Citations

ABSTRACT Artificial intelligence (AI), encompassing machine learning, deep learning and generative AI, is poised to redefine nephrology by enabling earlier detection, more precise risk stratification and workflow-integrated clinical decision support across the spectrum of kidney disease. This state-of-the-art review synthesizes emerging applications of AI in acute kidney injury (AKI), chronic kidney disease (CKD), dialysis and kidney transplantation, with attention to clinical integration, real-world validation, workflow implementation and translational challenges. In AKI, predictive models trained on high-frequency electronic health record data and intensive care unit telemetry have demonstrated strong performance in forecasting critical events, yet translation into routine clinical workflows remains limited. In CKD, machine learning tools support progression risk stratification and phenotype clustering, with the potential to inform individualized surveillance and therapy. AI-enabled dialysis management systems optimize ultrafiltration, anemia control and vascular access surveillance, while generative AI and large language models offer novel capabilities for clinical documentation, triage and patient education. In transplantation, AI applications span organ allocation, dynamic graft monitoring and digital pathology-assisted rejection classification, with validated tools such as the iBox system gaining regulatory recognition. Implementation challenges include data heterogeneity, bias, interpretability, regulatory uncertainty and workflow integration. Looking ahead, multimodal integration of imaging, pathology and multi-omics data may support biologically informed precision nephrology. Reinforcement learning, digital twins and ambient intelligence are emerging as adaptive decision-support paradigms rather than autonomous care systems. Regulatory frameworks are evolving to accommodate adaptive algorithms, underscoring the need for clinician engagement in model development, validation and deployment. As AI matures from pilot to practice, nephrologists who embrace and help shape these tools will lead the transition toward a more personalized, efficient and equitable future for kidney care.

Abstract Aquagenic palmoplantar keratoderma is a rare dermatological condition characterized by mild palmar thickening and burning pain upon exposure to water. A 31-year-old female with aquagenic keratoderma of the palms associated with palmoplantar hyperhidrosis presenting with wrinkling and hyperkeratosis within minutes of hand contact with water, resolving quickly after drying. While cystic fibrosis is a known association, most cases remain idiopathic. Treatment options such as 20% aluminum hydroxide, aluminum chloride (15%–20%), urea, salicylic acid, and botulinum toxin can be challenging, with the patient being evaluated after treatment with 16% aluminum chloride.

Abstract Background The incidence of early onset breast cancers (BCs) has increased, paralleling rising trends in delayed childbearing. We hypothesize that a distinct postpartum BC (PPBC) subtype, identifiable by time since last birth (TSLB) and biomarker expression, contributes to this trend. Methods We applied GeoMx Digital Spatial Profiling (DSP) to measure associations between TSLB and 71 proteins in 640 BCs from women aged ≤40 years included in the Young Women’s Breast Cancer Study. We analyzed data using univariable linear regression and multivariable sliced inverse regression to account for higher order interactions among biomarkers. Results In keratin-rich segments, progesterone receptor (PR) (P = 1.00 × 10−4) and PTEN (P = 1.00 × 10−3) were associated with longer TSLB; multivariable analyses revealed positive associations for GZMB (P = 4.00 × 10−4), SMA (P = 1.00 × 10−4), and NF-1 (P = 1.00 × 10−3). In keratin-poor segments, univariable significant positive associations were found for PR (P = 2.00 × 10−4) and PTEN (P = 1.00 × 10−3), whereas CD20 (P = 3.00 × 10−4) and CTLA4 (P = 4.00 × 10−5) were negatively associated; multivariable significant associations were found for fibronectin (P = 3.00 × 10−5) and pan-Akt (P = 1.00 × 10−3). Associations persisted after adjustment for multiple comparisons and BC molecular subtypes. Associations including for PR, PTEN, and CD20 were strongest among women with the shortest TSLB. OPAL multiplex immunofluorescence assays for PR, PTEN, CD20, SMA, and CTLA4 replicated several DSP findings, particularly when stratified by subtype and with compartment matching. In TCGA, RNA species linked to proteins associated with TSLB correlated strongly with a T-cell exhaustion signature previously linked to poor prognosis among premenopausal women. Conclusion These data support PPBC as a biologically coherent phenotype defined by TSLB and biomarker profile, with potential implications for prevention and therapy.

