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Abstract Background Minimally invasive left-sided pancreatectomy (MIS-LP) is increasingly adopted for pancreatic neuroendocrine tumours (PNETs), yet its oncological safety compared with open surgery (O-LP) remains debated. Robust population-level evidence is required to guide practice. Methods Patients ≥65 years with grade 1–2 PNETs undergoing left pancreatectomy were identified from the SEER-Medicare database (2010–2019). Overall and cancer-specific survival outcomes were analysed using multilevel Cox regression after propensity-score matching (PSM). Landmark analysis from 6 months excluded early postoperative deaths. Results 772 patients were included: 257 (33%) MIS-LP and 515 (67%) O-LP. Median age was 71 years; 87% had grade 1 and 13% grade 2 tumours. Before matching, MIS-LP patients had lower comorbidity (median Charlson Index 2 versus 3, P = 0.011) and fewer with metastatic disease (2.0% versus 5.7%, P = 0.019). After PSM (MIS-LP n = 248; O-LP n = 441), 5-year overall survival was higher with MIS-LP (88.1% versus 84.5%, P = 0.03), while 5-year cancer-specific survival was similar (95.2% versus 93.5%, P = 0.11). O-LP was associated with increased risk of death (HR 1.75, 95% c.i. 1.05–2.91, P = 0.030), though not cancer-specific death (HR 1.97, 95% c.i. 0.86–4.54, P = 0.110). Notably, all 90-day mortalities (n = 15, 1.9%) occurred in the O-LP group. In landmark analysis beyond 6 months, surgical approach was no longer predictive of OS or CSS. Conclusions MIS-LP confers an early survival advantage, driven by reduced perioperative mortality, without compromising long-term oncological outcomes. As the largest population-based analysis in this setting, these findings support MIS-LP as the standard surgical approach for older patients with low- to intermediate-grade PNETs.

Objective Identifying eligible patients for precision oncology clinical trials is challenging, particularly for rare molecular subpopulations. To address this challenge, A2 Biotherapeutics developed BASECAMP-1 ( NCT04981119 ), a non-interventional master screening study to identify patients eligible for interventional studies of logic-gated Tmod chimeric antigen receptor T-cell therapies. Eligible patients for these interventional trials have an advanced solid malignancy and are germline human leucocyte antigen (HLA)-A*02 heterozygous, with tumour-associated HLA-A loss of heterozygosity (LOH). HLA-A LOH occurs in ~16% of advanced solid malignancies; therefore, an efficient screening strategy is required. This report describes BASECAMP-1; compares the efficiency of two screening methods; and discusses the broader advantages of BASECAMP-1 beyond efficient enrolment. Methods and analysis Patients are identified for BASECAMP-1 using two approaches. In the traditional approach, common for clinical trials, investigators consent and screen all patients who might be good candidates for cell therapy trials, with no prior knowledge of patient HLA-A type or LOH status. To further optimise our approach, we co-developed with Tempus AI (Tempus) the bioinformatic programme Aware, which identifies potentially eligible patients with tumour-associated HLA-A*02 LOH within a clinico-genomic database that includes linked genomic and transcriptomic sequencing and clinical data collected during routine care. Results Over 42 months of using a traditional approach to identify eligible patients, 1918 patients at 13 study sites were consented and screened for BASECAMP-1; of these, 30 patients with tumour-associated HLA-A*02 LOH were enrolled (~0.7 participants per month). Over the last 30 months of that same period, Tempus Aware screening was implemented and 55 patients with tumour-associated HLA-A*02 LOH were enrolled (~1.8 participants per month). The bioinformatic approach identified more patients than the traditional approach and used sequencing results produced as part of the standard clinical tumour sequencing workflow, reducing resource use and study staff burden. Additional advantages of using a screening study, such as BASECAMP-1, include manufacturing efficiencies and collection of a large dataset of molecular and clinical parameters that can be used to supplement trial analyses. Conclusions The BASECAMP-1 study demonstrates a clinico-genomic screening approach can more efficiently identify patients for precision oncology trials. Furthermore, precision oncology can be enhanced through collaborative data-sharing. Trial registration number NCT04981119 .

