Objective: To compare the vessel geometry characteristics of color fundus photographs in normal control and diabetes mellitus (DM) patients and to find potential biomarkers for early diabetic retinopathy (DR) based on a neural network vessel segmentation system and automated vascular geometry parameter analysis software. Methods: A total of 102 consecutive patients with type 2 DM (T2DM) and 132 healthy controls were recruited. All participants underwent general ophthalmic examinations, and retinal fundus photographs were taken with a digital fundus camera without mydriasis. Color fundus photographs were input into a dense-block generative adversarial network (D-GAN)-assisted retinal vascular segmentation system (http://www.gdcerc.cn:8081/#/login) to obtain binary images. These images were then analyzed by customized software (ocular microvascular analysis system V2.9.1) for automatic processing of vessel geometry parameters, including the monofractal dimension (Dbox), multifractal dimension (D0), vessel area ratio (R), max vessel diameter (dmax), average vessel diameter (dave), arc–chord ratio (A/C), and tortuosity (τn). Geometric differences between the healthy subjects and DM patients were analyzed. Then, regression analysis and receiver operating characteristic (ROC) curve analysis were performed to evaluate the diagnostic efficiency of the vascular geometry parameters. Results: No significant differences were observed between the baseline characteristics of each group. DM patients had lower Dbox and D0 values (1.330 ± 0.041; 1.347 ± 0.038) than healthy subjects (1.343 ± 0.048, p < 0.05; 1.362 ± 0.042, p < 0.05) and showed increasing values of dmax, dave, A/C, and τn compared with normal controls, although only the differences in dave and τn between the groups were statistically significant. In the regression analysis, dave and τn showed a good correlation with diabetes (dave, OR 1.765, 95% CI 1.319–2.362, p < 0.001; τn, OR 9.323, 95% CI 1.492–58.262, p < 0.05). Conclusions: We demonstrated the relationship between retinal vascular geometry and the process in DM patients, showing that Dbox, D0, dave, and τn may be indicators of morphological changes in retinal vessels in DM patients and can be early biomarkers of DR.

Vascular tortuosity as an indicator of retinal vascular morphological changes can be quantitatively analyzed and used as a biomarker for the early diagnosis of relevant disease such as diabetes. While various methods have been proposed to evaluate retinal vascular tortuosity, the main obstacle limiting their clinical application is the poor consistency compared with the experts’ evaluation. In this research, we proposed to apply a multiple subdivision-based algorithm for the vessel segment vascular tortuosity analysis combining with a learning curve function of vessel curvature inflection point number, emphasizing the human assessment nature focusing not only global but also on local vascular features. Our algorithm achieved high correlation coefficients of 0.931 for arteries and 0.925 for veins compared with clinical grading of extracted retinal vessels. For the prognostic performance against experts’ prediction in retinal fundus images from diabetic patients, the area under the receiver operating characteristic curve reached 0.968, indicating a good consistency with experts’ predication in full retinal vascular network evaluation.

Purpose: To evaluate the morphologic and hemodynamic changes of bulbar conjunctival vessels in thyroid-associated ophthalmopathy (TAO) patients and the correlations with the activity. Methods: Patients diagnosed as TAO with different clinical activity scores (CAS) and healthy participants were recruited. All subjects underwent a complete ophthalmic examination and functional slit-lamp biomicroscope. Vascular variables including the vessel density, vessel complexity, average diameter, blood flow velocity and blood flow rate in microvascular networks were measured. The correlations among microvascular parameters, CAS and exophthalmos were analyzed. Areas under the receiver operating characteristic curves (AUROCs) were applied to evaluate the diagnostic accuracy of microvascular alterations for active TAO. Results: A total of 46 eyes were enrolled in our study. The vessel complexity and blood flow velocity increased in the active TAO group significantly compared with the inactive group and healthy controls (P < .05). Meanwhile, the vessel complexity and blood flow rate were positively correlated with CAS (r = 0.641 and r = 0.526). Bulbar conjunctival microvascular parameters performed a good ability in distinguishing the active stage of TAO (AUROC = 0.793). Conclusions: Increasing bulbar conjunctival vessel complexity and blood flow were evident in TAO with severe inflammation. The measurements of bulbar conjunctival microvasculature could be a reference to evaluate activity in TAO.

