免散瞳眼底图像在糖尿病视网膜病变自动筛查中的应用

为实现糖尿病视网膜病变(糖网)的自动筛查,建立了基于免散瞳眼底图像的糖网自动筛查方法。该方法包括视盘定位及提取、糖网白色病灶(硬性渗出、棉绒斑)自动检测以及微动脉瘤与视网膜内出血的自动检测。在此基础上设计并实现了基于免散瞳眼底图像的糖网自动筛查系统。利用已实现的系统对临床采集的7 687个样本共15 374幅眼底图像进行糖网自动筛查,对样本个体的检测结果为:灵敏度96.46%,特异性96.07%,平均处理时间57.87 s。测试结果表明,所构建的基于免散瞳眼底图像的糖网自动筛查系统满足英国糖尿病协会提出的糖网自动筛查标准(最低灵敏度80%,最低特异性95%)。     

通过SEC-UNet精准分割糖尿病视网膜病变眼底OCT图像脉络膜层

目的 糖尿病视网膜病变（DR）是糖尿病的严重并发症，可导致患者视力下降甚至失明。脉络膜的早期检查在DR诊断中起着至关重要的作用。然而，由于DR患者的光学相干层析成像（OCT）中存在脉络膜和巩膜边界模糊、视网膜病变阴影等问题，导致大多数现有算法无法精准分割脉络膜层。本文目的在于提高DR患者OCT图像中脉络膜层分割的精准度。方法 本文提出了一种结合挤压激励连接（SEC）模块和UNet的网络，简称SEC-UNet，不仅增强Unet的局部细节目标关注能力，且能跳出局部最优来增强整体表达能力。结果 SEC-UNet模型的ROC曲线下面积（AUC）达到0.993 0，优于传统UNet模型和SE-UNet模型。这表明SEC-UNet能够获得准确、完整的脉络膜层分割结果。统计分析脉络膜参数变化发现，与正常眼相比，87.1%的DR患者脉络膜中央凹1 mm内体积增加，这证明了DR很可能导致脉络膜增厚。结论 该技术有望成为一种新的辅助诊断工具，帮助医生研究脉络膜在糖尿病眼病的预防、发病机制和预后中的作用。     

Novel Diabetic Mouse Models as Tools for Investigating Diabetic Retinopathy

Objective

Mouse models possessing green fluorescent protein (GFP) and/or human aldose reductase (hAR) in vascular tissues have been established and crossed with naturally diabetic Akita mice to produce new diabetic mouse models.

Research Design and Methods

Colonies of transgenic C57BL mice expressing GFP (SMAA-GFP), hAR (SMAA-hAR) or both (SMAA-GFP-hAR) in vascular tissues expressing smooth muscle actin were established and crossbred with C57BL/6-Ins2^Akita/J (AK) mice to produce naturally diabetic offspring AK-SMAA-GFP and AK-SMAA-GFP-hAR. Aldose reductase inhibitor AL1576 (ARI) was administered in chow. Retinal and lenticular sorbitol levels were determined by HPLC. Retinal functions were evaluated by electroretinography (ERGs). Growth factor and signaling changes were determined by Western Blots using commercially available antibodies. Retinal vasculatures were isolated from the neural retina by enzymatic digestion. Flat mounts were stained with PAS-hematoxylin and analyzed.

Results

Akita transgenics developed DM by 8 weeks of age with blood glucose levels higher in males than females. Sorbitol levels were higher in neural retinas of AK-SMAA-GFP-hAR compared to AK-SMAA-GFP mice. AK-SMAA-GFP-hAR mice also had higher VEGF levels and reduced ERG scotopic b-wave function, both of which were normalized by AL1576. AK-SMAA-GFP-hAR mice showed induction of the retinal growth factors bFGF, IGF-1, and TGFβ, as well as signaling changes in P-Akt, P-SAPK/JNK and P-44/42 MAPK that were also reduced by ARI treatment. Quantitative analysis of flat mounts in 18 week AK-SMAA-GFP-hAR mice revealed increased loss of nuclei/capillary length and a significant increase in the percentage of acellular capillaries present which was not seen in AK-SMAA-GFP-hAR treated with ARI.

