Surface modification of ureteral stents: development history, classification, function, and future developments.

INTRODUCTION: Ureteral stents are commonly used in urology but are frequently associated with hematuria, abdominal discomfort, urinary tract infection, stent displacement, and stent encrustation. Surface modification of ureteral stents is beneficial to solve the problem, and these can be divided into coated stents and drug-eluting stents according to the modification method. Coated stents can be divided into hydrophilic coatings, antibacterial coatings, and anti-encrustation coatings. Drug-eluting stents can be divided into antimicrobial drug-eluting, antispasmodic analgesic drug-eluting, anti-ureteral stricture drug-eluting, and anti-tumor drug-eluting. Surface modification of ureteral stents can not only reduce complications related to ureteral stents but also strengthen the treatment of certain urologic diseases, which has a high clinical application value. AREAS COVERED: This review focuses on highlighting and summarizing the latest research progress about surface modification of ureteral stents, ureteral stent development history, classification, functions, and future development prospects. EXPERT OPINION: The purpose of this article is to discuss surface modification of ureteral stents to reduce stent-related complications and potential research directions for the treatment of urinary tract tumors are also briefly discussed, to help guide further innovation in ureteral stent coatings, which contribute to the future progress of ureteral stents surface modification.

Identify AGAP2 as prognostic biomarker in clear cell renal cell carcinoma based on bioinformatics and IHC staining.

Background: Arf GTPase-activating proteins are aberrantly expressed in a variety of tumors, but their role in clear cell renal cell carcinoma (ccRCC) was unclear. Exploring the biological role of Arf GAP with GTP binding protein like domain, Ankyrin repeat and PH domain 2 (AGAP2) in ccRCC could improve our understanding on the aggressiveness and immune relevance of ccRCC. Methods: The expression of AGAP2 was analyzed based on the Cancer Genome Atlas (TCGA) database and verified in ccRCC samples using immunohistochemistry. The association between AGAP2 and clinical cancer stages was explored by TCGA dataset and UALCAN. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were performed to analyze the biological functions of AGAP2-related genes. Moreover, the relationship between AGAP2 and immune cell infiltration was investigated with TIME and TCGA dataset. Results: Compared to normal tissues, AGAP2 was upregulated in ccRCC tissues. Higher expression of AGAP2 was associated with clinical cancer stages, TNM stages, pathologic stages, and status. Prognostic analysis on AGAP2 showed that AGAP2 overexpression was associated with KIRC overall survival (OS) reduction (P = 0.019). However, higher expression of AGAP2 may improve the OS of CESC (P = 0.002), THYM (P = 0.006) and UCEC (P = 0.049). GO and KEGG analysis showed that AGAP2-related genes was related to T cell activation, immune activity and PD-L1 expression and PD-1 checkpoint pathway. Furthermore, our study showed that AGAP2 were significantly associated with T cells, Cytotoxic cells, Treg, Th1 cells, CD8 T cells, T helper cells. And AGAP2 expression level affected the abundance of immune cells infiltration. The infiltrating level of immune cells was different between the AGAP2 high-expression and low-expression groups. Conclusion: The expression of AGAP2 in ccRCC was higher than that in normal kidney tissues. It was significantly associated with clinical stage, poor prognosis, and immune cell infiltration. Therefore, AGAP2 may become an important component for ccRCC patients who receive precision cancer therapy and may be a promising prognostic biomarker.

Texture analysis based on PI-RADS 4/5-scored magnetic resonance images combined with machine learning to distinguish benign lesions from prostate cancer.

