基于网络药理学探讨大承气汤治疗急性胰腺炎的物质基础和分子机制

采用网络药理学方法预测大承气汤治疗急性胰腺炎的物质基础和分子机制。通过数据库检索并预测大承气汤化合物的作用靶点及急性胰腺炎的疾病基因;并对关键靶点和相关活性化学成分进行分子对接验证;利用DAVID"富集分析"得到大承气汤的活性成分在治疗急性胰腺炎过程中所涉及的功能及通路,最终构建"中药材-活性化学成分-靶点-通路"综合网络。在大承气汤中发现了108种活性化学成分可与28个疾病靶点相关联,分子对接结果显示关键靶点与其对应化合物均有较强的结合能力。经DAVID富集分析得到,生物学过程438条,分子功能31条,细胞组成17条,KEGG通路富集96条。大承气汤可能通过药物反应、脂多糖反应和反向调节凋亡等生物过程,抗炎、抗氧化活性、反向调节细胞凋亡以及调节胰腺分泌等方式来实现其药效作用,涉及IL-17,TNF,NF-κB等多条信号通路。该研究运用网络药理学的方法,揭示了大承气汤以多种成分协同作用于多靶点、多信号通路治疗急性胰腺炎的分子机制,为生物实验提供理论基础。     

Multi‐scale network regression for brain‐phenotype associations

Brain networks are increasingly characterized at different scales, including summary statistics, community connectivity, and individual edges. While research relating brain networks to behavioral measurements has yielded many insights into brain‐phenotype relationships, common analytical approaches only consider network information at a single scale. Here, we designed, implemented, and deployed Multi‐Scale Network Regression (MSNR), a penalized multivariate approach for modeling brain networks that explicitly respects both edge‐ and community‐level information by assuming a low rank and sparse structure, both encouraging less complex and more interpretable modeling. Capitalizing on a large neuroimaging cohort (n = 1, 051) , we demonstrate that MSNR recapitulates interpretable and statistically significant connectivity patterns associated with brain development, sex differences, and motion‐related artifacts. Compared to single‐scale methods, MSNR achieves a balance between prediction performance and model complexity, with improved interpretability. Together, by jointly exploiting both edge‐ and community‐level information, MSNR has the potential to yield novel insights into brain‐behavior relationships.     

Finite Neuron Method and Convergence Analysis

We study a family of $H^m$-conforming piecewise polynomials based on the artificial neural network, referred to as the finite neuron method (FNM), for numerical solution of $2m$-th-order partial differential equations in$\mathbb{R}^d$ for any $m,d≥1$ and then provide convergence analysis for this method. Given a general domain Ω$⊂\mathbb{R}^d$ and a partition$\mathcal{T}_h$ of Ω, it is still an open problem in general how to construct a conforming finite element subspace of $H^m$(Ω) that has adequate approximation properties. By using techniques from artificial neural networks, we construct a family of $H^m$-conforming functions consisting of piecewise polynomials of degree $k$ for any $k≥m$ and we further obtain the error estimate when they are applied to solve the elliptic boundary value problem of any order in any dimension. For example, the error estimates that $‖u−u_N‖_{H^m(\rm{Ω})}=\mathcal{O}(N^{−\frac{1}{2}−\frac{1}{d}})$ is obtained for the error between the exact solution $u$ and the finite neuron approximation $u_N$. A discussion is also provided on the difference and relationship between the finite neuron method and finite element methods (FEM). For example, for the finite neuron method, the underlying finite element grids are not given a priori and the discrete solution can be obtained by only solving a non-linear and non-convex optimization problem. Despite the many desirable theoretical properties of the finite neuron method analyzed in the paper, its practical value requires further investigation as the aforementioned underlying non-linear and non-convex optimization problem can be expensive and challenging to solve. For completeness and the convenience of the reader, some basic known results and their proofs are introduced.     

