Objectives This study aimed to investigate and compare water sorption and solubility properties of current restorative materials with different contents. Materials and Methods Alkasite, self-adhesive restorative material (Cention N, Ivoclar Vivadent AG, Schaan, Liechtenstein), bulk-fill glass hybrid restorative material (EQUIA Forte HT, GC Corp., Tokyo, Japan), nanohybrid universal composite material (OptiShade, Kerr Dental, United States), and bulk-fill composite material (Filtek One Bulk Fill Restorative, 3M ESPE, St. Paul, Minnesota, United States) were used. Samples (n = 6) were prepared (2 × 10 mm) according to the ISO 4049 standards. Water sorption and solubility values were calculated according to the ISO 4049 standards. Statistical Analysis One-way ANOVA, Tukey's post-hoc, Tamhane's T2 post-hoc, Pearson's correlation, and independent samples t-tests were used for statistical analysis (p < 0.05). Results Group EQUIA Forte HT significantly showed the highest water sorption values (57.278 ± 3.174), while Group Filtek One Bulk Fill Restorative exhibited the lowest (4.429 ± 0.174; p < 0.05). The water sorption values for Group Cention N were 5.000 ± 0.542. Group EQUIA Forte HT significantly had the lowest water solubility values (−99.799 ± 1.909), while Group Cention N (−2.966 ± 0.402) significantly exhibited the highest (p < 0.05). There was no significant correlation between water sorption and solubility values for each material (p > 0.05). Conclusion The bulk-fill nano-filled composite resin material was successful in terms of water sorption while the bulk-fill glass hybrid restorative system in terms of water solubility. Alkasite can be recommended to be used as a base material due to its high solubility feature. Monomer, filler type, and amount had an impact on the water sorption and solubility properties of the tested materials.

Currently, there is not any specific treatment for chronic pancreatitis (CP). It was aimed to investigate the effects of melatonin administration on endoplasmic reticulum (ER) stress, oxidative stress, fibrosis, biochemical and histopathological parameters and Abcc2,Abcc5 and Abcg2 gene levels in an experimental rat CP model. Forty rats were randomized into five groups: Sham, CP, CP+25 mg/kg melatonin, CP+50 mg/kg melatonin, and CP+placebo. In all rats except the sham group, a model of chronic pancreatitis was accomplished with intraperitoneal caerulein administration. In treatment groups, melatonin was used as a therapeutic agent. Serum TGF-ß,TNF-?,MDA and GPx levels were studied. Pancreatic tissues were evaluated histopathologically. The expression levels of ?Sma,IR1?,Perk,Abcc2,Abcc5 and Abcg2 genes were measured with the qRT-PCR. Biochemical results of the melatonin groups exhibited favorable changes compared to the CP and placebo groups. ?Sma,IR1?,Perk expression levels were significantly lower in the melatonin groups. The expression levels of Abcc2, Abcc5 and Abcg2 were significantly higher in the CP group compared to the sham group and these gene levels were significantly lower in the melatonin groups compared to the CP group (p<0.01, p<0.05, p<0.05,respectively). In light of these favorable positive results, melatonin may be a useful preventive agent in the course of CP.

Emergency department (ED) overcrowding has necessitated more efficient triage processes. Traditional methods can struggle to keep up with increasing patient volumes, and interest in machine learning-based triage systems is increasing. However, the perspectives of emergency department professionals, who play a critical role in triage decision-making, are often overlooked in ML development. This qualitative study explores emergency department professionals’ perspectives on the potential for machine learning-based triage to enhance triage processes in emergency departments. Semi-structured interviews were conducted with 13 ED professionals (9 physicians, 4 nurses) from 6 hospitals in Istanbul. A grounded theory approach was used to analyze the data, identifying themes related to current triage challenges, attitudes toward machine learning-based triage, and suggestions for ML algorithm development. Three main themes emerged: (i) patient and public interaction with triage, (ii) technology and ML in triage, and (iii) triage processes and challenges. Healthcare professionals expressed optimism about the potential of machine learning-based triage but raised concerns about the accuracy of current technology and the need for ML models to integrate complex clinical judgments, particularly regarding pain assessment and patient behavior. While machine learning-based triage has the potential to significantly enhance ED triage, emergency department professionals’ experiential insights are crucial for the development of more accurate and usable ML models which focus on incorporating human expertise.