A Competition for the Diagnosis of Myopic Maculopathy by Artificial Intelligence Algorithms.

Importance: Myopic maculopathy (MM) is a major cause of vision impairment globally. Artificial intelligence (AI) and deep learning (DL) algorithms for detecting MM from fundus images could potentially improve diagnosis and assist screening in a variety of health care settings. Objectives: To evaluate DL algorithms for MM classification and segmentation and compare their performance with that of ophthalmologists. Design, Setting, and Participants: The Myopic Maculopathy Analysis Challenge (MMAC) was an international competition to develop automated solutions for 3 tasks: (1) MM classification, (2) segmentation of MM plus lesions, and (3) spherical equivalent (SE) prediction. Participants were provided 3 subdatasets containing 2306, 294, and 2003 fundus images, respectively, with which to build algorithms. A group of 5 ophthalmologists evaluated the same test sets for tasks 1 and 2 to ascertain performance. Results from model ensembles, which combined outcomes from multiple algorithms submitted by MMAC participants, were compared with each individual submitted algorithm. This study was conducted from March 1, 2023, to March 30, 2024, and data were analyzed from January 15, 2024, to March 30, 2024. Exposure: DL algorithms submitted as part of the MMAC competition or ophthalmologist interpretation. Main Outcomes and Measures: MM classification was evaluated by quadratic-weighted κ (QWK), F1 score, sensitivity, and specificity. MM plus lesions segmentation was evaluated by dice similarity coefficient (DSC), and SE prediction was evaluated by R2 and mean absolute error (MAE). Results: The 3 tasks were completed by 7, 4, and 4 teams, respectively. MM classification algorithms achieved a QWK range of 0.866 to 0.901, an F1 score range of 0.675 to 0.781, a sensitivity range of 0.667 to 0.778, and a specificity range of 0.931 to 0.945. MM plus lesions segmentation algorithms achieved a DSC range of 0.664 to 0.687 for lacquer cracks (LC), 0.579 to 0.673 for choroidal neovascularization, and 0.768 to 0.841 for Fuchs spot (FS). SE prediction algorithms achieved an R2 range of 0.791 to 0.874 and an MAE range of 0.708 to 0.943. Model ensemble results achieved the best performance compared to each submitted algorithms, and the model ensemble outperformed ophthalmologists at MM classification in sensitivity (0.801; 95% CI, 0.764-0.840 vs 0.727; 95% CI, 0.684-0.768; P = .006) and specificity (0.946; 95% CI, 0.939-0.954 vs 0.933; 95% CI, 0.925-0.941; P = .009), LC segmentation (DSC, 0.698; 95% CI, 0.649-0.745 vs DSC, 0.570; 95% CI, 0.515-0.625; P < .001), and FS segmentation (DSC, 0.863; 95% CI, 0.831-0.888 vs DSC, 0.790; 95% CI, 0.742-0.830; P < .001). Conclusions and Relevance: In this diagnostic study, 15 AI models for MM classification and segmentation on a public dataset made available for the MMAC competition were validated and evaluated, with some models achieving better diagnostic performance than ophthalmologists.

Integrated image-based deep learning and language models for primary diabetes care.

Primary diabetes care and diabetic retinopathy (DR) screening persist as major public health challenges due to a shortage of trained primary care physicians (PCPs), particularly in low-resource settings. Here, to bridge the gaps, we developed an integrated image-language system (DeepDR-LLM), combining a large language model (LLM module) and image-based deep learning (DeepDR-Transformer), to provide individualized diabetes management recommendations to PCPs. In a retrospective evaluation, the LLM module demonstrated comparable performance to PCPs and endocrinology residents when tested in English and outperformed PCPs and had comparable performance to endocrinology residents in Chinese. For identifying referable DR, the average PCP's accuracy was 81.0% unassisted and 92.3% assisted by DeepDR-Transformer. Furthermore, we performed a single-center real-world prospective study, deploying DeepDR-LLM. We compared diabetes management adherence of patients under the unassisted PCP arm (n = 397) with those under the PCP+DeepDR-LLM arm (n = 372). Patients with newly diagnosed diabetes in the PCP+DeepDR-LLM arm showed better self-management behaviors throughout follow-up (P < 0.05). For patients with referral DR, those in the PCP+DeepDR-LLM arm were more likely to adhere to DR referrals (P < 0.01). Additionally, DeepDR-LLM deployment improved the quality and empathy level of management recommendations. Given its multifaceted performance, DeepDR-LLM holds promise as a digital solution for enhancing primary diabetes care and DR screening.

