Verification of Multimarkers for Detection of Early Stage Diabetic Retinopathy Using Multiple Reaction Monitoring

2013 ◽  
Vol 12 (3) ◽  
pp. 1078-1089 ◽  
Author(s):  
Kyunggon Kim ◽  
Sang Jin Kim ◽  
Dohyun Han ◽  
Jonghwa Jin ◽  
Jiyoung Yu ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Early Stage ◽  
Multiple Reaction Monitoring ◽  
Reaction Monitoring

scholarly journals Development of Diagnostic Biomarkers for Detecting Diabetic Retinopathy at Early Stages Using Quantitative Proteomics

2016 ◽  
Vol 2016 ◽  
pp. 1-22 ◽  
Author(s):  
Jonghwa Jin ◽  
Hophil Min ◽  
Sang Jin Kim ◽  
Sohee Oh ◽  
Kyunggon Kim ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Quantitative Proteomics ◽  
Multiple Reaction Monitoring ◽  
Diabetic Patients ◽  
Reaction Monitoring ◽  
Protein Marker ◽  
Diagnostic Biomarkers ◽  
Stable Isotope Dilution ◽  
Early Stages ◽  
Larger Sample

Diabetic retinopathy (DR) is a common microvascular complication caused by diabetes mellitus (DM) and is a leading cause of vision impairment and loss among adults. Here, we performed a comprehensive proteomic analysis to discover biomarkers for DR. First, to identify biomarker candidates that are specifically expressed in human vitreous, we performed data-mining on both previously published DR-related studies and our experimental data; 96 proteins were then selected. To confirm and validate the selected biomarker candidates, candidates were selected, confirmed, and validated using plasma from diabetic patients without DR (No DR) and diabetics with mild or moderate nonproliferative diabetic retinopathy (Mi or Mo NPDR) using semiquantitative multiple reaction monitoring (SQ-MRM) and stable-isotope dilution multiple reaction monitoring (SID-MRM). Additionally, we performed a multiplex assay using 15 biomarker candidates identified in the SID-MRM analysis, which resulted in merged AUC values of 0.99 (No DR versus Mo NPDR) and 0.93 (No DR versus Mi and Mo NPDR). Although further validation with a larger sample size is needed, the 4-protein marker panel (APO4, C7, CLU, and ITIH2) could represent a useful multibiomarker model for detecting the early stages of DR.

Verification of Biomarkers for Diabetic Retinopathy by Multiple Reaction Monitoring

2010 ◽  
Vol 9 (2) ◽  
pp. 689-699 ◽  
Author(s):  
Kyunggon Kim ◽  
Sang Jin Kim ◽  
Hyeong Gon Yu ◽  
Jiyoung Yu ◽  
Kyong Soo Park ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Multiple Reaction Monitoring ◽  
Reaction Monitoring

scholarly journals Clinical Assay for the Early Detection of Colorectal Cancer Using Mass Spectrometric Wheat Germ Agglutinin Multiple Reaction Monitoring

Cancers ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 2190
Author(s):  
I-Jung Tsai ◽  
Emily Chia-Yu Su ◽  
I-Lin Tsai ◽  
Ching-Yu Lin
Keyword(s):  
Colorectal Cancer ◽  
Wheat Germ Agglutinin ◽  
Wheat Germ ◽  
Early Stage ◽  
Multiple Reaction Monitoring ◽  
Machine Learning Algorithms ◽  
Test Machine ◽  
Reaction Monitoring ◽  
Plasma Samples ◽  
Clinical Biomarkers

Colorectal cancer (CRC) is currently the third leading cause of cancer-related mortality in the world. U.S. Food and Drug Administration-approved circulating tumor markers, including carcinoembryonic antigen, carbohydrate antigen (CA) 19-9 and CA125 were used as prognostic biomarkers of CRC that attributed to low sensitivity in diagnosis of CRC. Therefore, our purpose is to develop a novel strategy for novel clinical biomarkers for early CRC diagnosis. We used mass spectrometry (MS) methods such as nanoLC-MS/MS, targeted LC-MS/MS, and stable isotope-labeled multiple reaction monitoring (MRM) MS coupled to test machine learning algorithms and logistic regression to analyze plasma samples from patients with early-stage CRC, late-stage CRC, and healthy controls (HCs). On the basis of our methods, 356 peptides were identified, 6 differential expressed peptides were verified, and finally three peptides corresponding wheat germ agglutinin (WGA)-captured proteins were semi-quantitated in 286 plasma samples (80 HCs and 206 CRCs). The novel peptide biomarkers combination of PF454–62, ITIH4429–438, and APOE198–207 achieved sensitivity 84.5%, specificity 97.5% and an AUC of 0.96 in CRC diagnosis. In conclusion, our study demonstrated that WGA-captured plasma PF454–62, ITIH4429–438, and APOE198–207 levels in combination may serve as highly effective early diagnostic biomarkers for patients with CRC.