An addition to the evolving spectrum of lipofibromatosis and lipofibromatosis-like neural tumor: Molecular findings in an unusual phenotype aid in accurate classification

Lipofibromatosis (LPF) and lipofibromatosis-like neural tumor (LPF-NT) are histologically and prognostically similar neoplasms having differences in immunophenotype as well as molecular biology. In most cases, LPF-NT is driven by fusions in the NTRK gene, whereas LPF has been associated with fusions in a variety of receptor tyrosine kinases. The distinction between the driver fusion event holds clinical significance because of the profound clinical response to tropomyosin receptor kinase (Trk) inhibitors (larotrectinib) in the NTRK-driven tumors. Immunohistochemically, and consistent with its namesake, to-date all reported cases classified as LPF-NT have shown positivity for S100-protein staining. Consequently, as S100-protein staining is widely available, it represents a cost-effective screening tool for LPF-NT where the more specific studies such as the pan-Trk stain or fluorescence in situ hybridization for NTRK rearrangement are not available. Herein, we present a case of presumed LPF-NT harboring the recurrent NTRK1-LMNA fusion, but which was negative for S100-protein immunostaining and was previously classified as classical LPF. This case reveals a potential pitfall in distinguishing these rare subcutaneous tumors by S100-protein staining and highlights the challenges in reconciling the rapid and novel discoveries made in the field of diagnostic pathology.     

基于网络药理学的黄芪抗心力衰竭作用机制

目的 基于网络药理学研究黄芪抗心力衰竭的作用机制。方法 采用活性成分筛选与靶点预测技术,结合生物信息学手段,预测黄芪抗心力衰竭的潜在作用靶点,并进行信号通路分析,从而探讨其治疗心力衰竭的分子机制。结果 在TCMSP数据中搜索筛选得到20个相应黄芪化学成分,同时利用靶点预测技术共筛选出相关靶点121个。同时在疾病基因数据库检索得到10 962个与心力衰竭发生、发展有关的已知靶点基因,利用string数据库和Cytoscape软件中的网络拓扑分析共筛选获取关键靶点8个。GO富集分析显示,黄芪可通过参与细胞能量代谢、血管内环境变化、神经活性配受体作用、激素活性等10个生物功能的PI3K/AKT、p38丝裂原活化蛋白激酶(p38MAPK)和MAPK/NF-κB等10条关键信号通路发挥抗心力衰竭的作用。结论 中药黄芪抗心力衰竭的作用具有多成分、多靶点、协同作用的特点。     

生脉散防治心房纤颤的作用机制及网络药理学分析

目的:基于网络药理学方法探讨生脉散治疗心房纤颤的作用靶点和相关信号通路并探讨其作用机制。方法:运用中药系统药理学成分分析平台(bioinformatics analysis tool for molecular mechanism of TCM,BATMAN-TCM)数据库获取生脉散的化学成分及作用靶标基因,通过GeneCards,OMIM,DisGeNET数据库收集心房纤颤的靶标基因。将两者取交集后得到生脉散-心房纤颤靶基因交集,运用STRING构建蛋白质间相互作用网络,并将结果进行网络可视化展示。将药物-疾病交集基因导入DAVID6.8数据库,进行基因本体(gene ontology,GO)分析和基于京都基因与基因组百科全书(Kyoto Encyclopedia of Genes and Geomes,KEGG)通路富集分析。结果:生脉散干预房颤的有效活性成分159个,药物靶点与疾病靶点交集后获得206个共有靶点,PPI蛋白互作网络分析发现AKT1,TP53,PRKACA,IL-1B,TNF,INS,PPAR,RXR,F2,CACAN1C,PKC等是生脉散治疗房颤的核心靶点。GO富集分析确定了175个条目(P0.05),其中生物过程主要心脏传导调节心率、动作电位时膜去极化等;分子功能主要包括电压门控钙通道、类固醇激素受体活性、肾上腺素结合等,在细胞组成方面,主要包括钠、钾、钙通道复合物等。KEGG通路富集分析确定了100条相关信号通路,主要有cGMP/PKG信号通路,cAMP信号通路,血清素能突触,肾素分泌,钙信号通路等。结论:生脉散治疗心房纤颤具有多途径、多靶点作用的特点。该研究初步探讨了其作用的关键靶点及涉及的生物学过程和信号通路,为生脉散治疗心房纤颤后续的实验研究提供一定的参考。