Electroanatomic voltage mapping and characterisation imaging for “right ventricle arrhythmic syndromes” beyond the arrhythmia definition: a comprehensive review

Zoppo, Franco; Gagno, Giulia; Perazza, Luca; Cocciolo, Andrea; Mugnai, Giacomo; Vaccari, Diego; Calzolari, Vittorio

doi:10.1007/s10554-021-02221-3

Cited by 3 publications

(1 citation statement)

References 52 publications

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“…Since the beginning of the 21st century, artificial intelligence (AI) has progressively permeated all aspects of our existence, particularly in the medical field [ 28 ]. With continued exploration of the potential of artificial intelligence, AI-based clinical research will result in a paradigm shift in medical practice, thus significantly improving the survival rate of many diseases including cancer [ 29 ]. Currently, the diagnosis of HF is primarily based on the clinical signs and symptoms of patients, with echocardiography and chest radiography serving as the most common auxiliary tests.…”

Section: Introductionmentioning

confidence: 99%

Identification of crucial genes related to heart failure based on GEO database

Chen

Xue

Yan

et al. 2023

BMC Cardiovasc Disord

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Background The molecular biological mechanisms underlying heart failure (HF) remain poorly understood. Therefore, it is imperative to use innovative approaches, such as high-throughput sequencing and artificial intelligence, to investigate the pathogenesis, diagnosis, and potential treatment of HF. Methods First, we initially screened Two data sets (GSE3586 and GSE5406) from the GEO database containing HF and control samples from the GEO database to establish the Train group, and selected another dataset (GSE57345) to construct the Test group for verification. Next, we identified the genes with significantly different expression levels in patients with or without HF and performed functional and pathway enrichment analyses. HF-specific genes were identified, and an artificial neural network was constructed by Random Forest. The ROC curve was used to evaluate the accuracy and reliability of the constructed model in the Train and Test groups. Finally, immune cell infiltration was analyzed to determine the role of the inflammatory response and the immunological microenvironment in the pathogenesis of HF. Results In the Train group, 153 significant differentially expressed genes (DEGs) associated with HF were found to be abnormal, including 81 down-regulated genes and 72 up-regulated genes. GO and KEGG enrichment analyses revealed that the down-regulated genes were primarily enriched in organic anion transport, neutrophil activation, and the PI3K-Akt signaling pathway. The upregulated genes were mainly enriched in neutrophil activation and the calcium signaling. DEGs were identified using Random Forest, and finally, 16 HF-specific genes were obtained. In the ROC validation and evaluation, the area under the curve (AUC) of the Train and Test groups were 0.996 and 0.863, respectively. Conclusions Our research revealed the potential functions and pathways implicated in the progression of HF, and designed an RNA diagnostic model for HF tissues using machine learning and artificial neural networks. Sensitivity, specificity, and stability were confirmed by ROC curves in the two different cohorts.

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Section: Introductionmentioning

confidence: 99%

Identification of crucial genes related to heart failure based on GEO database

Chen

Xue

Yan

et al. 2023

BMC Cardiovasc Disord

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Electroanatomic mapping in athletes: Why and when. An expert opinion paper from the Italian Society of Sports Cardiology

Russo

Compagnucci

Zorzi

et al. 2023

International Journal of Cardiology

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Identification of Hub Diagnostic Biomarkers and Candidate Therapeutic Drugs in Heart Failure

Guo¹,

Ning²,

Zhang³

et al. 2022

IJGM

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Purpose The objective of this study was to identify the potential regulatory mechanisms, diagnostic biomarkers, and therapeutic drugs for heart failure (HF). Methods Differentially expressed genes (DEGs) between HF and non-failing donors were screened from the GSE57345, GSE5406, and GSE3586 datasets. Database for Annotation Visualization and Integrated Discovery and Metascape were used for Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses respectively. The GSE57345 dataset was used for weighted gene co-expression network analysis (WGCNA). The intersecting hub genes from the DEGs and WGCNA were identified and verified with the GSE5406 and GSE3586 datasets. The diagnostic value of the hub genes was calculated through receiver operating characteristic analysis and net reclassification index (NRI). Gene set enrichment analysis (GSEA) was used to filter out the signaling pathways associated with the hub genes. SYBYL 2.1 was used for molecular docking of hub targets and potential HF drugs obtained from the connection map. Results Functional annotation of the DEGs showed enrichment of negative regulation of angiogenesis, endoplasmic reticulum stress response, and heart development. PTN, LUM, ISLR, and ASPN were identified as the hub genes of HF. GSEA showed that the key genes were related to the transforming growth factor-β (TGF-β) and Wnt signaling pathways. Sirolimus, LY-294002, and wortmannin have been confirmed as potential drugs for HF. Conclusion We identified new hub genes and candidate therapeutic drugs for HF, which are potential diagnostic, therapeutic and prognostic targets and warrant further investigation.

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Electroanatomic voltage mapping and characterisation imaging for “right ventricle arrhythmic syndromes” beyond the arrhythmia definition: a comprehensive review

Cited by 3 publications

References 52 publications

Identification of crucial genes related to heart failure based on GEO database

Identification of crucial genes related to heart failure based on GEO database

Electroanatomic mapping in athletes: Why and when. An expert opinion paper from the Italian Society of Sports Cardiology

Identification of Hub Diagnostic Biomarkers and Candidate Therapeutic Drugs in Heart Failure

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