Conclusions/Significance

These new mouse models of early onset diabetes may be valuable tools for assessing both the role of hyperglycemia and AR in the development of retinal lesions associated with diabetic retinopathy.     

High-Resolution Imaging of Parafoveal Cones in Different Stages of Diabetic Retinopathy Using Adaptive Optics Fundus Camera

PurposeTo assess cone density as a marker of early signs of retinopathy in patients with type II diabetes mellitus.MethodsAn adaptive optics (AO) retinal camera (rtx1^™; Imagine Eyes, Orsay, France) was used to acquire images of parafoveal cones from patients with type II diabetes mellitus with or without retinopathy and from healthy controls with no known systemic or ocular disease. Cone mosaic was captured at 0° and 2°eccentricities along the horizontal and vertical meridians. The density of the parafoveal cones was calculated within 100×100-μm squares located at 500-μm from the foveal center along the orthogonal meridians. Manual corrections of the automated counting were then performed by 2 masked graders. Cone density measurements were evaluated with ANOVA that consisted of one between-subjects factor, stage of retinopathy and the within-subject factors. The ANOVA model included a complex covariance structure to account for correlations between the levels of the within-subject factors.ResultsTen healthy participants (20 eyes) and 25 patients (29 eyes) with type II diabetes mellitus were recruited in the study. The mean (± standard deviation [SD]) age of the healthy participants (Control group), patients with diabetes without retinopathy (No DR group), and patients with diabetic retinopathy (DR group) was 55 ± 8, 53 ± 8, and 52 ± 9 years, respectively. The cone density was significantly lower in the moderate nonproliferative diabetic retinopathy (NPDR) and severe NPDR/proliferative DR groups compared to the Control, No DR, and mild NPDR groups (P < 0.05). No correlation was found between cone density and the level of hemoglobin A_1c (HbA_1c) or the duration of diabetes.ConclusionsThe extent of photoreceptor loss on AO imaging may correlate positively with severity of DR in patients with type II diabetes mellitus. Photoreceptor loss may be more pronounced among patients with advanced stages of DR due to higher risk of macular edema and its sequelae.     

The Reading of Components of Diabetic Retinopathy: An Evolutionary Approach for Filtering Normal Digital Fundus Imaging in Screening and Population Based Studies

In any diabetic retinopathy screening program, about two-thirds of patients have no retinopathy. However, on average, it takes a human expert about one and a half times longer to decide an image is normal than to recognize an abnormal case with obvious features. In this work, we present an automated system for filtering out normal cases to facilitate a more effective use of grading time. The key aim with any such tool is to achieve high sensitivity and specificity to ensure patients'' safety and service efficiency. There are many challenges to overcome, given the variation of images and characteristics to identify. The system combines computed evidence obtained from various processing stages, including segmentation of candidate regions, classification and contextual analysis through Hidden Markov Models. Furthermore, evolutionary algorithms are employed to optimize the Hidden Markov Models, feature selection and heterogeneous ensemble classifiers. In order to evaluate its capability of identifying normal images across diverse populations, a population-oriented study was undertaken comparing the software''s output to grading by humans. In addition, population based studies collect large numbers of images on subjects expected to have no abnormality. These studies expect timely and cost-effective grading. Altogether 9954 previously unseen images taken from various populations were tested. All test images were masked so the automated system had not been exposed to them before. This system was trained using image subregions taken from about 400 sample images. Sensitivities of 92.2% and specificities of 90.4% were achieved varying between populations and population clusters. Of all images the automated system decided to be normal, 98.2% were true normal when compared to the manual grading results. These results demonstrate scalability and strong potential of such an integrated computational intelligence system as an effective tool to assist a grading service.     

Relationship between C-Reactive Protein Level and Diabetic Retinopathy: A Systematic Review and Meta-Analysis

Objectives

To date, the relationship between C-reactive protein (CRP) level and diabetic retinopathy (DR) remains controversial. Therefore, a systematic review and meta-analysis was used to reveal the potential relationship between CRP level and DR.

Methods

A systematic search of PubMed, Embase.com, and Web of Science was performed to identify all comparative studies that compared the CRP level of two groups (case group and control group). We defined that diabetic patients without retinopathy and /or matched healthy persons constituted the control group, and patients with DR were the case group.

