INTRODUCTIONProblem-based learning (PBL), a pedagogic concept using a student-centred approach and problem-solving through small group discussions, has been adopted in varying degrees for years at all 11 medical institutes in Taiwan. Much evidence has shown that a number of factors can seriously affect student performance in PBL courses, such as the design of PBL scenarios, the tutors' character and students' attitudes and efforts.

MATERIALS AND METHODSThe aim of this study was to examine how the personal characters or knowledge base of Taiwanese medical students influence their performance in a hybrid-PBL curriculum. A total of 309 (234 male, 75 female) high-school entry undergraduate medical students participated in this survey. Self-assessed personal traits were presented in a 44-item questionnaire with a Big Five factor structure. Knowledge base was assessed by students' score point average (SPA) based on their previous 4-year education in medical school. Peer-assessed performance of students in the PBL curriculum was carried out using a well-developed, reliable and validated evaluation form.

RESULTSEach student's peer-evaluated PBL performance can be divided into 5 principal components, which included control-lead, assist-coordinate, written organisation and compromise- comply (Eigen value >1). The consistency and reliability of the Big Five questionnaire on personal traits was analysed and discordant items were deleted (Cronbach's alpha = 0.72 to 0.86 after deletion). The relationship between the personal traits, knowledge base and PBL performance, as analysed by simple regression, showed that "extraversion" and "openness to experience" were positively related to the "assist-coordinate" characteristic in PBL performance, and "conscientiousness" was positively related to the "control-lead" characteristic in PBL performance. The SPA was positively related to the "assist-coordinate" or "control-lead" characteristic in PBL performance. The "agreeableness" was negatively correlated with the "control-lead" characteristic in PBL performance. After stepwise regression between the Big Five and each component of PBL performance, only the correlation between conscientiousness and control/lead, and between extraversion and assist/coordinate remained significant.

CONCLUSIONKnowledge and personality characteristics appear to be associated with student performance in a hybrid-PBL curriculum. The implications of this study on the future development and application of this assessment tool in medical schools are presented.

Educational Measurement ; Female ; Humans ; Male ; Mental Competency ; psychology ; Problem-Based Learning ; methods ; Retrospective Studies ; Students, Medical ; psychology ; Surveys and Questionnaires ; Taiwan

OBJECTIVE: Postoperative delirium (POD) is a highly prevalent complex neuropsychiatric syndrome in elderly patients. However, its pathophysiology is currently unknown. Early detection and prevention of POD is important; therefore, the aim of this study was to investigate the link between preoperative insulin growth factor 1 (IGF-1) levels in the serum and POD in the Chinese elderly patients. METHODS: One hundred and three patients who were undergoing an orthopedic operation took part in the study. Preoperative serum IGF-1 levels were measured. POD was determined daily using the Confusion Assessment Method (CAM) and DSM-IV TR. Baseline serum IGF-1 levels were compared between patients who did and did not develop POD. Correlation coefficients were calculated to evaluate relationship between baseline characteristics and serum IGF-1 levels. The relationship between baseline biomarkers and delirium status was investigated using logistic regression analysis, adjusting for potential confounding variables. RESULTS: Twenty-three patients developed POD. The POD group had lower MMSE scores and higher CCI scores and proportions of acute admission. Preoperative serum IGF-1 levels were correlated with MMSE scores and age (MMSE: r=0.230, p<0.05; age: r=-0.419, p<0.001). Baseline serum IGF-1 levels did not differ between patients who did and did not develop POD, even after adjusting for potential confounding factors, MMSE score, and age. CONCLUSION: No association was found between preoperative IGF-1 levels and POD, suggesting that they are not direct biomarkers of the incidence of POD among the Chinese elderly population. Further research with larger sample sizes is warranted to clarify the relationship.
Aged* ; Asian Continental Ancestry Group* ; Biomarkers ; Confounding Factors (Epidemiology) ; Delirium* ; Diagnostic and Statistical Manual of Mental Disorders ; Humans ; Incidence ; Insulin ; Insulin-Like Growth Factor I ; Logistic Models ; Orthopedics ; Sample Size

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: 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: 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: 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: 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.

