Urinary biomarkers in multicentric studies: Shaping the future of bladder cancer diagnosis and follow‐up

Background: Salivary biomarkers are non-invasive molecules that indicate neurodegenerative illnesses, especially Alzheimer disease (AD) and Parkinson disease (PD).this study was conducted to determine the diagnostic precision of salivary proteomic and genomic biomarkers in terms of early AD and PD detection. Methods: A systematic literature search was conducted in PubMed, web of science and Google Scholar, and studies included from 2016 to 2025. Research that examined salivary biomarkers in AD and PD was eligible. The data were analyzed with a random-effects model and odds ratios (OR), standard mean differences (SMD), and 95% confidence interval (CI) was estimated. Also, subgroup and sensitivity analysis were performed. To assess the risk of bias, the Newcastle-Ottawa Scale (NOS) was applied for included observational studies. Results: A total of 11 eligible studies concerning proteomic biomarkers, including amyloid-β (Aβ42, Aβ40) and alpha-synuclein total (α-synTotal) and alpha-synuclein Oligomer (α-synOligo), and genomic biomarkers like different salivary microRNAs were included. Meta-analysis indicated that Aβ42 (OR=0.70; 95% CI: 0.41 to 1.1) and Aβ40 (OR=1.01; 95% CI: 0.97 to 1.06) had significant discriminatory potential in AD patients; but α-synOligo (SMD = 2.90; 95% CI: -0.59–6.39) and α-synTotal (SMD = 0.44; 95% CI: -3.14 to 4.02) was higher in PD patients as compared with controls. Genomic biomarkers demonstrated inconsistent findings (SMD = -0.18; 95% CI: -1.79–1.42) because of difference in microRNA types. Heterogeneity was high (I2 > 90%), which is caused by alterations in study design and in the methods to measure biomarkers. Discussion: Salivary biomarkers were found to be an insignificant yet exceptional method of early examination of AD and PD. Nonetheless, the inconsistency of different studies points to develop standardized protocols.

Serum urea (sU) is synthesized in liver and serum creatinine (sCr) is a degradation product of muscle cells, both represents for the efficacy of glomerular filtration. Both sU and sCr have poor predictive accuracy for renal injury, particularly in the early stages of kidney diseases because they are evaluated when 60-70% nephrons were already damaged and then kidney diseases changed to irreversible required only dialysis or kidney transplantation. Therefore, patients undergo into multi disorder health problems towards death. So, it is now most urgent required for early diagnosis of kidney injury by analysis of direct kidney injury markers, renal tubule injury markers and proteomic genomic markers of kidney disorders from both urine and serum and alternative treatment strategies for management of kidney diseases using medicinal plants. There are four major classes of markers viz., tubular injury markers e.g, KIM-1, proteomic and genomic marker from proximal nephron is IL-18, kidney functional marker-cystatin C and oxidation stress marker- Malon di aldehyde (MDA), alpha Glutathione S Transferase (alpha GST). IL-18, KIM-1, cystatin C and GST are important mediators appear in urine when renal tubules are stressed and injured. The medicinal plant like Terminellia arjuna (TA) has been a part of ayurvedic medicinal system contains phytoconstituents like triterpenoids, tannins, flavonoids and others they have proved anti cardiovascular properties, anticancer, antimicrobial properties, and neproprotective. Aqueous bark extract of TA possess antiuremic properties was already established in our laboratory on dehydration induced uremic rats. Therefore, the manuscript was designed to investigate the kidney injury proteomic and genomic biomarkers from both serum and urine on acetaminophen induced kidney disorder rats and analysis of therapeutic efficacy of phytocompounds from the bark of Terminalia arjuna (TA). Objective: There were main two objectives, first was the identification of best biomarkers for early screening of kidney diseases whether plasma urea and creatinine or novel urinary low molecular weight protein biomarkers Interlukin-18 (IL-18), Kidney injury molecule-1(KIM-1), cystatin –C. Second, the therapeutic efficacy of methanol fraction of Terminalia arjuna (MFTA) on urinary novel biomarkers. Methods: A total of 35 adult male rats were divided into three groups (n = 5), group 1 was fed normal food, group 2, normal food with administration of acetaminophen (APAP) for 5 days, 10 days and 15 days and group 3, normal food with administration of APAP and co-administration of MFTA for 5 days, 10 days and 15 days. All rats were sacrificed at 15th day of experiment. Results: Results showed 5 day, 10 day, 15 day administration of APAP increased novel urinary biomarkers as IL-18, KIM-1 near two folds and cystatin-C near six folds increased than old biomarkers plasma urea and plasma creatinine. Administration of APAP with co-administration of MFTA represented the protective effect by decreasing old and new novel biomarkers with Superoxide dismutase (SOD) and catalase but Malondialdehyde (MDA) level increased. SDS-PAGE showed new low molecular weight urinary protein bands in APAP administration rats, protective effect of MFTA present no band in this molecular level as normal rats. Conclusion: MFTA is the most potent nephroprotective agent and urinary low molecular proteins are the best diagnostic tools for early detection of kidney disease over common plasma urea and creatinine.

