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Retinol dehydrogenase-10 (RDH10) is a member of the short-chain dehydrogenase/reductase family, which plays an important role in retinoic acid (RA) synthesis. Here, we show that RDH10 is highly expressed in human gliomas, and its expression correlates with tumor grade and patient survival times. In vitro, lentivirus-mediated shRNA knockdown of RDH10 suppressed glioma cell proliferation, survival, and invasiveness and cell cycle progression. In vivo, RDH10 knockdown reduced glioma growth in nude mice. Microarray analysis revealed that RDH10 silencing reduces expression of TNFRSF12A (Fn14), TNFSF12 (TWEAK), TRAF3, IKBKB (IKK-β), and BMPR2, while it increases expression of TRAF1, NFKBIA (IκBα), NFKBIE (IκBε), and TNFAIP3. This suggests that RDH10 promotes glioma cell proliferation and survival by regulating the TWEAK-NF-κB axis, and that it could potentially serve as a novel target for human glioma treatment.

Purpose To develop deep learning models based on color fundus photographs that can automatically grade myopic maculopathy, diagnose pathologic myopia, and identify and segment myopia-related lesions. Methods Photographs were graded and annotated by four ophthalmologists and were then divided into a high-consistency subgroup or a low-consistency subgroup according to the consistency between the results of the graders. ResNet-50 network was used to develop the classification model, and DeepLabv3+ network was used to develop the segmentation model for lesion identification. The two models were then combined to develop the classification-and-segmentation–based co-decision model. Results This study included 1395 color fundus photographs from 895 patients. The grading accuracy of the co-decision model was 0.9370, and the quadratic-weighted κ coefficient was 0.9651; the co-decision model achieved an area under the receiver operating characteristic curve of 0.9980 in diagnosing pathologic myopia. The photograph-level F 1 values of the segmentation model identifying optic disc, peripapillary atrophy, diffuse atrophy, patchy atrophy, and macular atrophy were all >0.95; the pixel-level F 1 values for segmenting optic disc and peripapillary atrophy were both >0.9; the pixel-level F 1 values for segmenting diffuse atrophy, patchy atrophy, and macular atrophy were all >0.8; and the photograph-level recall/sensitivity for detecting lacquer cracks was 0.9230. Conclusions The models could accurately and automatically grade myopic maculopathy, diagnose pathologic myopia, and identify and monitor progression of the lesions. Translational Relevance The models can potentially help with the diagnosis, screening, and follow-up for pathologic myopic in clinical practice.

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