Results

Two cross sectional studies and twenty case control studies including a total of 3679 participants were identified. After pooling the data from all 22 studies, obvious heterogeneity existed between the studies, so a subgroup analysis and sensitivity analysis were performed. Removing the sensitivity studies, the blood CRP levels in the case group were observed to be higher than those in the control group [SMD = 0.22, 95% confidence interval (CI), 0.11–0.34], and the blood CRP levels in the proliferative diabetic retinopathy (PDR) group were also higher than those in the non-proliferative diabetic retinopathy (NPDR) group [SMD = 0.50, 95% CI, 0.30–0.70].

Conclusions

The results from this current meta-analysis indicate that the CRP level might be used as a biomarker to determine the severity of DR.     

Diabetic Retinopathy,Classified Using the Lesion-Aware Deep Learning System,Predicts Diabetic End-Stage Renal Disease in Chinese Patients

Objective: To characterize the relationship between diabetic retinopathy (DR) and diabetic nephropathy (DN) in Chinese patients and to determine whether the severity of DR predicts end-stage renal disease (ESRD).Methods: Bilateral fundic photographs of 91 Chinese type 2 diabetic patients with biopsy-confirmed DN, not in ESRD stage, were obtained at the time of renal biopsy in this longitudinal study. The baseline severity of DR was determined using the Lesion-aware Deep Learning System (RetinalNET) in an open framework for deep learning and was graded using the Early Treatment Diabetic Retinopathy Study severity scale. Cox proportional hazard models were used to estimate the hazard ratio (HR) for the effect of the severity of diabetic retinopathy on ESRD.Results: During a median follow-up of 15 months, 25 patients progressed to ESRD. The severity of retinopathy at the time of biopsy was a prognostic factor for progression to ESRD (HR 2.18, 95% confidence interval 1.05 to 4.53, P = .04). At baseline, more severe retinopathy was associated with poor renal function, and more severe glomerular lesions. However, 30% of patients with mild retinopathy and severe glomerular lesions had higher low-density lipo-protein-cholesterol and more severe proteinuria than those with mild glomerular lesions. Additionally, 3% of patients with severe retinopathy and mild glomerular changes were more likely to have had diabetes a long time than those with severe glomerular lesions.Conclusion: Although the severity of DR predicted diabetic ESRD in patients with type 2 diabetes mellitus and DN, the severities of DR and DN were not always consistent, especially in patients with mild retinopathy or microalbuminuria.Abbreviations: CI = confidence interval; DM = diabetic mellitus; DN = diabetic nephropathy; DR = diabetic retinopathy; eGFR = estimated glomerular filtration rate; ESRD = end-stage renal disease; HbA1c = hemoglobin A1c; HR = hazard ratio; NPDR = nonproliferative diabetic retinopathy; PDR = proliferative diabetic retinopathy; SBP = systolic blood pressure; T2DM = type 2 diabetes mellitus; VEGF = vascular endothelial growth factor     

A Novel Mouse Model of Advanced Diabetic Kidney Disease

Currently available rodent models exhibit characteristics of early diabetic nephropathy (DN) such as hyperfiltration, mesangial expansion, and albuminuria yet features of late DN (hypertension, GFR decline, tubulointerstitial fibrosis) are absent or require a significant time investment for full phenotype development. Accordingly, the aim of the present study was to develop a mouse model of advanced DN with hypertension superimposed (HD mice). Mice transgenic for human renin cDNA under the control of the transthyretin promoter (TTRhRen) were employed as a model of angiotensin-dependent hypertension. Diabetes was induced in TTRhRen mice through low dose streptozotocin (HD-STZ mice) or by intercrossing with OVE26 diabetic mice (HD-OVE mice). Both HD-STZ and HD-OVE mice displayed more pronounced increases in urinary albumin levels as compared with their diabetic littermates. Additionally, HD mice displayed renal hypertrophy, advanced glomerular scarring and evidence of tubulointerstitial fibrosis. Both HD-OVE and HD-STZ mice showed evidence of GFR decline as FITC-inulin clearance was decreased compared to hyperfiltering STZ and OVE mice. Taken together our results suggest that HD mice represent a robust model of type I DN that recapitulates key features of human disease which may be significant in studying the pathogenesis of DN and in the assessment of putative therapeutics.     

