Citations

ABSTRACT Background Uremic toxins are a growing area of research in nephrology, with significant implications in the progression and treatment of chronic kidney disease (CKD) and the management of end‐stage kidney disease (ESKD). This bibliometric analysis aims to evaluate the global research trends, key contributors, and the impact of publications in the field of uremic toxins from 1991 to 2024. Methods The data were retrieved from the Science Citation Index Expanded (SCI‐EXPANDED) database. The study analyzed 2210 articles using various bibliometric indicators, including total publications ( TP ), average number of citations per publication ( CPP 2024 ), and the Y ‐index for author contributions. Publication trends were examined across countries, institutions, and authors. The analysis included document types, languages, Web of Science categories, and journals. Results The study revealed a steady increase in uremic toxin research publications. Japan, the USA, and China remained the most productive countries, with Japan maintaining the highest output, while Belgium achieved the highest citation impact. Citations of uremic toxin‐related research peaked 2 years following publication and plateaued after approximately 6 years. The research foci evolved, with CKD, ESKD, indoxyl sulfate, protein‐bound solutes, and gut microbiota gaining prominence. Studies on oxidative stress, inflammation, metabolomics, and advanced adsorption‐based toxin removal strategies increased markedly. International collaboration and interdisciplinary integration across nephrology, microbiology, and biochemistry correlated with higher citation performance and research visibility. Conclusion This bibliometric study provides an in‐depth analysis of uremic toxin research trends, identifies major contributors, and suggests future research priorities. The findings can inform future research directions, guide funding allocation, and strengthen global collaborative efforts towards improved management of toxin‐related kidney disease.

ABSTRACT Bayesian statistics is gaining momentum in biomedical research, offering a flexible and intuitive framework for integrating prior knowledge, managing uncertainty, and informing decision-making. In nephrology, a field marked by complex pathophysiology, small patient populations, and evolving therapeutic landscapes, Bayesian approaches may provide critical advantages over traditional statistical methods. Bayesian methodologies may help reshape nephrology by improving trial efficiency, refining diagnostic and prognostic precision, and strengthening causal inference. We conducted a comprehensive narrative review of peer-reviewed literature on Bayesian applications in nephrology, highlighting studies in adaptive clinical trial designs, dynamic risk prediction models, network meta-analyses, pharmacokinetic modeling, and Bayesian Mendelian randomization approaches, including Bayesian model averaging frameworks. Bayesian adaptive designs enabled more efficient and ethically sound trials, such as the WIndow-of-opportunity clinical trial platform for evaluation of novel treatment strategies in REnal cell cancer (WIRE) platform study in renal cell carcinoma. In diagnostics and prognostics, Bayesian models allowed individualized inference from dynamic data, as demonstrated in estimated glomerular filtration rate trajectory analyses and mortality prediction. Bayesian network meta-analyses improved comparative effectiveness research by incorporating probabilistic treatment rankings, while Bayesian frameworks strengthened causal inference through methods like Mendelian randomization (MR) -Bayesian Model Averaging and Bayesian Kernel Machine Regression. Bayesian approaches have emerging applications in nephrology, promoting a probabilistic framework that enhances the interpretation of complex clinical and research data and supports more individualized patient care. Their integration into both research and clinical practice requires investment in education and training, interdisciplinary collaboration, and the development of accessible analytical tools. Embracing Bayesian methods is likely to play an important role in advancing precision medicine and improving patient outcomes in nephrology.

Background: Artificial intelligence in nephrology has largely focused on predictive models for outcomes such as acute kidney injury (AKI), chronic kidney disease (CKD) progression, and transplant complications. Although these models demonstrate technical performance, their real-world clinical impact has remained limited because prediction alone rarely translates into coordinated clinical action. Clinical artificial intelligence agents represent workflow-native systems that operate in real time, interact bidirectionally with clinical environments, adapt to evolving patient and workflow states, and support coordinated clinical action rather than generating isolated predictions. This review proposes clinical artificial intelligence agents as a new paradigm for integrating artificial intelligence directly into nephrology workflows. Methods: We conducted a narrative synthesis of emerging literature on artificial intelligence systems, agentic artificial intelligence architectures, clinical decision support, and digital health infrastructures relevant to kidney care. Drawing from interdisciplinary sources in medicine, health informatics, and artificial intelligence research, we developed a conceptual framework describing the architecture, governance requirements, and evaluation principles of clinical artificial intelligence agents in nephrology. Results: Clinical artificial intelligence agents represent workflow-integrated systems capable of continuously perceiving patient data, reasoning under clinical constraints, planning tasks, and supporting coordinated clinical actions over time. We describe a layered architecture consisting of perception, cognition, planning and control, action, and learning components. Potential applications span the nephrology care continuum, including CKD management, AKI monitoring, dialysis and continuous renal replacement therapy (CRRT) optimization, kidney transplantation care coordination, glomerulonephritis management, and supervised patient-facing systems. Conclusions: Clinical artificial intelligence agents shift the role of artificial intelligence from isolated prediction toward longitudinal clinical orchestration. Future evaluation should prioritize workflow integration, time-to-action, clinician oversight, safety, and patient-centered outcomes rather than relying solely on traditional model performance metrics. This roadmap provides a conceptual foundation for the responsible development and clinical integration of agentic artificial intelligence systems in nephrology.

