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.

Objective To explore the safety and effectiveness of the robot-assisted system for transforaminal percutaneous endoscopic in the treatment of lumbar disc herniation with lumbar instability.Methods From October 2021 to March 2023,26 patients with single-segment lumbar disc herniation and lumbar spinal instability were treated with robot-assisted system for transforaminal percutaneous endoscopic.The operation time,intraoperative blood loss,incision length,postoperative drainage volume,postoperative ambulation activity time,postoperative hospitalization time were record.The intervertebral space height and the lumbar lordosis angle before and after surgery were observed and compared.Pain level was evaluated using the visual analogue scale(VAS).The clinical efficacy was evaluated by Oswestry disability index(ODI).The interbody fusion was evalu-ated by Brantigan Steffee criteria.Results All patients successfully completed the operation,the operation time ranged form 105 to 109 min with an average of(150.8±24.1)min.Intraoperative blood loss ranged form 35 to 88 ml with an average of(55.5±16.4)ml.Incision length ranged form 1.4 to 3.5 cm with an average of(2.3±0.8)cm.Postoperative drainage volume ranged form 15 to 40 ml with an average of(28.5±7.8)ml.Postoperative ambulation time ranged form 15 to 30 h with an aver-age of(22.8±4.5)h.Postoperative hospitalization time was 3 to 7 d with an average of(4.2±1.3)d.Total of 26 patients were followed up,the duration ranged from 12 to 16 months with an average of(14.0±1.3)months.The VAS and ODI at 1 week[(2.96±0.72)points,(41.63±4.79)％]and 12 months[(1.27±0.60)points,(13.11±2.45)％]were significantly different from those before surgery[(6.69±0.93)points,(59.12±5.92)％],P＜0.0 1.The height of the intervertebral space(11.95±1.47)mm and lumbar lordosis(57.46±7.59)° at 12 months were significantly different from those before surgery[(6.67±1.20)mm,(44.08±7.79)°],P＜0.01.At 12 months after surgery,all patients had no pedicle screw rupture or dislocation of the fusion cage,and the intervertebral fusion was successful.According to Brantigan-Steffee classification,17 cases were grade D and 9 cases were grade E.Conclusion Robot-assisted system for transforaminal percutaneous endoscopic for the treatment of single-segment lumbar disc herniation with lumbar instability improved the accuracy and safety of the operation,and the clinical effect of early follow-up is accurate.

INTRODUCTION: An echocardiographic calcium score (ECS) predicts cardiovascular disease (CVD) in the general population. Its utility in peritoneal dialysis (PD) patients is unknown. METHODS: This cross-sectional study assessed 125 patients on PD. The ECS (range 0-8) was compared between subjects with CVD and those without. RESULTS: Among the subjects, 54 had CVD and 71 did not. Subjects with CVD were older (69 years vs. 56 years, P < 0.001) and had a higher prevalence of diabetes mellitus (DM) (81.5% vs. 45.1%, P < 0.001). They had lower diastolic blood pressure (72 mmHg vs. 81 mmHg, P < 0.001), lower phosphate (1.6 mmol/L vs. 1.9 mmol/L, P = 0.002), albumin (30 g/L vs. 32 g/L, P = 0.001), parathyroid hormone (34.4 pmol/L vs. 55.8 pmol/L, P = 0.002), total cholesterol (4.5 vs. 4.9, P = 0.047), LDL cholesterol (2.4 mmol/L vs. 2.8 mmol/L, P = 0.019) and HDL cholesterol (0.8 mmol/L vs. 1.1 mmol/L, P = 0.002). The ECS was found to be higher in subjects with CVD than in those without (2 vs. 1, P = 0.001). On multivariate analysis, only DM and age were independently associated with CVD. CONCLUSION The ECS was significantly higher in PD patients with CVD than in those without, reflecting a higher vascular calcification burden in the former. It is a potentially useful tool to quantify vascular calcification in PD patients.
Humans ; Cardiovascular Diseases/diagnostic imaging* ; Cross-Sectional Studies ; Calcium ; Peritoneal Dialysis/adverse effects* ; Vascular Calcification/epidemiology* ; Echocardiography

