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.

Vascular access performance determines the adequacy, safety, and cost of hemodialysis. Stenosis, thrombosis, cannulation difficulty, infection, and access-related cardiac stress remain major sources of morbidity. This narrative review examines artificial intelligence and sensing technologies for vascular access care, based on PubMed and ClinicalTrials.gov searches through 22 August 2026 with citation chaining. Existing reviews have examined artificial intelligence in dialysis broadly or prediction models in isolation. In this review, the vascular access itself is kept as the unit of analysis, and the evidence is followed across four connected stages: systemic risk stratification; prediction of maturation, dysfunction, and complications from clinical, ultrasound, and multimodal data; longitudinal monitoring with optical, acoustic, vibration, thermal, and pressure sensors; and procedural support with robotics and visualization technologies. Reported discrimination is high in retrospective, recording-level analyses, reaching areas under the curve of 0.99 and sample-level accuracy of 100%, but falls to approximately 0.71 in longitudinal designs that predict future events in the same patients; patient-level external validation and calibration are also rarely reported. No approach has yet been demonstrated to reduce thrombosis, prolong patency, avoid interventions, or lower cost relative to standard care in prospective multicenter studies. Deployment is further constrained by power budgets, duty cycling, and transmission bandwidth in wearable devices. Technical feasibility should not be read as clinical utility.