Abstract
Nanorobots are a promising development in targeted drug delivery and the treatment of neurological disorders, with potential for crossing the blood-brain barrier (BBB). These small devices leverage advancements in nanotechnology and bioengineering for precise navigation and targeted payload delivery, particularly for conditions like brain tumors, Alzheimer's disease, and Parkinson's disease. Recent progress in artificial intelligence (AI) and machine learning (ML) has improved the navigation and effectiveness of nanorobots, allowing them to detect and interact with cancer cells through biomarker analysis. This study presents a new reinforcement learning (RL) framework for optimizing nanorobot navigation in complex biological environments, focusing on cancer cell detection by analyzing the concentration gradients of surrounding biomarkers. We utilize a computer simulation model to explore the behavior of nanorobots in a three-dimensional space with cancer cells and biological barriers. The proposed method uses Q-learning to refine movement strategies based on real-time biomarker concentration data, enabling nanorobots to autonomously navigate to cancerous tissues for targeted drug delivery. This research lays the groundwork for future laboratory experiments and clinical applications, with implications for personalized medicine and less invasive cancer treatments. The integration of intelligent nanorobots could revolutionize therapeutic strategies, reducing side effects and enhancing treatment effectiveness for cancer patients. Further research will investigate the practical deployment of these technologies in medical settings, aiming to unlock the full potential of nanorobotics in healthcare.
Abstract (translated)
纳米机器人在靶向药物递送和神经障碍治疗方面展现出巨大的发展潜力，特别是在穿越血脑屏障（BBB）方面。这些小型设备利用纳米技术和生物工程的最新进展实现精确导航和定点载荷输送，特别适用于如脑肿瘤、阿尔茨海默病和帕金森病等病症。人工智能（AI）和机器学习（ML）领域的近期进步提高了纳米机器人的导航能力和效果，使其能够通过生物标志物分析检测并与癌细胞互动。本研究提出了一种新的强化学习（RL）框架，用于优化复杂生物环境中的纳米机器人导航，重点在于通过周围生物标志物浓度梯度的分析来识别癌细胞。我们使用计算机仿真模型探索了三维空间中纳米机器人的行为表现，该环境中包含了癌细胞和生物屏障。所提出的方法采用Q-learning算法，根据实时的生物标志物浓度数据优化移动策略，使纳米机器人能够自主导航至病变组织进行靶向药物递送。这项研究为未来的实验室实验和临床应用奠定了基础，并对个性化医疗和非侵入性癌症治疗具有重要意义。智能纳米机器人的整合可能将彻底变革治疗策略，减少副作用并提升癌症患者的治疗效果。进一步的研究将探索这些技术在医学领域的实际部署，旨在充分发挥纳米机器人在医疗保健中的潜力。
URL
https://arxiv.org/abs/2411.02345
