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/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.

Purpose: The aim of this study was to identify novel vaginal probiotics with the potential to prevent vulvovaginal candidiasis (VVC) and bacterial vaginosis (BV). Materials and Methods: Eighteen strains of Lactiplantibacillus plantarum were isolated from healthy Korean women, and their antimicrobial effects against Candida albicans and Gardnerella vaginalis were assessed. Three strains (L. plantarum LM1203, LM1209, and LM1215) were selected for further investigation, focusing on their growth inhibition, biofilm regulation, and cellular mechanisms against these vaginal pathogens. Additionally, electron microscopy revealed damage to G. vaginalis induced by L.plantarum LM1215, and genomic analysis was conducted on this strain. Results: L. plantarum LM1203, LM1209, and LM1215 showed approximately 1 and 2 Log CFU/mL growth reduction in C. albicans and G. vaginalis, respectively. These L. plantarum strains effectively inhibited biofilm formation and eliminated the mature biofilms formed by C. albicans. Furthermore, L. plantarum LM1215 decreased tricarboxylic acid cycle activity by 51.75 (p<0.001) and respiratory metabolic activity by 52.88% (p<0.001) in G. vaginalis. L. plantarum induced cellular membrane damage, inhibition of protein synthesis, and cell wall collapse in G. vaginalis. Genomic analysis confirmed L. plantarum LM1215 as a safe strain for vaginal probiotics. Conclusion The L. plantarum LM1215 is considered a safe probiotic agent suitable for the prevention of VVC and BV.

Objective: Understanding the specific fears associated with coronavirus disease-2019 (COVID-19), particularly within different cultural contexts, is crucial for developing effective mental health interventions. This study aims to develop and validate the COVID-19 Infection Fear Scale (CIFS) in a collectivist cultural context such as Korea. Methods: A total of 1,002 adults aged 19 to 70 participated in an online survey in May 2020. The CIFS was developed through a multidisciplinary approach, categorizing public fears into two domains: fear of infection and fear of negative outcomes post-infection. Exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were conducted to validate the factor structure. Reliability and construct validity were assessed through correlations with anxiety (Generalized Anxiety Disorder-7), depression (Patient Health Questionnaire-9), suicidal ideation, and coping strategies. Results: The CIFS demonstrated high internal consistency. EFA and CFA supported a two-factor model. The Rasch analysis confirmed good item fit, with infit and outfit indices within the acceptable range. Differential item functioning analysis indicated minor sex and age biases, addressed without removing items. Construct validity was supported by significant correlations with anxiety, depression, suicidal ideation, and coping strategies. Fear of negative consequences post-infection showed a stronger correlation with psychological distress than fear of infection. Conclusion The CIFS is a reliable and valid tool for measuring fear related to COVID-19 infection and its consequences, particularly within a collectivist cultural context. This scale can aid in identifying individuals at higher risk of psychological distress and inform targeted interventions.

Purpose: The aim of this study was to identify novel vaginal probiotics with the potential to prevent vulvovaginal candidiasis (VVC) and bacterial vaginosis (BV). Materials and Methods: Eighteen strains of Lactiplantibacillus plantarum were isolated from healthy Korean women, and their antimicrobial effects against Candida albicans and Gardnerella vaginalis were assessed. Three strains (L. plantarum LM1203, LM1209, and LM1215) were selected for further investigation, focusing on their growth inhibition, biofilm regulation, and cellular mechanisms against these vaginal pathogens. Additionally, electron microscopy revealed damage to G. vaginalis induced by L.plantarum LM1215, and genomic analysis was conducted on this strain. Results: L. plantarum LM1203, LM1209, and LM1215 showed approximately 1 and 2 Log CFU/mL growth reduction in C. albicans and G. vaginalis, respectively. These L. plantarum strains effectively inhibited biofilm formation and eliminated the mature biofilms formed by C. albicans. Furthermore, L. plantarum LM1215 decreased tricarboxylic acid cycle activity by 51.75 (p<0.001) and respiratory metabolic activity by 52.88% (p<0.001) in G. vaginalis. L. plantarum induced cellular membrane damage, inhibition of protein synthesis, and cell wall collapse in G. vaginalis. Genomic analysis confirmed L. plantarum LM1215 as a safe strain for vaginal probiotics. Conclusion The L. plantarum LM1215 is considered a safe probiotic agent suitable for the prevention of VVC and BV.