Importance Adjuvant chemotherapy regimens may be administered after pancreatic resection for adenocarcinoma arising from intraductal papillary mucinous neoplasms (A-IPMNs), although the evidence supporting their use is limited. Objective To evaluate the survival benefit associated with contemporary adjuvant chemotherapy regimens after resection in A-IPMNs between 2017 and 2023. Design, Setting, and Participants This retrospective cohort study was an international, multicenter study with 69 participating centers across Europe, North America, South America, and the Asia-Pacific region. Patients undergoing resection for A-IPMNs were included. Data were analyzed from May to August 2025. Intervention Contemporary adjuvant chemotherapy regimens, such as gemcitabine-capecitabine (GemCap); 5-fluorouracil, leucovorin, oxaliplatin, and irinotecan (FOLFIRINOX), modified FOLFIRINOX (mFOLFIRINOX), and S-1. Main Outcome and Measure Overall survival (months). Results Among 1321 patients (median [IQR] age 70 [63 to 76] years; 713 males [54.0%] and 608 females [46.0%]), 781 patients (59.1%) received adjuvant chemotherapy, while in 181 patients (13.7%) it was omitted due to poor patient fitness. Of patients who received adjuvant chemotherapy, 568 patients (72.6%) received contemporary regimens, including GemCap (232 patients [29.7%]), FOLFIRINOX (176 patients [22.5%]), mFOLFIRINOX (71 patients [9.1%]), and S-1 (70 patients [9.0%]). The median (IQR) follow-up for the cohort was 64.2 (40.4 to 85.0) months, and the median overall survival was 73.8 months (95% CI, 66.4 to 81.9 months). After 90-day landmark analysis and exclusion of patients ineligible for chemotherapy, adjuvant chemotherapy vs no adjuvant chemotherapy (propensity score–matched populations, 243:243 patients) was not associated with improved overall survival (median, 82.3 months; 95% CI, 78.2 months to not applicable [NA] vs not reached; 95% CI, 75.3 months to NA; P = .58). Contemporary regimens vs no adjuvant chemotherapy (propensity score–matched populations, 309:309 patients) was not associated with longer survival, and a mean survival benefit greater than 4.2 months over 5 years was excluded (difference in restricted mean survival, 1.26 months; 95%, −1.72 to 4.24 months). Treatment outcomes did not vary by chemotherapy regimen or disease characteristic (eg, N stage or carbohydrate antigen 19-9 level). Conclusions and Relevance In this study, contemporary adjuvant chemotherapy was not associated with improved overall survival in A-IPMNs, and a randomized clinical trial is indicated.

Background: Artificial intelligence in nephrology has largely focused on predictive models for outcomes such as acute kidney injury (AKI), chronic kidney disease (CKD) progression, and transplant complications. Although these models demonstrate technical performance, their real-world clinical impact has remained limited because prediction alone rarely translates into coordinated clinical action. Clinical artificial intelligence agents represent workflow-native systems that operate in real time, interact bidirectionally with clinical environments, adapt to evolving patient and workflow states, and support coordinated clinical action rather than generating isolated predictions. This review proposes clinical artificial intelligence agents as a new paradigm for integrating artificial intelligence directly into nephrology workflows. Methods: We conducted a narrative synthesis of emerging literature on artificial intelligence systems, agentic artificial intelligence architectures, clinical decision support, and digital health infrastructures relevant to kidney care. Drawing from interdisciplinary sources in medicine, health informatics, and artificial intelligence research, we developed a conceptual framework describing the architecture, governance requirements, and evaluation principles of clinical artificial intelligence agents in nephrology. Results: Clinical artificial intelligence agents represent workflow-integrated systems capable of continuously perceiving patient data, reasoning under clinical constraints, planning tasks, and supporting coordinated clinical actions over time. We describe a layered architecture consisting of perception, cognition, planning and control, action, and learning components. Potential applications span the nephrology care continuum, including CKD management, AKI monitoring, dialysis and continuous renal replacement therapy (CRRT) optimization, kidney transplantation care coordination, glomerulonephritis management, and supervised patient-facing systems. Conclusions: Clinical artificial intelligence agents shift the role of artificial intelligence from isolated prediction toward longitudinal clinical orchestration. Future evaluation should prioritize workflow integration, time-to-action, clinician oversight, safety, and patient-centered outcomes rather than relying solely on traditional model performance metrics. This roadmap provides a conceptual foundation for the responsible development and clinical integration of agentic artificial intelligence systems in nephrology.

