Intravesical onabotulinumtoxinA (BoNT-A) injection is an effective treatment for overactive bladder syndrome (OAB) that is refractory to antimuscarinics. An injectable dose of 100 U has been suggested to achieve the optimal balance of benefit and safety in patients with OAB. BoNT-A (total volume of 10 mL) was administered as evenly distributed intradetrusor injections (5 U) across 20 sites approximately 1 cm apart (0.5 mL per site) using a flexible or rigid cystoscope. Treatment with BoNT-A was generally well tolerated by most patients, and most treatment-related adverse events were localized to the urinary tract. The prevalence of OAB increases with age, and elderly patients are more vulnerable to complications. The short-term efficacy of intravesical BoNT-A injection for refractory OAB with no treatment-related complications in the elderly population has been documented. Frail elderly patients can experience the same treatment results, such as significantly improved urgent urinary incontinence and quality of life, as young and nonfrail elderly patients with 100-U BoNT-A injections. However, increased risk of larger postvoid residual (PVR) urine and lower long-term success rates were noted in frail elderly patients; around 11% had acute urinary retention, while 60% had PVR urine volume >150 mL after treatment. In addition, intravesical injection of BoNT-A effectively decreased urgency symptoms in elderly patients with OAB and central nervous system lesions. The adverse effects were acceptable, while the long-term effects were comparable to those in patients with OAB without central nervous system lesions. Nonetheless, the possibility of longstanding urinary retention and chronic catheterization in this vulnerable population requires careful evaluation before treatment with intravesical BoNT-A. In conclusion, the current findings indicate that intravesical BoNT-A is an effective and safe treatment for OAB in elderly patients.
Administration, Intravesical ; Aged ; Botulinum Toxins, Type A ; Catheterization ; Catheters ; Central Nervous System ; Cystoscopes ; Frail Elderly ; Humans ; Muscarinic Antagonists ; Prevalence ; Quality of Life ; Urinary Bladder, Overactive* ; Urinary Incontinence ; Urinary Retention ; Urinary Tract ; Vulnerable Populations

PURPOSE: To investigate long-term therapeutic effects and patient adherence to a combination therapy of a 5α-reductase inhibitor and an α-blocker and to identify causes of withdrawal from medication in patients with clinical benign prostatic hyperplasia (BPH). METHODS: BPH patients with lower urinary tract symptoms (LUTS) receiving combination therapy with follow-ups for 1–12 years were retrospectively analyzed. Therapeutic effects were assessed at baseline and annually by measuring International Prostatic Symptoms Score, quality of life index, total prostate volume (TPV), maximal flow rate, voided volume, postvoid residual volume and prostate-specific antigen level. Causes of discontinued combination therapy were also investigated. RESULTS: A total of 625 patients, aged 40–97 years (mean, 73 years) were retrospectively analyzed. All measured parameters showed significant improvements after combination therapy. Three hundred sixty-nine patients (59%) discontinued combination therapy with a mean treatment duration of 2.2 years. The most common reasons for discontinued treatment were changing medication to monotherapy with α-blockers or antimuscarinics (124 patients, 19.8%), receiving surgical intervention (39 patients, 6.2%), and LUTS improvement (53 patients, 8.5%). Only 64 patients (10.2%) were loss to follow-up and 6 (1.0%) discontinued combined treatment due to adverse effects. Smaller TPV after short-term combination treatment caused withdrawal from combination therapy. CONCLUSIONS: BPH patients receiving long-term combination therapy showed significant improvement in all measured parameters. Changing medication, improved LUTS and choosing surgery are common reasons for discontinuing combination herapy. A smaller TPV after short-term combination treatment was among the factors that caused withdrawal from combination therapy.
Adrenergic alpha-1 Receptor Antagonists ; Follow-Up Studies ; Humans ; Lower Urinary Tract Symptoms ; Medication Adherence ; Muscarinic Antagonists ; Patient Compliance* ; Prostate ; Prostate-Specific Antigen ; Prostatic Hyperplasia* ; Quality of Life ; Residual Volume ; Retrospective Studies ; Therapeutic Uses