Objective: Understanding the specific fears associated with coronavirus disease-2019 (COVID-19), particularly within different cultural contexts, is crucial for developing effective mental health interventions. This study aims to develop and validate the COVID-19 Infection Fear Scale (CIFS) in a collectivist cultural context such as Korea. Methods: A total of 1,002 adults aged 19 to 70 participated in an online survey in May 2020. The CIFS was developed through a multidisciplinary approach, categorizing public fears into two domains: fear of infection and fear of negative outcomes post-infection. Exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were conducted to validate the factor structure. Reliability and construct validity were assessed through correlations with anxiety (Generalized Anxiety Disorder-7), depression (Patient Health Questionnaire-9), suicidal ideation, and coping strategies. Results: The CIFS demonstrated high internal consistency. EFA and CFA supported a two-factor model. The Rasch analysis confirmed good item fit, with infit and outfit indices within the acceptable range. Differential item functioning analysis indicated minor sex and age biases, addressed without removing items. Construct validity was supported by significant correlations with anxiety, depression, suicidal ideation, and coping strategies. Fear of negative consequences post-infection showed a stronger correlation with psychological distress than fear of infection. Conclusion The CIFS is a reliable and valid tool for measuring fear related to COVID-19 infection and its consequences, particularly within a collectivist cultural context. This scale can aid in identifying individuals at higher risk of psychological distress and inform targeted interventions.

Objective: Understanding the specific fears associated with coronavirus disease-2019 (COVID-19), particularly within different cultural contexts, is crucial for developing effective mental health interventions. This study aims to develop and validate the COVID-19 Infection Fear Scale (CIFS) in a collectivist cultural context such as Korea. Methods: A total of 1,002 adults aged 19 to 70 participated in an online survey in May 2020. The CIFS was developed through a multidisciplinary approach, categorizing public fears into two domains: fear of infection and fear of negative outcomes post-infection. Exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were conducted to validate the factor structure. Reliability and construct validity were assessed through correlations with anxiety (Generalized Anxiety Disorder-7), depression (Patient Health Questionnaire-9), suicidal ideation, and coping strategies. Results: The CIFS demonstrated high internal consistency. EFA and CFA supported a two-factor model. The Rasch analysis confirmed good item fit, with infit and outfit indices within the acceptable range. Differential item functioning analysis indicated minor sex and age biases, addressed without removing items. Construct validity was supported by significant correlations with anxiety, depression, suicidal ideation, and coping strategies. Fear of negative consequences post-infection showed a stronger correlation with psychological distress than fear of infection. Conclusion The CIFS is a reliable and valid tool for measuring fear related to COVID-19 infection and its consequences, particularly within a collectivist cultural context. This scale can aid in identifying individuals at higher risk of psychological distress and inform targeted interventions.

Purpose: The aim of this study was to identify novel vaginal probiotics with the potential to prevent vulvovaginal candidiasis (VVC) and bacterial vaginosis (BV). Materials and Methods: Eighteen strains of Lactiplantibacillus plantarum were isolated from healthy Korean women, and their antimicrobial effects against Candida albicans and Gardnerella vaginalis were assessed. Three strains (L. plantarum LM1203, LM1209, and LM1215) were selected for further investigation, focusing on their growth inhibition, biofilm regulation, and cellular mechanisms against these vaginal pathogens. Additionally, electron microscopy revealed damage to G. vaginalis induced by L.plantarum LM1215, and genomic analysis was conducted on this strain. Results: L. plantarum LM1203, LM1209, and LM1215 showed approximately 1 and 2 Log CFU/mL growth reduction in C. albicans and G. vaginalis, respectively. These L. plantarum strains effectively inhibited biofilm formation and eliminated the mature biofilms formed by C. albicans. Furthermore, L. plantarum LM1215 decreased tricarboxylic acid cycle activity by 51.75 (p<0.001) and respiratory metabolic activity by 52.88% (p<0.001) in G. vaginalis. L. plantarum induced cellular membrane damage, inhibition of protein synthesis, and cell wall collapse in G. vaginalis. Genomic analysis confirmed L. plantarum LM1215 as a safe strain for vaginal probiotics. Conclusion The L. plantarum LM1215 is considered a safe probiotic agent suitable for the prevention of VVC and BV.