Identification of Novel Biomarkers for Behcet Disease Diagnosis Using Human Proteome Microarray Approach

Behcet disease (BD) is a chronic systemic vasculitis and considered as an autoimmune disease. Although rare, BD can be fatal due to ruptured vascular aneurysms or severe neurological complications. To date, no known biomarker has been reported for this disease, making it difficult to diagnosis in the clinics. To undertake this challenge, we employed the HuProt arrays, each comprised of ∼20,000 unique human proteins, to identify BD-specific autoantibodies using a Two-Phase strategy established previously. In Phase I, we profiled the autoimmunity on the HuProt arrays with 75 serum samples collected from 40 BD patients, 15 diagnosed autoimmune patients who suffer from Takayasu arteritis (TA; n = 5)), ANCA associated vasculitis (AAV; n = 5), and Sjogren's syndrome (SS; n = 5), and 20 healthy subjects, and identified 20 candidate autoantigens that were significantly associated with BD. To validate these candidates, in Phase II we constructed a focused array with these 20 candidate BD-associated antigens, and use it to profile a much larger cohort, comprised of serum samples collected from 130 BD patients, 103 autoimmune patients (i.e. 40TA, 40 AAV and 23 SS), and 110 healthy controls. This allowed us to validate CTDP1 (RNA polymerase II subunit A C-terminal domain phosphatase)as a BD-specific autoantigen. The association of anti-CTDP1 with BD patients was further validated using the traditional Western blotting analysis. In conclusion, anti-CTDP1 antibody serves a novel autoantibody for Behcet disease and is expected to help more accurate clinical diagnosis.     

Predicting Disease Risk Using Bootstrap Ranking and Classification Algorithms

Genome-wide association studies (GWAS) are widely used to search for genetic loci that underlie human disease. Another goal is to predict disease risk for different individuals given their genetic sequence. Such predictions could either be used as a “black box” in order to promote changes in life-style and screening for early diagnosis, or as a model that can be studied to better understand the mechanism of the disease. Current methods for risk prediction typically rank single nucleotide polymorphisms (SNPs) by the p-value of their association with the disease, and use the top-associated SNPs as input to a classification algorithm. However, the predictive power of such methods is relatively poor. To improve the predictive power, we devised BootRank, which uses bootstrapping in order to obtain a robust prioritization of SNPs for use in predictive models. We show that BootRank improves the ability to predict disease risk of unseen individuals in the Wellcome Trust Case Control Consortium (WTCCC) data and results in a more robust set of SNPs and a larger number of enriched pathways being associated with the different diseases. Finally, we show that combining BootRank with seven different classification algorithms improves performance compared to previous studies that used the WTCCC data. Notably, diseases for which BootRank results in the largest improvements were recently shown to have more heritability than previously thought, likely due to contributions from variants with low minimum allele frequency (MAF), suggesting that BootRank can be beneficial in cases where SNPs affecting the disease are poorly tagged or have low MAF. Overall, our results show that improving disease risk prediction from genotypic information may be a tangible goal, with potential implications for personalized disease screening and treatment.     

Chronic Kidney Disease and Diabetic Retinopathy in Patients with Type 2 Diabetes

Purpose

To explore the relationship between chronic kidney disease (CKD) and diabetic retinopathy (DR) in a representative population of type 2 diabetes mellitus (DM2) patients in Catalonia (Spain).

Methods

This was a population-based, cross-sectional study. A total of 28,344 patients diagnosed with DM2 who had recorded ophthalmologic and renal functional examinations were evaluated. Data were obtained from a primary healthcare electronic database of medical records. CKD was defined as an estimated glomerular filtration ratio (eGFR) of <60 ml/min/1.73m² and/or urine albumin to creatinine ratio (UACR) ≥30 mg/g. DR was categorized as non-vision threatening diabetic retinopathy and vision threatening diabetic retinopathy.

