Explore the vast range of titles available.

Background Paclitaxel-induced peripheral neuropathy (PIPN) is a challenging clinical problem during chemotherapy. Our previous work found that herbal formula Huangqi Guizhi Wuwu decoction (HGWD) could reduce oxaliplatin-induced neurotoxicity. However, its effect on PIPN remains unknown. In this study, we aim to investigate the therapeutic effect and the underlying mechanisms of HGWD against PIPN with pharmacological experiment and network pharmacology. Methods Male Wistar rats were used to establish an animal model of PIPN and treated with different doses of HGWD for 3 weeks. Mechanical allodynia, thermal hyperalgesia and body weight were measured to evaluate the therapeutic effect of HGWD on PIPN rats. On the day of the sacrifice, blood, DRGs, sciatic nerve, and hind-paw intra-plantar skins were collected to assess neuroprotective effect of HGWD on PIPN. Next, network pharmacology was performed to decipher the potential active components and molecular mechanisms of HGWD, as were further verified by western blotting analyses in PIPN rats. Finally, the effect of HGWD on the chemotherapeutic activity of paclitaxel was evaluated in vitro and in vivo. Results In rats with PIPN, HGWD reversed mechanical allodynia, thermal hyperalgesia, and ameliorated neuronal damage. Moreover, HGWD significantly increased the level of nerve growth factor, dramatically reduced IL-1β, IL-6, TNF-α levels and oxidative stress. Network pharmacology analysis revealed 30 active ingredients in HGWD and 158 candidate targets. Integrated pathway analysis identified PI3K/Akt and toll-like receptor as two main pathways responsible for the neuroprotective effect of HGWD. Further experimental validation demonstrated that HGWD expectedly inhibited the protein expression of TLR4, MyD88, IKKα, and p-NF-κB, and promoted PI3K, p-Akt, Nrf2, and HO-1 level in dorsal root ganglia. Last but not least, HGWD did not interfere with the antitumor activity of paclitaxel both in in vitro and in vivo models. Conclusion These combined data showed that HGWD could inhibit paclitaxel-evoked inflammatory and oxidative responses in peripheral nervous system viaTLR4/NF-κB and PI3K/Akt-Nrf2 pathways involvement. The neuroprotective property of HGWD on PIPN provides fundamental support to the potential application of HGWD for counteracting the side effects of paclitaxel during chemotherapy.

Here, we report the synthesis and super- conductivity of the La-doped iron-based compound Sm0.95La0.05O0.85F0.15FeAs; the resistivity and magnetic measurement indicate that the onset transition temperature as high as 57.3 K. This compound has the same crystal structure as ZrCuSiAs type tetragonal layered structure. The higher Tc compares to SmO0.85F0.15FeAs was attributed to partly La-substitution.

Three-dimensional (3D) mesostructures with distinct compressive deformation behaviors and tunable mechanical responses have gained increasing interest in recent years. 3D cage-shaped mesostructures are representative framework structures widely exploited in 3D flexible electronics, owing to their unique cellular geometry and unusual mechanical responses. The snap-through behavior of cage-shaped mesostructures could potentially result in the performance degradation of electronics, while it could also be harnessed to design reconfigurable electronics. Due to the complicated deformation modes and random characteristics in experiments, the snap-through behavior of cage-shaped mesostructures remains largely unexplored, especially in terms of probability-based analyses. In this work, we present a systematic study on the configuration evolution and snap-through of 3D cage-shaped mesostructures under out-of-plane compressions. Experimental and computational studies show the existence of two distinct deformation modes associated with the snap-through, which is controlled by the energy barrier based on the energetic analyses. Phase diagrams of the deformation modes decode how key geometric parameters and assembly strain affect the snap-through. Compressive experiments based on periodic arrays (10 × 10) of mesostructures provided a large amount of deformation data, allowing for statistical analyses of the snap-through behavior. These results provide new insights and useful guidelines for the design of 3D reconfigurable devices and multistable metamaterials based on 3D cage-shaped mesostructures.

Inkjet-based organ printing or 3D organ printing can be implemented using orifice-based and orifice-free approaches. Inkjetting, the most widely used orifice-based fabrication approach, has been applied to fabricate various biological constructs, such as alginate tubes. There are two main factors that may lead to failure during the fabrication of the tubular constructs with an overhang using horizontal printing: Structure instability due to the moment imbalance and structure failure due to the droplet impact-induced crash or buckling. This study aims to investigate how to theoretically construct a predicted tube printing path which can compensate for deformation in the printing process. First, we discuss the influence of stress in the printing process, subsequently we proceed to simulate the printing of the tube as a thin curved beam under droplet impact force, and obtain the unchanged shape information from the deformed shape, and then we put forward the predicted-path algorithm of the tube fabrication (PPATF). Second, we verify the method effectiveness and correctness by constructing the predicted path of a tube of 6 mm diameter and using finite element analysis (FEA) to simulate the deformation in the printing process. Lastly, we use the predicted path to print the tube. The cross section of the fabricated constructs can be nearly circular with the predictive compensation path.

Modeling and simulation of MEMS devices is a very complex task which involve the electrical, mechanical, fluidic and thermal domains, and there are still some uncertainties need to be accounted because of uncertain material and/or geometric parameters factors. According to these problems, we put forward to stochastic model order reduction method under random input conditions to facilitate fast time and frequency domain analyses, the method firstly process model order reduction by Structure Preserving Reduced-order Interconnect Macro Modeling method, then makes use of polynomial chaos expansions in terms of the random input and output variables for the matrices of a finite element model of the system; at last we give the expected values and standard deviations computing method to MEMS stochastic model. The simulation results verify the method is effective in large scale MEMS design process.

TP391.4; 跨摄像头场景中依赖面向标签映射关系的学习以提高识别精度,有监督行人重识别模型虽然识别精度较好,但存在可扩展问题,诸如算法识别精度严重依赖有效的监督信息,算法实时性差等;针对上述问题,提出一种基于软多标签的无监督行人重识别算法.为了提高标签匹配精度,首先利用软多标签逼近真实标签,通过计算参考数据集和参考代理在软多标签函数中的损失函数,预训练参考数据集,并构建预训练与训练结果的映射模型.再通过生成数据和真实数据分布的最小距离的期望即简化的2-Wasserstein距离计算相机视图中软多标签均值和标准差得到损失函数,解决跨视域标签一致性问题.为了提高软多标签对未标记目标数据集的有效性,计算联合嵌入损失,挖掘不同类别间的相似对,纠正跨域分布错位.针对残差网络训练时长和无监督学习精度低的问题,通过结合压缩激励网络(SENet)和多层级深度特征融合改进残差网络的结构,提高训练速度和精度.实验结果表明,该方法在标准数据集下的首位命中率和平均精度均值优于先进相关算法.