Objective: Gaits constitute the most fundamental and common form of human locomotion and are essential in daily activities. We aimed to investigate gait parameters in medically and cognitively healthy older adults to determine the independent effects of age, physical attributes, and cognition on these parameters. Methods: This retrospective study enrolled healthy older adult participants aged 50 years or older with normal cognition and no neurological symptoms or medical/surgical history that could affect gait. Quantitative gait analysis was conducted via the GAITRite Electronic Walkway, which categorizes gait parameters into spatiotemporal, spatial, temporal, phase, and variability. Gait parameters were compared between sexes across different age groups. The independent effects of age, Mini-Mental State Examination score, and physical characteristics were analyzed via a multiple regression model. Results: This study included 184 participants with an average age of 72.2 years. After adjusting for age, height, and footwear, only the base width and its variability differed between the sexes. Gait parameters varied significantly among different age groups, revealing multiple interparameter associations. Age was independently correlated with decreased velocity, step and stride lengths, single support time percentage and increased double support time, double support time percentage, and variability parameters, excluding the coefficient of variance of base width. Height was positively correlated with velocity, step and stride lengths, and base width, whereas leg length was negatively associated with cadence and positively associated with temporal parameters of gait. Conclusion Gait parameters in healthy older adults were not only associated with age and physical characteristics but also had interparameter correlations.

Objectives: This study aimed to identify factors associated with life satisfaction by developing machine learning (ML) models to predict life satisfaction in older adults living alone. Methods: Data were extracted from 3112 older adults participating in the 2020 Korea Senior Survey. We employed 5 ML models to classify the life satisfaction of older adults living alone: logistic Lasso regression, decision tree-based classification and regression tree (CART), C5.0, random forest, and extreme gradient boost (XGBoost). The variables used as predictors included demographics, health status, functional abilities, environmental factors, and activity participation. The performance of these ML models was evaluated based on accuracy, precision, recall, F1-score, and area under the curve (AUC). Additionally, we assessed the significance of variable importance as indicated by the final classification models. Results: Out of the 1411 older adults living alone, 45.3% expressed satisfaction with their lives. The XGBoost model surpassed the performance of other models, achieving an F1-score of 0.72 and an AUC of 0.75. According to the XGBoost model, the five most important variables influencing life satisfaction were overall community satisfaction, self-rated health, opportunities to interact with neighbors, proximity to a child, and satisfaction with residence. Conclusions Overall satisfaction with the community environment emerged as the most significant predictor of life satisfaction among older adults living alone. These findings indicate that enhancing the supportiveness of the community environment could improve life satisfaction for this demographic.

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.

Objective: Gaits constitute the most fundamental and common form of human locomotion and are essential in daily activities. We aimed to investigate gait parameters in medically and cognitively healthy older adults to determine the independent effects of age, physical attributes, and cognition on these parameters. Methods: This retrospective study enrolled healthy older adult participants aged 50 years or older with normal cognition and no neurological symptoms or medical/surgical history that could affect gait. Quantitative gait analysis was conducted via the GAITRite Electronic Walkway, which categorizes gait parameters into spatiotemporal, spatial, temporal, phase, and variability. Gait parameters were compared between sexes across different age groups. The independent effects of age, Mini-Mental State Examination score, and physical characteristics were analyzed via a multiple regression model. Results: This study included 184 participants with an average age of 72.2 years. After adjusting for age, height, and footwear, only the base width and its variability differed between the sexes. Gait parameters varied significantly among different age groups, revealing multiple interparameter associations. Age was independently correlated with decreased velocity, step and stride lengths, single support time percentage and increased double support time, double support time percentage, and variability parameters, excluding the coefficient of variance of base width. Height was positively correlated with velocity, step and stride lengths, and base width, whereas leg length was negatively associated with cadence and positively associated with temporal parameters of gait. Conclusion Gait parameters in healthy older adults were not only associated with age and physical characteristics but also had interparameter correlations.

Objectives: This study aimed to identify factors associated with life satisfaction by developing machine learning (ML) models to predict life satisfaction in older adults living alone. Methods: Data were extracted from 3112 older adults participating in the 2020 Korea Senior Survey. We employed 5 ML models to classify the life satisfaction of older adults living alone: logistic Lasso regression, decision tree-based classification and regression tree (CART), C5.0, random forest, and extreme gradient boost (XGBoost). The variables used as predictors included demographics, health status, functional abilities, environmental factors, and activity participation. The performance of these ML models was evaluated based on accuracy, precision, recall, F1-score, and area under the curve (AUC). Additionally, we assessed the significance of variable importance as indicated by the final classification models. Results: Out of the 1411 older adults living alone, 45.3% expressed satisfaction with their lives. The XGBoost model surpassed the performance of other models, achieving an F1-score of 0.72 and an AUC of 0.75. According to the XGBoost model, the five most important variables influencing life satisfaction were overall community satisfaction, self-rated health, opportunities to interact with neighbors, proximity to a child, and satisfaction with residence. Conclusions Overall satisfaction with the community environment emerged as the most significant predictor of life satisfaction among older adults living alone. These findings indicate that enhancing the supportiveness of the community environment could improve life satisfaction for this demographic.

