本研究旨在通过Meta分析探究饲粮添加白藜芦醇对猪肉品质的影响,并整合转录组数据挖掘与计算生物学方法揭示其潜在调控机制。系统检索2000年1月至2025年1月期间在中国知网、万方、Web of Science和Science Direct数据库中发表的白藜芦...本研究旨在通过Meta分析探究饲粮添加白藜芦醇对猪肉品质的影响,并整合转录组数据挖掘与计算生物学方法揭示其潜在调控机制。系统检索2000年1月至2025年1月期间在中国知网、万方、Web of Science和Science Direct数据库中发表的白藜芦醇对猪肉品质影响的相关文献,依据纳入与排除标准进行筛选后,最终纳入5项研究用于Meta分析。Meta分析结果表明,与对照组相比,饲粮添加白藜芦醇可显著提高屠宰后45 min猪肉红度值(P<0.05),显著降低屠宰后24 h猪肉亮度值(P<0.05),并显著降低猪肉滴水损失(P<0.05)。进一步通过转录组数据挖掘,筛选出与猪肉红度值和滴水损失相关的潜在基因,并与白藜芦醇的预测靶点进行交集分析,结果表明,白藜芦醇可能通过调控碳酸酐酶2(CA2)、膜联蛋白A5(ANXA5)、半胱氨酸-天冬氨酸蛋白酶7(CASP7)、11β-羟类固醇脱氢酶1(HSD11B1)和热休克蛋白90α家族A类成员1(HSP90AA1)的表达影响猪肉滴水损失;同时,可能通过调控雌激素受体1(ESR1)和CASP7的表达影响猪肉红度值。分子对接与分子动力学模拟进一步证实,白藜芦醇与上述基因编码的蛋白存在潜在结合作用。综上所述,饲粮添加白藜芦醇对猪肉品质的改善作用主要为提高红度值、降低亮度值和滴水损失,其调控猪肉滴水损失的潜在靶点为CA2、ANXA5、CASP7、HSD11B1和HSP90AA1,调控猪肉红度值的潜在靶点为ESR1和CASP7。这些发现可为后续基于上述潜在靶点解析白藜芦醇调控猪肉品质的机制研究提供依据。展开更多
Individual participant data (IPD) meta-analysis was developed to overcome several meta-analytical pitfalls of classical meta-analysis. One advantage of classical psychometric meta-analysis over IPD meta-analysis is th...Individual participant data (IPD) meta-analysis was developed to overcome several meta-analytical pitfalls of classical meta-analysis. One advantage of classical psychometric meta-analysis over IPD meta-analysis is the corrections of the aggregated unit of studies, namely study differences, i.e., artifacts, such as measurement error. Without these corrections on a study level, meta-analysts may assume moderator variables instead of artifacts between studies. The psychometric correction of the aggregation unit of individuals in IPD meta-analysis has been neglected by IPD meta-analysts thus far. In this paper, we present the adaptation of a psychometric approach for IPD meta-analysis to account for the differences in the aggregation unit of individuals to overcome differences between individuals. We introduce the reader to this approach using the aggregation of lens model studies on individual data as an example, and lay out different application possibilities for the future (e.g., big data analysis). Our suggested psychometric IPD meta-analysis supplements the meta-analysis approaches within the field and is a suitable alternative for future analysis.展开更多
The paper aims to discuss three interesting issues of statistical inferences for a common risk ratio (RR) in sparse meta-analysis data. Firstly, the conventional log-risk ratio estimator encounters a number of problem...The paper aims to discuss three interesting issues of statistical inferences for a common risk ratio (RR) in sparse meta-analysis data. Firstly, the conventional log-risk ratio estimator encounters a number of problems when the number of events in the experimental or control group is zero in sparse data of a 2 × 2 table. The adjusted log-risk ratio estimator with the continuity correction points based upon the minimum Bayes risk with respect to the uniform prior density over (0, 1) and the Euclidean loss function is proposed. Secondly, the interest is to find the optimal weights of the pooled estimate that minimize the mean square error (MSE) of subject to the constraint on where , , . Finally, the performance of this minimum MSE weighted estimator adjusted with various values of points is investigated to compare with other popular estimators, such as the Mantel-Haenszel (MH) estimator and the weighted least squares (WLS) estimator (also equivalently known as the inverse-variance weighted estimator) in senses of point estimation and hypothesis testing via simulation studies. The results of estimation illustrate that regardless of the true values of RR, the MH estimator achieves the best performance with the smallest MSE when the study size is rather large and the sample sizes within each study are small. The MSE of WLS estimator and the proposed-weight estimator adjusted by , or , or are close together and they are the best when the sample sizes are moderate to large (and) while the study size is rather small.展开更多
