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Key Points Genetic testing of individuals suspected of monogenic urinary stone disease resolved 34% of families with 22 different genes detected. Resolved individuals had a lower baseline and last visit eGFR and earlier age of stone presentation than the unresolved group. Specific phenotypes and younger diagnostic age were associated with a monogenic cause, allowing refinement of future screenings. Background Urinary stone disease with a clear genetic cause, monogenic stone disease (MSD), is increasingly recognized as a significant proportion of the total population. When MSD is suspected, genetic testing provides a firm diagnosis that can alter management and treatment. Here, we present testing results from a large cohort with suspected MSD. Methods Patients with features suggestive of MSD (early onset, family history, frequent stones, nephrocalcinosis [NC], and/or CKD) were recruited by the Rare Kidney Stone Consortium and genotyped for up to 160 known or candidate MSD genes via a targeted massively parallel sequencing panel. We compared clinical and biochemical features between genetically resolved MSD and unresolved individuals. Results Of 426 families (657 patients) enrolled, 145 (34%) were resolved with identified disease associated variants in 22 known MSD genes. Ninety-nine families were biallelic, 37 monoallelic, and two digenic. An additional 21 of the 231 screened family members were resolved. Genes identified in ten or more families include the following: AGXT , HOGA1 , SLC34A3 , CYP24A1 , SLC3A1 , and CLCN5 . Compared with the unresolved group, MSD probands had a lower baseline and last visit eGFR, earlier age of stone presentation, and more stone events and procedures/year of life. The resolve rate was higher in those <16 years, and NC was seen earlier in the MSD group. Overall, NC was a risk factor for lower eGFR. Among the specific disorders, patients with primary hyperoxaluria had the earliest age of stone and NC diagnosis, and as expected, the highest urinary oxalate level. Conclusions Our study emphasizes the value of selecting patients enriched for factors associated with MSD, and comprehensive genetic testing to achieve a high yield of genetic diagnoses. Significant clinical and biochemical characteristics of patients with MSD were defined. A definitive MSD diagnosis facilitates individualized management and strategies to delay disease progression in probands and affected family members.

Background Sex-based differences may influence the clinical management, complication risks, and healthcare resource utilization of hospitalized end-stage kidney disease (ESKD) patients undergoing peritoneal dialysis (PD). Understanding these disparities is essential for optimizing patient care and informing healthcare policy. Methods This study was conducted using the National Inpatient Sample to identify hospitalized adult ESKD patients receiving PD from the year 2003 to 2018. The outcomes included 1) PD-related outcomes, defined as a composite of peritonitis, mechanical complications, catheter removal or revision, and adequacy issues, 2) non-PD-related outcomes, defined as a composite of sepsis, cardiac arrest, and need for mechanical ventilation 3) transfer to hemodialysis, and 4) in-hospital mortality. The associations between sex and in-hospital outcomes were analyzed using multivariable logistic regression and were adjusted for demographic factors, comorbidities, primary diagnoses, admission types, and hospital characteristics. Discharge weights were applied to generate nationally representative estimates. Results Of 97,036 hospitalized ESKD patients receiving PD analyzed, 48,906 (50.4%) were females. In adjusted analyses, there were no overall sex differences in PD-related outcomes, non-PD-related outcomes, or in-hospital mortality. However, age- and comorbidity-based variations were observed in PD-related outcomes. Female sex was associated with lower odds of transfer to hemodialysis, particularly among younger patients and those without heart failure or peripheral vascular disease. Notably, sex differences in in-hospital mortality were observed only among patients with elective admissions. Conclusion There were sex-based disparities in the outcomes and healthcare utilization of hospitalized ESKD patients receiving PD. These findings underscore the need for sex-specific, individualized strategies to improve PD care and inform clinical and policy decisions.

Hepatocellular carcinoma (HCC) remains a leading cause of cancer-related mortality worldwide, with early detection playing a crucial role in improving survival rates. Artificial intelligence (AI), particularly in medical image analysis, has emerged as a potential tool for HCC diagnosis and surveillance. Recent advancements in deep learning-driven medical imaging have demonstrated significant potential in enhancing early HCC detection, particularly in ultrasound (US)-based surveillance. This review provides a comprehensive analysis of the current landscape, challenges, and future directions of AI in HCC surveillance, with a specific focus on the application in US imaging. Additionally, it explores AI’s transformative potential in clinical practice and its implications for improving patient outcomes. We examine various AI models developed for HCC diagnosis, highlighting their strengths and limitations, with a particular emphasis on deep learning approaches. Among these, convolutional neural networks have shown notable success in detecting and characterising different focal liver lesions on B-mode US often outperforming conventional radiological assessments. Despite these advancements, several challenges hinder AI integration into clinical practice, including data heterogeneity, a lack of standardisation, concerns regarding model interpretability, regulatory constraints, and barriers to real-world clinical adoption. Addressing these issues necessitates the development of large, diverse, and high-quality data sets to enhance the robustness and generalisability of AI models. Emerging trends in AI for HCC surveillance, such as multimodal integration, explainable AI, and real-time diagnostics, offer promising advancements. These innovations have the potential to significantly improve the accuracy, efficiency, and clinical applicability of AI-driven HCC surveillance, ultimately contributing to enhanced patient outcomes.

ABSTRACT Background Membranous nephropathy (MN) associated with phospholipase A2 receptor (PLA2R) antibodies is the most common cause of nephrotic syndrome in non-diabetic adult patients. This study investigated the relationship between clinical phenotypes and renal outcomes in this population, emphasizing the potential for phenotype-based treatment stratification. Methods We conducted a retrospective, multi-center cohort study of PLA2R-positive MN. Unsupervised cluster analysis grouped patients based on clinicopathological characteristics. Primary outcomes included complete or partial remission within 2 years of biopsy and end-stage kidney disease (ESKD) or death during follow-up. Results Among 178 patients, three distinct clusters emerged (n = 89, 70, and 19). Within 2 years of biopsy, 102 patients (57%) achieved complete or partial remission. Cluster 1, characterized by the mildest disease markers, including the lowest body mass index, serum anti-PLA2R titer, serum creatinine, proteinuria, and triglycerides, had the highest remission rate of 72%, compared with 50% in cluster 2 and 54% in cluster 3. Cluster 2 had significantly lower remission compared to cluster 1 (HR 0.64, 95% CI 0.42–0.96, P = .03); results were similar in cluster 3, albeit not statistically significant (HR 0.50, 95% CI 0.23–1.09, P = .08). Elevated anti-PLA2R levels (&gt;100 U/ml) and proteinuria (&gt;8 g/24-hour) predicted reduced remission (HR 0.56, 95% CI 0.36–0.88, P = .01; HR 0.43, 95% CI 0.26–0.73, P = .002, respectively), while high high-density lipoprotein levels were protective (HR 1.01, 95% CI 1.00–1.02, P = .048). Overall, 21 patients (12%) developed ESKD or died. ESKD-free survival differed significantly across clusters (log-rank test P = .04). Conclusions Unsupervised clustering identified distinct clinical phenotypes in PLA2R-positive MN, each associated with different renal prognoses. Phenotype-based risk stratification could enhance treatment precision, improve patient outcomes, and potentially reduce treatment-related adverse effects.