Detection of macular diseases in optical coherence tomography image

Vision decreasing and blindness are great threats to health, which can be caused by various kinds of macular diseases. Optical coherence tomography (OCT) is widely used in the diagnosis of macular disease. Ophthalmologists often need to check and analyse lots of OCT images and then give eye disease report for each image, which is time-consuming and inefficient. Thus, the automated classification method is necessary. In this paper, we proposed an approach to determine the status (normal or abnormal) of retina by OCT images. The proposed method can differentiate several kinds of macular diseases from normal image, including macular edema, macular hole and age-related macular degeneration. Besides the geometry features, our method takes advantage of two texture feature (local binary pattern histograms and histogram of oriented gradient) to improve the accuracy rate. The experimental results on a public available dataset demonstrate that our approach is effective and achieve higher accuracy (all accuracy >0.98, AUC can reach 1).

Flowchart of the proposed detection method.     

Multi-retinal disease classification by reduced deep learning features

This paper presents the retina-based disease diagnosis through deep learning-based feature extraction method. This process helps in developing automated screening system, which is capable of diagnosing retina for diseases such as age-related molecular degeneration, diabetic retinopathy, macular bunker, retinoblastoma, retinal detachment, and retinitis pigmentosa. Some of these diseases share a common characteristic, which makes the classification difficult. In order to overcome the above-mentioned problem, deep learning feature extraction and a multi-class SVM classifier are used. The main contribution of this work is the reducing the dimension of the features required to classify the retinal disease, which enhances the process of reducing the system requirement as well as good performance.

     

Proteomics in ocular fluids

The focus of this article is to review recent techniques in proteomic analysis of ocular fluids. These fluids include tears, aqueous humor, and vitreous, they will also be compared to serum analysis. Furthermore, we attempt to summarize some disease correlated biomarkers in ocular fluids that were discovered through different proteomic techniques in eye diseases like dry eye, glaucoma, age-related macular degeneration, uveitis, or diabetic retinopathy. This review is trying to point out the importance of these biomarkers for clinical applications.     

Age-related eye disease, visual impairment, and driving in the elderly.

As people age, a number of visual functions such as acuity, visual field, and night vision deteriorate. This decline in vision is associated in part with an increase in vehicular accidents per mile driven by the elderly. Four age-related ocular conditions--cataract, macular degeneration, open-angle glaucoma, and diabetic retinopathy--are primarily responsible for the decline in visual acuity and visual field in the elderly. Few epidemiologic data are available about these diseases, and at present they cannot be prevented. There is need for more information about visual decline and how it affects driving performance and for development of pragmatic approaches for detecting and assessing the elderly driver with functional visual deficits.     

The role of O-GlcNAc signaling in the pathogenesis of diabetic retinopathy

Diabetic retinopathy is a leading cause of blindness worldwide. Despite laser and surgical treatments, antiangiogenic and other therapies, and strict metabolic control, many patients progress to visual impairment and blindness. New insights are needed into the pathophysiology of diabetic retinopathy in order to develop new methods to improve the detection and treatment of disease and the prevention of blindness. Hyperglycemia and diabetes result in increased flux through the hexosamine biosynthetic pathway, which, in turn, results in increased PTM of Ser/Thr residues of proteins by O-linked β-N-acetylglucosamine (O-GlcNAc). O-GlcNAcylation is involved in regulation of many nuclear and cytoplasmic proteins in a manner similar to protein phosphorylation. Altered O-GlcNAc signaling has been implicated in the pathogenesis of diabetes and may play an important role in the pathogenesis of diabetic retinopathy. The goal of this review is to summarize the biology of the hexosamine biosynthesis pathway and O-GlcNAc signaling, to present the current evidence for the role of O-GlcNAc signaling in diabetes and diabetic retinopathy, and to discuss future directions for research on O-GlcNAc in the pathogenesis of diabetic retinopathy.     