Purpose: Although acellular porcine corneal stroma (APCS) is a promising alternative to the human donor cornea for lamellar keratoplasty, here, we report 2 patients who exhibited persistent epithelial defects and sterile keratolysis after APCS transplantation to treat peripheral corneal diseases. Methods: Two patients with different peripheral corneal diseases underwent lamellar keratoplasty by using D-shaped lamellar APCS as graft materials. Standard keratoplasty postoperative treatments, including topical antibiotic-corticosteroid, tacrolimus, and artificial tears, were applied. Results: Patient 1 was a 7-year-old boy with limbal dermoid, and patient 2 was a 50-year-old man suffered from simultaneous Mooren ulcer with pterygium. Both patients developed persistent graft epithelial defects postoperatively, which were refractory to conventional nonsurgical therapies. The APCS grafts were noted to start sterile keratolysis at approximately 1 month after implantation and were completely dissolved within 3 months, leaving vascularized scars in the previously grafted area. Conclusions: These 2 cases demonstrated that given the high risk of postoperative persistent epithelial defect and sterile keratolysis, the application of APCS in peripheral keratoplasty may need further evaluation.

Purpose: To characterize the sex- and age-related alterations of the macular vascular geometry in a population of healthy eyes using fundus photography. Methods: A cross-sectional study was conducted with 610 eyes from 305 healthy subjects (136 men, 169 women) who underwent fundus photography examination and was divided into four age groups (G1 with age ≤ 25 years, G2 with age 26–35 years, G3 with age 36–45 years, and G4 with age ≥ 46 years). A self-developed automated retinal vasculature analysis system allowed segmentation and separate multiparametric quantification of the macular vascular network according to the Early Treatment Diabetic Retinopathy Study (ETDRS). Vessel fractal dimension (Df), vessel area rate (VAR), average vessel diameter (Dm), and vessel tortuosity (τn) were acquired and compared between sex and age groups. Results: There was no significant difference between the mean age of male and female subjects (32.706 ± 10.372 and 33.494 ± 10.620, respectively, p > 0.05) and the mean age of both sexes in each age group (p > 0.05). The Df, VAR, and Dm of the inner ring, the Df of the outer ring, and the Df and VAR of the whole macula were significantly greater in men than women (p < 0.001, p < 0.001, p < 0.05, respectively). There was no significant change of τn between males and females (p > 0.05). The Df, VAR, and Dm of the whole macula, the inner and outer rings associated negatively with age (p < 0.001), whereas the τn showed no significant association with age (p > 0.05). Comparison between age groups observed that Df started to decrease from G2 compared with G1 in the inner ring (p < 0.05) and Df, VAR, and Dm all decreased from G3 compared with the younger groups in the whole macula, inner and outer rings (p < 0.05). Conclusion: In the healthy subjects, macular vascular geometric parameters obtained from fundus photography showed that Df, VAR, and Dm are related to sex and age while τn is not. The baseline values of the macular vascular geometry were also acquired for both sexes and all age groups.

Automatic and accurate segmentation of anatomical structures on medical images is crucial for detecting various potential diseases. However, the segmentation performance of established deep neural networks may degenerate on different modalities or devices owing to the significant difference across the domains, a problem known as domain shift. In this work, we propose an uncertainty-aware domain alignment framework to address the domain shift problem in the cross-domain Unsupervised Domain Adaptation (UDA) task. Specifically, we design an Uncertainty Estimation and Segmentation Module (UESM) to obtain the uncertainty map estimation. Then, a novel Uncertainty-aware Cross Entropy (UCE) loss is proposed to leverage the uncertainty information to boost the segmentation performance on highly uncertain regions. To further improve the performance in the UDA task, an Uncertainty-aware Self-Training (UST) strategy is developed to choose the optimal target samples by uncertainty guidance. In addition, the Uncertainty Feature Recalibration Module (UFRM) is applied to enforce the framework to minimize the cross-domain discrepancy. The proposed framework is evaluated on a private cross-device Optical Coherence Tomography (OCT) dataset and a public cross-modality cardiac dataset released by MMWHS 2017. Extensive experiments indicate that the proposed UESM is both efficient and effective for the uncertainty estimation in the UDA task, achieving state-of-the-art performance on both cross-modality and cross-device datasets.