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

Background: The occurrence of Graves’ disease and Hashimoto thyroiditis after coronavirus disease 2019 (COVID-19) raised concerns that severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) may trigger thyroid autoimmunity. We aimed to address the current uncertainties regarding incident thyroid dysfunction and autoimmunity among COVID-19 survivors. Methods: We included consecutive adult COVID-19 patients without known thyroid disorders, who were admitted to Queen Mary Hospital from July 21 to September 21, 2020 and had serum levels of thyroid-stimulating hormone, free thyroxine, free triiodothyronine (fT3), and anti-thyroid antibodies measured both on admission and at 3 months. Results: In total, 122 patients were included. Among 20 patients with abnormal thyroid function tests (TFTs) on admission (mostly low fT3), 15 recovered. Among 102 patients with initial normal TFTs, two had new-onset abnormalities that could represent different phases of thyroiditis. Among 104 patients whose anti-thyroid antibody titers were reassessed, we observed increases in anti-thyroid peroxidase (TPO) (P<0.001) and anti-thyroglobulin (P<0.001), but not anti-thyroid stimulating hormone receptor titers (P=0.486). Of 82 patients with negative anti-TPO findings at baseline, 16 had a significant interval increase in anti-TPO titer by >12 U, and four became anti-TPO-positive. Worse baseline clinical severity (P=0.018), elevated C-reactive protein during hospitalization (P=0.033), and higher baseline anti-TPO titer (P=0.005) were associated with a significant increase in anti-TPO titer. Conclusion Most patients with thyroid dysfunction on admission recovered during convalescence. Abnormal TFTs suggestive of thyroiditis occurred during convalescence, but infrequently. Importantly, our novel observation of an increase in anti-thyroid antibody titers post-COVID-19 warrants further follow-up for incident thyroid dysfunction among COVID-19 survivors.

Background: The occurrence of Graves’ disease and Hashimoto thyroiditis after coronavirus disease 2019 (COVID-19) raised concerns that severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) may trigger thyroid autoimmunity. We aimed to address the current uncertainties regarding incident thyroid dysfunction and autoimmunity among COVID-19 survivors. Methods: We included consecutive adult COVID-19 patients without known thyroid disorders, who were admitted to Queen Mary Hospital from July 21 to September 21, 2020 and had serum levels of thyroid-stimulating hormone, free thyroxine, free triiodothyronine (fT3), and anti-thyroid antibodies measured both on admission and at 3 months. Results: In total, 122 patients were included. Among 20 patients with abnormal thyroid function tests (TFTs) on admission (mostly low fT3), 15 recovered. Among 102 patients with initial normal TFTs, two had new-onset abnormalities that could represent different phases of thyroiditis. Among 104 patients whose anti-thyroid antibody titers were reassessed, we observed increases in anti-thyroid peroxidase (TPO) (P<0.001) and anti-thyroglobulin (P<0.001), but not anti-thyroid stimulating hormone receptor titers (P=0.486). Of 82 patients with negative anti-TPO findings at baseline, 16 had a significant interval increase in anti-TPO titer by >12 U, and four became anti-TPO-positive. Worse baseline clinical severity (P=0.018), elevated C-reactive protein during hospitalization (P=0.033), and higher baseline anti-TPO titer (P=0.005) were associated with a significant increase in anti-TPO titer. Conclusion Most patients with thyroid dysfunction on admission recovered during convalescence. Abnormal TFTs suggestive of thyroiditis occurred during convalescence, but infrequently. Importantly, our novel observation of an increase in anti-thyroid antibody titers post-COVID-19 warrants further follow-up for incident thyroid dysfunction among COVID-19 survivors.