Diagnostic Value of Urine Cytology in Pharmacologically Forced Diuresis for Upper Tract Urothelial Carcinoma Diagnosis and Follow-Up.

We performed a urine cytology analysis of a pharmacologically induced diuresis for the diagnosis of upper tract urothelial carcinoma. To evaluate the diagnostic value of cytology of pharmacologically forced diuresis, an initial cohort of 77 consecutive patients with primary upper tract urothelial carcinoma treated via radical surgery was enrolled. To evaluate pharmacologically forced diuresis cytology as a follow-up procedure, a second cohort of 1250 patients who underwent a radical cystectomy for bladder cancer was selected. In the first cohort, the sensitivity of cytology of pharmacologically forced diuresis in patients with invasive, high-grade, low-grade, and concomitant carcinoma in situ was 8%, 9%, 0%, and 14%, respectively. In the second cohort, cytology of pharmacologically forced diuresis was positive in 30/689 (4.3%) patients, in whom upper urinary tract recurrence was present in 21/30 (70%) of cases, and urethral recurrence was present in 8/30 (26%) of cases. As a follow-up tool, cytology of pharmacologically forced diuresis showed a sensitivity, specificity, and positive and negative predictive values of 60%, 99%, 70%, and 98%, respectively. Overall, as a diagnostic tool, the sensitivity of cytology of pharmacologically forced diuresis is slightly better in patients with invasive upper tract urothelial carcinoma and concomitant carcinoma in situ. As a follow-up method, positive cytology of pharmacologically forced diuresis is strongly related to cancer recurrence and can reveal urethral recurrence. Cytology of pharmacologically forced diuresis might be useful in cases with contraindications for imaging or when achieving endoscopic access to the upper urinary tract is difficult.

Machine Learning-Based Classification of Transcriptome Signatures of Non-Ulcerative Bladder Pain Syndrome.

Lower urinary tract dysfunction (LUTD) presents a global health challenge with symptoms impacting a substantial percentage of the population. The absence of reliable biomarkers complicates the accurate classification of LUTD subtypes with shared symptoms such as non-ulcerative Bladder Pain Syndrome (BPS) and overactive bladder caused by bladder outlet obstruction with Detrusor Overactivity (DO). This study introduces a machine learning (ML)-based approach for the identification of mRNA signatures specific to non-ulcerative BPS. Using next-generation sequencing (NGS) transcriptome data from bladder biopsies of patients with BPS, benign prostatic obstruction with DO, and controls, our statistical approach successfully identified 13 candidate genes capable of discerning BPS from control and DO patients. This set was validated using Quantitative Polymerase Chain Reaction (QPCR) in a larger patient cohort. To confirm our findings, we applied both supervised and unsupervised ML approaches to the QPCR dataset. A three-mRNA signature TPPP3, FAT1, and NCALD, emerged as a robust classifier for non-ulcerative BPS. The ML-based framework used to define BPS classifiers establishes a solid foundation for comprehending the gene expression changes in the bladder during BPS and serves as a valuable resource and methodology for advancing signature identification in other fields. The proposed ML pipeline demonstrates its efficacy in handling challenges associated with limited sample sizes, offering a promising avenue for applications in similar domains.

De-obstruction of bladder outlet in humans reverses organ remodelling by normalizing the expression of key transcription factors.

