Purpose: Developmentally regulated GTP-binding protein 2 (DRG2) regulates microtubule dynamics and G2/M arrest during docetaxel treatment. Poly ADP-ribose polymerase (PARP) acts as an important repair system for DNA damage caused by docetaxel treatment. This study investigated whether DRG2 expression affects response to PARP inhibitors (olaparib) using prostate cancer cell lines PC3, DU145, LNCaP-FGC, and LNCaP-LN3. Materials and Methods: The cell viability and DRG2 expression levels were assessed using colorimetric-based cell viability assay and western blot. Cells were transfected with DRG2 siRNA, and pcDNA6/V5-DRG2 was used to overexpress DRG2. Flow cytometry was applied for cell cycle assay and apoptosis analysis using the Annexing V cell death assay. Results: The expression of DRG2 was highest in LNCaP-LN3 and lowest in DU145 cells. Expressions of p53 in PC3, DU145, and the two LNCaP cell lines were null-type, high-expression, and medium-expression, respectively. In PC3 (DRG2 high, p53 null) cells, docetaxel increased G2/M arrest without apoptosis; however, subsequent treatment with olaparib promoted apoptosis. In DU145 and LNCaP-FGC (DRG2 low), docetaxel increased sub-G1 but not G2/M arrest and induced apoptosis, whereas olaparib had no additional effect. In LNCaP-LN3 (DRG2 high, p53 wild-type), docetaxel increased sub-G1 and G2/M arrest, furthermore olaparib enhanced cell death. Docetaxel and olaparib combination treatment had a slight effect on DRG2 knockdown PC3, but increased apoptosis in DRG2-overexpressed DU145 cells. Conclusions DRG2 and p53 expressions play an important role in prostate cancer cell lines treated with docetaxel, and DRG2 levels can predict the response to PARP inhibitors.

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.

Purpose: Although osimertinib is the standard-of-care treatment of epidermal growth factor receptor (EGFR) T790M mutation–positive non–small cell lung cancer, real-world evidence on the efficacy of osimertinib is not enough to reflect the complexity of the entire course of treatment. Herein, we report on the use of osimertinib in patients with EGFR T790M mutation–positive non–small cell lung cancer who had previously received EGFR tyrosine kinase inhibitor (TKI) treatment in Korea. Materials and Methods: Patients with confirmed EGFR T790M after disease progression of prior EGFR-TKI were enrolled and administered osimertinib 80 mg daily. The primary effectiveness outcome was progression-free survival, with time-to-treatment discontinuation, treatment and adverse effects leading to treatment discontinuation, and overall survival being the secondary endpoints. Results: A total of 558 individuals were enrolled, and 55.2% had investigator-assessed responses. The median progression-free survival was 14.2 months (95% confidence interval [CI], 13.0 to 16.4), and the median time-to-treatment discontinuation was 15.0 months (95% CI, 14.1 to 15.9). The median overall survival was 36.7 months (95% CI, 30.9 to not reached). The benefit with osimertinib was consistent regardless of the age, sex, smoking history, and primary EGFR mutation subtype. However, hepatic metastases at the time of diagnosis, the presence of plasma EGFR T790M, and the shorter duration of prior EGFR-TKI treatment were poor predictors of osimertinib treatment. Ten patients (1.8%), including three with pneumonitis, had to discontinue osimertinib due to severe adverse effects. Conclusion Osimertinib demonstrated its clinical effectiveness and survival benefit for EGFR T790M mutation–positive in Korean patients with no new safety signals.

