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.

Objective:To explore the potential relationship between sugar metabolism,acid production and cariogenicity of Prevotella denticola.Methods:Morphological features of Prevotella denticola were observed and respectively cultured under incubation conditions with and without sugar and at different pH values.The growth characteristics of Prevotella denticola were detected by UV-Vis spectro-photometer and pH meter,the organic acid content in the culture supernatants of the cultures was detected by HPLC.Dentin slices were divided into control group,phosphoric acid group and the Prevotella denticola group and cultured in the corresponding mediu for 1 and 2 weeks respectively,the degree of demineralization of the samples was examined SEM and VHM.Results:Prevotella denticola fermen-ted sucrose and glucose,produced acids with its final pH values as low as 4.7,Succinic acid and acetic acid were its main metabolites.Prevotella denticola was moderately acid-tolerant.Furthermore,Prevotella denticola was able to cause dentin demineralization,and the Vickers hardness value of dentin samples in the Prevotella denticola group was significantly decreased compared with the control group(P＜0.05).Conclusion:The cariogenic capacity of Prevotella denticola may be related to its sugar metabolism and acid production.

Aim To explore the mechanism of Bushen Tongfu decoction(BSTF)improving the learning and memory ability of APP/PS1 mice.Methods The APP/PS1 mice were administered by BSTF for eight weeks.The spatial learning and memory ability of all mice were detected by Morris water maze.The changes in the intestinal microbiota and gut metabolites of mice were detected by 16S rDNA and LC-MS/MS technolo-gy.Results Compared with the model group,the es-cape latency and swimming path in BSTF-H,BSTF-M and BSTF-L groups were shortened and the times of target crossings after removing the platform increased significantly(P＜0.01).The 16S rDNA results showed that BSTF could increase the abundance of beneficial bacteria of Firmicutes and reduce the abun-dance of harmful bacteria of Bacteroidota in APP/PS1 mice.A total of 25 differential metabolites were identi-fied by LC-MS/MS,including sphingolipid metabo-lism,lysine metabolism and tyrosine metabolism.KEGG enrichment analysis showed that the therapeutic effect of BSTF was mainly through inflammation-related signaling pathways such as NF-κB pathways and Apelin pathways.Conclusions The mechanism of BSTF im-proving the learning and memory ability of APP/PS1 mice may be related to the improvement of intestinal flora composition,the effect of sphingolipid metabo-lism,lysine metabolism and tyrosine metabolism and the regulation of inflammation-related signaling path-ways such as NF-κB and Apelin pathways.

Objectives: Presenteeism is known to be a much more economically damaging social cost than disease rest while going to work despite physical pain. Since COVID-19, social discussions on the sickness benefit have been taking place as a countermeasure against presenteeism, and in particular, farmers and fishermen do not have an institutional mechanism for livelihood support when a disease other than work occurs. This study attempted to examine the relationship between agricultural, fishing, and forestry workers and presenteeism using the 6th Korean Work Conditions Survey. Methods: From October 2020 to January 2021, data from the 6th working conditions survey conducted on 17 cities and provinces in Korea were used, and a total of 34,981 people were studied. Control variables were gender, age, self-health assessment, education level, night work, shift work, monthly income, occupation, working hours per week, and employment status. Results: As a result of the analysis, farmers and fishermen showed the characteristics of the self-employed and the elderly, and as a result of the regression analysis, when farmers and fishermen analyzed the relationship with presenteeism tendency compared to other industry workers, farmers and fishermen increased by 23% compared to other industry groups. Conclusion This study is significant in that it has representation by utilizing the 6th working conditions survey and objectively suggests the need for a sickness benefit for farmers and fishermen who may be overlooked in the sickness benefit.

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: Presenteeism is known to be a much more economically damaging social cost than disease rest while going to work despite physical pain. Since COVID-19, social discussions on the sickness benefit have been taking place as a countermeasure against presenteeism, and in particular, farmers and fishermen do not have an institutional mechanism for livelihood support when a disease other than work occurs. This study attempted to examine the relationship between agricultural, fishing, and forestry workers and presenteeism using the 6th Korean Work Conditions Survey. Methods: From October 2020 to January 2021, data from the 6th working conditions survey conducted on 17 cities and provinces in Korea were used, and a total of 34,981 people were studied. Control variables were gender, age, self-health assessment, education level, night work, shift work, monthly income, occupation, working hours per week, and employment status. Results: As a result of the analysis, farmers and fishermen showed the characteristics of the self-employed and the elderly, and as a result of the regression analysis, when farmers and fishermen analyzed the relationship with presenteeism tendency compared to other industry workers, farmers and fishermen increased by 23% compared to other industry groups. Conclusion This study is significant in that it has representation by utilizing the 6th working conditions survey and objectively suggests the need for a sickness benefit for farmers and fishermen who may be overlooked in the sickness benefit.

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: Presenteeism is known to be a much more economically damaging social cost than disease rest while going to work despite physical pain. Since COVID-19, social discussions on the sickness benefit have been taking place as a countermeasure against presenteeism, and in particular, farmers and fishermen do not have an institutional mechanism for livelihood support when a disease other than work occurs. This study attempted to examine the relationship between agricultural, fishing, and forestry workers and presenteeism using the 6th Korean Work Conditions Survey. Methods: From October 2020 to January 2021, data from the 6th working conditions survey conducted on 17 cities and provinces in Korea were used, and a total of 34,981 people were studied. Control variables were gender, age, self-health assessment, education level, night work, shift work, monthly income, occupation, working hours per week, and employment status. Results: As a result of the analysis, farmers and fishermen showed the characteristics of the self-employed and the elderly, and as a result of the regression analysis, when farmers and fishermen analyzed the relationship with presenteeism tendency compared to other industry workers, farmers and fishermen increased by 23% compared to other industry groups. Conclusion This study is significant in that it has representation by utilizing the 6th working conditions survey and objectively suggests the need for a sickness benefit for farmers and fishermen who may be overlooked in the sickness benefit.

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: This study aimed to assess the real-world clinical outcomes of consolidative durvalumab in patients with unresectable locally advanced non–small cell lung cancer (LA-NSCLC) and to explore the role of radiotherapy in the era of immunotherapy. Materials and Methods: This retrospective study assessed 171 patients with unresectable LA-NSCLC who underwent concurrent chemoradiotherapy (CCRT) with or without consolidative durvalumab at Asan Medical Center between May 2018 and May 2021. Primary outcomes included freedom from locoregional failure (FFLRF), distant metastasis-free survival (DMFS), progression-free survival (PFS), and overall survival (OS). Results: Durvalumab following CCRT demonstrated a prolonged median PFS of 20.9 months (p=0.048) and a 3-year FFLRF rate of 57.3% (p=0.008), compared to 13.7 months and 38.8%, respectively, with CCRT alone. Furthermore, the incidence of in-field recurrence was significantly greater in the CCRT-alone group compared to the durvalumab group (26.8% vs. 12.4%, p=0.027). While median OS was not reached with durvalumab, it was 35.4 months in patients receiving CCRT alone (p=0.010). Patients positive for programmed cell death ligand 1 (PD-L1) expression showed notably better outcomes, including FFLRF, DMFS, PFS, and OS. Adherence to PACIFIC trial eligibility criteria identified 100 patients (58.5%) as ineligible. The use of durvalumab demonstrated better survival regardless of eligibility criteria. Conclusion The use of durvalumab consolidation following CCRT significantly enhanced locoregional control and OS in patients with unresectable LA-NSCLC, especially in those with PD-L1–positive tumors, thereby validating the role of durvalumab in standard care.