Results

CKD was associated with a higher rate of DR [OR], 95% confidence interval [CI], 1.5 (1.4–1.7). When we analyzed the association between different levels of UACR and DR prevalence observed that DR prevalence rose with the increase of UACR levels, and this association was significant from UACR values ≥10 mg/g, and increased considerably with UACR values ≥300mg/g (Odds ratio [OR], 95% confidence interval [CI], 2.0 (1.6–2.5). This association was lower in patients with eGFR levels 44 to 30 mL/min/1.73m² [OR], 95% confidence interval [CI], 1.3 (1.1–1.6).

Conclusions

These results show that CKD, high UACR and/or low eGFR, appear to be associated with DR in this DM2 population.     

Angiogenic Potential of Vitreous from Proliferative Diabetic Retinopathy and Eales' Disease Patients

Purpose

Proliferative Diabetic Retinopathy (PDR) and Eales'' Disease (ED) have different aetiologies although they share certain common clinical symptoms including pre-retinal neovascularization. Since there is a need to understand if the shared end-stage angiogenic pathology of PDR and ED is driven by common stimulating factors, we have studied the cytokines contained in vitreous from both patient groups and analyzed the angiogenic potential of these samples in vitro.

Material and Methods

Vitreous samples from patients with PDR (n = 13) and ED (n = 5) were quantified for various cytokines using a cytokine biochip array and sandwich ELISA. An additional group of patients (n = 5) with macular hole (MH) was also studied for comparison. To determine the angiogenic potential of these vitreous samples, they were analyzed for their ability to induce tubulogenesis in human microvascular endothelial cells. Further, the effect of anti-VEGF (Ranibizumab) and anti-IL-6 antibodies were studied on vitreous-mediated vascular tube formation.

Results

Elevated levels of IL-6, IL-8, MCP-1 and VEGF were observed in vitreous of both PDR and ED when compared to MH. PDR and ED vitreous induced greater levels of endothelial cell tube formation compared to controls without vitreous (P<0.05). When VEGF in vitreous was neutralized by clinically-relevant concentrations of Ranibizumab, tube length was reduced significantly in 5 of 6 PDR and 3 of 5 ED samples. Moreover, when treated with IL-6 neutralizing antibody, apparent reduction (71.4%) was observed in PDR vitreous samples.

Conclusions

We have demonstrated that vitreous specimens from PDR and ED patients share common elevations of pro-inflammatory and pro-angiogenic cytokines. This suggests that common cytokine profiles link these two conditions.     

2型糖尿病眼底增殖改变与冠状动脉病变关系的研究

目的:探讨2型糖尿病眼底增殖改变与冠状动脉病变的关系.方法:对330例2型糖尿病合并冠心痛患者进行眼底检测及冠状动脉造影,根据眼底是否具有增殖性改变进行对比分析,并应用多因素分析方法探讨与眼底增殖改变相关的危险因素.结果:2型糖尿病伴眼底增殖改变者,年龄较大,病程较长,尿微量白蛋白阳性检出率增加,TG、TC及HbA1c显著性升高;多支血管病变及钙化病变受累比例增高;Logistic回归分析显示,经调整糖尿病病程、HbA1c、微量白蛋白尿后,2型糖尿病眼底增殖改变仍与冠状动脉钙化相关.结论:2型糖尿病眼底增殖改变与冠状动脉钙化相关.     

eSIP: A Novel Solution-Based Sectioned Image Property Approach for Microscope Calibration

Fluorescence confocal microscopy represents one of the central tools in modern sciences. Correspondingly, a growing amount of research relies on the development of novel microscopic methods. During the last decade numerous microscopic approaches were developed for the investigation of various scientific questions. Thereby, the former qualitative imaging methods became replaced by advanced quantitative methods to gain more and more information from a given sample. However, modern microscope systems being as complex as they are, require very precise and appropriate calibration routines, in particular when quantitative measurements should be compared over longer time scales or between different setups. Multispectral beads with sub-resolution size are often used to describe the point spread function and thus the optical properties of the microscope. More recently, a fluorescent layer was utilized to describe the axial profile for each pixel, which allows a spatially resolved characterization. However, fabrication of a thin fluorescent layer with matching refractive index is technically not solved yet. Therefore, we propose a novel type of calibration concept for sectioned image property (SIP) measurements which is based on fluorescent solution and makes the calibration concept available for a broader number of users. Compared to the previous approach, additional information can be obtained by application of this extended SIP chart approach, including penetration depth, detected number of photons, and illumination profile shape. Furthermore, due to the fit of the complete profile, our method is less susceptible to noise. Generally, the extended SIP approach represents a simple and highly reproducible method, allowing setup independent calibration and alignment procedures, which is mandatory for advanced quantitative microscopy.     

