We have proposed a new tumor sensitization and targeting (TST) framework, named in vivo computation, in our previous investigations. The problem of TST for an early and microscopic tumor is interpreted from the computational perspective with nanorobots being the “natural” computing agents, the high-risk tissue being the search space, the tumor targeted being the global optimal solution, and the tumor-triggered biological gradient field (BGF) providing the aided knowledge for fitness evaluation of nanorobots. This natural computation process can be seen as on-the-fly path planning for nanorobot swarms with an unknown target position, which is different from the traditional path planning methods. Our previous works are focusing on the TST for a solitary lesion, where we proposed the weak priority evolution strategy (WP-ES) to adapt to the actuating mode of the homogeneous magnetic field used in the state-of-the-art nanorobotic platforms, and some in vitro validations were performed. In this paper, we focus on the problem of TST for multifocal tumors, which can be seen as a multimodal optimization problem for the “natural” computation. To overcome this issue, we propose a sequential targeting strategy (Se-TS) to complete TST for the multiple lesions with the assistance of nanorobot swarms, which are maneuvered by the external actuating and tracking devices according to the WP-ES. The Se-TS is used to modify the BGF landscape after a tumor is detected by a nanorobot swarm with the gathered BGF information around the detected tumor. Next, another nanorobot swarm will be employed to find the second tumor according to the modified BGF landscape without being misguided to the previous one. In this way, all the tumor lesions will be detected one by one. In other words, the paths of nanorobots to find the targets can be generated successively with the sequential modification of the BGF landscape. To demonstrate the effectiveness of the proposed Se-TS, we perform comprehensive simulation studies by enhancing the WP-ES based swarm intelligence algorithms using this strategy considering the realistic in-body constraints. The performance is compared against that of the “brute-force” search, which corresponds to the traditional systemic tumor targeting, and also against that of the standard swarm intelligence algorithms from the algorithmic perspective. Furthermore, some in vitro experiments are performed by using Janus microparticles as magnetic nanorobots, a two-dimensional microchannel network as the human vasculature, and a magnetic nanorobotic control system as the external actuating and tracking system. Results from the in silico simulations and in vitro experiments verify the effectiveness of the proposed Se-TS for two representative BGF landscapes.

中文翻译：

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用于多焦点肿瘤敏化和靶向的纳米机器人辅助自然计算

我们提出了一个新的肿瘤敏化和靶向 (TST) 框架，命名为 体内计算，在我们之前的调查中。从计算角度解释早期微观肿瘤的 TST 问题，纳米机器人是“自然”计算代理，高危组织是搜索空间，目标肿瘤是全局最优解，肿瘤触发生物梯度场（BGF）为纳米机器人的适应性评估提供辅助知识。这种自然的计算过程可以看作是具有未知目标位置的纳米机器人群的动态路径规划，这与传统的路径规划方法不同。我们以前的工作重点是孤立病灶的 TST，体外进行了验证。在本文中，我们关注多灶性肿瘤的 TST 问题，可以将其视为“自然”计算的多模态优化问题。为了克服这个问题，我们提出了一种顺序靶向策略（Se-TS），在纳米机器人群的帮助下完成多处病变的 TST，纳米机器人群由外部驱动和跟踪装置根据 WP-ES 进行操纵。在纳米机器人群检测到肿瘤后，Se-TS 用于修改 BGF 景观，并在检测到的肿瘤周围收集 BGF 信息。接下来，将使用另一个纳米机器人群根据修改后的 BGF 景观寻找第二个肿瘤，而不会被误导到前一个。这样，所有的肿瘤病灶都会被一一检测出来。换句话说，纳米机器人寻找目标的路径可以随着 BGF 景观的顺序修改而连续生成。为了证明所提出的 Se-TS 的有效性，我们通过使用该策略增强基于 WP-ES 的群智能算法来进行全面的模拟研究，同时考虑到现实的体内约束。将性能与“蛮力”搜索的性能进行比较，该搜索对应于传统的系统性肿瘤靶向，并从算法的角度与标准的群体智能算法的性能进行了比较。此外，一些 我们考虑到现实的体内约束，通过使用这种策略增强基于 WP-ES 的群智能算法来执行全面的模拟研究。将性能与“蛮力”搜索的性能进行比较，该搜索对应于传统的系统性肿瘤靶向，并从算法的角度与标准的群体智能算法的性能进行了比较。此外，一些 我们考虑到现实的体内约束，通过使用这种策略增强基于 WP-ES 的群智能算法来执行全面的模拟研究。将性能与“蛮力”搜索的性能进行比较，该搜索对应于传统的系统性肿瘤靶向，并从算法的角度与标准的群体智能算法的性能进行了比较。此外，一些体外实验使用Janus微粒作为磁性纳米机器人，二维微通道网络作为人体脉管系统，磁性纳米机器人控制系统作为外部驱动和跟踪系统。结果来自电脑模拟 模拟和 体外 实验验证了所提出的 Se-TS 对两个有代表性的 BGF 景观的有效性。