Recent progress in urinary proteomics

Urinary proteomics has become one of the most attractive subdisciplines in clinical proteomics as the urine is an ideal source for the discovery of noninvasive biomarkers for kidney and nonkidney diseases. This field has been growing rapidly as indicated by >80 original research articles on urinary proteome analyses appearing since 2001, of which 28 (approximately 1/3) had been published within the year 2006. The most common technologies used in recent urinary proteome studies remain gel-based methods (1-DE, 2-DE and 2-D DIGE), whereas LC-MS/MS, SELDI-TOF MS, and CE-MS are other commonly used techniques. In addition, mass spectrometric immunoassay (MSIA) and array technology have also been applied. This review provides an extensive but concise summary of recent applications of urinary proteomics. Proteomic analyses of dialysate and ultrafiltrate fluids derived from renal replacement therapy (or artificial kidney) are also discussed.     

myStone: A system for automatic kidney stone classification

Kidney stone formation is a common disease and the incidence rate is constantly increasing worldwide. It has been shown that the classification of kidney stones can lead to an important reduction of the recurrence rate. The classification of kidney stones by human experts on the basis of certain visual color and texture features is one of the most employed techniques. However, the knowledge of how to analyze kidney stones is not widespread, and the experts learn only after being trained on a large number of samples of the different classes. In this paper we describe a new device specifically designed for capturing images of expelled kidney stones, and a method to learn and apply the experts knowledge with regard to their classification. We show that with off the shelf components, a carefully selected set of features and a state of the art classifier it is possible to automate this difficult task to a good degree. We report results on a collection of 454 kidney stones, achieving an overall accuracy of 63% for a set of eight classes covering almost all of the kidney stones taxonomy. Moreover, for more than 80% of samples the real class is the first or the second most probable class according to the system, being then the patient recommendations for the two top classes similar. This is the first attempt towards the automatic visual classification of kidney stones, and based on the current results we foresee better accuracies with the increase of the dataset size.     

2nd Combined Working Group and Management Committee Meeting of Urine and Kidney Proteomics COST Action 29-30 March 2009, Nafplio, Greece

EuroKUP (Urine and Kidney Proteomics; www.eurokup.org) is a COST (European Cooperation in the field of Scientific and Technical research: www.cost.esf.org Action fostering a multi-disciplinary network of investigators from 25 countries and focusing on facilitating translational proteomic research in kidney diseases. Four Working Groups focusing respectively on defining clinically important research questions in kidney diseases, kidney tissue proteomics, urine proteomics and bioinformatics have been generated. The EuroKUP members had their second combined Working Group and Management Committee (MC) meeting in Nafplio, Greece from March 29 to 30, 2009. This report summarizes the main presentations, discussions and agreed action points during this meeting. These refer to the design of collaborative projects and clinical center networks for specific kidney diseases; establishment of guidelines for kidney tissue proteomics analysis by laser-based imaging- and laser capture microdissection-MS; development and characterization of a "standard" urine specimen to be used for assessment of platform capability and data comparability in clinical proteomics applications; definition of statistical requirements in biomarker discovery studies; and development of a specialized kidney and urine ontology. Various training activities are planned involving training schools on laser capture microdissection- and imaging-MS, workshops on ontologies as well as short-term travel grants for junior investigators.     

基于能量活动曲线的肾脏超声图象的边界提取

与CT和MRI等医学图象相比，超声图象由于图象质量较差，相对难以分割，特别地，由于某些器官的边界不是很明显，尤其是肾脏的组织和组织之间的边界难以区分，因此，肾脏超声图象的边界提取对人们来说更富有挑战性。为了解决这一问题，本文提出了一种半自动的肾脏超声图象的边界提取方法。该算法基于能量活动曲线模型，并做了几点重要的改进，同时利用肾脏超声图象的统计模型，比较好地克服了肾脏复杂边界的影响，有效地提出了超声图象的肾脏边界。     

基于图割和水平集的肾脏医学图像分割

肾脏医学图像分割是医学图像分析和非侵入式计算机辅助诊断系统中的关键步骤。从CT、MRI图像中分割出肾脏及肾皮质,计算其体积和皮质厚度等信息,有助于评估肾脏的功能,从而制定相应的治疗方案。根据肾脏序列图像相邻切片之间结构灰度分布的相似性,提出了一种基于图割和水平集方法的自动肾脏及肾皮质分割方法。选取皮质区域具有足够对比度和清晰度的切片为初始参考图像,使用霍夫森林算法检测肾脏区域,对前景、背景进行均值聚类以估计其灰度分布,获取图割模型能量函数,分割出肾脏整体;通过形态学处理得到相邻切片肾脏的分割候选区域,重复上述分割。以此初步分割结果作为水平集方法的初始轮廓,进一步分割得到三维的肾脏整体和肾皮质区域。实验结果表明,基于图割和水平集的肾脏分割方法能够比较准确地分割出肾脏及肾皮质。     

Implementation of Proteomics Biomarkers in Nephrology: From Animal Models to Human Application?