Simple SummaryBladder cancer is a common cancer with a high risk of recurrence, making early detection and long-term monitoring essential. While cystoscopy remains the diagnostic gold standard, its invasiveness and cost highlight the need for noninvasive urinary biomarkers. Early FDA-approved assays, including Nuclear Matrix Protein 22 (NMP22), Bladder Tumor Antigen Stat and TRAK (BTA Stat/TRAK), Immunocytochemical Assay (ImmunoCyt/uCyt+), and the UroVysion fluorescence in situ hybridization (FISH) test, improved sensitivity but lacked sufficient specificity. Subsequent molecular assays in the 2000s–2010s, including genetic, epigenetic, and RNA-based panels, offered greater diagnostic accuracy and risk stratification. Recent innovations in next-generation sequencing (NGS), exosome analysis, and artificial intelligence (AI)-driven platforms have further advanced noninvasive detection. Despite limitations in sensitivity, specificity, cost, and clinical adoption, urinary biomarkers are increasingly valuable adjuncts to cystoscopy. Emerging multi-omics and computational approaches hold promise for more precise, patient-friendly bladder cancer management.Bladder cancer is a prevalent malignancy with high morbidity and mortality, particularly when diagnosed at an advanced stage. Early detection is critical, as it significantly improves prognosis and the patient’s outcomes. Bladder cancer also has a high recurrence rate, necessitating long-term surveillance. While cystoscopy remains the gold standard for diagnosis and monitoring, it is invasive and costly. Urine cytology, though widely used, has high specificity for detecting high-grade urothelial carcinoma but suffers from low sensitivity and limited effectiveness as a stand-alone diagnostic tool. Urinary biomarkers offer a promising, noninvasive alternative for early detection and disease surveillance. This review examines FDA-approved urinary biomarker tests, including NMP 22, UroVysion, and BTA, highlighting their clinical utility and limitations. Additionally, we explore emerging biomarkers such as DNA methylation assays, genomic alterations, and proteomic signatures as well as advanced technologies like next-generation sequencing and machine learning-based platforms. These innovations have the potential to enhance diagnostic accuracy, risk stratification, and recurrent monitoring, ultimately improving early detection and long-term disease management. By evaluating both established and emerging urinary biomarkers, this review aims to provide clinicians and researchers with insights into evolving tools for bladder cancer diagnosis and surveillance.

Impact of a six-week olive oil supplementation in healthy adults on urinary proteomic biomarkers of coronary artery disease, chronic kidney disease and diabetes

BackgroundMachine learning algorithms have been drawing attention at the joining of pathology and radiology in prostate cancer research. However, due to their algorithmic learning complexity and the variability of their architecture, there is an ongoing need to analyze their performance.ObjectiveThis study assesses the source of heterogeneity and the performance of machine learning applied to radiomic, genomic, and clinical biomarkers for the diagnosis of prostate cancer. One research focus of this study was on clearly identifying problems and issues related to the implementation of machine learning in clinical studies.MethodsFollowing the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) protocol, 816 titles were identified from the PubMed, Scopus, and OvidSP databases. Studies that used machine learning to detect prostate cancer and provided performance measures were included in our analysis. The quality of the eligible studies was assessed using the QUADAS-2 (quality assessment of diagnostic accuracy studies–version 2) tool. The hierarchical multivariate model was applied to the pooled data in a meta-analysis. To investigate the heterogeneity among studies, I2 statistics were performed along with visual evaluation of coupled forest plots. Due to the internal heterogeneity among machine learning algorithms, subgroup analysis was carried out to investigate the diagnostic capability of machine learning systems in clinical practice.ResultsIn the final analysis, 37 studies were included, of which 29 entered the meta-analysis pooling. The analysis of machine learning methods to detect prostate cancer reveals the limited usage of the methods and the lack of standards that hinder the implementation of machine learning in clinical applications.ConclusionsThe performance of machine learning for diagnosis of prostate cancer was considered satisfactory for several studies investigating the multiparametric magnetic resonance imaging and urine biomarkers; however, given the limitations indicated in our study, further studies are warranted to extend the potential use of machine learning to clinical settings. Recommendations on the use of machine learning techniques were also provided to help researchers to design robust studies to facilitate evidence generation from the use of radiomic and genomic biomarkers.

Urothelial bladder cancer is the fourth most prevalent male malignancy in the United States and also one of the ten most lethal. Superficial or non-muscle-invasive bladder cancer has a high rate of recurrence and can progress to muscle invasive disease. Conventional surveillance requires regular cystoscopy and is used often with urinary cytology. Unfortunately, the cystoscopy procedure is invasive for patients and costly for health care providers. Urinary biomarkers have the potential to improve bladder cancer diagnosis, the efficiency and also the cost-effectiveness of follow up. It may also be possible for urinary biomarkers to help prognosticate particularly for patients with high-grade bladder cancer who may want enhanced assessment of their risk of disease progression. In this review the important historical urinary biomarkers and the newly emerging biomarkers are discussed. As will be presented, although many of the tests have good performance characteristics, unfortunately no single test can fulfill all the roles currently provided by cystoscopy and cytology. It is likely that in the future, urinary biomarker testing will be used selectively in a personalized manner to try and improve prognostication or reduce the necessity for invasive cystoscopy in patients understanding the limits of the test.

