Objective: To evaluate the effects of Catalpa bignonioides fruit extract on the promotion of muscle growth and muscular capacity in vitro and in vivo. Methods: Cell viability was measured using the 3-(4,5-dimethylthiazol- 2-yl)-2,5-diphenyltetrazolium bromide assay. Cell proliferation was assessed using a 5-bromo-2’-deoxyuridine (BrdU) assay kit. Western blot analysis was performed to determine the protein expressions of related factors. The effects of Catalpa bignonioides extract were investigated in mice using the treadmill exhaustion test and whole-limb grip strength assay. Chemical composition analysis was performed using high-performance liquid chromatography (HPLC). Results: Catalpa bignonioides extract increased the proliferation of C2C12 mouse myoblasts by activating the Akt/mTOR signaling pathway. It also induced metabolic changes, increasing the number of mitochondria and glucose metabolism by phosphorylating adenosine monophosphate-activated protein kinase. In an in vivo study, the extract-treated mice showed improved motor abilities, such as muscular endurance and grip strength. Additionally, HPLC analysis showed that vanillic acid may be the main component of the Catalpa bignonioides extract that enhanced muscle strength. Conclusions: Catalpa bignonioides improves exercise performance through regulation of growth and metabolism in skeletal muscles, suggesting its potential as an effective natural agent for improving muscular strength.

Background: and Purpose: Dementia, particularly Alzheimer’s disease, is a significant global health concern, with early diagnosis and treatment development being critical goals. While numerous cohorts have advanced dementia research, there is a lack of comprehensive data on ethnic differences, particularly for the Korean population. The Korea-Registries to Overcome Dementia and Accelerate Dementia Research (K-ROAD) aims to establish a large-scale, hospital-based dementia cohort to address this gap, with a focus on understanding disease progression, developing early diagnostics, and supporting treatment advancements specific to the Korean population. Methods: K-ROAD comprises multiple prospective cohorts. Participants underwent clinical evaluations, neuroimaging, and biomarker analysis, with data collected on a range of clinical and genomic markers. Results: As of December 2023, K-ROAD has recruited over 5,800 participants, including individuals across the Alzheimer’s clinical syndrome, subcortical vascular cognitive impairment, and frontotemporal dementia spectra. Preliminary findings highlight significant ethnic differences in amyloid positivity, cognitive decline, and biomarker profiles, compared to Western cohorts. Conclusions The K-ROAD cohort offers a unique and critical resource for dementia research, providing insights into ethnic-specific disease characteristics and biomarker profiles. These findings will contribute to the development of personalized diagnostic and therapeutic approaches to dementia, enhancing global understanding of the disease.

Purpose: Unnecessary prostate biopsies for detecting prostate cancer (PCa) should be minimized. Therefore, this study developed a machine learning (ML) model to predict PCa in Korean men and evaluated its usability. Materials and Methods: We retrospectively analyzed clinical data from 928 patients who underwent prostate biopsies at Kangwon National University Hospital between May 2013 and May 2023. Of these, 377 (41.6%) were diagnosed with PCa, and 551 (59.4%) did not have cancer. For external validation, clinical data from 385 patients aged 48–89 years who underwent prostate biopsies from September 2005 to September 2023 at Wonju Severance Christian Hospital were also included. Twenty-two clinical features were used to develop an ML model to predict PCa. Features were selected based on their contributions to model performance, leading to the inclusion of 15 features. A meta-learner was constructed using logistic regression to predict the probability of PCa, and the classifier was trained and validated on randomly extracted training and test sets at an 8:2 ratio. Results: The prostate health index, prostate volume, age, nodule on digital rectal examination, and prostate-specific antigen were the top 5 features for predicting PCa. The area under the receiver operating characteristic curve (AUC) of the meta-learner logistic regression model was 0.89, and the accuracy, sensitivity, and specificity were 0.828, 0.711, and 0.909, respectively. Our model also showed excellent prediction performance for high-grade PCa, with a Gleason score of 7 or higher and an AUC of 0.903. Furthermore, we evaluated the performance of the model using external cohort clinical data and achieved an AUC of 0.863. Conclusions Our ML model excelled in predicting PCa, specifically clinically significant PCa. Although extensive cross-validation in other clinical cohorts is needed, this ML model is a promising option for future diagnostics.

