Mikolajewicz Nicholas, Komarova Svetlana V
Faculty of Dentistry, McGill University, Montreal, QC, Canada.
Shriners Hospital for Children-Canada, Montreal, QC, Canada.
Front Physiol. 2019 Mar 27;10:203. doi: 10.3389/fphys.2019.00203. eCollection 2019.
Basic life science literature is rich with information, however methodically quantitative attempts to organize this information are rare. Unlike clinical research, where consolidation efforts are facilitated by systematic review and meta-analysis, the basic sciences seldom use such rigorous quantitative methods. The goal of this study is to present a brief theoretical foundation, computational resources and workflow outline along with a working example for performing systematic or rapid reviews of basic research followed by meta-analysis. Conventional meta-analytic techniques are extended to accommodate methods and practices found in basic research. Emphasis is placed on handling heterogeneity that is inherently prevalent in studies that use diverse experimental designs and models. We introduce , a meta-analytic toolbox developed in MATLAB R2016b which implements the methods described in this methodology and is provided for researchers and statisticians at Git repository (https://github.com/NMikolajewicz/MetaLab). Through the course of the manuscript, a rapid review of intracellular ATP concentrations in osteoblasts is used as an example to demonstrate workflow, intermediate and final outcomes of basic research meta-analyses. In addition, the features pertaining to larger datasets are illustrated with a systematic review of mechanically-stimulated ATP release kinetics in mammalian cells. We discuss the criteria required to ensure outcome validity, as well as exploratory methods to identify influential experimental and biological factors. Thus, meta-analyses provide informed estimates for biological outcomes and the range of their variability, which are critical for the hypothesis generation and evidence-driven design of translational studies, as well as development of computational models.
基础生命科学文献蕴含着丰富的信息,然而,有条理地对这些信息进行定量整理的尝试却很少见。与临床研究不同,临床研究通过系统评价和荟萃分析来促进整合工作,而基础科学很少使用如此严格的定量方法。本研究的目的是提供一个简要的理论基础、计算资源和工作流程概述,并给出一个实例,用于对基础研究进行系统或快速回顾,随后进行荟萃分析。传统的荟萃分析技术得到扩展,以适应基础研究中发现的方法和实践。重点在于处理在使用不同实验设计和模型的研究中普遍存在的异质性。我们介绍了一个在MATLAB R2016b中开发的荟萃分析工具箱,它实现了本方法中描述的方法,并在Git仓库(https://github.com/NMikolajewicz/MetaLab)为研究人员和统计学家提供。在整个手稿中,以对成骨细胞内ATP浓度的快速回顾为例,展示基础研究荟萃分析的工作流程、中间结果和最终结果。此外,通过对哺乳动物细胞中机械刺激ATP释放动力学的系统评价,说明了与更大数据集相关的特征。我们讨论了确保结果有效性所需的标准,以及识别有影响力的实验和生物学因素的探索性方法。因此,荟萃分析为生物学结果及其变异性范围提供了有根据的估计,这对于转化研究的假设生成和证据驱动设计以及计算模型的开发至关重要。