Search Results (262)

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. Full article

The increasing prevalence of chronic kidney disease (CKD) and end-stage kidney disease (ESKD) has led to a growing demand for kidney transplantation (KTx). Identifying risk factors that enable improved allograft survival through novel therapeutic agents, advanced biomarkers, and artificial intelligence (AI)-driven data integration are critical to addressing this challenge. Drugs, such as SGLT2 inhibitors and finerenone, have demonstrated improved outcomes in patients but lack comprehensive long-term evidence in KTx patients. The use of biomarkers, including circulating cytokines and transcriptomics, coupled with AI, could enhance early detection and personalized treatment strategies. Addressing patient self-management and addressing health access disparities may be more achievable using technologies used at home rather than traditional models of healthcare and thus lead to increased transplant success, both in terms of transplantation rates and allograft longevity. Full article

Urinary tract infections (UTIs) are a common pathology worldwide, frequently associated with kidney stones. We aimed to determine how artificial intelligence (AI) could assist and enhance human medical activities in this field. We performed a search in PubMed using different sets of keywords. When using the keywords “AI, artificial intelligence, urinary tract infections, Escherichia coli (E. coli)”, we identified 16 papers, 12 of which fulfilled our research criteria. When using the keywords “urolithiasis, AI, artificial intelligence”, we identified 72 results, 30 of which were suitable for analysis. We identified that AI/machine learning can be used to detect Gram-negative bacilli involved in UTIs in a fast and accurate way and to detect antibiotic-resistant genes in E. coli. The most frequent AI applications for urolithiasis can be summarized into three categories: The first category relates to patient follow-up, trying to improve physical and medical conditions after specific urologic surgical procedures. The second refers to urinary stone disease (USD), focused on stone evaluation, using different AI and machine learning systems, regarding the stone’s composition in terms of uric acid, its dimensions, its volume, and its speed of detection. The third category comprises the comparison of the ChatGPT-4, Bing AI, Grok, Claude, and Perplexity chatbots in different applications for urolithiasis. ChatGPT-4 has received the most positive evaluations. In conclusion, the impressive number of papers published on different applications of AI in UTIs and urology suggest that machine learning will be exploited effectively in the near future to optimize patient follow-up, diagnosis, and treatment. Full article

Background/Objectives: Multivessel coronary artery disease (CAD) remains a challenging condition requiring multidisciplinary decision-making, particularly when determining between percutaneous coronary intervention (PCI) and coronary artery bypass grafting (CABG). Recent advancements in artificial intelligence (AI), particularly generative language models like ChatGPT, present an opportunity to assist in the decision-making process. However, their ability to replicate human clinical judgment in complex scenarios, such as multivessel CAD, remains untested. Methods: The aim of this study was to evaluate the concordance between recommendations from AI (ChatGPT) and those from heart team (HT) in the management of multivessel CAD, with a focus on comparing treatment strategies such as PCI and CABG. A retrospective observational study was conducted on 137 patients with multivessel CAD, discussed at multidisciplinary HT meetings in 2024. Standardized clinical vignettes, including clinical and anatomical data, were presented to ChatGPT for treatment recommendations. The AI’s responses were compared with the HT’s decisions regarding PCI or CABG. Statistical analysis was performed to assess the level of agreement and predictive value of ChatGPT’s recommendations. Results: ChatGPT achieved an overall accuracy of 65% in its recommendations. The agreement rate was higher for CABG (82.4%) than for PCI (44.4%). Discordance was identified in 48 patients, with a notable bias towards recommending CABG. Factors such as age, diabetes, and chronic kidney disease were predictors of discordance, although no significant factors emerged for the PCI or CABG subgroups. Conclusions: AI, particularly ChatGPT, demonstrated modest concordance with HT decisions in the management of multivessel CAD, especially favoring CABG. While AI offers potential as a decision-support tool, its current limitations highlight the continued need for human clinical judgment in complex cases. Further research is required to optimize AI integration into clinical decision-making frameworks. Full article

Artificial insemination (AI) is essential in intensive pig production, which significantly depends on semen quality from boars selected for health, genetics, and fertility. While AI aims to improve productivity, larger litters often result in smaller and less resistant piglets. Beyond fertility and genetic traits, boars also influence offspring health. This study investigated the relationship between sperm parameters of highly fertile boars and both reproductive outcomes and piglet physiological indicators. Multivariate analysis revealed significant paternal effects on blood markers reflecting organ function, including those of the pancreas, liver, and kidneys, as well as on glucose homeostasis, lipid metabolism, oxidative stress, protein and carbohydrate metabolism, muscle contraction, and neural signaling. Notably, sperm velocity was correlated with mitochondrial function, which is crucial for sperm motility, capacitation, DNA integrity, and embryo development—factors likely linked to healthier, more resilient offspring. Boars transmitting superior sperm velocity, erythropoiesis efficiency, and oxygen transport capacities produced piglets with better glucose regulation, growth, and resistance to neonatal hypoglycemia. These findings underscore the broader impact of sperm quality on offspring vitality and suggest that advanced sperm analysis could improve boar selection and enable more effective, health-oriented breeding strategies. Full article