Background: and Purpose: Dementia, particularly Alzheimer’s disease, is a significant global health concern, with early diagnosis and treatment development being critical goals. While numerous cohorts have advanced dementia research, there is a lack of comprehensive data on ethnic differences, particularly for the Korean population. The Korea-Registries to Overcome Dementia and Accelerate Dementia Research (K-ROAD) aims to establish a large-scale, hospital-based dementia cohort to address this gap, with a focus on understanding disease progression, developing early diagnostics, and supporting treatment advancements specific to the Korean population. Methods: K-ROAD comprises multiple prospective cohorts. Participants underwent clinical evaluations, neuroimaging, and biomarker analysis, with data collected on a range of clinical and genomic markers. Results: As of December 2023, K-ROAD has recruited over 5,800 participants, including individuals across the Alzheimer’s clinical syndrome, subcortical vascular cognitive impairment, and frontotemporal dementia spectra. Preliminary findings highlight significant ethnic differences in amyloid positivity, cognitive decline, and biomarker profiles, compared to Western cohorts. Conclusions The K-ROAD cohort offers a unique and critical resource for dementia research, providing insights into ethnic-specific disease characteristics and biomarker profiles. These findings will contribute to the development of personalized diagnostic and therapeutic approaches to dementia, enhancing global understanding of the disease.

Background: and Purpose: Dementia, particularly Alzheimer’s disease, is a significant global health concern, with early diagnosis and treatment development being critical goals. While numerous cohorts have advanced dementia research, there is a lack of comprehensive data on ethnic differences, particularly for the Korean population. The Korea-Registries to Overcome Dementia and Accelerate Dementia Research (K-ROAD) aims to establish a large-scale, hospital-based dementia cohort to address this gap, with a focus on understanding disease progression, developing early diagnostics, and supporting treatment advancements specific to the Korean population. Methods: K-ROAD comprises multiple prospective cohorts. Participants underwent clinical evaluations, neuroimaging, and biomarker analysis, with data collected on a range of clinical and genomic markers. Results: As of December 2023, K-ROAD has recruited over 5,800 participants, including individuals across the Alzheimer’s clinical syndrome, subcortical vascular cognitive impairment, and frontotemporal dementia spectra. Preliminary findings highlight significant ethnic differences in amyloid positivity, cognitive decline, and biomarker profiles, compared to Western cohorts. Conclusions The K-ROAD cohort offers a unique and critical resource for dementia research, providing insights into ethnic-specific disease characteristics and biomarker profiles. These findings will contribute to the development of personalized diagnostic and therapeutic approaches to dementia, enhancing global understanding of the disease.

Purpose: Unnecessary prostate biopsies for detecting prostate cancer (PCa) should be minimized. Therefore, this study developed a machine learning (ML) model to predict PCa in Korean men and evaluated its usability. Materials and Methods: We retrospectively analyzed clinical data from 928 patients who underwent prostate biopsies at Kangwon National University Hospital between May 2013 and May 2023. Of these, 377 (41.6%) were diagnosed with PCa, and 551 (59.4%) did not have cancer. For external validation, clinical data from 385 patients aged 48–89 years who underwent prostate biopsies from September 2005 to September 2023 at Wonju Severance Christian Hospital were also included. Twenty-two clinical features were used to develop an ML model to predict PCa. Features were selected based on their contributions to model performance, leading to the inclusion of 15 features. A meta-learner was constructed using logistic regression to predict the probability of PCa, and the classifier was trained and validated on randomly extracted training and test sets at an 8:2 ratio. Results: The prostate health index, prostate volume, age, nodule on digital rectal examination, and prostate-specific antigen were the top 5 features for predicting PCa. The area under the receiver operating characteristic curve (AUC) of the meta-learner logistic regression model was 0.89, and the accuracy, sensitivity, and specificity were 0.828, 0.711, and 0.909, respectively. Our model also showed excellent prediction performance for high-grade PCa, with a Gleason score of 7 or higher and an AUC of 0.903. Furthermore, we evaluated the performance of the model using external cohort clinical data and achieved an AUC of 0.863. Conclusions Our ML model excelled in predicting PCa, specifically clinically significant PCa. Although extensive cross-validation in other clinical cohorts is needed, this ML model is a promising option for future diagnostics.

Background: and Purpose: Dementia, particularly Alzheimer’s disease, is a significant global health concern, with early diagnosis and treatment development being critical goals. While numerous cohorts have advanced dementia research, there is a lack of comprehensive data on ethnic differences, particularly for the Korean population. The Korea-Registries to Overcome Dementia and Accelerate Dementia Research (K-ROAD) aims to establish a large-scale, hospital-based dementia cohort to address this gap, with a focus on understanding disease progression, developing early diagnostics, and supporting treatment advancements specific to the Korean population. Methods: K-ROAD comprises multiple prospective cohorts. Participants underwent clinical evaluations, neuroimaging, and biomarker analysis, with data collected on a range of clinical and genomic markers. Results: As of December 2023, K-ROAD has recruited over 5,800 participants, including individuals across the Alzheimer’s clinical syndrome, subcortical vascular cognitive impairment, and frontotemporal dementia spectra. Preliminary findings highlight significant ethnic differences in amyloid positivity, cognitive decline, and biomarker profiles, compared to Western cohorts. Conclusions The K-ROAD cohort offers a unique and critical resource for dementia research, providing insights into ethnic-specific disease characteristics and biomarker profiles. These findings will contribute to the development of personalized diagnostic and therapeutic approaches to dementia, enhancing global understanding of the disease.

