A comparison of two models for detecting inconsistency in network meta-analysis.

The application of network meta-analysis is becoming increasingly widespread, and for a successful implementation, it requires that the direct comparison result and the indirect comparison result should be consistent. Because of this, a proper detection of inconsistency is often a key issue in network meta-analysis as whether the results can be reliably used as a clinical guidance. Among the existing methods for detecting inconsistency, two commonly used models are the design-by-treatment interaction model and the side-splitting models.

Development, validation and visualization of a web-based nomogram for predicting risk of new-onset diabetes after percutaneous coronary intervention.

Simple and practical tools for screening high-risk new-onset diabetes after percutaneous coronary intervention (PCI) (NODAP) are urgently needed to improve post-PCI prognosis. We aimed to evaluate the risk factors for NODAP and develop an online prediction tool using conventional variables based on a multicenter database. China evidence-based Chinese medicine database consisted of 249, 987 patients from 4 hospitals in mainland China.

Elevated extracellular matrix protein 1 in circulating extracellular vesicles supports breast cancer progression under obesity conditions.

The cargo content in small extracellular vesicles (sEVs) changes under pathological conditions. Our data shows that in obesity, extracellular matrix protein 1 (ECM1) protein levels are significantly increased in circulating sEVs, which is dependent on integrin-β2. Knockdown of integrin-β2 does not affect cellular ECM1 protein levels but significantly reduces ECM1 protein levels in the sEVs released by these cells.

Detecting the skewness of data from the five-number summary and its application in meta-analysis.

For clinical studies with continuous outcomes, when the data are potentially skewed, researchers may choose to report the whole or part of the five-number summary (the sample median, the first and third quartiles, and the minimum and maximum values) rather than the sample mean and standard deviation. In the recent literature, it is often suggested to transform the five-number summary back to the sample mean and standard deviation, which can be subsequently used in a meta-analysis. However, if a study contains skewed data, this transformation and hence the conclusions from the meta-analysis are unreliable.

Efficacy and safety of Chinese patent medicine Xiao Yao San in polycystic ovary syndrome: A systematic review and meta-analysis.

Ethnopharmacological Relevance: Polycystic ovary syndrome (PCOS) is one of the most common endocrine-metabolic disorders in women of reproductive age worldwide. Previous studies using randomized controlled trials (RCTs) have revealed that Xiao Yao San (XYS), a classic Chinese patent medicine formula, can effectively treat PCOS. However, the entire evidence has yet to be systematically summarized.

Omics approach to reveal the effects of obesity on the protein profiles of the exosomes derived from different adipose depots.

Background: Obesity affects the cargo packaging of the adipocyte-derived exosomes. Furthermore, adipocytes in different adipose tissues have different genetic makeup, the cargo contents of the exosomes derived from different adipose tissues under obesity conditions should be different, and hence their impacts on the pathophysiological conditions.

Methods And Results: iTRAQ-based quantitative proteomics show that obesity has more prominent effects on the protein profiles of the exosomes derived from subcutaneous adipose tissue (SAT-Exos) in the high fat diet-induced obesity (DIO) mice than those derived from epididymal adipose tissue (EAT-Exos) and visceral adipose tissue (VAT-Exos).

A new method for estimating Sharpe ratio function via local maximum likelihood.

The Sharpe ratio function is a commonly used risk/return measure in financial econometrics. To estimate this function, most existing methods take a two-step procedure that first estimates the mean and volatility functions separately and then applies the plug-in method. In this paper, we propose a direct method via local maximum likelihood to simultaneously estimate the Sharpe ratio function and the negative log-volatility function as well as their derivatives.

scDLC: a deep learning framework to classify large sample single-cell RNA-seq data.

