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Ministry of Education, Singapore under its Academic Research Funding Tier

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Ministry of Education, Singapore under its Academic Research Funding Tier 1; Lee Kong Chian Fund for Excellenc

Intracoronary Sarcoplasmic Reticulum CalciumATPase Gene Therapy in Advanced Heart Failure Patients with reduced Ejection Fraction: A Prospective Cohort Study

OBJECTIVE: Heart failure is a progressive and debilitating disease. Intracoronary sarcoplasmic reticulum calciumATPase gene therapy may improve the function of cardiac muscle cells. This study aimed to test the hypothesis that intracoronary sarcoplasmic reticulum calcium-ATPase gene therapy can improve outcomes and reduce the number of recurrent and terminal events in advanced heart failure patients with reduced ejection fraction. METHODS: A total of 768 heart failure patients with reduced ejection fraction and New York Heart Association classification II to IV were included in this prospective cohort study. Patients either underwent intracoronary sarcoplasmic reticulum calcium-ATPase gene therapy (CA group, n=384) or received oral placebo (PA group; n=384). Data regarding recurrent and terminal event(s), treatment-emergent adverse effects, and outcome measures were collected and analyzed. RESULTS: After a follow-up period of 18 months, intracoronary sarcoplasmic reticulum calcium-ATPase gene therapy reduced the number of hospital admissions (p=0.001), ambulatory treatments (p=0.0004), and deaths (p=0.024). Additionally, intracoronary sarcoplasmic reticulum calcium-ATPase gene therapy improved the left ventricular ejection fraction (po0.0001) and Kansas City Cardiomyopathy Questionnaire score (po0.0001). The number of recurrent and terminal events/patients were higher in the PA group than in the CA group after the follow-up period of 18 months (p=0.015). The effect of the intracoronary sarcoplasmic reticulum calciumATPase gene therapy was independent of the confounding variables. No new arrhythmias were reported in the CA group. CONCLUSIONS: Intracoronary sarcoplasmic reticulum calcium-ATPase gene therapy reduces the number of recurrent and terminal events and improves the clinical course of advanced heart failure patients with reduced ejection fraction

Relation-dependent Contrastive Learning with Cluster Sampling for Inductive Relation Prediction

Relation prediction is a task designed for knowledge graph completion which aims to predict missing relationships between entities. Recent subgraph-based models for inductive relation prediction have received increasing attention, which can predict relation for unseen entities based on the extracted subgraph surrounding the candidate triplet. However, they are not completely inductive because of their disability of predicting unseen relations. Moreover, they fail to pay sufficient attention to the role of relation as they only depend on the model to learn parameterized relation embedding, which leads to inaccurate prediction on long-tail relations. In this paper, we introduce Relation-dependent Contrastive Learning (ReCoLe) for inductive relation prediction, which adapts contrastive learning with a novel sampling method based on clustering algorithm to enhance the role of relation and improve the generalization ability to unseen relations. Instead of directly learning embedding for relations, ReCoLe allocates a pre-trained GNN-based encoder to each relation to strengthen the influence of relation. The GNN-based encoder is optimized by contrastive learning, which ensures satisfactory performance on long-tail relations. In addition, the cluster sampling method equips ReCoLe with the ability to handle both unseen relations and entities. Experimental results suggest that ReCoLe outperforms state-of-the-art methods on commonly used inductive datasets