Review of Various Tasks Performed in the Preprocessing Phase of a Diabetic Retinopathy Diagnosis System

2020 ◽  
Vol 16 (4) ◽  
pp. 397-426
Author(s):  
Muhammad Nadeem Ashraf ◽  
Muhammad Hussain ◽  
Zulfiqar Habib
Keyword(s):  
Diabetic Retinopathy ◽  
Blood Vessels ◽  
Early Stage ◽  
Spherical Shape ◽  
Cost Effective ◽  
Lesion Detection ◽  
Diabetic Patients ◽  
Cad System ◽  
Non Invasive ◽  
Optic Discs

Diabetic Retinopathy (DR) is a major cause of blindness in diabetic patients. The increasing population of diabetic patients and difficulty to diagnose it at an early stage are limiting the screening capabilities of manual diagnosis by ophthalmologists. Color fundus images are widely used to detect DR lesions due to their comfortable, cost-effective and non-invasive acquisition procedure. Computer Aided Diagnosis (CAD) of DR based on these images can assist ophthalmologists and help in saving many sight years of diabetic patients. In a CAD system, preprocessing is a crucial phase, which significantly affects its performance. Commonly used preprocessing operations are the enhancement of poor contrast, balancing the illumination imbalance due to the spherical shape of a retina, noise reduction, image resizing to support multi-resolution, color normalization, extraction of a field of view (FOV), etc. Also, the presence of blood vessels and optic discs makes the lesion detection more challenging because these two artifacts exhibit specific attributes, which are similar to those of DR lesions. Preprocessing operations can be broadly divided into three categories: 1) fixing the native defects, 2) segmentation of blood vessels, and 3) localization and segmentation of optic discs. This paper presents a review of the state-of-the-art preprocessing techniques related to three categories of operations, highlighting their significant aspects and limitations. The survey is concluded with the most effective preprocessing methods, which have been shown to improve the accuracy and efficiency of the CAD systems.

Two-Tier Grading System for NPDR Severities of Diabetic Retinopathy in Retinal Fundus Images

2020 ◽  
Vol 14 ◽  
Author(s):  
Charu Bhardwaj ◽  
Shruti Jain ◽  
Meenakshi Sood
Keyword(s):  
Diabetic Retinopathy ◽  
Statistical Analysis ◽  
Early Stage ◽  
Image Feature ◽  
Grading System ◽  
Fundus Images ◽  
Fundus Image ◽  
Retinal Fundus Images ◽  
Retinal Fundus ◽  
Timely Treatment

: Diabetic Retinopathy is the leading cause of vision impairment and its early stage diagnosis relies on regular monitoring and timely treatment for anomalies exhibiting subtle distinction among different severity grades. The existing Diabetic Retinopathy (DR) detection approaches are subjective, laborious and time consuming which can only be carried out by skilled professionals. All the patents related to DR detection and diagnoses applicable for our research problem were revised by the authors. The major limitation in classification of severities lies in poor discrimination between actual lesions, background noise and other anatomical structures. A robust and computationally efficient Two-Tier DR (2TDR) grading system is proposed in this paper to categorize various DR severities (mild, moderate and severe) present in retinal fundus images. In the proposed 2TDR grading system, input fundus image is subjected to background segmentation and the foreground fundus image is used for anomaly identification followed by GLCM feature extraction forming an image feature set. The novelty of our model lies in the exhaustive statistical analysis of extracted feature set to obtain optimal reduced image feature set employed further for classification. Classification outcomes are obtained for both extracted as well as reduced feature set to validate the significance of statistical analysis in severity classification and grading. For single tier classification stage, the proposed system achieves an overall accuracy of 100% by k- Nearest Neighbour (kNN) and Artificial Neural Network (ANN) classifier. In second tier classification stage an overall accuracy of 95.3% with kNN and 98.0% with ANN is achieved for all stages utilizing optimal reduced feature set. 2TDR system demonstrates overall improvement in classification performance by 2% and 6% for kNN and ANN respectively after feature set reduction, and also outperforms the accuracy obtained by other state of the art methods when applied to the MESSIDOR dataset. This application oriented work aids in accurate DR classification for effective diagnosis and timely treatment of severe retinal ailment.

scholarly journals PSV-25 Detection of heart and aorta tissue peptide markers by the multiple-reaction monitoring method