Background: The global morbidity and mortality of prostate cancer (PCa) increase sharply every year. Early diagnosis is essential; it determines survival and outcome. So, this study extracted the texture features of apparent diffusion coefficient images in multiparametric magnetic resonance imaging (mp-MRI) and built machine learning models based on radiomics texture analysis (TA) to determine its ability to distinguish benign from PCa lesions using the Prostate Imaging Reporting and Data System (PI-RADS) 4/5 score. Methods: We enrolled 103 patients who underwent mp-MRI examinations and transrectal ultrasound and magnetic resonance fusion imaging (TRUS-MRI) targeted prostate biopsy and obtained pathological confirmation at our hospital from August 2017 to January 2020. We used ImageJ software to obtain texture feature parameters based on apparent diffusion coefficient (ADC) images, then standardized texture feature parameters, and used LASSO regression to reduce multiple feature parameters; 70% of the cases were randomly selected from the PCa group and the benign prostate hyperplasia group as the training set. The remaining 30% was used as the test set. The machine learning classification model for identifying benign and malignant prostate lesions was constructed using the feature parameters after dimensionality reduction. The clinical indicators were statistically analyzed, and we constructed a machine learning classification model based on clinical indicators of benign and malignant prostate lesions. Finally, we compared the model's performance based on radiomics texture features and clinical indicators to identify benign and malignant prostate lesions in PI-RADS 4/5 score. Results: The area under the curve (AUC) of the R-logistic model test set was 0.838, higher than the R-SVM and R-AdaBoost classification models. At this time, the corresponding R-logistic classification model formula is as follow: Y_radiomics=9.396-7.464*median ADC-0.584*kurtosis+0.627*skewness+0.576*MRI lesions volume; analysis of clinical indicators shows that the corresponding C-logistic classification model formula is as follows: Y_clinical =-2.608+0.324*PSA-3.045*Fib+4.147*LDL-C, the AUC value of the model training set was 0.860, smaller than the training set R-logistic classification model AUC value of 0.936. Conclusions: Radiomics combined with the machine learning classifier model has strong classification performance in identifying benign and PCa in PI-RADS 4/5 score. Various treatments and outcomes for PCa patients can be applied clinically.

A Modified Disposable Circumcision Suture Device with Application of Plastic Sheet to Avoid Severe Bleeding After Circumcision.

PURPOSE: To evaluate the effectiveness of a modified disposable circumcision suture device (DCSD) with application of plastic sheet to avoid severe bleeding after circumcision and compare the surgical effects and other postoperative complications of two DCSDs. MATERIALS AND METHODS: A total of 943 excess foreskin patients from January 2018 to January 2020 who underwent circumcision using two different DCSDs were recruited. Preoperative characteristics (patient age, height and weight), main surgical outcomes (surgical time, intraoperative blood loss, incision healing time) and postoperative complications (postoperative hemorrhage and hematoma rate, edema rate, incision infection rate, residual staples rate) were collected and analyzed. Patients' "satisfaction" or "dissatisfaction" was also investigated. RESULTS: Preoperative characteristics showed no significant statistical difference. The modified DCSD group has a lower intraoperative bleeding, postoperative hemorrhage or hematoma rate and residual staples rate compared with the conventional group. Incision healing time and incision infection rate between the two groups were similar. Nevertheless, conventional group has a shorter surgical time, a lower edema rate and a higher satisfaction rate. CONCLUSION: The modified DCSD with application of plastic sheet can avoid severe bleeding after circumcision effectively and can be served as a new choice for circumcision.

A nomogram for bladder pain syndrome/interstitial cystitis based on netrin-1.

PURPOSE: This study aimed to combine plasma netrin-1 and clinical parameters to construct a diagnostic model for bladder pain syndrome/interstitial cystitis (BPS/IC). METHODS: We analyzed the independent diagnostic value of netrin-1 and the correlation with clinical symptom scores of BPS/IC. Clinical parameters were selected using LASSO regression, and a multivariate logistic regression model based on netrin-1 was established, and then a nomogram of BPS/IC prevalence was constructed. The nomogram was evaluated using calibration curves, the C-index, and decision curve analysis (DCA). Finally, the model was validated using an internal validation method. RESULTS: The area under the curve for the ability of netrin-1 to independently predict BPS/IC diagnosis was 0.858 (p < 0.001), with a sensitivity of 85% and specificity of 82%. The predicted nomogram included three variables: age, CD3 + /CD4 + T lymphocyte ratio, and netrin-1. The C-index of this nomogram was 0.882, and the predicted values were highly consistent with the actual results in the calibration curve. In addition, the internally validated C-index of 0.870 confirms the high reliability of the model. DCA results show that the net patient benefit of the netrin-1 combined with other clinical parameters was higher than that of the single netrin-1 model. CONCLUSION: Netrin-1 can be used as a diagnostic marker for BPS/IC and is associated with pain. The nomogram constructed by combining netrin-1 and clinical parameters was able to predict BPS/IC with great accuracy. In addition, Netrin-1 may also serve as a novel therapeutic target for BPS/IC.

