Objective To summarize and review the heterogeneity of bone marrow derived stem cells (BMDSCs) and its formation mechanism and significance, and to analyze the possible roles and mechanisms in intestinal epithel ial reconstruction. Methods The related l iterature about BMDSCs heterogeneity and its role in intestinal epithel ial repair was reviewed and analyzed. Results The heterogeneity of BMDSCs provided better explanations for its multi-potency. The probable mechanisms of BMDSCs to repair intestinal epithel ium included direct implantation into intestinal epithel ium, fusion between BMDSCs and intestinal stem cells, and promotion of injury microcirculation reconstruction. Conclusion BMDSCs have a bright future in gastrointestinal injury caused by inflammatory bowl disease and regeneration.
Neutrophils are the most abundant myeloid-derived eukaryotic cells in human blood with increasingly recognized as important regulators of cancer progression. However, the functional importance of tumor-associated neutrophils (TANs) is often overlooked due to their short-lived, terminally differentiated, non-proliferative properties. In recent years, a wealth of evidences obtained from experimental tumor models and cancer patients had indicated that TANs had obvious heterogeneity in morphology and function, and TANs had dual functions of pro- and anti-tumor in cancer patients. This review provides an adequate overview of the heterogeneity and distinct roles of neutrophils.
Through collecting and synthesizing the paper concerning the method of dealing with heterogeneity in the meta analysis, to introduce the multi-levels statistical models, such as meta regression and baseline risk effect model based on random effects, and random effects model based on hierarchical bayes, and to introduce their application of controlling the meta analysis heterogeneity. The multi-levels statistical model will decompose the single random error in the traditional model to data structure hierarchical. Its fitting effect can not only make the meta-analysis result more robust and reasonable, but also guide clinical issues through the interpretation of association variable.
This paper is to discuss the research of heterogeneity in Meta-analysis, including the definition of the heterogeneity in Meta-analysis and classification it into clinical heterogeneity, methodological heterogeneity and statistical heterogeneity, the strategies for diminishing clinical heterogeneity and methodological heterogeneity, the five testing methods in statistical heterogeneity (Q statistic, I2 statistic, H statistic, Galbraith plot and L’Abbe plot) and the examples and applying conditions of the five testing methods, classification of meta-analysis into exploratory meta-analysis and analytic meta-analysis according if the meta-analysis has heterogeneity, and the strategies and the flowchart when existing the heterogeneity in meta-analysis.
Objective To investigate confidence interval estimation for the amount of heterogeneity in meta-analysis. Methods On the basis of BT’s method, the approximate Q-statistic distribution following linear transformation of Chi-square was applied to improve the accuracy of Q-statistic distribution, and to obtain the confidence interval for the amount of heterogeneity in meta-analysis. Results In case, the Q1 distribution obtained 95%CI 0.07 to 2.20, while the Q2 distribution obtained 95%CI 0.00 to 1.41; The proposed method Q2 narrowed down the range of confidence interval. Conclusion On account of improving the accuracy of Q-statistic distribution, the proposed method effectively strengthens the coverage probabilities of the confidence interval for the amount of heterogeneity. And the proposed method can also improve the precision of the confidence interval estimation for the amount of heterogeneity.
ObjectiveTo understand the single-cell RNA sequencing (scRNA-seq) and its research progress in the tumor microenvironment (TME) of breast cancer, in order to provide new ideas and directions for the research and treatment of breast cancer. MethodThe development of scRNA-seq technology and its related research literature in breast cancer TME at home and abroad in recent years was reviewed. ResultsThe scRNA-seq was a quantum technology in high-throughput sequencing of mRNA at the cellular level, and had become a powerful tool for studying cellular heterogeneity when tissue samples were fewer. While capturing rare cell types, it was expected to accurately describe the complex structure of the TME of breast cancer. ConclusionsAfter decades of development, scRNA-seq has been widely used in tumor research. Breast cancer is a malignant tumor with high heterogeneity. The application of scRNA-seq in breast cancer research can better understand its tumor heterogeneity and TME, and then promote development of personalized diagnosis and treatment.
Many meta-analysis studies evaluate rates as parameter to assess the overall estimate of effects. However, none of these studies address systematic approaches for the meta-analysis of rates. This paper outlines the conditions, analysis and software operation procedures for the meta-analysis of rates. It also compares different operation procedures of three types of commonly-used R software (Comprehensive Meta-Analysis, Stata and MetaAnalyst) through real application examples. The biggest challenge for the meta-analysis of rates is to determine whether rates can be pooled, and how to evaluate heterogeneity between studies' outcomes needs further discussion.
Diabetic retinopathy (DR) is one of the main causes of vision loss and irreversible blindness in the working-age population, closely regarded as the destruction of the retinal neurovascular unit (NVU). As an important component of the NVU, retinal microglia (RMG) plays a vital role in the progression of DR. In recent years, single-cell RNA sequencing (scRNA-seq) technology has emerged as an important tool in transcriptomic analysis. This latest method reveals the heterogeneity and complexity of RNA transcriptional profiles within individual cells, as well as the composition of different cell types and functions. Utilizing scRNA-seq technology, researchers have further revealed the role of RMG in the occurrence and development of DR, discovering phenotypic heterogeneity, regional heterogeneity, and cell-to-cell communication in RMG. It is anticipated that in the future, more omics technologies and multi-omics correlation analysis methods will be applied to DR and even other ophthalmic diseases, exploring potential diagnostic and therapeutic targets, providing different perspectives for the clinical diagnosis, treatment, and scientific research of DR, and truly promoting clinical translation through technological innovation, thereby benefiting patients with DR diseases.
Objective To analyze the heterogeneity of systematic reviews (SRs)/Meta-analysis on traditional Chinese medicine (TCM), and explore strategies for addressing heterogeneity correctly during the process of conducting TCM related to systematic reviews (SRs). Methods Both electronic and hand searches were used to identify TCM SRs in CBM, CNKI, VIP database, and Chinese Journal of Evidence-Based Medicine. Two researchers performed data extracting and heterogeneity evaluation independently. Results A total of 115 TCM SRs were included, involving 17 types of diseases, among which the cardiovascular and cerebrovascular diseases were the most addressed (n=36, 31.30%). There were 35.65% (n=41) of SRs which integrated two or more types of studies; interventions of the included studies were inconsistent in 53.91% (n=62) of TCM SRs; control groups of the included studies were completely different in 60 (52.17%) SRs; and 8.7% (n=10) of SRs failed to investigate heterogeneity in the process of synthesis analysis. Conclusion The heterogeneity is common in TCM related to SRs, and the most addressed is clinical heterogeneity. Addressing heterogeneity incorrectly would downgrade the quality of TCM related to SRs.
The assumption of fixed-effects model is based on that the true effect of the each trial is same. However, the assumption of random-effects model is based on that the true effect of included trials is normal distributed. The total variance is equal to the sum of within-trial variance and between-trial variance under the random-effects model. There are many estimators of the between-trial variance. The aim of this paper is to give a brief introduction of the estimators of between-trial variance in trial sequential analysis for random-effects model.