Automated detection of exudates from fundus images plays an important role in diabetic retinopathy (DR) screening and evaluation, for which supervised or semi-supervised learning methods are typically preferred. However, a potential limitation of supervised and semi-supervised learning based detection algorithms is that they depend substantially on the sample size of training data and the quality of annotations, which is the fundamental motivation of this work. In this study, we construct a dataset containing 1219 fundus images (from DR patients and healthy controls) with annotations of exudate lesions. In addition to exudate annotations, we also provide four additional labels for each image: left-versus-right eye label, DR grade (severity scale) from three different grading protocols, the bounding box of the optic disc (OD), and fovea location. This dataset provides a great opportunity to analyze the accuracy and reliability of different exudate detection, OD detection, fovea localization, and DR classification algorithms. Moreover, it will facilitate the development of such algorithms in the realm of supervised and semi-supervised learning.

PURPOSE: We evaluated the clinical utility of a novel anterior segment optical coherence tomography (AS-OCT) device, CASIA2, to evaluate parameters indicative of anterior chamber (AC) inflammation severity in uveitis, including AC cell number, flare, and keratic precipitates (KPs). DESIGN: Prospective evaluation of a diagnostic device. METHODS: Uveitis eyes were classified into active and inactive groups. The number of hyperreflective dots representing AC cells and optical density ratio (aqueous-to-air relative intensity [ARI] index) for flare qualification were calculated from AS-OCT images. In addition, a program was designed to quantify the posterior corneal surface smoothness (PCSS) of each image for KPs evaluation. The maximum, minimum, and average PCSS values were calculated from 128 images per eye and compared among active uveitis, inactive uveitis, and control eyes. Correlations between Standardization of Uveitis Nomenclature grade and both hyperreflective dot number and ARI index were evaluated. Receiver operating characteristic (ROC) curves were constructed to test the values of these indicators for uveitis diagnosis. RESULTS: AC hyperreflective dot count, ARI index, and maximum and average PCSS values were all significantly higher in the active uveitis group than in the inactive and control groups. Hyperreflective dot count and ARI index were associated with Standardization of Uveitis Nomenclature cell and flare grade. According to ROC curve analysis, maximum PCSS was the best indicator for the diagnosis of uveitis involving the anterior segment, meanwhile the hyperreflective dot number was the best to identify active AC inflammation from the inactive. CONCLUSIONS: Quantification of AC cell number, flare, and KPs using the CASIA2 device is a promising strategy for the objective assessment of AC inflammation. (C) 2020 Elsevier Inc. All rights reserved.

The "Great Unconformity" across the Proterozoic-Phanerozoic boundary has long been recognised as critical for understandings of the Cambrian Explosion and changes in sediment preservation. However in many parts of the Earth no rock strata are preserved for hundreds of millions of years leading up to the Cambrian Period. This study reports detrital zircon age and whole rock geochemical data from 43 samples of glaciogenic rocks in the Ediacaran Luoquan and Dongpo Formations. Detrital zircon age populations show a distinct peak at ca. 2.5 Ga and minor peaks at 2.2-1.6 Ga, typical of Mesoproterozoic provenance. It has been established that glaciogenic strata are uniquely suited as a proxy for the average upper continental crust (UCC). Applying this principle, the samples presented herein are used to provide insights into the UCC present in the North China Craton (NCC) during the Great Unconformity. This reveals UCC older than the Neoproterozoic but younger than the Great Oxygenation Event. Based upon these findings a tectonic and climatic model is proposed, using a modern analogue from Antarctica, wherein accommodation space was created by glacial abrasion whilst uplift continued throughout the NCC. This overdeepening process provides an explanation for the preservation of these glaciogenic strata, despite the Great Unconformity that surrounds them on all sides and this study contribute towards understanding of processes in the NCC during a critical time when little has survived into the rock record.