Purpose: Tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) is a promising cancer therapeutic agent because of its tumor selectivity and its ability to induce apoptosis in cancer cells while sparing most normal cells. We evaluated whether docetaxel enhances TRAIL-mediated apoptosis in prostate cancer (PCa) cells and its mechanism. Materials and Methods: LNCap-LN3, PC3, and DU 145 PCa cell lines were used to investigate the effects of TRAIL with docetaxel treatment (dosages, 1, 3, 5, and 10 nmol). To evaluate the mechanism, death receptor 4 (DR4), DR5, enhancer of zeste homolog 2 (EZH2) and E2F1 levels were assessed in PCa cells. Results: Hormone-sensitive LNCap-LN3 showed apoptosis in proportion to the concentration of docetaxel. Castration-resistant PC3 and DU 145 showed no change irrespective of the docetaxel concentration. However, combinations of docetaxel (2 nM) and TRAIL (100 ng/mL) had a significant effect on apoptosis of DU 145 cells. In DU 145 cells, docetaxel reduced EZH2 and elevated expression of DR4. The decrease of EZH2 by docetaxel was correlated with the E2F1 level, which was considered as the promoter of EZH2. DZNep reduced EZH2 and elevated DR4 in all PCa cells. Additionally, DZNep-enhanced TRAIL mediated reduction of PCa cell viability. Conclusions Docetaxel and the EZH2 inhibitor reduced EZH2 and elevated expression of DR4 in all PCa cell lines. Docetaxel-enhanced TRAIL mediated apoptosis in PCa via elevation of DR4 through epigenetic regulation by EZH2. To improve the efficacy of TRAIL for PCa treatment, adding docetaxel or EZH2 inhibitors to TRAIL may be promising.

Purpose: The purpose of this study was to compare the concurrent validity and test-retest reliability of ‘KPIMT Torticollis Protractor’, a smart phone and I-pad application for convenient range of motion measurement, and ‘Image J’, an analysis software with high reliability and validity, according to head tilt and active cervical rotation angle. This was done to determine the clinical utility of ‘KPIMT Torticollis Protractor’. Methods: Head tilt and active cervical spine rotation angles of 40 children with congenital muscular torticollis were measured using Image J and KPIMT Torticollis Protractor, respectively. The level of concurrent validity and inter-rater and intra-rater reliability between the two measurement methods were analyzed. Results: For forty participants, the concurrent validity between Image J and KPIMT Torticollis Protractor showed very high validity with ICC of ICC 0.977 (0.995-0.999), 0.994 (0.994-0.998), CVME% 0.71-0.72%, SEM% 0.31-0.34%, MDC% 0.86-0.94%. The test-retest intra-rater reliability showed very high reliability ICC 0.911 (0.911-0.966), CVME% 0.71%, SEM% 0.34-0.36%, MDC% 0.81-0.94%. The test-retest inter-rater showed very high reliability ICC 0.936 (0.933-0.957), CVME% 0.70%, SEM% 0.34-0.35%, MDC% 0.81-0.83%. Conclusion The KPIMT Torticollis Protractor, a smart phone and IPD application, is a highly reliable and valid device for angle measurement in children with congenital myotonia and can be easily used in clinical practice.

Background: This study aimed to identify the characteristics of the referral and return of patients to clinics in the endocrinology and cardiology departments at the National Health Insurance Service Ilsan Hospital to evaluate the “referral and return of patients to clinics” program and reduce the rate of returning patients. Methods: From May 2018 to December 2020, we identified the number of visits to referral hospitals and hospital usage status at Ilsan Hospital after returning to clinics. We also identified the patients who returned to Ilsan Hospital within 6 months, defined as “failure to transport,” among those recommended to be transported to clinics of the Medical Cooperation Center. Additionally, we evaluated the characteristics of the “failure to transport” patients. Results: Among the returning patients, the rate of visiting Ilsan Hospital within 6 months was higher in cardiology than in endocrinology (25.1% vs. 16.7%). Older age, more severe disease, and more number of visits to the department were associated with a high rate of failure to transport. The rate of failure to return was low in cases diagnosed with hyperlipidemia/lipoprotein metabolism disorder. With respect to diabetes, the rate of failure to transport differed according to each type of diagnosis of diabetes. Conclusion The success rate of the “referral and return of patient to clinics” program differed based on each patient’s characteristics, department of visit, and diagnosis. Individualizing according to the visit department and diagnosis is required to ensure successful transfers, and infrastructure expansion and institutional arrangements must be facilitated.