Green Touchable Nanorobotic Sensor Networks

Chen, Yifan; Nakano, Tadashi; Kosmas, Panagiotis; Yuen, Chau; Vasilakos, Athanasios V.; Asvial, Muhamad

doi:10.1109/mcom.2016.1500626cm

Cited by 27 publications

(6 citation statements)

References 13 publications

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“…However, this kind of "natural" computation has some peculiar properties, which distinguish it from traditional optimization and should be taken into account in the process of TST. Firstly, the BGF landscape compensation error should be considered as the nanorobots fabricated with natural materials could cause physical, chemical, and biological interactions with the tissue microenvironment [25], [26]. Secondly, the landscape quantization error caused by the the discrete human microvascular network is a basic characteristic of natural computation [27].…”

Section: A Tumor Sensitization and Targeting As In Vivo Computationmentioning

confidence: 99%

Nanorobots-Assisted Natural Computation for Multifocal Tumor Sensitization and Targeting

Shi

Yan

Xiong

et al. 2021

IEEE Trans.on Nanobioscience

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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 tumortriggered 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

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Section: A Tumor Sensitization and Targeting As In Vivo Computationmentioning

confidence: 99%

Nanorobots-Assisted Natural Computation for Multifocal Tumor Sensitization and Targeting

Shi

Yan

Xiong

et al. 2021

IEEE Trans.on Nanobioscience

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“…For example, the process of natural selection inspired the development of the classical genetic algorithm to solve complex optimization and search problems. It is also stimulating to look the other way by exploiting computing strategies for biomedical applications [30], [31]. There is an intriguing analogy between the knowledge-aided DTS in an externally manipulable nanosystem for tumor sensitization ( Fig.…”

Section: B Biosensing By Learningmentioning

confidence: 99%

“…This is in contrast to a traditional iterative method using a non-interacting approximate solution. An external observer can then infer the domain by monitoring the movement of the guess ("seeing-is-sensing" [31]), where the (n + 1) th approximation is derived from the n th one. This strategy is within the general framework of computing-inspired bio-detection proposed in our previous work [30].…”

Section: B Biosensing By Learningmentioning

confidence: 99%

Biosensing-by-Learning Direct Targeting Strategy for Enhanced Tumor Sensitization

Chen

Ali

Shi

et al. 2019

IEEE Trans.on Nanobioscience

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Objective: We propose a novel iterative-optimizationinspired direct targeting strategy (DTS) for smart nanosystems, which harness swarms of externally manipulable nanoswimmers assembled by magnetic nanoparticles (MNPs) for knowledgeaided tumor sensitization and targeting. We aim to demonstrate through computational experiments that the proposed DTS can significantly enhance the accumulation of MNPs in the tumor site, which serve as a contrast agent in various medical imaging modalities, by using the shortest possible physiological routes and with minimal systemic exposure.Methods: The epicenter of a tumor corresponds to the global maximum of an externally measurable objective function associated with an in vivo tumor-triggered biophysical gradient; the domain of the objective function is the tissue region at a high risk of malignancy; swarms of externally controllable magnetic nanoswimmers for tumor sensitization are modeled as the guess inputs. The objective function may be resulted from a passive phenomenon such as reduced blood flow or increased kurtosis of microvasculature due to tumor angiogenesis; otherwise, the objective function may involve an active phenomenon such as the fibrin formed during the coagulation cascade activated by tumortargeted "activator" nanoparticles. Subsequently, the DTS can be interpreted from the iterative optimization perspective: guess inputs (i.e., swarms of nanoswimmers) are continuously updated according to the gradient of the objective function in order to find the optimum (i.e., tumor) by moving through the domain (i.e., tissue under screening). Along this line of thought, we propose the computational model based on the gradient descent (GD) iterative method to describe the GD-inspired DTS, which takes into account the realistic in vivo propagation scenario of nanoswimmers.Results: By means of computational experiments, we show that the GD-inspired DTS yields higher probabilities of tumor sensitization and more significant dose accumulation compared to the "brute-force" search, which corresponds to the systemic targeting scenario where drug nanoparticles attempt to target a tumor by enumerating all possible pathways in the complex vascular network.Conclusion: The knowledge-aided DTS has potential to enhance the tumor sensitization and targeting performance remarkably by exploiting the externally measurable, tumor-triggered biophysical gradients.Significance: We believe that this work motivates a novel biosensing-by-learning framework facilitated by externally manipulable, smart nanosystems.

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“…For example, the process of natural selection inspired the development of the classical genetic algorithm to solve complex optimization and search problems. It is also stimulating to "look the other way" by exploiting computing strategies for biomedical applications [1], [2]. There is an intriguing analogy between iterative optimization and externally controllable cancer detection as depicted in Fig.…”

Section: Introductionmentioning

confidence: 99%

“…This is in contrast to a traditional iterative method using a non-interacting approximate solution. An external observer can then infer the domain by monitoring the movement of the guess ("seeing-is-sensing" [1]), where the ( + 1)th approximation is derived from the th one. This strategy is within the general framework of computinginspired bio-detection proposed in our previous work [2].…”

Section: Introductionmentioning

confidence: 99%

Biosensing by Learning: Cancer Detection as Iterative optimization

Chen

Sharifi

Holmes

et al. 2018

2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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We propose a novel cancer detection procedure (CDP) based on an iterative optimization method. The global minimum of a tumor-induced biological cost function indicates the tumor location, the domain of the cost function is the tissue region at high risk of malignancy, and the time-variant guess input is a swarm of externally controllable and trackable nanorobots for tumor sensing. We consider the spatial distribution of fibrin as the cost function; the fibrin is formed during the coagulation cascade activated by tumor-targeted signalling modules (nanoparticles) and recruits clot-targeted receiving modules (nanorobots) towards the site of disease. Subsequently, the CDP can be interpreted from the iterative optimization perspective: the guess input (i.e., a swarm of nanorobots) is continuously updated according to the gradient of the cost function in order to find the optimum (i.e., cancer) by moving through the domain (i.e., tissue under screening). Along this line of thought, we consider the gradient descent (GD) iterative method, and propose the GD-inspired CDP, which takes into account the realistic in vivo propagation scenario of nanorobots. Finally, we present numerical examples to demonstrate the features of the GD-inspired CDP.

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Green Touchable Nanorobotic Sensor Networks

Cited by 27 publications

References 13 publications

Nanorobots-Assisted Natural Computation for Multifocal Tumor Sensitization and Targeting

Nanorobots-Assisted Natural Computation for Multifocal Tumor Sensitization and Targeting

Biosensing-by-Learning Direct Targeting Strategy for Enhanced Tumor Sensitization

Biosensing by Learning: Cancer Detection as Iterative optimization

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