Objective: To address the gap in timely diagnosis of dementia due to limited screening tools, we investigated the validity and reliability of the Hellocog, computerized neuropsychological test based on tablets for screening dementia. The higher the probability score on the Hellocog, the higher the likelihood of dementia. Methods: This study included 100 patients with dementia and 100 individuals with normal cognition who were aged 60 years or older and free of other major psychiatric, neurological, or medical conditions. They administered the Hellocog on a tablet under the supervision of a neuropsychologist. To determine test-retest reliability, 20 took the Hellocog again after 4 weeks. Diagnostic performance was assessed using the receiver operator characteristics (ROC) analysis. Results: The Hellocog showed adequate internal consistency (Cronbach’s alpha=0.69) and good test-retest reliability (intraclass correlation coefficient=0.86, p<0.001). Participants with dementia scored higher on the Hellocog than those with normal cognition (p<0.001), confirming its high criterion validity. Strong correlations with the Mini-Mental Status Examination (MMSE) score and the total score of the Consortium to Establish a Registry for Alzheimer’s Disease Neuropsychological Assessment Battery (CERAD-TS) highlight the concurrent validity of the Hellocog. The area under the ROC curve for dementia of the Hellocog was excellent (0.971) and comparable to that of the MMSE and CERAD-TS. The sensitivity and specificity for dementia were 0.945 and 0.872%, respectively, which were slightly better than those of the MMSE and CERAD-TS. Conclusion Hellocog stands out as a valid and reliable tool for self-administered dementia screening, with promise for improving early detection of dementia.

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.

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.

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.