Hydrogels are attractive biomaterials with favorable characteristics due to their water uptake capacity. However, hydrogel properties are determined by the cross-linking degree and nature, the tacticity, and the crystallinity of the polymer. These biomaterials can be sorted out according to the internal structure and by their response to external factors. In this case, the internal interaction can be reversible when the internal chains are led by physicochemical interactions. These physical hydrogels can be synthesized through several techniques such as crystallization, amphiphilic copolymers, charge interactions, hydrogen bonds, stereo-complexing, and protein interactions. In contrast, the internal interaction can be irreversible through covalent cross-linking. Synthesized hydrogels by chemical interactions present a high cross-linking density and are employed using graft copolymerization, reactive functional groups, and enzymatic methods. Moreover, specific smart hydrogels have also been denoted by their external response, pH, temperature, electric, light, and enzyme. This review deeply details the type of hydrogel, either the internal structure or the external response. Furthermore, we detail some of the main applications of these hydrogels in the biomedicine field, such as drug delivery systems, scaffolds for tissue engineering, actuators, biosensors, and many other applications.

The study of neuroimaging is a very important tool in the diagnosis of central nervous system tumors. This paper presents the evaluation of seven deep convolutional neural network (CNN) models for the task of brain tumor classification. A generic CNN model is implemented and six pre-trained CNN models are studied. For this proposal, the dataset utilized in this paper is Msoud, which includes Fighshare, SARTAJ, and Br35H datasets, containing 7023 MRI images. The magnetic resonance imaging (MRI) in the dataset belongs to four classes, three brain tumors, including Glioma, Meningioma, and Pituitary, and one class of healthy brains. The models are trained with input MRI images with several preprocessing strategies applied in this paper. The CNN models evaluated are Generic CNN, ResNet50, InceptionV3, InceptionResNetV2, Xception, MobileNetV2, and EfficientNetB0. In the comparison of all CNN models, including a generic CNN and six pre-trained models, the best CNN model for this dataset was InceptionV3, which obtained an average Accuracy of 97.12%. The development of these techniques could help clinicians specializing in the early detection of brain tumors.

Nanomedicine is a new discipline resulting from the combination of nanotechnology and biomedicine. Nanomedicine has contributed to the development of new and improved treatments, diagnoses, and therapies. In this field, nanoparticles have notable importance due to their unique properties and characteristics, which are useful in different applications, including tissue engineering, biomarkers, and drug delivery systems. Electrospinning is a versatile technique used to produce fibrous mats. The high surface area of the electrospun mats makes them suitable for applications in fields using nanoparticles. Electrospun mats are used for tissue engineering, wound dressing, water-treatment filters, biosensors, nanocomposites, medical implants, protective clothing materials, cosmetics, and drug delivery systems. The combination of nanoparticles with nanofibers creates hybrid systems that acquire properties that differ from their components’ characteristics. By utilizing nanoparticles and nanofibers composed of dissimilar polymers, the two synergize to improve the overall performance of electrospinning mats and nanoparticles. This review summarizes the hybrid systems of polymeric nanoparticles and polymeric nanofibers, critically analyzing how the combination improves the properties of the materials and contributes to the reduction of some disadvantages found in nanometric devices and systems.

New medical devices with anti-inflammatory properties are critical to prevent inflammatory processes and infections in medical/surgical procedures. In this work, we present a novel functionalization of silicone for medical use with a polymeric prodrug and a thermosensitive polymer, by graft polymerization (gamma rays), for the localized release of salicylic acid, an analgesic, and anti-inflammatory drug. Silicone rubber (SR) films were functionalized in two stages using graft polymerization from ionizing radiation (⁶⁰Co). The first stage was grafting poly(N-vinylcaprolactam) (PNVCL), a thermo-sensitive polymer, onto SR to obtain SR-g-PNVCL. In the second stage, poly(2-methacryloyloxy-benzoic acid) (P2MBA), a polymeric prodrug, was grafted to obtain (SR-g-PNVCL)-g-P2MBA. The degree of functionalization depended on the concentrations of monomers and the irradiation dose. The films were characterized by attenuated total reflectance Fourier-transform infrared spectroscopy (ATR-FTIR), scanning electron microscopy/energy-dispersive X-ray spectrometry (SEM–EDX), thermogravimetric analysis (TGA), and contact angle. An upper critical solution temperature (UCST) of the films was demonstrated by the swelling degree as a temperature function. (SR-g-PNVCL)-g-P2MBA films demonstrated hydrolysis-mediated drug release from the polymeric prodrug, pH, and temperature sensitivity. GC–MS confirmed the presence of the drug (salicylic acid), after polymer hydrolysis. The concentration of the drug in the release media was quantified by HPLC. Cytocompatibility and thermo-/pH sensitivity of functionalized medical silicone were demonstrated in cancer and non-cancer cells.