以人工智能为基础的预测信号通路在心脏纤维化中的概述建议一种新的组合治疗策略.
Bo Yang1, Jie Chen1, Qiongjie Mi1
1Center for Organoid and Regeneration Medicine, Greater Bay Area Institute of Precision Medicine (Guangzhou); School of Life Sciences, Fudan University, China.
bioRxiv : the preprint server for biology
|January 7, 2026
概括
人工智能发现了心脏纤维化治疗的新治疗策略. 暂时抑制JAK-STAT和TGF-β信号通路可以减少纤维化,而不会损害急性损伤的修复.
科学领域:
- 心血管研究的心血管研究.
- 生物医学工程 生物医学工程
- 计算生物学是一种计算生物学.
背景情况:
- 心血管疾病 (CVD) 是全球主要的死亡原因.
- 心肌纤维化,标志着细胞外基质 (ECM) 沉积过多,导致心脏功能障碍.
- 目前的抗纤维化疗法受限于对纤维化过程的不完全理解.
研究的目的:
- 利用人工智能 (AI) 识别心脏纤维化中的关键信号通路.
- 开发心脏纤维化治疗的优化,时间调节的治疗策略.
- 为了区分急性和慢性纤维状况的治疗目标.
主要方法:
- 在 6,528 篇心脏纤维化出版物上训练了一个人工智能模型,以识别 10 个节点信号通路.
- 利用单细胞RNA测序来分析小鼠模型和临床样本中的途径活性.
- 开发并测试了用于JAK-STAT和TGF-β信号的时间抑制策略.
主要成果:
- 人工智能确定了JAK-STAT和TGF-β作为心脏纤维化中的关键途径.
- 在急性心肌梗塞 (MI) 和慢性横向大动脉收缩 (TAC) 中,JAK-STAT信号在早期达到峰值.
- 纤维细胞特异性Jak1/2删除减少了TAC纤维化,但没有MI纤维化,表明了差异性通路作用.
- 对JAK-STAT和TGF-β的时间抑制策略改善了心脏功能,并减少了MI后纤维化.
结论:
- 人工智能驱动的,暂时调节的JAK-STAT和TGF-β信号的抑制是心脏纤维化的一种有前途的治疗策略.
- 这种方法有效地减少了病理性纤维化,同时保持了必要的急性补偿性痕.
- 该策略涉及Ruxolitinib (RUX) 用于JAK-STAT抑制和Pirfenidone (PFD) 用于TGF-β抑制后MI.
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