Adipose-Derived exosome from Diet-Induced-Obese mouse attenuates LPS-Induced acute lung injury by inhibiting inflammation and Apoptosis: In vivo and in silico insight.

BACKGROUND: Acute lung injury (ALI) is a severe clinical condition in the intensive care units, and obesity is a high risk of ALI. Paradoxically, obese ALI patients had better prognosis than non-obese patients, and the mechanism remains largely unknown. METHODS: Mouse models of ALI and diet-induced-obesity (DIO) were used to investigate the effect of exosomes derived from adipose tissue. The adipose-derived exosomes (ADEs) were isolated by ultracentrifugation, and the role of exosomal miRNAs in the ALI was studied. RESULTS: Compared with ADEs of control mice (C-Exo), ADEs of DIO mice (D-Exo) increased survival rate and mitigated pulmonary lesions of ALI mice. GO and KEGG analyses showed that the target genes of 40 differentially expressed miRNAs between D-Exo and C-Exo were mainly involved with inflammation, apoptosis and cell cycle. Furthermore, the D-Exo treatment significantly decreased Ly6G+ cell infiltration, down-regulated levels of pro-inflammatory cytokines (IL-6, IL-12, TNF-α, MCP-1) and chemokines (IL-8 and MIP-2), reduced pulmonary apoptosis and arrest at G0G1 phase (P < 0.01). And the protective effects of D-Exo were better than those of C-Exo (P < 0.05). Compared with the C-Exo mice, the levels of miR-16-5p and miR-335-3p in the D-Exo mice were significantly up-regulated (P < 0.05), and the expressions of IKBKB and TNFSF10, respective target of miR-16-5p and miR-335-3p by bioinformatic analysis, were significantly down-regulated in the D-Exo mice (P < 0.05). CONCLUSIONS: Exosomes derived from adipose tissue of DIO mice are potent to attenuate LPS-induced ALI, which could be contributed by exosome-carried miRNAs. Our data shed light on the interaction between obesity and ALI.

DRAC 2022: A public benchmark for diabetic retinopathy analysis on ultra-wide optical coherence tomography angiography images.

We described a challenge named "DRAC - Diabetic Retinopathy Analysis Challenge" in conjunction with the 25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2022). Within this challenge, we provided the DRAC datset, an ultra-wide optical coherence tomography angiography (UW-OCTA) dataset (1,103 images), addressing three primary clinical tasks: diabetic retinopathy (DR) lesion segmentation, image quality assessment, and DR grading. The scientific community responded positively to the challenge, with 11, 12, and 13 teams submitting different solutions for these three tasks, respectively. This paper presents a concise summary and analysis of the top-performing solutions and results across all challenge tasks. These solutions could provide practical guidance for developing accurate classification and segmentation models for image quality assessment and DR diagnosis using UW-OCTA images, potentially improving the diagnostic capabilities of healthcare professionals. The dataset has been released to support the development of computer-aided diagnostic systems for DR evaluation.

A deep learning system for predicting time to progression of diabetic retinopathy.

Diabetic retinopathy (DR) is the leading cause of preventable blindness worldwide. The risk of DR progression is highly variable among different individuals, making it difficult to predict risk and personalize screening intervals. We developed and validated a deep learning system (DeepDR Plus) to predict time to DR progression within 5 years solely from fundus images. First, we used 717,308 fundus images from 179,327 participants with diabetes to pretrain the system. Subsequently, we trained and validated the system with a multiethnic dataset comprising 118,868 images from 29,868 participants with diabetes. For predicting time to DR progression, the system achieved concordance indexes of 0.754-0.846 and integrated Brier scores of 0.153-0.241 for all times up to 5 years. Furthermore, we validated the system in real-world cohorts of participants with diabetes. The integration with clinical workflow could potentially extend the mean screening interval from 12 months to 31.97 months, and the percentage of participants recommended to be screened at 1-5 years was 30.62%, 20.00%, 19.63%, 11.85% and 17.89%, respectively, while delayed detection of progression to vision-threatening DR was 0.18%. Altogether, the DeepDR Plus system could predict individualized risk and time to DR progression over 5 years, potentially allowing personalized screening intervals.

