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.

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.

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.

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.

Purpose: The study aimed to investigate the recruiting of T lymphocytes including IL-7Rαlow CX3CR1+ effector memory (EM) CD8+ T cells and α4β7 integrin tagged T cells to inflamed intestinal mucosa. Methods: Whole blood and mucosal tissues of intestine were collected from 40 children with or without inflammatory bowel disease (IBD). T cell surface staining and immunohistochemistry were done with several antibodies in peripheral blood mononuclear cells (PBMCs) and intestinal mucosa, respectively. Serum levels of cytokines were measured by ELISA. Results: The frequency of IL-7Rαlow CX3CR1+ EM CD8+ T cells in the PBMC was significantly higher in the ulcerative colitis group than in the control group (57.9±17.80% vs. 33.9±15.70%, p=0.021). The frequency of integrin α4β7+ CD4+ T cells in the PBMC was significantly lower in the ulcerative colitis group than in the control group (53.2±27.6% vs. 63.9±13.2%, p=0.022). Serum concentration of TNF-α was higher in the Crohn’s disease group than in the control group (26.13±5.01 pg/mL vs. 19.65±6.07 pg/mL, p=0.008). Of the three groups, the ulcerative colitis group had the highest frequency of integrin α4β7+ T cells based on immunohistochemistry analyses for intestinal tissues, followed by the Crohn’s disease group and the control group (4.63±1.29 cells vs. 2.0±0.57 cells vs. 0.84±0.52 cells, p<0.001). Conclusion Trafficking immune cells with effector memory CD8+ T cells clarified by IL-7Rαlow CX3CR1+ and integrin α4β7+ CD4+ T cells might be highly associated with the pathogenesis of ulcerative colitis.

Purpose: The study aimed to investigate the recruiting of T lymphocytes including IL-7Rαlow CX3CR1+ effector memory (EM) CD8+ T cells and α4β7 integrin tagged T cells to inflamed intestinal mucosa. Methods: Whole blood and mucosal tissues of intestine were collected from 40 children with or without inflammatory bowel disease (IBD). T cell surface staining and immunohistochemistry were done with several antibodies in peripheral blood mononuclear cells (PBMCs) and intestinal mucosa, respectively. Serum levels of cytokines were measured by ELISA. Results: The frequency of IL-7Rαlow CX3CR1+ EM CD8+ T cells in the PBMC was significantly higher in the ulcerative colitis group than in the control group (57.9±17.80% vs. 33.9±15.70%, p=0.021). The frequency of integrin α4β7+ CD4+ T cells in the PBMC was significantly lower in the ulcerative colitis group than in the control group (53.2±27.6% vs. 63.9±13.2%, p=0.022). Serum concentration of TNF-α was higher in the Crohn’s disease group than in the control group (26.13±5.01 pg/mL vs. 19.65±6.07 pg/mL, p=0.008). Of the three groups, the ulcerative colitis group had the highest frequency of integrin α4β7+ T cells based on immunohistochemistry analyses for intestinal tissues, followed by the Crohn’s disease group and the control group (4.63±1.29 cells vs. 2.0±0.57 cells vs. 0.84±0.52 cells, p<0.001). Conclusion Trafficking immune cells with effector memory CD8+ T cells clarified by IL-7Rαlow CX3CR1+ and integrin α4β7+ CD4+ T cells might be highly associated with the pathogenesis of ulcerative colitis.

Purpose: The purpose of this study was to explore the experiences of healthcare disparities in the individuals with acquired vision impairments. Methods: This study is a qualitative research using thematic analysis. Data were collected from January to March 2024 through one-on-one semi-structured interviews with a total of 11 individuals with acquired vision impairments. Results: The analysis revealed 5 main themes and 19 subthemes. The identified main themes were physical injury and aggravation, psychological tension, difficulty maintaining a healthy lifestyle, mastery of self-management and emergence of social requirements. Conclusion The findings of this study contribute to a deep understanding of the health management experiences of individuals with acquired vision impairments. Additionally, this study identifies their healthcare needs and provides directions for rehabilitation nursing and health promotion behaviors. It is necessary to explore methods for developing tailored health care programs for individuals with acquired vision impairments and to address their needs for physical environments and social systems.

Purpose: The purpose of this study was to explore the experiences of healthcare disparities in the individuals with acquired vision impairments. Methods: This study is a qualitative research using thematic analysis. Data were collected from January to March 2024 through one-on-one semi-structured interviews with a total of 11 individuals with acquired vision impairments. Results: The analysis revealed 5 main themes and 19 subthemes. The identified main themes were physical injury and aggravation, psychological tension, difficulty maintaining a healthy lifestyle, mastery of self-management and emergence of social requirements. Conclusion The findings of this study contribute to a deep understanding of the health management experiences of individuals with acquired vision impairments. Additionally, this study identifies their healthcare needs and provides directions for rehabilitation nursing and health promotion behaviors. It is necessary to explore methods for developing tailored health care programs for individuals with acquired vision impairments and to address their needs for physical environments and social systems.

BACKGROUND/OBJECTIVES: Okra seed is a rich source of various nutritional and bioactive constituents, but its mechanism of action is still unclear. The aim of this study was to evaluated the effects on glucose uptake and serum lipid profiles of unsaponifiable matter (USM) from okra seed in adipocytes and diabetic animal models.MATERIALS/METHODSUSM was prepared from okra seed powder by saponification. The contents of phytosterols and vitamin E in USM were measured. 3T3-L1 preadipocytes were cultured for 6 days with different concentrations of USM (0–200 μg/mL). The diabetic rats were administered with or without USM for 5 wk. RESULTS: In the USM, the contents of phytosterols and vitamin E were 394.13 mg/g USM and 31.16 mg/g USM, respectively. USM showed no cytotoxicity and led to an approximately 1.4-fold increase in glucose uptake in 3T3-L1 adipocytes. The treatment of USM also increased the expressions of peroxisome proliferator-activated receptor-γ and glucose transporter-4 in a dose-dependent manner in adipocytes. The body weight change was not significantly different in all diabetic rats. However, blood glucose and the weights of liver and adipose tissues were significantly reduced compared to those in the control diabetic rats. Treatment with USM decreased the levels of triglycerides, total cholesterol, and low-density lipoprotein cholesterol compared to the control group. The USM group also showed significantly decreased atherogenic indices and cardiac risk factors. CONCLUSION These results suggest that USM from okra seed improves the hypoglycemic and hypolipidemic effects in diabetic rats, and provides valuable information for improving the functional properties of okra seed.