Purpose: Unnecessary prostate biopsies for detecting prostate cancer (PCa) should be minimized. Therefore, this study developed a machine learning (ML) model to predict PCa in Korean men and evaluated its usability. Materials and Methods: We retrospectively analyzed clinical data from 928 patients who underwent prostate biopsies at Kangwon National University Hospital between May 2013 and May 2023. Of these, 377 (41.6%) were diagnosed with PCa, and 551 (59.4%) did not have cancer. For external validation, clinical data from 385 patients aged 48–89 years who underwent prostate biopsies from September 2005 to September 2023 at Wonju Severance Christian Hospital were also included. Twenty-two clinical features were used to develop an ML model to predict PCa. Features were selected based on their contributions to model performance, leading to the inclusion of 15 features. A meta-learner was constructed using logistic regression to predict the probability of PCa, and the classifier was trained and validated on randomly extracted training and test sets at an 8:2 ratio. Results: The prostate health index, prostate volume, age, nodule on digital rectal examination, and prostate-specific antigen were the top 5 features for predicting PCa. The area under the receiver operating characteristic curve (AUC) of the meta-learner logistic regression model was 0.89, and the accuracy, sensitivity, and specificity were 0.828, 0.711, and 0.909, respectively. Our model also showed excellent prediction performance for high-grade PCa, with a Gleason score of 7 or higher and an AUC of 0.903. Furthermore, we evaluated the performance of the model using external cohort clinical data and achieved an AUC of 0.863. Conclusions Our ML model excelled in predicting PCa, specifically clinically significant PCa. Although extensive cross-validation in other clinical cohorts is needed, this ML model is a promising option for future diagnostics.

As part of our current projects to discover biologically active compounds from natural sources, we conducted a phytochemical investigation of Juncus decipiens, a species generally distributed throughout Korea. J. decipiens has been used in traditional Chinese medicine to control diuresis for strangury and clear out heart fire. The phytochemical investigation of the EtOH extract of J. decipiens led to the isolation of five phenolic compounds (1–5) via semi-preparative HPLC purification. The chemical structures of the isolated compounds were identified as isosaponarin (1), isovitexin 7,2′′-di-O-glucoside (2), 4-O-feruloylquinic acid (3), 5-O-feruloylquinic acid (4), and 3-O-caffeoylquinic acid (5) based on comparisons of their spectroscopic and physical properties with those reported in previous studies. Notably, this is the first report of the presence of compounds (1–4) in J. decipiens. Then, compounds 1–5 were tested to determine their effects on osteogenesis and adipogenesis in the mouse mesenchymal stem cell line C3H10T1/2. We found that quinic acid derivatives (3–5) promoted the osteogenic differentiation of stem cells. These findings demonstrate that the bioactive quinic acid derivatives might be effective for the treatment of menopause-associated syndromes, such as osteoporosis, as the isolated compounds were shown to promote osteogenesis of stem cells.

The global resurgence of bed bug infestations, exacerbated by increasing international travel, trade, and insecticide resistance, has significantly impacted Korea. This study identified the bed bug species and performed pyrethroid resistance genotyping of recently resurgent bed bugs in Korea. Thirty-one regional bed bug samples were collected from 5 administrative regions: Gyeonggi-do (n=14), Seoul (n=13), Busan (n=2), Jeonllanam-do (n=1), and Chungcheongbuk-do (n=1). The samples underwent morphological and molecular identification. Twenty-four regional samples (77.4%) were identified as the tropical bed bug, Cimex hemipterus, and the remaining 7 regional samples (22.6%) were identified as the common bed bug, Cimex lectularius. The C. hemipterus regional samples carried at least three mutations associated with knockdown resistance (kdr), including 2 super-kdr mutations. The 7 C. lectularius regional samples possessed at least one of the 3 kdr-related mutations associated with pyrethroid resistance. This study confirms that the prevalent bed bug species recently in Korea is C. hemipterus, replacing the previously endemic C. lectularius. Additionally, the rise in bed bug populations with pyrethroid resistance underscores the necessity of introducing alternative insecticides.