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.

Over the last decade oxycodone has become one of the most widely abused drugs. The emergence of oxycodone dependence as a serious health crisis has prompted a major need for animal models of oxycodone dependence with face and predictive validity. Oxycodone use in humans is more prevalent in women (Administration, 2014) and leads to pronounced hyperalgesia and irritability. However, it is unclear if the current animal model of oxycodone self-administration recapitulates these characteristics. We assessed the face validity of an extended access oxycodone self-administration model in rats by examining escalation of oxycodone intake and behavioral symptoms of withdrawal including irritability like behavior and mechanical nociception in male and female rats. We found that male and female rats escalated oxycodone intake over the course of 14 self-administration sessions, however, female rats escalated took more drug than male rats once escalated. When we assessed irritability-like behavior we found no differences between baseline or withdrawal, however when tested immediately after a 12-h self-administration session rats showed a decreased number of aggressive responses and a increased number of defensive responses. When tested for mechanical threshold during withdrawal rats showed pronounced hyperalgesia that was only partially reversed by oxycodone self-administration. The results of the present study demonstrate the face validity of the extended access model of oxycodone self-administration by identifying sex differences in the escalation of oxycodone intake and demonstrating pronounced changes to pain and affective states.

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.

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