Ana Zulijani

Background and Objectives: The rehabilitation of the atrophic posterior maxilla with pterygoid implant presents a significant challenge due to complex regional anatomy and limited visual access. This study aimed to compare the accuracy of a dynamic computer-assisted implant surgery system (dCAIS) for pterygoid implant placement in fully and partially edentulous maxillae using radiographic marker registration (RMR) and markerless tracing registration (MTR), respectively. Materials and Methods: Forty pterygoid implants were retrospectively evaluated in 40 patients, divided into two groups: fully edentulous (n = 20) and partially edentulous (n = 20). Implant placement was performed using a dynamic navigation system (dCAIS), utilizing either bone-anchored mini-screws or tooth-surface tracing as reference points. Accuracy was assessed by superimposing preoperative plans with postoperative cone beam computer tomography (CBCT) scans. Coronal, apical, and angular deviations were measured and analyzed using the Data Science Workbench (version 14). The level of statistical significance was set at α = 0.05 (two-tailed). Results: The mean deviations for fully and partially edentulous maxillae measured 1.24 ± 0.27 mm and 1.40 ± 0.27 mm at the coronal level (p = 0.065), 1.30 ± 0.49 mm and 1.30 ± 0.54 at the apical level (p = 0.995), and 0.72 ± 0.38° and 0.82 ± 0.43° in angular deviation (p = 0.560), respectively. No statistically significant differences were found between the two groups. Notably, mean angular deviation was remarkably low (Conclusions: No statistically significant between-group differences in accuracy were detected in coronal, apical, and angular deviations between the markerless tracing registration method in partially edentulous patients and the bone-anchored radiographic marker registration method in fully edentulous patients.

Survival rates for children treated for malignant diseases continue to improve, yet many survivors face persistent late oral complications that affect function, aesthetics, and quality of life. Oncological therapy, especially at a young age and following head and neck radiotherapy or intensive chemotherapy, can disrupt dental and craniofacial development, resulting in dental developmental disorders, enamel defects, salivary gland dysfunction, caries susceptibility, periodontal problems, trismus, and osteoradionecrosis of the jaw. Although these effects are partially known, they are frequently underrecognized in routine practice, and many children do not receive adequate long-term dental follow-up. A key challenge highlighted in the recent literature is the absence of structured, evidence-based guidelines for monitoring and managing late oral effects. The article emphasizes the need for clearer recommendations, better communication of oncological treatment histories, and stronger integration of dental professionals within survivorship care. Developing standardized follow-up protocols will be essential to ensure timely detection, consistent management, and improved oral health outcomes for childhood cancer survivors. This article is intended as a narrative review, synthesizing available evidence from key publications to highlight clinically relevant late oral complications and gaps in current survivorship care.

The aim of the study was to determine potential deviation in measurements of commonly used orthodontic variables, including intermolar width (IMW), intercanine width (ICW), and arch perimeter (AP), when measured on three-dimensional images compared to measurements performed on plaster models obtained from alginate impressions using a hand-held digital vernier caliper. A total of 120 sets of models were divided into four groups: plaster models made from an alginate impression, digitized plaster models made from an alginate and silicone impression, and digital models obtained via intraoral scan. The repeated measurements analysis of variance (ANOVA) with Sidak post-hoc test was applied to test differences in measurements between the four methods. The results show better agreement for intermolar and intercanine width (r = 0.468–0.967) and smaller measurement error (ME = 0.53–1.07) compared to arch length (r = 0.422–0.477; ME = 2.04–2.89). Better agreement levels were more frequently observed between plaster models with intraoral scans than between digitized plaster models. The deviations in intermolar and intercanine width measurements between the hand-held digital vernier caliper and digital analyses were found to be clinically insignificant. The observed difference in arch perimeter suggests that curved measurements may not be clinically acceptable.

(1) Background: The objective of this study was to assess which digitization method produces the biggest deviation in the 3D images of tooth size from plaster models made using alginate impressions, which are considered the gold standard in orthodontics. (2) Methods: The sample used in this study included 30 subjects (10 males and 20 females). Measurements were made on four types of models: (1) digital models obtained through intraoral scanning and digitized models of plaster cast made from (2) alginate impressions, (3) silicone impressions, and (4) conventional plaster models. Mesio-distal (MD) and buccal/labial–lingual/palatal (BL) dimensions were measured on the reference teeth of the right side of the jaw (central incisor, canine, first premolar, and first molar). Comparisons of tooth size between the methods were conducted using a repeated measurement analysis of variance and the Friedman test, while the intraclass correlation coefficient was used to determine agreement between the different methods. (3) Results: The results showed a similar level of agreement between the conventional and digital models in both jaws and the anterior, middle, and posterior segments. Better agreement was found for the MD measurements (r = 0.337–0.798; p ≤ 0.05) compared to the BL measurements (r = 0.016–0.542), with a smaller mean difference for MD (0.001–0.50 mm) compared to BL (0.02–1.48 mm) and a smaller measurement error for MD (0.20–0.39) compared to BL (0.38–0.89). There was more frequently a better level of agreement between 3D images than measurements made using a digital caliper on the plaster models with 3D images. (4) Conclusions: The differences in measurements between the digital models and conventional plaster models were small and clinically acceptable.