BACKGROUND: Benign prostatic hyperplasia in elderly males often causes bladder outlet obstruction termed benign prostatic obstruction (BPO). BPO induces lower urinary tract symptoms and quantifiable urodynamic alterations in bladder function. When conservative medical treatments are exhausted, surgical interventions like transurethral resection of the prostate (TURP) are employed for bladder outlet de-obstruction. Elucidating the molecular changes in the human bladder resulting from BPO and their reversal post-de-obstruction is pivotal for defining the "point of no return", when the organ deterioration becomes irreversible. In this study we carried out a comprehensive molecular and urodynamic characterization of the bladders in men with BPO before TURP and 3 months after the relief of obstruction. METHODS: We report integrated transcriptome and proteome analysis of bladder samples from male patients with BPO before and 3 months after de-obstruction surgery (TURP). mRNA and protein profiles were correlated with urodynamic findings, specifically voiding detrusor pressure (PdetQmax) before TURP. We delineated the molecular classifiers of each group, pointing at the different pre-TURP bladder status. RESULTS: Age-matched patients with BPO without DO were divided into two groups based on the PdetQmax values recorded by UDI before de-obstruction: high and medium pressure (HP and MP) groups. Three months after de-obstruction surgery, the voiding parameters PdetQmax, Qmax and RV were significantly improved in both groups, without notable inter-group differences in the values after TURP. Patients with high PdetQmax showed less advanced remodeling and inflammatory changes than those with lower values. We detected significant dysregulation of gene expression, which was at least partially reversed by de-obstruction in both patients' groups. Transcription factor SOX21 and its target thrombospondin 4 (THBS4) demonstrated normalization post-TURP. CONCLUSIONS: Our findings reveal substantial yet incomplete reversal of cell signalling pathways three months after TURP, consistent with improved urodynamic parameters. We propose a set of biomarker genes, indicative of BPO, and possibly contributing to the bladder changes. This study unveils the stages of progressive obstruction-induced bladder decompensation and offers insights into selecting an optimal intervention point to mitigate loss of contractility.

Machine Learning-based Classification of transcriptome Signatures of non-ulcerative Bladder Pain Syndrome.

Lower urinary tract dysfunction (LUTD) presents a global health challenge with symptoms impacting a substantial percentage of the population. The absence of reliable biomarkers complicates the accurate classification of LUTD subtypes with shared symptoms such as non-ulcerative Bladder Pain Syndrome (BPS) and overactive bladder caused by bladder outlet obstruction with Detrusor Overactivity (DO). This study introduces a machine learning (ML)-based approach for the identification of mRNA signatures specific to non-ulcerative BPS. Using next-generation sequencing (NGS) transcriptome data from bladder biopsies of patients with BPS, benign prostatic obstruction with DO and controls, our statistical approach successfully identified 13 candidate genes capable of discerning BPS from control and DO patients. This set was subsequently validated using Quantitative Polymerase Chain Reaction (QPCR) in a larger patient cohort. To confirm our findings, we applied both supervised and unsupervised ML approaches to the QPCR dataset. Notably, a three-mRNA signature TPPP3, FAT1, and NCALD, emerged as a robust classifier, effectively distinguishing patients with non-ulcerative BPS from controls and patients with DO. This signature was universally selected by both supervised and unsupervised approaches. The ML-based framework used to define BPS classifiers not only establishes a solid foundation for comprehending the specific gene expression changes in the bladder of the patients with BPS but also serves as a valuable resource and methodology for advancing signature identification in other fields. The proposed ML pipeline demonstrates its efficacy in handling challenges associated with limited sample sizes, offering a promising avenue for applications in similar domains.

Machine Learning Made Easy (MLme): a comprehensive toolkit for machine learning-driven data analysis.

BACKGROUND: Machine learning (ML) has emerged as a vital asset for researchers to analyze and extract valuable information from complex datasets. However, developing an effective and robust ML pipeline can present a real challenge, demanding considerable time and effort, thereby impeding research progress. Existing tools in this landscape require a profound understanding of ML principles and programming skills. Furthermore, users are required to engage in the comprehensive configuration of their ML pipeline to obtain optimal performance. RESULTS: To address these challenges, we have developed a novel tool called Machine Learning Made Easy (MLme) that streamlines the use of ML in research, specifically focusing on classification problems at present. By integrating 4 essential functionalities-namely, Data Exploration, AutoML, CustomML, and Visualization-MLme fulfills the diverse requirements of researchers while eliminating the need for extensive coding efforts. To demonstrate the applicability of MLme, we conducted rigorous testing on 6 distinct datasets, each presenting unique characteristics and challenges. Our results consistently showed promising performance across different datasets, reaffirming the versatility and effectiveness of the tool. Additionally, by utilizing MLme's feature selection functionality, we successfully identified significant markers for CD8+ naive (BACH2), CD16+ (CD16), and CD14+ (VCAN) cell populations. CONCLUSION: MLme serves as a valuable resource for leveraging ML to facilitate insightful data analysis and enhance research outcomes, while alleviating concerns related to complex coding scripts. The source code and a detailed tutorial for MLme are available at https://github.com/FunctionalUrology/MLme.

