{"id":395,"title":"Single-cell-level digital twins for preterm birth prevention strategies","title_zh":"单细胞水平数字孪生用于早产预防策略：SIMCO 框架与候选药物模拟筛选","url":"https://pubmed.ncbi.nlm.nih.gov/42905824","doi":"10.1126/sciadv.aed2741","pmid":"42905824","journal":"Sci Adv","kind":"paper","lang":"en","published_at":"2026-10-21T00:00:00+00:00","fetched_at":"2026-11-08T03:44:12+00:00","authors":["Einhaus J","Neidlinger P","Fondeur O","Sato M","Anronikov S","Miyazaki K","Maechler A","Amar JN","Ando K","Badea V","Gaudilliere DK","Sabayev M","Feyaerts D","Diop M","Tsai AS","Cambriel A","Ganio EA","Lagarde R","O'Kelly E","Stelzer IA","Hedou J","Wong RJ","Blumenfeld YJ","Lyell DJ","Shaw GM","Oskotsky TT","Sirota M","Giudice LC","Stevenson DK","Aghaeepour N","Gaudilliere B"],"primary_topic":"early-pregnancy","topics":[{"slug":"ai-compute","score":4.0,"hits":["digital"]},{"slug":"omics-methods","score":4.0,"hits":["single-cell"]},{"slug":"early-pregnancy","score":3.0,"hits":["preterm birth"]},{"slug":"ethics-policy","score":1.0,"hits":["ethical"]}],"topic_score":4.0,"preference_score":0.0,"preference_labels":[],"llm_score":80.0,"final_score":88.0,"score_dims":{"novelty":88,"rigor":80,"impact":82,"relevance":72,"translation":75},"recommend_reason":"将单细胞免疫组学与数字孪生模拟结合用于早产药物筛选，方法学新颖且给出可验证的机制假设，对生殖免疫与计算医学研究有直接启发；但结论均为计算预测，需临床验证，且不直接涉及 ART 实验室流程。","keywords":["数字孪生","早产临产","单细胞质谱流式","免疫组学","药物重定位","计算模拟"],"selected":1,"story_id":342,"llm_provider":"deepseek","llm_model":"deepseek-flash","llm_effort":"low","source_key":"pubmed-top-journals","source_name":"PubMed · CNS/大子刊/四大医学刊（生殖相关）","source_tier":1,"mention_count":1,"has_interpretation":false,"journal_key":"sci-adv","journal_tier":"cns-sub","journal_if":11.7,"is_mock":false,"summary_zh":"该研究提出 SIMCO（Simulated Immunome Modeling of Clinical Outcomes），一个单细胞分辨率的\"数字孪生\"框架，用于模拟免疫调节治疗对分娩发动时间的影响，以规避早产临产（PTL）临床试验的伦理与经费障碍。作者基于新构建的孕期免疫组质谱流式图谱（涵盖九种针对早产预防预选的候选药物）训练并验证数字孪生，并在独立孕妇队列中模拟各药物对孕周长度的影响，同时给出系统层面的机制解释。结果显示四氢叶酸以及阿司匹林联合兰索拉唑为最佳候选，主要通过增强固有免疫细胞 mTOR 信号、减弱初始 CD4+ T 细胞 JAK/STAT 信号延迟分娩发动。结果基于计算模拟与体外免疫图谱，尚需前瞻性临床试验验证。","summary":"Digital twin models can accelerate therapeutic development by enabling low-risk testing of candidate interventions. In preterm labor (PTL), a major pregnancy complication where clinical trials face ethical and financial barriers, digital twins are especially valuable for evaluating new therapies targeting immune dysfunctions driving PTL. Yet, current models lack single-cell resolution, limiting detection of cell type-specific mechanisms, off-target effects, and the design of personalized interventions. We present Simulated Immunome Modeling of Clinical Outcomes (SIMCO), a single-cell-level digital twin framework that models immunomodulatory treatment effects on the timing of labor using immunome-wide, single-cell simulations. SIMCO's digital twins are trained and validated on a newly generated mass cytometry atlas of the pregnant immunome exposed to nine candidate drugs preselected for PTL prevention. Applying SIMCO to an independent cohort of pregnant individuals, we simulate treatment effects on gestational length, screening for candidate drugs that delay labor timing and providing system-level mechanistic insight for each drug candidate. Tetrahydrofolate and the combination of aspirin and lansoprazole emerged as top candidates for PTL prevention, delaying labor onset primarily through enhanced mTOR signaling in innate immune cells and attenuated JAK/STAT signaling in naïve CD4+ T cells.","interpretation_basis":null,"fulltext_status":"ok","interpretation":null,"fulltext":{"available":true,"status":"ok","reason":"已取得 Europe PMC OA 全文","pmcid":"PMC13693830","license":"CC BY","chars":29996,"source":"Europe PMC","fetched_at":"2026-11-08T04:16:47+00:00"}}