Urea-Self Powered Biosensors: A Predictive Evolutionary Model for Human Energy Harvesting

The objective of this study is to create a reliable predictive model for the electrochemical performance of self-powered biosensors that rely on urea-based biological energy sources. Specifically, this model focuses on the development of a human energy harvesting model based on the utilization of urea found in sweat, which will enable the development of self-powered biosensors. In the process, the potential of urea hydrolysis in the presence of a urease enzyme is employed as a bioreaction for self-powered biosensors. The enzymatic reaction yields a positive potential difference that can be harnessed to power biofuel cells (BFCs) and act as an energy source for biosensors. This process provides the energy required for self-powered biosensors as biofuel cells (BFCs). To this end, initially, the platinum electrodes are modified by multi-walled carbon nanotubes to increase their conductivity. After stabilizing the urease enzyme on the surface of the platinum electrode, the amount of electrical current produced in the process is measured. The optimal design of the experiments is performed based on the Taguchi method to investigate the effect of urea concentration, buffer concentration, and pH on the generated electrical current. A general equation is employed as a prediction model and its coefficients calculated using an evolutionary strategy. Also, the evaluation of effective parameters is performed based on error rates. The obtained results show that the established model predicts the electrical current in terms of urea concentration, buffer concentration, and pH with high accuracy.

Largest Lyapunov Exponent Optimization for Control of a Bionic-Hand: A Brain Computer Interface Study

This paper introduces a brain control bionic-hand, and several methods have been developed for predicting and quantifying the behavior of a non-linear system such as a brain. Non-invasive investigations on the brain were conducted by means of electroencephalograph (EEG) signal oscillations. One of the prominent concepts necessary to understand EEG signals is the chaotic concept named the fractal dimension and the largest Lyapunov exponent (LLE). Specifically, the LLE algorithm called the chaotic quantifier method has been employed to compute the complexity of a system. The LLE helps us to understand how the complexity of the brain changes while making a decision to close and open a fist. The LLE has been used for a long time, but here we optimize the traditional LLE algorithm to attain higher accuracy and precision for controlling a bionic hand. In the current study, the main constant input parameters of the LLE, named the false nearest neighbor and mutual information, are parameterized and then optimized by means of the Water Drop (WD) and Chaotic Tug of War (CTW) optimizers. The optimized LLE is then employed to identify imaginary movement patterns from the EEG signals for control of a bionic hand. The experiment includes 21 subjects for recording imaginary patterns. The results illustrated that the CTW solution achieved a higher average accuracy rate of 72.31% in comparison to the traditional LLE and optimized LLE by using a WD optimizer. The study concluded that the traditional LLE required enhancement using optimization methods. In addition, the CTW approximation method has the potential for more efficient solutions in comparison to the WD method.

Nanocaged platforms: modification, drug delivery and nanotoxicity. Opening synthetic cages to release the tiger

Nanocages (NCs) have emerged as a new class of drug-carriers, with a wide range of possibilities in multi-modality medical treatments and theranostics. Nanocages can overcome such limitations as high toxicity caused by anti-cancer chemotherapy or by the nanocarrier itself, due to their unique characteristics. These properties consist of: (1) a high loading-capacity (spacious interior); (2) a porous structure (analogous to openings between the bars of the cage); (3) enabling smart release (a key to unlock the cage); and (4) a low likelihood of unfavorable immune responses (the outside of the cage is safe). In this review, we cover different classes of NC structures such as virus-like particles (VLPs), protein NCs, DNA NCs, supramolecular nanosystems, hybrid metal-organic NCs, gold NCs, carbon-based NCs and silica NCs. Moreover, NC-assisted drug delivery including modification methods, drug immobilization, active targeting, and stimulus-responsive release mechanisms are discussed, highlighting the advantages, disadvantages and challenges. Finally, translation of NCs into clinical applications, and an up-to-date assessment of the nanotoxicology considerations of NCs are presented.