Sex Does Not Affect Survival: A Propensity Score-Matched Comparison in a Homogenous Contemporary Radical Cystectomy Cohort.

OBJECTIVES: To determine whether biological sex affects oncological outcome after extended pelvic lymph node dissection, radical cystectomy, and urinary diversion for muscle-invasive bladder cancer, and to identify risk factors impacting outcome. PATIENTS AND METHODS: We performed a single-center, retrospective observational cohort study with prospective data collection with a propensity score matched population. A total of 1165 consecutive patients from 2000 to 2020, (317 women and 848 men) scheduled for open extended pelvic lymph node dissection, radical cystectomy, and urinary diversion for urothelial bladder cancer were included in the final analysis. Overall Survival (OS), Cancer-Specific-Survival (CSS), and Recurrence-Free-survival (RFS) were assessed with multivariable weighted Cox regression analysis as well as with propensity score matched Cox-Regression. RESULTS: No significant difference was found between sexes regarding OS (HR 1.18, [0.93-1.49], P = .16), CSS (HR 0.87, [0.64-1.18], P = .38), or RFS (HR 0.80, [0.59-1.07], P = .13). These results were confirmed after propensity score matching: female sex was not associated with inferior OS (HR 1.20, [0.91-1.60], P = .19), CSS (HR 1.01, [0.75-1.35], P = .97) or RFS (HR 0.98, [0.75-1.27], P = .86). CONCLUSIONS: We did not find a significant difference in cancer-related outcomes or overall survival after extended pelvic lymph node dissection, open radical cystectomy, and urinary diversion for urothelial cancer between males and females even after adjustment with propensity matching score for multiple factors including oncological parameters, smoking status, and renal function.

SpheroScan: A User-Friendly Deep Learning Tool for Spheroid Image Analysis.

Background: In recent years, three-dimensional (3D) spheroid models have become increasingly popular in scientific research as they provide a more physiologically relevant microenvironment that mimics in vivo conditions. The use of 3D spheroid assays has proven to be advantageous as it offers a better understanding of the cellular behavior, drug efficacy, and toxicity as compared to traditional two-dimensional cell culture methods. However, the use of 3D spheroid assays is impeded by the absence of automated and user-friendly tools for spheroid image analysis, which adversely affects the reproducibility and throughput of these assays. Results: To address these issues, we have developed a fully automated, web-based tool called SpheroScan, which uses the deep learning framework called Mask Regions with Convolutional Neural Networks (R-CNN) for image detection and segmentation. To develop a deep learning model that could be applied to spheroid images from a range of experimental conditions, we trained the model using spheroid images captured using IncuCyte Live-Cell Analysis System and a conventional microscope. Performance evaluation of the trained model using validation and test datasets shows promising results. Conclusion: SpheroScan allows for easy analysis of large numbers of images and provides interactive visualization features for a more in-depth understanding of the data. Our tool represents a significant advancement in the analysis of spheroid images and will facilitate the widespread adoption of 3D spheroid models in scientific research. The source code and a detailed tutorial for SpheroScan are available at https://github.com/FunctionalUrology/SpheroScan.

Impact of Intraoperative Fluid Balance and Norepinephrine on Postoperative Acute Kidney Injury after Cystectomy and Urinary Diversion over Two Decades: A Retrospective Observational Cohort Study.

The use of norepinephrine and the restriction of intraoperative hydration have gained increasing acceptance over the last few decades. Recently, there have been concerns regarding the impact of this approach on renal function. The objective of this study was to examine the influence of norepinephrine, intraoperative fluid administration and their interaction on acute kidney injury (AKI) after cystectomy. In our cohort of 1488 consecutive patients scheduled for cystectomies and urinary diversions, the overall incidence of AKI was 21.6% (95%-CI: 19.6% to 23.8%) and increased by an average of 0.6% (95%-CI: 0.1% to 1.1%, p = 0.025) per year since 2000. The fluid and vasopressor regimes were characterized by an annual decrease in fluid balance (-0.24 mL·kg-1·h-1, 95%-CI: -0.26 to -0.22, p < 0.001) and an annual increase in the amount of norepinephrine of 0.002 µg·kg-1·min-1 (95%-CI: 0.0016 to 0.0024, p < 0.001). The interaction between the fluid balance and norepinephrine levels resulted in a U-shaped association with the risk of AKI; however, the magnitude and shape depended on the reference categories of confounders (age and BMI). We conclude that decreased intraoperative fluid balance combined with increased norepinephrine administration was associated with an increased risk of AKI. However, other potential drivers of the observed increase in AKI incidence need to be further investigated in the future.

