The identification of gene signature and critical pathway associated with childhood-onset type 2 diabetes

In general, type 2 diabetes (T2D) usually occurs in middle-aged and elderly people. However, the incidence of childhood-onset T2D has increased all across the globe. Therefore, it is very important to determine the molecular and genetic mechanisms of childhood-onset T2D. In this study, the dataset GSE9006 was downloaded from the GEO (Gene Expression Omnibus database); it includes 24 healthy children, 43 children with newly diagnosed Type 1 diabetes (T1D), and 12 children with newly diagnosed T2D. These data were used for differentially expressed genes (DGEs) analysis and weighted co-expression network analysis (WGCNA). We identified 192 up-regulated genes and 329 down-regulated genes by performing DEGs analysis. By performing WGGNA, we found that blue module (539 genes) was highly correlated to cyan module (97 genes). Gene ontology (GO) and pathway enrichment analyses were performed to figure out the functions and related pathways of genes, which were identified in the results of DEGs and WGCNA. Genes with conspicuous logFC and in the high correlated modules were input into GeneMANIA, which is a plugin of Cytoscape application. Thus, we constructed the protein-protein interaction (PPI) network (92 nodes and 254 pairs). Eventually, we analyzed the transcription factors and references related to genes with conspicuous logFC or high-degree genes, which were present in both the modules of WGCNA and PPI network. Current research shows that EGR1 and NAMPT can be used as marker genes for childhood-onset T2D. Gestational diabetes and chronic inflammation are risk factors that lead to the development of childhood-onset T2D

Neutrophil

<p>https://github.com/wu-yc/neutrophil</p&gt

scProgram: quantifying transcriptional programs at the single-cell resolution

<h1>scProgram</h1> <p><code>scProgram</code> is a R package for quantifying transcriptional programs at the single-cell resolution</p> <h2><a href="https://github.com/wu-yc/scProgram#requirements"></a>Requirements</h2> <div> <div> <div> <div> <div> </div> Explain</div> </div> </div> <pre><code>install.packages("philentropy") install.packages("Seurat") #Please use the version ≤4 install.packages("data.table") install.packages("dplyr") install.packages("tidyverse") install.packages("Matrix") install.packages("pheatmap") install.packages("RColorBrewer") install.packages("clusterProfiler") install.packages("ggplot2") </code></pre> <div> </div> </div> <h2><a href="https://github.com/wu-yc/scProgram#install"></a>Install</h2> <div> <pre><code>devtools::install_github("wu-yc/scProgram") </code></pre> <div> </div> </div> <h2><a href="https://github.com/wu-yc/scProgram#quick-start"></a>Quick Start</h2> <p><code>scProgram</code> generally supports the quantification and visualization of transcriptional programs at the single-cell resolution.</p> <h3><a href="https://github.com/wu-yc/scProgram#1-load-packages-and-demo-data"></a>1. Load packages and demo data</h3> <p>The demo data is the dataset of Peripheral Blood Mononuclear Cells (PBMC) from 10X Genomics open access dataset (~2,700 single cells, also used by Seurat tutorial). The demo Seurat object can be downloaded from <a href="https://figshare.com/articles/dataset/scProgram_-_pbmc_demo_rda/13670038" rel="nofollow">here</a>.</p> <div> <pre><code>load(file = "pbmc_demo.rda") library(scProgram) </code></pre> <div> </div> </div> <h3><a href="https://github.com/wu-yc/scProgram#2-get-the-features-of-each-cluster"></a>2. Get the features of each cluster</h3> <div> <pre><code>FeatureMatrix = GetFeatures(obj = countexp.Seurat, group.by = "ident", genenumber = 50, pct_exp = 0.1, mode = "fast") </code></pre> <div> </div> </div> <p><code>obj</code> is a Seurat object containing the UMI count matrix.</p> <p><code>group.by</code> is the cell cluster or identity column of the given Seurat object.</p> <p><code>genenumber</code> is the number of featured genes of each cluster.</p> <p><code>pct_exp</code> is the percentage of the gene expressed in each cell cluster.</p> <p><code>mode</code> supports <code>fast</code>, <code>standard</code>, in which fast is the default method.</p> <h3><a href="https://github.com/wu-yc/scProgram#3-plot-the-heatmap-of-the-features-of-each-cluster"></a>3. Plot the heatmap of the features of each cluster</h3> <div> <pre><code>HeatFeatures(obj = countexp.Seurat, features = FeatureMatrix, group.by = "ident", show_rownames = F, show_colnames = T, cols = c("white","white", "white", "#52A85F")) </code></pre> <div> </div> </div> <p><code>obj</code> is a Seurat object containing the UMI count matrix.</p> <p><code>features</code> is the output matrix generated by GetFeatures function.</p> <p><code>group.by</code> is the cell cluster or identity column of the given Seurat object.</p> <p><a href="https://github.com/wu-yc/scProgram/blob/main/scg_fig1.png" target="_blank" rel="noopener noreferrer"></a></p> <h3><a href="https://github.com/wu-yc/scProgram#4-get-the-features-of-each-cluster"></a>4. Get the features of each cluster</h3> <div> <pre><code>GetProgram(features = FeatureMatrix, geneset = "KEGG", pvalue_cutoff = 0.05, cols = c("#F47E5D", "#CA3D74", "#7F2880", "#463873"), plot_term_number =3) </code></pre> <div> </div> </div> <p><code>features</code> is the output matrix generated by GetFeatures function.</p> <p><code>geneset</code> supports <code>KEGG</code> and <code>HALLMARK</code></p> <p><code>pvalue_cutoff</code> is the cutoff value for the enrichment analysis.</p> <p><a href="https://github.com/wu-yc/scProgram/blob/main/scg_fig2.png" target="_blank" rel="noopener noreferrer"></a></p> <h2><a href="https://github.com/wu-yc/scProgram#citations"></a>Citations</h2> <p><strong><em>scProgram</em></strong></p> <h2><a href="https://github.com/wu-yc/scProgram#author"></a>Author</h2> <p>Ying-Cheng Wu <a href="mailto:[email protected]">[email protected]</a></p> <p>Copyright (C) 2021-2999 Gao Lab @ Fudan University.</p&gt