Proteomic profiling of animal models mimicking skeletal muscle disorders

Over the last few decades of biomedical research, animal models of neuromuscular diseases have been widely used for determining pathological mechanisms and for testing new therapeutic strategies. With the emergence of high-throughput proteomics technology, the identification of novel protein factors involved in disease processes has been decisively improved. This review outlines the usefulness of the proteomic profiling of animal disease models for the discovery of new reliable biomarkers, for the optimization of diagnostic procedures and the development of new treatment options for skeletal muscle disorders. Since inbred animal strains show genetically much less interindividual differences as compared to human patients, considerably lower experimental repeats are capable of producing meaningful proteomic data. Thus, animal model proteomics can be conveniently employed for both studying basic mechanisms of molecular pathogenesis and the effects of drugs, genetic modifications or cell-based therapies on disease progression. Based on the results from comparative animal proteomics, a more informed decision on the design of clinical proteomics studies could be reached. Since no one animal model represents a perfect pathobiochemical replica of all of the symptoms seen in complex human disorders, the proteomic screening of novel animal models can also be employed for swift and enhanced protein biochemical phenotyping.     

一种快速的视网膜光学相干图像分割方法

在医学领域,黄斑厚度可以用来量化糖尿病黄斑水肿和年龄相关性黄斑变性等疾病,临床上通常使用光学相干断层扫描的影像技术来获取黄斑图像。但现有的黄斑图像分割方法运算速度较慢,阻碍了其临床使用。本文提出一种新的基于多分辨率及水平集的黄斑图像分割方法,首先使用高斯滤波对原始图像按行进行滤波,再运用多分辨率方法获取图像初始局部轮廓,最后使用水平集方法可以快速获取黄斑图像的中间轮廓,得到最终的图像分割结果。通过在311幅黄斑图像的仿真实验对比,本文方法在边缘检测结果和运算速度上比传统方法有很大改进。     

Proteomics and Leishmaniasis: Potential Clinical Applications

Leishmaniases are diseases caused by protozoan parasites of the genus Leishmania. They are endemic in 98 countries, affect around 12 million people worldwide and may present several distinct clinical forms. Unfortunately, there are only a few drugs available for treatment of leishmaniasis, which are toxic and not always effective. Different parasite species and different clinical forms require optimization of the treatment or more specific therapies, which are not available. The emergence of resistance is also a matter of concern. Besides, diagnosis can sometimes be complicated due to atypical manifestations and associations with other pathologies. In this review, proteomic data are presented and discussed in terms of their application in important issues in leishmaniasis such as parasite resistance to chemotherapy, diagnosis of active disease in patients and dogs, markers for different clinical forms, identification of virulence factors, and their potential use in vaccination. It is shown that proteomics has contributed to the discovery of potential biomarkers for prognosis, diagnosis, therapeutics, monitoring of disease progression, treatment follow‐up and identification of vaccine candidates for specific diseases. However, the authors believe its capabilities have not yet been fully explored for routine clinical analysis for several reasons, which will be presented in this review.     

Automated detection of exudates and macula for grading of diabetic macular edema

Medical systems based on state of the art image processing and pattern recognition techniques are very common now a day. These systems are of prime interest to provide basic health care facilities to patients and support to doctors. Diabetic macular edema is one of the retinal abnormalities in which diabetic patient suffers from severe vision loss due to affected macula. It affects the central vision of the person and causes total blindness in severe cases. In this article, we propose an intelligent system for detection and grading of macular edema to assist the ophthalmologists in early and automated detection of the disease. The proposed system consists of a novel method for accurate detection of macula using a detailed feature set and Gaussian mixtures model based classifier. We also present a new hybrid classifier as an ensemble of Gaussian mixture model and support vector machine for improved exudate detection even in the presence of other bright lesions which eventually leads to reliable classification of input retinal image in different stages of macular edema. The statistical analysis and comparative evaluation of proposed system with existing methods are performed on publicly available standard retinal image databases. The proposed system has achieved average value of 97.3%, 95.9% and 96.8% for sensitivity, specificity and accuracy respectively on both databases.     

