一种基于Meta分析固定效应模型的简明统计学方法

为了让临床医生更方便地使用M eta分析方法,我们根据四格表数据特点,提出了一种基于固定效应模型的简明方法。该方法简单而容易操作,且统计结果与原方法完全等价,在循证医学领域很有推广价值。     

Impact of low-frequency ultrasound technology on physical,chemical and technological properties of cereals and pseudocereals

Cereals (CE) and pseudocereals (PSCE) play a pivotal role in nourishing the human population. Low-frequency ultrasound (LFUS) modifies the structure of CE and PSCE macromolecules such as starch and proteins, often improving their technological, functional and bioactive properties. Hence, it is employed for enhancing the traditional processes utilized for the preparation of CE- and PSCE-based foods as well as for the upcycling of their by-products. We report recent advances in LFUS treatments for hydration, germination, extraction of bioactive compounds from by-products, and fortification of CEs and PSCE, including kinetic modelling and underlying action mechanisms. Meta-analyses of LFUS influence on compounds extraction and starch gelatinization are also presented. LFUS enhances hydration rate and time lag phase of CE and PSCE, essential for germination, extraction, fermentation and cooking. The germination is improved by increasing hydration, releasing promoters and eliminating inhibitors. Furthermore, LFUS boosts the extraction of phenolic compounds, polysaccharides and other food components; modifies starch structure, affecting pasting properties; causes partial denaturation of proteins, improving their interfacial properties and their peptides availability. Overall, LFUS has an outstanding potential to improve transformation processes and functionalities of CE and PSCE.     

Borrowing information from relevant microarray studies for sample classification using weighted partial least squares

With an increasing number of publicly available microarray datasets, it becomes attractive to borrow information from other relevant studies to have more reliable and powerful analysis of a given dataset. We do not assume that subjects in the current study and other relevant studies are drawn from the same population as assumed by meta-analysis. In particular, the set of parameters in the current study may be different from that of the other studies. We consider sample classification based on gene expression profiles in this context. We propose two new methods, a weighted partial least squares (WPLS) method and a weighted penalized partial least squares (WPPLS) method, to build a classifier by a combined use of multiple datasets. The methods can weight the individual datasets depending on their relevance to the current study. A more standard approach is first to build a classifier using each of the individual datasets, then to combine the outputs of the multiple classifiers using a weighted voting. Using two quite different datasets on human heart failure, we show first that WPLS/WPPLS, by borrowing information from the other dataset, can improve the performance of PLS/PPLS built on only a single dataset. Second, WPLS/WPPLS performs better than the standard approach of combining multiple classifiers. Third, WPPLS can improve over WPLS, just as PPLS does over PLS for a single dataset.     

Can diffusion-weighted imaging be used as a reliable sequence in the detection of malignant pulmonary nodules and masses?

Recent developments in diffusion-weighted magnetic resonance imaging (DWI) make it possible to image malignant tumors to provide tissue contrast based on difference with the diffusion of water molecules among tissues, which can be measured by the apparent diffusion coefficient (ADC) value. We aimed to assess the diagnostic accuracy of DWI for benign/malignant discrimination of pulmonary nodules/masses with a meta-analysis. The MEDLINE, EMBASE, Cancerlit and Cochrane Library database, from January 2001 to August 2011, were searched for studies evaluating the diagnostic accuracy of DWI for benign/malignant discrimination of pulmonary nodules. We determined sensitivities and specificities across studies, calculated positive and negative likelihood ratios (LRP and LRN), and constructed summary receiver operating characteristic SROC) curves. Across 10 studies (545 patients), there was no evidence of publication bias (P= .22, bias=−19.19). DWI had a pooled sensitivity of 0.84 (95% CI, 0.76–0.90) and a pooled specificity of 0.84 (95% CI, 0.64–0.94). Overall, LRP was 5.3 (95% CI, 2.1–13.0) and LRN was 0.19 (95% CI, 0.12–0.30). In patients with high pretest probabilities, DWI enabled confirmation of malignant pulmonary lesion; in patients with low pretest probabilities, DWI enabled exclusion of malignant pulmonary lesion. Worst-case-scenario (pretest probability, 50%) posttest probabilities were 84% and 16% for positive and negative DWI results, respectively. Diffusion-weighted magnetic resonance imaging can be used to differentiate malignant from benign pulmonary lesions. High-quality prospective studies regarding DWI in the evaluation of pulmonary nodules are still needed to be conducted.     

Gender difference on spatial visualization by college students’ major types as STEM and non-STEM: a meta-analysis

Gender difference on spatial visualization has been considered an important factor that impacts the gender gap in academic performance in STEM-related disciplines. The purpose of this meta-analysis was to examine the gender difference on spatial visualization between STEM and non-STEM undergraduate majors. Twenty effect sizes representing STEM majors’ spatial ability and 14 effect sizes representing non-STEM majors’ spatial ability were included in the present meta-analysis. Results indicated that the overall effect size equalled 0.613 (95% CI?=?[0.444, 0.782]) in STEM and 0.481 (95% CI?=?[0.359, 0.604]) in non-STEM. These findings showed that the gender differences on spatial visualization were statistically significant in both STEM and non-STEM majors. The gender difference on spatial visualization in STEM was larger than the gender difference on spatial visualization in non-STEM. In addition, male students’ spatial visualization ability was higher than female students’ spatial visualization in both STEM and non-STEM majors.     