A Novel and Fast Approach for Population Structure Inference Using Kernel-PCA and Optimization

Population structure is a confounding factor in genome-wide association studies, increasing the rate of false positive associations. To correct for it, several model-based algorithms such as ADMIXTURE and STRUCTURE have been proposed. These tend to suffer from the fact that they have a considerable computational burden, limiting their applicability when used with large datasets, such as those produced by next generation sequencing techniques. To address this, nonmodel based approaches such as sparse nonnegative matrix factorization (sNMF) and EIGENSTRAT have been proposed, which scale better with larger data. Here we present a novel nonmodel-based approach, population structure inference using kernel-PCA and optimization (PSIKO), which is based on a unique combination of linear kernel-PCA and least-squares optimization and allows for the inference of admixture coefficients, principal components, and number of founder populations of a dataset. PSIKO has been compared against existing leading methods on a variety of simulation scenarios, as well as on real biological data. We found that in addition to producing results of the same quality as other tested methods, PSIKO scales extremely well with dataset size, being considerably (up to 30 times) faster for longer sequences than even state-of-the-art methods such as sNMF. PSIKO and accompanying manual are freely available at https://www.uea.ac.uk/computing/psiko.     

改良眼底激光光凝合康柏西普治疗增殖性糖尿病视网膜病变的效果及对视力水平、黄斑区血流密度的影响

摘要 目的：探究改良眼底激光光凝联合康柏西普治疗增殖性糖尿病视网膜病变（PDR）的效果及对视力水平、黄斑区血流密度的影响。方法：回顾性分析2019.06-2022.06于我院接受改良眼底激光光凝治疗的62例（124眼）PDR患者临床资料,纳入对照组,回顾性分析同期于我院接受改良眼底激光光凝联合康柏西普治疗的62例（124眼）PDR患者临床资料,纳入试验组。比较治疗前和治疗后3个月两组患者视力水平[最佳矫正视力（BCVA）、视野指数（VFI）、视野平均缺损（MD）]、视网膜中央动脉血流动力学[峰值血流速度（PSV）、平均血流速度（MV）、搏动指数（PI）、阻力指数（RI）]、黄斑区血流密度[浅层毛细血管丛（SCP）、深层毛细血管丛（DCP）]、生活质量[低视力者生存质量量表（CLVQOL）]差异,记录3个月内两组患者并发症（黄斑水肿、高眼压、视网膜出血）发生情况。结果：治疗后3个月,两组患者BCVA、PSV、MV、SCP、DCP、CLVQOL较治疗前升高,试验组高于对照组（P均<0.05）;而VFI、MD、PI、RI水平降低,试验组低于对照组（P均<0.05）;两组患者术后并发症无显著性差异（P>0.05）。结论：改良眼底激光光凝治疗联合康柏西普治疗可增强PDR患者视力功能,改善患者视网膜中央动脉血流动力学及黄斑区血流密度,提高生活质量。     

Results of Automated Retinal Image Analysis for Detection of Diabetic Retinopathy from the Nakuru Study,Kenya

Objective

Digital retinal imaging is an established method of screening for diabetic retinopathy (DR). It has been established that currently about 1% of the world’s blind or visually impaired is due to DR. However, the increasing prevalence of diabetes mellitus and DR is creating an increased workload on those with expertise in grading retinal images. Safe and reliable automated analysis of retinal images may support screening services worldwide. This study aimed to compare the Iowa Detection Program (IDP) ability to detect diabetic eye diseases (DED) to human grading carried out at Moorfields Reading Centre on the population of Nakuru Study from Kenya.

