Excessive apoptosis and inflammatory responses of nucleus pulposus (NP) cells induced by oxidative stress contribute to intervertebral disc degeneration (IVDD). Though some microRNAs are associated with IVDD, the specific microRNA that can mediate apoptotic and inflammatory responses of NP cells induced by oxidative stress synchronously still needs further identification. Here, we find that microRNA-623 (miR-623) is downregulated in IVDD and its expression is regulated by hypoxia-inducible factor-1α (HIF-1α) under oxidative stress conditions. Mechanistically, HIF-1α is observed to promote miR-623 expression by directly binding to its promoter region (−1,994/−1,987 bp). Functionally, miR-623 is found to work as an intermediator in alleviating apoptosis and inflammatory responses of NP cells induced by oxidative stress via regulating thioredoxin-interacting protein (TXNIP) expression by directly targeting its 3 ′ -untranslated region (3 ′ -UTR). Thus, on elucidating the expression and functional mechanisms of miR-623, our study suggests that miR-623 can be a valuable therapeutic target for treating oxidative stress-induced IVDD.
The bio-inspired porous metallic scaffolds have tremendous potential as artificial bone substitutes. In this work, a radially graded lattice structure (RGLS), which mimic the structures of natural human bones, were designed and processed by laser powder bed fusion (LPBF) of martensitic Ti-rich TiNi powder. The morphologies, mechanical properties, deformation behaviour, and biological compatibility of RGLS samples were characterised in comparison to those in the uniform lattice structure. The results demonstrated that RGLS has superior biomechanical properties and biological compatibility than the uniform structures, which highlights a novel strategy for improving the performance of additively manufactured artificial implants.
Well begun is half done. In the crowdfunding market, the early fundraising performance of the project is a concerned issue for both creators and platforms. However, estimating the early fundraising performance before the project published is very challenging and still under-explored. To that end, in this paper, we present a focused study on this important problem in a market modeling view. Specifically, we propose a Graph-based Market Environment model (GME) for estimating the early fundraising performance of the target project by exploiting the market environment. In addition, we discriminatively model the market competition and market evolution by designing two graph-based neural network architectures and incorporating them into the joint optimization stage. Finally, we conduct extensive experiments on the real-world crowdfunding data collected from Indiegogo.com. The experimental results clearly demonstrate the effectiveness of our proposed model for modeling and estimating the early fundraising performance of the target project.
Well begun is half done. In the crowdfunding market, the early fundraising performance of the project is a concerned issue for both creators and platforms. However, estimating the early fundraising performance before the project published is very challenging and still under-explored. To that end, in this paper, we present a focused study on this important problem in a market modeling view. Specifically, we propose a Graphbased Market Environment model (GME) for estimating the early fundraising performance of the target project by exploiting the market environment. In addition, we discriminatively model the market competition and market evolution by designing two graph-based neural network architectures and incorporating them into the joint optimization stage. Finally, we conduct extensive experiments on the real-world crowdfunding data collected from Indiegogo.com. The experimental results clearly demonstrate the effectiveness of our proposed model for modeling and estimating the early fundraising performance of the target project.
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