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

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

The impact of pelvic venous pressure on blood loss during open radical cystectomy and urinary diversion: Results from a secondary analysis of a randomized clinical trial

PURPOSE Blood loss and blood substitution are associated with higher morbidity after major abdominal surgery. During major liver resection, low local venous pressure, has been shown to reduce blood loss. Ambiguity persists concerning the impact of local venous pressure on blood loss during open radical cystectomy. We aimed to determine the association between intraoperative blood loss and pelvic venous pressure (PVP) and determine factors affecting PVP. MATERIAL AND METHODS In the frame of a single-center, double-blind, randomized trial, PVP was measured in 82 patients from a norepinephrine/low-volume group and in 81 from a control group with liberal hydration. For this secondary analysis, patients from each arm were stratified into subgroups with PVP <5 mmHg or ≥5 mmHg measured after cystectomy (optimal cut-off value for discrimination of patients with relevant blood loss according to the Youden's index). RESULTS Median blood loss was 800 ml [range: 300-1600] in 55/163 patients (34%) with PVP <5 mmHg and 1200 ml [400-3000] in 108/163 patients (66%) with PVP ≥5 mmHg; (P<0.0001). A PVP <5 mmHg was measured in 42/82 patients (51%) in the norepinephrine/low-volume group and 13/81 (16%) in the control group (P<0.0001). PVP dropped significantly after removal of abdominal packing and abdominal lifting in both groups at all time points (at begin and end of pelvic lymph node dissection, end of cystectomy) (P<0.0001). No correlation between PVP and central venous pressure could be detected. CONCLUSIONS Blood loss was significantly reduced in patients with low PVP. Factors affecting PVP were fluid management and abdominal packing

Urinary miRNA profiles discriminate between obstruction-induced bladder dysfunction and healthy controls.

Urgency, frequency and incomplete emptying are the troublesome symptoms often shared between benign prostatic obstruction-induced (BLUTD) and neurogenic (NLUTD) lower urinary tract dysfunction. Previously, using bladder biopsies, we suggested a panel of miRNA biomarkers for different functional phenotypes of the bladder. Urine is a good source of circulating miRNAs, but sex- and age-matched controls are important for urinary metabolite comparison. In two groups of healthy subjects (average age 32 and 57 years old, respectively) the total protein and RNA content was very similar between age groups, but the number of secreted extracellular vesicles (uEVs) and expression of several miRNAs were higher in the young healthy male volunteers. Timing of urine collection was not important for these parameters. We also evaluated the suitability of urinary miRNAs for non-invasive diagnosis of bladder outlet obstruction (BOO). A three urinary miRNA signature (miR-10a-5p, miR-301b-3p and miR-363-3p) could discriminate between controls and patients with LUTD (BLUTD and NLUTD). This panel of representative miRNAs can be further explored to develop a non-invasive diagnostic test for BOO. The age-related discrepancy in the urinary miRNA content observed in this study points to the importance of selecting appropriate, age-matched controls

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

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

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

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

Clinical utilization of genomics data produced by the international Pseudomonas aeruginosa consortium

The International Pseudomonas aeruginosa Consortium is sequencing over 1000 genomes and building an analysis pipeline for the study of Pseudomonas genome evolution, antibiotic resistance and virulence genes. Metadata, including genomic and phenotypic data for each isolate of the collection, are available through the International Pseudomonas Consortium Database (http://ipcd.ibis.ulaval.ca/). Here, we present our strategy and the results that emerged from the analysis of the first 389 genomes. With as yet unmatched resolution, our results confirm that P. aeruginosa strains can be divided into three major groups that are further divided into subgroups, some not previously reported in the literature. We also provide the first snapshot of P. aeruginosa strain diversity with respect to antibiotic resistance. Our approach will allow us to draw potential links between environmental strains and those implicated in human and animal infections, understand how patients become infected and how the infection evolves over time as well as identify prognostic markers for better evidence-based decisions on patient care