Foot and Ankle Tendoscopy: Evidence-Based Recommendations

Novel biomarkers of acute kidney injury: time for implementation?

Can We Move on From the Discussion of Direct Antiviral Agents and Risk of Hepatocellular Carcinoma Recurrence?

Bladder cancer (BC) represents a major global health issue with high recurrence and significant mortality rates in cases of advanced disease. Currently, the development of molecular profiling, liquid biopsy technologies, and artificial intelligence (AI) software has resulted in unprecedented opportunities to improve diagnosis, prognostic assessment, and treatment selection. Recent multicenter studies have identified emerging metabolomic, proteomic, and genomic biomarkers with high sensitivity and specificity that may help replace or complement invasive approaches. AI-driven models that combine multi-omics datasets with radiomics and clinical parameters have demonstrated improved accuracy for predicting both therapeutic response and long-term outcomes, compared to standard approaches for risk stratification. Additionally, the incremental clinical usefulness of liquid biopsy platforms has been demonstrated for the monitoring of non-muscle-invasive bladder cancer and minimal disease detection. As these innovations converge, they herald the advent of a new era of personalized management of urothelial carcinoma; however, broad-based clinical implementation will require large-scale validation, standardization, regulatory harmonization, and economic analyses. Background: Bladder cancer continues to be a global health problem, particularly in the advanced disease setting where treatment options are limited, and mortality remains high. The exciting advances in precision medicine, including breakthrough molecular profiling techniques, liquid biopsy, and opportunities to apply AI to interpret these molecular data, hold unprecedented promise in improving the accuracy of diagnosis, prognostic stratification, and therapeutic decision-making.

Age-related macular degeneration (AMD) is a progressive disease and major cause of severe visual loss. Toward the discovery of tools for early identification of AMD susceptibility, we evaluated the combined predictive capability of proteomic and genomic AMD biomarkers. We quantified plasma carboxyethylpyrrole (CEP) oxidative protein modifications and CEP autoantibodies by ELISA in 916 AMD and 488 control donors. CEP adducts are uniquely generated from oxidation of docosahexaenoate-containing lipids that are abundant in the retina. Mean CEP adduct and autoantibody levels were found to be elevated in AMD plasma by ∼60 and ∼30%, respectively. The odds ratio for both CEP markers elevated was 3-fold greater or more in AMD than in control patients. Genotyping was performed for AMD risk polymorphisms associated with age-related maculopathy susceptibility 2 (ARMS2), high temperature requirement factor A1 (HTRA1), complement factor H, and complement C3, and the risk of AMD was predicted based on genotype alone or in combination with the CEP markers. The AMD risk predicted for those exhibiting elevated CEP markers and risk genotypes was 2–3-fold greater than the risk based on genotype alone. AMD donors carrying the ARMS2 and HTRA1 risk alleles were the most likely to exhibit elevated CEP markers. The results compellingly demonstrate higher mean CEP marker levels in AMD plasma over a broad age range. Receiver operating characteristic curves suggest that CEP markers alone can discriminate between AMD and control plasma donors with ∼76% accuracy and in combination with genomic markers provide up to ∼80% discrimination accuracy. Plasma CEP marker levels were altered slightly by several demographic and health factors that warrant further study. We conclude that CEP plasma biomarkers, particularly in combination with genomic markers, offer a potential early warning system for assessing susceptibility to this blinding, multifactorial disease.

Combinations of Urinary Biomarkers for Surveillance of Patients with Incident Nonmuscle Invasive Bladder Cancer: The European FP7 UROMOL Project

In vitro approaches for the identification and characterization of urinary metabolite biomarkers of synthetic cannabinoid ADB-BUTINACA

To understand the current state of research and to also reveal the challenges and opportunities for future research in the field of internet of water things for water quality monitoring, in this study, we conduct a bibliometric analysis and a comprehensive review of the published research from 2012 to 2022 on internet of water things for water quality monitoring. The bibliometric analysis method was used to analyze the collected published papers from the Scopus database. This helped to determine the majority of research topics in the internet of water things for water quality monitoring research field. Subsequently, an in depth comprehensive review of the relevant literature was conducted to provide insight into recent advances in internet of water things for water quality monitoring, and to also determine the research gaps in the field. Based on the comprehensive review of literature, we identified that reviews of the research topic of resource management in internet of water things for water quality monitoring is less common. Hence, this study aimed to fill this research gap in the field of internet of water things for water quality monitoring. To address the resource management challenges associated with the internet of water things designed for water quality monitoring applications, this paper is focused on the use of game theory methods. Game theory methods are embedded with powerful mathematical techniques that may be used to model and analyze the behaviors of various individual, or any group, of water quality sensors. Additionally, various open research issues are pointed out as future research directions.

Genomic and proteomic biomarker landscape in clinical trials