Objective To understand the current situation of nutrition literacy of primary and secondary school students in Shijingshan District of Beijing, and analyze its influencing factors, and to put forward targeted suggestions for improving the students’ nutrition literacy and promoting their healthy growth. Methods A multi-stage stratified cluster sampling method was used to select 2480 primary and secondary school students and their parents from 5 primary schools, 3 middle schools and 1 high school in Shijingshan District. The multivariate logistic regression model was used to analyze the factors influencing the attainment rate of nutrition literacy. Results The median score of nutrition literacy of 2480 primary and secondary school students from grades 1 to 12 was 77.86 (in hundred-mark system), the quartile range (IQR) was 16.96, and the attainment rate of nutrition literacy was 42.46%. The cognitive level (45.12%) was higher than the skill level (41.20%) among students from grades 3 to 12. In terms of skills, the attainment rate of food preparation was the lowest, at 30.38%. The scores of nutrition literacy of girls were higher than those of boys, and the scores of primary school students were higher than those of secondary school students. Students with different levels of caregiver’s education, family income, and family food environment had different scores of nutrition literacy, and the differences were statistically significant (P<0.05). Multivariate logistic regression analysis showed that the attainment rate of nutrition literacy was closely related to student’s gender and study stage, caregiver’s education level, and family food environment. Conclusion The nutrition literacy of primary and secondary school students in Shijingshan District still needs to be improved, especially in the aspect of skills. Targeted nutrition education should be carried out.

Background: Respiratory infections play a major role in acute exacerbation of chronic obstructive pulmonary disease (AECOPD). This study assessed the prevalence of bacterial and viral pathogens and their clinical impact on patients with AECOPD. Methods: This retrospective study included 1,186 patients diagnosed with AECOPD at 28 hospitals in South Korea between 2015 and 2018. We evaluated the identification rates of pathogens, basic patient characteristics, clinical features, and the factors associated with infections by potentially drug-resistant (PDR) pathogens using various microbiological tests. Results: Bacteria, viruses, and both were detected in 262 (22.1%), 265 (22.5%), and 129 (10.9%) of patients, respectively. The most common pathogens included Pseudomonas aeruginosa (17.8%), Mycoplasma pneumoniae (11.2%), Streptococcus pneumoniae (9.0%), influenza A virus (19.0%), rhinovirus (15.8%), and respiratory syncytial virus (6.4%). Notably, a history of pulmonary tuberculosis (odds ratio [OR], 1.66; p=0.046), bronchiectasis (OR, 1.99; p=0.032), and the use of a triple inhaler regimen within the past 6 months (OR, 2.04; p=0.005) were identified as significant factors associated with infection by PDR pathogens. Moreover, patients infected with PDR pathogens exhibited extended hospital stays (15.9 days vs. 12.4 days, p=0.018) and higher intensive care unit admission rates (15.9% vs. 9.5%, p=0.030). Conclusion This study demonstrates that a variety of pathogens are involved in episodes of AECOPD. Nevertheless, additional research is required to confirm their role in the onset and progression of AECOPD.

Background/Aims: The incidence of steatotic liver disease (SLD) is increasing across all age groups as the incidence of obesity increases worldwide. The existing noninvasive prediction models for SLD require laboratory tests or imaging and perform poorly in the early diagnosis of infrequently screened populations such as young adults and individuals with healthcare disparities. We developed a machine learning-based point-of-care prediction model for SLD that is readily available to the broader population with the aim of facilitating early detection and timely intervention and ultimately reducing the burden of SLD. Methods: We retrospectively analyzed the clinical data of 28,506 adults who had routine health check-ups in South Korea from January to December 2022. A total of 229,162 individuals were included in the external validation study. Data were analyzed and predictions were made using a logistic regression model with machine learning algorithms. Results: A total of 20,094 individuals were categorized into SLD and non-SLD groups on the basis of the presence of fatty liver disease. We developed three prediction models: SLD model 1, which included age and body mass index (BMI); SLD model 2, which included BMI and body fat per muscle mass; and SLD model 3, which included BMI and visceral fat per muscle mass. In the derivation cohort, the area under the receiver operating characteristic curve (AUROC) was 0.817 for model 1, 0.821 for model 2, and 0.820 for model 3. In the internal validation cohort, 86.9% of individuals were correctly classified by the SLD models. The external validation study revealed an AUROC above 0.84 for all the models. Conclusions As our three novel SLD prediction models are cost-effective, noninvasive, and accessible, they could serve as validated clinical tools for mass screening of SLD.

