of 102 glycan isomers separating isomers that differ only in sialic acid linkage (α-2,3, α-2,6) or in fucose positions (core, antenna). Comparative assessment of the samples revealed that upregulation of certain fucosylated glycans and downregulation of their nonfucosylated counterparts occurred in cancer patients. An increased ratio of isomers with more α-2,6-linked sialic acids was also observed. Linear discriminant analysis (LDA) combining 10 variables with the highest discriminatory power was employed to categorize the samples based on their glycosylation pattern. The performance of the method was tested by cross-validation, resulting in an overall classification success rate of 96.7%. The approach presented here is significantly superior to serological marker S100B protein in terms of sensitivity and negative predictive power in the population studied. Therefore, it may effectively support the diagnosis of malignant melanoma as a biomarker.

scholarly journals Imaging Mass Spectrometry-Based Proteomic Analysis to Differentiate Melanocytic Nevi and Malignant Melanoma

Cancers ◽  
2021 ◽  
Vol 13 (13) ◽  
pp. 3197
Author(s):  
Rita Casadonte ◽  
Mark Kriegsmann ◽  
Katharina Kriegsmann ◽  
Isabella Hauk ◽  
Rolf R. Meliß ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Malignant Melanoma ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Imaging Mass Spectrometry ◽  
Independent Set ◽  
Molecular Techniques ◽  
Charge Ratio ◽  
Linear Discriminant ◽  
Melanocytic Nevi

The discrimination of malignant melanoma from benign nevi may be difficult in some cases. For this reason, immunohistological and molecular techniques are included in the differential diagnostic toolbox for these lesions. These methods are time consuming when applied subsequently and, in some cases, no definitive diagnosis can be made. We studied both lesions by imaging mass spectrometry (IMS) in a large cohort (n = 203) to determine a different proteomic profile between cutaneous melanomas and melanocytic nevi. Sample preparation and instrument setting were tested to obtain optimal results in term of data quality and reproducibility. A proteomic signature was found by linear discriminant analysis to discern malignant melanoma from benign nevus (n = 113) with an overall accuracy of >98%. The prediction model was tested in an independent set (n = 90) reaching an overall accuracy of 93% in classifying melanoma from nevi. Statistical analysis of the IMS data revealed mass-to-charge ratio (m/z) peaks which varied significantly (Area under the receiver operating characteristic curve > 0.7) between the two tissue types. To our knowledge, this is the largest IMS study of cutaneous melanoma and nevi performed up to now. Our findings clearly show that discrimination of melanocytic nevi from melanoma is possible by IMS.

scholarly journals Measuring and Managing Credit Risk for Chinese Microfinance Institutions

2018 ◽  
Vol 10 (7) ◽  
pp. 56
Author(s):  
Jie Li ◽  
Zhenyu Sheng
Keyword(s):  
Credit Risk ◽  
Predictive Power ◽  
Direct Method ◽  
Credit Scoring ◽  
Microfinance Institutions ◽  
Linear Discriminant ◽  
Management Level ◽  
Scoring Model ◽  
Subjective Appraisals ◽  
Credit Scoring Model

Chinese microfinance institutions need to measure and manage credit risk in a quantitative way in order to improve competitiveness. To establish a credit scoring model (CSM) with sound predictive power, they should examine various models carefully, identify variables, assign values to variables and reduce variable dimensions in an appropriate way. Microfinance institutions could employ both CSM and loan officer’s subjective appraisals to improve risk management level gradually. The paper sets up a CSM based on the data of a microfinance company running from October 2009 to June 2014 in Jiangsu province. As for establishing the model, the paper uses Linear Discriminant Analysis (LDA) method, selects 16 initial variables, employs direct method to assign variables and adopts all the variables into the model. Ten samples are constructed by randomly selecting records. Based on the samples, the coefficients are determined and the final none-standardized discriminant function is established. It is found that Bank credit, Education, Old client and Rate variables have the greatest impact on the discriminant effect. Compared with the same international models, this model’s classification effect is fine. The paper displays the key technical points to build a credit scoring model based on a practical application, which provides help and references for Chinese microfinance institutions to measure and manage credit risk quantitatively.