Isoliensinine affects the proliferation, apoptosis and autophagy of colon cancer SW480 cells through PI3K/Akt/mTOR signaling pathway / 中国肿瘤生物治疗杂志

@#[摘 要] 目的：探讨异莲心碱（Iso）通过PI3K/Akt/mTOR信号通路对结肠癌SW480细胞增殖、凋亡和自噬的影响。方法：用10、20和40 μmol/L的Iso处理结肠癌SW480细胞，CCK-8法、流式细胞术和WB法分别检测Iso对细胞增殖活力、凋亡和自噬相关蛋白LC3Ⅰ、LC3Ⅱ、p62表达的影响。然后，用20 μmol/L的Iso和25 μmol/L的PI3K激活剂740 Y-P分别处理SW480细胞，将细胞分为对照组、740 Y-P组、Iso组和Iso+740 Y-P组，流式细胞术、WB法检测Iso和740 Y-P对各组细胞凋亡及细胞中LC3Ⅰ、LC3Ⅱ、p62、PI3K、p-PI3K、 mTOR和p-mTOR蛋白表达的影响。结果：10、20和40 μmol/L的Iso处理后，SW480细胞增殖活力均显著下降（均P<0.05），细胞凋亡率均显著升高（均P<0.05），LC3Ⅱ/LC3Ⅰ表达均显著上调（均P<0.05），p26蛋白表达显著下调（P<0.05）。Iso和740 Y-P处理后，与对照组相比，740 Y-P组细胞凋亡率、LC3Ⅱ/LC3Ⅰ表达均显著下降（均P<0.05），p26、p-PI3K/PI3K和p-mTOR/mTOR表达均显著升高（均P<0.05）；Iso组细胞凋亡率、LC3Ⅱ/LC3Ⅰ表达升高（均P<0.05），p26、p-PI3K/PI3K和p-mTOR/mTOR表达均显著下降（均P<0.05）；与740 Y-P组相比，Iso+740 Y-P组细胞凋亡率、LC3Ⅱ/LC3Ⅰ表达升高（P<0.05），p26、p-PI3K/PI3K和p-mTOR/mTOR表达均显著下降（均P<0.05）；与Iso组相比，Iso+740 Y-P组细胞凋亡率、LC3Ⅱ/LC3Ⅰ表达下降（均P<0.05），p26、p-PI3K/PI3K和p-mTOR/mTOR表达均显著升高（均P<0.05）。结论：Iso通过抑制PI3K/Akt/mTOR信号通路抑制结肠癌SW480细胞增殖并诱导细胞凋亡和自噬。

Imaging Artifacts and Quality Evaluation with Ultrawide-Field Swept-Source OCTA in Diabetic Retinopathy.

PURPOSE: To evaluate the prevalence and types of artifacts in ultrawide-field swept-source optical coherence tomography angiography (SS-OCTA) scans of diabetic retinopathy (DR) patients. METHODS: This study was a prospective, observational study conducted from May 2022 to October 2022. Participants comprised individuals with proliferative diabetic retinopathy (PDR), nonproliferative diabetic retinopathy (NPDR), no diabetic retinopathy, and healthy controls. SS-OCTA imaging was performed, and a 5-scan composite with a larger field of view (23.5 mm × 17.5 mm) was captured using built-in software. Two experienced ophthalmologists analyzed the images independently, and the image quality and artifact prevalence were recorded and analyzed. RESULTS: The study included 70 eyes (16 with PDR, 24 with NPDR, 12 eyes of diabetic patients without DR, and 18 healthy eyes) in 70 subjects. Imaging artifacts were observed in a high percentage of eyes, with 98.57% of eyes presenting at least one type of artifact. A significant proportion of eyes (58.57%) exhibited a severe degree of artifacts. The most prevalent artifacts were loss of signal in 63 eyes (90%) and displacement artifact and masking artifact in 43 eyes (61.4%). Patients with more severe stages of DR had higher artifact scores (p < 0.05). Multivariate regression analysis indicated that DR severity was the most important factor influencing artifact scores (p < 0.05). CONCLUSIONS: In OCTA photos, various artifacts arise at different frequencies. It is crucial to qualitatively evaluate the images to ensure their quality. The results demonstrate that DR severity has a significant correlation with artifact scores.

