Scholar - SciOpen

This review explores recent advances in risk stratification systems for early hematoma expansion (HE), focusing on predictive models using CTA, NCCT, radiomics, and artificial intelligence to enhance accuracy and clinical utility. The study analyzes literature on CTA spot signs, NCCT imaging features, and radiomics combined with machine learning for hematoma expansion prediction. It compares traditional biomarkers with artificial intelligence-driven models.

Methods

A narrative review was performed to synthesize the available literature and explore the predictive value of CTA spot signs, NCCT imaging features, and radiomics combined with machine learning for hematoma expansion and outcomes.

Results

Integrating multimodal imaging and artificial intelligence significantly enhances HE prediction. Future work should focus on multicenter validation and standardized models to optimize personalized treatment and improve intracerebral hemorrhage outcomes.

Conclusion

Intelligent risk stratification systems, incorporating multimodal imaging and artificial intelligence, enhance hematoma expansion prediction and clinical utility. Future research could prioritize multicenter validation, standardization of models, and the development of personalized treatment strategies to ultimately improve outcomes for ICH patients.

Open Access Full Length Article Issue

Genome-wide profiling of long noncoding RNA expression patterns and CeRNA analysis in mouse cortical neurons infected with different strains of borna disease virus

Lin Sun, Yujie Guo, Peng He, Xiaoyan Xu, Xiong Zhang, Haiyang Wang, Tian Tang, Wei Zhou, Ping Xu, Peng Xie

Genes & Diseases 2019, 6(2): 147-158

Published: 17 April 2019

Abstract

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Borna disease virus 1 (BoDV-1) is neurotropic prototype of Bornaviruses causing neurological diseases and maintaining persistent infection in brain cells of mammalian species. Long non-coding RNA (lncRNA) is transcript of more than 200 nucleotides without protein-coding function regulating various biological processes as proliferation, apoptosis, cell migration and viral infection. However, regulatory of lncRNAs in BoDV-1 infection remains unknown. To identify differential expression profiles and predict functions of lncRNA in BoDV-1 infection, microarray data showed that 3528 lncRNAs and 2661 lncRNAs were differentially expressed in Strain V and Hu-H1 BoDV-infected groups compared with control groups, respectively. Gene Ontology (GO) and pathway analysis suggested that differential lncRNAs may be involved in regulation of metabolic, biological regulation, cellular process, endocytosis, viral infections and cell adhesion processes, cancer in both BoDV-infected strains. ENSMUST00000128469 was found down-regulated in both BoDV-infected groups compared with control groups consistent with microarray (p < 0.05). ceRNA analysis indicated possible interaction networks as ENSMUST00000128469/miR-22-5p, miR-206-3p, miR-302b-5p, miR-302c-3p, miR-1a-3p/Igf1. Igf1 was found up-regulated in both BoDV-infected groups compared with control groups (p < 0.05). Possible functions of predicted target mRNAs and miRNAs of ENSMUST00000128469 were involved in cell proliferation, transcriptional misregulation and proteoglycan pathways enriched in cancer. lncRNA may be involved in regulation of Hu-H1 inhibited cell proliferation and promoted apoptosis through NF-kB, JNK/MAPK signaling, BCL2 and CDK6/E2F1 pathways different from Strain V. Possible interaction networks as ENSMUST00000128469/miR-22-5p, miR-206-3p, miR-302b-5p, miR-302c-3p, miR-1a-3p/Igf1 may involve in regulation of cell proliferation, apoptosis, and cancer.

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