Simple correlations for calculating NHTPB enthalpy of formation through molecular structures

2019 ◽  
Vol 15 (1) ◽  
pp. 258-264 ◽  
Author(s):  
Hamid Reza Ghaieni ◽  
Saeed Tavangar ◽  
Mohammad Moein Ebrahimzadeh Qhomi
Keyword(s):  
Enthalpy Of Formation ◽  
Design Methodology ◽  
Predictive Power ◽  
Cross Validation ◽  
Molecular Structures ◽  
Mean Square ◽  
Mean Square Deviation ◽  
Simple Correlation ◽  
Content Type ◽  
Regression Methods

Purpose The purpose of this paper is to present simple correlation for calculating nitrated hydroxyl-terminated polybutadiene (NHTPB) enthalpy of formation. Design/methodology/approach It uses multiple linear regression methods. Findings The proposed correlation has determination coefficient 0.96. The correlation has root mean square deviation and the average absolute deviations values 53.4 and 46.1 respectively. Originality/value The predictive power of correlation is checked by cross-validation method (R2=0.96, Q L O O 2 = 0.96 ).

Sweetened kallikrein-related peptidases (KLKs): glycan trees as potential regulators of activation and activity

2014 ◽  
Vol 395 (9) ◽  
pp. 959-976 ◽  
Author(s):  
Shihui Guo ◽  
Wolfgang Skala ◽  
Viktor Magdolen ◽  
Hans Brandstetter ◽  
Peter Goettig
Keyword(s):  
Prostate Cancer ◽  
Endoplasmic Reticulum ◽  
Protein Synthesis ◽  
Benign Prostatic Hyperplasia ◽  
Active Site ◽  
Prostatic Hyperplasia ◽  
Healthy Individuals ◽  
Glycosylation Pattern ◽  
Structural Stabilization ◽  
Respective Function

Abstract Most kallikrein-related peptidases (KLKs) are N-glycosylated with N-acetylglucosamine2-mannose9 units at Asn-Xaa-Ser/Thr sequons during protein synthesis and translocation into the endoplasmic reticulum. These N-glycans are modified in the Golgi machinery, where additional O-glycosylation at Ser and Thr takes place, before exocytotic release of the KLKs into the extracellular space. Sequons are present in all 15 members of the KLKs and comparative studies for KLKs from natural and recombinant sources elucidated some aspects of glycosylation. Although glycosylation of mammalian KLKs 1, 3, 4, 6, and 8 has been analyzed in great detail, e.g., by crystal structures, the respective function remains largely unclear. In some cases, altered enzymatic activity was observed for KLKs upon glycosylation. Remarkably, for KLK3/PSA, changes in the glycosylation pattern were observed in samples of benign prostatic hyperplasia and prostate cancer with respect to healthy individuals. Potential functions of KLK glycosylation in structural stabilization, protection against degradation, and activity modulation of substrate specificity can be deduced from a comparison with other glycosylated proteins and their regulation. According to the new concept of protein sectors, glycosylation distant from the active site might significantly influence the activity of proteases. Novel pharmacological approaches can exploit engineered glycans in the therapeutical context.

scholarly journals Using Chemometric Analyses for Tracing the Regional Origin of Multifloral Honeys of Montenegro

Foods ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 210 ◽  
Author(s):  
Vesna Vukašinović-Pešić ◽  
Nada Blagojević ◽  
Snežana Brašanac-Vukanović ◽  
Ana Savić ◽  
Vladimir Pešić
Keyword(s):  
Industrial Pollution ◽  
Physicochemical Parameters ◽  
Mineral Content ◽  
Cross Validation ◽  
Classification Rate ◽  
Linear Discriminant ◽  
Allowable Level ◽  
Regional Origin ◽  
Maximum Allowable Level

This is the first study of mineral content and basic physicochemical parameters of honeys of Montenegro. We examined honey samples from eight different micro-regions of Montenegro, and the results confirm that, with the exception of cadmium in samples from two regions exposed to industrial pollution, none of the 12 elements analyzed exceeded the maximum allowable level. The samples from areas exposed to industrial pollution were clearly distinguished from samples from other regions of Montenegro in the detectable contents of Pb, Cd, and Sr. This study showed that chemometric techniques might enhance the classification of Montenegrin honeys according to their micro-regional origin using the mineral content. Linear discriminant analysis revealed that the classification rate was 79.2% using the cross-validation method.

