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.

Gastric outlet obstruction (GOO) is a condition characterized by a mechanical obstruction of the stomach or duodenum, caused by either benign or malignant disease. Traditionally, surgical gastrojejunostomy (SGJ) has been the standard treatment for malignant GOO and endoscopic stenting (ES) offers a less invasive option, but it often requires repeat interventions. Recently, endoscopic ultrasound (EUS)-guided gastroenterostomy (EUS-GE), an innovative technique, has been applied as an alternative to SGJ and ES for GOO patients. Direct EUS-GE, device-associated EUS-GE, and EUS-guided double balloon-occluded gastrojejunostomy bypass are the most commonly used techniques with reported technical success rates ranging from 80% to 100%, and clinical success rates between 68% and 100%. Adverse event (AE) rates range from 0% to 28.2% and the stent misdeployment is the most common while other AEs include abdominal pain, bleeding, infection, peritonitis, bowel perforation, gastric leakage, and stent migration. It is clear that EUS-GE may achieve a similar clinical success to SGJ with fewer AEs and a shorter hospital stay. Compared to ES, EUS-GE showed higher clinical success, fewer stent obstructions, and lower reintervention rates.

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.

Gastric outlet obstruction (GOO) is a condition characterized by a mechanical obstruction of the stomach or duodenum, caused by either benign or malignant disease. Traditionally, surgical gastrojejunostomy (SGJ) has been the standard treatment for malignant GOO and endoscopic stenting (ES) offers a less invasive option, but it often requires repeat interventions. Recently, endoscopic ultrasound (EUS)-guided gastroenterostomy (EUS-GE), an innovative technique, has been applied as an alternative to SGJ and ES for GOO patients. Direct EUS-GE, device-associated EUS-GE, and EUS-guided double balloon-occluded gastrojejunostomy bypass are the most commonly used techniques with reported technical success rates ranging from 80% to 100%, and clinical success rates between 68% and 100%. Adverse event (AE) rates range from 0% to 28.2% and the stent misdeployment is the most common while other AEs include abdominal pain, bleeding, infection, peritonitis, bowel perforation, gastric leakage, and stent migration. It is clear that EUS-GE may achieve a similar clinical success to SGJ with fewer AEs and a shorter hospital stay. Compared to ES, EUS-GE showed higher clinical success, fewer stent obstructions, and lower reintervention rates.

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.

Gastric outlet obstruction (GOO) is a condition characterized by a mechanical obstruction of the stomach or duodenum, caused by either benign or malignant disease. Traditionally, surgical gastrojejunostomy (SGJ) has been the standard treatment for malignant GOO and endoscopic stenting (ES) offers a less invasive option, but it often requires repeat interventions. Recently, endoscopic ultrasound (EUS)-guided gastroenterostomy (EUS-GE), an innovative technique, has been applied as an alternative to SGJ and ES for GOO patients. Direct EUS-GE, device-associated EUS-GE, and EUS-guided double balloon-occluded gastrojejunostomy bypass are the most commonly used techniques with reported technical success rates ranging from 80% to 100%, and clinical success rates between 68% and 100%. Adverse event (AE) rates range from 0% to 28.2% and the stent misdeployment is the most common while other AEs include abdominal pain, bleeding, infection, peritonitis, bowel perforation, gastric leakage, and stent migration. It is clear that EUS-GE may achieve a similar clinical success to SGJ with fewer AEs and a shorter hospital stay. Compared to ES, EUS-GE showed higher clinical success, fewer stent obstructions, and lower reintervention rates.

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: AMP-activated protein kinase (AMPK) is a key enzyme for cellular energy homeostasis and improves metabolic disorders. Brown and beige adipose tissues exert thermogenesis capacities to dissipate energy in the form of heat. Here, we investigated the beneficial effects of the antioxidant alpha-lipoic acid (ALA) in menopausal obesity and the underlying mechanisms. Methods: Female Wistar rats (8 weeks old) were subjected to bilateral ovariectomy (Ovx) and divided into four groups: Sham (n=8), Ovx (n=11), Ovx+ALA2 (n=10), and Ovx+ALA3 (n=6) (ALA 200 and 300 mg/kg/day, respectively; gavage) for 8 weeks. 3T3-L1 cells were used for in vitro study. Results: Rats receiving ALA2 and ALA3 treatment showed significantly lower levels of body weight and white adipose tissue (WAT) mass than those of the Ovx group. ALA improved plasma lipid profiles including triglycerides, total cholesterol, low-density lipoprotein cholesterol, and high-density lipoprotein cholesterol. Hematoxylin & eosin staining of inguinal WAT showed that ALA treatment reduced Ovx-induced adipocyte size and enhanced uncoupling protein 1 (UCP1) expression. Moreover, plasma levels of irisin were markedly increased in ALA-treated Ovx rats. Protein expression of brown fat-specific markers including UCP1, PRDM16, and CIDEA was downregulated by Ovx but markedly increased by ALA. Phosphorylation of AMPK, its downstream acetyl-CoA carboxylase, and its upstream LKB1 were all significantly increased by ALA treatment. In 3T3-L1 cells, administration of ALA (100 and 250 μM) reduced lipid accumulation and enhanced oxygen consumption and UCP1 protein expression, while inhibition of AMPK by dorsomorphin (5 μM) significantly reversed these effects. Conclusion ALA improves estrogen deficiency-induced obesity via browning of WAT through AMPK signaling.

Purpose: Protective ileostomy and colostomy are performed in patients undergoing low anterior resection with a high leakage risk. We aimed to compare surgical, medical, and daily care complications between these 2 ostomies in order to make individual choice. Methods: Patients who underwent low anterior resection for rectal tumors with protective stomas between January 2011 and September 2018 were enrolled. Stoma-related complications were prospectively recorded by wound, ostomy, and continence nurses. The cancer stage and treatment data were obtained from the Taiwan Cancer Database of our Big Data Center. Other demographic data were collected retrospectively from medical notes. The complications after stoma creation and after the stoma reversal were compared. Results: There were 176 patients with protective colostomy and 234 with protective ileostomy. Protective ileostomy had higher proportions of high output from the stoma for 2 consecutive days than protective colostomy (11.1% vs. 0%, P<0.001). Protective colostomy resulted in more stoma retraction than protective ileostomy (21.6% vs. 9.4%, P=0.001). Female, open operation, ileostomy, and carrying stoma more than 4 months were also significantly associated with a higher risk of stoma-related complications during diversion. For stoma retraction, the multivariate analysis revealed that female (odds ratio [OR], 4.00; 95% confidence interval [CI], 2.13–7.69; P<0.001) and long diversion duration (≥4 months; OR, 2.33; 95% CI, 1.22–4.43; P=0.010) were independent risk factors, but ileostomy was an independent favorable factor (OR, 0.40; 95% CI, 0.22–0.72; P=0.003). The incidence of complication after stoma reversal did not differ between colostomy group and ileostomy group (24.3% vs. 20.9%, P=0.542). Conclusion We suggest avoiding colostomy in patients who are female and potential prolonged diversion when stoma retraction is a concern. Otherwise, ileostomy should be avoided for patients with impaired renal function. Wise selection and flexibility are more important than using one type of stoma routinely.