北京市三级甲等医院结直肠癌患者就医流向规律分析

目的 分析不同特征的结直肠癌患者就医行为选择在中医院（含中西医结合医院）、西医院及肿瘤专科医院间的差异，为合理引导结直肠癌患者适宜就医及制订相关政策提供依据。方法 收集北京地区2018年1月-2019年12月17家三级甲等医院21894例首诊结直肠癌成年住院患者的病案首页数据，采用EmpowerStats 2.0对数据进行描述性分析。结果 21894例结直肠癌患者中就诊于中医院的有862例（3.93%），西医院的有8723例（39.85%），肿瘤专科医院的有12309例（56.22%）。对于不同医疗机构，男性占比均大于女性，58-68岁患者占比最大。且结直肠癌患者年龄、医疗付款方式及肿瘤分期在不同医疗机构间的分布存在差异（P<0.001）。西医院及肿瘤专科医院结直肠癌Ⅲ期患者占比最大，而就诊于中医院患者中结直肠癌Ⅳ期最多。从地域分布来看，异地就诊比例（57.32%）大于本地，且就诊于肿瘤专科医院的患者中73.66%来自外地。患者来源前三名分别是北京市、河北省及内蒙古自治区。而在北京市内，西医院患者主要来源于朝阳区、海淀区及西城区，中医院患者主要来源于海淀区、朝阳区及丰台区，肿瘤专科医院则主要来源于朝阳区、海淀区及丰台区。结论 应大力倡导年轻以及早期结直肠癌患者向中医院分流，充分施展中医药在结直肠癌患者中的治疗优势；发挥三级医院带动作用，建立对口帮扶医院，减少不必要的跨省流动及提倡结直肠癌的早筛早治，以降低结直肠癌死亡率。     

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.     

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.     

鼠巨细胞病毒感染C57BL/6小鼠急性肝炎动物模型的建立与鉴定

目的：建立鼠巨细胞病毒（MCMV）感染C57BL/6小鼠急性肝炎模型并对其感染特点进行分析及鉴定。方法：将24 只C57BL/6小鼠随机分为阴性对照组（n =12）及病毒感染组（n =12），病毒感染组腹腔注射1.0×106 PFU（200 μL）MCMV悬液，阴性对照组注射等体积小鼠胚胎成纤维细胞（MEF）悬液。于感染后第3天和第7天取外周血分离血清检测谷丙转氨酶（ALT）及谷草转氨酶（AST）。同时进行肝组织病毒分离、组织病理学及MCMV IE和M55基因、细胞因子白细胞介素-6（IL-6）、白细胞介素-1β（IL-1β）、肿瘤坏死因子α（TNF-α）的检测。结果：病毒感染组肝组织匀浆病毒分离均为阳性，肝炎发生率为100%。在感染后第3天即发生肝炎病理改变，病毒感染组血清ALT及AST较阴性对照组明显升高（P ＜0.01）；病毒感染组肝脏HE染色第3天可见局灶性炎性细胞浸润及肝脏点灶状坏死，持续至第7天，Ishak评分较阴性对照组明显升高（P ＜0.01）；在感染后第3天病毒感染组肝组织内可检测到MCMV IE及M55基因，且在感染后第7天仍可测得IE基因；感染后第3天及第7天病毒感染组炎性细胞因子IL-6、TNF-α及IL-1β mRNA表达水平明显升高（P ＜0.05）。 结论：成功建立MCMV感染C57BL/6小鼠急性动物肝炎模型，其感染表现主要集中在急性感染前期。     

Learning to Discretize: Solving 1D Scalar Conservation Laws via Deep Reinforcement Learning

Conservation laws are considered to be fundamental laws of nature. It has broad applications in many fields, including physics, chemistry, biology, geology, and engineering. Solving the differential equations associated with conservation laws is a major branch in computational mathematics. The recent success of machine learning, especially deep learning in areas such as computer vision and natural language processing, has attracted a lot of attention from the community of computational mathematics and inspired many intriguing works in combining machine learning with traditional methods. In this paper, we are the first to view numerical PDE solvers as an MDP and to use (deep) RL to learn new solvers. As proof of concept, we focus on 1-dimensional scalar conservation laws. We deploy the machinery of deep reinforcement learning to train a policy network that can decide on how the numerical solutions should be approximated in a sequential and spatial-temporal adaptive manner. We will show that the problem of solving conservation laws can be naturally viewed as a sequential decision-making process, and the numerical schemes learned in such a way can easily enforce long-term accuracy. Furthermore, the learned policy network is carefully designed to determine a good local discrete approximation based on the current state of the solution, which essentially makes the proposed method a meta-learning approach. In other words, the proposed method is capable of learning how to discretize for a given situation mimicking human experts. Finally, we will provide details on how the policy network is trained, how well it performs compared with some state-of-the-art numerical solvers such as WENO schemes, and supervised learning based approach L3D and PINN, and how well it generalizes.     

Protein kinase Cδ mediates trimethyltin-induced neurotoxicity in mice in vivo via inhibition of glutathione defense mechanism

We investigated whether protein kinase C (PKC) is involved in trimethyltin (TMT)-induced neurotoxicity. TMT treatment (2.8 mg/kg, i.p.) significantly increased PKCδ expression out of PKC isozymes (i.e., α, βI, βII, δ, and ?) in the hippocampus of wild-type (WT) mice. Consistently, treatment with TMT resulted in significant increases in cleaved PKCδ expression. Genetic or pharmacological inhibition (PKCδ knockout or rottlerin) was less susceptible to TMT-induced seizures than WT mice. TMT treatment increased glutathione oxidation, lipid peroxidation, protein oxidation, and levels of reactive oxygen species. These effects were more pronounced in the WT mice than in PKCδ knockout mice. In addition, the ability of TMT to induce nuclear translocation of Nrf2, Nrf2 DNA-binding activity, and upregulation of γ-glutamylcysteine ligase was significantly increased in the PKCδ knockout mice and rottlerin (10 or 20 mg/kg, p.o. × 6)-treated WT mice. Furthermore, neuronal degeneration (as shown by nuclear chromatin clumping and TUNEL staining) in WT mice was most pronounced 2 days after TMT. At the same time, TMT-induced inhibition of phosphoinositol 3-kinase (PI3K)/Akt signaling was evident, thereby decreasing phospho-Bad, expression of Bcl-xL and Bcl-2, and the interaction between phospho-Bad and 14-3-3 protein, and increasing Bax expression and caspase-3 cleavage were observed. Rottlerin or PKCδ knockout significantly protected these changes in anti- and pro-apoptotic factors. Importantly, treatment of the PI3K inhibitor LY294002 (0.8 or 1.6 µg, i.c.v.) 4 h before TMT counteracted protective effects (i.e., Nrf-2-dependent glutathione induction and pro-survival phenomenon) of rottlerin. Therefore, our results suggest that down-regulation of PKCδ and up-regulations of Nrf2-dependent glutathione defense mechanism and PI3K/Akt signaling are critical for attenuating TMT neurotoxicity.