Chronic kidney disease (CKD) is a global health burden, with uremic toxins (UTs) playing a central role in its pathophysiology. In this review, we systematically examined the evolution of UT classification from the 2003 European Uremic Toxin Work Group (EUTox) system based on molecular weight and protein-binding properties to the 2023 multidimensional framework integrating clinical outcomes, clearance technologies, and artificial intelligence. We highlighted the toxicity mechanisms of UTs across the cardiovascular, immune, and nervous systems and evaluated traditional (e.g., low-/high-flux hemodialysis) and advanced (e.g., high-cutoff dialysis and hemoadsorption) clearance strategies. Despite progress, challenges persist in toxin detection, clearance efficiency, and personalized therapy. Future directions include multi-omics-based biomarker discovery, optimized dialysis membranes, advanced adsorption technology, and AI-driven treatment personalization. This synthesis aims to bridge translational gaps and guide precision medicine in nephrology. Full article

Background/Objectives: Predicting kidney stone recurrence after active stone treatment remains challenging due to its multifactorial nature. Artificial intelligence, particularly machine learning, provides new methods for identifying hidden patterns in high-dimensional clinical data. We conducted a study applying machine learning to identify key predictors of recurrence following endoscopic combined intrarenal surgery (ECIRS) in patients with calcium stones. Methods: This retrospective cohort analysis included 72 patients with calcium stones who underwent ECIRS between June 2019 and May 2022 and achieved a complete stone-free status on postoperative CT. Patients were followed for two years, with recurrence assessed through protocolized imaging. We collected 235 variables, including clinical data, 24 h urine collections, stone composition, imaging features, and perioperative findings. Several machine learning models were developed, and SHapley Additive exPlanations (SHAP) analysis identified features associated with recurrence. Results: Within two years, 29 of 72 patients (40.3%) experienced recurrence. The TabNet model demonstrated the highest predictive accuracy (AUC = 0.89), outperforming traditional machine learning algorithms. SHAP analysis identified urinary oxalate ≥ 25.4 mg/day and hemoglobin (Hb) drop ≥ 0.3 g/dL at 3 months postoperatively as independent predictors, even within normal limits. A simplified TabNet-based model using three key features (oxalate, urine volume, and 3-month ΔHb) maintained a strong performance (AUC = 0.75), supporting its clinical utility. Conclusions: Machine learning enabled the accurate prediction of kidney stone recurrence after ECIRS. The inclusion of 24 h urine data significantly improved the performance. Even patients with “normal” oxalate levels showed increased risk, suggesting current clinical thresholds may require re-evaluation. Full article

Urogenital transplantation has emerged as a ground-breaking field with the potential to revolutionize the treatment of end-stage organ failure and congenital or acquired defects of the kidney and urinary bladder. This review provides a comprehensive analysis of the current state, clinical experiences, and experimental progress in kidney and bladder transplantation, with a particular focus on immunological, surgical, and ethical challenges. While kidney transplantation is now a well-established procedure offering improved survival and quality of life for patients with chronic renal failure, bladder transplantation remains in the experimental phase, facing hurdles in vascularization, tissue integration, and functional restoration. Recent advancements in tissue engineering, regenerative medicine, and immunosuppressive strategies are critically discussed, highlighting their role in shaping the future of urogenital grafts. This review also explores xenotransplantation and bio-artificial organ development as promising frontiers. Continued interdisciplinary research is essential to overcome the current limitations and enable routine clinical application of bladder transplantation while optimizing outcomes in kidney grafts. Full article