Preclinical animal models are extensively used in nephrology. In this review, the utility of performing proteome analysis of kidney tissue or urine in such models and transfer of the results to human application has been assessed. Analysis of the literature identified 68 relevant publications. Pathway analysis of the reported proteins clearly indicated links with known biological processes in kidney disease providing validation of the observed changes in the preclinical models. However, although most studies focused on the identification of early markers of kidney disease or prediction of its progression, none of the identified makers has made it to substantial validation in the clinic or at least in human samples. Especially in renal disease where urine is an abundant source of biomarkers of diseases of the kidney and the urinary tract, it therefore appears that the focus should be on human material based discovery studies. In contrast, the most valid information of proteome analysis of preclinical models in nephrology for translation in human disease resides in studies focusing on drug evaluation, both efficacy for translation to the clinic and for mechanistic insight.     

An Optimized Transfer Learning Model Based Kidney Stone Classification

The kidney is an important organ of humans to purify the blood. The healthy function of the kidney is always essential to balance the salt, potassium and pH levels in the blood. Recently, the failure of kidneys happens easily to human beings due to their lifestyle, eating habits and diabetes diseases. Early prediction of kidney stones is compulsory for timely treatment. Image processing-based diagnosis approaches provide a greater success rate than other detection approaches. In this work, proposed a kidney stone classification method based on optimized Transfer Learning(TL). The Deep Convolutional Neural Network (DCNN) models of DenseNet169, MobileNetv2 and GoogleNet applied for classification. The combined classification results are processed by ensemble learning to increase classification performance. The hyperparameters of the DCNN model are adjusted by the metaheuristic algorithm of Gorilla Troops Optimizer (GTO). The proposed TL model outperforms in terms of all the parameters compared to other DCNN models.     

Dynamic urinary proteomic analysis reveals stable proteins to be potential biomarkers

Human urinary proteome analysis is a convenient and efficient approach for understanding disease processes affecting the kidney and urogenital tract. Many potential biomarkers have been identified in previous differential analyses; however, dynamic variations of the urinary proteome have not been intensively studied, and it is difficult to conclude that potential biomarkers are genuinely associated with disease rather then simply being physiological proteome variations. In this paper, pooled and individual urine samples were used to analyze dynamic variations in the urinary proteome. Five types of pooled samples (first morning void, second morning void, excessive water‐drinking void, random void, and 24 h void) collected in 1 day from six volunteers were used to analyze intra‐day variations. Six pairs of first morning voids collected a week apart were used to study inter‐day, inter‐individual, and inter‐gender variations. The intra‐day, inter‐day, inter‐individual, and inter‐gender variation analyses showed that many proteins were constantly present with relatively stable abundances, and some of these had earlier been reported as potential disease biomarkers. In terms of sensitivity, the main components of the five intra‐day urinary proteomes were similar, and the second morning void is recommended for clinical proteome analysis. The advantages and disadvantages of pooling samples are also discussed. The data presented describe a pool of stable urinary proteins seen under different physiological conditions. Any significant qualitative or quantitative changes in these stable proteins may mean that such proteins could serve as potential urinary biomarkers.     

Sources of urinary proteins and their analysis by urinary proteomics for the detection of biomarkers of disease

Renal disorders account for a substantial fraction of the budget for health care in many countries. Proteinuria is a frequent manifestation in afflicted patients, but the origin of the proteins varies based on the nature of the disorder. The emerging field of urinary proteomics has the potential to replace kidney biopsy as the diagnostic procedure of choice for patients with some glomerular forms of renal disease. To fully realize this potential, it is vital to understand the basis for the urinary excretion of protein in physiological and pathological conditions. In this review, we discuss the structure of the nephron, the functional unit of the kidney, and the process by which proteins/peptides enter the urine. We discuss several aspects of proteinuria that impact the proteomic analysis of urine of patients with renal diseases.     

Multiple reconstruction and dynamic modeling of 3D digital objects using a morphing approach

Organ segmentation and motion simulation of organs can be useful for many clinical purposes such as organ study, diagnostic aid, therapy planning or even tumor destruction. In this paper we present a full workflow starting from a CT-Scan resulting in kidney motion simulation and tumor tracking. Our method is divided into three major steps: kidney segmentation, surface reconstruction and animation. The segmentation is based on a semi-automatic region-growing approach that is refined to improve its results. The reconstruction is performed using the Poisson surface reconstruction and gives a manifold three-dimensional (3D) model of the kidney. Finally, the animation is accomplished using an automatic mesh morphing among the models previously obtained. Thus, the results are purely geometric because they are 3D animated models. Moreover, our method requires only a basic user interaction and is fast enough to be used in a medical environment, which satisfies our constraints. Finally, this method can be easily adapted to magnetic resonance imaging acquisition because only the segmentation part would require minor modifications.     

