The advent of anti-vascular endothelial growth factor (VEGF) therapies has remarkably improved the functional outcomes of neovascular age-related macular degeneration (nAMD) patients. However, there are guidelines on how to start treatment, the guidelines for discontinuing treatment are not yet clear. In this respect, the treat-extend-stop (TES) protocol have showed us the possibility of discontinuing treatment. In this study, we tried to investigate optical coherence tomography angiography (OCTA) biomarkers related to recurrence of neovascular activity in eyes with nAMD undergoing treatment using TES protocol. A total of 134 eyes with nAMD were divided into two groups (stop, non-stop) depending on whether they met criteria for stopping anti-VEGF treatment. Quantitative and qualitative OCTA parameters including the morphologic pattern of choroidal neovascularization (CNV) were compared between groups. Of these, 44 eyes (32.8%) were in the stop group and 90 eyes (67.2%) were in the non-stop group. In multivariate regression analysis, closed-circuit pattern of CNV and the presence of peripheral loop were associated with the non-stop group (all p < 0.001). Our results imply that the morphologic appearance of CNV on OCTA after anti-VEGF treatment may be a useful biomarker to predict weaning from treatment.
This study aimed to validate and evaluate deep learning (DL) models for screening of high myopia using spectral-domain optical coherence tomography (OCT). This retrospective cross-sectional study included 690 eyes in 492 patients with OCT images and axial length measurement. Eyes were divided into three groups based on axial length: a “normal group,” a “high myopia group,” and an “other retinal disease” group. The researchers trained and validated three DL models to classify the three groups based on horizontal and vertical OCT images of the 600 eyes. For evaluation, OCT images of 90 eyes were used. Diagnostic agreements of human doctors and DL models were analyzed. The area under the receiver operating characteristic curve of the three DL models was evaluated. Absolute agreement of retina specialists was 99.11% (range: 97.78–100%). Absolute agreement of the DL models with multiple-column model was 100.0% (ResNet 50), 90.0% (Inception V3), and 72.22% (VGG 16). Areas under the receiver operating characteristic curves of the DL models with multiple-column model were 0.99 (ResNet 50), 0.97 (Inception V3), and 0.86 (VGG 16). The DL model based on ResNet 50 showed comparable diagnostic performance with retinal specialists. The DL model using OCT images demonstrated reliable diagnostic performance to identify high myopia.
Scanning ion conductance microscopy (SICM) is an increasingly useful nanotechnology tool for non-contact, high resolution imaging of live biological specimens such as cellular membranes. In particular, approach-retract-scanning (ARS) mode enables fast probing of delicate biological structures by rapid and repeated approach/retraction of a nano-pipette tip. For optimal performance, accurate control of the tip position is a critical issue. Herein, we present a novel closed-loop control strategy for the ARS mode that achieves higher operating speeds with increased stability. The algorithm differs from that of most conventional (i.e., constant velocity) approach schemes as it includes a deceleration phase near the sample surface, which is intended to minimize the possibility of contact with the surface. Analysis of the ion current and tip position demonstrates that the new mode is able to operate at approach speeds of up to 250 μm s(-1). As a result of the improved stability, SICM imaging with the new approach scheme enables significantly improved, high resolution imaging of subtle features of fixed and live cells (e.g., filamentous structures & membrane edges). Taken together, the results suggest that optimization of the tip approach speed can substantially improve SICM imaging performance, further enabling SICM to become widely adopted as a general and versatile research tool for biological studies at the nanoscale level.
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