Phosphorylation of ERK-Dependent NF-κB Triggers NLRP3 Inflammasome Mediated by Vimentin in EV71-Infected Glioblastoma Cells

Enterovirus 71 (EV71) is a dominant pathogenic agent that may cause severe central nervous system (CNS) diseases among infants and young children in the Asia-pacific. The inflammasome is closely implicated in EV71-induced CNS injuries through a series of signaling pathways. However, the activation pathway of NLRP3 inflammasome involved in EV71-mediated CNS injuries remains poorly defined. In the studies, EV71 infection, ERK1/2 phosphorylation, and activation of NLRP3 are abolished in glioblastoma cells with low vimentin expression by CRISPR/Cas9-mediated knockdown. PD098059, an inhibitor of p-ERK, remarkably blocks the vimentin-mediated ERK1/2 phosphorylation in EV71-infected cells. Nuclear translocation of NF-κB p65 is dependent on p-ERK in a time-dependent manner. Moreover, NLRP3 activation and caspase-1 production are limited in EV71-infected cells upon the caffeic acid phenethyl ester (CAPE) administration, an inhibitor of NF-κB, which contributes to the inflammasome regulation. In conclusion, these results suggest that EV71-mediated NLRP3 inflammasome could be activated via the VIM-ERK-NF-κB pathway, and the treatment of the dephosphorylation of ERK and NF-κB inhibitors is beneficial to host defense in EV71-infected CNS.     

Investigation on the Peroxidase-like Activity of Vitamin B6 and Its Applications in Colorimetric Detection of Hydrogen Peroxide and Total Antioxidant Capacity Evaluation

The peroxidase-like activity of vitamin B6 (VB6) was firstly demonstrated by catalyzing the peroxidase chromogenic substrate 3,3′,5,5′-tetramethylbenzidine (TMB) at the existence of H₂O₂. The influence of different factors on the catalytic property of VB6, including pH, temperature, VB6 concentration, and incubation time, were investigated. The steady-state kinetic study results indicate that VB6 possesses higher affinity to H₂O₂ than natural horseradish peroxidase and some other peroxidase mimics. Besides, the radical quenching experiment results confirm that hydroxyl radical (•OH) accounts for the catalytic process. Based on the excellent peroxidase-like catalytic activity of VB6, the colorimetric methods for H₂O₂ and gallic acid (GA) detection were developed by measuring the absorbance variance of the catalytic system. Under the optimal conditions, the linear ranges of the methods for H₂O₂ and GA determination with good selectivity are 50.0–600.0 μM and 10.0–50.0 μM, respectively. In addition, the developed method was applied in the detection of H₂O₂ in milk samples and evaluation of total antioxidant capacity of different tea infusions. This study may broaden the application prospect of VB6 in environmental and biomedical analysis fields, contribute to profound insight of the physiological functions of VB6, as well as lay foundation for further excavation of small-molecule peroxidase mimics.     

Interfacial Charge Transfer Induced Electronic Property Tuning of MoS_2 by Molecular Functionalization

The modulation of electrical properties of MoS_2 has attracted extensive research interest because of its potential applications in electronic and optoelectronic devices.Herein,interfacial charge transfer induced electronic property tuning of MoS_2 are investigated by in situ ultraviolet photoelectron spectroscopy and x-ray photoelectron spectroscopy measurements.A downward band-bending of MoS_2-related electronic states along with the decreasing work function,which are induced by the electron transfer from Cs overlayers to MoS_2,is observed after the functionalization of MoS_2 with Cs,leading to n-type doping.Meanwhile,when MoS_2 is modified with 2,3,5,6-tetrafluoro-7,7,8,8-tetracyanoquinodimethane(F_4-TCNQ),an upward band-bending of MoS_2-related electronic states along with the increasing work function is observed at the interfaces.This is attributed to the electron depletion within MoS_2 due to the strong electron withdrawing property of F_4-TCNQ,indicating p-type doping of MoS_2.Our findings reveal that surface transfer doping is an effective approach for electronic property tuning of MoS_2 and paves the way to optimize its performance in electronic and optoelectronic devices.     

REGULARITY OF WEAK SOLUTIONS TO A CLASS OF NONLINEAR PROBLEM

We study the regularity of weak solutions to a class of second order parabolic system under only the assumption of continuous coefficients.We prove that the wea...     

改进型单晶炉提拉系统偏心平衡研究

随着光伏行业的快速发展, 对硅单晶的品质和长晶装备的稳定性的要求也不断提高。直拉法是生产硅单晶的主要方法,通过提高单晶炉副室的高度以扩大单晶硅的生产规模。由于副室高度的大幅增加,且单晶炉提拉头质心相对于旋转轴心有一定距离,对单晶炉整体稳定性有较大影响,从而降低了单晶硅的生产质量。针对此问题,对单晶炉建立可靠的力学分析模型,采用数值仿真方法,对单晶炉整体进行动力学响应分析,计算得到副室高度增加后的单晶炉工作时中钨丝绳下端晶棒的运动规律以及最大摆动幅度,为改进设计提供依据。数值仿真分析表明提高单晶炉副室高度后,提拉头较大的质心偏心是单晶炉提拉系统发生摆动的主要原因。在此基础上提出在提拉头上添加质心调节装置,通过控制系统调节可保证提拉头质心位置在旋转轴线上以降低提拉系统的摆动。     

Evaluation of granule structure and strength properties of green packed beds in iron ore sintering using high-resolution X-ray tomography and uniaxial compression testing

The strength properties of green sinter beds,including the Young's modulus and maximum bed strain,were evaluated using uniaxial compression tests.The green-sint...     

