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Updated: Jan 13, 2026

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Analytical Techniques for Assaying Nitric Oxide Bioactivity
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人工智能驱动的氧化的临床应用:一个参考书目和科学绘图分析
Zhen Liu1, Ainikaer Abulaiti1, Yan Zhao1
1Orthopedic Research Center, Sixth Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang Uygur Autonomous Region, China.
Medical gas research
|January 7, 2026
概括
人工智能 (AI) 正在彻底改变氧化研究,特别是在心血管和呼吸系统疾病方面. 这项研究绘制了AI-氧化领域的地图,确定了临床翻译中的关键主题,合作和挑战.
科学领域:
- 生物医学工程 生物医学工程
- 计算生物学 计算生物学
- 人工智能的人工智能
背景情况:
- 氧化 (NO) 是心血管调节,免疫反应和神经调节中的关键信号分子.
- 人工智能 (AI) 为NO监测,剂量和毒性预测提供了先进的方法.
- 对人工智能驱动的NO系统的现有研究是分散的,缺乏对知识结构和演变的系统分析.
研究的目的:
- 系统地分析人工智能驱动的氧化系统的知识架构和演变.
- 确定这个跨学科领域的研究趋势,关键参与者和翻译障碍.
- 提供AI-NO融合格局的全面概述.
主要方法:
- 来自Web of Science核心集合的384篇文章 (2005-2024) 的图书统计分析.
- 使用CiteSpace,VOSviewer和Bibliometrix R包进行数据分析.
- 执行关键词演变和网络分析,绘制研究主题和合作.
主要成果:
- 2017年后,出版物增长加速,与人工智能进步相吻合.
- 中国和美国领导研究,以核心-外围国际合作结构.
- 主要的研究主题包括心血管/呼吸系统疾病;欠发达的领域包括神经免疫学和传染病.
- 关键词分析显示,从传统机制转向人工智能驱动的方法,如机器学习和深度学习.
- 临床翻译存在重大挑战,包括数据异质性和算法可解释性.
结论:
- 这项研究为人工智能驱动的NO研究提供了系统的框架,突出了知识差距和翻译瓶.
- 未来的研究应该专注于对NO代谢的AI建模,可解释的AI算法和临床验证.
- 弥合实验室发现和临床应用之间的差距对于推进AI-NO融合至关重要.
关键词:
城市空间 (Citespace) 是一个城市空间.在VOSviewer中使用VOSviewer.科学网络的网络.人工智能的人工智能是人工智能.文献计量数据库 (bibliometrics) 是一个心血管疾病心血管疾病深度学习是一种深度学习.知识图表知识图表机器学习是机器学习.氧化氧化的使用方法更多相关视频
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