Background Kidney transplantation offers substantial clinical and economic benefits for patients with end-stage kidney disease (ESKD), yet organ shortages persist. Enhancing public awareness and health literacy regarding kidney donation is essential for effective donor recruitment. While online patient education materials are primary drivers of public perception, their readability, digital engagement, and accessibility remain underexplored barriers. Methods We analyzed the most prominent kidney donation websites identified through U.S.-based Google searches, including 11 primary organizations and 16 affiliated subdomains, for a total of 27 websites. Readability was benchmarked using Flesch-Kincaid, SMOG, and Gunning Fog indices. To assess qualitative metrics, we deployed a generative AI framework utilizing a Large Language Model (Claude) to conduct automated sentiment analysis and tone evaluation, validated by human review. We systematically mapped digital engagement features, including multimedia, interactive tools, and multilingual support, to determine content comprehensiveness. Results Websites consistently provided accurate information with a generally positive or neutral tone. Average readability exceeded recommended levels, with a combined mean grade of 12.3; 34% of websites were written at a college-level reading standard. Consensus across content was high. Multimedia elements were widely used, but engagement features were limited; only 30% of sites included extensive testimonials, and interactive tools were absent. AI-based analysis enabled standardized and reproducible evaluation, highlighting opportunities to improve accessibility, tone, and inclusivity. Conclusion Current U.S. kidney donation digital resources present a barrier to health equity due to excessive reading complexity and static engagement models. AI provides a scalable, reproducible framework to audit and optimize patient education materials. Future initiatives must leverage AI-guided content optimization to bridge the literacy gap, potentially increasing donor registration and access to transplantation.

Key Points Both sodium zirconium cyclosilicate and patiromer effectively lower serum potassium and provide clinical benefits. Sodium zirconium cyclosilicate is associated with higher risk of edema. Patiromer is associated with the increased risk of constipation and hypokalemia. Background Sodium zirconium cyclosilicate (SZC) and patiromer have recently been approved for the treatment of hyperkalemia. However, their efficacy in lowering serum potassium according to the phase of intervention, safety profile, and impact clinical outcomes remains to be fully defined. This study aims to assess their efficacy and tolerability in the management of hyperkalemia. Methods A comprehensive search of PubMed, Scopus, and Cochrane Central Register of Controlled Trials was conducted up to June 6, 2025. Eligible studies included randomized controlled trials reporting the effects of SZC and patiromer on laboratory parameters, clinical outcomes, and safety profiles. Meta-analyses were synthesized using a random-effects model, with results presented as weighted mean differences (WMD) or relative risks (RR), along with 95% confidence intervals (CI). Results Nineteen randomized controlled trials involving 3627 patients (1911 on SZC; 1716 on patiromer) were analyzed. During the initial 48–72 hours, only SZC significantly reduced serum potassium levels (WMD, −0.45 mEq/L; 95% CI, –0.61 to –0.29). At the end of treatment, both agents effectively lowered serum potassium (SZC: WMD, –0.50 mEq/L; 95% CI, –0.63 to –0.38; patiromer: WMD, –0.36 mEq/L; 95% CI, –0.51 to –0.21). SZC significantly normalized serum potassium and reduced hyperkalemia incidence, while patiromer enabled renin-angiotensin-aldosterone system inhibitor optimization by supporting target dosing and reducing discontinuation due to hyperkalemia. However, SZC was associated with a higher incidence of edema (RR, 2.92; 95% CI, 1.08 to 7.90) and patiromer with increased rates of constipation (RR, 2.96; 95% CI, 1.21 to 7.22) and hypokalemia (RR, 1.46; 95% CI, 1.05 to 2.03). Conclusions Both SZC and patiromer effectively lower serum potassium and provide clinical benefits, with distinct safety considerations. Therapy selection should be guided by treatment goals and the patient's individual risk profile.

Background Effective patient education is critical for informed decision-making and adherence in kidney transplantation. Generative artificial intelligence (AI), particularly large language models (LLMs), has the potential to enhance patient education in kidney transplantation; however, its factual accuracy and readability remain incompletely characterized. Methods We evaluated the performance of the GPT-5.1 (2025) model in generating plain-language explanations for 100 clinically relevant kidney transplantation terms. Explanations were generated using a standardized prompt (first round) and a revised prompt explicitly requesting an eighth-grade reading level or lower (second round). Accuracy was assessed by expert reviewers using a 5-point Likert scale, while readability was evaluated using the Flesch Reading Ease (higher score indicated easier readability of the text) and Flesch–Kincaid Grade Level (higher score indicated higher education level required to understand the text) score. The study was conducted in November 2025. Results All AI-generated explanations demonstrated high accuracy, with no clinically significant errors. In the first round, the mean Flesch Reading Ease score was 23.6 ± 23.4, indicating very difficult readability, and 46% of explanations required a college-level reading ability (mean Flesch–Kincaid Grade Level 13.4 ± 4.8). Following prompt revision, readability improved substantially. The mean Flesch Reading Ease score increased to 62.4 ± 7.5, corresponding to standard readability, and all explanations were written at a middle school level or below (mean Flesch–Kincaid Grade Level 6.3 ± 1.1). Conclusion GPT-5.1 generated highly accurate explanations of kidney transplantation terms across prompting strategies. Explicit readability-focused prompting substantially improved readability without compromising accuracy, underscoring the importance of prompt design when deploying LLMs for patient-centered education in transplantation.