Design,Synthesis, and In Vitro and In Vivo Antifungal Activity of Novel Triazoles Containing Phenylethynyl Pyrazole Side Chains

A series of triazole derivatives containing phenylethynyl pyrazole moiety as side chain were designed, synthesized, and most of them exhibited good in vitro antifungal activities. Especially, compounds 5k and 6c showed excellent in vitro activities against C. albicans (MIC = 0.125, 0.0625 μg/mL), C. neoformans (MIC = 0.125, 0.0625 μg/mL), and A. fumigatus (MIC = 8.0, 4.0 μg/mL). Compound 6c also exerted superior activity to compound 5k and fluconazole in inhibiting hyphae growth of C. albicans and inhibiting drug-resistant strains of C. albicans, and it could reduce fungal burdens in mice kidney at a dosage of 1.0 mg/kg. An in vivo efficacy evaluation indicated that 6c could effectively protect mice models from C. albicans infection at doses of 0.5, 1.0, and 2.0 mg/kg. These results suggested that compound 6c deserves further investigation.     

Learning size-adaptive molecular substructures for explainable drug–drug interaction prediction by substructure-aware graph neural network

Drug–drug interactions (DDIs) can trigger unexpected pharmacological effects on the body, and the causal mechanisms are often unknown. Graph neural networks (GNNs) have been developed to better understand DDIs. However, identifying key substructures that contribute most to the DDI prediction is a challenge for GNNs. In this study, we presented a substructure-aware graph neural network, a message passing neural network equipped with a novel substructure attention mechanism and a substructure–substructure interaction module (SSIM) for DDI prediction (SA-DDI). Specifically, the substructure attention was designed to capture size- and shape-adaptive substructures based on the chemical intuition that the sizes and shapes are often irregular for functional groups in molecules. DDIs are fundamentally caused by chemical substructure interactions. Thus, the SSIM was used to model the substructure–substructure interactions by highlighting important substructures while de-emphasizing the minor ones for DDI prediction. We evaluated our approach in two real-world datasets and compared the proposed method with the state-of-the-art DDI prediction models. The SA-DDI surpassed other approaches on the two datasets. Moreover, the visual interpretation results showed that the SA-DDI was sensitive to the structure information of drugs and was able to detect the key substructures for DDIs. These advantages demonstrated that the proposed method improved the generalization and interpretation capability of DDI prediction modeling.

SA-DDI is designed to learn size-adaptive molecular substructures for drug–drug interaction prediction and can provide explanations that are consistent with pharmacologists.     

Mesoporous sodium four-coordinate aluminosilicate nanoparticles modulate dendritic cell pyroptosis and activate innate and adaptive immunity

Pyroptosis is a programmed cell death widely studied in cancer cells for tumour inhibition, but rarely in dendritic cell (DC) activation for vaccine development. Here, we report the synthesis of sodium stabilized mesoporous aluminosilicate nanoparticles as DC pyroptosis modulators and antigen carriers. By surface modification of sodium-stabilized four-coordinate aluminium species on dendritic mesoporous silica nanoparticles, the resultant Na-^IVAl-DMSN significantly activated DC through caspase-1 dependent pyroptosis via pH responsive intracellular ion exchange. The released proinflammatory cellular contents further mediated DC hyperactivation with prolonged cytokine release. In vivo studies showed that Na-^IVAl-DMSN induced enhanced cellular immunity mediated by natural killer (NK) cells, cytotoxic T cells, and memory T cells as well as humoral immune response. Our results provide a new principle for the design of next-generation nanoadjuvants for vaccine applications.

Na-^IVAl-DMSN acts as both antigen carriers and modulators to “hyperactivate” dendritic cells (DCs) via potassium (K⁺) efflux dependent pyroptosis, eventually leading to enhanced adaptive and innate immunity.     

Deep Multilabel Multilingual Document Learning for Cross-Lingual Document Retrieval

Cross-lingual document retrieval, which aims to take a query in one language to retrieve relevant documents in another, has attracted strong research interest in the last decades. Most studies on this task start with cross-lingual comparisons at the word level and then represent documents via word embeddings, which leads to insufficient structure information. In this work, the cross-lingual comparison at the document level is achieved through the cross-lingual semantic space. Our method, MDL (deep multilabel multilingual document learning), leverages a six-layer fully connected network to project cross-lingual documents into a shared semantic space. The semantic distances can be calculated when the cross-lingual documents are transformed into embeddings in semantic space. The supervision signals are automatically extracted from the data and then used to construct the semantic space via a linear classifier. The ambiguity of manual labels could be avoided and the multilabel supervision signals can be acquired instead of a single label. The representation of the semantic space is enriched by multilabel supervision signals, which improves the discriminative ability of the embeddings. The MDL is easy to extend to other fields since it does not depend on specific data. Furthermore, MDL is more efficient than the models training all languages jointly, since each language is trained individually. Experiments on Wikipedia data showed that the proposed method outperforms the state-of-the-art cross-lingual document retrieval methods.