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Catalytic hydrodechlorination (HDC) is considered as one of the most promising strategies for the treatment of chlorinated environmental pollutants. Here we proposed a strategy for rapid complete dechlorination by exploiting a B–N co-doped hierarchically porous carbon material (BNC) to immobilize Pd NPs, which makes use of not only the micro/meso porous structure to accelerate the mass transport and to provide enormous active sites, but also the synergistic effect for codoping of B–N to enhance the interaction between Pd NPs and support and thus to stabilize and disperse the small size of the Pd particle. Ascribed to these combined advantages, Pd/BNC exhibited more distinctive catalytic dehalogenation activity (k = −0.14 min−1) and better stability (about 90.0% activity retention after 5 cycles) than Pd/C (k = −0.0070 min−1). Additionally, density functional theory (DFT) calculation indicates that the incorporation of N and B atoms in the carbon skeleton will reduce the adsorption energy of Cl− and thus Pd/BNC exhibits more remarkable stability than Pd/C during long-term HDC reactions. As a result, ultrafine Pd particles and high Pd2+/(Pd2+ + Pd0) ratios were found in Pd/BNC. [ABSTRACT FROM AUTHOR]

Moyamoya disease (MMD) is a serious intracranial cerebrovascular disease. Cerebral hemorrhage caused by MMD will bring life risk to patients. Therefore, MMD detection is of great significance in the prevention of cerebral hemorrhage. In order to improve the accuracy of digital subtraction angiography (DSA) in the diagnosis of ischemic MMD, in this paper, a deep network architecture combined with 3D convolutional neural network (3D CNN) and bidirectional convolutional gated recurrent unit (BiConvGRU) is proposed to learn the spatiotemporal features for ischemic MMD detection. Firstly, 2D convolutional neural network (2D CNN) is utilized to extract spatial features for each frame of DSA. Secondly, the long-term spatiotemporal features of DSA sequence are extracted by BiConvGRU. Thirdly, the short-term spatiotemporal features of DSA are further extracted by 3D convolutional neural network (3D CNN). In addition, different features are extracted when gray images and optical flow images pass through the network, and multiple features are extracted by features fusion. Finally, the fused features are utilized to classify. The proposed method was quantitatively evaluated on a data sets of 630 cases. The experimental results showed a detection accuracy of 0.9788, sensitivity and specificity were 0.9780 and 0.9796, respectively, and area under curve (AUC) was 0.9856. Compared with other methods, we can get the highest accuracy and AUC. The experimental results show that the proposed method is stable and reliable for ischemic MMD detection, which provides an option for doctors to accurately diagnose ischemic MMD. [ABSTRACT FROM AUTHOR]

Objective: This study aims to investigate the regulatory effect of hypoxia on human periodontal ligament stem cells (PDLSCs) through RNA sequencing (RNA SEQ). Human PDLSCs were cultured in normoxia (20% O2) or hypoxia (2% O2). Material and Methods: Total RNA was extracted and sequenced. The expression profiles of circRNAs, lncRNAs, and miRNAs were determined, and the lncRNA/circRNA‐miRNA‐mRNA networks were analyzed. Results: In total, 15 miRNAs, 449 lncRNAs, and 53 circRNAs were differentially expressed. Among them, 21 circRNAs, 262 lncRNAs, 5 miRNAs, and 5 mRNAs were selected to construct competing endogenous RNA (ceRNA) networks. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were carried out to explore potential related pathways and regulatory functions. Several ceRNA axes (lncRNA‐FTX/circRNA‐FAT1‐hsa‐miR‐4781‐3p‐SMAD5 and circRNA LPAR1‐hsa‐miR‐342‐3p‐ADAR) may provide a theoretical basis on the study of osteogenic differentiation of PDLSCs under hypoxia. Conclusion: This study revealed that the expression profiles of circRNAs, lncRNAs, and miRNAs had changed significantly in PDLSCs cultured in 2% O2; specific circRNAs/lncRNAs may play a competitive role in the differentiation of PDLSCs. [ABSTRACT FROM AUTHOR]