Association between late gadolinium enhancement and outcome in dilated cardiomyopathy:A meta-analysis

BACKGROUND The prognostic value of late gadolinium enhancement(LGE)derived from cardiovascular magnetic resonance(CMR)is well studied,and several new metrics of LGE have emerged.However,some controversies remain;therefore,further discussion is needed,and more precise risk stratification should be explored.AIM To investigate the associations between the positivity,extent,location,and pattern of LGE and multiple outcomes in dilated cardiomyopathy(DCM).METHODS PubMed,Ovid MEDLINE,and Cochrane Library were searched for studies that investigated the prognostic value of LGE in patients with DCM.Pooled hazard ratios(HRs)and 95%confidence intervals were calculated to assess the role of LGE in the risk stratification of DCM.RESULTS Nineteen studies involving 7330 patients with DCM were included in this metaanalysis and covered a wide spectrum of DCM,with a mean left ventricular ejection fraction between 21%and 50%.The meta-analysis revealed that the presence of LGE was associated with an increased risk of multiple adverse outcomes(all-cause mortality,HR:2.14;arrhythmic events,HR:5.12;and composite endpoints,HR:2.38;all P<0.001).Furthermore,every 1%increment in the extent of LGE was associated with an increased risk of all-cause mortality.Analysis of a subgroup revealed that the prognostic value varied based on different location and pattern of LGE.Additionally,we found that LGE was a stronger predictor of arrhythmic events in patients with greater left ventricular ejection fraction.CONCLUSION LGE by CMR in patients with DCM exhibited a substantial value in predicting adverse outcomes,and the extent,location,and pattern of LGE could provide additional information for risk stratification.

GAEBic:A Novel Biclustering Analysis Method for miRNA-Targeted Gene Data Based on Graph Autoencoder

Unlike traditional clustering analysis,the biclustering algorithm works simultaneously on two dimensions of samples(row)and variables(column).In recent years,biclustering methods have been developed rapidly and widely applied in biological data analysis,text clustering,recommendation system and other fields.The traditional clustering algorithms cannot be well adapted to process high-dimensional data and/or large-scale data.At present,most of the biclustering algorithms are designed for the differentially expressed big biological data.However,there is little discussion on binary data clustering mining such as miRNA-targeted gene data.Here,we propose a novel biclustering method for miRNA-targeted gene data based on graph autoencoder named as GAEBic.GAEBic applies graph autoencoder to capture the similarity of sample sets or variable sets,and takes a new irregular clustering strategy to mine biclusters with excellent generalization.Based on the miRNA-targeted gene data of soybean,we benchmark several different types of the biclustering algorithm,and find that GAEBic performs better than Bimax,Bibit and the Spectral Biclustering algorithm in terms of target gene enrichment.This biclustering method achieves comparable performance on the high throughput miRNA data of soybean and it can also be used for other species.