Latticed Channel Model of Touchable Communication Over Capillary Microcirculation Network

Tension-Relaxation In Vivo Computing Principle for Tumor Sensitization and Targeting

By modeling the tumor sensitization and targeting (TST) as a natural computational process, we have proposed the framework of nanorobots-assisted in vivo computation. The externally manipulable nanorobots are steered to detect the tumor in the high-risk tissue, which is analogous to the process of searching for the optimal solution by the computing agents in the search space. To overcome the constraint of a nanorobotic platform that can only generate a uniform magnetic field to actuate the nanorobots, we have proposed the weak priority evolution strategy (WP-ES) in our previous works. However, these works do not consider the proportions of the nanorobot control and tracking operations, which are part and parcel of in vivo computation as the control operation aims at searching for the tumor effectively while the tracking mode is used for gathering information about the biological gradient function (BGF). Careful planning about the durations spent in these operations is needed for optimal performance of the TST strategy. To account for this issue, in the current article, we propose a novel computational principle, called the tension-relaxation (T-R) principle, to balance the displacements of nanorobots during each control and tracking cycle. Furthermore, we build three tumor vascular models with different sizes to represent three different targeting regions as the morphology of tumor vasculature determined by the tumor growth process is an important factor affecting TST. We carry out the computational experiments for tumors with three different sizes for several representative landscapes by introducing the T-R principle into the WP-ES-based swarm intelligence algorithms and considering the realistic internal constraints. The experimental outcomes demonstrate the effectiveness of the proposed TST strategy.

In Vivo Computing for Smart Tumor Targeting in Taxicab-Geometry Vasculature

This paper investigates the tumor microenvironment regulated by densely interconnected capillaries, resulting in the distribution of tumor-induced biological gradient field (BGF) in taxicab-geometry vasculature (TGV). We aim to improve the efficiency of tumor targeting with the knowledge of BGF in TGV, which is facilitated by a swarm of magnetic nanorobots. An external system observes and records the nanorobot swarm (NS) reaction to the BGF. Then the NS is controlled to move toward the potential tumor location by an external magnetic field. In this way, the BGF formed under the constraint of TGV is the objective function to be optimized, where the tumor center corresponds to the maximum value. The high-risk tissue area is the domain of the objective function, while the NS plays the role of a computing agent. Subsequently, we propose the coordinate gradient descent (CGD) targeting strategy for NS steering. This strategy estimates the BGF in the direction perpendicular to the propagation direction of NS to improve the efficiency of tumor detection. In addition, it considers the limited lifespan of NS in vivo, where a memory step-size mechanism (MSM) is utilized to reduce the targeting time. We use computational experiments to show that the CGD strategy yields higher tumor-targeting probabilities than the brute-force search and the original gradient-descent-inspired targeting strategy for the BGF subject to TGV.

An Optimal Strategy for Individualized Drug Delivery Therapy: A Molecular Communication Inspired Waveform Design Perspective

To enhance the drug delivery therapy efficacy and reduce the adverse effects on patients, we propose a molecular communication (MC) inspired optimization strategy to maintain the locoregional concentration of drug within a therapeutic window, the safe drug concentration range for individualized therapy. Observing the parallels between the propagation of information molecules and the delivery of drugs, MC, as a new paradigm, has drawn devised to overcome the challenges of the drug delivery system. The drug delivery is mapping to the transmission process of information molecules from the transmitter to the receiver. In addition, the locoregional concentration-time profile of the administered drug particles corresponds to the signalling waveform. Different from conventional drug delivery strategy, this work focuses on locoregional concentration instead of plasma concentration. Furthermore, with sustained-release preparations schemes, we also propose an algorithm to obtain the optimal administration time. The simulation results demonstrate that this strategy effectively maintains the relative steady-state drug concentration.

A Feasibility Study of In Vivo Control and Tracking of Microrobot Using Taxicab Geometry for Direct Drug Targeting

In vivo direct drug targeting aims at delivering drug molecules loaded on microrobots to the diseased site using the shortest possible physiological routes, which potentially improves targeting efficiency and reduces systemic toxicity. It is thus essential to consider realistic in-body limitations for direct drug targeting applications. Here, we present a novel controller for microrobot maneuver by considering four key in vivo constraints: non-Euclidean structure of capillaries, irreversibility of blood flow, invisibility of microvasculature, and inaccuracy of microrobot tracking. We use the taxicab geometry of capillaries as the a priori knowledge for steering and tracking a microrobot in lattice-like vessels. Furthermore, we introduce a minimax repulsive boundary function to prevent the microrobot from getting too close to the boundaries imposed by the direction of blood flow. We also propose a novel Kalman filtering algorithm to reduce tracking error, while avoiding possible obstacles such as vessel walls without knowing their actual locations. The proposed control method consists of four modules, namely a model predictive control module for tumor targeting, a Kalman filtering module for microrobot tracking, a blind obstacle detection module, and a vessel structure estimation module. The interplay of these four modules offers successful maneuver and tracking of the microrobot while avoiding obstacles in a blind manner by utilizing the taxicab geometry of blood vessels. We present a comprehensive in silico simulation study to verify our designed controller.

A Molecular Communication Detection Method for the Deformability of Erythrocyte Membrane in Blood Vessels

Molecular communication (MC) inspired drug delivery holds considerable promise as a new design for targeted therapy with high efficiency and minimal toxicity. The process of drug delivery can be modelled in a blood flow-based MC system, where nanoparticles (NPs) carry therapeutic agents through the blood vessel channels to the targeted diseased tissue. Most previous studies in the flow-based MC consider a Newtonian fluid with a laminar flow, which ignores the influence of red blood cells (RBCs). However, the nature of blood flow is a complex and non-Newtonian fluid composed of proteins, platelets, plasma and deformable cells, especially RBCs. The ability to change their shapes is essential to the proper functioning of RBCs in the microvasculature. Different shapes of RBCs have a great impact on the performance of blood flow. Changes in the properties and shapes of RBCs are often associated with different diseases, such as sickle cell anemia, diabetes, and malaria. Thus, it is highly important to establish a more realistic blood flow MC model considering the deformable cells. According to our previous study, the motion and adhesion of individual NPs are modelled through the Brownian adhesion dynamics. Subsequently, this paper establishes a particle-cell hybrid model in the flow-based MC, which focuses on the RBC deformation, aggregation, and dispersion in the blood suspension. Based on the state of the RBC deformation and aggregation in the vessels with different flow rates, this paper proposes a novel methodology for detecting the deformability of the cells. The blood state in terms of RBC deformability is determined by the difference in NPs' concentration at the receiving end., this paper sheds some light on the influence of RBCs on the motion of NPs, which provides new insights on the design of targeted drug delivery and the detection of vascular diseases.