Background: Using single-cell RNA sequencing (scRNA-seq) data to diagnose disease is an effective technique in medical research. Several statistical methods have been developed for the classification of RNA sequencing (RNA-seq) data, including, for example, Poisson linear discriminant analysis (PLDA), negative binomial linear discriminant analysis (NBLDA), and zero-inflated Poisson logistic discriminant analysis (ZIPLDA). Nevertheless, few existing methods perform well for large sample scRNA-seq data, in particular when the distribution assumption is also violated.

Development of a Method for Quantitative Evaluation of Facial Swelling in a Rat Model of Cerebral Ischemia by Facial Image Processing.

A quantitative method for the evaluation of facial swelling in rats with middle cerebral artery occlusion (MCAO) was established using a mathematical method for the first time. The rat model of MCAO was established bilateral common carotid artery ligation. Three groups of rats with the same baseline were selected (model group, positive drug group, and control group) according to their behavioral score and body weight 24 h after surgery.

Meta-Analyzing Multiple Omics Data With Robust Variable Selection.

High-throughput omics data are becoming more and more popular in various areas of science. Given that many publicly available datasets address the same questions, researchers have applied meta-analysis to synthesize multiple datasets to achieve more reliable results for model estimation and prediction. Due to the high dimensionality of omics data, it is also desirable to incorporate variable selection into meta-analysis.

Meta-analysis with zero-event studies: a comparative study with application to COVID-19 data.

Background: Meta-analysis is a statistical method to synthesize evidence from a number of independent studies, including those from clinical studies with binary outcomes. In practice, when there are zero events in one or both groups, it may cause statistical problems in the subsequent analysis.

Methods: In this paper, by considering the relative risk as the effect size, we conduct a comparative study that consists of four continuity correction methods and another state-of-the-art method without the continuity correction, namely the generalized linear mixed models (GLMMs).

Estimating the reference interval from a fixed effects meta-analysis.

A reference interval provides a basis for physicians to determine whether a measurement is typical of a healthy individual. It can be interpreted as a prediction interval for a new individual from the overall population. However, a reference interval based on a single study may not be representative of the broader population.

[Efficacy of curcumin in the treatment of oral submucous fibrosis: a Meta-analysis].

Objectives: This study was performed to review the efficacy of curcumin in the treatment of oral submucous fibrosis systematically.

Methods: We searched seven databases, including Web of Science, PubMed, EBSCO, The Cochrane Library, CNKI, WanFang Data, and VIP, to obtain randomized controlled trials related to the treatment of oral submucous fibrosis by curcumin. Each database was searched from inception to 30 June 2019.

Efficacy and safety of Si-Jun-Zi-Tang-based therapies for functional (non-ulcer) dyspepsia: a meta-analysis of randomized controlled trials.

Background: The traditional Chinese medicine formula Si-Jun-Zi-Tang (SJZT) has a long history of application in the treatment of functional dyspepsia (non-ulcer dyspepsia, FD)-like symptoms. SJZT-based therapies have been claimed to be beneficial in managing FD. This study aimed to assess the efficacy and safety of SJZT-based therapies in treating FD by meta-analysis.

Optimally estimating the sample standard deviation from the five-number summary.

When reporting the results of clinical studies, some researchers may choose the five-number summary (including the sample median, the first and third quartiles, and the minimum and maximum values) rather than the sample mean and standard deviation (SD), particularly for skewed data. For these studies, when included in a meta-analysis, it is often desired to convert the five-number summary back to the sample mean and SD. For this purpose, several methods have been proposed in the recent literature and they are increasingly used nowadays.

Pathway-Based Single-Cell RNA-Seq Classification, Clustering, and Construction of Gene-Gene Interactions Networks Using Random Forests.

Single-cell RNA-Sequencing (scRNA-Seq), an advanced sequencing technique, enables biomedical researchers to characterize cell-specific gene expression profiles. Although studies have adapted machine learning algorithms to cluster different cell populations for scRNA-Seq data, few existing methods have utilized machine learning techniques to investigate functional pathways in classifying heterogeneous cell populations. As genes often work interactively at the pathway level, studying the cellular heterogeneity based on pathways can facilitate the interpretation of biological functions of different cell populations.

