Osteoblastic lineage cells are commonly used to evaluate the in vitro osteogenic ability of bone biomaterials. However, contradictory results obtained from in vivo and in vitro studies are not uncommon. With the increasing understanding of osteoimmunology, the immune response has been recognized as playing an important role in bone regeneration. In this study, we examined the effect of submicron-scaled titanium surface roughness (ranging from approximately 100 to 400 nm) on the response of osteoblasts and macrophages. The results showed that osteoblast differentiation enhanced with increased surface roughness of titanium substrates. The cytoskeleton of macrophages altered with the variation in titanium surface roughness. The production of cytokines (TNF-α, IL-6, IL-4 and IL-10) could be regulated by titanium surface roughness. Moreover, macrophages cultured on titanium surfaces exhibited a tendency to polarize to M1 phenotype with the increase of surface roughness. Material/macrophage conditioned medium tended to promote osteoblast differentiation with the increase of surface roughness. The results indicate that increasing surface roughness in the submicron range is beneficial for osteogenesis via modulating the immune response of macrophages. Modifying biomaterial surfaces based on their immunomodulatory effects is considered as a novel strategy for the improvement of their biological performance.
The initial stage of rice blast fungus, Magnaporthe oryzae, infection, before 36 h postinoculation, is a critical timespan for deploying pathogen effectors to overcome the host's defences and ultimately cause the disease. However, how this process is regulated at the transcription level remains largely unknown. This study functionally characterized two M. oryzae Early Infection-induced Transcription Factor genes (MOEITF1 and MOEITF2) and analysed their roles in this process. Target gene deletion and mutant phenotype analysis showed that the mutants Δmoeitf1 and Δmoeitf2 were only defective for infection growth but not for vegetative growth, asexual/sexual sporulation, conidial germination, and appressoria formation. Gene expression analysis of 30 putative effectors revealed that most effector genes were down-regulated in mutants, implying a potential regulation by the transcription factors. Artificial overexpression of two severely down-regulated effectors, T1REP and T2REP, in the mutants partially restored the pathogenicity of Δmoeitf1 and Δmoeitf2, respectively, indicating that these are directly regulated. Yeast one-hybrid assay and electrophoretic mobility shift assay indicated that Moeitf1 specifically bound the T1REP promoter and Moeitf2 specifically bound the T2REP promoter. Both T1REP and T2REP were predicted to be secreted during infection, and the mutants of T2REP were severely reduced in pathogenicity.Our results indicate crucial roles for the fungal-specific Moeitf1 and Moeitf2 transcription factors in regulating an essential step in M. oryzae early establishment after penetrating rice epidermal cells, highlighting these as possible targets for disease control.
Computer-aided diagnosis (CAD) is an important work which can improve the working efficiency of physicians. With the availability of large-scale data sets, several methods have been proposed to classify pathology on chest X-ray images. However, most methods report performance based
on a frontal chest radiograph, ignoring the effect of the lateral chest radiography on the diagnosis. This paper puts forward a kind of model, Dual-Ray Net, of a deep convolutional neural network which can deal with the front and lateral chest radiography at the same time by referring the
method of using lateral chest radiography to assist diagnose during the diagnosis used by radiologists. Firstly, we evaluated the performance of parameter migration to small data after pre-training for large datasets. The data sets for pre-training are chest X-ray 14 and ImageNet respectively.
The results showed that pre-training with chest X-ray 14 performed better than with the generic dataset ImageNet. Secondly, We evaluated the performance of the Frontal and lateral chest radiographs in different modes of input model for the diagnosis of assisted chest disease. Finally, by comparing
different feature fusion methods of addition and concatenation, we found that the fusion effect of concatenation is better, which average AUC reached 0.778. The comparison results show that whether it is a public or a non-public dataset, our Dual-Ray Net (concatenation) architecture shows
improved performance in recognizing findings in CXR images when compared to applying separate baseline frontal and lateral classes.
Shape memory hydrogels (SMHs) can fix the hydrogels in a provisional shape and restore the initial shape under external stimulation. Herein, a dualresponsive shape memory hydrogel with dual-responsive swelling and selfhealing properties is presented in this work. The SMHs were fabricated by one-step emulsion copolymerization of acrylic acid (AAc), acrylamide (AAm) and stearyl methacrylate (SMA). Sodium alginate (SA) was introduced as an interpenetrating polymer in the network. With ionic cross-linking between-COO − and Fe 3+ or saline-reinforced hydrophobic association, the hydrogels
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