Artificial intelligence in diabetes management: Advancements, opportunities, and challenges.

The increasing prevalence of diabetes, high avoidable morbidity and mortality due to diabetes and diabetic complications, and related substantial economic burden make diabetes a significant health challenge worldwide. A shortage of diabetes specialists, uneven distribution of medical resources, low adherence to medications, and improper self-management contribute to poor glycemic control in patients with diabetes. Recent advancements in digital health technologies, especially artificial intelligence (AI), provide a significant opportunity to achieve better efficiency in diabetes care, which may diminish the increase in diabetes-related health-care expenditures. Here, we review the recent progress in the application of AI in the management of diabetes and then discuss the opportunities and challenges of AI application in clinical practice. Furthermore, we explore the possibility of combining and expanding upon existing digital health technologies to develop an AI-assisted digital health-care ecosystem that includes the prevention and management of diabetes.

Heterologous Expression of Platycodon grandiflorus PgF3'5'H Modifies Flower Color Pigmentation in Tobacco.

Flavonoid-3',5'-hydroxylase (F3'5'H) is the key enzyme for the biosynthesis of delphinidin-based anthocyanins, which are generally required for purple or blue flowers. Previously, we isolated a full-length cDNA of PgF3'5'H from Platycodon grandiflorus, which shared the highest homology with Campanula medium F3'5'H. In this study, PgF3'5'H was subcloned into a plant over-expression vector and transformed into tobacco via Agrobacterium tumefaciens to investigate its catalytic function. Positive transgenic tobacco T0 plants were obtained by hygromycin resistance screening and PCR detection. PgF3'5'H showed a higher expression level in all PgF3'5'H transgenic tobacco plants than in control plants. Under the drive of the cauliflower mosaic virus (CaMV) 35S promoter, the over-expressed PgF3'5'H produced dihydromyricetin (DHM) and some new anthocyanin pigments (including delphinidin, petunidin, peonidin, and malvidin derivatives), and increased dihydrokaempferol (DHK), taxifolin, tridactyl, cyanidin derivatives, and pelargonidin derivatives in PgF3'5'H transgenic tobacco plants by ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) analysis, resulting in a dramatic color alteration from light pink to magenta. These results indicate that PgF3'5'H products have F3'5'H enzyme activity. In addition, PgF3'5'H transfer alters flavonoid pigment synthesis and accumulation in tobacco. Thus, PgF3'5'H may be considered a candidate gene for gene engineering to enhance anthocyanin accumulation and the molecular breeding project for blue flowers.

Cytokine status and significant increase of IL-6 and sIL-6R in the aqueous humor of diabetic cataract patients revealed by quantitative multiplexed assays.

OBJECTIVE: This study aimed to investigate inflammatory cytokine expression profiles in the aqueous humor (AH) of diabetic cataract (DC) patients. METHODS: A quantitative multiplexed antibody assay was performed to measure the expression levels of 40 inflammatory cytokines in AH samples from DC and age-related cataract (ARC) patients. Bioinformatics analysis was used to examine the functions of the cytokines. Enzyme-linked immunosorbent assays (ELISAs) and western blots were performed to verify the data. RESULTS: The multiplexed antibody assay revealed that the expression levels of IL-6, sIL-6R, IL-17A, IL-8, MCP-1, TNF-ß, RANTES, TIMP-1, and TIMP-2 were higher in the AH of DC patients compared with ARC patients. However, IL-1ra and IL-1a expression levels were lower in the DC patient AH samples. Pathway analysis indicated that IL-6 and sIL-6R belong to the class I helical cytokine family, which is associated with many biological functions. ELISA and western blot results confirmed that IL-6R and IL-6 expression levels were significantly higher in DC patients compared with ARC patients. CONCLUSIONS: Our results revealed the status of 40 inflammatory cytokines in the AH by quantitative multiplexed assays. Additionally, IL-6 and sIL-6R were expressed markedly higher in DC compared with ARC, which may play critical roles in DC pathophysiology.

