Objective: This study aimed to identify factors associated with the recovery of mandibular nerve disturbance and to predict the possibility of recovery tailored to individual patients. Materials and Methods: Patients who visited the dental hospital with symptoms of mandibular nerve disturbance from April 2015 to September 2020 were studied. Patients were divided into two groups based on treatment outcomes: recovered or non-recovered. Variables related to recovery included age, sex, onset event of the nerve disturbance, affected area, imaging findings, and treatment methods. The correlation between recovery and these variables was analyzed using the Chi-square test and Fisher’s exact test. Results: A total of 328 patients were included in the study.Among the variables associated with recovery, the onset event of the symptom (P-value=0.02) and imaging findings (P-value=0.04) were statistically significant. Among the significant variables, the highest proportion of patients (77.78%) recovered without symptoms of onset event, while implant surgery showed the lowest recovery rate (34.25%). Regarding imaging findings, the recovery rate was highest in cases of suspected canal damage (58.82%), while no patients recovered from compression of the canal (0.00%). Conclusion This study highlights the importance of large-scale data analysis and a thorough evaluation of clinical variables to understand mandibular nerve disturbances. The findings provide a basis for improving treatment strategies and reducing the impact of nerve disturbances on patients’ quality of life.

Purpose: This study aimed to perform a genome characterization of Streptococcus mitis KHUD 011, a strain isolated from the oral microbiome of a healthy Korean individual, and to compare its genomic features with other S. mitis strains. Materials and Methods: The strain was identified through 16S rRNA gene sequencing, and its genome was sequenced using the PacBio Sequel II platform. De novo assembly and annotation were performed, followed by comparative genomic analysis with three additional strains (S. mitis NCTC 12261, S022-V3-A4, and B6). Pan-genome and phylogenetic analyses were conducted to identify strain-specific genes and assess inter-strain genomic diversity. Results: The genome of S. mitis KHUD 011 consisted of 1,782 protein-coding genes, with a G+C content of 40.24%. Pan-genome analysis identified 1,263 core gene clusters (50.0%), 496 dispensable clusters (19.7%), and 763 strain-specific clusters (30.3%). KHUD 011 displayed 88 strain-specific genes, particularly associated with cell wall/membrane biogenesis, transcriptional regulation, and carbohydrate metabolism. Phylogenetic analysis placed KHUD 011 closely with NCTC 12261, forming a distinct cluster apart from other strains. Conclusion The genome characterization of S. mitis KHUD 011 underscores substantial inter-strain genomic diversity influenced by host interactions, ecological niches, and health status. The identified strain-specific genes, particularly those associated with cell wall/ membrane biogenesis, transcriptional regulation, and carbohydrate metabolism, suggest adaptations to the oral microbiome and its interaction with the host. These findings highlight the ecological versatility of S. mitis and the importance of exploring strains from diverse environments to better understand their role within the host and the broader microbiome.

Purpose: The opacity of zirconia sometimes requires a veneering material; thus lithium disilicate, a veneer material with excellent strength, can be used. This study investigated the fracture resistance of zirconia–lithium disilicate (Zr-LS2) bi-layered crowns according to the design of the substructure. Materials and Methods: Five groups of posterior Zr-LS2 restorations (Zirtooth and Amber LiSi-POZ) were fabricated with different zirconia substructure coverage (control group, groups half occlusal zirconia coverage; 1/2OZ, and three-quarter occlusal zirconia coverage; 3/4OZ) and margin designs (control group, groups collar margin; C-M, and collarless margin; Cl-M). All restorations were cemented with self-adhesive resin cement followed by 24-h water storage and thermocycling (10,000 cycles, 5°C and 55°C). The fracture load was measured, and failure mode analysis, fractography, and elemental analysis were performed. The one-way analysis of variance and Fisher’s exact test were performed for statistical analyses (α=.05). Results: A significant difference was found in the fracture load of Zr-LS2 restorations according to the zirconia coverage of the occlusal area and margin design (P<.05). Group 3/4OZ was significantly larger than the control group C and 1/2OZ (P<.05). The C-M group had greater fracture loads based on margin design than the control group C and Cl-M (P<.05). Conclusion The fracture resistance of posterior Zr-LS2 restorations increased with the zirconia coverage, occlusal thickness, and collar margin.