Introduction: Among aortic diseases in children, congenital defects such as coarctation of the aorta (CoA), interrupted aortic arch (IAA), hypoplastic aortic arch (HAA), and hypoplastic left heart syndrome (HLHS) predominate. Tissue patches are applied in pediatric cardiovascular surgery for the repair of congenital aortic defects as a filling material to replenish missing tissue or as a substitute material for the complete reconstruction of the vascular wall along the course of the vessel. This retrospective single-center study aimed to present the safety and feasibility of extracellular matrix (ECM) biological scaffolds in pediatric aortic surgery. Patients and methods: There were 26 patients (17 newborns and nine children), who underwent surgical procedures in the Department of Pediatric Cardiac Surgery (Poznań, Poland) between 2023 and 2024. The patients’ population was divided into two subgroups according to the hemodynamic nature of the primary diagnosis of the congenital heart defect and the performed pediatric cardiovascular surgery. The first group included 18 (72%) patients after aortic arch repair for interrupted aortic arch and/or hypoplastic aortic arch, while the second group included seven (28%) patients after aortopulmonary anastomosis. In the first group, patches were used to reconstruct the aortic arch by forming an artificial arch with three separate patches sewn together, primarily addressing the hypoplastic or interrupted segments. In the second group, patches were applied to augment the anastomosis site between the pulmonary trunk and the aortic arch, specifically at the connection points in procedures, such as the Damus–Kaye–Stansel or Norwood procedures. The analysis was based on data acquired from the national cardiac surgery registry. Results: The overall mortality in the presented group was 15%. All procedures were performed using median sternotomy with a cardiopulmonary bypass. The cardiopulmonary bypass (CPB) and aortic cross-clamp (AoX) median times were 144 (107–176) and 53 (33–79) min, respectively. There were two (8%) cases performed in deep hypothermic circulatory arrest (DHCA). The median postoperative stay in the intensive care unit (ICU) was 284 (208–542) h. The median mechanical ventilation time was 226 (103–344) h, including 31% requiring prolonged mechanical ventilation support. Postoperative acute kidney failure requiring hemodiafiltration (HDF) was noticed in 12% of cases. Follow-up data, collected via routine transthoracic echocardiography (TTE) and clinical assessments over a median of 418 (242.3–596.3) days, showed no evidence of patch-related complications such as restenosis, aneurysmal dilation, or calcification in surviving patients. One patient required reintervention on the same day due to a significantly narrow ascending aorta, unrelated to patch failure. No histological data from explanted patches were available, as no patches were removed during the study period. The median (Q1–Q3) hospitalization time was 21 (16–43) days. Conclusions: ProxiCor^® biological patches derived from the extracellular matrix can be safely used in pediatric patients with congenital aortic arch disease. Long-term follow-up is necessary to confirm the durability and growth potential of these patches, particularly regarding their resistance to calcification and dilation. Full article

End-stage renal disease occurs when there is permanent loss of the kidney’s ability to filter toxins from the blood. Due to the limited number of transplants, dialysis is currently the most common treatment, but it significantly limits a patient’s lifestyle and has significant side effects. One solution is an artificial kidney, but significant challenges remain in its development. One challenge is the separation of glucose from urea. Nanofiltration is ideal for this separation; however, there is little understanding of the important parameters for this separation under physiological conditions. In this study, operating parameters (pressure and temperature) as well as feed conditions (increased glucose/salt) were explored for their effects on the separation of glucose from urea in six commercial membranes. The rejection of monovalent and divalent ions was also characterized. While increasing pressure increased flux, it had little effect on metabolite rejection, except for glucose, which increased above 20 psi. Increasing temperature led to a slight increase in flux and a slight decrease in the rejection of divalent ions. Glucose rejection was sensitive to feed conditions, while urea rejection was less affected. Divalent ions were rejected more strongly than monovalent ions and were also more affected by feed conditions. Full article

Generalized secondary peritonitis is a life-threatening intra-abdominal infection requiring urgent surgical intervention. Despite advances in surgical and antimicrobial therapy, morbidity and mortality remain high. Identifying key prognostic factors is crucial for improving patient outcomes. This review examines significant prognostic indicators and explores the potential role of scoring systems and artificial intelligence in risk stratification. A review was conducted using PubMed, Web of Science, Scopus, and Medline databases. Studies published from 2000 to 2024 focusing on prognostic factors in secondary peritonitis were included. A total of 145 studies were identified, with 40 selected based on relevance and methodological quality. Data extraction included patient demographics, comorbidities, severity scores, microbiological profiles, and artificial intelligence applications in peritonitis management. Poor prognosis was associated with advanced age, severe sepsis, organ failure, chronic kidney disease, cardiovascular comorbidities, and diabetes mellitus. The Mannheim Peritonitis Index (MPI) remains a widely validated prognostic tool, while APACHE II and SOFA scores also provide valuable risk estimates. Increasing multidrug-resistant infections further complicate management and impact outcomes. Emerging evidence suggests that machine learning algorithms may improve early risk stratification and individualized outcome prediction when integrated with conventional scoring systems. Identifying prognostic factors remains essential for optimizing outcomes in secondary peritonitis, and future research should prioritize the clinical validation and integration of AI-based models into perioperative management protocols. Full article

