Recent advances in nanocarriers containing Bromelain: In vitro and in vivo studies

Medicinal products of plant origin have long been considered the most affordable and accessible sources to treat different health problems. Bromelain (Br) is a mixture of enzymes derived from pineapple (Ananas comosus L.) with a wide field of applications including medicine, health, food, and cosmetics. Br has various therapeutic effects, such as antimicrobial, antioxidant, anticancer, wound healing, burn treatment, pain relief, anti-inflammatory, inhibition of platelet aggregation, and fibrinolytic activity. On the other hand, most proteins are susceptible to denaturation and structural changes that may reduce their activities. Encapsulation of drug molecules into nanoparticles (NPs) could increase their stability, bioavailability, and overcome other challenges in drug delivery and therapy. This review aimed to highlight various Br nano-formulations approaches, toward the improvement of Br therapeutic efficiency

On the possibility of using waste disposable gloves as recycled fibers in sustainable 3D concrete printing using different additives

Abstract Due to the Covid-19 pandemic, using large amounts of personal protective equipment (PPE) throughout the world has extensively increased in recent years. The lack of a practical method to dispose of these recycled materials is one of the main concerns of researchers. Hence, comprehensive experimental tests were conducted in the present study to investigate the feasibility of using disposable gloves in mortars to achieve a sustainable mixture. Accordingly, latex and vinyl gloves as recycled fibers were considered in the experimental program to improve the sustainability of 3D printing concrete. As using these recycled materials causes some deficiencies for printing layers, different mineral and chemical admixtures were used in the present study, including graphene oxide nanomaterials, polyvinyl alcohol, Cloisite 15A nanoclay, and micro silica fume. Also, the hybrid use of latex, vinyl, and polypropylene (PP) fiber was considered to improve the printability of concrete mixtures containing waste fibers. Moreover, the effect of internal reinforcement was also considered by using plain steel wire mesh to increase the composite behavior of printed layers in this simplified experimental program. Results indicate that the synergic influence of recycled fibers and admixtures meaningfully enhanced the 3D printing properties of mortar so that about 20%, 80%, 50%, and more than 100% improvements were obtained for workability, direct tensile strength, flexural strength, and buildability index respectively. However, an average percentage − 28.3% reduction was recorded for the concrete compressive strength. Sustainability analysis also showed that using waste disposable gloves considerably reduced CO2 emissions

An efficient machine learning approach for predicting concrete chloride resistance using a comprehensive dataset

Abstract By conducting an analysis of chloride migration in concrete, it is possible to enhance the durability of concrete structures and mitigate the risk of corrosion. In addition, the utilization of machine learning techniques that can effectively forecast the chloride migration coefficient of concrete shows potential as a financially viable and less complex substitute for labour-intensive experimental evaluations. The existing models for predicting chloride resistance encounter two primary challenges: the constraints imposed by a limited dataset and the absence of certain input variables. These factors collectively contribute to a decrease in the overall effectiveness of these models. Therefore, this study aims to propose an advanced approach for dataset cleaning, utilizing a comprehensive experimental dataset comprising 1073 pre-existing experimental outcomes. The proposed model for predicting the chloride diffusion coefficient incorporates various input variables, such as water content, cement content, slag content, fly ash content, silica fume content, fine aggregate content, coarse aggregate content, superplasticizer content, fresh density, compressive strength, age of compressive strength test, and age of migration test. The utilization of the artificial neural network (ANN) technique is also employed for the processing of missing data. The current supervised learning incorporates both regression and classification tasks. The efficacy of the proposed models for accurately predicting the chloride diffusion coefficient has been effectively validated. The findings indicate that the XGBoost and SVM algorithms exhibit superior performance compared to other regression prediction algorithms, as evidenced by their high R2 scores of 0.94 and 0.91, respectively. In relation to classification algorithms, the findings demonstrate that the Random Forest, LightGBM, and XGBoost models exhibit the highest levels of accuracy, specifically 0.93, 0.96, and 0.97, respectively. Furthermore, a website has been developed that is capable of predicting the chloride migration coefficient and chloride penetration resistance of concrete