Meta分析包括已发表文献的Meta分析(meta-analysis of the published literature,MPL)和单个病例资料的Meta分析(meta-analysis of individual patient data,MIPD)。递归累积Meta分析是一种可对已有资料重新整理并及时更新,还能对现有...Meta分析包括已发表文献的Meta分析(meta-analysis of the published literature,MPL)和单个病例资料的Meta分析(meta-analysis of individual patient data,MIPD)。递归累积Meta分析是一种可对已有资料重新整理并及时更新,还能对现有试验的延续随访进行分析的Meta分析方法,递归累积Meta分析在每纳入一项新研究或纳入更新的研究时,可以检测每一合并步骤中效应量的波动,从而判断纳入研究间是否存在偏倚或异质性,并判断合并结果的稳定性。本文主要介绍了递归累积Meta分析的概念并结合具体实例来讲解如何实现。展开更多
文摘Individual participant data (IPD) meta-analysis was developed to overcome several meta-analytical pitfalls of classical meta-analysis. One advantage of classical psychometric meta-analysis over IPD meta-analysis is the corrections of the aggregated unit of studies, namely study differences, i.e., artifacts, such as measurement error. Without these corrections on a study level, meta-analysts may assume moderator variables instead of artifacts between studies. The psychometric correction of the aggregation unit of individuals in IPD meta-analysis has been neglected by IPD meta-analysts thus far. In this paper, we present the adaptation of a psychometric approach for IPD meta-analysis to account for the differences in the aggregation unit of individuals to overcome differences between individuals. We introduce the reader to this approach using the aggregation of lens model studies on individual data as an example, and lay out different application possibilities for the future (e.g., big data analysis). Our suggested psychometric IPD meta-analysis supplements the meta-analysis approaches within the field and is a suitable alternative for future analysis.
文摘The paper aims to discuss three interesting issues of statistical inferences for a common risk ratio (RR) in sparse meta-analysis data. Firstly, the conventional log-risk ratio estimator encounters a number of problems when the number of events in the experimental or control group is zero in sparse data of a 2 × 2 table. The adjusted log-risk ratio estimator with the continuity correction points based upon the minimum Bayes risk with respect to the uniform prior density over (0, 1) and the Euclidean loss function is proposed. Secondly, the interest is to find the optimal weights of the pooled estimate that minimize the mean square error (MSE) of subject to the constraint on where , , . Finally, the performance of this minimum MSE weighted estimator adjusted with various values of points is investigated to compare with other popular estimators, such as the Mantel-Haenszel (MH) estimator and the weighted least squares (WLS) estimator (also equivalently known as the inverse-variance weighted estimator) in senses of point estimation and hypothesis testing via simulation studies. The results of estimation illustrate that regardless of the true values of RR, the MH estimator achieves the best performance with the smallest MSE when the study size is rather large and the sample sizes within each study are small. The MSE of WLS estimator and the proposed-weight estimator adjusted by , or , or are close together and they are the best when the sample sizes are moderate to large (and) while the study size is rather small.
文摘Meta分析包括已发表文献的Meta分析(meta-analysis of the published literature,MPL)和单个病例资料的Meta分析(meta-analysis of individual patient data,MIPD)。递归累积Meta分析是一种可对已有资料重新整理并及时更新,还能对现有试验的延续随访进行分析的Meta分析方法,递归累积Meta分析在每纳入一项新研究或纳入更新的研究时,可以检测每一合并步骤中效应量的波动,从而判断纳入研究间是否存在偏倚或异质性,并判断合并结果的稳定性。本文主要介绍了递归累积Meta分析的概念并结合具体实例来讲解如何实现。
