长春新碱治疗糖尿病肾病大鼠的实验研究

目的验证长春新碱对糖尿病肾病大鼠的治疗作用。方法用链脲佐菌素(STZ)诱导大鼠糖尿病模型。将模型大鼠分为治疗组与非治疗组,治疗组给予长春新碱(VCR)0.2 mg/kg,尾静脉注射,每周2次,观察给药后大鼠血糖、尿蛋白、肾功能、肾重,体重等值的改变以及肾脏病理的改善情况。结果 治疗组大鼠尿蛋白明显减少,肾重/体重低于非治疗组。光镜下观察治疗组肾脏病理学变化情况有明显改善。结论长春新碱可降低糖尿病肾病大鼠的尿蛋白并改善糖尿病肾病早期病理损害。     

一种改进的视网膜图像中微小动脉瘤的检测算法

基于糖尿病性视网膜病变中最早出现的微小动脉瘤病症进行了研究,提出一种有效的微小动脉瘤检测算法。首先在传统模板匹配算法的基础上提出了一种动态多参数模板匹配算法,并且使用相对误差和与相关系数来共同制约匹配度,从而实现了更为精确的匹配提取;其次提出了基于分布特性的计分策略和自适应加权的汇总策略,避免了单纯采用各个特征量作为独立约束指标进行筛选时忽视各个特征量的约束力大小的弊端。实验结果表明,该检测算法能够有效地提高微小动脉瘤的检测真阳性率。     

基于自适应阈值和区域生长的SD-OCT糖网图像亮斑分割

硬性渗出是糖尿病性视网膜病变的一个比较显著的症状,频域光学相干断层视网膜图像中的高信号亮斑与渗出有着密切的联系。为了研究渗出与病变的关系,有必要找到一种提取亮斑的方法。但是,目前关于糖网图像亮斑提取的研究还非常少。首先运用层分割算法限制亮斑所在区域,然后采用自适应阈值法确定种子集合,最后使用基于人类视觉特性的区域生长方法提取出亮斑。实验结果表明,本方法可以较准确地分割出糖网图像中的亮斑。     

An effective deep learning model for grading abnormalities in retinal fundus images using variational auto-encoders

Diabetic retinopathy (DR) and Diabetic Macular Edema (DME) are severe diseases that affect the eyes due to damage in blood vessels. Computer-aided automated grading will help clinicians conduct disease diagnoses at ease. Experiments of automated image processing with deep learning techniques using CNN produce promising results, especially in the medical imaging domain. However, the disease grading tasks in retinal images using CNN struggle to retain high-quality information at the output. A novel deep learning model based on variational auto-encoder to grade DR and DME abnormalities in retinal images is proposed. The objective of the proposed model is to extract the most relevant retinal image features efficiently. It focuses on addressing less relevant candidate region generation and translational invariance present in images. The experiments are conducted in IDRID dataset and evaluated using accuracy, U-kappa, sensitivity, specificity and precision metrics. The results outperform compared with other state-of-art techniques.     

The discovery of biomarkers for type 2 diabetic nephropathy by serum proteome analysis

Diabetic nephropathy (DN) is a serious kidney complication of diabetes, and constitutes the leading cause of end-stage renal disease. The earliest clinical evidence of DN is microalbuminuria, a term which refers to the appearance of small but abnormal amounts of albumin in the urine. However, screening methods for DN, such as biomarker assays, are yet to be developed for type 2 DN. In the present study, in an attempt to identify the biomarkers for initial diagnoses of type 2 DN, the protein profiles of human sera collected from 30 microalbuminuric type 2 diabetic patients were compared with those collected from 30 normoalbuminuric type 2 diabetic patients, via 2-DE. As a result, a total of 18 spots were determined to have different protein levels in the microalbuminuric patients. Twelve spots had lower protein levels of approximately 50%, and the other six had higher levels of approximately 100-300% as compared to the spots of normoalbuminuric patients. These spots were identified with ESI-Q-TOF (ESI-quadrupole-TOF) MS. Among the identified proteins, vitamin D-binding protein (DBP) and pigment epithelium-derived factor (PEDF) were verified by Western blotting. The results of this study indicate that the DBP may be employed as diagnostic and monitoring biomarkers of type 2 DN, contingent on further study into the matter.     

Retinal vessel segmentation employing ANN technique by Gabor and moment invariants-based features

Diabetic retinopathy (DR) is the major ophthalmic pathological cause for loss of eye sight due to changes in blood vessel structure. The retinal blood vessel morphology helps to identify the successive stages of a number of sight threatening diseases and thereby paves a way to classify its severity. This paper presents an automated retinal vessel segmentation technique using neural network, which can be used in computer analysis of retinal images, e.g., in automated screening for diabetic retinopathy. Furthermore, the algorithm proposed in this paper can be used for the analysis of vascular structures of the human retina. Changes in retinal vasculature are one of the main symptoms of diseases like hypertension and diabetes mellitus. Since the size of typical retinal vessel is only a few pixels wide, it is critical to obtain precise measurements of vascular width using automated retinal image analysis. This method segments each image pixel as vessel or nonvessel, which in turn, used for automatic recognition of the vasculature in retinal images. Retinal blood vessels are identified by means of a multilayer perceptron neural network, for which the inputs are derived from the Gabor and moment invariants-based features. Back propagation algorithm, which provides an efficient technique to change the weights in a feed forward network is utilized in our method. The performance of our technique is evaluated and tested on publicly available DRIVE database and we have obtained illustrative vessel segmentation results for those images.     

