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.

Serological tests for Helicobacter pylori needs local validation as the diagnostic accuracy may vary depending on the prevalence of H.pylori. This study examined the diagnostic performance of two ELISA, GastroPanel® (GastroPanel ELISA; Biohit Oyj) and GENE-DIA® (GENEDIA® H. pylori ELISA, Green Cross Co.) in Korean population. One thousand seventy seven patients who visited for esophagogastroduodenoscopy between 2013 and 2023 were prospectively enrolled, and serum samples from the subjects were tested using both GastroPanel® and GENEDIA® . The two tests were compared for their diagnostic accuracy in detecting atrophic gastritis (AG), intestinal metaplasia (IM), gastric adenoma (GA), and gastric cancer (GC), and the positivity rates by age and sexwere observed. There was substantial correlation (Pearson coefficient [r] = 0.512, P < 0.001) and agreement (Cohen’s Kappa coefficient [κ] = 0.723, P < 0.001) between the results obtained using GastroPanel® and GENEDIA® . The test results from the two kits did not match perfectly with a discrepancy observed in approximately 16% of cases, that 67 subjects were positive only on GENE-DIA® while 75 subjects were positive only on GastroPanel® . The area under receiver operating characteristic curve for AG, IM, GA,and GC using GastroPanel® were 0.666, 0.635, 0.540, and 0.575, while the results tested using GENEDIA® were 0.649, 0.604, 0.553, and 0.555, respectively, without significant difference between the two results. GastroPanel® and GENEDIA® showed similar performance in terms of diagnostic accuracy; but the test results did not match perfectly. A large-scale validation study in Koreansis needed.

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.

OBJECTIVES: This study aimed to establish the validity—specifically, the sensitivity and specificity—of the screening questionnaire and diagnostic criteria for oral frailty proposed by the Korean Academy of Geriatric Dentistry (KAGD) among community-dwelling older adults. METHODS: This study enrolled 100 participants. Among various definitions of oral frailty, this study used the criteria proposed by Tanaka as the reference test. The screening questionnaire consisted of 11 items for screening physical frailty, chewing ability, swallowing difficulties, oral dryness, and tongue and lip motor function. Each question had a different scoring weight, and if the total score was 1 or higher, an oral frailty diagnostic examination proposed by the KAGD would be recommended. The diagnostic test was the oral frailty diagnostic criteria proposed by the KAGD including 6 measures: chewing ability, occlusal force, tongue pressure, oral dryness, swallowing difficulty, and oral hygiene. If a participant exhibited 2 or more positive measures, this participant was classified as “oral frail.” The screening questionnaire was analyzed using a cut-off value of 1 or higher, while the diagnostic criteria utilized a cut-off of 2 or more positive measures. Sensitivity and specificity were calculated. RESULTS: The screening questionnaire showed significant power for screening oral frailty (area under the receiver operating characteristic curve, 0.783; sensitivity, 87.8%; specificity, 52.5%). The diagnostic accuracy of the newly proposed diagnostic criteria was acceptable (sensitivity, 95.1%; specificity, 42.4%). CONCLUSIONS The newly proposed screening questionnaire and diagnostic criteria in Korea appear to be a useful tool to identify oral frailty in community-dwelling older adults.

Serological tests for Helicobacter pylori needs local validation as the diagnostic accuracy may vary depending on the prevalence of H.pylori. This study examined the diagnostic performance of two ELISA, GastroPanel® (GastroPanel ELISA; Biohit Oyj) and GENE-DIA® (GENEDIA® H. pylori ELISA, Green Cross Co.) in Korean population. One thousand seventy seven patients who visited for esophagogastroduodenoscopy between 2013 and 2023 were prospectively enrolled, and serum samples from the subjects were tested using both GastroPanel® and GENEDIA® . The two tests were compared for their diagnostic accuracy in detecting atrophic gastritis (AG), intestinal metaplasia (IM), gastric adenoma (GA), and gastric cancer (GC), and the positivity rates by age and sexwere observed. There was substantial correlation (Pearson coefficient [r] = 0.512, P < 0.001) and agreement (Cohen’s Kappa coefficient [κ] = 0.723, P < 0.001) between the results obtained using GastroPanel® and GENEDIA® . The test results from the two kits did not match perfectly with a discrepancy observed in approximately 16% of cases, that 67 subjects were positive only on GENE-DIA® while 75 subjects were positive only on GastroPanel® . The area under receiver operating characteristic curve for AG, IM, GA,and GC using GastroPanel® were 0.666, 0.635, 0.540, and 0.575, while the results tested using GENEDIA® were 0.649, 0.604, 0.553, and 0.555, respectively, without significant difference between the two results. GastroPanel® and GENEDIA® showed similar performance in terms of diagnostic accuracy; but the test results did not match perfectly. A large-scale validation study in Koreansis needed.

