Epitope spreading is a critical mechanism driving the progression of autoimmune glomerulonephritis. This phenomenon, where immune responses broaden from a single epitope to encompass additional targets, contributes to the complexity and severity of diseases such as membranous nephropathy (MN), lupus nephritis (LN), and ANCA-associated vasculitis (AAV). In MN, intramolecular spreading within the phospholipase A2 receptor correlates with a worse prognosis, while LN exemplifies both intra- and intermolecular spreading, exacerbating renal involvement. Similarly, ANCA reactivity in AAV highlights the destructive potential of epitope diversification. Understanding these immunological cascades reveals therapeutic opportunities—targeting early epitope spreading could curb disease progression. Despite promising insights, the clinical utility of epitope spreading as a prognostic tool remains debated. This review provides a complete overview of the current evidence, exploring the dual-edged nature of epitope spreading, the intricate immune mechanisms behind it, and its therapeutic implications. By elucidating these dynamics, we aim to pave the way for more precise, targeted interventions in autoimmune glomerular diseases.

Background: Interdialytic weight gain (IDWG), defined as the accumulation of salt and water intake between dialysis sessions, is a critical parameter of fluid management and a marker of adherence to dietary and fluid restrictions in hemodialysis patients. Excessive IDWG has been strongly associated with increased cardiovascular risk, including left ventricular hypertrophy, cardiac dysfunction, and cerebrovascular complications. Additionally, it necessitates more aggressive ultrafiltration, potentially compromising hemodynamic stability, impairing quality of life, and escalating healthcare costs. Despite international guidelines recommending an IDWG target of <4–4.5% of body weight, many patients struggle to achieve this due to barriers in adhering to dietary and fluid restrictions. This review explores the current state-of-the-art strategies to mitigate IDWG and evaluates emerging diagnostic and therapeutic perspectives to improve fluid management in dialysis patients. Methods: A literature search was conducted in PubMed/MEDLINE, Scopus, and Web of Science to identify studies on IDWG in hemodialysis. Keywords and MeSH terms were used to retrieve peer-reviewed articles, observational studies, RCTs, meta-analyses, and systematic reviews. Non-English articles, case reports, and conference abstracts were excluded. Study selection followed PRISMA guidelines, with independent screening of titles, abstracts, and full texts. Data extraction focused on IDWG definitions, risk factors, clinical outcomes, and management strategies. Due to study heterogeneity, a narrative synthesis was performed. Relevant data were synthesized thematically to evaluate both established strategies and emerging perspectives. Results: The current literature identifies three principal strategies for IDWG control: cognitive–behavioral interventions, dietary sodium restriction, and dialysis prescription adjustments. While educational programs and behavioral counseling improve adherence, their long-term effectiveness remains constrained by patient compliance and logistical challenges. Similarly, low-sodium diets, despite reducing thirst, face barriers to adherence and potential nutritional concerns. Adjustments in dialysate sodium concentration have yielded conflicting results, with concerns regarding hemodynamic instability and intradialytic hypotension. Given these limitations, alternative approaches are emerging. Thirst modulation strategies, including chewing gum to stimulate salivation and acupuncture for autonomic regulation, offer potential benefits in reducing excessive fluid intake. Additionally, technological innovations, such as mobile applications and telemonitoring, enhance self-management by providing real-time feedback on fluid intake. Biofeedback-driven dialysis systems enable dynamic ultrafiltration adjustments, improving fluid removal efficiency while minimizing hemodynamic instability. Artificial intelligence (AI) is advancing predictive analytics by integrating wearable bioimpedance sensors and dialysis data to anticipate fluid overload and refine individualized dialysis prescriptions, driving precision-based volume management. Finally, optimizing dialysis frequency and duration has shown promise in achieving better fluid balance and cardiovascular stability, suggesting that a personalized, multimodal approach is essential for effective IDWG management. Conclusions: Despite decades of research, IDWG remains a persistent challenge in hemodialysis, requiring a multifaceted, patient-centered approach. While traditional interventions provide partial solutions, integrating thirst modulation strategies, real-time monitoring, biofeedback dialysis adjustments, and AI-driven predictive tools represent the next frontier in fluid management. Future research should focus on long-term feasibility, patient adherence, and clinical efficacy, ensuring these innovations translate into tangible improvements in quality of life and cardiovascular health for dialysis patients.