Abstract Background Critical illness is a risk factor for carnitine deficiency. Carnitine deficiency may result in serious medical complications and poor clinical outcomes. This study aimed to identify the prevalence and potential predictors of carnitine deficiency. Methods This was a descriptive cross‐sectional study conducted in a convenience sample of 144 critically ill adults admitted to a surgical or cardiovascular intensive care unit at the University of Utah Hospital, with serum carnitine levels measured from January 1, 2022, to December 31, 2024. Binary and multivariable logistic regression models were constructed to explore potential predictors of carnitine deficiency, which was defined as a free serum carnitine level <36 µmol/L. Results The mean age of patients was 56.5 years; 41.7% of the sample had carnitine deficiency. For each unit decrease in body mass index, the odds of developing carnitine deficiency increased by 5% compared with maintaining normal free carnitine status (odds ratio [OR], 0.95; 95% confidence interval [CI], 0.9–0.99). Those prescribed continuous renal replacement therapy were 2.43 times more likely to develop carnitine deficiency than those not on continuous renal replacement therapy (OR, 2.43; 95% CI, 1.12–5.25). Conclusion A high proportion of our study population had carnitine deficiency. Continuous renal replacement therapy and a lower body mass index were significant predictors in the multivariable model. Female sex and a history of chronic kidney disease may warrant further exploration based on significance in the univariable analysis.

Background: Mosquito-borne viral diseases are a growing global health threat, and artificial intelligence (AI) and machine learning (ML) are increasingly proposed as forecasting tools to support early-warning and response. However, the available evidence is fragmented across pathogens, settings and modelling approaches. This review provides, to the best of our knowledge, the first comprehensive comparative assessment of AI/ML models forecasting mosquito-borne viral diseases in human populations, jointly synthesising predictive performance across model families and appraising both methodological quality and operational readiness. Methods: Following PRISMA 2020, we searched PubMed, Embase and Scopus up to August 2025. We included studies applying AI/ML or statistical models to predict arboviral incidence, outbreaks or temporal trends and reporting at least one quantitative performance metric. Given the substantial heterogeneity in outcomes, predictors and time–space scales, we conducted a descriptive synthesis. Risk of bias and applicability were evaluated using PROBAST. Results: Ninety-eight studies met the inclusion criteria, of which 91 focused on dengue. The forecasts spanned national to city-level settings and annual-to-weekly resolutions. Across classification tasks, tree-ensemble models showed the most consistent performance, with accuracies typically above 0.85, while classical ML and deep-learning models showed wider variability. For regression tasks, errors increased with temporal horizon and spatial aggregation: short-term, fine-scale forecasts (e.g., weekly city level) often achieved low absolute errors, whereas long-horizon national models frequently exhibited very large errors and unstable performance. PROBAST assessment indicated that most studies (63/98) were at high risk of bias, with only 24 judged at low risk and limited external validation. Conclusions: AI/ML models, especially tree-ensemble approaches, show strong potential for short-term, fine-scale forecasting, but their reliability drops substantially at broader spatial and temporal scales. Most remain research-stage, with limited external validation and minimal operational deployment. This review clarifies current capabilities and highlights three priorities for real-world use: standardised reporting, rigorous external validation, and context-specific calibration.

Purpose of review TP53- mutated ( TP53 -MT) myeloid neoplasms (MN) represent one of the most challenging disease subsets due to their distinct pathobiology, frequent association with therapy-related disease, chemo-resistant phenotype, and dismal outcomes. The prognostic impact of TP53 -MT is not uniform and is influenced by disease state, allelic burden, and co-occurring cytogenetic and molecular aberrations that shape disease trajectory. While current prognostic models incorporate TP53 status, they incompletely capture the heterogeneity within TP53 -MT MN. Therapeutic options remain limited, with allogeneic stem cell transplantation being the only intervention offering long-term survival in selected patients. Recent findings Novel approaches, including p53 reactivators and anti-CD47 antibodies in combination with standard-of-care therapies, have been explored; however, none has yet transformed the natural history of TP53 -MT MN. Summary This review highlights the current understanding of TP53 -MT MN, evolving strategies of prognostic stratification and propose a practical framework for clinical management. We further discuss the critical unmet need for collaborative, biologically informed clinical trials to improve outcomes of this challenging disease entity.

