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Quantitative analysis of proteins in the tear fluid of patients with diabetic retinopathy

Diabetic retinopathy is the leading cause of new cases of legal blindness among adults in the developed countries. Approximately 40% of all people with diabetes have diabetic retinopathy and 5% of these have sight-threatening form. As the advanced stage, where there is a high risk for vision loss, can develop without any serious symptoms, sometimes it is hard to detect it. A non invasive method to detect biomarkers characteristic for diabetic retinopathy from the tear fluid was developed. Tear samples from diabetic patients with no retinopathy, non proliferative and proliferative stages of diabetic retinopathy were analyzed and the protein content of each sample was compared to the protein content of tear pool from healthy volunteers. The samples were labeled with iTRAQ fourplex labels and were analyzed with nanoHPLC coupled ESI-MS/MS mass spectrometry. The lipocalin 1, lactotransferrin, lacritin, lysozyme C, lipophilin A and immunoglobulin lambda chain were identified as possible biomarker candidates with significantly higher relative levels in the tear of patients with diabetic retinopathy.

A Versatile Method to Design Stem-Loop Primer-Based Quantitative PCR Assays for Detecting Small Regulatory RNA Molecules

Short regulatory RNA-s have been identified as key regulators of gene expression in eukaryotes. They have been involved in the regulation of both physiological and pathological processes such as embryonal development, immunoregulation and cancer. One of their relevant characteristics is their high stability, which makes them excellent candidates for use as biomarkers. Their number is constantly increasing as next generation sequencing methods reveal more and more details of their synthesis. These novel findings aim for new detection methods for the individual short regulatory RNA-s in order to be able to confirm the primary data and characterize newly identified subtypes in different biological conditions. We have developed a flexible method to design RT-qPCR assays that are very sensitive and robust. The newly designed assays were tested extensively in samples from plant, mouse and even human formalin fixed paraffin embedded tissues. Moreover, we have shown that these assays are able to quantify endogenously generated shRNA molecules. The assay design method is freely available for anyone who wishes to use a robust and flexible system for the quantitative analysis of matured regulatory RNA-s.

Tear fluid proteomics multimarkers for diabetic retinopathy screening

BackgroundThe aim of the project was to develop a novel method for diabetic retinopathy screening based on the examination of tear fluid biomarker changes. In order to evaluate the usability of protein biomarkers for pre-screening purposes several different approaches were used, including machine learning algorithms.MethodsAll persons involved in the study had diabetes. Diabetic retinopathy (DR) was diagnosed by capturing 7-field fundus images, evaluated by two independent ophthalmologists. 165 eyes were examined (from 119 patients), 55 were diagnosed healthy and 110 images showed signs of DR. Tear samples were taken from all eyes and state-of-the-art nano-HPLC coupled ESI-MS/MS mass spectrometry protein identification was performed on all samples. Applicability of protein biomarkers was evaluated by six different optimally parameterized machine learning algorithms: Support Vector Machine, Recursive Partitioning, Random Forest, Naive Bayes, Logistic Regression, K-Nearest Neighbor.ResultsOut of the six investigated machine learning algorithms the result of Recursive Partitioning proved to be the most accurate. The performance of the system realizing the above algorithm reached 74% sensitivity and 48% specificity.ConclusionsProtein biomarkers selected and classified with machine learning algorithms alone are at present not recommended for screening purposes because of low specificity and sensitivity values. This tool can be potentially used to improve the results of image processing methods as a complementary tool in automatic or semiautomatic systems.

Combined Methods for Diabetic Retinopathy Screening, Using Retina Photographs and Tear Fluid Proteomics Biomarkers

Background. It is estimated that 347 million people suffer from diabetes mellitus (DM), and almost 5 million are blind due to diabetic retinopathy (DR). The progression of DR can be slowed down with early diagnosis and treatment. Therefore our aim was to develop a novel automated method for DR screening. Methods. 52 patients with diabetes mellitus were enrolled into the project. Of all patients, 39 had signs of DR. Digital retina images and tear fluid samples were taken from each eye. The results from the tear fluid proteomics analysis and from digital microaneurysm (MA) detection on fundus images were used as the input of a machine learning system. Results. MA detection method alone resulted in 0.84 sensitivity and 0.81 specificity. Using the proteomics data for analysis 0.87 sensitivity and 0.68 specificity values were achieved. The combined data analysis integrated the features of the proteomics data along with the number of detected MAs in the associated image and achieved sensitivity/specificity values of 0.93/0.78. Conclusions. As the two different types of data represent independent and complementary information on the outcome, the combined model resulted in a reliable screening method that is comparable to the requirements of DR screening programs applied in clinical routine.

