Ant-Behavior Inspired Intelligent NanoNet for Targeted Drug Delivery in Cancer Therapy

Point biserial correlation symbiotic organism search nanoengineering based drug delivery for tumor diagnosis

Nanoparticulate systems have the prospect of accounting for a new making of drug delivery systems. Nanotechnology is manifested to traverse the hurdle of both physical and biological sciences by implementing nanostructures indistinct fields of science, particularly in nano-based drug delivery. The low delivery efficiency of nanoparticles is a critical obstacle in the field of tumor diagnosis. Several nano-based drug delivery studies are focused on for tumor diagnosis. But, the nano-based drug delivery efficiency was not increased for tumor diagnosis. This work proposes a method called point biserial correlation symbiotic organism search nanoengineering-based drug delivery (PBC-SOSN). The objective and aim of the PBC-SOSN method is to achieve higher drug delivery efficiency and lesser drug delivery time for tumor diagnosis. The contribution of the PBC-SOSN is to optimized nanonengineering-based drug delivery with higher r drug delivery detection rate and smaller drug delivery error detection rate. Initially, raw data acquired from the nano-tumor dataset, and nano-drugs for glioblastoma dataset, overhead improved preprocessed samples are evolved using nano variational model decomposition-based preprocessing. After that, the preprocessed samples as input are subjected to variance analysis and point biserial correlation-based feature selection model. Finally, the preprocessed samples and features selected are subjected to symbiotic organism search nanoengineering (SOSN) to corroborate the objective. Based on these findings, point biserial correlation-based feature selection and a symbiotic organism search nanoengineering were tested for their modeling performance with a nano-tumor dataset and nano-drugs for glioblastoma dataset, finding the latter the better algorithm. Incorporated into the method is the potential to adjust the drug delivery detection rate and drug delivery error detection rate of the learned method based on selected features determined by nano variational model decomposition for efficient drug delivery.

Target Convergence Analysis of Cancer-Inspired Swarms for Early Disease Diagnosis and Targeted Collective Therapy

Sensing and perception is generally a challenging aspect of decision-making. In the nanoscale, however, these processes face further complications due to the physical limitations of devising the nanomachines with more limited perception, more noise, and fewer sensors. There is, hence, higher dependence on swarm sensing and perception of many nanomachines. Here, taking hardware and software bioinspiration, we propose Chemo-Mechanical Cancer-Inspired Swarm Perception (CMCISP) based on online nano fuzzy haptic feedback for early disease diagnosis and targeted therapy. Particularly, we use epithelial cancer cell's scaffold as a carrier, its properties as a distributed perception mechanism, and its motility patterns as the swarm movements such as anti-durotaxis, blebbing, and chemotaxis. We implement the in-silico model of CMCISP using a hybrid computational framework of the cellular Potts model, swarm intelligence, and fuzzy decision-making. Furthermore, the target convergence of CMCISP is analytically proved using swarm control theory. Finally, several numerical experiments and validations for cancer target therapy, cellular stiffness measurement, anti-durotaxis movement, and robustness analysis are also conducted and compared with a mathematical chemotherapy model and authors' previous works on targeted therapy. Results show improvements of up to 57.49% in early cancer detection, 26.64% in target convergence, and 68.01% in increased normoxic cell density. The study also reveals the strategy's robustness to environmental/sensory noise by applying six SNR levels of 0, 2, 5, 10, 30, and 50 dB, with an average diagnosis error of only 0.98% and at most 2.51%.

Release Rate Optimization in Molecular Communication for Local Nanomachine-based Targeted Drug Delivery

Targeted drug delivery has become an important direction in anticancer therapy research. In nanomachine-based targeted drug delivery, where a nanomachine containing anticancer drugs moves towards cancer cells and releases drugs to kill cancer cells, it should be noted that the nanomachine has limited space to carry drugs, and on the other hand the cancer cells have finite receptors to bind drugs. Therefore, to efficiently utilize cancer drugs, this paper aims to calculate and optimize drug release rate of nanomachines to produce a full drug response in local targeted drug delivery. A drug reception model reflecting ligand-receptors binding is established based on M/M/c/c queue. The minimum released concentration of drug molecules is derived from the minimum effective occupancy ratio of receptors according to the drug occupancy theory. We then derive the optimized release rates of each nanomachine from the minimum effective concentration of drug molecules according to diffusion channel response in terms of continuous emission of single nanomachine and multi-nanomachine, respectively. The simulation results match well with the analytical results. The study paves the way for designing local targeted drug delivery systems.

Clock Synchronization for Mobile Molecular Communication Systems

Molecular communication (MC) is a promising paradigm, which conveys messages at the nano- or micro- scale for information exchange by using molecules or particles as signal carriers. Clock synchronization between transmitters and receivers in MC systems plays an important role in information exchange and nanodevices' collaboration. The current research about the synchronization mainly focuses on fixed MC systems. However, the movements of transmitters and receivers commonly exist in MC systems. In this paper, two clock synchronization schemes, the least square method and the peak time method, are proposed to estimate the clock offset between a mobile transmitter and a mobile receiver in mobile MC systems. The synthesis time of information molecules is also taken into consideration in the proposed schemes, and by using different types of molecules, the influence of the synthesis time of molecules can be solved. The effects of the movement of receiver on the received signal are discussed. The performance of the proposed schemes is evaluated by simulations and discussed.

Initial Distance Estimation and Signal Detection for Diffusive Mobile Molecular Communication

Mobile molecular communication (MC) attracts much attention in recent years where mobile nanomachines exchange information using molecules. In this paper, we consider a diffusion-based mobile MC system consisting of a pair of diffusive nanomachines. Due to the Brownian motion of nanomachines, the communication distance between the transmitter and the receiver is dynamic. Thus, the channel impulse response (CIR) is a stochastic process. The stochastic CIR brings the difficulty in the detection process. Contrast to the common static MC characterized by the deterministic CIR, in a mobile MC system, the receiver needs to estimate the dynamic distance for CIR reconstruction and detection threshold setting at each bit interval, which achieve high computational complexity. To tackle these difficulties, a new detection technique for mobile MC is proposed in this paper. It is unnecessary to estimate the dynamic communication distance at each bit interval. Instead, the receiver estimates the initial distance between it and the transmitter. The estimated initial distance can be used to reconstruct the statistical characteristics of CIR, setting detection threshold for all the bit intervals in advance. To achieve this goal, a novel two-step scheme based on maximum likelihood (ML) is proposed to estimate the initial distance. In the first step, the transmitter releases some molecules as a pilot signal before the information bits transmission. Then the receiver estimates releasing distance by observations of received signal. In the second step, the estimated value obtained in the previous step is used to estimate the initial distance by ML estimation. The performances of proposed two-step scheme and the detection technique are evaluated via particle-based simulation of the Brownian motion.