An integrated bioinformatics analysis and experimental study identified key biomarkers CD300A or CXCL1, pathways and immune infiltration in diabetic nephropathy mice

Diabetic nephropathy (DN) is a common microvascular complication that easily leads to end-stage renal disease. It is important to explore the key biomarkers and molecular mechanisms relevant to diabetic nephropathy (DN). We used highthroughput RNA sequencing to obtain the genes related to DN glomerular tissues and healthy glomerular tissues of mice. Then we used LIMMA to analyze differentially expressed genes (DEGs) between DN and non-diabetic glomerular samples. And we performed KEGG, gene ontology functional (GO) enrichment, and gene set enrichment analysis to reveal the signaling pathway of the disease. The CIBERSORT algorithm based on support vector machine was used to determine the immune infiltration score. Random forest algorithm and Cytoscape obtained hub genes. Finally, we applied co-staining, immunohistochemical staining, RT-qPCR and western blotting to validate the protein and mRNA expression of both hub genes. We obtained 913 DEGs mainly related to inflammatory factors and immunity. GSEA results showed that differential genes were mainly enriched in IL-17 signaling pathway, lipid and atherosclerosis, rheumatoid arthritis, TNF signaling pathway, neutrophil extracellular trap formation, Staphylococcus aureus infection and other pathways. The intersection of the random forest algorithm and Cytoscape revealed both hub genes of CD300A and CXCL1. Experiments have shown that the both key genes of CD300A and CXCL1 shown increased expression in glomerular podocytes, and are related to the inflammation of diabetic nephropathy. And immunohistochemical staining and RT-qPCR further confirmed that the protein and mRNA expression level of CD300A or CXCL1 in glomeruli tissue in DN mice were increased. The expression levels of CD300A and CXCL1 increased significantly under HG (high glucose) stimulation, further confirming that diabetes can lead to increased levels of CD300A and CXCL1 at the cellular level. Through bioinformatics analysis, machine learning algorithms, and experimental research, CD300A and CXCL1 are confirmed as both potential biomarkers in diabetic nephropathy. And we further revealed the main pathways of differential genes and the differentially distributed immune infiltrating cells in diabetic nephropathy.     

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.     

Effective optic disc detection method based on swarm intelligence techniques and novel pre-processing steps

Diabetic retinopathy affects the vision of a significant fraction of the population worldwide. Retinal fundus images are used to detect the condition before vision loss develops to enable medical interventions. Optic disc detection is an essential step for the automatic detection of the disease. Several techniques have been introduced in the literature to detect the optic disc with different performance characteristics such as speed, accuracy and consistency. For optic disc detection, a nature-inspired algorithm called swarm intelligence has been shown to have clear superiority in terms of speed and accuracy compared to traditional detection algorithms. We therefore further investigated and compared several swarm intelligence techniques. Our study focused on five popular swarm intelligence algorithms: artificial bee colony, particle swarm optimization, bat algorithm, cuckoo search and firefly algorithm. This work also featured a novel pre-processing scheme that enhances the detection accuracy of the swarm techniques by making the optic disc region the highest grayscale value in the image. The pre-processing involves multiple stages of background subtraction, median filtering and mean filtering and is named Background Subtraction-based Optic Disc Detection (BSODD). The best result was obtained by combining our pre-processing technique, firefly algorithm and the parameters used for the algorithm. The obtained accuracy was superior to the other tested algorithms and published results in the literature. The accuracy of the firefly algorithm was 100%, 100%, 98.82% and 95% when using the DRIVE, DiaRetDB1, DMED and STARE databases, respectively.     

2型糖尿病肾病地区发病率及危险因素分析

目的研究本地区2型糖尿病发生糖尿病肾病(DN)发病率及相关危险因素。方法以2008-2010年在本区域就诊的1603例2型糖尿病患者中确诊为糖尿病肾病的517例患者为研究对象。根据尿微量白蛋白(UACR)将患者分为早期糖尿病肾病组(MA组)、临床糖尿病肾病组(CP组),随机选取60例糖尿病无DN患者作为糖尿病无糖尿病肾病组(NA组),对3组患者的临床资料与生化指标进行分析。结果本地区2型糖尿病患者肾脏疾病发病率为32.25%,患者组间年龄无统计学意义,CP组与MA组病程有统计学意义。MA组与CP组血压、血糖、HbA1c、LP(α)、TG水平明显高于NA组。结论病程、血压、血糖、HbA1c、LP(α)、TG是DN的危险因素。DN早期临床表现不明显,尿常规无法检测微量白蛋白尿,应对糖尿病患者定期进行有效的尿白蛋白检查。控制血压、血糖、血脂等指标是控制DN进展的关键。