99mTc-DTPA核素肾动态显像评价主动脉夹层患者术后肾功能受损研究

目的:应用99mTc-DTPA肾动脉显像评价Stanford不同分型主动脉夹层患者术后左、右侧肾功能及总体肾功能受损程度,帮助临床制定进一步的治疗方案,改善患者预后。方法:回顾性分析2018年3月8日至2019年7月19日,在本院核医学科行99mTc-DTPA肾动态显像的主动脉夹层术后患者48例,评价患者双肾血流灌注、总肾小球滤过率(GFR)和分肾的GFR,比较Stanford主动脉夹层A型(简称A型)患者和主动脉夹层B型(简称B型)患者之间总肾功能及分肾功能,血肌酐、血尿素氮及血尿酸水平的差异。结果:B型患者术后总GFR低于A型患者(67.5 vs.80.6 m L/min,P<0.05),其中以左肾功能受损为著(30.9 vs.40.3 m L/min,P<0.05),差异有统计学意义。结论:肾动态显像对主动脉夹层术后患者早期评价肾功能有重要价值。主动脉夹层B型患者GFR较A型减低,且左侧肾GFR减低更明显,临床可以早期采取干预措施,改善主动脉夹层患者预后。     

Population Genetics Identifies Challenges in Analyzing Rare Variants

Geneticists have, for years, understood the nature of genome‐wide association studies using common genomic variants. Recently, however, focus has shifted to the analysis of rare variants. This presents potential problems for researchers, as rare variants do not always behave in the same way common variants do, sometimes rendering decades of solid intuition moot. In this paper, we present examples of the differences between common and rare variants. We show why one must be significantly more careful about the origin of rare variants, and how failing to do so can lead to highly inflated type I error. We then explain how to best avoid such concerns with careful understanding and study design. Additionally, we demonstrate that a seemingly low error rate in next‐generation sequencing can dramatically impact the false‐positive rate for rare variants. This is due to the fact that rare variants are, by definition, seen infrequently, making it hard to distinguish between errors and real variants. Compounding this problem is the fact that the proportion of errors is likely to get worse, not better, with increasing sample size. One cannot simply scale their way up in order to solve this problem. Understanding these potential pitfalls is a key step in successfully identifying true associations between rare variants and diseases.     

In Vitro-In Vivo Extrapolation and Hepatic Clearance-Dependent Underprediction

Accurately predicting the hepatic clearance of compounds using in vitro to in vivo extrapolation (IVIVE) is crucial within the pharmaceutical industry. However, several groups have recently highlighted the serious error in the process. Although empirical or regression-based scaling factors may be used to mitigate the common underprediction, they provide unsatisfying solutions because the reasoning behind the underlying error has yet to be determined. One previously noted trend was intrinsic clearance-dependent underprediction, highlighting the limitations of current in vitro systems. When applying these generated in vitro intrinsic clearance values during drug development and making first-in-human dose predictions for new chemical entities though, hepatic clearance is the parameter that must be estimated using a model of hepatic disposition, such as the well-stirred model. Here, we examine error across hepatic clearance ranges and find a similar hepatic clearance-dependent trend, with high clearance compounds not predicted to be so, demonstrating another gap in the field.     

Caution! Overestimation of treatment effects of corticosteroid therapy for community-acquired pneumonia in a meta-analysis of randomized controlled trials

It is well known that a meta-analysis of randomized controlled trials aims to increase the power and precision of the estimated intervention effects. However, when a meta-analysis includes a limited number of patients and a small number of events, overestimation of intervention effect estimates may occur and could cause spurious results. Although many biases can cause the overestimation, random error may be the most common cause. Trial sequential analysis (TSA) can explore the independent effect of random error on intervention effect estimates in meta-analyses and protect meta-analyses against overestimation due to random error.     

Measuring behaviour: The tools and the strategies

Animal behaviour can be viewed as a stream of elements, which, once accurately described, can be counted and timed. Data acquisition techniques and tools are reviewed, and some strategies for collection and analysis of data using PC computers are suggested. Automated instruments are not satisfactory for the study of complex behaviour and as such systemic observation remains irreplaceable. IBM PC-type computers, with a wide range of analytical software (e.g., spreadsheets, statistical packages, technical graphics), are practical for data acquisition. Several systems which can satisfy different applications are reviewed. Some systems can communicate with a videorecorder, a facility which remarkably increases the accuracy of measurement; this is essential for meaningful analyses of the internal structure of behavioural streams (sequences, time patterns) or communication processes. The power of new tools enables behavioural measurement with the necessary complexity to allow a whole new set of questions to be addressed. However, it also increases demands for meaningful content and analysis of data.