Robot-assisted surgery has transformed the landscape of genitourinary cancer treatment, offering enhanced precision, reduced morbidity, and improved recovery compared to open or conventional laparoscopic approaches. As the field matures, a new generation of technological innovations is redefining the boundaries of what robotic systems can achieve. This narrative review explores the integration of artificial intelligence, advanced imaging modalities, augmented reality, and connectivity in robotic urologic oncology. The applications of machine learning in surgical skill evaluation and postoperative outcome predictions are discussed, along with AI-enhanced haptic feedback systems that compensate for the lack of tactile sensation. The role of 3D virtual modeling, intraoperative augmented reality, and fluorescence-guided surgery in improving surgical planning and precision is examined for both kidney and prostate procedures. Emerging tools for real-time tissue recognition, including confocal microscopy and Raman spectroscopy, are evaluated for their potential to optimize margin assessment. This review also addresses the shift toward single-port systems and the rise of telesurgery enabled by 5G connectivity, highlighting global efforts to expand expert surgical care across geographic barriers. Collectively, these innovations represent a paradigm shift in robot-assisted urologic oncology, with the potential to enhance functional outcomes, surgical safety, and access to high-quality care. Full article

As artificial intelligence advances in medical image analysis, its environmental impact remains largely overlooked. This study analyzes the energy demands of AI workflows for medical image segmentation using the popular Kidney Tumor Segmentation-2019 (KiTS-19) dataset. It examines how training and inference differ in energy consumption, focusing on factors that influence resource usage, such as computational complexity, memory access, and I/O operations. To address these aspects, we evaluated three variants of convolution—Standard Convolution, Depthwise Convolution, and Group Convolution—combined with optimization techniques such as Mixed Precision and Gradient Accumulation. While training is energy-intensive, the recurring nature of inference often results in significantly higher cumulative energy consumption over a model’s life cycle. Depthwise Convolution with Mixed Precision achieves the lowest energy consumption during training while maintaining strong performance, making it the most energy-efficient configuration among those tested. In contrast, Group Convolution fails to achieve energy efficiency due to significant input/output overhead. These findings emphasize the need for GPU-centric strategies and energy-conscious AI practices, offering actionable guidance for designing scalable, sustainable innovation in medical image analysis. Full article

Artificial intelligence (AI) has become commonplace in our everyday lives and in healthcare. Peritoneal dialysis (PD) is a cost-effective method of treatment for kidney failure that is preferred by many patients, but its uptake is limited by several barriers. With the rapid advancements in AI, researchers are developing new tools that could mitigate some of these barriers to promote uptake and improve patient outcomes. AI has the capacity to assist with patient selection and management, predict patient technique failure, predict patient outcomes, and improve accessibility of patient education. Patients already have access to some open-source AI tools, and others are being rapidly developed for implementation in the dialysis space. For ethical implementation, it is essential for providers to understand the advantages and limitations of AI-based approaches and be able to interpret the common metrics used to evaluate their performance. In this review, we provide a general overview of AI with information necessary for clinicians to critically evaluate AI models and tools. We then review existing AI models and tools for PD. Full article

Chronic kidney disease (CKD) remains a significant global health burden, often diagnosed at advanced stages due to the limitations of traditional biomarkers such as serum creatinine and estimated glomerular filtration rate (eGFR). This review aims to critically evaluate recent advancements in novel biomarkers, multi-omics technologies, and artificial intelligence (AI)-driven diagnostic strategies, specifically addressing existing gaps in early CKD detection and personalized patient management. We specifically explore key advancements in CKD diagnostics, focusing on emerging biomarkers—including neutrophil gelatinase-associated lipocalin (NGAL), kidney injury molecule-1 (KIM-1), soluble urokinase plasminogen activator receptor (suPAR), and cystatin C—and their clinical applications. Additionally, multi-omics approaches integrating genomics, proteomics, metabolomics, and transcriptomics are reshaping disease classification and prognosis. Artificial intelligence (AI)-driven predictive models further enhance diagnostic accuracy, enabling real-time risk assessment and treatment optimization. Despite these innovations, challenges remain in biomarker standardization, large-scale validation, and integration into clinical practice. Future research should focus on refining multi-biomarker panels, improving assay standardization, and facilitating the clinical adoption of precision-driven diagnostics. By leveraging these advancements, CKD diagnostics can transition toward earlier intervention, individualized therapy, and improved patient outcomes. Full article