Participants

Retinal images were taken from participants of the Nakuru Eye Disease Study in Kenya in 2007/08 (n = 4,381 participants [NW6 Topcon Digital Retinal Camera]).

Methods

First, human grading was performed for the presence or absence of DR, and for those with DR this was sub-divided in to referable or non-referable DR. The automated IDP software was deployed to identify those with DR and also to categorize the severity of DR.

Main Outcome Measures

The primary outcomes were sensitivity, specificity, and positive and negative predictive value of IDP versus the human grader as reference standard.

Results

Altogether 3,460 participants were included. 113 had DED, giving a prevalence of 3.3% (95% CI, 2.7–3.9%). Sensitivity of the IDP to detect DED as by the human grading was 91.0% (95% CI, 88.0–93.4%). The IDP ability to detect DED gave an AUC of 0.878 (95% CI 0.850–0.905). It showed a negative predictive value of 98%. The IDP missed no vision threatening retinopathy in any patients and none of the false negative cases met criteria for treatment.

Conclusions

In this epidemiological sample, the IDP’s grading was comparable to that of human graders’. It therefore might be feasible to consider inclusion into usual epidemiological grading.     

A Novel Approach for Prediction of Vitamin D Status Using Support Vector Regression

Background

Epidemiological evidence suggests that vitamin D deficiency is linked to various chronic diseases. However direct measurement of serum 25-hydroxyvitamin D (25(OH)D) concentration, the accepted biomarker of vitamin D status, may not be feasible in large epidemiological studies. An alternative approach is to estimate vitamin D status using a predictive model based on parameters derived from questionnaire data. In previous studies, models developed using Multiple Linear Regression (MLR) have explained a limited proportion of the variance and predicted values have correlated only modestly with measured values. Here, a new modelling approach, nonlinear radial basis function support vector regression (RBF SVR), was used in prediction of serum 25(OH)D concentration. Predicted scores were compared with those from a MLR model.

Methods

Determinants of serum 25(OH)D in Caucasian adults (n = 494) that had been previously identified were modelled using MLR and RBF SVR to develop a 25(OH)D prediction score and then validated in an independent dataset. The correlation between actual and predicted serum 25(OH)D concentrations was analysed with a Pearson correlation coefficient.

Results

Better correlation was observed between predicted scores and measured 25(OH)D concentrations using the RBF SVR model in comparison with MLR (Pearson correlation coefficient: 0.74 for RBF SVR; 0.51 for MLR). The RBF SVR model was more accurately able to identify individuals with lower 25(OH)D levels (<75 nmol/L).

Conclusion

Using identical determinants, the RBF SVR model provided improved prediction of serum 25(OH)D concentrations and vitamin D deficiency compared with a MLR model, in this dataset.     

A Novel Method for Assessment of Natural Killer Cell Cytotoxicity Using Image Cytometry

Natural killer (NK) cells belong to the innate arm of the immune system and though activated NK cells can modulate immune responses through the secretion of cytokines, their primary effector function is through target cell lysis. Accordingly, cytotoxicity assays are central to studying NK cell function. The ⁵¹Chromium release assay, is the “gold standard” for cytotoxicity assay, however, due to concerns over toxicity associated with the use and disposal of radioactive compounds there is a significant interest in non-radioactive methods. We have previously used the calcein release assay as a non-radioactive alternative for studying NK cell cytotoxicity. In this study, we show that the calcein release assay varies in its dynamic range for different tumor targets, and that the entrapped calcein could remain unreleased within apoptotic bodies of lysed tumor targets or incompletely released resulting in underestimation of percent specific lysis. To overcome these limitations, we developed a novel cytotoxicity assay using the Cellometer Vision Image Cytometer and compared this method to standard calcein release assay for measuring NK cell cytotoxicity. Using tumor lines K562, 721.221, and Jurkat, we demonstrate here that image cytometry shows significantly higher percent specific lysis of the target cells compared to the standard calcein release assay within the same experimental setup. Image cytometry is able to accurately analyze live target cells by excluding dimmer cells and smaller apoptotic bodies from viable target cell counts. The image cytometry-based cytotoxicity assay is a simple, direct and sensitive method and is an appealing option for routine cytotoxicity assay.