Machine Learning Made Easy (MLme): A Comprehensive Toolkit for Machine Learning-Driven Data Analysis.

Background: Machine learning (ML) has emerged as a vital asset for researchers to analyze and extract valuable information from complex datasets. However, developing an effective and robust ML pipeline can present a real challenge, demanding considerable time and effort, thereby impeding research progress. Existing tools in this landscape require a profound understanding of ML principles and programming skills. Furthermore, users are required to engage in the comprehensive configuration of their ML pipeline to obtain optimal performance. Results: To address these challenges, we have developed a novel tool called Machine Learning Made Easy (MLme) that streamlines the use of ML in research, specifically focusing on classification problems at present. By integrating four essential functionalities, namely Data Exploration, AutoML, CustomML, and Visualization, MLme fulfills the diverse requirements of researchers while eliminating the need for extensive coding efforts. To demonstrate the applicability of MLme, we conducted rigorous testing on six distinct datasets, each presenting unique characteristics and challenges. Our results consistently showed promising performance across different datasets, reaffirming the versatility and effectiveness of the tool. Additionally, by utilizing MLme's feature selection functionality, we successfully identified significant markers for CD8+ naive (BACH2), CD16+ (CD16), and CD14+ (VCAN) cell populations. Conclusion: MLme serves as a valuable resource for leveraging machine learning (ML) to facilitate insightful data analysis and enhance research outcomes, while alleviating concerns related to complex coding scripts. The source code and a detailed tutorial for MLme are available at https://github.com/FunctionalUrology/MLme.

Fluid mechanical performance of ureteral stents: The role of side hole and lumen size.

Ureteral stents are indispensable devices in urological practice to maintain and reinstate the drainage of urine in the upper urinary tract. Most ureteral stents feature openings in the stent wall, referred to as side holes (SHs), which are designed to facilitate urine flux in and out of the stent lumen. However, systematic discussions on the role of SH and stent lumen size in regulating flux and shear stress levels are still lacking. In this study, we leveraged both experimental and numerical methods, using microscopic-Particle Image Velocimetry and Computational Fluid Dynamic models, respectively, to explore the influence of varying SH and lumen diameters. Our results showed that by reducing the SH diameter from 1.1 to 0.4 mm the median wall shear stress levels of the SHs near the ureteropelvic junction and ureterovesical junction increased by over 150 % , even though the flux magnitudes through these SH decreased by about 40 % . All other SHs were associated with low flux and low shear stress levels. Reducing the stent lumen diameter significantly impeded the luminal flow and the flux through SHs. By means of zero-dimensional models and scaling relations, we summarized previous findings on the subject and argued that the design of stent inlet/outlet is key in regulating the flow characteristics described above. Finally, we offered some clinically relevant input in terms of choosing the right stent for the right patient.

Molecular Characterization of Non-Neurogenic and Neurogenic Lower Urinary Tract Dysfunction (LUTD) in SCI-Induced and Partial Bladder Outlet Obstruction Mouse Models.

