Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: This study assessed the performance of the ultrasound-guided attenuation parameter (UGAP) in diagnosing and grading hepatic steatosis in patients with metabolic dysfunctionassociated steatotic liver disease (MASLD). Magnetic resonance imaging proton density fat fraction (MRI-PDFF) served as the reference standard. Methods: Patients with hepatic steatosis were enrolled in this prospective study and underwent UGAP measurements. MRI-PDFF values of ≥5%, ≥15%, and ≥25% were used as references for the diagnosis of steatosis grades ≥S1, ≥S2, and S3, respectively. Spearman correlation coefficients and area under the receiver operating characteristic curves (AUCs) were calculated. Results: Between July 2023 and June 2024, the study included 88 patients (median age, 40 years; interquartile range [IQR], 36 to 46 years), of whom 54.5% (48/88) were men and 45.5% (40/88) were women. Steatosis grades exhibited the following distribution: 22.7% (20/88) had S0, 50.0% (44/88) had S1, 21.6% (19/88) had S2, and 5.7% (5/88) had S3. The success rate for UGAP measurements was 100%. The median UGAP value was 0.74 dB/cm/MHz (IQR, 0.65 to 0.82 dB/ cm/MHz), and UGAP values were positively correlated with MRI-PDFF (r=0.77, P<0.001). The AUCs of UGAP for the diagnoses of ≥S1, ≥S2, and S3 steatosis were 0.91, 0.90, and 0.88, respectively. In the subgroup analysis, 98.4% (60/61) of patients had valid controlled attenuation parameter (CAP) values. UGAP measurements were positively correlated with CAP values (r=0.65, P<0.001). Conclusion Using MRI-PDFF as the reference standard, UGAP demonstrates good diagnostic performance in the detection and grading of hepatic steatosis in patients with MASLD.

Purpose: Interviews play a crucial role in the medical school selection process, although little is known about interviewers’ non-verbal observable communications (NoVOC) during the interviews. This study investigates how interviewers perceive NoVOC exhibited by interviewees in two medical schools, one in Taiwan and the other in Australia. The study also explores potential cross-cultural differences in these perceptions. Methods: A 26-item questionnaire was developed using a Delphi-like method to identify NoVOC. Interviewers from the University of New South Wales, Australia, and National Yang Ming Chiao Tung University, Taiwan (n=47 and N=78, respectively) rated these NoVOC between 2018 and 2021. Factor analyses identified and validated underlying factors. Measurement invariance across countries and genders was examined. Results: A total of 125 interviewers completed the questionnaire, including 78 from Taiwan and 47 from Australia. Using exploratory factor analysis, 14 items yielded reliable three factors “charming,” “disengaged,” and “anxious” (Cronbach’s α=0.853, 0.714, and 0.628, respectively). The measurement invariance analysis indicated that the factor models were invariant across genders but significantly different between the two countries. Further analysis revealed inconsistencies in interpreting the “anxious” factor between Taiwan and Australia. Conclusion The three distinct factors revealed in this study provide valuable insights into the NoVOC that interviewers perceive and evaluate during the interview process. The findings highlight the importance of considering non-verbal communication in selecting medical students and emphasize the need for training and awareness among interviewers. Understanding the impact of non-verbal behaviors can improve selection processes to mitigate bias and enhance the fairness and reliability of medical student selection.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: This study assessed the performance of the ultrasound-guided attenuation parameter (UGAP) in diagnosing and grading hepatic steatosis in patients with metabolic dysfunctionassociated steatotic liver disease (MASLD). Magnetic resonance imaging proton density fat fraction (MRI-PDFF) served as the reference standard. Methods: Patients with hepatic steatosis were enrolled in this prospective study and underwent UGAP measurements. MRI-PDFF values of ≥5%, ≥15%, and ≥25% were used as references for the diagnosis of steatosis grades ≥S1, ≥S2, and S3, respectively. Spearman correlation coefficients and area under the receiver operating characteristic curves (AUCs) were calculated. Results: Between July 2023 and June 2024, the study included 88 patients (median age, 40 years; interquartile range [IQR], 36 to 46 years), of whom 54.5% (48/88) were men and 45.5% (40/88) were women. Steatosis grades exhibited the following distribution: 22.7% (20/88) had S0, 50.0% (44/88) had S1, 21.6% (19/88) had S2, and 5.7% (5/88) had S3. The success rate for UGAP measurements was 100%. The median UGAP value was 0.74 dB/cm/MHz (IQR, 0.65 to 0.82 dB/ cm/MHz), and UGAP values were positively correlated with MRI-PDFF (r=0.77, P<0.001). The AUCs of UGAP for the diagnoses of ≥S1, ≥S2, and S3 steatosis were 0.91, 0.90, and 0.88, respectively. In the subgroup analysis, 98.4% (60/61) of patients had valid controlled attenuation parameter (CAP) values. UGAP measurements were positively correlated with CAP values (r=0.65, P<0.001). Conclusion Using MRI-PDFF as the reference standard, UGAP demonstrates good diagnostic performance in the detection and grading of hepatic steatosis in patients with MASLD.