scholarly journals A Nonparametric Ensemble Postprocessing Approach for Short-Range Visibility Predictions in Data-Sparse Areas

2018 ◽  
Vol 33 (3) ◽  
pp. 835-855 ◽  
Author(s):  
William R. Ryerson ◽  
Joshua P. Hacker
Keyword(s):  
Air Force ◽  
Short Range ◽  
Predictive Power ◽  
Cross Validation ◽  
Physical Mechanism ◽  
Cloud Water ◽  
Weather Research And Forecasting ◽  
Forecast System ◽  
Clear Cases ◽  
Observational Record

Abstract This work develops and tests the viability of obtaining skillful short-range (<20 h) visibility predictions using statistical postprocessing of a 4-km, 10-member Weather Research and Forecasting (WRF) ensemble configured to closely match the U.S. Air Force Mesoscale Ensemble Forecast System. The raw WRF predictions produce excessive forecasts of zero cloud water, which is simultaneously predicted by all ensemble members in 62% of observed fog cases, leading to zero ensemble dispersion and no skill in these cases. Adding dispersion to the clear cases by making upward adjustments to cloud water predictions from individual members not predicting fog on their own provides the best chance to increase the resolution and reliability of the ensemble. The technique leverages traits of a joint parameter space in the predictions and is generally most effective when the space is defined with a moisture parameter and a low-level stability parameter. Cross-validation shows that the method adds significant overnight skill to predictions in valley and coastal regions compared to the raw WRF forecasts, with modest skill increases after sunrise. Postprocessing does not improve the highly skillful raw WRF predictions at the mountain test sites. Since the framework addresses only systematic WRF deficiencies and identifies parameter pairs with a clear, non-site-specific physical mechanism of predictive power, it has geographical transferability with less need for recalibration or observational record compared to other statistical postprocessing approaches.

Development of a novel liquid biopsy test to diagnose and locate gastrointestinal cancers.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. 1557-1557
Author(s):  
Yuying Wang ◽  
Jianchao Zheng ◽  
Zhilong Li ◽  
Ruijingfang Jiang ◽  
Jiaxi Peng ◽  
...  
Keyword(s):  
Liquid Biopsy ◽  
Cross Validation ◽  
Validation Cohort ◽  
Classification Model ◽  
Healthy Controls ◽  
Healthy Individuals ◽  
Training Cohort ◽  
Cancer Types ◽  
Gi Cancers ◽  
Tissue Of Origin

1557 Background: Cancers of the gastrointestinal (GI) system, including esophagus, stomach, pancreas, gallbladder, liver, bile duct, colon, and rectum are estimated to account for 38% of all cancer incidences and nearly 46% of cancer-related deaths in China. We conducted a multi-center study to evaluate the feasibility of using genetic and epigenetic abnormalities in plasma cfDNA to diagnose and locate GI cancers. Methods: We performed parallel genetic and epigenetic profiling of plasma cfDNA from hepatocellular carcinoma (HCC), colorectal cancer (CRC) and pancreatic cancer (PC) patients as well as age-matched healthy individuals by ultra-deep sequencing targeting cancer driver genes, and by targeted bisulfite sequencing covering genome-wide CpG islands, shelves, and shores. Results: Using a pre-specified mutation scoring system, we found that cfDNA mutation profiling achieved a sensitivity of 59.6%, 67.2%, and 46.8% for detecting HCC (n = 322), CRC (n = 244) and PC (n = 141) respectively, with a specificity of 95% in healthy controls (n = 207). For 901 plasma cfDNA samples that underwent methylome profiling, we first applied a machine learning approach to classify each cancer type versus healthy controls in the training cohort (HCC: n = 125; CRC: n = 105; PC: n = 97; healthy individuals: n = 84). Random Forest models with 10-fold cross validation achieved an AUC of 0.96±0.04,0.89±0.06, 0.91±0.07 for HCC, CRC, and PC, respectively. Further analyses were performed on the validation cohort, including 172 HCC patients, 162 CRC patients, 60 PC patients, and an independent cohort of healthy individuals (HCC validation: n = 63; HCC independent validation: n = 109; CRC validation: n = 104; CRC external validation: n = 58; PC validation: n = 60; healthy controls: n = 96). The trained model achieved a sensitivity of 83.1% (specificity = 95.8%), 89.5% (specificity = 95.8%), and 76.7% (specificity = 91.7%) for HCC, CRC, and PC, respectively. Using regional methylation markers from diagnostic models for individual cancer types, we built a tissue-of-origin classification model, which achieved a cross-validation accuracy of 83.3% in the training cohort and an accuracy of 80.1% in the validation cohort in assigning correct cancer types. Conclusions: Plasma cfDNA methylome profiling identified effective biomarkers for the detection and tissue-of-origin determination of GI cancers, and outperformed mutation-based detection approach. Therefore, a liquid biopsy test capable of detecting and locating GI cancers is feasible and may serve as a valuable tool for early detection and intervention.