PURPOSE: To determine the duration of antimuscarinic therapy for overactive bladder syndrome (OAB) appropriate for assessment of the efficacy of treatment, and to evaluate the possible predictive factors for response to therapy. METHODS: All OAB patients who visited a urology outpatient clinic of a tertiary referral center and who were prescribed 5 mg of solifenacin or 4 mg of tolterodine extended release capsules daily were enrolled in the study. Patients were asked to continue therapy for 6 months. All enrolled patients completed the patient perception of bladder condition, overactive bladder symptom score (OABSS), and the modified Indevus Urgency Severity Scale questionnaires. All patients underwent uroflowmetry. RESULTS: A total of 164 patients were enrolled and 125 patients (76%) had at least one follow-up visit. The mean follow-up interval was 1 month (range, 0.5-6 months). Sixty-two patients (49.6%; 95% confidence interval [CI], 40.7-58.5) responded to antimuscarinic treatment. The median time for the onset of response was 3 months (95% CI, 1-6). Multivariate Cox proportional-hazards model revealed that elevated baseline OABSS was an independent predictor of responsiveness to therapy. Receiver operating characteristic (ROC) curve analysis revealed an optimal OABSS cutoff value of > or =7, with an area under the ROC curve of 0.79 (95% CI, 0.70-0.88; sensitivity, 91.9%; specificity, 60.7%). CONCLUSIONS: The median time for a therapeutic response was 3 months, and OABSS was the only predictor for responsiveness. These findings may serve as a guideline when prescribing antimuscarinic treatment for OAB patients.
Ambulatory Care Facilities ; Capsules ; Cholinergic Antagonists ; Follow-Up Studies ; Humans ; ROC Curve ; Sensitivity and Specificity ; Solifenacin Succinate ; Tertiary Care Centers ; Tolterodine Tartrate ; Treatment Outcome ; Urinary Bladder ; Urinary Bladder, Overactive* ; Urology

To investigate the effects of microenvironment of aorta-gonad-mesonephros (AGM) on embryonic hematopoiesis, mesenchymal stem cell like stromal cells (MSC like stromal cells) derived from dorsal aorta (DA) in AGM region were separated and identified by their growth characteristics, related molecules expression and mesenchymal lineage potentials. Stromal cells from DA region in mouse embryos (E11.5) were isolated and cultured in vitro. After transfected by pSV3neo-SV40, the clones with G418 resistance were selected, and their growth characteristics were studied. The related molecules were analyzed by flow cytometry, and each clone was induced to differentiate into adipocytes, osteocytes, and chondrocytes. The results showed that most clones (20 clones) selected in the mouse DA region held the morphology of fibroblastoid cells. mDAF3 and mDAF18 could be grown in culture for more than 50 passages with G418 resistance, both have the potential to differentiate into adipocytes, osteocytes, and chondrocytes. At the logarithmic growth period, the cell population doubling time is about 24 hours. Surface markers, such as CD29, CD44, CD105 and Sca-1 were positively detected, while low levels of CD34, CD45, and CD31 were detected. It is concluded that immortalized mDAF3 and mDAF18 have the specific phenotype and differential potency of MSC, which suggests that MSC maybe exist in mouse embryonic DA region, where the MSC like stromal cells can be used as a cell model for further research on the modulation activity of DA microenvironment for embryonic hematopoiesis.
Animals ; Aorta ; cytology ; Cell Culture Techniques ; methods ; Cell Differentiation ; physiology ; Cells, Cultured ; Cells, Immobilized ; cytology ; Coculture Techniques ; Embryo, Mammalian ; Gonads ; cytology ; Hematopoiesis ; Mesenchymal Stromal Cells ; cytology ; Mesonephros ; cytology ; Mice ; Stromal Cells ; cytology

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.

To establish a content determination method for 2,3,5,4'-tetrahydroxy stilbene-2-O-β-D-glucoside (TSG) of the crude/processed root of Polygonum multiflorum from different habitats in China and set up the fingerprint by using UPLC. Various samples were pretreated by macro-porous resin. Then UPLC analysis was performed on Waters ACQUITY UPLC@BEH C18 chromatographic column (2.1 mm×50 mm, 1.7 μm) at (25±5) ℃. A binary gradient elution system was composed of acetonitrile (phase A) and 0.5% acetic acid solution (phase B). Detection was performed at the wavelength of 254 nm, and the mobile flow rate was set at 0.3 mL•min⁻¹. Results showed that the yield of extraction of the 2,3,5,4'-tetrahydroxy stilbene-2-O-β-D-glucoside from root of P. multiflorum was all over 25.0% after macro-porous resin separation; an exclusive UPLC fingerprint method of the crude/processed root of P. multiflorum from different habitats was successfully set up and 17 chromatographic peaks were calibrated. Cluster analysis can not entirely distinguish the crude one from the processed one, while principal component analysis absolutely can. 2,3,5,4'-tetrahydroxy stilbene-2-O-β-D-glucoside is the composition that has largest differences in variable importance in projection (VIP) between crude and processed root of P. multiflorum. The separating method can gain high-purity 2,3,5,4'-tetrahydroxy stilbene-2-O-β-D-glucoside, and the determination method is simple, sensitive, reliable and can be used in fast identifying the crude/processed root of P. multiflorum or as a method for overall quality control of root of P. multiflorum.