Purpose: Interviews play a crucial role in the medical school selection process, although little is known about interviewers’ non-verbal observable communications (NoVOC) during the interviews. This study investigates how interviewers perceive NoVOC exhibited by interviewees in two medical schools, one in Taiwan and the other in Australia. The study also explores potential cross-cultural differences in these perceptions. Methods: A 26-item questionnaire was developed using a Delphi-like method to identify NoVOC. Interviewers from the University of New South Wales, Australia, and National Yang Ming Chiao Tung University, Taiwan (n=47 and N=78, respectively) rated these NoVOC between 2018 and 2021. Factor analyses identified and validated underlying factors. Measurement invariance across countries and genders was examined. Results: A total of 125 interviewers completed the questionnaire, including 78 from Taiwan and 47 from Australia. Using exploratory factor analysis, 14 items yielded reliable three factors “charming,” “disengaged,” and “anxious” (Cronbach’s α=0.853, 0.714, and 0.628, respectively). The measurement invariance analysis indicated that the factor models were invariant across genders but significantly different between the two countries. Further analysis revealed inconsistencies in interpreting the “anxious” factor between Taiwan and Australia. Conclusion The three distinct factors revealed in this study provide valuable insights into the NoVOC that interviewers perceive and evaluate during the interview process. The findings highlight the importance of considering non-verbal communication in selecting medical students and emphasize the need for training and awareness among interviewers. Understanding the impact of non-verbal behaviors can improve selection processes to mitigate bias and enhance the fairness and reliability of medical student selection.

Purpose: Interviews play a crucial role in the medical school selection process, although little is known about interviewers’ non-verbal observable communications (NoVOC) during the interviews. This study investigates how interviewers perceive NoVOC exhibited by interviewees in two medical schools, one in Taiwan and the other in Australia. The study also explores potential cross-cultural differences in these perceptions. Methods: A 26-item questionnaire was developed using a Delphi-like method to identify NoVOC. Interviewers from the University of New South Wales, Australia, and National Yang Ming Chiao Tung University, Taiwan (n=47 and N=78, respectively) rated these NoVOC between 2018 and 2021. Factor analyses identified and validated underlying factors. Measurement invariance across countries and genders was examined. Results: A total of 125 interviewers completed the questionnaire, including 78 from Taiwan and 47 from Australia. Using exploratory factor analysis, 14 items yielded reliable three factors “charming,” “disengaged,” and “anxious” (Cronbach’s α=0.853, 0.714, and 0.628, respectively). The measurement invariance analysis indicated that the factor models were invariant across genders but significantly different between the two countries. Further analysis revealed inconsistencies in interpreting the “anxious” factor between Taiwan and Australia. Conclusion The three distinct factors revealed in this study provide valuable insights into the NoVOC that interviewers perceive and evaluate during the interview process. The findings highlight the importance of considering non-verbal communication in selecting medical students and emphasize the need for training and awareness among interviewers. Understanding the impact of non-verbal behaviors can improve selection processes to mitigate bias and enhance the fairness and reliability of medical student selection.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.