ABSTRACT
Hydrogels are widely used biomaterials in the delivery of therapeutic agents, including drugs, genes, proteins, etc., as well as tissue engineering, due to obvious properties such as biocompatibility and their similarity to natural body tissues. Some of these substances have the feature of injectability, which means that the substance is injected into the desired place in the solution state and then turns into the gel, which makes it possible to administer them from a way with a minimal amount of invasion and eliminate the need for surgery to implant pre-formed materials. Gelation can be caused by a stimulus and/or spontaneously. Suppose this induces due to the effect of one or many stimuli. In that case, the material in question is called stimuli-responsive because it responds to the surrounding conditions. In this context, we introduce the different stimuli that cause gelation and investigate the different mechanisms of the transformation of the solution into the gel in them. Also, we study special structures, such as nano gels or nanocomposite gels.
ABSTRACT
Stimuli-responsive drug delivery has attracted tremendous attention in the past decades. It provides a spatial- and temporal-controlled release in response to different triggers, thus enabling highly efficient drug delivery and minimizing drug side effects. Graphene-based nanomaterials have been broadly explored, and they show great potential in smart drug delivery due to their stimuli-responsive behavior and high loading capacity for an extended range of drug molecules. These characteristics are a result of high surface area, mechanical stability and chemical stability, and excellent optical, electrical, and thermal properties. Their great and infinite functionalization potential also allows them to be integrated into several types of polymers, macromolecules, or other nanoparticles, leading to the fabrication of novel nanocarriers with enhanced biocompatibility and trigger-sensitive properties. Thus, numerous studies have been dedicated to graphene modification and functionalization. In the current review, we introduce graphene derivatives and different graphene-based nanomaterials utilized in drug delivery and discuss the most important advances in their functionalization and modification. Also, their potential and progress in an intelligent drug release in response to different types of stimuli either endogenous (pH, redox conditions, and reactive oxygen species (ROS)) or exogenous (temperature, near-infrared (NIR) radiation, and electric field) will be debated.
ABSTRACT
In the last decade, injectable hydrogels have attracted a lot of attention due to their excellent functional properties in the field of drug delivery for precise, non-invasive and focused tissue locations. Therefore, designing drug delivery systems (DDS) responsive to hydrogel stimuli to release a drug to an external stimulus with various advantages, can be very challenging. Due to their biocompatibility, mucosal adhesion, and hemostatic activity, chitosan (Chitosan)-based hydrogels offer a lot of potential for tissue engineering and drug delivery. It can be difficult to manage the structure of these stimuli-responsive CS hydrogels or they may require additional crosslinking agents, such as hydrogels with dual pH and thermo-responsiveness. Therefore, it is crucial to create these hydrogels for medicinal applications.
ABSTRACT
One of the fundamental requirements for an artificial hand to successfully grasp and manipulate an object is to be able to distinguish different objects' shapes and, more specifically, the objects' surface curvatures. In this study, we investigate the possibility of enhancing the curvature detection of embedded tactile sensors by proposing a ridged fingertip structure, simulating human fingerprints. In addition, a curvature detection approach based on machine learning methods is proposed to provide the embedded sensors with the ability to discriminate the surface curvature of different objects. For this purpose, a set of experiments were carried out to collect tactile signals from a 2 × 2 tactile sensor array, then the signals were processed and used for learning algorithms. To achieve the best possible performance for our machine learning approach, three different learning algorithms of Naïve Bayes (NB), Artificial Neural Networks (ANN), and Support Vector Machines (SVM) were implemented and compared for various parameters. Finally, the most accurate method was selected to evaluate the proposed skin structure in recognition of three different curvatures. The results showed an accuracy rate of 97.5% in surface curvature discrimination.