Bladder muscle regeneration enhanced by sustainable delivery of heparin from bilayer scaffolds carrying stem cells in a rat bladder partial cystectomy model.

In bladder tissue engineering, regeneration of muscle is of equal importance to epithelial regeneration. However, as yet there is no effective strategy for promoting bladder muscle regeneration. In this study we aim to promote bladder muscle regeneration by sustainably delivering heparin from a bilayer scaffold carrying stem cells. The bilayer scaffold [heparin-polycaprolactone (PCL)/bladder decellularized matrix (BAM) Hep-PB/PCL] comprises an electrospun layer (Hep-PB electrospun membrane) and a three-dimensional (3D) printed layer (PCL scaffold), fabricated via coaxial-electrospinning and 3D printing, respectively. Heparin was encapsulated into the core of the Hep-PB fibers with a core-shell structure to sustain its release. The morphology of the bilayer scaffold and the microstructure of the electrospun fibers were characterized. The release behavior of heparin from various electrospun membranes was evaluated. The role of Hep-PB in promoting myogenic differentiation of the adipose-derived stem cells (ADSCs) through sustainable release of heparin was also evaluated. After 7 d culture, Hep-PB/PCL scaffolds carrying ADSCs (defined as ASHP) were used for bladder reconstruction in a rat partial cystotomy model. The result shows that the PCL printed scaffold has ordered macropores (∼370 µm), unlike the compact microstructure of electrospun films. The Hep-PB membrane exhibits a sustained release behavior for heparin. This membrane also shows better growth and proliferation of ADSCs than the other membranes. The polymerase chain reaction results show that the expression of smooth muscle cell markers in ADSCs is enhanced by the Hep-PB scaffold. The results of retrograde urethrography and histological staining indicate that the bladder volume in the ASHP group recovers better, and the regenerated bladder muscle bundles are arranged in a more orderly fashion compared with the direct suture and bladder decellularized matrix groups. Therefore, findings from this study show that bladder muscle regeneration could be enhanced by bilayer scaffolds delivering heparin and carrying stem cells, which may provide a new strategy for bladder tissue engineering.

Construction of five microRNAs prognostic markers and a prognostic model for clear cell renal cell carcinoma.

BACKGROUND: To determine the role of miRNA in the progression and outcome of renal clear cell carcinoma (ccRCC), establish a model for predicting outcome in patients with ccRCC and verify it using a Cox regression model. The miRNA target genes were predicted to understand their biological functions. METHODS: The microRNAs of 71 normal tissues and 545 tumor tissues were downloaded from TCGA (https://tcga-data.nci.nih.gov/tcga/). We also downloaded 537 clinical materials from this website. The miRNA difference analysis was carried out. A prognostic model was constructed using differential miRNA. The model was verified using Cox survival analysis, receiver operator characteristic (ROC), and independent predictive analysis. RESULTS: MiR-130b-3p, miR-365b-3p, miR-149-5p, miR-155-5p, and miR-144-5p can be used as independent prognostic indicators. We also analyzed the related functions of the target gene and found that target genes of miRNAs are involved in the signal pathways of some tumors, including cholesterol metabolism, HIF-1 signal pathway, focus adhesion, the Rap1 signal pathway, and hepatitis C. CONCLUSIONS: The prognostic model constructed using five miRNAs is an independent and accurate factor. These miRNAs target genes are involved in regulating a variety of tumorigenesis and signal pathways. Therefore, we have reason to believe that the regulation of signal pathways by miRNA may play a critical role in the occurrence, development, and outcome of ccRCC, provide a new therapeutic target for ccRCC, and improve outcomes.

A nomogram combined with radiomics features, albuminuria, and metabolic syndrome to predict the risk of myometrial invasion of bladder cancer.

