Spread of an Enterococcus faecalis sequence type 6 (CC2) clone in patients undergoing selective decontamination of the digestive tract

Enterococcus faecalis (E. faecalis) is a common cause of nosocomial infection in immunocompromised patients. The presence and dissemination of high‐risk clonal complexes, such as CC2, is an ongoing problem in hospitals. The aim of this work was to characterize 24 E. faecalis isolates from ICU patients undergoing selective decontamination of the digestive tract (SDD) by phenotypical (antimicrobial susceptibility) and genotypical (presence of virulence genes, RAPD‐PCR and MLST) methods. Our results showed high prevalence of the ST6 E. faecalis clone (91.6%), especially adapted to the hospital environment, with a multidrug resistance pattern and a multitude of putative virulence genes. In addition, ST179 (4.2%) and ST191 (4.2%) were detected. By RAPD–PCR analysis, the 22 isolates identified as ST6 showed six different DNA patterns, while the two remaining isolates, ST179 and ST191, showed two additional profiles. CC2 is a known clonal complex with high adaptability to hospital environment and worldwide distribution. The high prevalence of the ST6 clone in the studied population could be related to the presence of gentamicin in the SDD mixture since most strains were gentamicin resistant. Consequently, strict surveillance should be applied for rapid detection and control of this clone to prevent future spread outside the ICU.     

基于UPLC-LTQ-Orbitrap-MS分析刺果番荔枝叶的化学成分

目的利用UPLC-LTQ-Orbitrap-MS技术对刺果番荔枝叶的化学成分进行定性分析。方法采用Waters Acquity UPLC HSS T 3色谱柱(2.1 mm×100 mm,1.8μm),流动相为0.1%甲酸水(A)和乙腈(B)梯度洗脱,流速为0.3 mL/min,进样量2μL,在电喷雾正负离子模式下采集数据。经Reaxys数据库检索番荔枝属类化合物信息,通过质谱信息比对各化合物的m/z值、保留时间、质谱特征碎片等,并结合文献数据对鉴定的化合物进行验证。结果根据各化合物的特征裂解规律,从刺果番荔枝叶中共鉴定出45个化合物,包括16个生物碱类,14个番荔枝内酯类,7个黄酮类和8个其他类化合物,其中以番荔枝内酯类和生物碱类成分居多,与文献报道番荔枝内酯与生物碱类化合物是发挥抗癌的主要活性成分一致。结论利用UPLC-LTQ-Orbitrap-MS技术对刺果番荔枝叶中的化学成分进行了快速、准确的定性分析,为刺果番荔枝叶的提取分离与药效物质基础的研究提供依据。     

A Multi-Scale DNN Algorithm for Nonlinear Elliptic Equations with Multiple Scales

Algorithms based on deep neural networks (DNNs) have attracted increasing attention from the scientific computing community. DNN based algorithms are easy to implement, natural for nonlinear problems, and have shown great potential to overcome the curse of dimensionality. In this work, we utilize the multi-scale DNN-based algorithm (MscaleDNN) proposed by Liu, Cai and Xu (2020) to solve multi-scale elliptic problems with possible nonlinearity, for example, the p-Laplacian problem. We improve the MscaleDNN algorithm by a smooth and localized activation function. Several numerical examples of multi-scale elliptic problems with separable or non-separable scales in low-dimensional and high-dimensional Euclidean spaces are used to demonstrate the effectiveness and accuracy of the MscaleDNN numerical scheme.