Description of Flavimaribacter sediminis gen. nov., sp. nov., a New Member of the Family Rhizobiaceae Isolated from Marine Sediment.

A novel Gram-staining-negative, aerobic and rod-shaped bacterium, designated WL0058T, was isolated from coastal sediment sample collected in Nantong city, Jiangsu province of China (120° 51' 13″ E, 32° 6' 26″ N) in October 2020. Strain WL0058T was found to grow at 4-37 °C (optimum, 28 °C) with 1.5-4.0% NaCl (optimum, 4.0%) and displayed alkaliphilic growth with the pH range of pH 6.0-10.0 (optimum, pH 6.0). Phylogenetic trees constructed based on 16S rRNA gene sequence indicated that strain WL0058T is a member of the family Rhizobiaceae, shared the highest similarity with "Hoeflea prorocentri" CCTCC AB 2016294T (97.7%) and constituted a sub-cluster within the family with it, while the similarity with others in the family Rhizobiaceae was lower than 97.0%. The G + C content of genomic DNA was 59.5 mol%. Polar lipids profile of strain WL0058T included phosphatidylcholine (PC), phosphatidylethanolamine (PE), and glycolipid (GL), phosphatidylmonomethylethanolamine (PME) and two unidentified polar lipids (L). The major isoprenoid quinone was determined to be Q-10 and the major fatty acids were C16:0, C18:0, summed features 4 (iso-C17:1 and/or anteiso-C17:1), and summed features 8 (C18:1ω6c and/or C18:1ω7c). As inferred from the morphology, physiology, and biochemical analysis, genotypic characteristics, and the phylogenetic trees, strain WL0058T ought to be recognized as a novel genus in the family Rhizobiaceae, for which the name Flavimaribacter sediminis gen. nov., sp. nov. The type strain of Flavimaribacter sediminis gen. nov., sp. nov. is WL0058T (= MCCC 1K06063T = JCM 34659T = GDMCC 1.2448T).

Serum cholinesterase is associated with incident diabetic retinopathy: the Shanghai Nicheng cohort study.

BACKGROUND: Serum cholinesterase (ChE) is positively associated with incident diabetes and dyslipidemia. We aimed to investigate the relationship between ChE and the incidence of diabetic retinopathy (DR). METHODS: Based on a community-based cohort study followed for 4.6 years, 1133 participants aged 55-70 years with diabetes were analyzed. Fundus photographs were taken for each eye at both baseline and follow-up investigations. The presence and severity of DR were categorized into no DR, mild non-proliferative DR (NPDR), and referable DR (moderate NPDR or worse). Binary and multinomial logistic regression models were used to estimate the risk ratio (RR) and 95% confidence interval (CI) between ChE and DR. RESULTS: Among the 1133 participants, 72 (6.4%) cases of DR occurred. The multivariable binary logistic regression showed that the highest tertile of ChE (≥ 422 U/L) was associated with a 2.01-fold higher risk of incident DR (RR 2.01, 95%CI 1.01-4.00; P for trend < 0.05) than the lowest tertile (< 354 U/L). The multivariable binary and multinomial logistic regression showed that the risk of DR increased by 41% (RR 1.41, 95%CI 1.05-1.90), and the risk of incident referable DR was almost 2-fold higher than no DR (RR 1.99, 95%CI 1.24-3.18) with per 1-SD increase of loge-transformed ChE. Furthermore, multiplicative interactions were found between ChE and elderly participants (aged 60 and older; P for interaction = 0.003) and men (P for interaction = 0.044) on the risk of DR. CONCLUSIONS: In this study, ChE was associated with the incidence of DR, especially referable DR. ChE was a potential biomarker for predicting the incident DR.

