This study aimed to evaluate the surface hardness and wear resistance of a novel glass-hybrid restorative material in comparison with those of high-viscosity glass ionomer cement (GIC). Additionally, this study examined how the application of a nano-filled resin coating affected these mechanical properties. This study utilized 80 disk-shaped samples prepared from two distinct GI materials: Equia Forte HT Fil and Fuji IX GP. Half of the specimens from each material group were treated with an Equia Forte Coat. Vickers hardness tests were conducted on a set of 40 specimens, and wear resistance was measured on a separate set of 40 specimens. Equia Forte HT Fil showed significantly higher hardness than Fuji IX GP (p < 0.05). The nano-filled resin coating did not significantly affect the hardness in both groups (p > 0.05). In wear depth measurements, uncoated Equia Forte HT Fil showed significantly lower wear depth compared to uncoated Fuji IX GP (p < 0.05). After coating application, both GI groups showed significantly decreased wear depth (p < 0.05). In terms of both hardness and wear resistance, the properties of the glass-hybrid restorative material were superior to those of the high-viscosity GIC. Nano-filled resin coating exhibited no significant positive effect on the hardness of either GI cement material but significantly increased their wear resistance.

This study aimed to evaluate the surface hardness and wear resistance of a novel glass-hybrid restorative material in comparison with those of high-viscosity glass ionomer cement (GIC). Additionally, this study examined how the application of a nano-filled resin coating affected these mechanical properties. This study utilized 80 disk-shaped samples prepared from two distinct GI materials: Equia Forte HT Fil and Fuji IX GP. Half of the specimens from each material group were treated with an Equia Forte Coat. Vickers hardness tests were conducted on a set of 40 specimens, and wear resistance was measured on a separate set of 40 specimens. Equia Forte HT Fil showed significantly higher hardness than Fuji IX GP (p < 0.05). The nano-filled resin coating did not significantly affect the hardness in both groups (p > 0.05). In wear depth measurements, uncoated Equia Forte HT Fil showed significantly lower wear depth compared to uncoated Fuji IX GP (p < 0.05). After coating application, both GI groups showed significantly decreased wear depth (p < 0.05). In terms of both hardness and wear resistance, the properties of the glass-hybrid restorative material were superior to those of the high-viscosity GIC. Nano-filled resin coating exhibited no significant positive effect on the hardness of either GI cement material but significantly increased their wear resistance.

This study aimed to evaluate the surface hardness and wear resistance of a novel glass-hybrid restorative material in comparison with those of high-viscosity glass ionomer cement (GIC). Additionally, this study examined how the application of a nano-filled resin coating affected these mechanical properties. This study utilized 80 disk-shaped samples prepared from two distinct GI materials: Equia Forte HT Fil and Fuji IX GP. Half of the specimens from each material group were treated with an Equia Forte Coat. Vickers hardness tests were conducted on a set of 40 specimens, and wear resistance was measured on a separate set of 40 specimens. Equia Forte HT Fil showed significantly higher hardness than Fuji IX GP (p < 0.05). The nano-filled resin coating did not significantly affect the hardness in both groups (p > 0.05). In wear depth measurements, uncoated Equia Forte HT Fil showed significantly lower wear depth compared to uncoated Fuji IX GP (p < 0.05). After coating application, both GI groups showed significantly decreased wear depth (p < 0.05). In terms of both hardness and wear resistance, the properties of the glass-hybrid restorative material were superior to those of the high-viscosity GIC. Nano-filled resin coating exhibited no significant positive effect on the hardness of either GI cement material but significantly increased their wear resistance.

This study aims to evaluate the color stability and surface roughness of the single-shade composite resin after finishing and polishing for primary molars. A single-shade composite resin (OM, OMNICHROMA) and two multi-shade composite resins (FT, FiltekTM Z350XT; ES, ESTELITE® SIGMA QUICK) were included. The specimens were divided into three subgroups using different polishing methods: control, Sof-Lex XT, and Sof-Lex Diamond. For color stability tests, cavities were prepared on extracted primary second molars and restored with experimental composite resins. Each specimen was immersed in the coffee solution for 48 hours. The color difference of each specimen was calculated. For surface roughness tests, cylindrical specimens were crafted with experimental composite resins. Surface roughness was analyzed using an atomic force microscope and a scanning electron microscope. In the color stability tests, FT demonstrated a significantly lower ΔEab than ES among the control groups, but no significant differences were observed between the ΔEab values of OM and FT or OM and ES. Additionally, no significant differences were found between the Sof-Lex XT and Sof-Lex Diamond subgroups in the three composite groups. Moreover, no significant differences in the surface roughness were found between the three composite groups, regardless of the polishing methods. The single-shade composite resin demonstrated comparable color stability and surface roughness to that of the multi-shade composite resins regardless of the polishing methods used in restoring primary molars. The single-shade composite resin is expected to be applicable in clinical pediatric dentistry reducing chair time due to the easy shade matching procedures.

