After several decades’ development, meta-analysis has become the pillar of evidence-based medicine. However, heterogeneity is still the threat to the validity and quality of such studies. Currently, Q and its descendant I² (I square) tests are widely used as the tools for heterogeneity evaluation. The core mission of this kind of test is to identify data sets from similar populations and exclude those are from different populations. Although Q and I² are used as the default tool for heterogeneity testing, the work we present here demonstrates that the robustness of these two tools is questionable. We simulated a strictly normalized population S. The simulation successfully represents randomized control trial data sets, which fits perfectly with the theoretical distribution (experimental group: p = 0.37, control group: p = 0.88). And we randomly generate research samples Si that fits the population with tiny distributions. In short, these data sets are perfect and can be seen as completely homogeneous data from the exactly same population. If Q and I² are truly robust tools, the Q and I² testing results on our simulated data sets should not be positive. We then synthesized these trials by using fixed model. Pooled results indicated that the mean difference (MD) corresponds highly with the true values, and the 95% confidence interval (CI) is narrow. But, when the number of trials and sample size of trials enrolled in the meta-analysis are substantially increased; the Q and I² values also increase steadily. This result indicates that I² and Q are only suitable for testing heterogeneity amongst small sample size trials, and are not adoptable when the sample sizes and the number of trials increase substantially. Every day, meta-analysis studies which contain flawed data analysis are emerging and passed on to clinical practitioners as “updated evidence”. Using this kind of evidence that contain heterogeneous data sets leads to wrong conclusion, makes chaos in clinical practice and weakens the foundation of evidence-based medicine. We suggest more strict applications of meta-analysis: it should only be applied to those synthesized trials with small sample sizes. We call upon that the tools of evidence-based medicine should keep up-to-dated with the cutting-edge technologies in data science. Clinical research data should be made available publicly when there is any relevant article published so the research community could conduct in-depth data mining, which is a better alternative for meta-analysis in many instances.

Spinal cord injury (SCI) is a devastating event with a limited hope for recovery and represents an enormous public health issue. It is crucial to understand the disturbances in the metabolic network after SCI to identify injury mechanisms and opportunities for treatment intervention. Through plasma 1H-nuclear magnetic resonance (NMR) screening, we identified 15 metabolites that made up an “Eigen-metabolome” capable of distinguishing rats with severe SCI from healthy control rats. Forty enzymes regulated these 15 metabolites in the metabolic network. We also found that 16 metabolites regulated by 130 enzymes in the metabolic network impacted neurobehavioral recovery. Using the Eigen-metabolome, we established a linear discrimination model to cluster rats with severe and mild SCI and control rats into separate groups and identify the interactive relationships between metabolic biomarkers in the global metabolic network. We identified 10 clusters in the global metabolic network and defined them as distinct metabolic disturbance domains of SCI. Metabolic paths such as retinal, glycerophospholipid, arachidonic acid metabolism; NAD–NADPH conversion process, tyrosine metabolism, and cadaverine and putrescine metabolism were included. In summary, we presented a novel interdisciplinary method that integrates metabolomics and global metabolic network analysis to visualize metabolic network disturbances after SCI. Our study demonstrated the systems biological study paradigm that integration of 1H-NMR, metabolomics, and global metabolic network analysis is useful to visualize complex metabolic disturbances after severe SCI. Furthermore, our findings may provide a new quantitative injury severity evaluation model for clinical use.

Introduction: To establish a plasma metabolomics fingerprint spectrum for severe burn patients and to use it to identify a set of biomarkers that could be used for clinical monitoring. Methods: Twenty-one severe burn patients and three healthy control individuals were enrolled in this study, and the plasma samples from patients and healthy individuals were collected for nuclear magnetic resonance (NMR) measurements. The NMR spectra were analyzed using principal component analysis (PCA) and partial least squares (PLS) in order to establish the metabolomics fingerprint representing the changes in metabolism and to select the major biomarkers. Results: NMR spectra of the plasma samples showed significant differences between burn patients and healthy individuals. Using metabolomics techniques, we found an Eigen-metabolome that consists of 12 metabolites, which are regulated by 103 enzymes in a global metabolic network. Among these metabolites, α-ketoisovaleric acid, 3-methylhistidine, and β-hydroxybutyric acid were the most important biomarkers that were significantly increased during the early stage of burn injury. These results suggest that the mitochondrial damage and carbohydrate, protein and fatty acid metabolism disturbances occur after burn injury. Our analysis also show that histone deacetylases, which are protein transcription suppressors, were remarkably increased and indicate that protein transcription was inhibited and anabolism was restrained during the early stage of burn injury. Conclusions: Metabolomics techniques based on NMR can be used to monitor metabolism in severe burn patients. Our study demonstrates that integrated ¹H-NMR metabolome and global metabolic network analysis is useful for visualizing complex metabolic disturbances after severe burn injury and may provide a new quantitative injury severity evaluation for future clinical use. Trial registration: Chinese Clinical Trial Registry ChiCTR-OCC-12002145. Registered 25 April 2012.

In February 2013, H7N9 (A/H7N9/2013_China), a novel avian influenza virus, broke out in eastern China and caused human death. It is a global priority to discover its origin and the point in time at which it will become transmittable between humans. We present here an interdisciplinary method to track the origin of H7N9 virus in China and to establish an evolutionary dynamics model for its human-to-human transmission via mutations. After comparing influenza viruses from China since 1983, we established an A/H7N9/2013_China virus evolutionary phylogenetic tree and found that the human instances of virus infection were of avian origin and clustered into an independent line. Comparing hemagglutinin (HA) and neuraminidase (NA) gene sequences of A/H7N9/2013_China viruses with all human-to-human, avian, and swine influenza viruses in China in the past 30 years, we found that A/H7N9/2013_China viruses originated from Baer’s Pochard H7N1 virus of Hu Nan Province 2010 (HA gene, EPI: 370846, similarity with H7N9 is 95.5%) and duck influenza viruses of Nanchang city 2000 (NA gene, EPI: 387555, similarity with H7N9 is 97%) through genetic re-assortment. HA and NA gene sequence comparison indicated that A/H7N9/2013_China virus was not similar to human-to-human transmittable influenza viruses. To simulate the evolution dynamics required for human-to-human transmission mutations of H7N9 virus, we employed the Markov model. The result of this calculation indicated that the virus would acquire properties for human-to-human transmission in 11.3 years (95% confidence interval (CI): 11.2–11.3, HA gene).