败血症毒性网络重建 - 动态信号和多器官损伤:一项审查
Biomolecules & biomedicine
|September 2, 2025
概括
败血症涉及复杂的全身毒性和免疫失调导致器官损伤. 网络建模和多组学集成可以识别关键毒结并预测毒症的进展以进行个性化治疗.
科学领域:
- 系统生物学
- 免疫学
- 计算生物学
背景情况:
- 败血症是一种危及生命的疾病,其特征是全身炎症,免疫失调和多器官损伤.
- 关键机制包括氧化应激,编程细胞死亡 (亡,热亡,铁亡) 和代谢重编程.
研究的目的:
- 综合体内和器官之间的信号通路在败血症的演变.
- 评估多组合和网络建模在识别有毒节点和预测疾病进展方面的实用性.
主要方法:
- 进行了PubMed,Web of Science和Scopus的机械学研究 (2015-2025) 的叙述性审查.
- 补充了文献选,不包括非机械工作和病例报告.
主要成果:
- 败血症涉及一个动态的系统网络,由炎症转变为免疫抑制和器官特定损伤.
- 通过损伤相关的分子模式 (DAMPs),外体和代谢物进行器官间的通信.
- 像动态贝叶斯网络 (DBN) 和图形神经网络 (GNN) 这样的计算模型可以识别监管中心并帮助预测.
结论:
- 败血症可以被视为一个暂时阶段的全身毒性网络.
- 多组学,DBN/GNN建模和基于人工智能的决策支持为个性化,基于机制的败血症护理提供了框架.
- 需要严格的验证,以确保这些先进方法的临床适用性.
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