高危结直肠腺瘤的危险因素及风险预测模型的构建与验证

摘要 目的：探讨高危结直肠腺瘤的影响因素,构建风险预测模型并验证。方法：回顾性分析2021年1月至2021年12月期间在江苏大学附属人民医院进行诊疗的1408例结直肠腺瘤患者的资料,根据病理特征分为高危结直肠腺瘤组（759例）和非高危结直肠腺瘤组（649例）。采用Logistic回归分析筛选高危结直肠腺瘤的独立危险因素并建立风险预测模型,并验证预测模型的应用效能。结果：Logistic回归分析结果显示,病灶部位为直肠、高血压、高脂血症、年龄≥53岁、吸烟是高危结直肠腺瘤的独立危险因素（P＜0.05）。基于以上因素建立预测高危结直肠腺瘤风险的列线图模型,经Hosmer-Lemeshow检验和受试者工作特征曲线（ROC）分析显示,该风险预测模型具有较好的拟合度和预测效能,可以用于高危腺瘤的风险预测。结论：病灶部位为直肠、高血压、高脂血症、年龄≥53岁、吸烟是高危结直肠腺瘤的独立危险因素,临床医生可尽早对高危患者进行预防性干预以减缓高危腺瘤的发生。     

《山海经》三大神木的植物学考订

《山海经》中有太阳东出扶桑、日中建木、西归若木的传说,长期以来一直存疑,争论不休,直到四川广汉三星堆遗址出土了青铜神树,才证明了传说的真实性.我们结合《山海经》等相关典籍,从植物学角度对扶桑、建木、若木的原植物进行探讨,基于典籍描述、考古资料、植物形态特征、生态习性和地理分布等,初步考订扶桑的植物原型为桦木科白桦(Betula platyphylla),建木的植物原型为杉科杉木(Cunninghamia lanceolata),若木的植物原型为木棉科木棉(Bombax ceiba).     

Generalized estimating equations for ordinal categorical data: arbitrary patterns of missing responses and missingness in a key covariate

We propose methods for regression analysis of repeatedly measured ordinal categorical data when there is nonmonotone missingness in these responses and when a key covariate is missing depending on observables. The methods use ordinal regression models in conjunction with generalized estimating equations (GEEs). We extend the GEE methodology to accommodate arbitrary patterns of missingness in the responses when this missingness is independent of the unobserved responses. We further extend the methodology to provide correction for possible bias when missingness in knowledge of a key covariate may depend on observables. The approach is illustrated with the analysis of data from a study in diagnostic oncology in which multiple correlated receiver operating characteristic curves are estimated and corrected for possible verification bias when the true disease status is missing depending on observables.     

Accounting for nonignorable verification bias in assessment of diagnostic tests

A "gold" standard test, providing definitive verification of disease status, may be quite invasive or expensive. Current technological advances provide less invasive, or less expensive, diagnostic tests. Ideally, a diagnostic test is evaluated by comparing it with a definitive gold standard test. However, the decision to perform the gold standard test to establish the presence or absence of disease is often influenced by the results of the diagnostic test, along with other measured, or not measured, risk factors. If only data from patients who received the gold standard test were used to assess the test performance, the commonly used measures of diagnostic test performance--sensitivity and specificity--are likely to be biased. Sensitivity would often be higher, and specificity would be lower, than the true values. This bias is called verification bias. Without adjustment for verification bias, one may possibly introduce into the medical practice a diagnostic test with apparent, but not truly, high sensitivity. In this article, verification bias is treated as a missing covariate problem. We propose a flexible modeling and computational framework for evaluating the performance of a diagnostic test, with adjustment for nonignorable verification bias. The presented computational method can be utilized with any software that can repetitively use a logistic regression module. The approach is likelihood-based, and allows use of categorical or continuous covariates. An explicit formula for the observed information matrix is presented, so that one can easily compute standard errors of estimated parameters. The methodology is illustrated with a cardiology data example. We perform a sensitivity analysis of the dependency of verification selection process on disease.     

骨骼肌特异表达线虫ω-3脂肪酸去饱和酶基因载体的构建与验证

根据猪α-actin基因已知DNA序列设计合成了两个特异性引物,以猪基因组DNA为模板,通过PCR扩增得到α-actin 5'调控序列,然后与线虫ω-3脂肪酸去饱和酶基因cDNA、去除CMV启动子的表达载体pcDNA3.1连接构成肌肉特异性表达载体pcDNA3.1-AF,小鼠股四头肌注射该重组载体,RT-PCR检测证明...     