Inapproximability of the kidney exchange problem

To overcome the shortage of kidneys available for transplantation, several countries have started various programmes of kidney exchanges between incompatible patient-donor pairs. This situation can be modeled as a cooperative game in which the players are the patient-donor pairs and their preferences are derived in the first step from the suitability of the donated kidney and in the second step from the length of the obtained cycle of exchanges. Although the core of such a cooperative game is always nonempty and one core solution can be found by the famous Top Trading Cycles algorithm, many questions concerning the structure of the core are NP-hard. In this paper we show that the problem of finding a core permutation that maximizes the number of patients who obtain a suitable kidney is not approximable within n1−ε for any ε>0 unless P=NP.     

慢性肾病辅助诊断系统的设计与实现

针对慢性肾病在全球占比之高、病情发展不可逆转、病情极易出现恶化的特点,设计了慢性肾病辅助诊断系统。利用数据挖掘随机森林算法的分类功能对病人化验数据进行处理,判断病人是否患有慢性肾病。设计并开发了基于B/S的慢性肾病辅助诊断系统,该系统集慢性肾病辅助诊断、诊断信息查看、用户管理于一体。该系统用于给经验不足的医生提供诊断参考,助其提高诊断水平,降低误诊率,从而使慢性肾病患者尽早进行正确的治疗,避免病情治疗延误带来的严重后果。     

Aberrant protein expression in plasma and kidney tissue during experimental obstructive nephropathy

Kidney failure is a major health problem worldwide. Patients with end-stage renal disease require intensive medical support by dialysis or kidney transplantation. Current methods for diagnosis of kidney disease are either invasive or insensitive, and renal function may decline by as much as 50% before it can be detected using current techniques. The goal of this study was, therefore, to identify biomarkers of kidney disease (associated with renal fibrosis) that can be used for the development of a non-invasive clinical test for early disease detection. We utilized two protein-profiling technologies (SELDI-TOF MS and 2-D) to screen the plasma and kidney proteome for aberrantly expressed proteins in an experimental mouse model of unilateral uretric obstruction, which mimics the pathology of human renal disease. Several differentially regulated proteins were detected at the plasma level of day-3-obstructed animals, which included serum amyloid A1, fibrinogen α, haptoglobin precursor protein, haptoglobin and major urinary proteins 11 and 8. Differentially expressed proteins detected at the tissue level included ras-like activator protein 2, haptoglobin precursor protein, malate dehydrogenase, α enolase and murine urinary protein (all p<0.05 versus controls). Immunohistochemistry was used to confirm the up-regulation of fibrinogen. Interestingly, these proteins are largely separated into four major classes: (i) acute-phase reactants (ii) cell-signaling molecules (iii) molecules involved in cell growth and metabolism and (iv) urinary proteins. These results provide new insights into the pathology of obstructive nephropathy and may facilitate the development of specific assay(s) to detect and monitor renal fibrosis.     

Medical image diagnosis of kidney regions by deep feedback GMDH-type neural network using principal component-regression analysis

The deep feedback Group Method of Data Handling (GMDH)-type neural network is applied to the medical image recognition of kidney regions. In this algorithm, the principal component-regression analysis is used for the learning calculation of the neural network, and the accurate and stable predicted values are obtained. The neural network architecture is automatically organized so as to fit the complexity of the medical images using the prediction error criterion defined as Akaike’s Information Criterion (AIC) or Prediction Sum of Squares (PSS). The recognition results show that the deep feedback GMDH-type neural network algorithm is useful for the medical image recognition of kidney regions, because the optimum neural network architecture is automatically organized.     

Circular Stable Matching and 3-way Kidney Transplant

We consider the following version of the stable matching problem. Suppose that men have preferences for women, women have preferences for dogs, and dogs have preferences for men. The goal is to organize them into family units so that no three of them have incentive to desert their assigned family members to join in a new family. This problem is called circular stable matching, allegedly originated by Knuth. We also investigate a generalized version of this problem, in which every participant has preference among all others. The goal is similarly to partition them into oriented triples so that no three persons have incentive to deviate from the assignment. This problem is motivated by recent innovations in kidney exchange, and we call it the 3-way kidney transplant problem. We report complexity, structural and counting results on these two problems.     

Fragment class analysis for testing of polymorphism in Java software

Testing of polymorphism in object-oriented software may require coverage of all possible bindings of receiver classes and target methods at call sites. Tools that measure this coverage need to use class analysis to compute the coverage requirements. However, traditional whole-program class analysis cannot be used when testing incomplete programs. To solve this problem, we present a general approach for adapting whole-program class analyses to operate on program fragments. Furthermore, since analysis precision is critical for coverage tools, we provide precision measurements for several analyses by determining which of the computed coverage requirements are actually feasible for a set of subject components. Our work enables the use of whole-program class analyses for testing of polymorphism in partial programs, and identifies analyses that potentially are good candidates for use in coverage tools.     

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.