Using a monotone single-index model to stabilize the propensity score in missing data problems and causal inference

The augmented inverse weighting method is one of the most popular methods for estimating the mean of the response in causal inference and missing data problems. An important component of this method is the propensity score. Popular parametric models for the propensity score include the logistic, probit, and complementary log-log models. A common feature of these models is that the propensity score is a monotonic function of a linear combination of the explanatory variables. To avoid the need to choose a model, we model the propensity score via a semiparametric single-index model, in which the score is an unknown monotonic nondecreasing function of the given single index. Under this new model, the augmented inverse weighting estimator (AIWE) of the mean of the response is asymptotically linear, semiparametrically efficient, and more robust than existing estimators. Moreover, we have made a surprising observation. The inverse probability weighting and AIWEs based on a correctly specified parametric model may have worse performance than their counterparts based on a nonparametric model. A heuristic explanation of this phenomenon is provided. A real-data example is used to illustrate the proposed methods.     

基于中医体质辨识的糖尿病风险评估模型建立与验证

背景　中医体质因素在糖尿病的发生发展过程中具有重要作用，但目前糖尿病的预测预警模型仅涵盖一般人口学资料、客观检查指标、生活方式等内容。在糖尿病风险评估模型中纳入中医体质辨识内容，对有针对性地防治糖尿病的发生发展具有重要意义。目的　根据健康体检数据建立基于中医体质辨识的糖尿病风险模型并对其进行验证。方法　于2016年1月-2018年12月，以2014-2015年某省级综合性医院健康管理中心体检数据为训练集数据（n=30 951），对是否患糖尿病进行单因素和多因素Logistic回归分析，纳入有意义的影响因素指标建立糖尿病风险评估模型；以2016-2017年的健康体检数据作为测试集数据（n=24 061），采用受试者工作特征曲线（ROC）对模型进行验证。结果　训练集人群中，患有糖尿病者1 315例（4.25%），未患糖尿病者29 636例（95.75%）。多因素Logistic回归分析结果显示，logit（P）（糖尿病患病情况）=-4.632-0.198×（女）+0.864×（年龄45~59岁）+1.684×（年龄≥60岁）+0.635×（高血压）+0.149×（超重）+0.376×（肥胖）-0.531×（偏轻）-0.234×（淋巴细胞百分数偏高）+0.279×（淋巴细胞百分数偏低）+0.304×（红细胞计数异常）-0.430×（红细胞比容偏低）+0.722×（平均红细胞血红蛋白浓度异常）+0.532×（血小板分布宽度异常）+1.016×（癌胚抗原异常）-0.406×（尿酸异常）+1.341×（肌酐偏低）+0.488×（血尿素氮偏高）+0.473×（三酰甘油异常）+0.257×（总胆固醇偏高）+0.544×（高密度脂蛋白偏低）+0.290×（总蛋白异常）+0.395×（丙氨酸氨基转移酶异常）+0.362×（谷氨酰转肽酶异常）+0.993×（阴虚质）+1.016×（气虚质）+0.601×（痰湿质）。模型验证结果显示，训练集ROC曲线下面积（AUC）为0.792，95%CI为0.779~0.816（P<0.05），最佳截断值为0.405，灵敏度为0.771，特异度为0.690；测试集验证准确率达到95.69%，Kappa=0.636（P<0.001）。结论　初步构建了糖尿病风险评估模型，且此模型具有较高诊断效应。中医体质辨识作为重要的影响因素纳入糖尿病发病风险评估的模型中来，可以提高其预测能力，为糖尿病的中医药早期防治提供一定的依据。     

胰腺囊性肿瘤恶性风险预测模型研究

目的 明确与恶性胰腺囊性肿瘤（PCN）相关的术前危险因素，建立准确的预测模型，并予以验证。方法 纳入2013年1月至2020年5月复旦大学附属华东医院经术后病理检查证实的114例PCN病例，分为模型组（n=80）和验证组（n=34）。回顾性分析模型组术前的临床资料并探索与恶性PCN相关的影响因素，建立PCN恶性风险预测模型，绘制受试者工作特征（ROC）曲线和校正曲线评价模型，最后基于验证组数据对模型进行临床验证。结果 单因素回归分析提示临床症状、CA19-9水平升高、中性粒细胞淋巴细胞比值（NLR）、淋巴细胞单核细胞比值（LMR）、肿瘤最大直径、胰管扩张和实性成分与恶性PCN显著相关，进一步行多因素回归分析确定了NLR≥2.146、CA19-9水平升高、胰管扩张是恶性PCN的独立预测因素。基于多因素回归分析结果建立恶性PCN预测模型，绘制模型的ROC曲线，计算AUC为0.921（95%CI 0.863~0.979），Youden指数最大时取得最佳临界预测值为0.203，此时相对应的特异度为83.3%，敏感度为92.9%，准确率为85%。同时校正曲线显示模型具有较好的拟合度，最后代入验证组数据显示模型预测准确率为82.4%，特异度81.2%，敏感度100%。结论 CA19-9水平升高、NLR升高以及胰管扩张是恶性PCN的高危因素，基于此建立的恶性胰腺囊性肿瘤的预测模型具有较好的准确率，可为今后的临床诊疗提供辅助参考。     

