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.

Introduction: This study aimed to determine the prevalence and factors associated with female sexual dysfunction in an outpatient clinic in Malaysia. Methods: The study was conducted among female patients aged 50 years and older who attended the outpatient clinic of a public hospital in Malaysia. A self-administered questionnaire was used that was based on the Malay version of the Female Sexual Function Index questionnaire. The predictors of female sexual dysfunction were identified using multivariate logistic regression analysis. Results: A total of 263 females were recruited in this study, with a mean age of 60.6 ± 6.7 years. The distribution of the respondents’ ethnicities was mostly Malay (42.2%), followed by Chinese (41.8%) and Indian (16.0%). The prevalence of female sexual dysfunction among participants was 68.8%. The prevalence of the subscales of female sexual dysfunction was as follows: desire (85.2%), satisfaction (74.9%), arousal (71.1%), lubrication (66.9%), pain (61.2%), and orgasm (60.8%). According to multivariate logistic regression, patients of Indian ethnicity had an increased risk of female sexual dysfunction (OR=16.60, 95% CI=2.54–108.63), and a higher frequency of sexual intercourse was correlated with a lower risk of female sexual dysfunction (OR=0.13, 95% CI=0.08–0.24). Conclusion Seven-tenths of the middle-aged female patients attending the outpatient clinic suffered from female sexual dysfunction. Indian ethnicity and having a lower frequency of sexual intercourse were predictors of female sexual dysfunction. Future intervention studies are needed to address this problem.
Prevalence ; Sexual Dysfunction, Physiological ; Women ; Ambulatory Care Facilities ; Middle Aged

The Singapore Health Services cluster (SingHealth) radiology film archives are a valuable repository of local radiological cases dating back to the 1950s. Some of the cases in the archives are of historical medical interest, i.e. cerebral angiography in the workup of patients with hemiplegia. Other cases are of historical social interest, being conditions seen during earlier stages of Singapore's development, i.e. bound feet. The archives form a unique portal into the development of local radiology as well as the national development of Singapore. A selection from the archives is published in commemoration of the International Day of Radiology in 2020, as well as the 200th anniversary of the Singapore General Hospital in 2021. This pictorial essay comprises gastroenterology, musculoskeletal and obstetrics and gynaecology cases from the archives.

The Singapore Health Services cluster (SingHealth) radiology film archives are a valuable repository of local radiological cases dating back to the 1950s. Some of the cases in the archives are of historical medical interest, i.e. cerebral angiography in the workup of patients with hemiplegia. Other cases are of historical social interest, being conditions seen during earlier stages of Singapore's development, i.e. bound feet. The archives form a unique portal into the development of local radiology as well as the national development of Singapore. A selection from the archives is published in 2020 in commemoration of the 20th anniversary of the formation of SingHealth, the 55th National Day of Singapore, and the 125th anniversary of the International Day of Radiology. This pictorial essay comprises cardiovascular, respiratory and neurological cases from the archives.

Diabetic nephropathy (DN) is a hyperglycemia-induced progressivedevelopment of renal insufficiency. Excessive glucose can increase mitochondrialreactive oxygen species (ROS) and induce cell damage, causing mitochondrial dysfunction.Our previous study indicated that cilostazol (CTZ) can reduce ROS levelsand decelerate DN progression in streptozotocin (STZ)-induced type 1 diabetes.This study investigated the potential mechanisms of CTZ in rats with DN and in highglucose-treated mesangial cells. Male Sprague–Dawley rats were fed 5 mg/kg/day ofCTZ after developing STZ-induced diabetes mellitus. Electron microscopy revealedthat CTZ reduced the thickness of the glomerular basement membrane and improvedmitochondrial morphology in mesangial cells of diabetic kidney. CTZ treatmentreduced excessive kidney mitochondrial DNA copy numbers induced by hyperglycemiaand interacted with the intrinsic pathway for regulating cell apoptosis as anantiapoptotic mechanism. In high-glucose-treated mesangial cells, CTZ reduced ROSproduction, altered the apoptotic status, and down-regulated transforming growthfactor beta (TGF-) and nuclear factor kappa light chain enhancer of activated B cells(NF-B). Base on the results of our previous and current studies, CTZ decelerationof hyperglycemia-induced DN is attributable to ROS reduction and thereby maintenanceof the mitochondrial function and reduction in TGF- and NF-B levels.