Purpose: To estimate the sample size required for developing artificial intelligence (AI) that can predict soft-tissue and alveolar bone changes following orthodontic treatment. Materials and Methods: From the original data sets with N=887, consisting of 132 input and 88 output variables used to create AI models for predicting treatment changes following orthodontic treatment, six subsets of the data (n=75, 150, 300, 450, 600, and 750) were generated through random resampling procedures. The process was repeated four times, resulting in 24 different data subsets. Each data subset was used to create a total of 24 AI models using the TabNet deep neural network algorithm. The clinically acceptable prediction accuracy was defined as a less than 1.5 mm prediction error on the lower lip. The prediction errors from each AI model were compared according to sample sizes and analyzed to estimate the optimal sample size. Results: The prediction error decreased with increasing sample sizes. A training sample size greater than approximately 1650 was estimated to develop an AI model with less than 1.5 mm of prediction errors at the lower lip area. Conclusion From a statistical and research design perspective, a considerable amount of training data appears necessary to develop an AI prediction model with clinically acceptable accuracy.

Objective: This study aimed to identify factors associated with the recovery of mandibular nerve disturbance and to predict the possibility of recovery tailored to individual patients. Materials and Methods: Patients who visited the dental hospital with symptoms of mandibular nerve disturbance from April 2015 to September 2020 were studied. Patients were divided into two groups based on treatment outcomes: recovered or non-recovered. Variables related to recovery included age, sex, onset event of the nerve disturbance, affected area, imaging findings, and treatment methods. The correlation between recovery and these variables was analyzed using the Chi-square test and Fisher’s exact test. Results: A total of 328 patients were included in the study.Among the variables associated with recovery, the onset event of the symptom (P-value=0.02) and imaging findings (P-value=0.04) were statistically significant. Among the significant variables, the highest proportion of patients (77.78%) recovered without symptoms of onset event, while implant surgery showed the lowest recovery rate (34.25%). Regarding imaging findings, the recovery rate was highest in cases of suspected canal damage (58.82%), while no patients recovered from compression of the canal (0.00%). Conclusion This study highlights the importance of large-scale data analysis and a thorough evaluation of clinical variables to understand mandibular nerve disturbances. The findings provide a basis for improving treatment strategies and reducing the impact of nerve disturbances on patients’ quality of life.

Purpose: This study aimed to perform a genome characterization of Streptococcus mitis KHUD 011, a strain isolated from the oral microbiome of a healthy Korean individual, and to compare its genomic features with other S. mitis strains. Materials and Methods: The strain was identified through 16S rRNA gene sequencing, and its genome was sequenced using the PacBio Sequel II platform. De novo assembly and annotation were performed, followed by comparative genomic analysis with three additional strains (S. mitis NCTC 12261, S022-V3-A4, and B6). Pan-genome and phylogenetic analyses were conducted to identify strain-specific genes and assess inter-strain genomic diversity. Results: The genome of S. mitis KHUD 011 consisted of 1,782 protein-coding genes, with a G+C content of 40.24%. Pan-genome analysis identified 1,263 core gene clusters (50.0%), 496 dispensable clusters (19.7%), and 763 strain-specific clusters (30.3%). KHUD 011 displayed 88 strain-specific genes, particularly associated with cell wall/membrane biogenesis, transcriptional regulation, and carbohydrate metabolism. Phylogenetic analysis placed KHUD 011 closely with NCTC 12261, forming a distinct cluster apart from other strains. Conclusion The genome characterization of S. mitis KHUD 011 underscores substantial inter-strain genomic diversity influenced by host interactions, ecological niches, and health status. The identified strain-specific genes, particularly those associated with cell wall/ membrane biogenesis, transcriptional regulation, and carbohydrate metabolism, suggest adaptations to the oral microbiome and its interaction with the host. These findings highlight the ecological versatility of S. mitis and the importance of exploring strains from diverse environments to better understand their role within the host and the broader microbiome.

Purpose: The opacity of zirconia sometimes requires a veneering material; thus lithium disilicate, a veneer material with excellent strength, can be used. This study investigated the fracture resistance of zirconia–lithium disilicate (Zr-LS2) bi-layered crowns according to the design of the substructure. Materials and Methods: Five groups of posterior Zr-LS2 restorations (Zirtooth and Amber LiSi-POZ) were fabricated with different zirconia substructure coverage (control group, groups half occlusal zirconia coverage; 1/2OZ, and three-quarter occlusal zirconia coverage; 3/4OZ) and margin designs (control group, groups collar margin; C-M, and collarless margin; Cl-M). All restorations were cemented with self-adhesive resin cement followed by 24-h water storage and thermocycling (10,000 cycles, 5°C and 55°C). The fracture load was measured, and failure mode analysis, fractography, and elemental analysis were performed. The one-way analysis of variance and Fisher’s exact test were performed for statistical analyses (α=.05). Results: A significant difference was found in the fracture load of Zr-LS2 restorations according to the zirconia coverage of the occlusal area and margin design (P<.05). Group 3/4OZ was significantly larger than the control group C and 1/2OZ (P<.05). The C-M group had greater fracture loads based on margin design than the control group C and Cl-M (P<.05). Conclusion The fracture resistance of posterior Zr-LS2 restorations increased with the zirconia coverage, occlusal thickness, and collar margin.