基于药动学与药效学相关联的养阴通脑颗粒主要有效部位正交配伍研究

目的探讨养阴通脑颗粒主要有效部位(总生物碱、总黄酮、总皂苷、总酚酸)配伍后在脑缺血再灌注模型大鼠体内药物浓度及其药动学与药效学变化。方法采用正交试验法组成上述主要有效部位用量配比不同的9个组方,供脑缺血再灌注模型大鼠ig给药,高效液相色谱-二极管阵列检测器(HPLC-DAD)测定不同时间点血浆中的葛根素、阿魏酸和川芎嗪血浆药物浓度。DAS 3.2.6软件以非房室模型拟合药动学参数,并运用总量统计矩法和综合评分法对整体药动学特征进行评价。同时采用酶联免疫吸附测定(ELISA)法测定大鼠血浆中超氧化物歧化酶(SOD)和过氧化氢酶(CAT)的含量。最后进行药动学-药效学(PK-PD)模型研究,获得各药物浓度与药效之间的定量方程。结果葛根素、阿魏酸和川芎嗪在模型大鼠体内的药动学特征有所差异。总量统计矩和综合评分研究表明不同配伍对总量零阶矩、总量平均滞留时间、综合评分等参数影响不一。主要有效部位正交配伍给药后,一定程度上会抑制脑缺血再灌注大鼠血浆中SOD和CAT的降低。各PK-PD模型均采用Sigmoid-Emax模型,拟合结果与实测数据之间相关性良好,R值均大于0.85。结论养阴通脑颗粒主要有效部位配伍对模型大鼠体内的药动学行为和抗氧化指标具有一定影响;中药复方多成分药物代谢动力学可采用总量统计矩和综合评分法进行研究;PK-PD结合模型可用于中药复方多成分药动学与药效学之间相关性的评价与预测。     

翻转课堂教学法在天然药物化学实验中的应用

目的利用翻转课堂教学法改进传统的天然药物化学实验课教学模式,提高该课程的教学效果。方法从实验教学的课程设计与开发,课前自主学习、课中内化及课后评估和讨论等环节进行了一系列的探讨与改革。结果翻转课堂教学模式为解决学生长期以来在天然药物化学实验课中缺乏主动参与性、探索性等问题提供一剂良方。结论在天然药物化学实验课教学中引入翻转课堂教学模式的初步探索为天然药物化学实验课的改革提供了依据和思路。     

基于Andersen行为模型的消化道肿瘤患者支持性照顾需求现状及影响因素分析

目的调查消化道肿瘤患者的支持性照顾需求现状并分析其影响因素。方法于2019年1—10月间,采用便利抽样的方法,抽取重庆市三峡中心医院内消化道肿瘤患者共370例,采用一般资料调查表、癌症病人支持性照顾需求简明问卷(SCNS-SF34)、医院焦虑和抑郁量表(HADS)、社会网络支持量表(LSNS)进行问卷调查。以Andersen行为模型为基础,将调查内容中关于消化道肿瘤患者支持性照顾需求的影响因素分别纳入倾向性因素、使能因素和需求性因素中,构建4个线性回归模型进行分析,并检验各模型的拟合优度,采用多元线性回归分析消化道肿瘤患者支持性照顾需求的影响因素。结果共发放问卷370份,回收有效问卷365份,有效回收率为98.6%。365例消化道肿瘤患者支持性照顾需求总均分为(3.42±0.54)分,其中生理与日常生活需求>健康信息需求>照顾与支持需求>心理需求>性需求。多元线性回归分析结果显示:倾向性因素中年龄、婚姻状况,使能因素中的家庭平均月收入、疾病负担能力、社会网络支持,需求性因素中的病程、疾病分期、疼痛情况、焦虑程度、抑郁程度是消化道肿瘤患者支持性照顾需求的主要影响因素(P<0.05)。结论消化道肿瘤患者有较高的支持性照顾需求,其需求受多种因素的影响,建议临床医护人员针对不同需求患者进行针对性满足,并对不同影响因素进行综合考量并加以分类分析。     

2-Dodecyl-6-methoxycyclohexa-2,5-diene-1,4-dione,isolated from the root of Averrhoa carambola L., protects against diabetic kidney disease by inhibiting TLR4/TGFβ signaling pathway

