基于网络药理学的三仙汤治疗骨质疏松症作用机制研究

目的通过网络药理学方法探讨三仙汤干预骨质疏松症(OP)的有效成分、作用靶点及信号通路等,从而明确其作用机制。方法应用中药系统药理学数据库与分析平台(TCMSP)检索获得三仙汤中3味药物的化学成分,通过Uniprot数据库,将已筛选出的靶点名称进行标准化。通过Genecards、OMIM数据库检索骨质疏松症的相关靶基因。将药物靶点与疾病靶点取交集,筛选得到疾病-药物成分共同靶蛋白在String网站构建蛋白-蛋白相互作用(PPI)网络模型。在Cytoscape软件中绘制药物、疾病、靶基因网络图,并进行基因GO功能和KEGG通路富集分析,探讨三仙汤治疗骨质疏松症的作用机制。结果通过数据库筛选得到三仙汤的28种有效成分以及与骨质疏松症相关的靶点127个,其中蛋白激酶、白介素6、血管内皮生长因子等基因为PPI网络中的核心基因; GO富集分析显示靶点主要影响核受体活性、转录因子活性、类固醇激素受体活性、细胞因子受体结合、蛋白质异二聚活性、血红素结合等生物过程;以及影响AGE-RAGE信号通路、流体剪切应力与动脉粥样硬化通路、IL-17信号通路、前列腺癌通路、肿瘤坏死因子信号通路、乙型肝炎等信号通路。结论三仙汤中的有效成分通过关键信号通路作用于核心基因,影响细胞分化、凋亡、炎症、氧化应激等多种生物学过程发挥抗骨质疏松的作用,为其后续研究提供了科学依据。     

基于Bevilacqua模型的乳腺癌保乳术患者术后淋巴水肿风险预测的临床研究

目的验证Bevilacqua乳腺癌术后淋巴水肿风险预测模型的临床适用性及可行性。方法回顾性分析2010年1月至2015年12月203例乳腺癌患者临床资料,临床数据分析使用统计学软件SPSS 24.0。Cox回归模型分析乳腺癌患者术后发生上肢淋巴水肿的危险因素,以P<0.05为有统计学意义;绘制ROC曲线,以曲线下面积检验模型预测效果;应用Hosmere-Lemeshow检验评估预测值与实际值的校准程度,以P>0.05为预测模型校准能力较好,预测与实际没有区别。结果所有患者随访共计62~86个月,中位随访时间70个月。术后5年内共发生上肢淋巴水肿患者45例(22.2%)。Cox回归模型分析结果显示,高身体质量指数(BMI)、接受过新辅助化疗、全腋窝淋巴结清扫、接受过放疗是上肢淋巴水肿的独立危险因素。Becilacqua上肢淋巴水肿风险预测模型ROC曲线分析结果显示,模型AUC值为0.711,95%CI(0.651~0.760),有较好的的预测效果。Hosmer-Lemeshow检验结果显示,风险预测模型预测风险与实际无明显差异(P=0.262),校准能力较好,与实际差别不大。结论Bevilacqua术后6个月淋巴水肿风险预测模型的准确性及适用性较高,可用于临床对乳腺癌保乳术后淋巴水肿的预测,可为预防淋巴水肿的发生制定干预决策提供参考。     

Derivation and validation of a novel comorbidity‐based delirium risk index to predict postoperative delirium using national administrative healthcare database

ObjectiveTo derive and validate a comorbidity‐based delirium risk index (DRI) to predict postoperative delirium.Data Source/Study SettingData of 506 438 hip fracture repair surgeries from 2006 to 2016 were collected to derive DRI and perform internal validation from the Premier Healthcare Database, which provided billing information on 20‐25 percent of hospitalizations in the USA. Additionally, data of 1 130 569 knee arthroplasty surgeries were retrieved for external validation.Study DesignThirty‐six commonly seen comorbidities were evaluated by logistic regression with the outcome of postoperative delirium. The hip fracture repair surgery cohort was separated into a training dataset (60 percent) and an internal validation (40 percent) dataset. The least absolute shrinkage and selection operator (LASSO) procedure was applied for variable selection, and weights were assigned to selected comorbidities to quantify corresponding risks. The newly developed DRI was then compared to the Charlson‐Deyo Index for goodness‐of‐fit and predictive ability, using the Akaike information criterion (AIC), Bayesian information criterion (BIC), area under the ROC curve (AUC) for goodness‐of‐fit, and odds ratios for predictive performance. Additional internal validation was performed by splitting the data by four regions and in 4 randomly selected hospitals. External validation was conducted in patients with knee arthroplasty surgeries.Data CollectionHip fracture repair surgeries, knee arthroplasty surgeries, and comorbidities were identified by using ICD‐9 codes. Postoperative delirium was defined by using ICD‐9 codes and analyzing billing information for antipsychotics (specifically haloperidol, olanzapine, and quetiapine) typically recommended to treat delirium.Principal FindingsThe derived DRI includes 14 comorbidities and assigns comorbidities weights ranging from 1 to 6. The DRI outperformed the Charlson‐Deyo Comorbidity Index with better goodness‐of‐fit and predictive performance.ConclusionsDelirium risk index is a valid comorbidity index for covariate adjustment and risk prediction in the context of postoperative delirium. Future work is needed to test its performance in different patient populations and varying definitions of delirium.     

