The biological compatibility of endodontic sealers is a key determinant of periapical tissue healing. This in vitro study investigated the cytotoxic, pro-inflammatory, and redox-related effects of eight endodontic sealers on human periodontal ligament fibroblasts (HPdLFs): Biopulp (Chema-Elektromet), AH Plus (Dentsply Sirona), MTA Fillapex (Angelus), EndoSeal MTA (Maruchi), GuttaFlow (Coltène), AH Plus Bioceramic (Dentsply Sirona), TotalFill BC (FKG Dentaire SA), and BioRoot TM (Septodont). Cells were exposed for 24 h to 10-fold-diluted sealer extracts prepared in accordance with the manufacturers’ instructions, while control samples underwent identical procedures without sealer contact. Oxidative stress biomarkers, antioxidant defense parameters, protein oxidation indices, apoptotic activity (caspase-3), pro-inflammatory cytokines (IL-1, IL-6), and cell viability (MTT assay) were assessed. Under the applied conditions, all materials induced only limited global oxidative stress, with most alterations reflecting selective protein and glycoxidative modifications. Nevertheless, AH Plus, MTA Fillapex, and the calcium hydroxide-based Biopulp exhibited a less favorable redox profile and greater protein oxidation compared with calcium silicate-based sealers. AH Plus and EndoSeal MTA were associated with increased IL-6 release, whereas EndoSeal MTA moderately elevated IL-1 levels. BioRoot TM demonstrated the lowest cytokine expression, and TotalFill BC preserved high cell viability. Caspase-3 activity remained comparable across all experimental groups, indicating minimal induction of apoptosis.

Radioactive iodine therapy requires individualized decisions regarding patient selection, administered activity planning, imaging assessment, and follow-up. This systematic review summarizes current applications of artificial intelligence (AI) in the ¹³¹ I treatment pathway and evaluates barriers limiting clinical implementation. Following PRISMA 2020 guidelines, PubMed, Scopus, Web of Science, and Wiley databases were searched for original studies involving human patients treated with ¹³¹ I for differentiated thyroid cancer or benign thyroid disease, where AI, deep learning (DL), or machine learning (ML) methods were utilized. Evidence was synthesized, and study quality was assessed using the NIH/NHLBI Study Quality Assessment Tool. Among 775 identified records, 24 studies met inclusion criteria. AI applications included pretreatment decision support, detection and characterization of iodine-avid disease, dosimetry and activity optimization, radiation-safety assessment, treatment response prediction, levothyroxine dose estimation, and recurrence-risk stratification. The available evidence was dominated by retrospective, single-center studies, primarily focused on differentiated thyroid cancer, with limited external validation. AI has the potential to improve standardization and personalization of ¹³¹ I therapy, but heterogeneous methodologies and insufficient evidence of clinical utility currently restrict routine adoption. Prospective multicenter studies with harmonized endpoints and robust validation are required to facilitate clinical translation.

This article addresses the issue of the conceptualisation of diabetes based on statements made by patients hospitalised in Białystok. The analysis falls within the scope of narrative medicine, which views the patient holistically – as an individual functioning on physical, psychological, and social levels. Given the cultural and social nature of illness, the topic also invites reflection within the field of cognitive linguistics. The analysis has shown that diabetes affects many aspects of life and requires patients to develop adaptive mechanisms, the understanding of which may support more effective therapeutic interventions.