Citations

Artificial intelligence (AI) is rapidly transforming the delivery of kidney care through predictive analytics, machine learning, deep learning, and generative AI technologies. To meet this challenge, the American Society of Nephrology convened an AI Workgroup to provide a framework for the responsible use of AI in nephrology. The group outlines foundational principles to guide AI development: prioritizing patient benefit, ensuring clinician oversight, and advancing innovation in high-burden disease areas. Its set of foundational assumptions are grounded in the physician always being in the loop and an overarching goal to benefit patients with kidney diseases. This review provides an overview of the clinical uses of AI in nephrology and offers practical guidance for nephrologists seeking to incorporate AI into CKD and AKI management, dialysis, and transplantation care. It also highlights key challenges—such as data quality, equity, transparency, and clinical integration—that must be addressed to ensure the responsible and effective implementation of AI in kidney care.

Abstract Background: This study investigated the experience of intimate partner violence (IPV) and its association with quality of life (QoL) in cancer patients. Methods: A descriptive quantitative cross-sectional survey design was used. Study participants comprising 144 patients with cancer were recruited from the Radiation Oncology Clinic, University College Hospital (UCH), Ibadan. The Quality of Life in Adult Cancer Survivors (QLACS), Intimate Partner Conflict Experience (IPCE), and a demographic form were used for data collection. Data were analyzed using frequency, percentages, Mann-Whitney U test, Kruskal-Wallis, and Fisher exact tests. Results: The results showed that 25 (17.4%) of the 144 patients with cancer in the study reported experiencing IPV in the past 12 months. Psychological IPV was most commonly reported by 21 (84.0%) of those who experienced IPV. The results showed significant differences in IPV severity by gender (U = 28.50, Z = −2.176, P = 0.030) and marital status (U = 10.50, Z = −2.651, P = 0.008). Female patients with cancer and those who formerly lived with a spouse/partner reported more IPV severity. Fisher exact test ( P = 0.042) and χ 2 [(df = 1, n = 25) = 4.738, P = 0.030] confirm a significant association between IPV and QoL. Patients with cancer experiencing high IPV were more likely to have poorer QoL than those experiencing low IPV. Conclusion: These findings suggest that some patients with cancer experience the double jeopardy of IPV and cancer with significant consequences on their QoL. This underscores the need for targeted psychosocial interventions to identify and mitigate the adverse effects of IPV to improve QoL outcomes for affected patients with cancer.

Abstract Eosinophils play an important role in mediating itch and inflammation in dermatitis. The role of the eosinophil granule protein eosinophil peroxidase (EPX) in mediating inflammation and itch was tested in a dermatitis mouse model. Mice were sensitized to trimellitic anhydride (TMA) and subsequently challenged chronically on the ear to establish dermatitis. Loss of EPX (in EPX−/− mice) or blocking EPX with the drug resorcinol significantly reduced dermatitis in mice exposed to TMA. Resorcinol also reduced levels of thymic stromal lymphopoietin protein (TSLP) in skin. Further studies showed that EPX increased different cytokines in keratinocytes in cell culture via 2 distinct mechanisms. EPX-induced TSLP expression requires lysophosphatidic acid signaling while EPX-induced expression of TNF-α, CSF2, CSF3, and IL1a required IL-1 signaling. We also showed that blocking IL-1 reduced inflammation in skin following TMA exposure in mice. Thus, EPX is an important mediator of inflammation and itch, that are mediated via at least 2 pathways. This suggests that both EPX and its signaling pathways may provide novel therapeutic strategies in dermatitis.

