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1.
Ocul Immunol Inflamm ; 6(1): 43-50, 1998 Mar.
Article in English | MEDLINE | ID: mdl-9798193

ABSTRACT

PURPOSE: The aim of this study was to classify the human IgG autoantibody repertoire of sera from patients suffering from endocrine ophthalmopathy (EOP) and healthy subjects (CTRL) for diagnostic purposes using the recently developed Megablot technique. This technique allows for the simultaneous and quantitative screening of a large set of antigens and uses multivariate statistical techniques and an artificial neural network. METHODS: Sera were tested against Western blots (WBs) of SDS-PAGE preparations of proteins from human extraorbital eye muscle (EOP: n = 16; CTRL: n = 11). Digital image analysis was performed. The blots were subsequently analyzed by multivariate statistical techniques (analysis of discriminance) and an artificial neural network (probalistic neural network). RESULTS: The sera of both the EOP and CTRL groups showed a complex staining pattern against WBs of SDS-PAGEs from human eye muscle. Using the multivariate statistical technique for classification, all of the known samples and 85% of the unknown samples (not presented during calculation) were assigned to their correct clinical group. Using the artificial neural network as classifier, all of the samples presented during training and 96.3% of the unknown samples (not trained) were assigned correctly. CONCLUSIONS: The artificial neural network exceeds the ability of multivariate statistical techniques such as analysis of discriminance to assign unknown samples to their correct predefined group. Thus, the neural network exceeds other methods in generalizing some similarities of blots used for classification. This study reveals that our new technique and its evaluation using a neural network can be used as a helpful diagnostic tool in autoimmune diseases such as endocrine ophthalmopathy.


Subject(s)
Autoantibodies/classification , Graves Disease/diagnosis , Graves Disease/immunology , Immunologic Tests , Neural Networks, Computer , Adult , Autoantibodies/analysis , Blotting, Western , Discriminant Analysis , Electrophoresis, Polyacrylamide Gel , Female , Humans , Male , Middle Aged , Multivariate Analysis , Reference Values
2.
Eur J Ophthalmol ; 8(2): 90-7, 1998.
Article in English | MEDLINE | ID: mdl-9673477

ABSTRACT

PURPOSE: To analyze the electrophoretic patterns of tear proteins from diabetic (DIDRY) and non-diabetic (DRY) patients with dry-eye disease in comparison with the patterns in healthy subjects (CTRL). The patterns were classified using multivariate statistical methods. METHODS: A total of 119 eyes were examined in this study (50 DIDRY, 39 DRY, 30 CTRL). The patients were primarily grouped according to the results of the basic secretory test (BST) and subjective symptoms (burning, foreign body sensations, tearing, "dryness" of the eyes). Patients with values < or = 10/5' plus subjective symptoms were classified as dry-eye patients. Tear proteins were separated by sodium-dodecyl-sulfate poly-acrylamide gel electrophoresis (SDS-PAGE). Digital image analysis was done using the ScanPacK (Biometra, Gottingen, Germany). A data set was created from each electrophoretic pattern. The data were analyzed by multivariate analysis of discriminance, k-means cluster analysis and with a factor analysis before k-means cluster analysis as a data reduction tool. RESULTS: The protein patterns of the three groups were significantly different (Wilks' lambda: 0.1425; P < 0.01): P < 0.05 (CTRL-DRY), P < 0.00005 (CTRL-DIDRY), and P < 0.0005 (DIDRY-DRY). There were more peaks/electrophoretic lane in the DRY and DIDRY groups (P < 0.05) than CTRL. Classification of all samples as DRY and CTRL gave the following results: known patterns 97% correct; unknown patterns 71.4% correct. Classification in DIDRY, DRY or CTRL was 92% correct for known patterns and 43% for unknown patterns. Using k-means cluster analysis, 72% of patients previously classified as "dry-eye" according to the BST value were classified as "dry-eye" based on their electrophoretic data too. CONCLUSIONS: Analysis of protein patterns and statistical evaluation are suitable for the detection of dry eyes. Tear-protein pattern analysis can provide more information on the pathogenesis of the disease. Thus, this new method might be more reliable than the BST value for both diagnostic and therapeutic purposes.


Subject(s)
Dry Eye Syndromes/diagnosis , Eye Proteins , Tears/chemistry , Diabetes Complications , Dry Eye Syndromes/complications , Electrophoresis, Polyacrylamide Gel , Eye Proteins/analysis , Humans , Multivariate Analysis
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