BACKGROUND: To establish a preoperative prediction model of myometrial invasion of bladder cancer (BC) based on the radiomics characteristics of multi-parameter thin-slice enhanced computed tomography (CT) imaging. METHODS: Data from 100 patients with BC were analyzed retrospectively. The patients were divided into two groups: muscular invasive BC and non-muscular invasive BC. The tumor region was segmented from enhanced CT images (arterial- and venous-phase calibration maps) of all patients using Slicer-3D software. We extracted 1,223 texture features from tumor image data based on the shape and gray-level co-occurrence matrix, gray size region matrix, gray run-length matrix, adjacent gray difference matrix, and gray correlation matrix. The patients were randomly divided into a training group (n=70) and a verification group (n=30) in a 7:3 ratio. Interclass correlation coefficients >0.75, least absolute shrinkage, and selection operator regression were used for feature selection. The prediction model was established by combining Rad-score, independent clinical factors, and support vector machine (SVM), and a radiomics nomogram was constructed. The nomogram was tested using the consistency index, calibration curve, time-dependent receiver operating characteristic curve, and clinical decision curve to predict the myometrial invasion of the bladder preoperatively. RESULTS: Six radiomics features that were significantly related to myometrial invasion of BC were selected to construct a predictive model. The area under the curve (AUC) values of training group and verification group based on SVM were 0.898 (95% CI: 0.820-0.976) and 0.702 (95% CI: 0.495-0.909), respectively. Single factor and multiple factor analysis showed that albuminuria (95% CI: 0.243-2.206, P=0.0014) and metabolic syndrome (95% CI: 0.850-2.935, P<0.001) were independent influencing factors of BC myometrial invasion. Clinical factors and 11 radiomics features were used to construct a comprehensive model for predicting the pathological grade of BC (radiomics + clinical). After a comprehensive comparison, we found that the overall effectiveness of the model (radiomics + clinical) was the highest (AUC =0.8457). CONCLUSIONS: Based on the multi-parameter thin-layer enhanced CT radiomics feature can be used as a potential independent predictor of BC myometrial invasion, the model based on parameters can initially quantitatively characterize the risk of myometrial invasion, and has excellent potential for predicting myometrial invasion of BC.

A pan-cancer study of PD-1 and CTLA-4 as therapeutic targets.

BACKGROUND: Immunotherapy is a new and powerful weapon against tumors, represented by inhibitors of programmed death-1 (PD-1) and cytotoxic T lymphocyte-associated protein-4 (CTLA-4). This study aimed to determine the similarities and differences between PD-1 and CTLA-4 in 33 cancers in The Cancer Genome Atlas (TCGA) and the impact of subtypes of the immune environment on tumor production and treatment. METHODS: From the Xena browser, we downloaded TNM stage, immune subtypes, and tumor microenvironment scores for 33 tumors from TCGA. Expression of CTLA-4 and PD-1 in normal and tumor samples were compared for various tumors with normal tissue sample sizes greater than five. The relationship between expression and overall survival was investigated using one-way Cox analysis. The immune scores of 33 tumors were assessed using ESTIMATE prediction software to predict the degree of immune cell infiltration across tumors and calculate the correlation between PD-1 and CTLA-4 expression with the tumor microenvironment and tumor stem cells. We also examined the correlation between genes and drug sensitivity. RESULTS: PD-1 and CTLA-4 were highly expressed in breast invasive carcinoma (BRCA), cholangiocarcinoma (CHOL), esophageal carcinoma (ESCA), and kidney renal clear cell carcinoma (KIRC) (P<0.05), highly correlated with immune subtypes C2 (IFN-γ-dominant) and C6 (TGF-ß-dominant), and positively correlated with tumor microenvironmental immune scores (P<0.05). In renal clear cell carcinoma, PD-1 and CTLA-4 expression was positively correlated with clinical stage and microenvironmental score (r>0.7, P<0.05). CONCLUSIONS: The finding that PD1 and CTLA-4 are associated with the prognosis of most tumour patients and are closely related to the tumour microenvironment is of great value and provides a research direction for the screening of populations benefiting from immunotherapy.