Analysis on internal mechanism of zedoary turmeric in treatment of liver cancer based on pharmacodynamic substances and pharmacodynamic groups

Zedoary tumeric (Curcumae Rhizoma, Ezhu in Chinese) has a long history of application and has great potential in the treatment of liver cancer. The anti liver cancer effect of zedoary tumeric depends on the combined action of multiple pharmacodynamic substances. In order to clarify the specific mechanism of zedoary tumeric against liver cancer, this paper first analyzes the mechanism of its single pharmacodynamic substance against liver cancer, and then verifies the joint anti liver cancer mechanism of its "pharmacodynamic group". By searching the research on the anti hepatoma effect of active components of zedoary tumeric in recent years, we found that pharmacodynamic substances, including curcumol, zedoarondiol, curcumenol, curzerenone, curdione, curcumin, germacrone, β-elemene, can act on multi-target and multi-channel to play an anti hepatoma role. For example, curcumin can regulate miR, GLO1, CD133, VEGF, YAP, LIN28B, GPR81, HCAR-1, P53 and PI3K/Akt/mTOR, HSP70/TLR4 and NF-κB. Wnt/TGF/EMT, Nrf2/Keap1, JAK/STAT and other pathways play an anti hepatoma role. Network pharmacological analysis showed that the core targets of the "pharmacodynamic group" for anti-life cancer are AKT1, EGFR, MAPK8, etc, and the core pathways are neuroactive live receiver interaction, nitrogen metabolism, HIF-1 signaling pathway, etc. At the same time, by comparing and analyzing the relationship between the specific mechanisms of pharmacodynamic substance and "pharmacodynamic group", it is found that they have great reference significance in target, pathway, biological function, determination of core pharmacodynamic components, formation of core target protein interaction, in-depth research of single pharmacodynamic substance, increasing curative effect and so on. By analyzing the internal mechanism of zedoary tumeric pharmacodynamic substance and "pharmacodynamic group" in the treatment of liver cancer, this paper intends to provide some ideas and references for the deeper pharmacological research of zedoary tumeric and the relationship between pharmacodynamic substance and "pharmacodynamic group".     

液质联用技术联合网络药理学探讨白鲜皮治疗湿疹的作用机制＊

目的 采用液质联用技术分析白鲜皮中的主要化学成分；基于网络药理学技术研究白鲜皮治疗皮肤湿疹的活性成分、作用靶点及作用机制。方法 利用液质联用技术（LC-MS）对白鲜皮主要成分进行分析，通过质谱数据结合文献资料，对各主要成分进行快速识别；通过利用SwissADME数据库对白鲜皮潜在活性成分进行预测，筛选主要作用靶点。进行KEGG通路富集分析，最终得到白鲜皮成分-靶点-疾病-通路网络图。采用分子对接技术对主要成分和作用靶点进行验证。结果 从白鲜皮中共分析鉴定了12种成分，进一步筛选得到4种潜在活性成分，可作用于48个关键靶点和14条主要通路。结论 采用LC-MS技术结合网络药理学方法，可以快速分析白鲜皮的潜在活性成分，并初步揭示了成分-靶点-通路之间的关系。通过分子对接技术可以做作用靶点进行验证。     

基于网络药理学和分子对接技术研究黄芪-赤芍治疗COPD的作用机制＊

目的 基于网络药理学和分子对接技术探究黄芪-赤芍配伍对治疗慢性阻塞性肺疾病（chronic obstructive pulmonary disease，COPD）的作用机制。方法 利用TCMSP，Pharmmaper数据库，筛选黄芪-赤芍治疗COPD的活性成分和潜在靶点；结合Genecards数据库挖掘的COPD相关靶点，对黄芪-赤芍药对与COPD靶点进行PPI网络构建，交互处理得到黄芪-赤芍药对治疗COPD的关键靶点，并进行GO分析和KEGG通路富集分析；并采用分子对接技术将主要活性成分与TNF-α（肿瘤坏死因子），IL-6（白细胞介素6）等进行分子对接；最后利用A549炎症细胞与人脐静脉内皮细胞缺氧损伤模型进行体外细胞实验对结果加以验证。结果 黄芪-赤芍药对中44个有效成分作用于COPD，核心成分为：槲皮素、山奈酚、丁子香萜、芍药苷、（2R，3R）-4-methoxyl-distylin、二氢异黄酮；黄芪-赤芍药对通过IL6、PTGS2、TNF等113个靶蛋白，调控Ras、PI3KAkt、IL-17等多条信号通路治疗COPD，且分子对接结果显示槲皮素、山奈酚、丁子香萜、芍药苷与IL-6、PTGS2、TNF大分子蛋白有良好的结合性，体外细胞试验证实，槲皮素与山奈酚均能减少IL-8，MMP-9炎症因子的分泌，具有不同程度的抗炎效果；芍药苷有明显的扩血管、抗血栓之效。结论 黄芪-赤芍药对治疗COPD具有多成分、多靶点、多通路、整体调节的作用特点。初步揭示了黄芪-赤芍药对通过抑制炎症反应、调节上皮细胞生长增强保护屏障等预测出黄芪-赤芍药对治疗COPD的潜在作用机制，以期为其活性成分的药效物质基础提供理论研究和思路。     

