Chronic kidney disease (CKD) patients are hospitalized for various conditions. Hospitalization increases the readmission rate and mortality rate, seriously deteriorating patients’ quality of life. Consequently, it is crucial to analyze the reasons for hospitalization in CKD patients from a broader perspective according to CKD grade. Methods: This is a prospective cohort study of CKD patients entitled the KoreaN cohort study for Outcomes in patients With Chronic Kidney Disease (KNOW-CKD). A total of 2,238 patients were examined, and the reasons for hospitalization were classified into 16 disease categories. The incidence rate ratio (IRR) according to CKD stage was compared using negative bimodal regression analysis. Results: The all-cause hospitalization incidence was 184.96 per 1,000 person-years. The most common reason for hospitalization was circulatory system disease, followed by infection and digestive system disease. Among hospitalizations for acute kidney injury, endocrine-nutrition-metabolic–related illness, blood-related disease, and diseases of the nervous system and sensory organs, IRR increased as CKD grade advanced. The incidence of ophthalmologic surgery during hospitalization increased according to the CKD stage. The IRR of KNOW-CKD patients was 6.19 (95% confidence interval, 5.92–6.48; p < 0.001) compared with the general population. Conclusion: This in-depth analysis of hospitalizations among CKD patients confirmed that CKD patients were hospitalized for various reasons, such as metabolic, ophthalmic, and hematologic diseases. Early detection and intervention regarding causative diseases of CKD are important to reduce the hospitalization burden and improve patients’ quality of life.

New supplements with preventive effects against skin photodamage are receiving increasing attention. This study evaluated the anti-photoaging effects of salmon nasal cartilage proteoglycan (SPG), acting as a functional material for skin health. We administered SPG to in vitro and in vivo models exposed to ultraviolet B (UVB) radiation and assessed its moisturizing and anti-wrinkle effects on dorsal mouse skin and keratinocytes and dermal fibroblasts cell lines. These results showed that SPG restored the levels of filaggrin, involucrin, and AQP3 in the epidermis of UVB-irradiated dorsal skin and keratinocytes, thereby enhancing the keratinization process and water flow. Additionally, SPG treatment increased the levels of hyaluronan and skin ceramide, the major components of intercellular lipids in the epidermis. Furthermore, SPG treatment significantly increased the levels of collagen and procollagen type 1 by down-regulating matrix metalloproteinase 1, which play a crucial role in skin fibroblasts, in both in vitro and in vivo models. In addition, SPG strongly inhibited mitogen-activated protein kinase (MAPKs) signaling, the including extracellular signal-regulated kinase, c-Jun N-terminal kinase (JNK), and p38. These findings suggest that dietary SPG may be an attractive functional food for preventing UVB-induced photoaging. And this SPG product may provide its best benefit when treating several signs of skin photoaging.

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.

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.

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.

Objective: The Scales for Outcomes in Parkinson’s Disease–Cognition (SCOPA-Cog) was developed to assess cognition in patients with Parkinson’s disease (PD). In this study, we aimed to evaluate the validity and reliability of the Korean version of the SCOPACog (K-SCOPA-Cog). Methods: We enrolled 129 PD patients with movement disorders from 31 clinics in South Korea. The original version of the SCOPA-Cog was translated into Korean using the translation-retranslation method. The test–retest method with an intraclass correlation coefficient (ICC) and Cronbach’s alpha coefficient were used to assess reliability. Spearman’s rank correlation analysis with the Montreal Cognitive Assessment-Korean version (MOCA-K) and the Korean Mini-Mental State Examination (K-MMSE) were used to assess concurrent validity. Results: The Cronbach’s alpha coefficient was 0.797, and the ICC was 0.887. Spearman’s rank correlation analysis revealed a significant correlation with the K-MMSE and MOCA-K scores (r = 0.546 and r = 0.683, respectively). Conclusion Our results demonstrate that the K-SCOPA-Cog has good reliability and validity.