Hypoxia-inducible factor (HIF) is a transcription factor that facilitates cellular adaptation to hypoxia and ischemia. Long-standing evidence suggests that one isotype of HIF, HIF-1α, is involved in the pathogenesis of various solid tumors and cardiac diseases. However, the role of HIF-1α in retina remains poorly understood. HIF-1α has been recognized as neuroprotective in cerebral ischemia in the past two decades. Additionally, an increasing number of studies has shown that HIF-1α and its target genes contribute to retinal neuroprotection. This review will focus on recent advances in the studies of HIF-1α and its target genes that contribute to retinal neuroprotection. A thorough understanding of the function of HIF-1α and its target genes may lead to identification of novel therapeutic targets for treating degenerative retinal diseases including glaucoma, age-related macular degeneration, diabetic retinopathy, and retinal vein occlusions.

Lens thickness, lens location, and axial length do not appear to play a significant role in the progression from PAC/PACS to CPACG. A thin and anterior iris bowing may be related to the progression from PAC/PACS to CPACG.

Background: Diabetic retinopathy (DR) is the leading cause of blindness in the working-age population worldwide, and there is a large unmet need for DR screening in China. This observational, prospective, multicenter, gold standard-controlled study sought to evaluate the effectiveness and safety of the AIDRScreening system (v. 1.0), which is an artificial intelligence (AI)-enabled system that detects DR in the Chinese population based on fundus photographs.Methods: Participants with diabetes mellitus (DM) were recruited. Fundus photographs (field 1 and field 2) of 1 eye in each participant were graded by the AIDRScreening system (v. 1.0) to detect referable DR (RDR).The results were compared to those of the masked manual grading (gold standard) system by the Zhongshan Image Reading Center. The primary outcomes were the sensitivity and specificity of the AIDRScreening system in detecting RDR. The other outcomes evaluated included the system's diagnostic accuracy, positive predictive value, negative predictive value, diagnostic accuracy gain rate, and average diagnostic time gain rate.Results: Among the 1,001 enrolled participants with DM, 962 (96.1%) were included in the final analyses.The participants had a median age of 60.61 years (range: 20.18-85.78 years), and 48.2% were men. The manual grading system detected RDR in 399 (41.48%) participants. The AIDRScreening system had a sensitivity of 86.72% (95% CI: 83.39-90.05%) and a specificity of 96.09% (95% CI: 94.14-97.54%) in the detection of RDR, and a false-positive rate of 3.91%. The diagnostic accuracy gain rate of the AIDRScreening system was 16.57% higher than that of the investigator, while the average diagnostic time gain rate was −37.32% lower.Conclusions: The automated AIDRScreening system can detect RDR with high accuracy, but cannot detect maculopathy. The implementation of the AIDRScreening system may increase the efficiency of DR screening.

To predict visual acuity (VA) and post-therapeutic optical coherence tomography (OCT) images 1, 3, and 6 months after laser treatment in patients with central serous chorioretinopathy (CSC) by artificial intelligence (AI). Real-world clinical and imaging data were collected at Zhongshan Ophthalmic Center (ZOC) and Xiamen Eye Center (XEC). The data obtained from ZOC (416 eyes of 401 patients) were used as the training set; the data obtained from XEC (64 eyes of 60 patients) were used as the test set. Six different machine learning algorithms and a blending algorithm were used to predict VA, and a pix2pixHD method was adopted to predict post-therapeutic OCT images in patients after laser treatment. The data for VA predictions included clinical features obtained from electronic medical records (20 features) and measured features obtained from fundus fluorescein angiography, indocyanine green angiography, and OCT (145 features). The data for OCT predictions included 480 pairs of pre- and post-therapeutic OCT images. The VA and OCT images predicted by AI were compared with the ground truth. In the VA predictions of XEC dataset, the mean absolute errors (MAEs) were 0.074–0.098 logMAR (within four to five letters), and the root mean square errors were 0.096–0.127 logMAR (within five to seven letters) for the 1-, 3-, and 6-month predictions, respectively; in the post-therapeutic OCT predictions, only about 5.15% (5 of 97) of synthetic OCT images could be accurately identified as synthetic images. The MAEs of central macular thickness of synthetic OCT images were 30.15 ± 13.28 μm and 22.46 ± 9.71 μm for the 1- and 3-month predictions, respectively. This is the first study to apply AI to predict VA and post-therapeutic OCT of patients with CSC. This work establishes a reliable method of predicting prognosis 6 months in advance; the application of AI has the potential to help reduce patient anxiety and serve as a reference for ophthalmologists when choosing optimal laser treatments.

BackgroundThe optimal treatment for polypoidal choroidal vasculopathy (PCV) is still under debate. Little knowledge is known about the treatment effect of “1+pro re nata(PRN)” treatment regimen for PCV. The aim of this study was to compare the outcomes of photodynamic therapy (PDT), intravitreal ranibizumab injection (IVR) and combination therapy under the “1 + PRN” treatment regimen for PCV.MethodsFifty-seven eyes of 57 patients completed the 12 months’ follow-up in this prospective study. The patients in the PDT arm(n = 23), ranibizumab arm(n = 18), or combination arm(n = 16) underwent a session of PDT, IVR or combination of both at baseline followed by additional IVR as needed. Mean change of logarithm of the minimal angle of resolution (logMAR) visual acuity (VA), central foveal thickness (CFT) and the regression rate of polyps were evaluated. Cost-benefit analysis was also performed.ResultsAt Month 12, the mean logMAR VA improved from 0.90 ± 0.52 to 0.75 ± 0.57 in the PDT group (P < 0.05), from 0.96 ± 0.58 to 0.77 ± 0.41 in the IVR group (P < 0.05), and from 0.94 ± 0.55 to 0.72 ± 0.44 in the combination group (P < 0.05), respectively. The CFT decreased from 478.04 ± 156.70 μm, 527.5 ± 195.90 μm, and 522.63 ± 288.40 μm at the baseline to 366.43 ± 148.28 μm, 373.17 ± 134.88 μm and 328.44 ± 103.25 in the PDT group (P < 0.05), IVR group (P < 0.01), and the combination group (P < 0.05), respectively. However, no statistical difference was found between groups (P > 0.05). PDT treatment (60.87%) was superior to the IVR therapy (22.22%) in achieving complete regression of polyps (P < 0.05). Cost-benefit analysis showed that IVR treatment cost the least money for improving per 0.1logMAR units and the combination therapy demanded the least money for reducing per 100 μm of CFT.ConclusionsPDT, IVR and the combination therapy have similar efficacy in the VA improvement as well as the reduction of CFT under the “1 + PRN” treatment regimen.Trial registrationCurrent Controlled Trials NCT03459144. Registered retrospectively on March 2, 2018.