We examined bladder function following spinal cord injury (SCI) by repeated urodynamic investigation (UDI), including external urethral sphincter (EUS) electromyography (EMG) in awake restrained mice and correlated micturition parameters to gene expression and morphological changes in the bladder. A partial bladder outlet obstruction (pBOO) model was used for comparison to elucidate both the common and specific features of obstructive and neurogenic lower urinary tract dysfunction (LUTD). Thirty female C57Bl/6J mice in each group received an implanted bladder catheter with additional electrodes placed next to the EUS in the SCI group. UDI assessments were performed weekly for 7 weeks (pBOO group) or 8 weeks (SCI group), after which bladders were harvested for histological and transcriptome analysis. SCI mice developed detrusor sphincter dyssynergia (DSD) one week after injury with high-pressure oscillations and a significantly increased maximal bladder pressure Pmax and were unable to void spontaneously during the whole observation period. They showed an increased bladder-to-bodyweight ratio, bladder fibrosis, and transcriptome changes indicative of extracellular matrix remodeling and alterations of neuronal signaling and muscle contraction. In contrast, pBOO led to a significantly increased Pmax after one week, which normalized at later time points. Increased bladder-to-bodyweight ratio and pronounced gene expression changes involving immune and inflammatory pathways were observed 7 weeks after pBOO. Comparative transcriptome analysis of SCI and pBOO bladders revealed the activation of Wnt and TGF-beta signaling in both the neurogenic and obstructive LUTD and highlighted FGF2 as a major upregulated transcription factor during organ remodeling. We conclude that SCI-induced DSD in mice leads to profound changes in neuronal signaling and muscle contractility, leading to bladder fibrosis. In a similar time frame, significant bladder remodeling following pBOO allowed for functional compensation, preserving normal micturition parameters.

Prediction of Biochemical Recurrence Based on Molecular Detection of Lymph Node Metastasis After Radical Prostatectomy.

Background: Molecular detection of lymph node (LN) micrometastases by analyzing mRNA expression of epithelial markers in prostate cancer (PC) patients provides higher sensitivity than histopathological examination. Objective: To investigate which type of marker to use and whether molecular detection of micrometastases in LNs was predictive of biochemical recurrence. Design setting and participants: LN samples from PC patients undergoing radical prostatectomy with extended LN dissection between 2009 and 2011 were examined for the presence of micrometastases by both routine histopathology and molecular analyses. Outcome measurements and statistical analysis: The mRNA expression of a panel of markers of prostate epithelial cells, prostate stem cell-like cells, epithelial-to-mesenchymal transition, and stromal activation, was performed by quantitative real-time polymerase chain reaction. The expression levels of these markers in LN metastases from three PC patients were compared with the expression levels in LN from five control patients without PC in order to identify the panel of markers best suited for the molecular detection of LN metastases. The predictive value of the molecular detection of micrometastases for biochemical recurrence was assessed after a follow-up of 10 yr. Results and limitations: Prostate epithelial markers are better suited for the detection of occult LN metastases than molecular markers of stemness, epithelial-to-mesenchymal transition, or reactive stroma. An analysis of 1023 LNs from 60 PC patients for the expression of prostate epithelial cell markers has revealed different expression levels and patterns between patients and between LNs of the same patient. The positive predictive value of molecular detection of occult LN metastasis for biochemical recurrence is 66.7% and the negative predictive value is 62.5%. Limitations are sample size and the hypothesis-driven selection of markers. Conclusions: Molecular detection of epithelial cell markers increases the number of positive LNs and predicts tumor recurrence already at surgery. Patient summary: We show that a panel of epithelial prostate markers rather than single genes is preferred for the molecular detection of lymph node micrometastases not visible at histopathological examination.

An in vitro bladder model with physiological dynamics: Vesicoureteral reflux alters stent encrustation pattern.

In vitro models are indispensable to study the physio-mechanical characteristics of the urinary tract and to evaluate ureteral stent performances. Yet previous models mimicking the urinary bladder have been limited to static or complicated systems. In this study, we designed a simple in vitro bladder model to simulate the dynamics of filling and voiding. The physio-mechanical condition of the model was verified using a pressure-flow test with different bladder outlet obstruction levels, and a reflux test was performed to qualitatively demonstrate the stent associated vesicoureteral reflux (VUR). Finally, the setup was applied with and without the bladder model to perform encrustation tests with artificial urine on commercially available double-J stents, and the volumes of luminal encrustations were quantified using micro-Computed Tomography and image segmentation. Our results suggest that, VUR is an important factor contributing to the dynamics in the upper urinary tract with indwelling stents, especially in patients with higher bladder outlet obstruction levels. The influence of VUR should be properly addressed in future in vitro studies and clinical analyses.

Effects of Deep Brain Stimulation on Lower Urinary Tract Function in Neurological Patients.