In this paper, we proposed and validated a novel and effective pipeline for automatically detecting hemorrhage from coarsely-annotated fundus images in diabetic retinopathy. The proposed framework consisted of three parts: image pre-processing, training data refining, and object detection using a convolutional neural network with label smoothing. Contrast limited adaptive histogram equalization and adaptive gamma correction with weighting distribution were adopted to improve image quality by enhancing image contrast and correcting image illumination. To refine coarsely-annotated training data, we designed a bounding box refining network (BBR-net) to provide more accurate bounding box annotations. Combined with label smoothing, RetinaNet was implemented to alleviate mislabeling issues and automatically detect hemorrhages. The proposed method was trained and evaluated on a publicly available IDRiD dataset and also one of our private datasets 1 1 This dataset will be released soon.. Experimental results showed that our BBR-net could effectively refine manually-delineated coarse hemorrhage annotations, with the average IoU being 0.8715 when compared with well-annotated bounding boxes. The proposed hemorrhage detection pipeline was compared to pure RetinaNet and superior performance was observed.

In this paper, we propose and validate a novel neural network named Lesion Guided Network (LGN) for automatic diagnosis of Diabetic Retinopathy (DR) from fundus images. RetinaNet is first adopted and trained on a coarsely-annotated dataset for rough lesion detection. Lesion-Aware Module (LAM) in LGN is proposed to highlight regions of interest in fundus images utilizing the coarse lesion maps. Then, the outputs of LAM are fed into a covolutional neural network (CNN) for DR identification. The proposed method is evaluated on a private dataset consisting of 4465 fundus images. Experimental results demonstrate the superiority of the proposed LGN, achieving comparable performance with ophthalmologists.

Purpose:To assess how DNA damage-inducible transcript 4 (DDIT4) and autophagic flux are altered in dry eye disease and reveal the underlying mechanisms.Methods:C57BL/6 mice were exposed to desiccating stress (subcutaneous scopolamine [0.5 mg/0.2 mL] 3 times a day, humidity < 30%) for 7 days. Primary human corneal epithelial cells and cells from a human corneal epithelial cell line were cultured under hyperosmolarity. Western blot assays and immunofluorescence staining were used to measure changes in protein expression. mRNA expression was analyzed by RT-PCR and quantitative real-time PCR. Autophagosomes were observed through electron microscopy. Cellular reactive oxygen species and mitochondrial function were detected with 2',7'-dichlorodihydrofluorescein diacetate and mitochondrial membrane potential assays. Cell Counting Kit-8 and lactate dehydrogenase assays were used to measure cell death. Apoptosis was analyzed by Annexin V-PI flow cytometry.Results:Increased expression of microtubule-associated protein 1 light chain 3 (LC3-II), sequestosome 1 (SQSTM1), and DDIT4 were observed in corneal epithelial cells in in vitro and mice models of dry eye. The electron microscopy revealed large autophagic vacuoles with poorly degraded materials in human corneal epithelial cells under hyperosmolarity. In addition, we found that DDIT4 knockdown significantly suppressed the expression of LC3-II and SQSTM1 by disrupting reactive oxygen species release and restoring mitochondrial function under hyperosmolarity. Moreover, the ablation of DDIT4 effectively preserved cell viability and inhibited apoptosis.Conclusions:Excessive reactive oxygen species release through DDIT4 induction can lead to impaired autophagy and decreased cell viability in dry eye disease.