Characterization of the farnesyl pyrophosphate synthase of Trypanosoma cruzi by homology modeling and molecular dynamics

Chagas' disease, caused by the Trypanosoma cruzi parasite, is one of the largest public health problems in the Western hemisphere, with 16-18 million people infected, and approximately 100 million people at risk. Many efforts towards the development of targeted antiparasitic agents have recently been described. Of interest, bisphosphonates, pyrophosphate analogs in which the oxygen bridge between the two phosphorus atoms has been replaced by a carbon substituted with different side chains, are able to inhibit the growth of T. cruzi. The enzyme T. cruzi farnesyl pyrophosphate synthase (TcFPPS) involved in the mevalonate pathway, has been recently identified as the target of bisphosphonates. The protein has 362 amino acids and a molecular mass of 41.2 kDa. Several sequence motifs found in other FPPSs are present in TcFPPS. In this study we have modeled the structure of TcFPPS based on the structure of the avian FPPS. We have characterized the interaction with its substrates, isopentyl pyrophosphate and dimethylallyl pyrophosphate, and the mechanism of inhibition by the potent bisphosphonate risedronate (K(i) of 0.032+/-0.002 microM) by means of molecular dynamics techniques. We propose that homorisedronate, which has an extra methylene and a K(i) of 8.17+/-1.36 microM, does not form strong hydrogen bonds with TYR 211 and THR 208, which may be responsible for its lower activity as compared to risedronate. Moreover, we were able to reproduce the structural changes that occur upon the binding of the third Mg2+ to the active site of the protein. Taken together, our results provide a structural model for the design of novel inhibitors that may prove useful for the treatment of Chagas' disease.     

Proteome profiling of vitreoretinal diseases by cluster analysis

Vitreous samples collected in retinopathic surgeries have diverse properties, making proteomics analysis difficult. We report a cluster analysis to evade this difficulty. Vitreous and subretinal fluid samples were collected from 60 patients during surgical operation of non‐proliferative diabetic retinopathy, proliferative diabetic retinopathy, proliferative vitreoretinopathy, and rhegmatogenous retinal detachment. For controls, we collected vitreous fluid from patients of idiopathic macular hole, epiretinal, and from a healthy postmortem donor. Proteins from these samples were subjected to quantitative proteomics using two‐dimensional gel electrophoresis. We selected 105 proteins robustly expressed among ca. 400 protein spots and subjected them to permutation test. By using permutation test analysis we observed unique variations in the expression of some of these proteins in vitreoretinal diseases when compared to the control and to each other: (i) the levels of inflammation‐associated proteins such as alpha1‐antitrypsin, apolipoprotein A4, albumin, and transferrin were significantly higher in all four types of vitreoretinal diseases, and (ii) each vitreoretinal disease elevated a unique set of proteins, which can be interpreted based on the pathology of retinopathy. Our protocol will be effective for the study of protein expression in other types of clinical samples of diverse properties.     

A decade of proteomics studies of glaucomatous neurodegeneration

Glaucoma is a leading cause of blindness; however, limited understanding of the molecular mechanisms involved in optic nerve degeneration hinders the development of improved treatment strategies. Proteomics techniques that combine the protein chemistry, MS, and bioinformatics offer the opportunity to shed light on molecular mechanisms so that new treatment strategies can be developed for immunomodulation, neuroprotection, neurorescue, neuroregeneration, and function gain in glaucoma. The proteomics technologies also hold great promise for biomarker discovery, another important goal of glaucoma research. As much as developing new treatment strategies, molecular biomarkers are strongly needed for early diagnosis of glaucoma, prediction of its prognosis, and monitoring the responses to new treatments. It is now a decade that the proteomics analysis techniques have been using to move glaucoma research forward. This review will focus on valuable applications of proteomics in the field of glaucoma research and highlight the power of this analytical toolbox in translational and clinical research toward better characterization and improved treatment of glaucomatous neurodegeneration and discovery of glaucoma-related molecular biomarkers.     

How can proteomics elucidate the complexity of multiple sclerosis?