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.

Fine particulate matter (FPM) is a major component of air pollution and has emerged as a significant global health concern owing to its adverse health effects. Previous studies have investigated the correlation between bone health and FPM through cohort or review studies. However, the effects of FPM exposure on bone health are poorly understood. This study aimed to investigate the effects of FPM on bone health and elucidate these effects in vitro and in vivo using mice. Micro-CT analysis in vivo revealed FPM exposure decreased bone mineral density, trabecular bone volume/total volume ratio, and trabecular number in the femurs of mice, while increasing trabecular separation. Histological analysis showed that the FPM-treated group had a reduced trabecular area and an increased number of osteoclasts in the bone tissue. Moreover, in vitro studies revealed that low concentrations of FPM significantly enhanced osteoclast differentiation. These findings further support the notion that short-term FPM exposure negatively impacts bone health, providing a foundation for further research on this topic.

BACKGROUND: Osteoporosis, characterized by decreased bone mineral density due to an imbalance between osteoblast and osteoclast activity, poses significant challenges in bone healing, particularly in postmenopausal women. Current treatments, such as bisphosphonates, are effective but associated with adverse effects like medication-related osteonecrosis of the jaw, necessitating safer alternatives. METHODS: This study investigated the use of L-serine-incorporated gelatin sponges for bone regeneration in calvarial defects in an ovariectomized rat model of osteoporosis. Thirty rats were divided into three groups: a control group, a group treated with a gelatin sponge containing an amino acid mixture, and a group treated with a gelatin sponge containing L-serine. Bone regeneration was assessed using micro-computed tomography (micro-CT) and histological analyses. RESULTS: The L-serine group showed a significant increase in bone volume (BV) and bone area compared to the control and amino acid groups. The bone volume to total volume (BV/TV) ratio was also significantly higher in the L-serine group.Immunohistochemical analysis demonstrated that L-serine treatment suppressed the expression of cathepsin K, a marker of osteoclast activity, while increasing serine racemase activity. CONCLUSION These findings suggest that L-serine-incorporated gelatin sponges not only enhance bone formation but also inhibit osteoclast-mediated bone resorption, providing a promising and safer alternative to current therapies for osteoporosis-related bone defects. Further research is needed to explore its clinical applications in human patients.

Purpose: This study assessed the validity of the hospital standardized mortality ratio (HSMR) risk-adjusted model by comparing models that include clinical information and the current model based on administrative information in South Korea. Materials and Methods: The data of 53976 inpatients were analyzed. The current HSMR risk-adjusted model (Model 1) adjusts for sex, age, health coverage, emergency hospitalization status, main diagnosis, surgery status, and Charlson Comorbidity Index (CCI) using administrative data. As candidate variables, among clinical information, the American Society of Anesthesiologists score, Acute Physiology and Chronic Health Evaluation (APACHE) II, Simplified Acute Physiology Score (SAPS) 3, present on admission CCI, and cancer stage were collected. Surgery status, intensive care in the intensive care unit, and CCI were selected as proxy variables among administrative data. In-hospital death was defined as the dependent variable, and a logistic regression analysis was performed. The statistical performance of each model was compared using C-index values. Results: There was a strong correlation between variables in the administrative data and those in the medical records. The C-index of the existing model (Model 1) was 0.785; Model 2, which included all clinical data, had a higher C-index of 0.857. In Model 4, in which APACHE II and SAPS 3 were replaced with variables recorded in the administrative data from Model 2, the C-index further increased to 0.863. Conclusion The HSMR assessment model improved when clinical data were adjusted. Simultaneously, the validity of the evaluation method could be secured even if some of the clinical information was replaced with the information in the administrative data.

Objective: Gaits constitute the most fundamental and common form of human locomotion and are essential in daily activities. We aimed to investigate gait parameters in medically and cognitively healthy older adults to determine the independent effects of age, physical attributes, and cognition on these parameters. Methods: This retrospective study enrolled healthy older adult participants aged 50 years or older with normal cognition and no neurological symptoms or medical/surgical history that could affect gait. Quantitative gait analysis was conducted via the GAITRite Electronic Walkway, which categorizes gait parameters into spatiotemporal, spatial, temporal, phase, and variability. Gait parameters were compared between sexes across different age groups. The independent effects of age, Mini-Mental State Examination score, and physical characteristics were analyzed via a multiple regression model. Results: This study included 184 participants with an average age of 72.2 years. After adjusting for age, height, and footwear, only the base width and its variability differed between the sexes. Gait parameters varied significantly among different age groups, revealing multiple interparameter associations. Age was independently correlated with decreased velocity, step and stride lengths, single support time percentage and increased double support time, double support time percentage, and variability parameters, excluding the coefficient of variance of base width. Height was positively correlated with velocity, step and stride lengths, and base width, whereas leg length was negatively associated with cadence and positively associated with temporal parameters of gait. Conclusion Gait parameters in healthy older adults were not only associated with age and physical characteristics but also had interparameter correlations.