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: and Purpose Although amyloid positron emission tomography (PET) might provide a molecular diagnosis for cerebral amyloid angiopathy (CAA), it does not have sufficient specificity for this condition relative to incipient Alzheimer’s disease (AD). To identify a regional amyloid uptake pattern specific to CAA, we attempted to reduce this overlap by selecting “pure CAA” (i.e., fulfilling the criteria for probable CAA but without tau PET AD signature) and “pure AD” (i.e., positive amyloid PET and presence of tau PET AD signature, but without lobar hemorrhagic lesions). We hypothesized that occipital tracer uptake relative to the whole cortex (WC) would be higher in patients with pure CAA and may serve as a specific diagnostic marker. Methods: Patients who fulfilled these criteria were identified. In addition to the occipital region of interest (ROI), we assessed the frontal and posterior cingulate cortex (PCC) ROIs that are sensitive to AD. Amyloid PET uptake was expressed as the absolute standardized uptake value ratio (SUVR) and ROI/WC ratio. The diagnostic utility of amyloid PET was assessed using the Youden index cutoff. Results: Eighteen patients with AD and 42 patients with CAAs of comparable age were eligible. The occipital/WC was significantly higher in CAA than AD (1.02 [0.97–1.06] vs. 0.95 [0.87–1.01], P=0.001), with an area under curve of 0.762 (95% confidence interval [CI] 0.635–0.889) and a specificity of 72.2% (95% CI 46.5–90.3) at Youden cutoff (0.98). The occipital lobe, frontal lobe, PCC and WC SUVRs were significantly lower in CAA than in AD. The frontal/WC and PCC/WC ratios did not differ significantly between the groups. Conclusion Using stringent patient selection to minimize between-condition overlap, this study demonstrated the specificity of higher relative occipital amyloid uptake in CAA than in AD.

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: and Purpose Although amyloid positron emission tomography (PET) might provide a molecular diagnosis for cerebral amyloid angiopathy (CAA), it does not have sufficient specificity for this condition relative to incipient Alzheimer’s disease (AD). To identify a regional amyloid uptake pattern specific to CAA, we attempted to reduce this overlap by selecting “pure CAA” (i.e., fulfilling the criteria for probable CAA but without tau PET AD signature) and “pure AD” (i.e., positive amyloid PET and presence of tau PET AD signature, but without lobar hemorrhagic lesions). We hypothesized that occipital tracer uptake relative to the whole cortex (WC) would be higher in patients with pure CAA and may serve as a specific diagnostic marker. Methods: Patients who fulfilled these criteria were identified. In addition to the occipital region of interest (ROI), we assessed the frontal and posterior cingulate cortex (PCC) ROIs that are sensitive to AD. Amyloid PET uptake was expressed as the absolute standardized uptake value ratio (SUVR) and ROI/WC ratio. The diagnostic utility of amyloid PET was assessed using the Youden index cutoff. Results: Eighteen patients with AD and 42 patients with CAAs of comparable age were eligible. The occipital/WC was significantly higher in CAA than AD (1.02 [0.97–1.06] vs. 0.95 [0.87–1.01], P=0.001), with an area under curve of 0.762 (95% confidence interval [CI] 0.635–0.889) and a specificity of 72.2% (95% CI 46.5–90.3) at Youden cutoff (0.98). The occipital lobe, frontal lobe, PCC and WC SUVRs were significantly lower in CAA than in AD. The frontal/WC and PCC/WC ratios did not differ significantly between the groups. Conclusion Using stringent patient selection to minimize between-condition overlap, this study demonstrated the specificity of higher relative occipital amyloid uptake in CAA than in AD.

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: and Purpose Although amyloid positron emission tomography (PET) might provide a molecular diagnosis for cerebral amyloid angiopathy (CAA), it does not have sufficient specificity for this condition relative to incipient Alzheimer’s disease (AD). To identify a regional amyloid uptake pattern specific to CAA, we attempted to reduce this overlap by selecting “pure CAA” (i.e., fulfilling the criteria for probable CAA but without tau PET AD signature) and “pure AD” (i.e., positive amyloid PET and presence of tau PET AD signature, but without lobar hemorrhagic lesions). We hypothesized that occipital tracer uptake relative to the whole cortex (WC) would be higher in patients with pure CAA and may serve as a specific diagnostic marker. Methods: Patients who fulfilled these criteria were identified. In addition to the occipital region of interest (ROI), we assessed the frontal and posterior cingulate cortex (PCC) ROIs that are sensitive to AD. Amyloid PET uptake was expressed as the absolute standardized uptake value ratio (SUVR) and ROI/WC ratio. The diagnostic utility of amyloid PET was assessed using the Youden index cutoff. Results: Eighteen patients with AD and 42 patients with CAAs of comparable age were eligible. The occipital/WC was significantly higher in CAA than AD (1.02 [0.97–1.06] vs. 0.95 [0.87–1.01], P=0.001), with an area under curve of 0.762 (95% confidence interval [CI] 0.635–0.889) and a specificity of 72.2% (95% CI 46.5–90.3) at Youden cutoff (0.98). The occipital lobe, frontal lobe, PCC and WC SUVRs were significantly lower in CAA than in AD. The frontal/WC and PCC/WC ratios did not differ significantly between the groups. Conclusion Using stringent patient selection to minimize between-condition overlap, this study demonstrated the specificity of higher relative occipital amyloid uptake in CAA than in AD.

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: 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.

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.