Multiple sclerosis affects more than 2.5 million people worldwide. Although multiple sclerosis was described almost 150 years ago, there are many knowledge gaps regarding its etiology, diagnosis, prognosis, and pathogenesis. Multiple sclerosis is an inflammatory, demyelinating, neurodegenerative disease of the CNS. During the last several decades, experimental models of multiple sclerosis have contributed to our understanding of the inflammatory disease mechanisms and have aided drug testing and development. However, little is known about the neurodegenerative mechanisms that operate during the evolution of the disease. Currently, all therapeutic approaches are primarily based on the inflammatory aspect of the disease. During the last decade, proteomics has emerged as a promising tool for revealing molecular pathways as well as identifying and quantifying differentially expressed proteins. Therefore, proteomics may be used for the discovery of biomarkers, potential drug targets, and new regulatory mechanisms. To date, a considerable number of proteomics studies have been conducted on samples from experimental models and patients with multiple sclerosis. These data form a solid base for further careful analysis and validation.     

Proteomics of Alzheimer's disease: Unveiling protein dysregulation in complex neuronal systems

Alzheimer's disease (AD) and its progressive dementia is multifactorial, develops relatively sporadically, and involves multiple pathologies in the complex biological system of the brain. For these reasons, genetics alone is not likely to explain the molecular basis of this disease. Proteomics is contributing valuable information about the underlying molecular defects involved in AD development and progression, which includes oxidative damage to specific proteins, altered levels of synaptic components, and protein compositions of Aβ plaques and neurofibrillary tangles (pathological hallmarks of AD). However, emerging strategies in the field of proteomics which include more specific targeting of disease-related anatomical regions, targeting of specific subcellular compartments of functional relevance, novel approaches for large scale identification of regulatory post-translational modifications such as phosphorylation and O-GlcNAc, improved chromatographic separations of peptides for more comprehensive analysis of samples, and high-throughput quantitative strategies directly coupled with MS should greatly enhance the future of AD proteomics. The characterization of AD-specific alterations in proteomes should provide further insight into mechanisms of disease development and progression, provide biomarkers predicting disease development, and provide novel targets for therapeutic intervention.     

Proteomics of bacterial pathogenicity: therapeutic implications

Identification of the molecular mechanisms of host-pathogen interaction is becoming a key focus of proteomics. Analysis of these interactions holds promise for significant developments in the identification of new therapeutic strategies to combat infectious diseases, a process that will also benefit parallel improvements in molecular diagnostics, biomarker identification and drug discovery. This review highlights recent advances in functional proteomics initiatives in infectious disease with emphasis on studies undertaken within physiologically relevant parameters that enable identification of the infectious proteome rather than that of the vegetative state. Deciphering the molecular details of what constitutes physiologically relevant host-pathogen interactions remains an underdeveloped aspect of research into infectious disease. The magnitude of this deficit will be largely influenced by the ease with which model systems can be established to investigate such interactions. As the selective pressures exerted by the host on an infecting pathogen are numerous, the adequacy of certain model systems should be considered carefully.     

The effect of electrostatics on factor H function and related pathologies

Factor H (FH) contributes to the regulation of the complement system by binding to polyanionic surfaces and the proteins C3b/C3c/C3d. This implicates charge and electrostatic interactions in recognition and binding of FH. Despite the large amount of experimental and pathology data the exact mechanism at molecular level is not yet known. We have implemented a computational framework for comparative analysis of the charge and electrostatic diversity of FH modules and C3b domains to identify electrostatic hotspots and predict potential binding sites. Our electrostatic potential clustering analysis shows that charge distributions and electrostatic potential distributions are more useful in understanding C3b-FH interactions than net charges alone. We present a model of non-specific electrostatic interactions of FH with polyanion-rich surfaces and specific interactions with C3b, using our computational data and existing experimental data. We discuss the electrostatic contributions to the formation of the C3b-FH complex and the competition between FH and Factor Bb (Bb) for binding to C3b. We also discuss the significance of mutations of charged amino acids in the pathobiology of FH-mediated disease, such as age-related macular degeneration, atypical hemolytic uremic syndrome, and dense deposit disease. Our data can be used to guide future experimental studies.     