Analysis of time‐to‐event for observational studies: Guidance to the use of intensity models

This paper provides guidance for researchers with some mathematical background on the conduct of time‐to‐event analysis in observational studies based on intensity (hazard) models. Discussions of basic concepts like time axis, event definition and censoring are given. Hazard models are introduced, with special emphasis on the Cox proportional hazards regression model. We provide check lists that may be useful both when fitting the model and assessing its goodness of fit and when interpreting the results. Special attention is paid to how to avoid problems with immortal time bias by introducing time‐dependent covariates. We discuss prediction based on hazard models and difficulties when attempting to draw proper causal conclusions from such models. Finally, we present a series of examples where the methods and check lists are exemplified. Computational details and implementation using the freely available R software are documented in Supplementary Material. The paper was prepared as part of the STRATOS initiative.     

Clinical prediction models to predict the risk of multiple binary outcomes: a comparison of approaches

Clinical prediction models (CPMs) can predict clinically relevant outcomes or events. Typically, prognostic CPMs are derived to predict the risk of a single future outcome. However, there are many medical applications where two or more outcomes are of interest, meaning this should be more widely reflected in CPMs so they can accurately estimate the joint risk of multiple outcomes simultaneously. A potentially naïve approach to multi‐outcome risk prediction is to derive a CPM for each outcome separately, then multiply the predicted risks. This approach is only valid if the outcomes are conditionally independent given the covariates, and it fails to exploit the potential relationships between the outcomes. This paper outlines several approaches that could be used to develop CPMs for multiple binary outcomes. We consider four methods, ranging in complexity and conditional independence assumptions: namely, probabilistic classifier chain, multinomial logistic regression, multivariate logistic regression, and a Bayesian probit model. These are compared with methods that rely on conditional independence: separate univariate CPMs and stacked regression. Employing a simulation study and real‐world example, we illustrate that CPMs for joint risk prediction of multiple outcomes should only be derived using methods that model the residual correlation between outcomes. In such a situation, our results suggest that probabilistic classification chains, multinomial logistic regression or the Bayesian probit model are all appropriate choices. We call into question the development of CPMs for each outcome in isolation when multiple correlated or structurally related outcomes are of interest and recommend more multivariate approaches to risk prediction.     

四川省卫生总费用的结构变动及预测研究

目的 分析2008—2019年四川省卫生总费用结构变动及影响因素和预测研究,为助力“健康四川”建言献策。方法 使用四川省卫生统计年鉴的相关卫生总费用数据,在描述性分析的基础上,使用结构变动度分析卫生总费用的结构变动,灰色关联法研究卫生总费用的影响因素并进行GM(1,1)预测。结果 2008—2019年卫生总费用持续增加,政府、社会和个人卫生支出结构逐步合理;人均卫生费用水平排名靠后,社会卫生支出成为卫生总费用的主要构成单元;千人口床位、常住人口和农村居民人均可支配收入是影响卫生总费用的主要因素;预测发现,2020—2025年卫生总费用仍呈增加趋势,个人卫生支出占比进一步下降。结论 四川省卫生总费用结构渐趋合理,医疗服务水平得到大幅度改善;但仍需重点关注人均卫生费用等相对性指标。     

NLR及PLR对晚期尿路上皮癌免疫治疗疗效和预后的预测价值

目的：探讨外周血中性粒细胞与淋巴细胞比值（neutrophil to lymphocyte ratio,NLR）和血小板与淋巴细胞比值（platelet to lymphocyte ratio,PLR）对晚期尿路上皮癌免疫治疗疗效和预后的预测价值。方法：回顾性分析2019年9月至2022年7月接受过免疫检查点抑制剂治疗的晚期尿路上皮癌患者的临床资料,绘制受试者工作特征曲线,根据曲线下面积（area under the curve,AUC）筛选的最佳截断值分组,应用Kaplan-Meier生存分析比较组间生存差异,运用Cox模型进行多因素分析筛选出独立预测因素。结果：共纳入57例患者,经3周期免疫治疗后NLR较治疗前显著下降（P<0.001）,分别为2.40±1.08和3.05±1.07;而治疗前后PLR无明显变化（P=0.402）,分别为226.41±111.70和246.97±113.17;外周血中治疗前后NLR、PLR及NLR变化幅度的AUC分别为0.829 1(P=0.004)、0.889 6(P<0.001)、0.743 6(P=0.003)、0.740 4(P&l...     

The Development of a Predictive Method for the Estimation of Flux Through Polydimethylsiloxane Membranes. IV. Application to a Series of Substituted Quinolines

The steady-state flux of 33 substituted quinoline derivatives was determined in polydimethylsiloxane membranes using isopropyl alcohol as the receiver solvent. These diffusants constituted a diverse group of compounds possessing a wide range of hydrophobic, steric, and electronic characteristics. Various parameters representing these physicochemical properties such as cyclohexane–water fragmental constants, molar refractivity, Hammett's constants, intramolecular hydrogen bonding ability, melting point, and mole fraction solubility were employed to develop empirical models capable of relating the rate of diffusion to these characteristics of either the substituent on the quinoline ring or the compound itself.     

自然界中华按蚊种群研究工具及其在生命表制作中的应用

目的：为了能在自然界大型水体和旷野中对中华按蚊种群进行研究。方法：研制出适合于稻田浅水定量研究的实验简,适合于深水的实验网和接近自然状态的大实验笼。应用这些工具进行了自然界中华按蚊种群生命表的研究。结果：利用系列研究工具定量研究出1988年第8代中华按蚊自然种群生命表。求出各发育历期的死亡数和死亡因素,死亡率,存活率和死亡因素k值,分析出各发育阶段的关键死亡因素,计算下代预计产卵量为8288粒,种群趋势指数I为6．3027。结论：系列研究工具使中华按蚊自然种群的定量研究成为可能。郑州中华按蚊第9代种群数量还趋于增加之中。