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.

STUDY DESIGN: In Vitro cell culture study. PURPOSE: This study aims to investigate the impact of transforming growth factor-beta1 (TGF-β1) and lovastatin on differentiating human mesenchymal stem cells (MSCs) toward nucleus pulposus (NP)-like phenotype. OVERVIEW OF LITERATURE: MSCs offer a cell source to the cell-based therapy for intervertebral disc degeneration. TGF-β1 is used to induce MSCs to differentiate into NP-like cells; however, an undesired expression of collagen type I has been reported. Statins reportedly stimulate expression of bone morphogenetic protein-2 (BMP-2) and promote the chondrogenic phenotype to NP cells. However, the effects of statins with or without TGF-β1 on the differentiation of MSCs into NP-like cells remain unclear. METHODS: Human MSCs were treated with TGF-β1 alone, lovastatin alone, and simultaneous or sequential treatment with TGF-β1 and lovastatin. After the proposed stimulation, the total RNA was extracted to assess the expression profile of NP cells-specific genes. Hematoxylin–eosin staining was used for examining the microscopic morphology. Furthermore, we detected the syntheses of S-100 protein, aggrecan, and collagen type II in the extracellular matrix using immunohistochemical staining. RESULTS: Simultaneous or sequential treatment of TGF-β1 and lovastatin could further augment the BMP-2 overexpression compared with lovastatin-alone treatment. However, the mRNA expression of aggrecan and collagen type II was not compatible with the expression level of BMP-2. Immunohistochemical studies revealed compatible production of aggrecan, collagen type II, and S-100 protein in all three groups treated with lovastatin. Cells in groups treated with lovastatin were less populated than that in the group treated with TGF-β1 alone. CONCLUSIONS: This study demonstrates a promising role of lovastatin in inducing human MSCs into NP-like cells. However, further optimization of cell density before lovastatin treatment, treatment duration, and combination with TGF-β1 are warranted to attain better stimulatory effects.

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

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.

OBJECTIVE: To determine whether treatment outcome is associated with visualization of contrast extravasation in patients with acute massive gastrointestinal bleeding after endoscopic failure. MATERIALS AND METHODS: From January 2007 to December 2009, patients that experienced a first attack of acute gastrointestinal bleeding after failure of initial endoscopy were referred to our interventional department for intra-arterial treatment. We enrolled 79 patients and divided them into two groups: positive and negative extravasation. For positive extravasation, patients were treated by coil embolization; and in negative extravasation, patients were treated with intra-arterial vasopressin infusion. The two groups were compared for clinical parameters, hemodynamics, laboratory findings, endoscopic characteristics, and mortality rates. RESULTS: Forty-eight patients had detectable contrast extravasation (positive extravasation), while 31 patients did not (negative extravasation). Fifty-six patients survived from this bleeding episode (overall clinical success rate, 71%). An elevation of hemoglobin level was observed in the both two groups; significantly greater in the positive extravasation group compared to the negative extravasation group. Although these patients were all at high risk of dying, the 90-day mortality rate was significantly lower in the positive extravasation than in the negative extravasation (20% versus 42%, p < 0.05). A multivariate analysis suggested that successful hemostasis (odds ratio [OR] = 28.66) is the most important predictor affecting the mortality in the two groups of patients. CONCLUSION: Visualization of contrast extravasation on angiography usually can target the bleeding artery directly, resulting in a higher success rate to control of hemorrhage.
Acute Disease ; Adult ; Aged ; Aged, 80 and over ; *Angiography ; *Embolization, Therapeutic ; Extravasation of Diagnostic and Therapeutic Materials/*radiography ; Female ; Gastrointestinal Hemorrhage/mortality/radiography/*therapy ; Hemostasis, Endoscopic ; Hemostatics/*administration & dosage ; Humans ; Infusions, Intra-Arterial ; Male ; Middle Aged ; *Radiography, Interventional ; Treatment Failure ; Vasopressins/*administration & dosage ; Young Adult