胰腺癌患者血清CEA、CA242、CA199水平变化及联合诊断的ROC曲线分析

目的:探讨胰腺癌患者血清癌胚抗原(CEA)、糖类抗原242(CA242)、糖类抗原199(CA199)水平变化,并分析上述指标对胰腺癌的联合诊断价值,为胰腺癌的临床诊断提供参考。方法:选择2014年2月至2018年2月我院收治的186例胰腺癌患者(胰腺癌组)、89例胰腺炎患者(胰腺炎组)作为研究对象,并取同期来我院检查的268例健康人作为对照组。比较三组受试者的血清CEA、CA242、CA199水平变化,对比分析血清CEA、CA242、CA199的单一以及联合诊断的准确度、特异度以及灵敏度,并绘制ROC曲线以分析上述指标的诊断价值。结果:三组受试者血清CEA、CA242、CA199水平差异具有统计学意义(P<0.05)。且胰腺炎组和胰腺癌组的血清CEA、CA242、CA199水平明显高于对照组,胰腺癌组患者的血清CEA、CA242、CA199水平明显高于胰腺炎组,差异均有统计学意义(P<0.05)。ROC曲线结果显示,CEA诊断价值最大,CA199诊断价值最小。CEA是胰腺癌单项肿瘤标志物中敏感度最高的,为85.48%;特异度最高的为CA242(96.72%);三项肿瘤标志物联合诊断的准确度增加至92.27%,敏感度增加至95.16%,特异度相比略有下降。结论:与单一肿瘤标记物诊断胰腺癌相比,CEA、CA242、CA199联合诊断的敏感度和准确度均明显升高,可以明显改善胰腺癌的漏诊率,提高患者的生存率,具有较好的临床应用价值。     

柽柳灌丛关键物候参数多种植被指数遥感提取的适用性——基于CO2通量观测和Sentinel-2数据

本研究以额济纳绿洲四道桥超级站为研究区,结合2018—2019年涡度通量、气象数据和2017—2020年Sentinel-2遥感影像,分析通量塔总初级生产力(GPP)与环境因子的关系,评估12种遥感植被指数对柽柳灌丛长势模拟和关键物候参数提取的适用性。采用7参数双逻辑斯蒂函数(DL-7)+全局模型函数(GMF)拟合GPP和各植被指数生长曲线,并逐年提取生长季始期(SOS)、生长季峰期(POS)和生长季末期(EOS)3种关键物候参数。结果表明: 有效积温(GDD)和土壤含水量是影响柽柳灌丛物候动态的主要环境因子。与2018年相比,2019年由于气温较低,SOS前的积温累积速率较慢,柽柳灌丛需要更长时间的热量积累来进入生长季,从而导致2019年SOS比2018年晚。在SOS与POS之间,2018和2019年水热条件相似,但2019年POS比2018年晚8 d,可能是2019年SOS较晚所致。POS以后,2019年较高的GDD和较低的土壤含水量使柽柳灌丛遭受水分胁迫,导致其生长季后期时间缩短。标准化的Sentinel-2植被指数与10:00—14:00 GPP均值的线性回归结果表明,宽波段植被指数中的增强型植被指数和窄波段植被指数中的叶绿素红边指数、倒红边叶绿素指数、红边归一化植被指数(NDVI705)能够较好地反映与柽柳灌丛GPP具有较高的一致性。柽柳灌丛SOS和EOS的遥感提取结果表明,Sentinel-2窄波段植被指数比宽波段植被指数的准确性更高,尤其是修正叶绿素吸收反射率指数提取SOS最准确,MERIS陆地叶绿素指数提取EOS最准确;Sentinel-2宽波段植被指数提取POS的准确性更高,尤其是两波段增强型植被指数和植被近红外反射率指数最准确。综合所有物候参数来看,NDVI705综合表现最佳。     

实时定量PCR构建法夫酵母内参基因β-actin、 gpd、18S rRNA标准品质粒和标准曲线

为建立检测法夫酵母JMU-MVP14中虾青素合成相关基因在不同生长时期表达水平的实时定量PCR方法,构建法夫酵母JMU-MVP14的管家基因β-actin、gpd、18S rRNA的标准质粒,进行实时定量PCR,制作标准曲线及回归方程.β-actin基因标准曲线相关系数（R2）=0.9956,扩增效率（E） =96.93％;gpd基因标准曲线相关系数（R2） =0.9901,扩增效率（E） =93.78％;18S rRNA基因标准曲线相关系数（R2） =0.9981,扩增效率（E）=98.76％.3个基因片段的熔解曲线均呈单峰;扩增曲线呈典型的S型动力学曲线,指数期和平台期明显,为理想的熔解曲线和扩增曲线.用geNorm软件对三个管家基因的稳定性进行分析,三个基因的稳定性排序为β-actin＞ 18S rRNA＞ gpd,故β-actin和18S rRNA较适合作为研究法夫酵母JMU-MVP14定量实验的内参基因.     

Validation of discrete time-to-event prediction models in the presence of competing risks

Clinical prediction models play a key role in risk stratification, therapy assignment and many other fields of medical decision making. Before they can enter clinical practice, their usefulness has to be demonstrated using systematic validation. Methods to assess their predictive performance have been proposed for continuous, binary, and time-to-event outcomes, but the literature on validation methods for discrete time-to-event models with competing risks is sparse. The present paper tries to fill this gap and proposes new methodology to quantify discrimination, calibration, and prediction error (PE) for discrete time-to-event outcomes in the presence of competing risks. In our case study, the goal was to predict the risk of ventilator-associated pneumonia (VAP) attributed to Pseudomonas aeruginosa in intensive care units (ICUs). Competing events are extubation, death, and VAP due to other bacteria. The aim of this application is to validate complex prediction models developed in previous work on more recently available validation data.     

One-step targeted maximum likelihood estimation for time-to-event outcomes

Researchers in observational survival analysis are interested in not only estimating survival curve nonparametrically but also having statistical inference for the parameter. We consider right-censored failure time data where we observe n independent and identically distributed observations of a vector random variable consisting of baseline covariates, a binary treatment at baseline, a survival time subject to right censoring, and the censoring indicator. We assume the baseline covariates are allowed to affect the treatment and censoring so that an estimator that ignores covariate information would be inconsistent. The goal is to use these data to estimate the counterfactual average survival curve of the population if all subjects are assigned the same treatment at baseline. Existing observational survival analysis methods do not result in monotone survival curve estimators, which is undesirable and may lose efficiency by not constraining the shape of the estimator using the prior knowledge of the estimand. In this paper, we present a one-step Targeted Maximum Likelihood Estimator (TMLE) for estimating the counterfactual average survival curve. We show that this new TMLE can be executed via recursion in small local updates. We demonstrate the finite sample performance of this one-step TMLE in simulations and an application to a monoclonal gammopathy data.     

ROC-guided survival trees and ensembles

Tree-based methods are popular nonparametric tools in studying time-to-event outcomes. In this article, we introduce a novel framework for survival trees and ensembles, where the trees partition the dynamic survivor population and can handle time-dependent covariates. Using the idea of randomized tests, we develop generalized time-dependent receiver operating characteristic (ROC) curves for evaluating the performance of survival trees. The tree-building algorithm is guided by decision-theoretic criteria based on ROC, targeting specifically for prediction accuracy. To address the instability issue of a single tree, we propose a novel ensemble procedure based on averaging martingale estimating equations, which is different from existing methods that average the predicted survival or cumulative hazard functions from individual trees. Extensive simulation studies are conducted to examine the performance of the proposed methods. We apply the methods to a study on AIDS for illustration.