ObjectiveDiabetic kidney disease (DKD) is the leading cause of death and disability of diabetes mellitus. However, there is still a lack of specific drugs for the treatment of DKD. The chief aim of this research is to investigate the role and mechanism of 2-Dodecyl-6-methoxycyclohexa-2,5-diene-1,4-dione (DMDD) for DKD.MethodsWild type and TLR4 knockout mice were induced to diabetes. After 4-week treatment with DMDD, blood sugar, renal function, blood lipid and pathological changes were assessed. Real-time PCR, western blotting, and immunohistochemistry were employed to detect the expressions of TLR4, TGFβ1 and Smad2/3 in the renal tissue.ResultsDMDD improved the serum lipid and decreased fasting blood glucose levels in diabetic mice. CysC and urinary albumin levels increased markedly in the diabetic group, and they were obviously decreased after 4 weeks of DMDD treatment. Compared with the WT diabetic mice, the urinary albumin and CysC in the TLR4^-/- mice were expressed at lower levels. HE and Masson’s staining revealed that DMDD clearly ameliorated pathological changes and renal fibrosis. When TLR4 gene was knock out, the pathological was improved. Mechanistically, TLR4, TGF-β1 and Smad2/3 were obvious up-regulation in the renal tissues of diabetic mice. The expressions of these proteins were significantly down-regulated after DMDD treatment (p < 0.05). In the TLR4^-/- mice, mRNA and protein levels of TGF-β1 and Smad2/3 were obviously lower than those in the WT mice. In addition, IHC revealed that a strong in situ expressions of TLR4, TGF-β1 and Smad2/3 were seen in the kidney tissues of diabetic mice, which were distinctly weakened in the DMDD-treated mice. In the TLR4^-/- mice, however, expressions of TGF-β1 and Smad2/3 were not remarkable increase in the diabetic mice compared with normal mice.ConclusionsThese results strongly indicate that TLR4 is essential for DMDD protection against renal dysfunction in diabetic mice. Its hypoglycemic and anti-fibrosis effects were likely mediated by the TLR4/TGFβ signaling pathway.     

Comprehensive analysis of the relationship between competitive endogenous RNA (ceRNA) networks and tumor infiltrating-cells in hepatocellular carcinoma

BackgroundThe innovation of immune checkpoint blockade (ICB) represents a promising shift in the treatment of advanced hepatocellular carcinoma (HCC). However, response to ICB has varied largely due to the high tumor heterogeneity and complex tumor microenvironment (TME). The competitive endogenous RNA (ceRNA) network also plays an important role in tumor occurrence and progression, but its relation with tumor-infiltrating immune cells (TICs) remains largely unexplored in HCC. The overriding objective of our study was thus to construct a prognosis-related risk model and to further evaluate the relationship between ceRNA networks and TICs.MethodsDifferentially expressed gene (DEG) analysis was performed to identify the differentially expressed RNAs. Lasso and multivariable Cox regression analyses were used to construct risk models, which were assessed by the area under the receiver operating characteristic curve (AUC of ROC) and Kaplan-Meier (K-M) curves. Then, a single-sample gene set enrichment analysis (ssGSEA) algorithm was adopted to dissect the TICs in HCC samples. Nomograms were constructed and calibration curves were used to verify the discrimination and accuracy of the nomograms. Finally, integration analysis was performed to validate the correlation of ceRNA and TICs.ResultsIn the study, 7 differentially expressed RNAs [5 messenger RNA s (mRNAs) and 2 micro RNAs (miRNAs)] were incorporated to construct a ceRNA risk model. The AUC of the 1-, 3-, and 5-year overall survival (OS) were 0.784, 0.685, and 0.691 respectively. Likewise, 7 types TICs were in the TICs signature model and the AUC of the 1-, 3-, and 5-year OS were 0.706, 0.731, and 0.721 respectively. The integration analysis showed that 7 pairs of mRNA-TICs and 1 pair of miRNA-TICs had a close relation (all correlation coefficients >0.2, P<0.001).ConclusionsThrough constructing two risk models based on ceRNA network and TICs, we identified the hub RNAs and key TICs in the progression and prognosis of HCC, and further explored the relationship between ceRNA and TME. Importantly, targeting these hub RNAs may facilitate the remodeling of the TME and be a potential therapeutic alternative to enhancing the response to ICB, thus improving the prognosis of HCC patients.     

Analyzing Survival Rate of Leukemia Patients Applying Long Term Exponential Model

Background: Making progressin treatment of all branches of cancers has increasedthe percent of patients that never experience the event of interest. These cases are called immune or cure and models for handling the data included cure fraction rate, are referred to as cure model or long-term survival models. Methods:The data for this historical cohort study, were collected from leukemia patients diagnosed between 2007 to 2014 and followed up until 2016 in Taleghani hospital and received BMT (Bone Marrow Transplant). Some data had to be excluded because of incomplete information. Using recorded files mostly and phone calls rarely, were made to confirm whether the patients were still alive or not. Death due to leukemia was regarded as interested event. Analysis were performed by R version 3.4.1and Stata version 14. Results: Number of recurrents after receiving BMT, pre-transplant Hb and age at diagnosis were found as significant prognostics of survival time. HD patients had the highest 5-years overall survival in category of diagnosis type with 81.3%. Cure fraction was estimated to be 64.1%. Conclusion: According to high percentage of censoring, using long-term model had better fit.