BACKGROUND: This study investigated whether xenotransplantation of human Wharton’s jelly-derived mesenchymal stem cells (WJ-MSCs) reduces thioacetamide (TAA)-induced mouse liver fibrosis and the underlying molecular mechanism. METHODS: Recipient NOD/SCID mice were injected intraperitoneally with TAA twice weekly for 6 weeks before initial administration of WJ-MSCs. Expression of regenerative and pro-fibrogenic markers in mouse fibrotic livers were monitored post cytotherapy. A hepatic stallate cell line HSC-T6 and isolated WJ-MSCs were used for in vitro adhesion, migration and mechanistic studies. RESULTS: WJ-MSCs were isolated from human umbilical cords by an explant method and characterized by flow cytometry. A single infusion of WJ-MSCs to TAA-treated mice significantly reduced collagen deposition and ameliorated liver fibrosis after 2-week therapy. In addition to enhanced expression of hepatic regenerative factor, hepatocyte growth factor, and PCNA proliferative marker, WJ-MSC therapy significantly blunted pro-fibrogenic signals, including Smad2, RhoA, ERK. Intriguingly, reduction of plasma fibronectin (pFN) in fibrotic livers was noted in MSC-treated mice. In vitro studies further demonstrated that suspending MSCs triggered pFN degradation, soluble pFN conversely retarded adhesion of suspending MSCs onto type I collagen-coated surface, whereas pFN coating enhanced WJ-MSC migration across mimicked wound bed. Moreover, pretreatment with soluble pFN and conditioned medium from MSCs with pFN strikingly attenuated the response of HSC-T6 cells to TGF-b1-stimulation in Smad2 phosphorylation and RhoA upregulation. CONCLUSION These findings suggest that cytotherapy using WJ-MSCs may modulate hepatic pFN deposition for a better regenerative niche in the fibrotic livers and may constitute a useful anti-fibrogenic intervention in chronic liver diseases.

BACKGROUND: This study investigated whether xenotransplantation of human Wharton’s jelly-derived mesenchymal stem cells (WJ-MSCs) reduces thioacetamide (TAA)-induced mouse liver fibrosis and the underlying molecular mechanism. METHODS: Recipient NOD/SCID mice were injected intraperitoneally with TAA twice weekly for 6 weeks before initial administration of WJ-MSCs. Expression of regenerative and pro-fibrogenic markers in mouse fibrotic livers were monitored post cytotherapy. A hepatic stallate cell line HSC-T6 and isolated WJ-MSCs were used for in vitro adhesion, migration and mechanistic studies. RESULTS: WJ-MSCs were isolated from human umbilical cords by an explant method and characterized by flow cytometry. A single infusion of WJ-MSCs to TAA-treated mice significantly reduced collagen deposition and ameliorated liver fibrosis after 2-week therapy. In addition to enhanced expression of hepatic regenerative factor, hepatocyte growth factor, and PCNA proliferative marker, WJ-MSC therapy significantly blunted pro-fibrogenic signals, including Smad2, RhoA, ERK. Intriguingly, reduction of plasma fibronectin (pFN) in fibrotic livers was noted in MSC-treated mice. In vitro studies further demonstrated that suspending MSCs triggered pFN degradation, soluble pFN conversely retarded adhesion of suspending MSCs onto type I collagen-coated surface, whereas pFN coating enhanced WJ-MSC migration across mimicked wound bed. Moreover, pretreatment with soluble pFN and conditioned medium from MSCs with pFN strikingly attenuated the response of HSC-T6 cells to TGF-b1-stimulation in Smad2 phosphorylation and RhoA upregulation. CONCLUSION These findings suggest that cytotherapy using WJ-MSCs may modulate hepatic pFN deposition for a better regenerative niche in the fibrotic livers and may constitute a useful anti-fibrogenic intervention in chronic liver diseases.

Background/Aims: Chronic hepatitis C (CHC) patients who failed antiviral therapy are at increased risk for hepatocellular carcinoma (HCC). This study assessed the potential role of metformin and statins, medications for diabetes mellitus (DM) and hyperlipidemia (HLP), in reducing HCC risk among these patients. Methods: We included CHC patients from the T-COACH study who failed antiviral therapy. We tracked the onset of HCC 1.5 years post-therapy by linking to Taiwan’s cancer registry data from 2003 to 2019. We accounted for death and liver transplantation as competing risks and employed Gray’s cumulative incidence and Cox subdistribution hazards models to analyze HCC development. Results: Out of 2,779 patients, 480 (17.3%) developed HCC post-therapy. DM patients not using metformin had a 51% increased risk of HCC compared to non-DM patients, while HLP patients on statins had a 50% reduced risk compared to those without HLP. The 5-year HCC incidence was significantly higher for metformin non-users (16.5%) versus non-DM patients (11.3%; adjusted sub-distribution hazard ratio [aSHR]=1.51; P=0.007) and metformin users (3.1%; aSHR=1.59; P=0.022). Statin use in HLP patients correlated with a lower HCC risk (3.8%) compared to non-HLP patients (12.5%; aSHR=0.50; P<0.001). Notably, the increased HCC risk associated with non-use of metformin was primarily seen in non-cirrhotic patients, whereas statins decreased HCC risk in both cirrhotic and non-cirrhotic patients. Conclusions Metformin and statins may have a chemopreventive effect against HCC in CHC patients who failed antiviral therapy. These results support the need for personalized preventive strategies in managing HCC risk.