Yunjia Tang

The purpose of this study was to identify the clinical features and laboratory factors that are predictive of intravenous immunoglobulin (IVIG)-resistant Kawasaki disease. Multiple databases were searched for relevant studies on IVIG-resistant Kawasaki disease published from January 2002 to April 2017. Eligible studies were retrieved by manual review of the references. Stata 12 was used for the meta-analysis. Weighted mean differences and odds ratios with 95% confidence intervals were calculated for several indices. Twenty-eight studies involving 26,260 patients comprising 4442 IVIG-resistant Kawasaki disease patients and 21,818 IVIG-sensitive Kawasaki disease patients were included. The meta-analysis showed that the erythrocyte sedimentation rate (ESR) in the IVIG-resistant group was significantly higher than that in the IVIG-sensitive group, and that platelet count and hemoglobin levels were significantly lower in the IVIG-resistant group. The patients with oral mucosa alterations, cervical lymphadenopathy, swelling of the extremities, polymorphous rash, and initial administration of IVIG ≤ 4.0 days after the onset of symptoms were more likely to be IVIG resistant. CONCLUSION: The initial administration of IVIG ≤ 4.0 days after the onset of symptoms increased ESR and decreased hemoglobin and platelet counts, oral mucosa alterations, cervical lymphadenopathy, swelling of the extremities, and polymorphous rash and are the risk factors for IVIG-resistant Kawasaki disease. What is Known: • Recent reports on this topic are about aspartate aminotransferase (AST), alanine aminotransferase (ALT), gammaglutamyl transferase, total bilirubin, white blood cells, platelets, erythrocyte sedimentation rate (ESR), polymorphonuclear leukocytes (PMN), C-reactive protein (CRP), pro-brain natriuretic peptide (BNP), albumin, and sodium as the risk factors in the IVIG-resistant Kawasaki disease; however, no studies have been published on clinical features as predictors of IVIG resistance. What is New: • This meta-analysis identified the clinical features, the initial administration of IVIG ≤ 4.0 days after the onset of symptoms, and much more comprehensive laboratory indicators, such as hemoglobin, as predictors of IVIG-resistant Kawasaki disease.

Motivation: The epitranscriptome, also known as chemical modifications of RNA (CMRs), is a newly discovered layer of gene regulation, the biological importance of which emerged through analysis of only a small fraction of CMRs detected by high-throughput sequencing technologies. Understanding of the epitranscriptome is hampered by the absence of computational tools for the systematic analysis of epitranscriptome sequencing data. In addition, no tools have yet been designed for accurate prediction of CMRs in plants, or to extend epitranscriptome analysis from a fraction of the transcriptome to its entirety. Results: Here, we introduce PEA, an integrated R toolkit to facilitate the analysis of plant epitranscriptome data. The PEA toolkit contains a comprehensive collection of functions required for read mapping, CMR calling, motif scanning and discovery and gene functional enrichment analysis. PEA also takes advantage of machine learning (ML) technologies for transcriptome-scale CMR prediction, with high prediction accuracy, using the Positive Samples Only Learning algorithm, which addresses the two-class classification problem by using only positive samples (CMRs), in the absence of negative samples (non-CMRs). Hence PEA is a versatile epitranscriptome analysis pipeline covering CMR calling, prediction and annotation and we describe its application to predict N6-methyladenosine (m6A) modifications in Arabidopsis thaliana. Experimental results demonstrate that the toolkit achieved 71.6% sensitivity and 73.7% specificity, which is superior to existing m6A predictors. PEA is potentially broadly applicable to the in-depth study of epitranscriptomics. Availability and implementation: PEA Docker image is available at https://hub.docker.com/r/malab/pea, source codes and user manual are available at https://github.com/cma2015/PEA. Supplementary information: Supplementary data are available at Bioinformatics online.