The weighted generalized estimating equations approach for the evaluation of medical diagnostic test at subunit level

Sensitivity and specificity are common measures used to evaluate the performance of a diagnostic test. A diagnostic test is often administrated at a subunit level, e.g. at the level of vessel, ear or eye of a patient so that the treatment can be targeted at the specific subunit. Therefore, it is essential to evaluate the diagnostic test at the subunit level. Often patients with more negative subunit test results are less likely to receive the gold standard tests than patients with more positive subunit test results. To account for this type of missing data and correlation between subunit test results, we proposed a weighted generalized estimating equations (WGEE) approach to evaluate subunit sensitivities and specificities. A simulation study was conducted to evaluate the performance of the WGEE estimators and the weighted least squares (WLS) estimators (Barnhart and Kosinski, 2003) under a missing at random assumption. The results suggested that WGEE estimator is consistent under various scenarios of percentage of missing data and sample size, while the WLS approach could yield biased estimators due to a misspecified missing data mechanism. We illustrate the methodology with a cardiology example.     

A novel design for estimating relative accuracy of screening tests when complete disease verification is not feasible

The accuracy (sensitivity and specificity) of a new screening test can be compared with that of a standard test by applying both tests to a group of subjects in which disease status can be determined by a gold standard (GS) test. However, it is not always feasible to administer a GS test to all study subjects. For example, a study is planned to determine whether a new screening test for cervical cancer ("ThinPrep") is better than the standard test ("Pap"), and in this setting it is not feasible (or ethical) to determine disease status by biopsy in order to identify women with and without disease for participation in a study. When determination of disease status is not possible for all study subjects, the relative accuracy of two screening tests can still be estimated by using a paired screen-positive (PSP) design in which all subjects receive both screening tests, but only have the GS test if one of the screening tests is positive. Unfortunately in the cervical cancer example, the PSP design is also infeasible because it is not technically possible to administer both the ThinPrep and Pap at the same time. In this article, we describe a randomized paired screen-positive (RPSP) design in which subjects are randomized to receive one of the two screening tests initially, and only receive the other screening test and GS if the first screening test is positive. We derive maximum likelihood estimators and confidence intervals for the relative accuracy of the two screening tests, and assess the small sample behavior of these estimators using simulation studies. Sample size formulae are derived and applied to the cervical cancer screening trial example, and the efficiency of the RPSP design is compared with other designs.     

Testing the stability of transfer functions

In dendroclimatology, testing the stability of transfer functions using cross-calibration verification (CCV) statistics is a common procedure. However, the frequently used statistics reduction of error (RE) and coefficient of efficiency (CE) merely assess the skill of reconstruction for the validation period, which does not necessarily reflect possibly instable regression parameters. Furthermore, the frequently used rigorous threshold of zero which sharply distinguishes between valid and invalid transfer functions is prone to an underestimation of instability. To overcome these drawbacks, we here introduce a new approach – the Bootstrapped Transfer Function Stability test (BTFS). BTFS relies on bootstrapped estimates of the change of model parameters (intercept, slope, and r²) between calibration and verification period as well as the bootstrapped significance of corresponding models. A comparison of BTFS, CCV and a bootstrapped CCV approach (BCCV) applied to 42,000 pseudo-proxy datasets with known properties revealed that BTFS responded more sensitively to instability compared to CCV and BCCV. BTFS performance was significantly affected by sample size (length of calibration period) and noise (explained variance between predictor and predictand). Nevertheless, BTFS performed superior with respect to the detection of instable transfer functions in comparison to CCV.     

Analytical platform for verification and quantitation of target peptides in human serum: application to cathelicidin

A selective and sensitive, fully automated platform for verification and quantitative determination of target peptides in biofluids is proposed and then validated by development of a method for analysis of cathelicidin in human serum. The method is based on the on-line coupling of solid-phase extraction (SPE) and tandem mass spectrometry with direct infusion. Mass spectrometry analysis was carried out by multiple reaction monitoring using three transitions (one for quantitative analysis and two for qualitative analysis), all them confirmed by in silico fragmentation of the target peptide. Samples were prepared in the SPE workstation on a polymeric divinylbenzene resin by preconcentration, deproteinization, and cleanup, removing salts and interferences after direct injection of human serum. The analytical process required 12 min. The limits of detection and quantitation were 2.5 and 8.25 μg/L, respectively (0.20 and 0.66 pg on column). Repeatability and within-laboratory reproducibility were 2.4% and 2.7%, respectively. A dual-cartridge configuration was used to test recovery of cathelicidin in serum, resulting in 80%. Because quantitative retention in the cartridge was assessed, determination of cathelicidin was validated without using synthetic peptides labeled with stable isotopes. The hyphenated system allows full automation, thereby improving reproducibility and accuracy, as demanded by clinical analysis.