Lactate was originally thought to be a metabolic waste product of glycolysis produced by cells in hypoxic environment. In recent years, increasing evidence has indicated that lactate plays a crucial role in the physiological and pathological processes of the retina. Lactate is transported via monocarboxylate transporters in different retinal cell types such as photoreceptor cells and Müller cells to maintain the high metabolic demand of the retina. In addition to serving as oxiditive substrate for energy, lactate can mediate intracellular signal transduction through receptor G protein-coupled receptor 81, participating in the maintenance of retinal homeostasis and the progression of pathological neovascularization. Moreover, lactate-mediated protein lactylation directly regulates gene expression in microglia and T lymphocytes, which has gradually become a new hotspot in the field of retinal pathological neovascularization and neuroinflammation. Therefore, the regulation of lactate metabolism may provide novel perspectives for the treatment of retinal lactic acid metabolism disorders such as age-related macular degeneration and retinitis pigmentosa.
ObjectiveTo propose automatic measurement of global and local tessellation density on color fundus images based on a deep convolutional neural network (DCNN) method. MethodsAn applied study. An artificial intelligence (AI) database was constructed, which contained 1 005 color fundus images captured from 1 024 eyes of 514 myopic patients in the Northern Hospital of Qingdao Eye Hospital from May to July, 2021. The images were preprocessed by using RGB color channel re-calibration method (CCR algorithm), CLAHE algorithm based on Lab color space, Retinex algorithm for multiple iterative illumination estimation, and multi-scale Retinex algorithm. The effects on the segmentation of tessellation by adopting the abovemetioned image enhancement methods and utilizing the Dice, Edge Overlap Rate and clDice loss were compared and observed. The tessellation segmentation model for extracting the tessellated region in the full fundus image as well as the tissue detection model for locating the optic disc and macular fovea were built up. Then, the fundus tessellation density (FTD), macular tessellation density (MTD) and peripapillary tessellation density (PTD) were calculated automatically. ResultsWhen applying CCR algorithm for image preprocessing and the training losses combination strategy, the Dice coefficient, accuracy, sensitivity, specificity and Jordan index for fundus tessellation segmentation were 0.723 4, 94.25%, 74.03%, 96.00% and 70.03%, respectively. Compared with the manual annotations, the mean absolute errors and root mean square errors of FTD, MTD, PTD automatically measured by the model were 0.014 3, 0.020 7, 0.026 7 and 0.017 8, 0.032 3, 0.036 5, respectively. ConclusionThe DCNN-based segmentation and detection method can automatically measure the tessellation density in the global and local regions of the fundus of myopia patients, which can more accurately assist clinical monitoring and evaluation of the impact of fundus tessellation changes on the development of myopia.
ObjectiveTo compare and observe the curative effect of different body positions after pars plana vitrectomy (PPV) combined with inert gas filling for rhegmatogenous retinal detachment (RRD). MethodsA retrospective clinical study. From October 2019 to September 2021, 192 eyes of 192 RRD patients who were diagnosed and received PPV combined with inert gas filling in Qingdao Eye Hospital of Shandong First Medical University were included in the study. Best corrected visual acuity (BCVA), intraocular pressure, ultra-wide-angle fundus photography, optical coherence tomography, and B-mode ultrasonography were performed in all affected eyes. The BCVA examination was performed using a standard logarithmic visual acuity chart, which was converted into logarithm of the minimum angle of resolution (logMAR) visual acuity during statistics. According to the post-operative position requirements, the affected eyes were divided into the face-down positioning group and the adjustable positioning group, with 97 eyes in 97 patients and 95 eyes in 95 patients, respectively. Age (Z=0.804), course of disease (Z=－0.490), eye type (χ2=0.175), logMAR BCVA (Z=－0.895), intraocular pressure (Z=0.178), lens status (χ2=1.090), number of detached clocks (Z=0.301) and macular involvement (χ2=0.219), number of holes (Z=－1.051) and number of lower holes (χ2=0.619) were compared, there was no significant difference (P＞0.05). The gender composition ratio was compared, and the difference was statistically significant (χ2=5.341, P＜0.05). The follow-up time after surgery was more than 3 months. The retinal reattachment rate in one operation, the improvement of BCVA and the incidence of complications were observed. The independent sample Mann-Whitney test was used for the comparison of continuous variables between groups; the χ2 test was used for the comparison of categorical variables. ResultsIn the face-down positioning group and the adjustable positioning group, retinal reattachment in one operation was performed in 92 (94.8%, 92/97) and 89 (93.7%, 89/95) eyes, respectively; logMAR BCVA was 0.45±0.34, 0.41±0.21. There was no significant difference in the retinal reattachment rate in one operation (χ2=0.120, P=0.729) and logMAR BCVA (Z=－0.815, P=0.416) between the two groups. After surgery, the intraocular pressure increased in 11 (11.3%, 11/97) and 5 (5.3%, 5/95) eyes in the face-down positioning group and the adjustable positioning group, respectively; the secondary epimacular membrane was 2 (2.1%, 2/97), 3 (3.2%, 3/95) eyes. There was no significant difference in the incidence of elevated intraocular pressure and secondary epimacular membrane between the two groups after surgery (χ2=2.320, 0.227; P=0.128, 0.634). ConclusionIt is safe and effective to adopt adjustable positioning after PPV combined with inert gas filling for RRD, which is equivalent to the effect of face-down positioning.