蛋白质折叠计算机模拟研究进展

蛋白质折叠过程中的结构变异可能导致"折叠病",比如老年痴呆症和多聚谷氨酰胺疾病等。因此蛋白质折叠研究对于揭示"折叠病"致病机理、指导药物设计等具有重大意义。文章阐述了蛋白质折叠计算机模拟研究的研究近况,分别介绍了蛋白质侧链研究、蛋白质折叠算法、蛋白质折叠病研究、蛋白质的分子动力学模拟和蛋白质结构预测等几个方面。     

Screening for diabetic retinopathy using computer based image analysis and statistical classification

Diabetic retinopathy is one of the most common causes of blindness in Europe. However, efficient therapies do exist. An accurate and early diagnosis and correct application of treatment can prevent blindness in more than 50% of all cases. Digital imaging is becoming available as a means of screening for diabetic retinopathy. As well as providing a high quality permanent record of the retinal appearance, which can be used for monitoring of progression or response to treatment, and which can be reviewed by an ophthalmologist, digital images have the potential to be processed by automatic analysis systems. We have described the preliminary development of a tool to provide automatic analysis of digital images taken as part of routine monitoring of diabetic retinopathy in our clinic. Various statistical classifiers, a Bayesian, a Mahalanobis, and a KNN classifier were tested. The system was tested on 134 retinal images. The Mahalanobis classifier had the best results: microaneurysms, haemorrhages, exudates, and cotton wool spots were detected with a sensitivity of 69, 83, 99, and 80%, respectively.     

Automatic diabetic retinopathy diagnosis using adjustable ophthalmoscope and multi-scale line operator

Diabetic Retinopathy (DR), the most common one of diabetic eye diseases that cause loss of vision and blindness, has become one of major health problems today. However, DR can be eased through timely treatment and periodical screening. In this paper, we proposes an automatic diabetic retinopathy diagnostic system to help patients know about their retinal conditions. We design a portable ophthalmoscope, which is composed of a retinal lens, a smartphone and a frame between them to help patients take fundus images anywhere and anytime. Then the images are transmitted to be analyzed, including localization of optic disk and macular, vessel segmentation, detection of lesions, and grading of DR. We use a multi-scale line operator to improve accuracy in segmenting small-scale vessels, a binary mask and image restoration to reduce the effect of the existence of the vessels on optic disk localization. After the analysis, the fundus image are then graded as normal, mild Non-Proliferative Diabetic Retinopathy (NPDR), moderate NPDR or severe NPDR. The grading process uses region segmentation to improve the efficiency. The final grading results are tested based on the fundus images provided by the hospitals. We evaluate our system through comparing our grading result with those graded by experts, which comes out with an overall accuracy of up to 85%.     

Human kidney glomerulus proteome and biomarker discovery of kidney diseases

The kidney glomerulus is the site of plasma filtration and production of primary urine in the kidney. The structure not only plays a pivotal role in ultrafiltration of plasma into urine but also is the locus of kidney diseases progressing to chronic renal failure. Patients afflicted with these glomerular diseases frequently progress to irreversible loss of renal function and inevitably require replacement therapies. The diagnosis and treatment of glomerular diseases are now based on clinical manifestations, urinary protein excretion level, and renal pathology of needle biopsy specimens. The molecular mechanisms underlying the progression of glomerular diseases are still obscure despite a great number of clinical and experimental studies. Proteomics is a particularly promising approach for the discovery of proteins relevant to physiological and pathophysiological processes, and has been recently employed in nephrology. Although until now most efforts of proteomic analysis have been conducted with urine, the biological fluid that is easily collected without invasive procedures, proteomic analysis of the glomerulus, the tissue most proximal to the disease loci, is the most straightforward approach. In this review, we attempt to outline the current status of clinical proteomics of the glomerulus and provide a perspective of protein biomarker discovery of glomerular diseases.