A systematic review: Virtual-reality-based techniques for human exercises and health improvement.

Virtual Reality (VR) has emerged as a new safe and efficient tool for the rehabilitation of many childhood and adulthood illnesses. VR-based therapies have the potential to improve both motor and functional skills in a wide range of age groups through cortical reorganization and the activation of various neuronal connections. Recently, the potential for using serious VR-based games that combine perceptual learning and dichoptic stimulation has been explored for the rehabilitation of ophthalmological and neurological disorders. In ophthalmology, several clinical studies have demonstrated the ability to use VR training to enhance stereopsis, contrast sensitivity, and visual acuity. The use of VR technology provides a significant advantage in training each eye individually without requiring occlusion or penalty. In neurological disorders, the majority of patients undergo recurrent episodes (relapses) of neurological impairment, however, in a few cases (60-80%), the illness progresses over time and becomes chronic, consequential in cumulated motor disability and cognitive deficits. Current research on memory restoration has been spurred by theories about brain plasticity and findings concerning the nervous system's capacity to reconstruct cellular synapses as a result of interaction with enriched environments. Therefore, the use of VR training can play an important role in the improvement of cognitive function and motor disability. Although there are several reviews in the community employing relevant Artificial Intelligence in healthcare, VR has not yet been thoroughly examined in this regard. In this systematic review, we examine the key ideas of VR-based training for prevention and control measurements in ocular diseases such as Myopia, Amblyopia, Presbyopia, and Age-related Macular Degeneration (AMD), and neurological disorders such as Alzheimer, Multiple Sclerosis (MS) Epilepsy and Autism spectrum disorder. This review highlights the fundamentals of VR technologies regarding their clinical research in healthcare. Moreover, these findings will raise community awareness of using VR training and help researchers to learn new techniques to prevent and cure different diseases. We further discuss the current challenges of using VR devices, as well as the future prospects of human training.

Transcriptome Analysis of Key Genes Involved in Color Variation between Blue and White Flowers of Iris bulleyana.

Iris bulleyana Dykes (Southwest iris) is an extensively distributed Iridaceae species with blue or white flowers. Hereby, we performed a systematic study, employing metabolomics and transcriptomics to uncover the subtle color differentiation from blue to white in Southwest iris. Fresh flower buds from both cultivars were subjected to flavonoid/anthocyanin and carotenoid-targeted metabolomics along with transcriptomic sequencing. Among 297 flavonoids, 24 anthocyanins were identified, and 13 showed a strong down-accumulation pattern in the white flowers compared to the blue flowers. Significant downregulation of 3GT and 5GT genes involved in the glycosylation of anthocyanins was predicted to hinder the accumulation of anthocyanins, resulting in white coloration. Besides, no significant altered accumulation of carotenoids and expression of their biosynthetic genes was observed between the two cultivars. Our study systematically addressed the color differentiation in I. bulleyana flowers, which can aid future breeding programs.

Wide-field swept-source OCTA in the assessment of retinal microvasculature in early-stage diabetic retinopathy.

BACKGROUND: To perform a quantitative analysis of retinal microvasculature in patients with early-stage diabetic retinopathy (DR) using wide-field swept-source optical coherence tomography angiography (SS-OCTA).  METHODS: One hundred nineteen eyes of 119 patents (67 eyes with no DR and 52 eyes with mild-moderate nonproliferative diabetic retinopathy (NPDR)) were enrolled in this observational and cross-sectional cohort study, and an age-matched group consisting of 39 eyes of 39 non-diabetic subjects were set as the control. Each participant underwent a full ophthalmic examination, including wide-field SS-OCTA imaging. On OCTA scans (12 mm * 12 mm), the mean perfusion area (PA) and vessel density (VD) were independently measured in all 16 Early Treatment Diabetic Retinopathy Study (ETDRS) sectors. Linear regression analyses were conducted to evaluate the influences of PA. RESULTS: In the central ring, there were no significant differences in the average PA and VD among the groups. In the 3 mm radius, the PA and VD of the no DR and mild-moderate NPDR were significantly decreased compared with the control group in superior and inferior quadrants. In the wide-field scans (9 and 12 mm radius), there was no significant difference in average PA and VD between the groups in each sectors (p > 0.05). Regression analysis found that the effect of VD on PA was statistically different (b = 1.311, p < 0.001). CONCLUSION: Wide-field OCTA imaging is useful for evaluating peripheral capillary perfusion in eyes with early-stage DR. Decrease in PA and VD was greater in the S3 and I3 sectors, and reductions in PA and VD were uneven in wide-filed sectors (9 and 12 mm radius).