Abstract Background Despite advancements in care, pregnant individuals with congenital heart disease (CHD) face disproportionately high risks. Objectives To address these challenges, a national consortium was established to improve maternal outcomes in CHD through multidisciplinary collaboration and patient engagement. Study design As part of a 2.5‐year multi‐phase consortium initiative, an in‐person research summit followed by three virtual focus groups (Project PHASE 3) was conducted with patients, clinicians, researchers, advocates, and policy representatives. Qualitative data were collected, and reflexive thematic analysis was performed to identify shared priorities. Results A total of 48 individuals participated in the in‐person meeting, including 18 patients (83% female; 11% Black/African American, 17% Asian, 11% Hispanic, 61% White) and 30 clinicians/researchers. An additional 19 patients (100% female; 32% rural/non‐urban; 16% Black/African American, 10% Hispanic, 74% White) participated in virtual focus groups. Key priorities identified included improved patient–healthcare provider communication, expanded access to multidisciplinary and adult CHD‐specialized care, and greater inclusion of patient voices in reproductive planning and research. Focus groups underscored racial disparities in care, limited CHD education for patients of color, and the need for culturally tailored resources. Participants emphasized the importance of representative research cohorts, integration of genetic counseling, and dissemination of research in accessible, plain‐language formats. Conclusions Findings demonstrate the value of integrating patient expertise and partnership in shaping cardio‐obstetric research, care models, and education. Addressing maternal health concerns in CHD research requires both structural changes and community‐driven, inclusive research strategies.

Erdheim-Chester disease (ECD) is a rare disease characterized by the accumulation of neoplastic histiocytes in various extra-nodal tissues. Tissue biopsies involved by ECD are difficult to distinguish from reactive inflammatory infiltrates given the bland appearance of the neoplastic histiocytes. Confirmation of the ECD diagnosis often relies on molecular studies to confirm BRAF V600E mutation or other activating mutations involving MAPK pathway genes. In this study, we examined the diagnostic utility of cyclin D1 and pERK as immunohistochemical markers of MAPK pathway activation in ECD compared with its histopathologic mimics. The cohort included 41 clinically confirmed ECD patients, most with known genetic alterations in MAPK pathway genes (n=38). In 3 cases no mutation was identified. 37 of 41 (90%) of ECD cases showed cyclin D1 overexpression, with frequent staining in the cytoplasm as well as the nucleus. pERK expression was observed in 32 of 39 (82%) cases. Cyclin D1 staining was negative in histopathologic mimics of ECD, apart from weak patchy staining in fat necrosis and uniform staining in a subset of cases of juvenile/adult xanthogranuloma. While not entirely sensitive or specific, in the proper clinical and radiologic context strong nuclear and cytoplasmic cyclin D1 expression within histiocytic infiltrates helps to support a diagnosis of ECD.

Abstract Cutaneous squamous cell carcinoma (cSCC) poses significant clinical challenges due to its rising incidence and potential for metastasis. Histopathologic risk stratification is further limited by substantial inter-observer variability. Unsupervised AI approaches based on content-based image retrieval offer scalable and interpretable decision support for diagnostic pathology. The objective of this study was to evaluate the use of image retrieval within histopathology atlases to stratify cSCC tumor differentiation from whole-slide images (WSIs), while comparing different patch selection and feature extraction strategies. This retrospective study included 552 archived WSIs comprising 385 well-differentiated, 102 moderately differentiated, and 66 poorly differentiated cases collected across Mayo Clinic sites in Arizona, Florida, and Minnesota. Image atlases were constructed using multiple patch aggregation strategies (Mosaic, Collage, and Montage) and deep learning encoders (KimiaNet, PathDino, and H-Optimus-0). A leave-one-WSI-out evaluation framework was used to assess differentiation classification performance using accuracy, specificity, sensitivity, and F1 score. Mosaic combined with KimiaNet achieved the highest Top-1 accuracy (74.9%) and specificity (92.6%), while Mosaic with H-Optimus-0 yielded the best Top-5 accuracy (79.0%) and macro-F1 score (62.6%). Collage combined with KimiaNet produced the highest Top-5 specificity (99.5%). The generalizability of the evaluated AI models varied across hospitals, reflecting differences in imaging protocols, staining practices, and patient populations. Overall, unsupervised image search and retrieval provides effective, annotation-free support for cSCC differentiation and has the potential to enhance dermatopathology workflows when appropriate combinations of patch selection and feature ex-traction methods are employed.