Objectives: Bipolar hemiarthroplasty is commonly performed to treat displaced femoral neck fractures in osteo porotic patients. This study aimed to assess the occurrence and outcomes of unplanned return visits to the emergency department (ED) within 90 days following bipolar hemiarthroplasty for displaced femoral neck fractures. Methods: The clinical data of 1322 consecutive patients who underwent bipolar hemiarthroplasty for osteoporotic femoral neck fractures at a tertiary medical center were analyzed. Data from the patients’ electronic medical records, including demographic information, comorbidities, and operative details, were collected. The risk factors and mortality rates were analyzed. Results: Within 90 days after surgery, 19.9% of patients returned to the ED. Surgery-related reasons accounted for 20.2% of the patient’s returns. Older age, a high Charlson comorbidity index score, chronic kidney disease, and a history of cancer were identified as significant risk factors for unplanned ED visits. Patients with uncemented implants had a significantly greater risk of returning to the ED due to periprosthetic fractures than did those with cemented implants (P = 0.04). Patients who returned to the ED within 90 days had an almost fivefold greater 1-year mortality rate (15.2% vs 3.1%, P < 0.001) and a greater overall mortality rate (26.2% vs 10.5%, P < 0.001). Conclusions This study highlights the importance of identifying risk factors for unplanned ED visits after bipolar hemiarthroplasty, which may contribute to a better prognosis. Consideration should be given to the use of cemented implants for hemiarthroplasty, as uncemented implants are associated with a greater risk of peri prosthetic fractures.

OBJECTIVE: Since the inflammatory process has been implicated in the pathophysiology of psychiatric disorder, an important issue emerging is to assess the test-retest reliability of cytokine measurement in healthy individuals and patients with schizophrenia. The objective of the present study was to investigate the test-retest reliability of bead-based multiplex immunoassay technology (BMIT) for cytokine measurement by using a Bland-Altman plot (BAP). METHODS: Twenty healthy individuals and twenty patients with schizophrenia were enrolled, and a 17-plex cytokine assay was used to measure inflammatory biomarkers at baseline and two weeks later. The test-retest reliability was examined by BAP, 95% limits of agreement (LOA), intraclass correlation coefficient (ICC), and coefficient of repeatability (CoR). RESULTS: In the healthy controls, only interleukin (IL)-2, IL-13, IL-10, IL-17, and macrophage inflammatory protein-1β showed excellent ICC. The BAP with 95% LOA determined that 13 cytokines showed acceptable 95% LOA for a 2-week test-retest reliability, and only IL-1β, IL-12 and tumor necrosis factor (TNF)-α had significant test-retest bias. The CoR of cytokines varied significantly, ranging from 1.72 to 218.1. Compared with healthy controls, patients with schizophrenia showed significantly higher levels of IL-5, IL-13, and TNF-α and significantly lower levels of IL-4, IL-12, and interferon-gamma (IFN-γ). Of these six cytokines, IL-12 and TNF-α were considered suboptimal reliability. CONCLUSION: The findings from ICC and CoR implied that the test-retest reliability of BMIT for cytokine measurement were suboptimal. However, the BAP with 95% LOA confirmed that BMIT can reliably distinguish schizophrenia from healthy individuals in cytokine measurement, while significant within-subject variation and between-group overlapping were evident in cytokine expression.
Bias (Epidemiology) ; Biomarkers ; Cytokines ; Humans ; Immunoassay ; Inflammation ; Interferon-gamma ; Interleukin-10 ; Interleukin-12 ; Interleukin-13 ; Interleukin-17 ; Interleukin-4 ; Interleukin-5 ; Interleukins ; Loa ; Macrophages ; Reproducibility of Results ; Schizophrenia ; Tumor Necrosis Factor-alpha