Objective To study the efficiency and accuracy of artificial intelligence (AI) system based on fundus photograph in diabetic retinopathy(DR)screening,and evaluate the clinical application value of AI system. Methods A diagnostic trial was adopted. Total of 13683 color fundus photos were collected in Zhaoqing Gaoyao People's Hospital from March,2017 to November,2018. The AI system for DR (ZOC-DR-V1) was established,based on transfer learning + NASNet algorithm,by training 4465 precisely labeled fundus images (2510 normal,and 1955 with any stage of DR). One thousand confirmed fundus images (300 normal and 700 with any stage of DR),diagnosed by AI ( AI group ) and doctors ( 3 ophthalmologist doctors and 3 endocrinologist doctors ) ( doctor group ) , respectively. Ophthalmologist group and endocrinologist group were both composed of primary,intermediate and senior physicians. The mean reading time of each image and the total time of 1000 images were recorded. The accuracy and efficiency of AI system and doctor groups were compared. The reading process was divided into two stages. The diagnostic coincidence rate and the average reading time of each group between the two parts were calculated and compared. This study protocol was approved by Ethic Committee of Zhongshan Ophthalmic Center, Sun Yat-sen University (No. 2017KYPJ104). Results After training,the diagnostic coincidence rate of AI system (ZOC-DR-V1) in test set was 94. 7％,AUC was 0. 994. In this "man-machine to war",the diagnostic coincidence rate of primary,intermediate and senior endocrinologist was 94. 0％,91. 4％ and 93. 4％;the diagnostic coincidence rate of primary,intermediate and senior ophthalmologist was 92. 7％,94. 4％ and 95. 6％;the diagnostic coincidence rate of AI system was 95. 2％. There was no difference in the diagnostic coincidence rate between AI system and senior ophthalmologist ( P = 0. 749 ) . The mean reading time of each image of primary, intermediate and senior endocrinologists was (4. 63±1. 87),(3. 74±3. 47) and (5. 71±3. 47) seconds,and the total time of 1000 images of primary,intermediate and senior endocrinologists was 1. 29,1. 04 and 1. 58 hours;the mean reading time of each image of primary,intermediate and senior ophthalmologists was ( 7. 25 ± 6. 58 ) , ( 5. 18 ± 5. 01 ) and ( 5. 18 ± 3. 47 ) seconds,and the total time of 1000 images of primary,intermediate and senior endocrinologists was 2. 02,1. 44 and 1. 44 hours;the mean and total time of AI system was (1. 62±0. 67) seconds and 0. 45 hours,and the reading time of AI system was significantly shorter than that of the doctor groups (all at P=0. 000). The diagnostic coincidence rates between previous and posterior part of primary endocrinologist, primary and intermediate ophthalmologist were significantly different (χ2=11. 986,6. 517,10. 896;all at P<0. 05),and the mean reading time in the posterior part was significantly shorter than that in the previous part of intermediate and senior endocrinologist and primary ophthalmologist (t=4. 175,8. 189,5. 160;all at P<0. 01). While the reading time of AI system remained stable throughout the process(χ2=3. 151,P=0. 103;t=0. 038,P=0. 970). Conclusions The ophthalmic AI system based on fundus images has a good diagnostic efficiency,and its diagnostic coincidence rate can compare with senior ophthalmologist,providing a new method and platform for large-scale DR screening.

Background A worldwide lack of donor corneas demands the bioengineered corneas be developed as an alternative. The primary objective of the current study was to evaluate the efficacy of acellular porcine corneal stroma (APCS) transplantation in various types of infectious keratitis and identify risk factors that may increase APCS graft failure. Methods In this prospective interventional study, 39 patients with progressive infectious keratitis underwent therapeutic lamellar keratoplasty using APCS and were followed up for 12 months. Data collected for analysis included preoperative characteristics, visual acuity, graft survival and complications. Graft survival was evaluated by the Kaplan–Meier method and compared with the log-rank test. Results The percentage of eyes that had a visual acuity of 20/40 or better increased from 10.3% preoperatively to 51.2% at 12 months postoperatively. Twelve patients (30.8%) experienced graft failure within the follow-up period. The primary reasons given for graft failure was noninfectious graft melting (n = 5), and the other causes included recurrence of primary infection (n = 4) and extensive graft neovascularization (n = 3). No graft rejection was observed during the follow-up period. A higher relative risk (RR) of graft failure was associated with herpetic keratitis (RR = 8.0, P = 0.046) and graft size larger than 8 mm (RR = 6.5, P < 0.001). Conclusions APCS transplantation is an alternative treatment option for eyes with medically unresponsive infectious keratitis. Despite the efficacy of therapeutic lamellar keratoplasty with APCS, to achieve a good prognosis, restriction of surgical indications, careful selection of patients and postoperative management must be emphasized. Trial registration Prospective Study of Deep Anterior Lamellar Keratoplasty Using Acellular Porcine Cornea, NCT03105466. Registered 31 August 2016, ClinicalTrails.gov