Peripapillary vessel density and retinal nerve fiber layer thickness changes in early diabetes retinopathy.

AIM: To perform a quantitative analysis of the peripapillary vessel density (VD) and retinal nerve fiber layer (RNFL) thickness changes in patients with early stage of diabetic retinopathy (DR). METHODS: In this case-control study, swept-source optical coherence tomography angiography (SS-OCTA) imaging was used to examine diabetic and age-matched healthy subjects. The optic disc HD 6×6 mm2 blood flow imaging scan mode was selected. Automatic software was used to measure the peripapillary VD, capillary vessel density (CVD), and RNLF in an optic nerve head (ONH) filed based on the Garway-Heath map. In addition, the correlation between peripapillary VD, CVD, and RNFL was further investigated. RESULTS: The cohort consisted of 32 healthy individuals and 72 patients with diabetes (34 eyes with no DR and 38 eyes with mild-moderate NPDR). Peripapillary VD decreased in the mild-moderate NPDR group compared to the control group in most regions (P<0.05). Peripapillary CVD and RNFL thickness were significantly lower in the mild-moderate NPDR group in the superior temporal (ST) quadrants (P=0.018, P=0.030). In the correlation analysis of each region, the RNFL thickness in the NS region was positively correlated with the peripapillary VD and CVD (r=0.233, P=0.05; r=0.288, P=0.015). In the TI region, the RNFL thickness was positively correlated with the peripapillary CVD (r=0.237, P=0.047). CONCLUSION: The measurement based on the ONH topographic map may be helpful in detecting functional and structural impairments in DR. The peripapillary VD, CVD and RNFL decrease in early DR, and the RNFL thickness altered in association with the CVD or/and VD in some regions.

Surface Condition Evolution and Fatigue Evaluation after Different Surface Processes for TiAl47Cr2Nb2 Alloy.

The TiAl47Cr2Nb2 alloy fatigue specimens were prepared by investment casting, and three kinds of surface processes were applied to fatigue specimens. These three processes were sand-blasting (SB), sand-blasting and shot-peening (SBSP) and sand-blasting and mechanical grinding (SBMG). The surface condition evolutions before and after thermal exposure at 700 °C for 24 h were investigated. The fatigue performances of specimens after thermal exposure were evaluated. The results show that the surface roughness Ra after SB, SBSP and SBMG processes were 3.14, 2.35 and 0.04 µm, respectively. After thermal exposure, they almost remained unchanged for all three processes. The SB process caused work hardening in the near-surface region and the work hardening reached saturation after the SB process. Due to the mechanical grinding (MG) process removing the uncertain thick hardening layer, the maximum hardness after SBMG process was noticeably lower than those after SB and SBSP processes. After thermal exposure, the maximum hardness after SB, SBSP and SBMG processes significantly recovered. The SBMG specimens had the highest fatigue limit of 350 MPa. This is attributed to the SBMG specimens having very smooth surfaces and some work hardening remaining near their surface layers.

DeepDRiD: Diabetic Retinopathy-Grading and Image Quality Estimation Challenge.

