基于网络药理学和实验验证研究柴胡疏肝散治疗慢性萎缩性胃炎的作用机制＊

目的 通过网络药理学的方法进行预测，再深一步进行动物实验验证来研究柴胡疏肝散治疗CAG的作用机制。方法 首先在TCMSP数据库中检索柴胡疏肝散的所有活性成分与药物靶点；通过收集PharmGkb、OMIM、GeneCards和DrugBank数据库中收录的慢性萎缩性胃炎的相关靶点。将药物靶点与疾病靶点进行映射筛选出交集靶点，将得到的交集靶点构建PPI网络与活性成分-共同靶点网络，并对其进行GO和KEGG富集分析。最后利用Vina软件进行分子对接实验验证，并通过免疫印迹法验证柴胡疏肝散对两种受体蛋白EGFR和STAT1的影响。结果 最终筛选得到柴胡疏肝散活性成分104个，潜在靶点238个，与慢性萎缩性胃炎的交集靶点52个；GO与KEGG富集分析分别得到2166条目和148条目，主要涉及到JAK-STAT信号通路、TNF信号通路、HIF-1信号通路等；分子对接结果显示EGFR、STAT1两个靶点能够与核心活性成分能够自发结合成较为稳定的构像；免疫印迹法实验证明柴胡疏肝散能够降低大鼠胃黏膜组织EGFR和STAT1蛋白表达。结论 通过网络药理学和实验验证，发现柴胡疏肝散可能通过调节EGFR和STAT1蛋白表达来共同调控胃黏膜细胞增殖与凋亡，进而发挥着治疗慢性萎缩性胃炎的效果，为深入进行柴胡疏肝散治疗慢性萎缩性胃炎的作用机制研究提供新思路和新方法。     

Proteomic Profiling of Small Extracellular Vesicles Isolated from the Plasma of Vietnamese Patients with Non-Small Cell Lung Cancer Reveals Some Potential Biomarkers

Background: Considering the poor prognosis of non-small cell lung cancer (NSCLC), the objective of this study was to examine the potential of plasma-derived vesicles as a source of lung cancer-specific proteins. Extracellular vesicle (EV) cargos are specific to the source cells, hence they have the potential of being a source of cancer-specific proteins.  Methods: The proteins differently expressed in cancer were determined and derived from EVs isolated from the plasma of NSCLC patients at the National Lung Hospital. To this end, purification was done using gel filtration chromatography and ultracentrifugation. In addition, nano liquid chromatography mass spectrometry (LC–MS/MS) was used for analyzing. Results: Fifty-seven EV-derived proteins related to NSCLC were highlighted in this research. Some of them have not been addressed before, such as EEF1A1 (elongation factor 1-α1), KPNB1 (Importin subunit beta 1), SRC (proto-oncogene tyrosine-protein kinase) and ACTC1 (actin, alpha cardiac muscle 1). This list was further confirmed through a comparison with ExoCarta and Vesiclepedia. Conclusion: This study is the first work to show the involvement of several novel proteins of small EV (EEF1A1, KPNB1, SRC, and ACTC1) in the progression of NSCLC. The results suggested that they could serve as novel biomarkers for non-small cell lung cancer in the future.     

Uncertainty evaluation at three spatial scales for the NDVI-based VWC estimation method used in the SMAP algorithm

Vegetation water content (VWC) is the key input parameter for a soil moisture retrieval algorithm based on microwave remote sensing, and VWC uncertainty can limit the estimated accuracy of soil moisture. There has been little research on VWC algorithm development and validation in China, and the uncertainty of the VWC estimation method has not been well evaluated. Therefore, the aim of this study is to evaluate the uncertainty of the VWC estimation method used in the SMAP (Soil Moisture Active Passive) algorithm on three spatial scales (the point-scale, 30 m scale, and 1 km scale) for maize in northeast China. Results from three ground experimental datasets showed that the SMAP VWC estimation method was strongly biased with an average overestimation of 1.16 kg m^?2,1.04 kg m^?2, and 1.13 kg m^?2 for the point-scale, 30 m scale, and 1 km scale respectively, and maximum bias occurred in the mid-stage of maize. Also, a new power relationship between NDVI (Normalized Difference Vegetation Index) and VWC was proposed for the 30 m scale based on Sentinel 2 NDVI and field VWC values from 2017 experiment, with respective R² (coefficient of determination) and Root Mean Squared Error (RMSE) values of 0.80 and 0.67 kg m^?2. The results confirmed that this power relationship was still suitable for VWC estimation at the 1 km scale, and it has smaller bias than the original SMAP VWC method. Future work will be carried out to evaluate the applicability of this VWC estimation method over a lager region. It is expected that it can improve the accuracy of soil moisture by providing high precision VWC input parameters.     

Quantile regression and empirical likelihood for the analysis of longitudinal data with monotone missing responses due to dropout,with applications to quality of life measurements from clinical trials

The analysis of quality of life (QoL) data can be challenging due to the skewness of responses and the presence of missing data. In this paper, we propose a new weighted quantile regression method for estimating the conditional quantiles of QoL data with responses missing at random. The proposed method makes use of the correlation information within the same subject from an auxiliary mean regression model to enhance the estimation efficiency and takes into account of missing data mechanism. The asymptotic properties of the proposed estimator have been studied and simulations are also conducted to evaluate the performance of the proposed estimator. The proposed method has also been applied to the analysis of the QoL data from a clinical trial on early breast cancer, which motivated this study.