Megalin (LRP2) is a rapidly recycling multiligand endocytic receptor primarily expressed in polarized epithelial cells. Although megalin might be involved in tumor growth and invasiveness through several mechanisms, its role has been understudied in the field of molecular oncology so far. The present study aimed to evaluate the impact of megalin expression in oral squamous cell carcinoma (OSCC) on disease progression. Megalin expression was evaluated immunohistochemically in 63 OSCC specimens. Data obtained were retrospectively compared with patient clinicopathological features and their survival. The proportion of megalin-expressing cells in the primary OSCC tissue was significantly associated with metastatic spreading to lymph nodes, vascular invasion and lower overall survival rate. Results obtained by the study suggest that megalin can be considered as a novel molecule involved in OSCC pathogenesis, but also useful as a potential biomarker for cancer progression.

Megalin (LRP2) is a rapidly recycling multiligand endocytic receptor, primarily expressed in polarized epithelial cells. Despite megalin could promote tumor growth and invasiveness through several mechanisms, it has been understudied in the field of molecular oncology so far. The present study aimed to evaluate the impact of megalin expression in oral squamous cell carcinoma (OSCC) on disease progression. Megalin expression was evaluated immunohistochemically in 63 OSCC specimens. Data obtained were retrospectively compared with patients’ clinicopathological features and survival. The proportion of megalin-expressing cells in primary OSCC tissue was significantly associated with metastatic spreading to lymph nodes, vascular invasion and lower overall survival rate. Results obtained by the study suggest that megalin can be considered as a novel molecule involved in OSCC pathogenesis, but also useful as a potential biomarker for cancer progression.

Chemotherapy used on pediatric patients especially those below 3 years of age causes disturbances in dental development. The aim of this case report was to present the late dental effects of chemotherapy in a patient treated for anaplastic ependymoma (WHO III) at an early age. Radiographic findings at the age of 9 years showed oligomicrodontia of six teeth, maxillary lateral incisors, and maxillary and mandibular first premolars. Pediatric cancer survivors after chemotherapy have an increased risk of one or more dental development disorders. To ensure proper dental care and to assess the long-term effects on oral health, tooth development, and occlusion, the involvement of a dentist is crucial. Adequate diagnosis and well-planned treatment of the dental defect can significantly improve patient oral health-related quality of life.

Oral cancer (OC) is among the top ten cancers worlwide, with more than 90% being squamous cell carcinoma. Despite diagnostic and therapeutic development in OC patients’ mortality and morbidity rates remain high with no advancement in the last 50 years. Development of diagnostic tools in identifying pre-cancer lesions and detecting early-stage OC might contribute to minimal invasive treatment/surgery therapy, improving prognosis and survival rates, and maintaining a high quality of life of patients. For this reason, Artificial Intelligence (AI) algorithms are widely used as a computational aid in tumor classification and segmentation to help clinicians in the earlier discovery of cancer and better monitoring of oral lesions. In this paper, we propose an AI-based system for automatic segmentation of the epithelial and stromal tissue from oral histopathological images in order to assist clinicians in discovering new informative features. In terms of semantic segmentation, the proposed AI system based on preprocessing methods and deep convolutional neural networks produced satisfactory results, with 0.878 ± 0.027 mIOU and 0.955 ± 0.014 F1 score. The obtained results show that the proposed AI-based system has a great potential in diagnosing oral squamous cell carcinoma, therefore, this paper is the first step towards analysing the tumor microenvironment, specifically segmentation of the microenvironment cells.

This study aimed to assess the relationship and possible interactions between metallothioneins (MTs) and megalin (LRP-2) in different grades of oral squamous cell carcinoma (OSCC) and premalignant lesions of the oral mucosa (oral leukoplakia and oral lichen planus). The study included archived samples of 114 patients and control subjects. Protein expression was examined by immunohistochemistry and immunofluorescence, and staining quantification was performed by ImageJ software. Protein interaction in cancer tissue was tested and visualized by proximity ligation assay. Mann-Whitney and Kruskal-Wallis tests were used to determine the significance of differences between each group, whereas Pearson correlation coefficient was performed to test correlation. Expression of both proteins differed significantly between each group showing the same pattern of gradual increasing from oral lichen planus to poorly differentiated OSCC. Moreover, MTs and megalin were found to co-express and interact in cancer tissue, and their expression positively correlated within the overall study group. Findings of prominent nuclear and chromosomal megalin expression suggest that it undergoes regulated intramembrane proteolysis upon MTs binding, indicating its ability to directly affect gene expression and cellular division in cancer tissue. The data obtained point to the onco-driving potential of MTs-megalin interaction.

Oral squamous cell carcinoma is most frequent histological neoplasm of head and neck cancers, and although it is localized in a region that is accessible to see and can be detected very early, this usually does not occur. The standard procedure for the diagnosis of oral cancer is based on histopathological examination, however, the main problem in this kind of procedure is tumor heterogeneity where a subjective component of the examination could directly impact patient-specific treatment intervention. For this reason, artificial intelligence (AI) algorithms are widely used as computational aid in the diagnosis for classification and segmentation of tumors, in order to reduce inter- and intra-observer variability. In this research, a two-stage AI-based system for automatic multiclass grading (the first stage) and segmentation of the epithelial and stromal tissue (the second stage) from oral histopathological images is proposed in order to assist the clinician in oral squamous cell carcinoma diagnosis. The integration of Xception and SWT resulted in the highest classification value of 0.963 (σ = 0.042) AUCmacro and 0.966 (σ = 0.027) AUCmicro while using DeepLabv3+ along with Xception_65 as backbone and data preprocessing, semantic segmentation prediction resulted in 0.878 (σ = 0.027) mIOU and 0.955 (σ = 0.014) F1 score. Obtained results reveal that the proposed AI-based system has great potential in the diagnosis of OSCC.