scholarly journals Application of Discriminant, Classification Tree and Neural Network Analysis to Differentiate between Potential Glaucoma Suspects with and without Visual Field Defects

2003 ◽  
Vol 5 (3-4) ◽  
pp. 161-170 ◽  
Author(s):  
W. Hitzl ◽  
H. A. Reitsamer ◽  
K. Hornykewycz ◽  
A. Mistlberger ◽  
G. Grabner
Keyword(s):  
Optic Nerve ◽  
Visual Field ◽  
Confidence Intervals ◽  
Visual Field Defect ◽  
Cross Validation ◽  
Classification Tree ◽  
Test Sample ◽  
Linear Discriminant ◽  
Specificity And Sensitivity ◽  
Field Defect

Purpose: This study has two objectives. The first one is to investigate the question whether it is possible to discriminate between eyes with and without a glaucomateous visual field defect based on standard ophthalmologic examinations as well as optic nerve head topographic parameters. The second objective raises the question of the ability of several suggested statistical models to generalize their results to new, previously unseen patients.Methods: To investigate the above addressed question: (a) independent, two-sidedt-tests, (b) a linear discriminant analysis with a forward stepwise variable selection algorithm, (c) four classification tree analyses and (d) three different neural network models with a forward, backward and a genetic variable selection algorithm were applied to 1020 subjects with a normal visual field and 110 subjects with a glaucomateous visual field defect. The Humphrey Visual Field Analyzer was used to test the visual fields and the TopSS®Scanning Laser Tomograph measured the optic nerve topography. A 10-fold cross-validation method was used for the models (b), (c) and (d) to compute approximative 95% confidence intervals for the specificity and sensitivity rates.A literature study of 18 studies dealt with the question whether/how the generalization error was controlled (control of sample bias, cross-validation procedures, training net size for valid generalization). It was also looked up whether point estimators or 95% confidence intervals were used to report specificity and sensitivity rates.Results: (a) Only few differences of the means could be found between both groups, including age, existing eye diseases, best corrected visual acuity and visual field parameters. The linear discriminant analysis (b), the classification tree analyses (c) and the neural networks (d) ended up with high specificity rates, but low sensitivity rates.The literature study showed that three models did not apply a cross-validation procedure to report their results. Two models used a test sample cross-validation and thirteen used a v-fold cross-validation method. Although most authors reported confidence intervals for the area under the ROC, no author reported confidence intervals for the true, but unknown sensitivity and specificity rates.Conclusions: (a) The results of this study suggest that the combination of standard ophthalmologic eye parameters and optic nerve head topographic parameters of the TopSS®instrument are not sufficient to discriminate properly among normal eyes and eyes with a glaucomateous visual field defect. (b) We follow important suggestions given in statistical learning theory for proper generalization and suggest to apply these methods to the given models or to models in future. At least three conditions should be met: (1) confidence intervals instead of point estimators to assess the classification performance of a model (control of test sample bias); (2) sensitivity and specificity rates should be estimated instead of reporting a point estimator for the area under the ROC and (3) the generalization error should be reported both in a training and a test sample and methods should be applied to select an appropriate training sample size for valid generalization.

Sign in / Sign up

Export Citation Format

Share Document