Deepm5C: A deep-learning-based hybrid framework for identifying human RNA N5-methylcytosine sites using a stacking strategy

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Bench to bedside: recent advances in lung cancer pathological research with implications for diagnostic clinical practice

After decades of relative stagnation lung cancer is emerging as a disease type where rapid progress is being made in diagnosis and therapy, as well as in our understanding of disease biology. Much of this progress is of immediate impact to diagnosticians, and more is likely to affect diagnostic practice in the near future. In this review we seek to briefly summarize several key areas of active research of immediate or probable imminent value to trainee and consultant pulmonary pathologists alike. We cover some major changes in tumour classification, grading, and patient stratification, as well as considering the state of the art in machine-assisted interpretation of lung cancer histology, and the use of genetically modified lung cancer models.     

Future directions in managing aniridia-associated keratopathy

Congenital aniridia is a panocular disorder that is typically characterized by iris hypoplasia and aniridia-associated keratopathy (AAK). AAK results in the progressive loss of corneal transparency and thereby loss of vision. Currently, there is no approved therapy to delay or prevent its progression, and clinical management is challenging because of phenotypic variability and high risk of complications after interventions; however, new insights into the molecular pathogenesis of AAK may help improve its management. Here, we review the current understanding about the pathogenesis and management of AAK. We highlight the biological mechanisms involved in AAK development with the aim to develop future treatment options, including surgical, pharmacological, cell therapies, and gene therapies.     

Deep learning with biopsy whole slide images for pretreatment prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer：A multicenter study

IntroductionPredicting pathological complete response (pCR) for patients receiving neoadjuvant chemotherapy (NAC) is crucial in establishing individualized treatment. Whole-slide images (WSIs) of tumor tissues reflect the histopathologic information of the tumor, which is important for therapeutic response effectiveness. In this study, we aimed to investigate whether predictive information for pCR could be detected from WSIs.Materials and methodsWe retrospectively collected data from four cohorts of 874 patients diagnosed with biopsy-proven breast cancer. A deep learning pathological model (DLPM) was constructed to predict pCR using biopsy WSIs in the primary cohort, and it was then validated in three external cohorts. The DLPM could generate a deep learning pathological score (DLPs) for each patient; stromal tumor-infiltrating lymphocytes (TILs) were selected for comparison with DLPs.ResultsThe WSI feature-based DLPM showed good predictive performance with the highest area under the curve (AUC) of 0.72 among the cohorts. Alternatively, the combination of the DLPM and clinical characteristics offered a better prediction performance (AUC >0.70) in all cohorts. We also evaluated the performance of DLPM in three different breast subtypes with the best prediction for the triple-negative breast cancer (TNBC) subtype (AUC: 0.73). Moreover, DLPM combined with clinical characteristics and stromal TILs achieved the highest AUC in the primary cohort (AUC: 0.82) and validation cohort 1 (AUC: 0.80).ConclusionOur study suggested that WSIs integrated with deep learning could potentially predict pCR to NAC in breast cancer. The predictive performance will be improved by combining clinical characteristics. DLPs from DLPM can provide more information compared to stromal TILs for pCR prediction.     

Coupling online control and inhibitory systems in children with Developmental Coordination Disorder: Goal-directed reaching

For children with Developmental Coordination Disorder (DCD), the real-time coupling between frontal executive function and online motor control has not been explored despite reported deficits in each domain. The aim of the present study was to investigate how children with DCD enlist online control under task constraints that compel the need for inhibitory control. A total of 129 school children were sampled from mainstream primary schools. Forty-two children who met research criteria for DCD were compared with 87 typically developing controls on a modified double-jump reaching task. Children within each skill group were divided into three age bands: younger (6–7 years), mid-aged (8–9), and older (10–12). Online control was compared between groups as a function of trial type (non-jump, jump, anti-jump). Overall, results showed that while movement times were similar between skill groups under simple task constraints (non-jump), on perturbation (or jump) trials the DCD group were significantly slower than controls and corrected trajectories later. Critically, the DCD group was further disadvantaged by anti-jump trials where inhibitory control was required; however, this effect reduced with age. While coupling online control and executive systems is not well developed in younger and mid-aged children, there is evidence of age-appropriate coupling in older children. Longitudinal data are needed to clarify this intriguing finding. The theoretical and applied implications of these results are discussed.