广西巴马长寿乡壮族长寿老人永生细胞库的建立

目的：建立广西巴马长寿乡壮族长寿老人永生细胞库。方法：使用EB病毒转化外周血B淋巴细胞方法。结果：建立了长寿者外周血细胞转化永生细胞株的方法，转化成功率77．4％。建立了65株长寿老人、直系亲属永生细胞株及长寿老人资料库和完整的细胞株档案。结论：广西巴马长寿乡壮族长寿老人永生细胞库的建立为永久保存细胞系及后续研究提供了必要的基础。     

Immortal Time Bias in the Analysis of Time-to-Event Data in Orthopedics

Many outcomes in arthroplasty research are analyzed as time-to-event outcomes using survival analysis methods. When comparison groups are defined after a time-delayed exposure or intervention, a period of immortal time arises and can lead to biased results. In orthopedics research, immortal time bias often arises when a minimum amount of follow-up is required for study inclusion or when comparing outcomes in staged bilateral vs unilateral arthroplasty patients. We present an explanation of immortal time and the associated bias, describe how to correctly account for it using proper data preparation and statistical techniques, and provide an illustrative example using real-world arthroplasty data. We offer practical guidelines for identifying and properly handling immortal time to avoid bias. Please visit the following https://youtu.be/58p8w5o-ci4 for a video that explains the highlights of the paper in practical terms.     

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.     

Characterization of four newly established human colorectal adenocarcinoma cell lines from Chinese patients.

Four colon adenocarcinoma cell lines, CC-M2, CC-M3, CC-M4, and CC-M2NM, have been established from surgical specimens of 18 unselected patients without the use of "feeder" cells and additional growth factors (e.g., insulin, hydrocortisone, etc.) in the culture medium. The methods of primary cultivation of tissue explants are described. Studies of determination of morphology, growth curve, plating efficiency, chromosomal analysis, CEA and beta-HCG synthesis, and tumorigenicity, were done to characterize the cell lines. Significant variations have been found in one of the four cell lines, both in vitro and in vivo studies. There are distinct phenotypes in the established cell lines which may be useful in studying the cell differentiation and progression of colorectal cancer.     

姜黄素对人淋巴细胞低密度脂蛋白受体表达影响的研究

 目的建立一种简便的直接检测人类低密度脂蛋白受体(lowdensitylipoproteinrecepor,LDL-R)活性并筛选降脂中药的细胞模型:人B淋巴细胞永生化细胞系研究不同浓度姜黄素对人类LDL-R在人淋巴细胞中表达的影响,在细胞和受体水平上探讨姜黄素的作用机制。方法EBV转化技术建立的人B淋巴细胞永生化细胞系;以荧光试剂标记配体法,利用流式细胞仪技术和激光扫描共聚焦显微镜技术研究姜黄素对人淋巴细胞LDL-R表达的影响。结果姜黄素在5～50μmol·L^-1内可以增强人淋巴细胞LDL-R的表达,并且具有明显的量效关系。结论利用荧光试剂标记配体法检测人淋巴细胞LDL-R活性是一个简单有效的方法;姜黄素是一个非常强的LDL-R基因表达促进剂,可能通过增加人淋巴细胞LDL-R的表达起到降血脂和抗动脉粥样硬化的作用。     

Addressing different biases in analysing drug use on cancer risk in diabetes in non‐clinical trial settings—what,why and how?

Motivated by recent reports on associations between diabetes and cancer, many researchers have used administrative databases to examine risk association of cancer with drug use in patients with diabetes. Many of these studies suffered from major biases in study design and data analysis, which can lead to erroneous conclusions if these biases are not adjusted. This article discusses the sources and impacts of these biases and methods for correction of these biases. To avoid erroneous results, this article suggests performing sensitivity and specificity analysis as well as using a drug with a known effect on an outcome to ascertain the validity of the proposed methods. Using the Hong Kong Diabetes Registry, we illustrated the impacts of biases of drug use indication and prevalent user by examining the effects of statins on cardiovascular disease. We further showed that 'immortal time bias' may have a neutral impact on the estimated drug effect if the hazard is assumed to be constant over time. On the contrary, adjustment for 'immortal time bias' using time-dependent models may lead to misleading results biased towards against the treatment. However, artificial inclusion of immortal time in non-drug users to correct for immortal time bias may bias the result in favour of the therapy. In conclusion, drug use indication bias and prevalent user bias but not immortal time bias are major biases in the design and analysis of drug use effects among patients with diabetes in non-clinical trial settings.     

宁夏地区28株遗传性眼病永生淋巴细胞系的建立

目的建立宁夏地区多种遗传病性眼病的永生淋巴细胞系,从而为进一步深入研究这些遗传病的发病机理提供研究材料。方法采用EB病毒转化B淋巴细胞的方法,建立永生淋巴细胞系。结果成功建立先天性高度近视、先天性白内障、先天性上睑下垂等遗传病,8个家系的28个永生淋巴细胞株。结论遗传性眼病家系永生细胞株的建立,为从分子水平研究它们的发病机理提供了可用的资源。     

Use of the landmark method to address immortal person‐time bias in comparative effectiveness research: a simulation study

Observational comparative effectiveness and safety studies are often subject to immortal person‐time, a period of follow‐up during which outcomes cannot occur because of the treatment definition. Common approaches, like excluding immortal time from the analysis or naïvely including immortal time in the analysis, are known to result in biased estimates of treatment effect. Other approaches, such as the Mantel–Byar and landmark methods, have been proposed to handle immortal time. Little is known about the performance of the landmark method in different scenarios. We conducted extensive Monte Carlo simulations to assess the performance of the landmark method compared with other methods in settings that reflect realistic scenarios. We considered four landmark times for the landmark method. We found that the Mantel–Byar method provided unbiased estimates in all scenarios, whereas the exclusion and naïve methods resulted in substantial bias when the hazard of the event was constant or decreased over time. The landmark method performed well in correcting immortal person‐time bias in all scenarios when the treatment effect was small, and provided unbiased estimates when there was no treatment effect. The bias associated with the landmark method tended to be small when the treatment rate was higher in the early follow‐up period than it was later. These findings were confirmed in a case study of chronic obstructive pulmonary disease. Copyright © 2016 John Wiley & Sons, Ltd.