Abstract Background and Objectives To assess the efficacy of a local volunteer program, delirium optimization with volunteer engagement (DOVE), in preventing in-hospital delirium. Research Design and Methods Between February 2023 and June 2024, volunteers provided structured interventions to patients at risk for delirium, as identified by the Delirium Risk Assessment (DRA) score. Interventions are humanities-based and included sensory aide assistance, completion of “get to know me” forms, and conversation. Data were retrospectively acquired from electronic health records and compared to a control cohort from another medical unit. Delirium occurrence was captured when the nursing confusion assessment method (CAM) documentation was positive, or a clinician documented delirium diagnoses. Cohorts were matched 1:2 (DOVE vs Control) by age, gender, DRA score, month/year of admission, and length of hospital stay. Results A total of 168 patients received interventions by DOVE volunteers, of which 139 were matched with 261 controls. In-hospital delirium occurrence was lower in the DOVE cohort compared to the control cohort (46.8% vs 56.7%; p = .015). Conditional logistic regressions demonstrated a lower odds ratio (OR) for delirium occurrence, 0.55 (95% confidence interval [CI]; 0.34–0.89; p = .015) associated with DOVE intervention. Subgroup analyses of delirium occurrence among patients with DRA score of 3–4 demonstrated an OR of 0.56 (95% CI; 0.31–1.01; p = .053) between the DOVE and control cohorts. Discussion and Implications In-hospital delirium occurrence was ∼10% lower among DOVE cohort patients. This humanities-based intervention offers a feasible strategy, particularly during times of staffing shortages, and should be considered for broader implementation across healthcare institutions.

Abstract Senile osteoporosis (SOP) is a systemic bone disease characterized by increased susceptibility to fractures. In this study, it is found that senescent bone marrow mesenchymal stem cells (BMSCs) exhibit increased sensitivity to ferroptosis, a phenomenon associated with Steap4, a nicotinamide adenine dinucleotide phosphate hydrogen (NADPH)‐dependent metalloreductase that reduces Fe 3+ to Fe 2+ . Therefore, it is aimed to innovatively elucidate how Steap4 affects ferroptosis in senescent BMSCs. These findings indicate that Steap4 promotes intracellular Fe 2+ accumulation and elevates reactive oxygen species (ROS) levels, collectively driving the upregulation of CCAAT/enhancer binding protein beta (C/EBPβ) expression. Interestingly, a functional C/EBPβ binding site is identified within the Steap4 promoter region. Mechanistically, knockdown studies demonstrated that C/EBPβ depletion attenuated Steap4 expression, whereas C/EBPβ overexpression conversely upregulated Steap4 levels. These regulatory processes establish a self‐amplifying Steap4/Fe 2+ ‐ROS/C/EBPβ positive feedback loop. Notably, a large number of adipocytes are also observed in the bone marrow of aged mice. Knockdown of Steap4 and C/EBPβ suppressed the differentiation of BMSCs into adipocytes. Knockdown of Steap4 or deferoxamine (DFO) treatment in animal experiments effectively relieves SOP. In conclusion, Steap4 accelerates the onset of ferroptosis in senescent BMSCs and promotes their differentiation to adipocytes through the Steap4/Fe 2+ ‐ROS/C/EBPβ axis, ultimately impairing their osteogenic capacity.

Optineurin (OPTN) is a multifunctional adaptor protein that regulates diverse cellular processes, including inflammatory signaling, autophagy, vesicular trafficking, and immune responses. This multifaceted role of OPTN is made possible by the presence of a complex structure comprising multiple domains that interact with different proteins to exert various functions important for modulating key signaling processes. Mutations in OPTN are linked with several human pathologies including glaucoma, Paget’s disease of bone, Crohn’s disease, and neurodegenerative diseases such as amyotrophic lateral sclerosis, and dementia. Emerging evidence suggests that OPTN has a complex and context-dependent role in cancer biology as well. It is upregulated in pancreatic ductal adenocarcinoma and hepatocellular carcinoma but downregulated in lung and colorectal cancers, indicating its dual role as a potential oncogene or tumor suppressor depending on the cellular environment. Additionally, OPTN plays a critical role in preventing immune evasion in colorectal cancer by maintaining interferon-gamma receptor 1 (IFNGR1) expression and supporting dendritic cell-mediated T-cell priming, thereby enhancing antitumor immune responses. Despite its significance in oncogenic pathways and immune regulation, the therapeutic potential of targeting OPTN in cancer remains largely unexplored. This review aims to provide a comprehensive understanding of OPTN’s pleiotropic functions, highlighting its role in autophagy, inflammation, immune surveillance, and cancer progression. By elucidating its diverse regulatory mechanisms, we seek to encourage further research into the therapeutic implications of OPTN in cancer treatment and immunotherapy.

