Sepsis toxicity network reconstruction-Dynamic signaling and multi-organ injury: A review.

0 MEDICINE, RESEARCH & EXPERIMENTAL
Shuai Liu, Qun Liang
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Abstract

Sepsis is a complex systemic disease in which systemic toxicity-arising from inflammation-immune dysregulation, oxidative stress, programmed cell death (apoptosis, pyroptosis, ferroptosis), and metabolic reprogramming-drives multi-organ injury. The aim of this review was to synthesize how signaling pathways evolve within and between key organs (lungs, liver, kidneys, heart) and to evaluate whether multi-omics integration and network modeling can identify critical toxic nodes and predict disease progression. We conducted a narrative review of English-language mechanistic studies published between 2015 and 2025 in PubMed, Web of Science, and Scopus, supplemented by bibliography screening, while excluding case reports, conference abstracts, and non-mechanistic work. The evidence depicts a high-dimensional systemic network that remodels over time, with early pro-inflammatory modules transitioning toward immunosuppression and organ-specific injury patterns, while inter-organ propagation is mediated by damage-associated molecular patterns (DAMPs), exosomes, and metabolites. Oxidative stress and mitochondrial dysfunction, via reactive oxygen species (ROS), couple to pyroptosis and ferroptosis to reinforce toxicity loops, and computational approaches such as dynamic Bayesian networks (DBN) and graph neural networks (GNN) delineate regulatory hubs and support forecasting. Therapeutic progress has concentrated on nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB), the NOD-, leucine-rich repeat and pyrin domain-containing protein 3 (NLRP3) inflammasome, and glutathione peroxidase 4 (GPX4), alongside artificial intelligence (AI)-assisted personalized toxicity maps and dynamic early-warning systems, though challenges remain in specificity, safety, and resistance. In conclusion, sepsis can be conceived as a temporally staged systemic toxicity network, and when combined with multi-omics, DBN/GNN modeling, and AI-enabled decision support, this framework offers a path toward individualized, mechanism-based care, while requiring rigorous validation to ensure clinical durability.

脓毒症毒性网络重建-动态信号和多器官损伤:综述。
脓毒症是一种复杂的全身性疾病,由炎症免疫失调、氧化应激、程序性细胞死亡(凋亡、焦亡、铁亡)和代谢重编程引起的全身性毒性驱动多器官损伤。本综述的目的是综合关键器官(肺、肝、肾、心)内部和之间的信号通路如何进化,并评估多组学整合和网络建模是否可以识别关键毒性节点并预测疾病进展。我们对2015年至2025年间发表在PubMed、Web of Science和Scopus上的英语机械性研究进行了叙述性回顾,并辅以参考文献筛选,同时排除了病例报告、会议摘要和非机械性工作。证据描述了一个高维系统网络,随着时间的推移,早期的促炎模块向免疫抑制和器官特异性损伤模式过渡,而器官间传播是由损伤相关分子模式(DAMPs)、外泌体和代谢物介导的。氧化应激和线粒体功能障碍通过活性氧(ROS)与焦亡和铁亡相结合,从而加强毒性循环,而动态贝叶斯网络(DBN)和图神经网络(GNN)等计算方法描绘了调控中心并支持预测。治疗进展主要集中在活化B细胞的核因子kappa-轻链增强子(NF-κB)、NOD-、富含亮氨酸的重复和含pyrin结构域的蛋白3 (NLRP3)炎症小体、谷胱甘肽过氧化物酶4 (GPX4),以及人工智能(AI)辅助的个性化毒性图和动态预警系统,尽管在特异性、安全性和耐药性方面仍存在挑战。总之,脓毒症可以被认为是一个暂时分阶段的系统性毒性网络,当与多组学、DBN/GNN建模和人工智能支持的决策支持相结合时,该框架为个性化、基于机制的护理提供了一条途径,同时需要严格的验证以确保临床持久性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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