Background: Respiratory infections play a major role in acute exacerbation of chronic obstructive pulmonary disease (AECOPD). This study assessed the prevalence of bacterial and viral pathogens and their clinical impact on patients with AECOPD. Methods: This retrospective study included 1,186 patients diagnosed with AECOPD at 28 hospitals in South Korea between 2015 and 2018. We evaluated the identification rates of pathogens, basic patient characteristics, clinical features, and the factors associated with infections by potentially drug-resistant (PDR) pathogens using various microbiological tests. Results: Bacteria, viruses, and both were detected in 262 (22.1%), 265 (22.5%), and 129 (10.9%) of patients, respectively. The most common pathogens included Pseudomonas aeruginosa (17.8%), Mycoplasma pneumoniae (11.2%), Streptococcus pneumoniae (9.0%), influenza A virus (19.0%), rhinovirus (15.8%), and respiratory syncytial virus (6.4%). Notably, a history of pulmonary tuberculosis (odds ratio [OR], 1.66; p=0.046), bronchiectasis (OR, 1.99; p=0.032), and the use of a triple inhaler regimen within the past 6 months (OR, 2.04; p=0.005) were identified as significant factors associated with infection by PDR pathogens. Moreover, patients infected with PDR pathogens exhibited extended hospital stays (15.9 days vs. 12.4 days, p=0.018) and higher intensive care unit admission rates (15.9% vs. 9.5%, p=0.030). Conclusion This study demonstrates that a variety of pathogens are involved in episodes of AECOPD. Nevertheless, additional research is required to confirm their role in the onset and progression of AECOPD.

Background: Respiratory infections play a major role in acute exacerbation of chronic obstructive pulmonary disease (AECOPD). This study assessed the prevalence of bacterial and viral pathogens and their clinical impact on patients with AECOPD. Methods: This retrospective study included 1,186 patients diagnosed with AECOPD at 28 hospitals in South Korea between 2015 and 2018. We evaluated the identification rates of pathogens, basic patient characteristics, clinical features, and the factors associated with infections by potentially drug-resistant (PDR) pathogens using various microbiological tests. Results: Bacteria, viruses, and both were detected in 262 (22.1%), 265 (22.5%), and 129 (10.9%) of patients, respectively. The most common pathogens included Pseudomonas aeruginosa (17.8%), Mycoplasma pneumoniae (11.2%), Streptococcus pneumoniae (9.0%), influenza A virus (19.0%), rhinovirus (15.8%), and respiratory syncytial virus (6.4%). Notably, a history of pulmonary tuberculosis (odds ratio [OR], 1.66; p=0.046), bronchiectasis (OR, 1.99; p=0.032), and the use of a triple inhaler regimen within the past 6 months (OR, 2.04; p=0.005) were identified as significant factors associated with infection by PDR pathogens. Moreover, patients infected with PDR pathogens exhibited extended hospital stays (15.9 days vs. 12.4 days, p=0.018) and higher intensive care unit admission rates (15.9% vs. 9.5%, p=0.030). Conclusion This study demonstrates that a variety of pathogens are involved in episodes of AECOPD. Nevertheless, additional research is required to confirm their role in the onset and progression of AECOPD.

This report presents the case of severe tooth wear and vertical dimension loss in a 71-year-old male patient. A combined conventional and digital approach was employed for full-mouth rehabilitation. After determining an increase in the vertical dimension of 5.5 mm using an anterior jig and diagnostic wax-up, provisional restorations were fabricated and adjusted throughout the adaptation period.For the fabrication of the final prosthesis, digital methodologies such as oral scanning and occlusal acquisition were performed. To obtain precise margin data, a die model was fabricated using the traditional impression method, followed by model scanning, which was then combined with intraoral scan data. The final prosthesis was made of zirconia to enhance esthetics and strength. Consequently, the treatment enhanced both function and esthetics, leading to high patient satisfaction with the outcomes.

Background/Aims: The incidence of steatotic liver disease (SLD) is increasing across all age groups as the incidence of obesity increases worldwide. The existing noninvasive prediction models for SLD require laboratory tests or imaging and perform poorly in the early diagnosis of infrequently screened populations such as young adults and individuals with healthcare disparities. We developed a machine learning-based point-of-care prediction model for SLD that is readily available to the broader population with the aim of facilitating early detection and timely intervention and ultimately reducing the burden of SLD. Methods: We retrospectively analyzed the clinical data of 28,506 adults who had routine health check-ups in South Korea from January to December 2022. A total of 229,162 individuals were included in the external validation study. Data were analyzed and predictions were made using a logistic regression model with machine learning algorithms. Results: A total of 20,094 individuals were categorized into SLD and non-SLD groups on the basis of the presence of fatty liver disease. We developed three prediction models: SLD model 1, which included age and body mass index (BMI); SLD model 2, which included BMI and body fat per muscle mass; and SLD model 3, which included BMI and visceral fat per muscle mass. In the derivation cohort, the area under the receiver operating characteristic curve (AUROC) was 0.817 for model 1, 0.821 for model 2, and 0.820 for model 3. In the internal validation cohort, 86.9% of individuals were correctly classified by the SLD models. The external validation study revealed an AUROC above 0.84 for all the models. Conclusions As our three novel SLD prediction models are cost-effective, noninvasive, and accessible, they could serve as validated clinical tools for mass screening of SLD.