Aldosterone is a key regulator of sodium reabsorption, potassium secretion, and acid–base balance along the aldosterone-sensitive distal nephron (ASDN), where it exerts coordinated, segment-specific control over tubular transport. Although essential for volume conservation in terrestrial environments, aldosterone signaling has become maladaptive in modern sodium-rich contexts, contributing to systemic inflammation, fibrosis, and progression of cardiovascular and kidney disease. This review examines the regulation of aldosterone biosynthesis, the molecular diversity of mineralocorticoid receptor (MR) signaling, and the cellular mechanisms by which aldosterone shapes ion transport in the ASDN. A detailed classification of aldosterone-related disorders is presented, including hyperaldosteronism, pseudo-hyperaldosteronism, aldosterone resistance, and hypoaldosteronism. The therapeutic section focuses on MR overactivation in chronic kidney disease, critically appraising the clinical use of steroidal and non-steroidal MR antagonists. In addition, emerging strategies targeting aldosterone synthesis and downstream inflammatory pathways are discussed as potential approaches to address residual cardiorenal risk and the aldosterone breakthrough phenomenon. Together, these insights support a mechanistic reappraisal of aldosterone as both a physiological modulator and a pathologic driver, with implications for biomarker-guided, targeted therapy.

Clinical trials in nephrology have historically been hindered by significant challenges, including slow disease progression, patient heterogeneity, and recruitment difficulties. While recent therapeutic breakthroughs have transformed care, they have also created a ‘paradox of success’ by lowering baseline event rates, further complicating traditional trial designs. We hypothesize that integrating innovative trial methodologies with advanced computational tools is essential for overcoming these hurdles and accelerating therapeutic development in kidney disease. This narrative review synthesizes the literature on persistent challenges in nephrology trials and explores methodological innovations. It investigates the transformative impact of computational tools, specifically Artificial Intelligence (AI), techniques like Augmented Reality (AR) and Conditional Tabular Generative Adversarial Networks (CTGANs), in silico clinical trials (ISCTs) and Digital Health Technologies across the research lifecycle. Key methodological innovations include adaptive designs, pragmatic trials, real-world evidence, and validated surrogate endpoints. AI offers transformative potential in optimizing trial design, accelerating patient stratification, and enabling complex data analysis, while AR can improve procedural accuracy, and CTGANs can augment scarce datasets. ISCTs provide complementary capabilities for simulating drug effects and optimizing designs using virtual patient cohorts. The future of clinical research in nephrology lies in the synergistic convergence of methodological and computational innovation. This integrated approach offers a pathway for conducting more efficient, precise, and patient-centric trials, provided that critical barriers related to data quality, model validation, regulatory acceptance, and ethical implementation are addressed.

Background: The clinical heterogeneity of systemic lupus erythematosus exceeds the resolution of conventional disease activity instruments. Artificial intelligence offers the analytical infrastructure to engage with this complexity, yet the translation of AI models into clinical practice remains limited. Methods: This review critically appraises the current evidence for AI applications across the SLE clinical workflow, including computational phenotyping, diagnosis, disease activity monitoring, organ-specific predictive modelling, and treatment personalization. Studies were evaluated for external validation, prospective testing, algorithmic fairness, explainability, and regulatory status. Findings: AI applications across the SLE clinical workflow show uneven methodological maturity. Diagnostic and monitoring tools include the SLERPI index, with multinational external validation, and an LSTM flare-prediction model (C-index 0.897). Treatment personalization is anchored by serum IgA2 anti-dsDNA, the only SLE response biomarker validated across independent trials, and by multi-stain deep learning on renal biopsies (AUC 0.84, three external centers). Predictive modelling for organ-specific manifestations has progressed unevenly: cardiovascular risk stratification (SLECRISK) and a multicenter thrombocytopenia model are the most mature, while neuropsychiatric, gastrointestinal, and ocular applications remain single-center proofs of concept. External validation is the exception across SLE prediction models, no AI tool has received regulatory clearance, and populations most affected by SLE remain underrepresented in training data. Conclusions: AI demonstrates genuine analytical capability in SLE but the translation gap is defined by insufficient validation, limited explainability, and absent equity evidence. Closing it will require fewer published models and more validated tools, optimization for clinical impact rather than discrimination alone, and demonstration of performance equity across the demographic spectrum of the disease.