Introduction: Ubiquinol–cytochrome c reductase core protein II (UQCRC2) encodes a core subunit of the mitochondrial electron transport chain (ETC) complex III (CIII). Biallelic pathogenic variants in UQCRC2 have been associated with mitochondrial disease characterized by lactic acidosis, developmental delay, hepatopathy, and episodic metabolic decompensation. Methods: We reviewed the biochemical phenotypes of 14 individuals possessing UQCRC2 variants, including two novel cases. We performed biochemical studies of mitochondrial respiration and oxidative phosphorylation (OXPHOS) complex measurements in patient-derived fibroblasts. Results: We report reduced CIII activity in a majority of individuals possessing variants in UQCRC2, as well as biochemical findings consistent with impaired mitochondrial energy metabolism, though impairments in mitochondrial respiration were variable. The two previously unreported, unrelated patients possessing the likely pathogenic missense variant c.361T>C, p.Tyr121His in UQCRC2 in trans with a 16p12.2 microdeletion encompassing UQCRC2 showed milder phenotypes, less severe metabolic decompensations, and no long-term neurological impairments. Both individuals display reduced CIII activity and mitochondrial respiratory dysfunction. Discussion: These data expand the current understanding of genotypes associated with UQCRC2-associated mitochondrial disease to include the novel 16p12.2 microdeletion. These data also highlight the consistent biochemical phenotype associated with UQCRC2-associated mitochondrial disease, and the need for consistent biochemical and respiratory assessment of individuals possessing UQCRC2 variants to further our understanding of this phenotype.

This chapter provides an overview of Mayo Clinic's history, achievements, and supply chain management (SCM) strategies. As a prominent academic medical center in the United States, Mayo Clinic operates major campuses in Minnesota, Florida, and Arizona. The organization manages an annual supply expenditure of $6.8 billion and employs approximately 1,300 SCM staff. Mayo Clinic SCM leading practices encompass a vision, mission, and strategic plan; active engagement of senior leadership; clinical practice–supply chain integration; talent recruitment, development, and retention; partnerships (strategic) and supplier relationship management; commitment to service excellence and continuous improvement; digital information technology (IT) platform and strategy; innovation and automation; benchmarking; and business continuity.

This chapter focuses on key factors that impact effective teamwork in healthcare organizations, including a case study on the applications of a teamwork model at Mayo Clinic in Rochester, MN. Studies have found positive correlations among effective teamwork, patient care, and provider satisfaction. Key factors such as teamwork training, leaderless online group discussion, servant leadership, and top management support lead to effective teamwork, which would positively impact the quadruple aim—patient experience, high-quality and safe delivery of patient care, lower costs, and provider satisfaction.

Several reports show a shortage of primary care physicians in the US, and it seems likely to worsen as the population continues to grow and age. Therefore, it is critical to train the next generation of physicians and healthcare providers to effectively meet the needs of the US population. Alongside this structural challenge, the medical profession is also confronting a cultural one—an erosion of professionalism characterized by declining responsibility, integrity, and communication (Foster, 2019). In teaching and training resident physicians and medical students, some of these skills can be taught, but these skills are usually better “caught” than taught. This chapter presents two physicians' personal experiences in medical education. It discusses the significance of role modeling and best practices for effectively transmitting professionalism, competence, and communication to the next generation of resident physicians, medical students, and healthcare providers.

This chapter provides a high-level overview of clinical informatics (CI) and artificial intelligence (AI) in healthcare. It covers foundational concepts such as electronic health records (EHRs) and data standards, current applications including Clinical Decision Support Systems (CDSS) and ambient scribing/documentation, and near-term directions for both clinicians and patients. The chapter also introduces how foundational CI systems and healthcare data are leveraged to develop AI solutions, ranging from traditional machine learning to large language models, while addressing key issues such as bias, generalizability, and regulatory considerations. Finally, the chapter examines a potential future in which decentralized AI models augment clinicians in their daily work, generate deeper clinical insights, and transform patient care through advanced predictive models and digital twins.

This chapter will explore how telemedicine can increase access to care, address the physician shortage, reduce healthcare costs, support patients in managing chronic diseases, and improve the patient experience. Today, with so much growth and reliance on technology, healthcare reform requires understanding how to incorporate technology to optimize the patient experience and outcomes. This chapter will focus on how best to harness the benefits of technology to provide optimal healthcare in a post-2020 world, from a clinician's perspective.