Today, the synthesis of biocompatible and bioresorbable composite materials such as “polymer matrix-mineral constituent,” which stimulate the natural growth of living tissues and the restoration of damaged parts of the body, is one of the challenging problems in regenerative medicine. In this study, composite films of bioresorbable polymers of polyvinylpyrrolidone (PVP) and sodium alginate (SA) with hydroxyapatite (HA) were obtained. HA was introduced by two different methods. In one of them, it was synthesized in situ in a solution of polymer mixture, and in another one, it was added ex situ. Phase composition, microstructure, swelling properties and biocompatibility of films were investigated. The crosslinked composite PVP-SA-HA films exhibit hydrogel swelling characteristics, increasing three times in mass after immersion in a saline solution. It was found that composite PVP-SA-HA hydrogel films containing HA synthesized in situ exhibited acute cytotoxicity, associated with the presence of HA synthesis reaction byproducts—ammonia and ammonium nitrate. On the other hand, the films with HA added ex situ promoted the viability of dental pulp stem cells compared to the films containing only a polymer PVP-SA blend. The developed composite hydrogel films are recommended for such applications, such as membranes in osteoplastic surgery and wound dressing.

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<span>Novel coronavirus (COVID-19) is a newly discovered infectious disease that has received much attention in the literature because of its rapid spread and daily global deaths attributable to such disease. The White House, together with a coalition of leading research groups, has published the freely available COVID-19 Open Research Dataset to help the global research community apply the recent advances in natural language processing and other AI techniques in generating novel insights that can support the ongoing fight against this disease. In this paper, the hierarchical and k-means clustering techniques are used to create a tool for identifying similar articles on COVID-19 and filtering them based on their titles. These articles are classified by applying three data mining techniques, namely, random forest (RF), decision tree (DT) and bagging. By using this tool, specialists can limit the number of articles they need to study and pre-process these articles via data framing, tokenisation, normalisation and term frequency-inverse document frequency. Given its 2D nature, the dimensionality of this dataset is reduced by applying t-SNE. The aforementioned data mining techniques are then cross validated to test the accuracy, precision and recall performance of the proposed tool. Results show that the proposed tool effectively extracts the keywords for each cluster, with RF, DT and bagging achieving optimal accuracies of 98.267%, 97.633% and 97.833%, respectively.</span>

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