Tracheal stenosis is one of major challenging issues in clinical medicine because of the poor intrinsic ability of tracheal cartilage for repair. Tissue engineering provides an alternative method for the treatment of tracheal defects by generating replacement tracheal structures. In this study, we fabricated coaxial electrospun fibers using poly(L-lactic acid-co-caprolactone) and collagen solution as shell fluid and kartogenin solution as core fluid. Scanning electron microscope and transmission electron microscope images demonstrated that nanofibers had uniform and smooth structure. The kartogenin released from the scaffolds in a sustained and stable manner for about 2 months. The bioactivity of released kartogenin was evaluated by its effect on maintain the synthesis of type II collagen and glycosaminoglycans by chondrocytes. The proliferation and morphology analyses of mesenchymal stems cells derived from bone marrow of rabbits indicated the good biocompatibility of the fabricated nanofibrous scaffold. Meanwhile, the chondrogenic differentiation of bone marrow mesenchymal stem cells cultured on core-shell nanofibrous scaffold was evaluated by real-time polymerase chain reaction. The results suggested that the core-shell nanofibrous scaffold with kartogenin could promote the chondrogenic differentiation ability of bone marrow mesenchymal stem cells. Overall, the core-shell nanofibrous scaffold could be an effective delivery system for kartogenin and served as a promising tissue engineered scaffold for tracheal cartilage regeneration.

Remote sensing image dehazing is an extremely complex issue due to the irregular and non-uniform distribution of haze. In this paper, a prior-based dense attentive dehazing network (DADN) is proposed for single remote sensing image haze removal. The proposed network, which is constructed based on dense blocks and attention blocks, contains an encoder-decoder architecture, which enables it to directly learn the mapping between the input images and the corresponding haze-free image, without being dependent on the traditional atmospheric scattering model (ASM). To better handle non-uniform hazy remote sensing images, we propose to combine a haze density prior with deep learning, where an initial haze density map (HDM) is firstly extracted from the original hazy image, and is subsequently utilized as the input of the network, together with the original hazy image. Meanwhile, a large-scale hazy remote sensing dataset is created for training and testing of the proposed method, which contains both uniform and non-uniform, synthetic and real hazy remote sensing images. Experimental results on the created dataset illustrate that the developed dehazing method obtains significant progresses over the state-of-the-art methods.

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Heparin and rosuvastatin calcium cause P(LLA-CL) nanofibers to exhibit good cell viability and anticoagulation ability.

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