We described a challenge named "Diabetic Retinopathy (DR)-Grading and Image Quality Estimation Challenge" in conjunction with ISBI 2020 to hold three sub-challenges and develop deep learning models for DR image assessment and grading. The scientific community responded positively to the challenge, with 34 submissions from 574 registrations. In the challenge, we provided the DeepDRiD dataset containing 2,000 regular DR images (500 patients) and 256 ultra-widefield images (128 patients), both having DR quality and grading annotations. We discussed details of the top 3 algorithms in each sub-challenges. The weighted kappa for DR grading ranged from 0.93 to 0.82, and the accuracy for image quality evaluation ranged from 0.70 to 0.65. The results showed that image quality assessment can be used as a further target for exploration. We also have released the DeepDRiD dataset on GitHub to help develop automatic systems and improve human judgment in DR screening and diagnosis.

Optical coherence tomography angiography for the detection and evaluation of ptic disc neovascularization: a retrospective, observational study.

BACKGROUND: To assess and characterize neovascularization of the optic disc (NVD) using optical coherence tomography angiography (OCTA) and different OCTA-based methods. METHODS: This retrospective, observational study included patients who were suspected of having early PDR with no presence of clinically apparent neovascularization (NV) bur were clinically diagnosed with proliferative diabetic retinopathy (PDR), or severe NPDR. Patients underwent standard clinical examinations and OCTA imaging using a 6 × 6 montage scan. Two trained graders identified NVD using different imaging systems (ultra-widefield-colour fundus photography (UWF-CFP), OCT, OCTA and fluorescein angiography (FA)). Moreover, morphological classification of NVD was performed. The detection and morphological classification of NVD by different OCTA-based methods (B-scan OCTA, En-face OCTA, VRI Angio and VRI Structure) were compared. RESULTS: A total of 169 eyes (126 eyes with PDR and 43 eyes with severe NPDR) of 123 participants were included in this study. The detection rate of NVD was 34.91% by UWF-CFP compared with 59.76% by OCT, 59.76% by OCTA, and 62.72% by FA. After excluding 2 cases with epiretinal membranes, the NVD diagnosis detected by OCT was used as the standard. Among 99 eyes diagnosed with NVD by OCT, B-scan OCTA detected NVD with a sensitivity of 97.98%, which was higher than that by en face OCTA (80.81%), VRI Angio (65.66%), and VRI Structure (61.62%) (all P < 0.05). According to its characteristics on OCTA, NVD was divided into four types (12 cases of type I, 6 cases of type II, 39 cases of type III, and 42 cases of type IV). For type I, B-scan OCTA exhibited a higher diagnostic sensitivity than other methods (P < 0.05). For types II and IV, there were no statistically significant differences in the sensitivity of various methods between the two groups (P > 0.05). CONCLUSION: OCTA and different OCTA-based methods are significant to the diagnosis of NVD, and the diagnostic accuracy of different detection methods may be related to different types of NVD.

Sirt5-mediated desuccinylation of OPTN protects retinal ganglion cells from autophagic flux blockade in diabetic retinopathy.

Retinal neurodegeneration develops early in the course of diabetic retinopathy (DR), and our previous research showed that succinate accumulation results in retinal ganglion cells (RGCs) dysfunction in the retinas of rats with DR. Succinate can enhance lysine succinylation, but the succinylation of DR is not well understood. In this study, we investigated the role of the succinylome in DR and identified the key factor in this process. TMT labeling and LC-MS/MS analysis were combined to quantify the differentially succinylated proteins between vitreous humor (VH) samples from DR and non-DR patients. A total of 74 sites in 35 proteins were differentially succinylated between DR and non-DR vitreous humor samples, among which succinylation of the K108 site of optineurin (OPTN K108su) in the defense response was enriched by GO analysis based on the biological process category. Then, using a streptozotocin (STZ)-induced diabetic rat model, R28 cells and primary rat RGCs (rRGCs), we demonstrated that OPTN underwent lysine succinylation in the retinas of rats with DR and that OPTN K108su mediated autophagic flux blockade under high-glucose (HG) conditions. Sirt5 can desuccinylate OPTN K108su, thus protecting RGCs function from high glucose-induced RGCs autophagic flux blockade in the diabetic retina. Overall, desuccinylation of OPTN is an essential adaptive mechanism for ameliorating autophagic flux blockade in RGCs under DR conditions, and targeting the Sirt5-desuccK108-OPTN axis may thus open an avenue for therapeutic intervention in RCGs dysfunction.