Synthesis of isoindole and benzoisoindole derivatives of teicoplanin pseudoaglycon with remarkable antibacterial and antiviral activities

The primary amino function of teicoplanin pseudoaglycon has been transformed into arylthioisoindole or benzoisoindole and glycosylthioisoindole derivatives, in a reaction with o-phthalaldehyde or naphtalene-2,3-dicarbaldehyde and various thiols. All of the obtained semisynthetic antibiotics exhibited potent antibacterial activities against Gram-positive bacteria in the ng per ml concentration range. A few of them showed antiviral activity, in particular against influenza virus.

A multi-level ensemble-based system for detecting microaneurysms in fundus images

In this paper, we present a complex approach to improve microaneurysm detection in color fundus images. Microaneurysms are early signs of diabetic retinopathy, so it is essential to detect these lesions accurately in an automatic screening system. The recommended detection of microaneurysms is realized through several levels. First, a specific combination of different preprocessing methods for candidate extractors is found. Then, we select candidates voted by a certain number of the candidate extractor algorithms. At all these levels, optimal adjustments are determined by corresponding simulated annealing algorithms. Finally, we classify the candidates with a machine-learning based approach considering an optimal feature vector selection determined by a feature subset selection algorithm. Our framework improves the positive likelihood ratio for the microaneurysms and outperforms both the state-of-the-art individual candidate extractors and microaneurysm detectors in these measures.

Evaluation of the grading performance of an ensemble-based microaneurysm detector

In this paper, results of a diabetic retinopathy screening experiment are presented which is based solely on the findings of a microaneurysm detector. For this purpose, an ensemble-based algorithm developed by our research group was used; this provided promising results in our earlier experiments. At its best, the 1200 image of the Messidor database is classified by this detector with a sensitivity of 96%, a specificity of 51% and achieved an AUC of 0.87. As anticipated, larger microaneurysm counts are recognized with higher level of certainty. Therefore, this approach might be expected to have good performance in relation to the severity of the disease.

A two-phase decision support framework for the automatic screening of digital fundus images

Abstract In this paper we give a brief review on the present status of automated detection systems describe for the screening of diabetic retinopathy. We further detail an enhanced detection procedure that consists of two steps. First, a pre-screening algorithm is considered to classify the input digital fundus images based on the severity of abnormalities. If an image is found to be seriously abnormal, it will not be analysed further with robust lesion detector algorithms. As a further improvement, we introduce a novel feature extraction approach based on clinical observations. The second step of the proposed method detects regions of interest with possible lesions on the images that previously passed the pre-screening step. These regions will serve as input to the specific lesion detectors for detailed analysis. This procedure can increase the computational performance of a screening system. Experimental results show that both two steps of the proposed approach are capable to efficiently exclude a large amount of data from further processing, thus, to decrease the computational burden of the automatic screening system.

Model requirements for Biobank Software Systems.

Biobanks are essential tools in diagnostics and therapeutics research and development related to personalized medicine. Several international recommendations, standards and guidelines exist that discuss the legal, ethical, technological, and management requirements of biobanks. Todays biobanks are much more than just collections of biospecimens. They also store a huge amount of data related to biological samples which can be either clinical data or data coming from biochemical experiments. A well-designed biobank software system also provides the possibility of finding associations between stored elements. Modern research biobanks are able to manage multicenter sample collections while fulfilling all requirements of data protection and security. While developing several biobanks and analyzing the data stored in them, our research group recognized the need for a well-organized, easy-to-check requirements guideline that can be used to develop biobank software systems. International best practices along with relevant ICT standards were integrated into a comprehensive guideline: The Model Requirements for the Management of Biological Repositories (BioReq), which covers the full range of activities related to biobank development. The guideline is freely available on the Internet for the research community. Availability The database is available for free at http://bioreq.astridbio.com/bioreq_v2.0.pdf

Geno viewer, a SAM/BAM viewer tool.

The ever evolving Next Generation Sequencing technology is calling for new and innovative ways of data processing and visualization. Following a detailed survey of the current needs of researchers and service providers, the authors have developed GenoViewer: a highly user-friendly, easy-to-operate SAM/BAM viewer and aligner tool. GenoViewer enables fast and efficient NGS assembly browsing, analysis and read mapping. It is highly customized, making it suitable for a wide range of NGS related tasks. Due to its relatively simple architecture, it is easy to add specialised visualization functionalities, facilitating further customised data analysis. The softwares source code is freely available; it is open for project and task-specific modifications. Availability The database is available for free at http://www.genoviewer.com/