The aim of the study was to evaluate the effects of incorporation of Al₂O₃, ZrO₂ and TiO₂ nanoparticles into glass–ionomer cements (GICs). Two different GICs were used in the study. Four groups were prepared for each material: the control group (without nanoparticles) and three groups modified by the incorporation of nanoparticles at 2, 5 or 10 wt %, respectively. Cements were mixed and placed in moulds (4 mm × 6 mm); after setting, the samples were stored in saline (one day and one week). Compressive strengths were measured and the morphology of the fractured surfaces was analyzed by scanning electron microscopy. The elements released into the storage solutions were determined by Inductively coupled plasma-optical emission spectrometry (ICP-OES). Addition of nanoparticles was found to alter the appearance of cements as examined by scanning electron microscopy. Compressive strength increased with the addition of ZrO₂ and especially TiO₂ nanoparticles, whereas the addition of Al₂O₃ nanoparticles generally weakened the cements. The ion release profile of the modified cements was the same in all cases. The addition of Al₂O₃, ZrO₂ and TiO₂ nanoparticles into GICs is beneficial, since it leads to reduction of the microscopic voids in the set cement. Of these, the use of ZrO₂ and TiO₂ nanoparticles also led to increased compressive strength. Nanoparticles did not release detectable levels of ions (Al, Zr or Ti), which makes them suitable for clinical use.

Application of mesenchymal stem cells (MSC) in regenerative therapeutic procedures is becoming an increasingly important topic in medicine. Since the first isolation of dental tissue-derived MSC, there has been an intense investigation on the characteristics and potentials of these cells in regenerative dentistry. Their multidifferentiation potential, self-renewal capacity, and easy accessibility give them a key role in stem cell-based therapy. So far, several different dental stem cell types have been discovered and their potential usage is found in most of the major dental medicine branches. These cells are also researched in multiple fields of medicine for the treatment of degenerative and inflammatory diseases. In this review, we summarized dental MSC sources and analyzed their treatment modalities with particular emphasis on temporomandibular joint osteoarthritis (TMJ OA).

Objective: The objective was to compare the efficacy of three different lasers in disinfection of root canals inoculated with Enterococcus faecalis and Candida albicans biofilms. Background: Endodontic space disinfection depends on the type of root canal irrigant used and the way it is delivered and agitated because irrigants have limited ability to reach all parts of root canal system. Materials and methods: Thirty single-rooted human teeth were selected. Root canals were instrumented and root surfaces were sealed using adhesive and the apical openings with adhesive and composite resin. Roots were fixed in Eppendorf tubes and sterilized in autoclave. The specimens were randomly divided into three experimental groups ( n = 10) and inoculated with E. faecalis and C. albicans . After 7 days of incubation period, the number of E. faecalis and C. albicans colony-forming units (CFUs) was determined for each root canal. In the first experimental group, Er:YAG laser (0.3 W) with photon-induced photoacoustic streaming technique was used for root canal disinfection, in the second, Nd:YAG laser (1.5 W), and in the third, Er,Cr:YSGG (1.25 W) laser was used. After different root canal disinfection protocols, the number of E. faecalis and C. albicans CFUs was determined again for each root canal. Results: Er:YAG and Er,Cr:YSGG lasers eradicated significant number of E. faecalis and C. albicans CFUs ( p < 0.05), while Nd:YAG laser irradiation did not result in statistically significant reduction ( p > 0.05). Er,Cr:YSGG laser eradicated significantly more microorganisms in comparison with Er:YAG laser ( p < 0.05). Conclusions: Er,Cr:YSGG laser was the most efficient tool in eradication of E. faecalis and C. albicans biofilms.

Background and Objectives: The purpose of this study was to develop and evaluate a deep learning model capable of autonomously detecting and segmenting radiolucent lesions in the lower jaw by utilizing You Only Look Once (YOLO) v8. Materials and Methods: This study involved the analysis of 226 lesions present in panoramic radiographs captured between 2013 and 2023 at the Clinical Hospital Dubrava and the School of Dental Medicine, University of Zagreb. Panoramic radiographs included radiolucent lesions such as radicular cysts, ameloblastomas, odontogenic keratocysts (OKC), dentigerous cysts and residual cysts. To enhance the database, we applied techniques such as translation, scaling, rotation, horizontal flipping and mosaic effects. We have employed the deep neural network to tackle our detection and segmentation objectives. Also, to improve our model’s generalization capabilities, we conducted five-fold cross-validation. The assessment of the model’s performance was carried out through metrics like Intersection over Union (IoU), precision, recall and mean average precision (mAP)@50 and mAP@50-95. Results: In the detection task, the precision, recall, mAP@50 and mAP@50-95 scores without augmentation were recorded at 91.8%, 57.1%, 75.8% and 47.3%, while, with augmentation, were 95.2%, 94.4%, 97.5% and 68.7%, respectively. Similarly, in the segmentation task, the precision, recall, mAP@50 and mAP@50-95 values achieved without augmentation were 76%, 75.5%, 75.1% and 48.3%, respectively. Augmentation techniques led to an improvement of these scores to 100%, 94.5%, 96.6% and 72.2%. Conclusions: Our study confirmed that the model developed using the advanced YOLOv8 has the remarkable capability to automatically detect and segment radiolucent lesions in the mandible. With its continual evolution and integration into various medical fields, the deep learning model holds the potential to revolutionize patient care.