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内分类固醇-微生物群-神经免疫超级系统:系统弹性,崩轨迹和精确反医学的统一反架构
Cătălin Aliuș1, Alexandru Breazu1, Cosmin Pantu2,3
1Faculty of General Medicine, "Carol Davila" University of Medicine and Pharmacy, 050474 Bucharest, Romania.
International journal of molecular sciences
|November 27, 2025
概括
慢性疾病源于系统性不稳定,而不是孤立的机制. 内分类固醇-微生物群-神经免疫超级系统 (EMN-S) 模型解释了涉及微生物组,内分类固醇和神经免疫系统的反循环如何保持健康,以及它们的崩如何导致疾病.
科学领域:
- 综合生物学和系统医学 综合生物学和系统医学
- 关注相互连接的生理网络.
- 慢性疾病病理生理学的探索.
背景情况:
- 诸如阿尔茨海默氏症,多发性硬化和糖尿病等慢性疾病具有相同的系统性破坏稳定的模式.
- 健康源于反网络:微生物群,内分泌,神经免疫和代谢系统.
- 目前的生物医学往往侧重于孤立的分子或器官特异性机制.
研究的目的:
- 介绍内分泌-微生物群-神经免疫超级系统 (EMN-S) 作为健康和疾病的统一模型.
- 提出EMN-S作为理解生理性和开发精确治疗方法的框架.
- 反崩的细节机制导致慢性多系统性疾病.
主要方法:
- 概念模型正式化EMN-S. 的概念模型.
- 反机制的描述:编码器漂移,集成器脱节,执行器过度利用.
- 识别EMN-S签名 (微生物组,脂质组,免疫读数) 适应性崩.
- 对人工智能驱动的数字双胞胎进行讨论,以预测和干预反崩.
主要成果:
- 该EMN-S整合了微生物组,内分泌大麻素和神经免疫系统,这些系统对健康至关重要.
- 三种反干扰机制 (编码器漂移,集成器脱节,执行器过度利用) 驱动慢性疾病.
- 一个EMN-S签名可以预测临床前状态和弹性崩.
- 人工智能数字双胞胎显示出用于恢复平衡的闭环干预的潜力.
结论:
- 该EMN-S提供了一个有凝聚力的,经验可测的生理稳定性和弹性理论.
- 了解EMN-S内部的反动态为慢性疾病研究提供了一个新的范式.
- 针对EMN-S的精确反疗法有望恢复动态平衡和治疗复杂疾病.
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