Objective Lewy body disease (LBD) is a complex neurodegenerative disorder characterized by the accumulation of misfolded α‐synuclein in the brain. Neuroinflammation has long been implicated in LBD pathogenesis, and recent genetic studies in Parkinson's disease (a clinical manifestation of LBD) have shown consistent association with the human leukocyte antigen (HLA) gene complex. Here, we assessed whether variation in HLA alleles influences neuropathological burden in a neuropathologically‐defined series of LBD cases. Methods We conducted a comprehensive analysis of HLA allelic variants in a cohort of 539 LBD cases of European descent from the Mayo Clinic brain bank. High‐resolution whole‐genome sequencing was used, and the HLA alleles of each sample were called using the HLA*LA tool and assessed for association with neuropathological outcomes. Results Our analysis identified 1 significant ( P < 3.43 × 10 −5 ) and 4 suggestive ( P < 0.001) associations between certain HLA alleles and specific neuropathological outcomes in LBD, suggesting a potential role for HLA‐mediated immune mechanisms in disease progression and subtype differentiation. Specifically, HLA‐DPB1*06:01 was significantly associated with lower Lewy body counts in the parahippocampal gyrus ( P = 3.30 × 10 −5 ), with weaker and suggestive associations observed in the middle frontal ( P = 1.80 × 10 −4 ) and inferior parietal gyrus ( P = 6.33 × 10 −4 ). Additionally, although only suggestive, HLA‐DRB1*11:01 correlated with a lower Thal amyloid phase ( P = 1.56 x 10 −4 ), and HLA‐B*15:01 correlated with an increased risk of diffuse LBD ( P = 7.58 x 10 −4 ). Interpretation This study provides a detailed evaluation of the relationship between HLA alleles and LBD pathology, highlighting the importance of immune‐related genetic factors in the etiology of LBD. ANN NEUROL 2026;99:492–501

Abstract Alzheimer’s Disease (AD) 1 is a progressive neurodegenerative disorder with limited therapeutic options, driving interest in drug repurposing to accelerate treatment discovery. Drug repurposing has emerged as a promising strategy to accelerate therapeutic discovery by repositioning existing drugs for new clinical indications. Recent computational repurposing approaches, including knowledge graph reasoning, transcriptomic signature analysis, and integrative literature mining, have demonstrated strong predictive capabilities 2 . However, these methods often yield divergent drug rankings, which makes it difficult to decide which candidates to advance for experimental follow-up and results in substantial gaps between computational predictions and feasible in vivo validation 2 .To bridge this computational-experimental gap, we proposed an advanced prioritization framework leveraging large language models (LLMs). Our method systematically evaluated three state-of-the-art (SOTA) and representative computational methods (TxGNN 3 , Composition-based Graph Convolutional Network (CompGCN) 4 , and a regularized logistic regression (RLR) 5 , to analyze both their predictive performance and pharmaceutical class distributions. By integrating the strengths and divergences of these models, we generated a unified, streamlined list of 90 candidate drugs for further prioritization. We then utilized an LLM-based agent to perform evidence synthesis from biomedical literature abstracts for each candidate. This process mimics expert manual curation but significantly reduces human effort and time by efficiently distilling vast textual data into actionable insights. Applying consistent and transparent selection criteria, we obtained a refined and prioritized list of drug candidates suitable for subsequent in vivo experimental validation. The robustness and clinical relevance of our framework were validated using real-world data from Alzheimer’s patient cohorts, clinical trial registries, and expert pharmacological reviews. This comprehensive validation confirmed that our LLM-driven approach enhances efficiency, consistency, scalability, and generalizability. By integrating computational predictions with scalable evidence synthesis and multifaceted validation, our framework facilitated rapid and informed prioritization of repurposed drugs. Our framework can potentially accelerate the translational pathway toward viable AD therapeutics. Moreover, the versatility of our framework can also be applied to drug repurposing efforts for other diseases beyond AD.