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/Aims: Real-world studies assessing the effectiveness and safety of sofosbuvir/velpatasvir (SOF/VEL) plus ribavirin (RBV) for Child-Pugh B/C hepatitis C virus (HCV)-related cirrhosis are limited. Methods: We included 107 patients with Child-Pugh B/C HCV-related cirrhosis receiving SOF/VEL plus RBV for 12 weeks in Taiwan. The sustained virologic response rates at off-treatment week 12 (SVR12) for the evaluable population (EP), modified EP, and per-protocol population (PP) were assessed. Thesafety profiles were reported. Results: The SVR12 rates in the EP, modified EP and PP were 89.7% (95% confidence interval [CI], 82.5–94.2%), 94.1% (95% CI, 87.8–97.3%), and 100% (95% CI, 96.2–100%). Number of patients who failed to achieve SVR12 were attributed to virologic failures. The SVR12 rates were comparable regardless of patient characteristics. One patient discontinued treatment because of adverse events (AEs). Twenty-four patients had serious AEs and six died, but none were related to SOF/VEL or RBV. Among the 96 patients achieving SVR12, 84.4% and 64.6% had improved Child-Pugh and model for endstage liver disease (MELD) scores. Multivariate analysis revealed that a baseline MELD score ≥15 was associated with an improved MELD score of ≥3 (odds ratio, 4.13; 95% CI, 1.16–14.71; P=0.02). Patients with chronic kidney disease (CKD) stage 1 had more significant estimated glomerular filtration rate declines than patients with CKD stage 2 (-0.42 mL/min/1.73 m2/month; P=0.01) or stage 3 (-0.56 mL/min/1.73 m2/month; P<0.001). Conclusions SOF/VEL plus RBV for 12 weeks is efficacious and well-tolerated for Child-Pugh B/C HCV-related cirrhosis.

Background/Aims: Real-world studies assessing the effectiveness and safety of sofosbuvir/velpatasvir (SOF/VEL) plus ribavirin (RBV) for Child-Pugh B/C hepatitis C virus (HCV)-related cirrhosis are limited. Methods: We included 107 patients with Child-Pugh B/C HCV-related cirrhosis receiving SOF/VEL plus RBV for 12 weeks in Taiwan. The sustained virologic response rates at off-treatment week 12 (SVR12) for the evaluable population (EP), modified EP, and per-protocol population (PP) were assessed. Thesafety profiles were reported. Results: The SVR12 rates in the EP, modified EP and PP were 89.7% (95% confidence interval [CI], 82.5–94.2%), 94.1% (95% CI, 87.8–97.3%), and 100% (95% CI, 96.2–100%). Number of patients who failed to achieve SVR12 were attributed to virologic failures. The SVR12 rates were comparable regardless of patient characteristics. One patient discontinued treatment because of adverse events (AEs). Twenty-four patients had serious AEs and six died, but none were related to SOF/VEL or RBV. Among the 96 patients achieving SVR12, 84.4% and 64.6% had improved Child-Pugh and model for endstage liver disease (MELD) scores. Multivariate analysis revealed that a baseline MELD score ≥15 was associated with an improved MELD score of ≥3 (odds ratio, 4.13; 95% CI, 1.16–14.71; P=0.02). Patients with chronic kidney disease (CKD) stage 1 had more significant estimated glomerular filtration rate declines than patients with CKD stage 2 (-0.42 mL/min/1.73 m2/month; P=0.01) or stage 3 (-0.56 mL/min/1.73 m2/month; P<0.001). Conclusions SOF/VEL plus RBV for 12 weeks is efficacious and well-tolerated for Child-Pugh B/C HCV-related cirrhosis.

Ulcerative colitis (UC) is a chronic inflammation of the gastrointestinal tract and is characterized by alternating periods of inflammation and remission. Although UC incidence is lower in Taiwan than in Western countries, its impact remains considerable, demanding updated guidelines for addressing local healthcare challenges and patient needs. The revised guidelines employ international standards and recent research, emphasizing practical implementation within the Taiwanese healthcare system. Since the inception of the guidelines in 2017, the Taiwan Society of Inflammatory Bowel Disease has acknowledged the need for ongoing revisions to incorporate emerging therapeutic options and evolving disease management practices. This updated guideline aims to align UC management with local contexts, ensuring comprehensive and context-specific recommendations, thereby raising the standard of care for UC patients in Taiwan. By adapting and optimizing international protocols for local relevance, these efforts seek to enhance health outcomes for patients with UC.