Purpose: To estimate the sample size required for developing artificial intelligence (AI) that can predict soft-tissue and alveolar bone changes following orthodontic treatment. Materials and Methods: From the original data sets with N=887, consisting of 132 input and 88 output variables used to create AI models for predicting treatment changes following orthodontic treatment, six subsets of the data (n=75, 150, 300, 450, 600, and 750) were generated through random resampling procedures. The process was repeated four times, resulting in 24 different data subsets. Each data subset was used to create a total of 24 AI models using the TabNet deep neural network algorithm. The clinically acceptable prediction accuracy was defined as a less than 1.5 mm prediction error on the lower lip. The prediction errors from each AI model were compared according to sample sizes and analyzed to estimate the optimal sample size. Results: The prediction error decreased with increasing sample sizes. A training sample size greater than approximately 1650 was estimated to develop an AI model with less than 1.5 mm of prediction errors at the lower lip area. Conclusion From a statistical and research design perspective, a considerable amount of training data appears necessary to develop an AI prediction model with clinically acceptable accuracy.

Objective: This study aimed to identify factors associated with the recovery of mandibular nerve disturbance and to predict the possibility of recovery tailored to individual patients. Materials and Methods: Patients who visited the dental hospital with symptoms of mandibular nerve disturbance from April 2015 to September 2020 were studied. Patients were divided into two groups based on treatment outcomes: recovered or non-recovered. Variables related to recovery included age, sex, onset event of the nerve disturbance, affected area, imaging findings, and treatment methods. The correlation between recovery and these variables was analyzed using the Chi-square test and Fisher’s exact test. Results: A total of 328 patients were included in the study.Among the variables associated with recovery, the onset event of the symptom (P-value=0.02) and imaging findings (P-value=0.04) were statistically significant. Among the significant variables, the highest proportion of patients (77.78%) recovered without symptoms of onset event, while implant surgery showed the lowest recovery rate (34.25%). Regarding imaging findings, the recovery rate was highest in cases of suspected canal damage (58.82%), while no patients recovered from compression of the canal (0.00%). Conclusion This study highlights the importance of large-scale data analysis and a thorough evaluation of clinical variables to understand mandibular nerve disturbances. The findings provide a basis for improving treatment strategies and reducing the impact of nerve disturbances on patients’ quality of life.

Purpose: This study aimed to perform a genome characterization of Streptococcus mitis KHUD 011, a strain isolated from the oral microbiome of a healthy Korean individual, and to compare its genomic features with other S. mitis strains. Materials and Methods: The strain was identified through 16S rRNA gene sequencing, and its genome was sequenced using the PacBio Sequel II platform. De novo assembly and annotation were performed, followed by comparative genomic analysis with three additional strains (S. mitis NCTC 12261, S022-V3-A4, and B6). Pan-genome and phylogenetic analyses were conducted to identify strain-specific genes and assess inter-strain genomic diversity. Results: The genome of S. mitis KHUD 011 consisted of 1,782 protein-coding genes, with a G+C content of 40.24%. Pan-genome analysis identified 1,263 core gene clusters (50.0%), 496 dispensable clusters (19.7%), and 763 strain-specific clusters (30.3%). KHUD 011 displayed 88 strain-specific genes, particularly associated with cell wall/membrane biogenesis, transcriptional regulation, and carbohydrate metabolism. Phylogenetic analysis placed KHUD 011 closely with NCTC 12261, forming a distinct cluster apart from other strains. Conclusion The genome characterization of S. mitis KHUD 011 underscores substantial inter-strain genomic diversity influenced by host interactions, ecological niches, and health status. The identified strain-specific genes, particularly those associated with cell wall/ membrane biogenesis, transcriptional regulation, and carbohydrate metabolism, suggest adaptations to the oral microbiome and its interaction with the host. These findings highlight the ecological versatility of S. mitis and the importance of exploring strains from diverse environments to better understand their role within the host and the broader microbiome.