The efficacy of chimeric antigen receptor (CAR) immunotherapy in animal models for solid tumors: A systematic review and meta-analysis

<div><p>Background</p><p>Most recently, an emerging theme in the field of tumor immunology predominates: chimeric antigen receptor (CAR) therapy in treating solid tumors. The number of related preclinical trials was surging. However, an evaluation of the effects of preclinical studies remained absent. Hence, a meta-analysis was conducted on the efficacy of CAR in animal models for solid tumors.</p><p>Methods</p><p>The authors searched PubMed/Medline, Embase, and Google scholar up to April 2017. HR for survival was extracted based on the survival curve. The authors used fixed effect models to combine the results of all the trials. Heterogeneity was assessed by I-square statistic. Quality assessment was conducted following the Stroke Therapy Academic Industry Roundtable standard. Publication bias was assessed using Egger's test.</p><p>Results</p><p>Eleven trials were included, including 54 experiments with a total of 362 animals involved. CAR immunotherapy significantly improved the survival of animals (HR: 0.25, 95% CI: 0.13–0.37, P < 0.001). The quality assessment revealed that no study reported whether allocation concealment and blinded outcome assessment were conducted, and only five studies implemented randomization.</p><p>Conclusions</p><p>This meta-analysis indicated that CAR therapy may be a potential clinical strategy in treating solid tumors.</p></div

Experimental and Numerical Study of an Innovative Infill Web-Strips Steel Plate Shear Wall with Rigid Beam-to-Column Connections

Steel plate shear walls (SPSWs) offer good energy dissipation capability when subjected to seismic forces as a robust lateral load resisting structure. This research investigated the cyclic behaviors of innovative infill web-strips (IWS-SPSW) and conventional unstiffened steel plate shear (USPSW) experimentally and numerically. As a result, two specimens of a 1:3 scale three-story single-bay IWS-SPSW and USPSW were fabricated and tested under cyclic lateral loading. Rigid moment-resistant connections were used for the steel plate shear wall beam-column connection. The steel shear walls with infill web strips showed high ductility and less shear load-bearing than the USPSW. The hysteresis results showed that the IWS-SPSW had high energy dissipation with no severe beam-columns damages. On the other hand, the USPSW displayed severe post-buckling, infill panel cracks, and first-floor column damages. Moreover, the IWS-SPSW shear strength did not fall in the test specimen beyond 2.5% average story drift, where the structure exhibited great seismic behavior. FE models were created and validated with experimental data. It has been proven that the infill web-strips can affect an SPSW system’s high performance and overall energy dissipation. From a parametric study, the material features of the infill web-strips, such as steel strength and thickness, can enhance the system’s impact even more

Forest plot of the meta-analysis for the hazard ratio.

<p>Forest plot of the meta-analysis for the hazard ratio.</p

Subgroup analysis by cancer type, target, generation, animal model, and publication year.

<p>Subgroup analysis by cancer type, target, generation, animal model, and publication year.</p