标题： 通过基于量子行走的网络分析推进对长期新冠病理生理学的理解 显示英文标题

标题： Advancing Understanding of Long COVID Pathophysiology Through Quantum Walk-Based Network Analysis

摘要： 长期新冠肺炎是一种多系统疾病，其特征是在感染新冠病毒后出现持续的症状，如疲劳、认知障碍和全身性炎症，但其机制仍不清楚。 在本研究中，我们应用了量子游走（QW），一种利用量子干涉的计算方法，来探索大规模的SARS-CoV-2诱导蛋白（SIP）网络。 与传统的带重启的随机游走（RWR）方法相比，QW在遍历网络深层区域方面表现出更强的能力，揭示了与长期新冠肺炎相关的蛋白质和通路。 关键发现包括线粒体功能障碍、血栓炎性反应和神经炎症作为核心机制。 QW独特地识别出CDGSH铁硫结构域含蛋白家族和VDAC1，这是一种线粒体钙转运蛋白，作为这些过程的关键调节因子。 VDAC1被确定为潜在的生物标志物和治疗靶点，得到了如大麻二醇等美国食品药品监督管理局批准的化合物的支持。 这些发现突显了QW作为一种强大的工具，用于阐明复杂的生物系统，并为类似长期新冠肺炎等状况发现新的治疗靶点。 显示英文摘要

摘要： Long COVID is a multisystem condition characterized by persistent symptoms such as fatigue, cognitive impairment, and systemic inflammation, following COVID-19 infection, yet its mechanisms remain poorly understood. In this study, we applied quantum walk (QW), a computational approach leveraging quantum interference, to explore large-scale SARS-CoV-2-induced protein (SIP) networks. Compared to the conventional random walk with restart (RWR) method, QW demonstrated superior capacity to traverse deeper regions of the network, uncovering proteins and pathways implicated in Long COVID. Key findings include mitochondrial dysfunction, thromboinflammatory responses, and neuronal inflammation as central mechanisms. QW uniquely identified the CDGSH iron-sulfur domain-containing protein family and VDAC1, a mitochondrial calcium transporter, as critical regulators of these processes. VDAC1 emerged as a potential biomarker and therapeutic target, supported by FDA-approved compounds such as cannabidiol. These findings highlight QW as a powerful tool for elucidating complex biological systems and identifying novel therapeutic targets for conditions like Long COVID.