BACKGROUND: Deep brain stimulation (DBS) has clear beneficial effects on motor signs in movement disorders, but much less is known about its impact on lower urinary tract (LUT) function. OBJECTIVE: To evaluate the effects of DBS on LUT function in patients affected by movement disorders. DESIGN, SETTING, AND PARTICIPANTS: We prospectively enrolled 58 neurological patients affected by movement disorders, who were planned to receive DBS. INTERVENTION: DBS in the globus pallidus internus, ventral intermediate nucleus of the thalamus, or subthalamic nucleus. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Subjective symptom questionnaires (International Prostate Symptom Score) and objective urodynamic studies were carried out before implantation of the DBS leads and several months after surgery. After DBS surgery, urodynamic investigations were performed with DBS ON as well as DBS OFF. RESULTS AND LIMITATIONS: We enrolled patients suffering from Parkinson's disease (n = 39), dystonia (n = 11), essential tremor (n = 5), Holmes tremor (n = 2), and multiple sclerosis with tremor (n = 1). DBS of the globus pallidus internus resulted in worsening of LUT symptoms in 25% (four of 16) of the cases. DBS of the subthalamic nucleus in patients with Parkinson's disease led to normalization of LUT function in almost 20% (six of 31 patients), while a deterioration was seen in only one (3%) patient. DBS of the ventral intermediate nucleus of the thalamus improved LUT function in two (18%) and deteriorated it in one (9%) patient with tremor. CONCLUSIONS: DBS effects on LUT varied with stimulation location, highly warranting patient counseling prior to DBS surgery. However, more well-designed, large-volume studies are needed to confirm our findings. PATIENT SUMMARY: In this report, we looked at outcomes of deep brain stimulation on lower urinary tract function. We found that outcomes varied with stimulation location, concluding that counseling of patients about the effects on lower urinary tract function is highly recommended prior to surgery.

Impact of early postoperative creatinine increase on mid-term renal function after cystectomy.

OBJECTIVES: To determine whether early acute kidney injury affects mid-term renal function, to identify risk factors for impaired mid-term renal function, and to highlight the evolution of plasma creatinine and estimated glomerular filtration rate in the first 12 months after cystectomy and urinary diversion. METHODS: We conducted a single-center retrospective observational cohort study from 2000 to 2019. We included 900 consecutive patients undergoing cystectomy and urinary diversion. Patients with incomplete data and preoperative hemodialysis were excluded. Early acute kidney injury was defined as an increase in plasma creatinine of >50% or >26.5 µmol/L within 24 h after surgery. Multiple linear regression analysis was performed to model the association between risk factors and change in plasma creatinine and estimated glomerular filtration rate at 12 months. RESULTS: Early acute kidney injury was diagnosed in 183/900 patients (20.3%) and was associated with significant mid-term plasma creatinine increase compared to preoperative value (+10.0 µmol/L [95% confidence interval -1.5, 25.0] vs +4.0 µmol/L [-7.0, 13.0]; P < 0.001). Similarly, a significant estimated glomerular filtration rate change was found (-11.2 mL/min [95% confidence interval -19.8, 0.6] vs -4.9 mL/min [-15.6, 5.3]; P < 0.001). In the linear regression model, early acute kidney injury increased creatinine at 12 months by 9.8% (estimated glomerular filtration rate: decrease by 6.2 mL/min), male sex by 12.0%. Limitations include retrospective analysis from prospectively assessed data. CONCLUSIONS: Early acute kidney injury resulted in elevated plasma creatinine and decreased estimated glomerular filtration rate values 12 months postoperatively, albeit the clinical relevance remains questionable.

Metabolic syndrome and male lower urinary tract symptoms.