Background: Time-of-flight (TOF) magnetic resonance angiography (MRA) is widely used to identify aneurysm in human brain. Various deep learning models have been developed to help TOF-MRA reading in the field. The performance of those TOF-MRA analysis tools, however, faces several limitations in cerebral aneurysm detection. These challenges primarily come from the fact that cerebral aneurysms occupy less than 0.1% of the total TOF-MRA voxel size. This study aims to improve the efficiency of cerebral aneurysm detection by developing a landmark-based semi-supervised deep learning method, a technology that automatically generates landmark boxes in areas with a high probability of cerebral aneurysm occurrence. Methods: We used data from a total of 500 aneurysm-positive and 50 aneurysm-negative subjects. The aneurysm detection model was developed using clustering and a dilated residual network. Results: When the number of landmarks was ten and their size was 36 mm3, the best performance was achieved in our experiment. Although landmark occupies a small portion of the entire image, up to 98.2% of landmarks were cerebral aneurysms. The sensitivity of the model for cerebral aneurysm detection was 83.0%, with a false positive rate of 3.4%. Conclusions This study developed a deep learning model using TOF-MRA image. This model generates the most suitable landmarks for each individual, excluding unnecessary areas for cerebral aneurysm detection, which makes it possible to focus on areas with a high probability of occurrence. This model is expected to enhance the efficiency and accuracy of cerebral aneurysm detection in the field.

This study aims to evaluate the color stability and surface roughness of the single-shade composite resin after finishing and polishing for primary molars. A single-shade composite resin (OM, OMNICHROMA) and two multi-shade composite resins (FT, FiltekTM Z350XT; ES, ESTELITE® SIGMA QUICK) were included. The specimens were divided into three subgroups using different polishing methods: control, Sof-Lex XT, and Sof-Lex Diamond. For color stability tests, cavities were prepared on extracted primary second molars and restored with experimental composite resins. Each specimen was immersed in the coffee solution for 48 hours. The color difference of each specimen was calculated. For surface roughness tests, cylindrical specimens were crafted with experimental composite resins. Surface roughness was analyzed using an atomic force microscope and a scanning electron microscope. In the color stability tests, FT demonstrated a significantly lower ΔEab than ES among the control groups, but no significant differences were observed between the ΔEab values of OM and FT or OM and ES. Additionally, no significant differences were found between the Sof-Lex XT and Sof-Lex Diamond subgroups in the three composite groups. Moreover, no significant differences in the surface roughness were found between the three composite groups, regardless of the polishing methods. The single-shade composite resin demonstrated comparable color stability and surface roughness to that of the multi-shade composite resins regardless of the polishing methods used in restoring primary molars. The single-shade composite resin is expected to be applicable in clinical pediatric dentistry reducing chair time due to the easy shade matching procedures.

Background: Time-of-flight (TOF) magnetic resonance angiography (MRA) is widely used to identify aneurysm in human brain. Various deep learning models have been developed to help TOF-MRA reading in the field. The performance of those TOF-MRA analysis tools, however, faces several limitations in cerebral aneurysm detection. These challenges primarily come from the fact that cerebral aneurysms occupy less than 0.1% of the total TOF-MRA voxel size. This study aims to improve the efficiency of cerebral aneurysm detection by developing a landmark-based semi-supervised deep learning method, a technology that automatically generates landmark boxes in areas with a high probability of cerebral aneurysm occurrence. Methods: We used data from a total of 500 aneurysm-positive and 50 aneurysm-negative subjects. The aneurysm detection model was developed using clustering and a dilated residual network. Results: When the number of landmarks was ten and their size was 36 mm3, the best performance was achieved in our experiment. Although landmark occupies a small portion of the entire image, up to 98.2% of landmarks were cerebral aneurysms. The sensitivity of the model for cerebral aneurysm detection was 83.0%, with a false positive rate of 3.4%. Conclusions This study developed a deep learning model using TOF-MRA image. This model generates the most suitable landmarks for each individual, excluding unnecessary areas for cerebral aneurysm detection, which makes it possible to focus on areas with a high probability of occurrence. This model is expected to enhance the efficiency and accuracy of cerebral aneurysm detection in the field.