页岩油藏经过分段压裂水平井的储层改造,其高效开发和持续稳产与基质中页岩油的动用情况密切相关.虽然赋存原油的泥页岩储集孔隙尺寸小、渗透率极低,但如果砂岩-页岩互层的纹层结构发育,页岩油仍有望达到商业化开采所需的可动用储层条件.目前烃类流体在纹层状泥页岩储层中流动机理尚不明确,普遍应用于常规油藏级数值模拟的双重介质模型和等效介质模型无法反映烃类流体在纹层状页岩油储层中流动的微观复杂特征,所得的宏观渗流模拟结果是不可靠的.根据陆相页岩储层的特征,建立了宏观页岩油藏的储层概念模型,分析了纹层状页岩储层的流体流动机理;考虑纹层物性、流体粘度和裂缝间距等影响因素,模拟了弹性开采过程中页岩储层条件和裂缝分布对页岩油可动性的作用规律,验证了页岩油藏弹性开采过程中窜流机制的存在和砂岩纹层渗透率的重要性,证明了天然裂缝和压裂缝加强纹层间流体传递的重要作用.

Automatic and accurate segmentation for retinal and choroidal layers of Optical Coherence Tomography (OCT) is crucial for detection of various ocular diseases. However, because of the variations in different equipments, OCT data obtained from different manufacturers might encounter appearance discrepancy, which could lead to performance fluctuation to a deep neural network. In this paper, we propose an uncertainty-guided domain alignment method to aim at alleviating this problem to transfer discriminative knowledge across distinct domains. We disign a novel uncertainty-guided cross-entropy loss for boosting the performance over areas with high uncertainty. An uncertainty-guided curriculum transfer strategy is developed for the self-training (ST), which regards uncertainty as efficient and effective guidance to optimize the learning process in target domain. Adversarial learning with feature recalibration module (FRM) is applied to transfer informative knowledge from the domain feature spaces adaptively. The experiments on two OCT datasets show that the proposed methods can obtain significant segmentation improvements compared with the baseline models.

页岩孔隙类型多样,微纳米尺度孔隙发育,借助分段压裂水平井技术能够实商业化开采.原油分子与孔隙壁面作用较甲烷分子更加复杂,目前页岩油在无机和有机纳米孔中的运移机制尚不清楚.准确模拟页岩油微观运移机制和多重孔隙介质间耦合流动对页岩油藏产能评价和生产预测具有重要意义.结合润湿特性,考虑液体吸附、速度滑移及物性变化等机制,引入复杂结构参数(迂曲度、孔隙度和有机孔含量),建立了微纳米多孔介质液体表观渗透率模型,研究了不同运移机制对微纳米多孔介质表观渗透率影响.在此基础上,建立了基质-天然裂缝-人工裂缝耦合的页岩油藏分段压裂水平井数学模型,利用有限元方法求解,进行产能影响因素分析.结果表明,孔隙半径小于10 nm时,微纳米孔隙速度滑移影响明显,而孔隙半径大于100 nm时,微观运移机制作用可以忽略;有机孔隙含量越小、裂缝条数越多则分段压裂水平井产能越大;最优的缝网模式为缝网无间距且不重叠.本研究的重点是丰富微纳米孔隙内油气运移理论,为页岩油藏开发模拟研究提供理论方法.

To determine the inter-visit variability of retinal blood flow velocities (BFVs) using a retinal function imager (RFI) in healthy young subjects.