Background/Aims: The incidence of steatotic liver disease (SLD) is increasing across all age groups as the incidence of obesity increases worldwide. The existing noninvasive prediction models for SLD require laboratory tests or imaging and perform poorly in the early diagnosis of infrequently screened populations such as young adults and individuals with healthcare disparities. We developed a machine learning-based point-of-care prediction model for SLD that is readily available to the broader population with the aim of facilitating early detection and timely intervention and ultimately reducing the burden of SLD. Methods: We retrospectively analyzed the clinical data of 28,506 adults who had routine health check-ups in South Korea from January to December 2022. A total of 229,162 individuals were included in the external validation study. Data were analyzed and predictions were made using a logistic regression model with machine learning algorithms. Results: A total of 20,094 individuals were categorized into SLD and non-SLD groups on the basis of the presence of fatty liver disease. We developed three prediction models: SLD model 1, which included age and body mass index (BMI); SLD model 2, which included BMI and body fat per muscle mass; and SLD model 3, which included BMI and visceral fat per muscle mass. In the derivation cohort, the area under the receiver operating characteristic curve (AUROC) was 0.817 for model 1, 0.821 for model 2, and 0.820 for model 3. In the internal validation cohort, 86.9% of individuals were correctly classified by the SLD models. The external validation study revealed an AUROC above 0.84 for all the models. Conclusions As our three novel SLD prediction models are cost-effective, noninvasive, and accessible, they could serve as validated clinical tools for mass screening of SLD.

Background: Respiratory infections play a major role in acute exacerbation of chronic obstructive pulmonary disease (AECOPD). This study assessed the prevalence of bacterial and viral pathogens and their clinical impact on patients with AECOPD. Methods: This retrospective study included 1,186 patients diagnosed with AECOPD at 28 hospitals in South Korea between 2015 and 2018. We evaluated the identification rates of pathogens, basic patient characteristics, clinical features, and the factors associated with infections by potentially drug-resistant (PDR) pathogens using various microbiological tests. Results: Bacteria, viruses, and both were detected in 262 (22.1%), 265 (22.5%), and 129 (10.9%) of patients, respectively. The most common pathogens included Pseudomonas aeruginosa (17.8%), Mycoplasma pneumoniae (11.2%), Streptococcus pneumoniae (9.0%), influenza A virus (19.0%), rhinovirus (15.8%), and respiratory syncytial virus (6.4%). Notably, a history of pulmonary tuberculosis (odds ratio [OR], 1.66; p=0.046), bronchiectasis (OR, 1.99; p=0.032), and the use of a triple inhaler regimen within the past 6 months (OR, 2.04; p=0.005) were identified as significant factors associated with infection by PDR pathogens. Moreover, patients infected with PDR pathogens exhibited extended hospital stays (15.9 days vs. 12.4 days, p=0.018) and higher intensive care unit admission rates (15.9% vs. 9.5%, p=0.030). Conclusion This study demonstrates that a variety of pathogens are involved in episodes of AECOPD. Nevertheless, additional research is required to confirm their role in the onset and progression of AECOPD.

Background/Aims: The incidence of steatotic liver disease (SLD) is increasing across all age groups as the incidence of obesity increases worldwide. The existing noninvasive prediction models for SLD require laboratory tests or imaging and perform poorly in the early diagnosis of infrequently screened populations such as young adults and individuals with healthcare disparities. We developed a machine learning-based point-of-care prediction model for SLD that is readily available to the broader population with the aim of facilitating early detection and timely intervention and ultimately reducing the burden of SLD. Methods: We retrospectively analyzed the clinical data of 28,506 adults who had routine health check-ups in South Korea from January to December 2022. A total of 229,162 individuals were included in the external validation study. Data were analyzed and predictions were made using a logistic regression model with machine learning algorithms. Results: A total of 20,094 individuals were categorized into SLD and non-SLD groups on the basis of the presence of fatty liver disease. We developed three prediction models: SLD model 1, which included age and body mass index (BMI); SLD model 2, which included BMI and body fat per muscle mass; and SLD model 3, which included BMI and visceral fat per muscle mass. In the derivation cohort, the area under the receiver operating characteristic curve (AUROC) was 0.817 for model 1, 0.821 for model 2, and 0.820 for model 3. In the internal validation cohort, 86.9% of individuals were correctly classified by the SLD models. The external validation study revealed an AUROC above 0.84 for all the models. Conclusions As our three novel SLD prediction models are cost-effective, noninvasive, and accessible, they could serve as validated clinical tools for mass screening of SLD.