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Large scale, high concurrency, and vast amount of data are important trends for the new generation of website. Node.js becomes popular and successful to build data-intensive web applications. To study and compare the performance of Node.js, Python-Web and PHP, we used benchmark tests and scenario tests. The experimental results yield some valuable performance data, showing that PHP and Python-Web handle much less requests than that of Node.js in a certain time. In conclusion, our results clearly demonstrate that Node.js is quite lightweight and efficient, which is an idea fit for I/O intensive websites among the three, while PHP is only suitable for small and middle scale applications, and Python-Web is developer friendly and good for large web architectures . To the best of our knowledge, this is the first paper to evaluate these Web programming technologies with both objective systematic tests (benchmark) and realistic user behavior tests (scenario), especially taking Node.js as the main topic to discuss.

Combination therapy has been proved to be an effective strategy to inhibit metastasis, however, its efficacy was always compromised by the poor delivery efficiency of drugs. In this study, multi...

Cancer-associated fibroblasts (CAFs) were believed to establish a tight physical barrier and a dense scaffold for tumor cells to make them maintain immunosuppression and drug resistance, strongly hindering nanoparticles to penetrate into the core of tumor tissues and limiting the performance of tumor cell-targeted nanoparticles. Here, we fabricated the substrate Z-Gly-Pro of fibroblast activation protein α (FAPα) and folic acid-codecorated pH-responsive polymeric micelles (dual ligand-modified PEOz-PLA polymeric micelles, DL-PP-PMs) that possessed nanodrill and tumor cell-targeted functions based on Z-Gly-pro-conjugated poly­(2-ethyl-2-oxazoline)-poly­(D,l-lactide) (ZGP-PEOz-PLA), folic acid (FA)-conjugated PEOz-PLA (FA-PEOz-PLA), and PEOz-PLA for cancer therapy. The micelles with about 40 nm particle size and a narrow distribution exhibited favorable pH-activated endo/lysosome escape induced by their pH responsibility. In addition, the enhancement of in vitro cellular uptake and cytotoxicity to folate receptors or FAPα-positive cells for doxorubicin (DOX)/DL-PP-PMs compared with DOX/PP-PMs evidenced the dual target ability of DOX/DL-PP-PMs, which was further supported by in vivo biodistribution results. As expected, in the human oral epidermal carcinoma (KB) cells xenograft nude mice model, the remarkable enhancement of antitumor efficacy for DOX/DL-PP-PMs with low toxicity was observed compared with DOX/FA-PP-PMs and DOX/ZGP-PP-PMs. The possible mechanism was elucidated to be the dismantling of the stromal barrier by nanodrill-like DOX/DL-PP-PMs via the deletion of CAFs evidenced by the downregulation of α-SMA and inhibition of their functions proved by the decrease in the microvascular density labeled with CD31 and the reduction in the extracellular matrix detected by the collagen content, thereby promoting tumor penetration and enhancing their uptake by tumor cells. The present research offered an alternative approach integrating anticancer and antifibrosis effects in one delivery system to enhance the delivery efficiency and therapeutic efficacy of anticancer drugs.

The intestinal epithelium is known to be a main hindrance to oral delivery of nanoparticles. Even though surface ligand modification can enhance cellular uptake of nanoparticles, the “easy entry and hard across” was frequently observed for many active targeting nanoparticles. Here, we fabricated polymeric nanoparticles relayed by bile acid transporters with monomethoxy poly­(ethylene glycol)-poly­(D,l-lactide) and deoxycholic acid-conjugated poly­(2-ethyl-2-oxazoline)-poly­(D,l-lactide) based on structural characteristics of intestine epithelium and the absorption characteristics of endogenous substances. As anticipated, deoxycholic acid-modified polymeric nanoparticles featuring good stability in simulated gastrointestinal fluid could notably promote the internalization of their payload by Caco-2 cells through mediation of apical sodium-dependent bile acid transporter (ASBT) and transmembrane transport of the nanoparticles across Caco-2 cell monolayers via relay-guide of ASBT, ileal bile acid-binding protein, and the heteromeric organic solute transporter (OSTα-OSTβ) along with multidrug resistance-associated protein 3 (MRP3) evidenced by competitive inhibition and fluorescence immunoassay, which was further visually confirmed by the stronger fluorescence from C6-labeled nanoparticles inside enterocytes and the basal side of the intestinal epithelium of mice. The transcellular transport of deoxycholic acid-modified nanoparticles in an intact form was mediated by caveolin/lipid rafts and clathrin with intracellular trafficking trace of endosome-lysosome-ER-Golgi apparatus and bile acid transport route. Furthermore, the increased uptake by HepG2 cells compared with unmodified nanoparticles evidenced the target ability of deoxycholic acid-modified nanoparticles to the liver, which was further supported by ex vivo imaging of excised major organs of mice. Thus, this study provided a feasible and potential strategy to further enhance transepithelial transport efficiency and liver-targeted ability of nanoparticles by means of the specific enterohepatic circulation pathways of bile acid.

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Chain disasters often cause greater casualties and economic losses than single disasters. It plays an important role in the prevention and control to draw the susceptibility map and hazard map of geological hazards. To the best of our knowledge, the existing models are not suitable for the study of earthquake–geological disaster chains. Therefore, this study aims to establish a DNN model suitable for the study of earthquake–geological disaster chains. Firstly, nine key factors affecting geological disasters were selected and multi-source data sets were established based on geological disaster points in the study area. Secondly, the DNN model is trained to calculate the susceptibility of landslides and is discussed with the Support Vector Machine (SVM) model, Logistic Regression (LR) model, and Random Forest (RF) model. Finally, verify with the ROC curve. The verification results show that the DNN model has the highest accuracy among the proposed models. It is suitable for drawing geological hazard susceptibility maps and hazard maps. Therefore, it is proved that the model can be applied for the prediction of chain disasters and is a promising tool for geological hazard assessment.