Background: Due to insufficient accuracy, urine-based assays currently have a limited role in the management of patients with bladder cancer. The identification of multiplex molecular signatures associated with disease has the potential to address this deficiency and to assist with accurate, non-invasive diagnosis and monitoring.
Methods: To evaluate the performance of Oncuria™, a multiplex immunoassay for bladder detection in voided urine samples. The test was evaluated in a multi-institutional cohort of 362 prospectively collected subjects presenting for bladder cancer evaluation. The parallel measurement of 10 biomarkers (A1AT, APOE, ANG, CA9, IL8, MMP9, MMP10, PAI1, SDC1 and VEGFA) was performed in an independent clinical laboratory. The ability of the test to identify patients harboring bladder cancer was assessed. Bladder cancer status was confirmed by cystoscopy and tissue biopsy. The association of biomarkers and demographic factors was evaluated using linear discriminant analysis (LDA) and predictive models were derived using supervised learning and cross-validation analyses. Diagnostic performance was assessed using ROC curves.
Results: The combination of the 10 biomarkers provided an AUROC 0.93 [95% CI 0.87-0.98], outperforming any single biomarker. The addition of demographic data (age, sex, and race) into a hybrid signature improved the diagnostic performance AUROC 0.95 [95% CI 0.90-1.00]. The hybrid signature achieved an overall sensitivity of 0.93, specificity of 0.93, PPV of 0.65 and NPV of 0.99 for bladder cancer classification. Sensitivity values of the diagnostic panel for high-grade bladder cancer, low-grade bladder cancer, MIBC and NMIBC were 0.94, 0.89, 0.97 and 0.93, respectively.
Conclusions: Urinary levels of a biomarker panel enabled the accurate discrimination of bladder cancer patients and controls. The multiplex Oncuria™ test can achieve the efficient and accurate detection and monitoring of bladder cancer in a non-invasive patient setting.
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http://dx.doi.org/10.1186/s12967-021-02796-4 | DOI Listing |
Am J Case Rep
January 2025
Department of Anatomical Pathology, Jenderal Soedirman University, Purwokerto, Central Java, Indonesia.
BACKGROUND Vulvar melanoma during pregnancy is exceptionally rare. Hormonal and immunological changes in pregnancy have raised concerns about the potential for accelerated melanoma progression and poorer maternal outcomes. This case report describes an unusual presentation of vulvar melanoma in a pregnant patient, which rapidly progressed despite previous treatments, but resulted in a favorable fetal outcome.
View Article and Find Full Text PDFWorld J Urol
January 2025
Department of Urology, Renmin Hospital of Wuhan University, 99 Zhang Zhi-dong Road, Wuhan, Hubei, 430060, P.R. China.
Purpose: To develop a deep learning (DL) model based on primary tumor tissue to predict the lymph node metastasis (LNM) status of muscle invasive bladder cancer (MIBC), while validating the prognostic value of the predicted aiN score in MIBC patients.
Methods: A total of 323 patients from The Cancer Genome Atlas (TCGA) were used as the training and internal validation set, with image features extracted using a visual encoder called UNI. We investigated the ability to predict LNM status while assessing the prognostic value of aiN score.
J Org Chem
January 2025
College of Chemistry and Chemical Engineering, Jishou University, Jishou 416000, People's Republic of China.
A copper-catalyzed domino addition/cyclization reaction was developed to synthesize novel benzoselenazole-linked 1,2,3-triazole and tetracyclic fused 12-benzo[4,5]selenazole[2,3-]quinazolin-12-one derivatives from isoselenocyanates. This domino reaction efficiently constructed multiple new chemical bonds in a single step, forming either four (one C-Se and three C-) or three (one C-Se and two C-) bonds. The reaction offers several key advantages, including mild conditions, broad substrate compatibility, and straightforward and safe operation.
View Article and Find Full Text PDFCells
December 2024
Department of Mechanical Engineering, Tufts University, Medford, MA 02155, USA.
The development of noninvasive methods for bladder cancer identification remains a critical clinical need. Recent studies have shown that atomic force microscopy (AFM), combined with pattern recognition machine learning, can detect bladder cancer by analyzing cells extracted from urine. However, these promising findings were limited by a relatively small patient cohort, resulting in modest statistical significance.
View Article and Find Full Text PDFCureus
December 2024
Biostatistics and Epidemiology, Rutgers University, Piscataway, USA.
Background Various studies have evaluated the quality of health-related information on TikTok (ByteDance Ltd., Beijing, China), including topics such as COVID-19, diabetes, varicoceles, bladder cancer, colorectal cancer, and others. However, there is a paucity of data on studies that examined TikTok as a source of quality health information on human papillomavirus (HPV).
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