The eye is a complex structure with a variety of anatomical barriers and clearance mechanisms, so the provision of safe and effective ophthalmic drug delivery technology is a major challenge....

Osteoarthritis (OA) seriously affects people's quality of life due to joint pain, stiffness, disability, and dyskinesia worldwide. Long non-coding RNAs zinc finger antisense 1 (ZFAS1) is downregulated and tightly associated with proliferation, migration, apoptosis, and matrix synthesis of chondrocyte in OA. However, the molecular mechanisms of ZFAS1 in OA remain unknown. The expression correlation between ZFAS1, miR-302d-3p and SMAD2 in OA tissues was analyzed by Pearson correlation analysis.ZFAS1 was a lower expression, and expedited proliferation and repressed apoptosis of chondrocytes. MiR-302d-3p was a direct target of ZFAS1. MiR-302d-3p hindered proliferation and facilitated apoptosis of chondrocytes. MiR-302d-3p partially reversed the effect of ZFAS1 on proliferation and apoptosis of chondrocytes. SMAD2 was positively regulated by the ZFAS1/miR-302d-3p. MiR-302d-3p-mediated proliferation and apoptosis were partly abrogated by targeting SMAD2.ZFAS1 promoted chondrocytes proliferation and repressed apoptosis possibly by regulating miR-302d-3p/SMAD2 axis, providing a potential target for OA treatment.

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The use of chest X-ray images (CXI) to detect Severe Acute Respiratory Syndrome Coronavirus 2 (SARS CoV-2) caused by Coronavirus Disease 2019 (COVID19) is life-saving important for both patients and doctors. This research proposes a multi-channel feature deep neural network (MFDNN) algorithm to screen people infected with COVID19. The algorithm integrates data over-sampling technology and MFDNN model to carry out the training. The oversampling technique reduces the deviation of the prior probability of the MFDNN algorithm on unbalanced data. Multi-channel feature fusion technology improves the efficiency of feature extraction and the accuracy of model diagnosis. In the experiment, Compared with traditional deep learning models (VGG19, GoogLeNet, Resnet50, Desnet201), the MFDNN model obtains an average test accuracy of 93.19% in all data. Furthermore, in each type of screening, the precision, recall, and F1 Score of the MFDNN model are also better than traditional deep learning networks. Furthermore, through ablation experiments, we proved that a multi-channel convolutional neural network (CNN) is superior to single-channel CNN, additional layer and PSN module, and indirectly proved the sufficiency and necessity of each step of the MFDNN classification method. Finally, our experimental code will be placed at https://github.com/panliangrui/covid19 .

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