INTRODUCTION: Historically, urologists regarded prostate enlargement as the sole cause for male bladder problems. Over time, subdivision of symptoms into storage, voiding and post-voiding symptoms raised awareness of the urinary tract fine-tuning associated with urine storage and voiding, and led to the umbrella term lower urinary tract symptoms (LUTS), which respects bladder and prostate function. While research goes on, it seems as if the more we know about urine storage and voiding, the more complicated it gets: different mechanism can mimic the same symptoms. Clinically, it remains ever the more challenging to understand the pathophysiological context of each patient. Metabolic syndrome (MetS), too, is an umbrella term. Metabolic changes caused by MetS pathophysiologically start with visceral adiposity. It leads to different changes in the signaling pathway including cytokines, elevated transmitters of inflammation, higher levels of free fatty acids (FFA), and adipokines, resulting in vasoconstriction, insulin resistance, impaired glucose uptake and high insulin secretion. Furthermore, MetS is thought to be associated with nephrolithiasis, BPH, LUTS, erectile dysfunction (ED), and infertility. This review aims at synthesizing interactions and consequences of LUTS with MetS. EVIDENCE ACQUISITION: On May 31st, 2021, PubMed and Cochrane were searched with no language nor time restrictions, using the terms "Metabolic syndrome" and "male LUTS." A total of 252 publications were identified. Only papers that mentioned both terms either in their title or in their abstract were included. Hence, 120 papers were reviewed. EVIDENCE SYNTHESIS: Many different findings cause LUTS, which are summarized in the EAU Guidelines on male non-neurogenic LUTS. These different symptoms belong to different phases of bladder function. Frequency, nocturia, urgency and incontinence are symptoms of the filling phase, while hesitancy, intermittency, weak or irregular stream, straining and terminal dribble belong to the voiding phase of the bladder. Post-voiding symptoms include post-void dribbling and sensation of incomplete voiding. Symptoms of any phase of bladder function impair quality of life, and are associated with ageing. CONCLUSIONS: Inflammation links both umbrella terms, LUTS and MetS. Understanding the exact role of the different elements will not only help to better understand both findings, but also lead to more efficacious treatment, and hopefully, in the future, personalized medicine, by understanding each individual's driving mechanism for LUTS. Reducing inflammation is likely to help patients with MetS and LUTS; further research could therefore focus on how to manage inflammation.

SpheroScan: a user-friendly deep learning tool for spheroid image analysis.

BACKGROUND: In recent years, 3-dimensional (3D) spheroid models have become increasingly popular in scientific research as they provide a more physiologically relevant microenvironment that mimics in vivo conditions. The use of 3D spheroid assays has proven to be advantageous as it offers a better understanding of the cellular behavior, drug efficacy, and toxicity as compared to traditional 2-dimensional cell culture methods. However, the use of 3D spheroid assays is impeded by the absence of automated and user-friendly tools for spheroid image analysis, which adversely affects the reproducibility and throughput of these assays. RESULTS: To address these issues, we have developed a fully automated, web-based tool called SpheroScan, which uses the deep learning framework called Mask Regions with Convolutional Neural Networks (R-CNN) for image detection and segmentation. To develop a deep learning model that could be applied to spheroid images from a range of experimental conditions, we trained the model using spheroid images captured using IncuCyte Live-Cell Analysis System and a conventional microscope. Performance evaluation of the trained model using validation and test datasets shows promising results. CONCLUSION: SpheroScan allows for easy analysis of large numbers of images and provides interactive visualization features for a more in-depth understanding of the data. Our tool represents a significant advancement in the analysis of spheroid images and will facilitate the widespread adoption of 3D spheroid models in scientific research. The source code and a detailed tutorial for SpheroScan are available at https://github.com/FunctionalUrology/SpheroScan.

MLcps: machine learning cumulative performance score for classification problems.

BACKGROUND: Assessing the performance of machine learning (ML) models requires careful consideration of the evaluation metrics used. It is often necessary to utilize multiple metrics to gain a comprehensive understanding of a trained model's performance, as each metric focuses on a specific aspect. However, comparing the scores of these individual metrics for each model to determine the best-performing model can be time-consuming and susceptible to subjective user preferences, potentially introducing bias. RESULTS: We propose the Machine Learning Cumulative Performance Score (MLcps), a novel evaluation metric for classification problems. MLcps integrates several precomputed evaluation metrics into a unified score, enabling a comprehensive assessment of the trained model's strengths and weaknesses. We tested MLcps on 4 publicly available datasets, and the results demonstrate that MLcps provides a holistic evaluation of the model's robustness, ensuring a thorough understanding of its overall performance. CONCLUSIONS: By utilizing MLcps, researchers and practitioners no longer need to individually examine and compare multiple metrics to identify the best-performing models. Instead, they can rely on a single MLcps value to assess the overall performance of their ML models. This streamlined evaluation process saves valuable time and effort, enhancing the efficiency of model evaluation. MLcps is available as a Python package at https://pypi.org/project/MLcps/.