Testing for heteroskedasticity in two-way fixed effects panel data models.

In this paper, we propose a new method for testing heteroskedasticity in two-way fixed effects panel data models under two important scenarios where the cross-sectional dimension is large and the temporal dimension is either large or fixed. Specifically, we will develop test statistics for both cases under the conditional moment framework, and derive their asymptotic distributions under both the null and alternative hypotheses. The proposed tests are distribution free and can easily be implemented using the simple auxiliary regressions.

A statistical normalization method and differential expression analysis for RNA-seq data between different species.

Background: High-throughput techniques bring novel tools and also statistical challenges to genomic research. Identifying genes with differential expression between different species is an effective way to discover evolutionarily conserved transcriptional responses. To remove systematic variation between different species for a fair comparison, normalization serves as a crucial pre-processing step that adjusts for the varying sample sequencing depths and other confounding technical effects.

The association of depression following percutanous coronary intervention with adverse cardiovascular events: Protocol for a systematic review and meta-analysis.

Introduction: Percutanous coronary intervention (PCI) has been increasingly used for patients suffered from severe coronary artery disease. However, physical trauma and potential adverse events related to the procedure often result in detrimental psychological stress. Accumulating evidences have shown that depression is closely related to coronary artery disease.

Weight reduction and cardiovascular benefits: Protocol for a systematic review and meta-analysis.

Background: There is widespread obesity paradox in cardiovascular diseases, the cardiovascular influence from weight management remains controversial. Moreover, previous publications indicating that different weight reduction extent might lead to various results. Thus, it is of importance to reassess the cardiovascular benefits of weight management strategies.

Diagonal likelihood ratio test for equality of mean vectors in high-dimensional data.

We propose a likelihood ratio test framework for testing normal mean vectors in high-dimensional data under two common scenarios: the one-sample test and the two-sample test with equal covariance matrices. We derive the test statistics under the assumption that the covariance matrices follow a diagonal matrix structure. In comparison with the diagonal Hotelling's tests, our proposed test statistics display some interesting characteristics.

Longitudinal study of esophageal mucosal damage after esophagectomy and gastric interposition: relationship between reflux-related mucosal injury and Notch signaling.

Background: Esophagectomy with gastric interposition could serve as a good human reflux model to study the molecular pathogenesis of esophageal mucosal damage induced by gastroesophageal reflux. This study was to investigate the role of Notch signaling in reflux injury of esophageal mucosa.

Methods: Patients undergoing Ivor-Lewis esophagectomy for early stage esophageal squamous cell carcinoma were included.

Classifying next-generation sequencing data using a zero-inflated Poisson model.

Motivation: With the development of high-throughput techniques, RNA-sequencing (RNA-seq) is becoming increasingly popular as an alternative for gene expression analysis, such as RNAs profiling and classification. Identifying which type of diseases a new patient belongs to with RNA-seq data has been recognized as a vital problem in medical research. As RNA-seq data are discrete, statistical methods developed for classifying microarray data cannot be readily applied for RNA-seq data classification.

A Comparison of Methods for Estimating the Determinant of High-Dimensional Covariance Matrix.

The determinant of the covariance matrix for high-dimensional data plays an important role in statistical inference and decision. It has many real applications including statistical tests and information theory. Due to the statistical and computational challenges with high dimensionality, little work has been proposed in the literature for estimating the determinant of high-dimensional covariance matrix.

GD-RDA: A New Regularized Discriminant Analysis for High-Dimensional Data.

High-throughput techniques bring novel tools and also statistical challenges to genomic research. Identification of which type of diseases a new patient belongs to has been recognized as an important problem. For high-dimensional small sample size data, the classical discriminant methods suffer from the singularity problem and are, therefore, no longer applicable in practice.