Serological tests for Helicobacter pylori needs local validation as the diagnostic accuracy may vary depending on the prevalence of H.pylori. This study examined the diagnostic performance of two ELISA, GastroPanel® (GastroPanel ELISA; Biohit Oyj) and GENE-DIA® (GENEDIA® H. pylori ELISA, Green Cross Co.) in Korean population. One thousand seventy seven patients who visited for esophagogastroduodenoscopy between 2013 and 2023 were prospectively enrolled, and serum samples from the subjects were tested using both GastroPanel® and GENEDIA® . The two tests were compared for their diagnostic accuracy in detecting atrophic gastritis (AG), intestinal metaplasia (IM), gastric adenoma (GA), and gastric cancer (GC), and the positivity rates by age and sexwere observed. There was substantial correlation (Pearson coefficient [r] = 0.512, P < 0.001) and agreement (Cohen’s Kappa coefficient [κ] = 0.723, P < 0.001) between the results obtained using GastroPanel® and GENEDIA® . The test results from the two kits did not match perfectly with a discrepancy observed in approximately 16% of cases, that 67 subjects were positive only on GENE-DIA® while 75 subjects were positive only on GastroPanel® . The area under receiver operating characteristic curve for AG, IM, GA,and GC using GastroPanel® were 0.666, 0.635, 0.540, and 0.575, while the results tested using GENEDIA® were 0.649, 0.604, 0.553, and 0.555, respectively, without significant difference between the two results. GastroPanel® and GENEDIA® showed similar performance in terms of diagnostic accuracy; but the test results did not match perfectly. A large-scale validation study in Koreansis needed.

Purpose: This study aimed to compare the short-term outcomes and safety profiles of androgen-deprivation therapy (ADT)+abiraterone/prednisone with those of ADT+docetaxel in patients with de novo metastatic hormone-sensitive prostate cancer (mHSPC). Materials and Methods: A web-based database system was established to collect prospective cohort data for patients with mHSPC in Korea. From May 2019 to November 2022, 928 patients with mHSPC from 15 institutions were enrolled. Among these patients, data from 122 patients who received ADT+abiraterone/prednisone or ADT+docetaxel as the primary systemic treatment for mHSPC were collected. The patients were divided into two groups: ADT+abiraterone/prednisone group (n=102) and ADT+docetaxel group (n=20). We compared the demographic characteristics, medical histories, baseline cancer status, initial laboratory tests, metastatic burden, oncological outcomes for mHSPC, progression after mHSPC treatment, adverse effects, follow-up, and survival data between the two groups. Results: No significant differences in the demographic characteristics, medical histories, metastatic burden, and baseline cancer status were observed between the two groups. The ADT+abiraterone/prednisone group had a lower prostate-specific antigen (PSA) progression rate (7.8% vs. 30.0%; p=0.011) and lower systemic treatment discontinuation rate (22.5% vs. 45.0%; p=0.037). No significant differences in adverse effects, oncological outcomes, and total follow-up period were observed between the two groups. Conclusions ADT+abiraterone/prednisone had lower PSA progression and systemic treatment discontinuation rates than ADT+docetaxel. In conclusion, further studies involving larger, double-blinded randomized trials with extended follow-up periods are necessary.

BACKGROUND: Rheumatoid arthritis (RA) is characterized by chronic inflammation and joint damage. Methotrexate (MTX), a commonly used disease-modifying anti-rheumatic drug (DMARD) used in RA treatment. However, the continued use of DMARDs can cause adverse effects and result in limited therapeutic efficacy. Cartilage extracellular matrix (CECM) has anti-inflammatory and anti-vascular effects and promotes stem cell migration, adhesion, and differentiation into cartilage cells. METHODS: CECM was assessed the dsDNA, glycosaminoglycan, collagen contents and FT-IR spectrum of CECM.Furthermore, we determined the effects of CECM and MTX on cytocompatibility in the SW 982 cells and RAW 264.7 cells. The anti-inflammatory effects of CECM and MTX were assessed using macrophage cells. Finally, we examined the in vivo effects of CECM in combination with MTX on anti-inflammation control and cartilage degradation in collageninduced arthritis model. Anti-inflammation control and cartilage degradation were assessed by measuring the serum levels of RA-related cytokines and histology. RESULTS: CECM in combination with MTX had no effect on SW 982, effectively suppressing only RAW 264.7 activity.Moreover, anti-inflammatory effects were enhanced when low-dose MTX was combined with CECM. In a collageninduced arthritis model, low-dose MTX combined with CECM remarkably reduced RA-related and pro-inflammatory cytokine levels in the blood. Additionally, low-dose MTX combined with CECM exerted the best cartilage-preservation effects compared to those observed in the other therapy groups. CONCLUSION Using CECM as an adjuvant in RA treatment can augment the therapeutic effects of MTX, reduce existing drug adverse effects, and promote joint tissue regeneration.

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.

Exon skipping is an efficient technique to inhibit specific gene expression induced by a short-sequence peptide nucleic acid (PNA).To date, there has been no study on the effects of PNA on skin pigmentation. In melanocytes, the tripartite complex is responsible for the transport of mature melanosomes from the nucleus to the dendrites. The tripartite complex is composed of Rab27a, Mlph (Melanophilin), and Myosin Va. Defects in the protein Mlph, a melanosome transport-related protein, are known to cause hypopigmentation. Our study shows that Olipass peptide nucleic acid (OPNA), a cell membrane-permeable PNA, targets exon skipping in the Mlph SHD domain, which is involved in Rab27a binding. Our findings demonstrate that OPNA induced exon skipping in melan-a cells, resulting in shortened Mlph mRNA, reduced Mlph protein levels, and melanosome aggregation, as observed by microscopy. Therefore, OPNA inhibits the expression of Mlph by inducing exon skipping within the gene. These results suggest that OPNA, which targets Mlph, may be a potential new whitening agent to inhibit melanosome movement.