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用于癌症治疗的石墨烯-磁铁纳米混合体中的增强磁热:通过外源输入-长期短期记忆 (NARX-LSTM) 预测的非线性自行回归通过人工智能驱动的验证

M S Dar¹, K B Akram², Liping Zhu¹

¹State Key Laboratory of Advanced Fiber Materials, College of Materials Science and Engineering, Donghua University, Shanghai, 201620, China.

Biomaterials advances

|October 28, 2025

概括

石墨烯-磁铁的纳米混合体显示了针对性癌症治疗的希望. 一个深度学习模型准确地预测了优化磁性高热治疗的加热效率.

科学领域:

纳米技术和材料科学材料科学
生物医学工程生物医学工程
人工智能的人工智能

背景情况:

磁热过热使用交替磁场 (AMF) 来加热用于癌症治疗的磁纳米粒子.
石墨烯-磁石纳米混合物 (GMNHs) 正在探索,以提高治疗疗效.

研究的目的:

为了优化GMNHs和AMF参数的组成,以实现高效的磁热过热.
开发和验证用于预测纳米粒子加热行为的深度学习模型.

主要方法:

合成和表征了具有不同Fe3O4:石墨烯比率的GMNH.
在不同的AMF频率和磁场强度下评估了加热效率.
一个非线性自回归与外源输入-长期短期记忆 (NARX-LSTM) 模型被用于温度预测.

主要成果:

在优化的AMF条件下,特定的GMNH组合物 (F75G25,F65G35) 在优化的AMF条件下达到治疗温度 (40-45°C).
F65G35组合表现出更高的特定吸收率 (SAR) 在518kHz/23.9mT.
高频 (982 kHz) 降低了由于纳米粒子放松动态的加热效率.
该NARX-LSTM模型显示了高的预测准确性 (R2 ≥0.997).

结论:

纳米混合组合和AMF参数的AI驱动优化对于有效的磁热过热至关重要.
使用人工智能模型准确的热预测可以实现个性化癌症治疗.
这种协同方法有可能开发出更安全,更有效的癌症治疗方法,具有最小的毒性.

关键词:

交替磁场 (AMF) 是一种可变的磁场.癌症治疗治疗癌症治疗深度学习是一种深度学习.石墨烯磁铁的纳米混合物石墨烯磁铁的纳米混合物磁性高热症是一种磁性高热症.特定的吸收率 (SAR) 是指

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