2020 ◽  
Vol 98 (Supplement_4) ◽  
pp. 362-363
Author(s):  
Daniil Khvostov ◽  
Natalya Vostrikova ◽  
Irina M Chernukha
Keyword(s):  
Amino Acid ◽  
High Performance ◽  
Multiple Reaction Monitoring ◽  
Meat Product ◽  
Meat Products ◽  
Amino Acid Sequences ◽  
Composition Analysis ◽  
Reaction Monitoring ◽  
Russian Science ◽  
Monitoring Method

Abstract Functional, particularly personalized meat-based foods are of more in demand by a consumer today. Functional additives, such as plant components and animal proteins from bovine or porcine tissues have been successfully used. With many ingredients added to foods, it is important to provide quality and composition monitoring to confirm the products’ authenticity, to identify undeclared or rarely used types of raw meat in product formulations. For example, if animal heart tissue is a component of a product formulation or if aorta tissue presents in a product due to improper trimming. Different methods are used to identify raw materials, including new approaches in proteomics and peptidomics that are considered the most effective modern methods nowadays. The purpose of the study is meat product composition analysis and special biomarker peptide identification to confirm the presence of heart and aorta tissue in a finished meat product. Over 20 amino acid sequences were checked based on earlier obtained data. Those amino acid sequences were analyzed with a high-performance liquid chromatography with mass spectrometric detection as described. The MS settings were selected using the Skyline. Signal-to-Noise ratio (S/N) over 10 units were used to choose the best peptide candidates. Seven peptides were found in porcine hearts. The best candidate was peptide VNVDEVGGEALGR (S/N - 73.10±5.3) from β-Hemoglobin. Two marker peptides from serum albumin were selected for pork aorta: TVLGNFAAFVQK (S/N 53.51±2.4) and EVTEFAK (S/N 31.69±4.1). These biomarkers showed the best detection and specificity. The multiply reaction monitoring method made it possible to identify the most/best specific peptides—biomarkers that could confirm the heart and/or aorta in meat products. The method can be used for comparative research or identification of best peptides that are specific to any type of animal tissue. The work was supported by the Russian Science Foundation, project no. 16-16- 10073.

scholarly journals Calcineurin Activity Assay Measurement by Liquid Chromatography–Tandem Mass Spectrometry in the Multiple Reaction Monitoring Mode

2014 ◽  
Vol 60 (2) ◽  
pp. 353-360 ◽  
Author(s):  
Lynn Carr ◽  
Anne-Laure Gagez ◽  
Marie Essig ◽  
François-Ludovic Sauvage ◽  
Pierre Marquet ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Liquid Chromatography ◽  
Mononuclear Cells ◽  
Multiple Reaction Monitoring ◽  
Internal Standard ◽  
Reaction Monitoring ◽  
Activity Assay ◽  
Peripheral Blood Mononuclear ◽  
Blood Concentrations ◽  
Transplant Patients

Abstract BACKGROUND Blood concentrations of the calcineurin inhibitors (CNIs) cyclosporine and tacrolimus are currently measured to monitor immunosuppression in transplant patients. The measurement of calcineurin (CN) phosphatase activity has been proposed as a complementary pharmacodynamic approach. However, determining CN activity with current methods is not practical. We developed a new method amenable to routine use. METHODS Using liquid chromatography–multiple reaction monitoring mass spectrometry (LC-MRM-MS), we quantified CN activity by measuring the dephosphorylation of a synthetic phosphopeptide substrate. A stable isotope analog of the product peptide served as internal standard, and a novel inhibitor cocktail minimized dephosphorylation by other major serine/threonine phosphatases. The assay was used to determine CN activity in peripheral blood mononuclear cells (PBMCs) isolated from 20 CNI-treated kidney transplant patients and 9 healthy volunteers. RESULTS Linearity was observed from 0.16 to 2.5 μmol/L of product peptide, with accuracy in the 15% tolerance range. Intraassay and interassay recoveries were 100.6 (9.6) and 100 (7.5), respectively. Michaelis–Menten kinetics for purified CN were Km = 10.7 (1.6) μmol/L, Vmax = 2.8 (0.3) μmol/min · mg, and for Jurkat lysate, Km = 182.2 (118.0) μmol/L, Vmax = 0.013 (0.006) μmol/min · mg. PBMC CN activity was successfully measured in a single tube with an inhibitor cocktail. CONCLUSIONS Because LC-MRM-MS is commonly used in routine clinical dosage of drugs, this CN activity assay could be applied, with parallel blood drug concentration monitoring, to a large panel of patients to reevaluate the validity of PBMC CN activity monitoring.

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