DNA methylation-regulated microRNA pathways in ovarian serous cystadenocarcinoma: A meta-analysis

Epigenetic regulation has been linked to the initiation and progression of cancer. Aberrant expression of microRNAs (miRNAs) is one such mechanism that can activate or silence oncogenes (OCGs) and tumor suppressor genes (TSGs) in cells. A growing number of studies suggest that miRNA expression can be regulated by methylation modification, thus triggering cancer development. However, there is no comprehensive in silico study concerning miRNA regulation by direct DNA methylation in cancer. Ovarian serous cystadenocarcinoma (OSC) was therefore chosen as a tumor model for the present work.Twelve batches of OSC data, with at least 35 patient samples in each batch, were obtained from The Cancer Genome Atlas (TCGA) database. The Spearman rank correlation coefficient (SRCC) was used to quantify the correlation between the CpG DNA methylation level and miRNA expression level. Meta-analysis was performed to reduce the effects of biological heterogeneity among different batches. MiRNA-target interactions were also inferred by computing SRCC and meta-analysis to assess the correlation between miRNA expression and cancer-associated gene expression and the interactions were further validated by a query against the miRTarBase database.A total of 26 potential epigenetic-regulated miRNA genes that can target OCGs or TSGs in OSC were found to show biological relevance between DNA methylation and miRNA gene expression. Furthermore, some of the identified DNA-methylated miRNA genes; for instance, the miR-200 family, were previously identified as epigenetic-regulated miRNAs and correlated with poor survival of ovarian cancer. We also found that several miRNA target genes, BTG3, NDN, HTRA3, CDC25A, and HMGA2 were also related to the poor outcomes in ovarian cancer.The present study proposed a systematic strategy to construct highly confident epigenetic-regulated miRNA pathways for OSC. The findings are validated and are in line with the literature. The inclusion of direct DNA methylated miRNA events may offer another layer of explanation that along with genetics can give a better understanding of the carcinogenesis process.     

Meta-analysis for ranked discovery datasets: theoretical framework and empirical demonstration for microarrays

The combination of results from different large-scale datasets of multidimensional biological signals (such as gene expression profiling) presents a major challenge. Methodologies are needed that can efficiently combine diverse datasets, but can also test the extent of diversity (heterogeneity) across the combined studies. We developed METa-analysis of RAnked DISCovery datasets (METRADISC), a generalized meta-analysis method for combining information across discovery-oriented datasets and for testing between-study heterogeneity for each biological variable of interest. The method is based on non-parametric Monte Carlo permutation testing. The tested biological variables are ranked in each study according to the level of statistical significance. METRADISC tests for each biological variable of interest its average rank and the between-study heterogeneity of the study-specific ranks. After accounting for ties and differences in tested variables across studies, we randomly permute the ranks of each study and the simulated metrics of average rank and heterogeneity are calculated. The procedure is repeated to generate null distributions for the metrics. The use of METRADISC is demonstrated empirically using gene expression data from seven studies comparing prostate cancer cases and normal controls. We offer a new tool for combining complex datasets derived from massive testing, discovery-oriented research and for examining the diversity of results across the combined studies.     

Sonolysis of per- and poly fluoroalkyl substances (PFAS): A meta-analysis

Human ingestion of per- and polyfluoroalkyl substances (PFAS) from contaminated food and water is linked to the development of several cancers, birth defects and other illnesses. The complete mineralisation of aqueous PFAS by ultrasound (sonolysis) into harmless inorganics has been demonstrated in many studies. However, the range and interconnected nature of reaction parameters (frequency, power, temperature etc.), and variety of reaction metrics used, limits understanding of degradation mechanisms and parametric trends. This work summarises the state-of-the-art for PFAS sonolysis, considering reaction mechanisms, kinetics, intermediates, products, rate limiting steps, reactant and product measurement techniques, and effects of co-contaminants. The meta-analysis showed that mid-high frequency (100 – 1,000 kHz) sonolysis mechanisms are similar, regardless of reaction conditions, while the low frequency (20 – 100 kHz) mechanisms are specific to oxidative species added, less well understood, and generally slower than mid-high frequency mechanisms. Arguments suggest that PFAS degradation occurs via adsorption (not absorption) at the bubble interface, followed by headgroup cleavage. Further mechanistic steps toward mineralisation remain to be proven. For the first time, complete stoichiometric reaction equations are derived for perfluorooctanoic acid (PFOA) and perfluorooctane sulfonic acid (PFOS) sonolysis, which add H₂ as a reaction product and consider CO an intermediate. Fluorinated intermediate products are derived for common, and more novel PFAS, and a naming system proposed for novel perfluoroether carboxylates. The meta-analysis also revealed the transition between pseudo first and zero order PFOA/S kinetics commonly occurs at 15 – 40 µM. Optimum values of; ultrasonic frequency (300 – 500 kHz), concentration (>15 – 40 μM), temperature (≈20 °C), and pH range (3.2 – 4) for rapid PFOX degradation are derived by evaluation of prior works, while optimum values for the dilution factor applied to PFAS containing firefighting foams and applied power require further work. Rate limiting steps are debated and F⁻ is shown to be rate enhancing, while SO₄²⁻ and CO₂ by products are theorised to be rate limiting. Sonolysis was compared to other PFAS destructive technologies and shown to be the only treatment which fully mineralises PFAS, degrades different PFAS in order of decreasing hydrophobicity, is parametrically well studied, and has low-moderate energy requirements (several kWh g⁻¹ PFAS). It is concluded that sonolysis of PFAS in environmental samples would be well incorporated within a treatment train for improved efficiency.