Rongli Fan

This study aimed to determine the possible risk factors for stroke outcomes based on prospective cohort study in China. A total of 146 stroke patients were recruited and divided into 2 groups, which assessed using the modified Rankin Scale (mRS), good outcomes (mRS <= 2) and poor outcomes group (mRS > 2). Demographic, clinical, and laboratory characteristics of participants were obtained from the medical record. The multivariable logistic regression analysis was employed to assess the risk factors for stroke outcomes. Of 146 participants, 28 (19.18%) were presented with poor outcomes at day 90. As a result of multivariable logistic regression analysis, a significantly increased risk of stroke outcomes was found in patients with Barthel Index (BI) score (stroke (OR 1.50, 95% CI 1.21 ~ 1.85, P < .001) and IS (OR 1.48, 95% CI 1.20 ~ 1.83, P < .001)).

Introduction: Stroke, of which ischemic stroke (IS) is the major type, is the second leading cause of disability and death worldwide. Circular RNAs (circRNAs) are reported to play important role in the physiology and pathology of IS. CircRNAs often act as competing endogenous RNA (ceRNA) to regulate gene expression by acting as miRNA sponges. However, whole transcriptome-wide screenings of circRNA-mediated ceRNA networks associated with IS are still lacking. In the present study, we constructed a circRNA-miRNA-mRNA ceRNA network by whole transcriptome-wide analysis.Methods: CircRNAs, miRNAs and mRNAs expression profiles were downloaded from the Gene Expression Omnibus (GEO) datasets. We identified differentially expressed (DE) circRNAs, miRNAs, and mRNAs in IS patients. StarBase and CircBank databases were used to predict the miRNA targets of DEcircRNAs, and mirDIP database was used to predict the mRNA targets of DEmiRNAs. CircRNA-miRNA pairs and miRNA-mRNA pairs were established. Then, we identified hub genes via protein-protein interaction analysis and constructed a core ceRNA sub-network.Results: In total, 276 DEcircRNAs, 43 DEmiRNAs, and 1926 DEmRNAs were explored. The ceRNA network included 69 circRNAs, 24 miRNAs, and 92 mRNAs. The core ceRNA subnetwork included hsa_circ_0011474, hsa_circ_0023110, CDKN1A, FHL2, RPS2, CDK19, KAT6A, CBX1, BRD4, and ZFHX3.Discussion: In conclusion, we established a novel hsa_circ_0011474 - hsa-miR-20a-5p/hsa-miR-17-5p - CDKN1A ceRNA regulatory axis associated with IS. Our findings provide new insights into the pathogenesis of IS and offer promising diagnostic and predictive biomarkers.

Natural language processing (NLP) technology has played a pivotal role in health monitoring as an important artificial intelligence method. As a key technology in NLP, relation triplet extraction is closely related to the performance of health monitoring. In this paper, a novel model is proposed for joint extraction of entities and relations, combining conditional layer normalization with the talking-head attention mechanism to strengthen the interaction between entity recognition and relation extraction. In addition, the proposed model utilizes position information to enhance the extraction accuracy of overlapping triplets. Experiments on the Baidu2019 and CHIP2020 datasets demonstrate that the proposed model can effectively extract overlapping triplets, which leads to significant performance improvements compared with baselines.