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Background: Metacarpophalangeal (MCP) arthroplasty is an established surgical option for end-stage MCP arthritis. We analyzed trends in case volumes and 30-day perioperative outcomes after MCP arthroplasty performed for rheumatoid arthritis (RA) and osteoarthritis (OA). Methods: We used the National Surgical Quality Improvement Program database to identify all MCP arthroplasties performed for RA (n = 216) or OA (n = 452) between 2010 and 2023. Bivariate analysis was used to compare patient demographics, operative characteristics, and 30-day outcomes. We analyzed temporal trends in case volumes using Poisson regression models and trends in patient characteristics using linear regression. Results: Patients with RA were more likely to be female (85% vs 54%) and have a history of smoking (22% vs 10%) and chronic steroid use (58% vs 4%). Patients with OA had more prevalent comorbidities including obesity, diabetes, and hypertension. Patients with RA more frequently required multi-joint replacement, concomitant soft tissue balancing procedures, plastic surgery involvement, and inpatient admission. The annual case volume for OA increased at +5.9% per year ( P < .001), whereas the volume for RA remained steady. The outpatient case volume grew at a rate of +4.6% per year ( P < .001). Both groups had similarly low rates of 30-day complications (0.9% RA, 1.8% OA), readmission (0.5% RA, 1.5% OA), and reoperation (0% RA, 0.9% OA). Conclusions: Despite distinct patient profiles and operative complexity, both OA and RA patients experience low rates of perioperative adverse events after MCP arthroplasty. Osteoarthritis is a growing indication for this procedure, which is increasingly being performed in the outpatient setting.

BACKGROUND: Pulsed field ablation is now widely used for atrial fibrillation (AF) ablation, but atrial tachyarrhythmia recurrence remains common and postablation risk tools are limited. We assessed whether a validated sinus‑rhythm artificial intelligence— enabled ECG AF probability score, and its early postablation change, predicts recurrence after pulsed field ablation. METHODS: We studied consecutive patients undergoing index pulsed field ablation across 3 Mayo Clinic sites (February 2024–March 2025) with ≥1 sinus‑rhythm ECG within 180 days preprocedure. Baseline artificial intelligence—enabled ECG AF score (range 0–1) was the mean across baseline ECGs. Recovery indices (RI30/60/90), defined as mean postprocedure artificial intelligence—enabled ECG score within days 1–30/60/90 minus baseline, predicted post‑blanking recurrence using corresponding 30‑, 60‑, and 90‑day blanking windows. The primary outcome was time to first documented AF/atrial flutter/atrial tachycardia episode >30 seconds, ascertained via clinic ECGs, ambulatory monitoring, and device interrogations. Cox regression adjusted for prespecified clinical and echocardiographic covariates assessed associations; Harrell C‑statistic quantified discrimination. RESULTS: Among 1052 patients (67±10 years; 33% women), median baseline score was 0.35; 320 (30%) recurred over 188 days. Higher baseline score independently predicted recurrence (hazard ratio per 0.1 increase, 1.08 [95% CI, 1.03–1.14]). Scores rose on day 0 after ablation, then declined over subsequent weeks, with persistently higher, less‑improving trajectories among patients who recurred (time×recurrence interaction P <0.001). Day 0 score was not independently associated with recurrence after adjustment. Attenuated early recovery (higher recovery index) was independently associated with recurrence, most robustly for RI-90 (hazard ratio per 0.1 increase, 1.15 [95% CI, 1.07–1.24]), and improved discrimination beyond baseline and covariates (C‑statistic, 0.63–0.67; ΔC=0.04). Findings were consistent in a historical thermal ablation cohort. CONCLUSIONS: Baseline and early postablation dynamics of a sinus‑rhythm artificial intelligence—enabled ECG AF probability score are associated with pulsed field ablation recurrence and may aid multivariable risk stratification; prospective validation is warranted.

ABSTRACT Persistent gaps in anatomical knowledge among incoming residents have raised concerns about clinical preparedness, especially in surgical and interventional disciplines. Conventional anatomy instruction and limited cadaveric exposure and learning opportunities in medical curricula have prompted calls for innovative, clinically focused anatomical curricular integration in clinical training. This article describes the development, implementation, and impact of a tailored Year 4 Clinical Anatomy Elective in the Department of Clinical Anatomy at Mayo Clinic, designed to reinforce competency in clinically relevant anatomical understanding and support learners' perceived preparedness during transitions to residency. A 4‐week elective was designed for fourth‐year medical students interested in pursuing a specific residency program. The course was personalized for students' clinical interests and incorporated fresh tissue dissection, simulation, procedural skills training, and imaging using an immersive learning platform. The elective was structured within the experiential learning model using a guided, self‐directed format within a multicapability laboratory and instructional environment, grounded in constructivist theory. A 5‐year review of elective records and informal thematic analysis of student feedback were conducted. Most participants pursued surgically related specialties, with orthopedic surgery being the most common. Exploratory thematic analysis identified six core themes: Professional Identity Formation, Educational Benefits, Learning Challenges, Connection to Clinical Practice, Patient Care Implications, and Future Preparation and Transferability. These students perceived gains in areas aligned with ACGME milestones: increased confidence, greater preparedness for residency, and improvement in their ability to apply anatomical knowledge in clinical settings. This perception may be indicative of lifelong learning and early educator identity formation. A specialty‐specific, clinically integrated anatomy elective may support learners' perceived preparedness for residency, reinforce clinically relevant anatomical understanding, and support the development of educational and professional competencies. Such targeted interventions may help address perceived gaps in anatomical preparedness during the transition to residency and contribute to postgraduate training within a competency‐based medical education framework.

Abstract Historically, the study of cellular metabolism and the cellular proteome have largely focused on specific proteins and metabolites in certain cellular pathways. With the advent of new “omics” technologies, researchers have been able to probe disruptions and alterations in cellular homeostasis on a near proteome-wide and metabolome-wide basis. Yet the truly interconnected and interdependent nature of the cellular metabolome and proteome has only become more evident with the discovery of proteome-wide protein post-translational modifications (PTMs) such as lysine acylation by acetyl-CoA or succinyl-CoA. Such PTMs link proteome function to fluctuating concentrations of tricarboxylic acid (TCA) cycle metabolites. Tumorigenesis in TCA cycle-defective tumors that upregulate acylating agents such as succinyl-CoA and acetyl-CoA provides a relevant context for understanding the breadth of protein acylation in disease. In particular, succinate dehydrogenase (SDH)-loss chromaffin cells can be transformed to the neuroendocrine tumors pheochromocytoma and paraganglioma (PPGL), but sensitive diagnostic and effective therapeutic options for metastatic disease remain limited. Interrogating cell models of SDH-loss PPGL provides a unique opportunity to capture the effects of SDH loss on cellular metabolism and protein acylation while exploring new avenues for improved diagnostics and novel therapeutic targets in these rare diseases.

Abstract Context Current guidelines for primary aldosteronism (PA) recommend screening all patients with hypertension. Given low screening rates, an automated artificial intelligence (AI)-based risk-stratified approach may facilitate guideline implementation. Objective To develop an AI-based prescreening tool using electronic health record (EHR) data to identify at-risk patients. Design Model development retrospective cohort study. Ensemble architecture using eXtreme Gradient Boosting, random forest, and extremely randomized trees was trained on EHR data (1986-2025) and evaluated on a test set of patients with hypertension. Setting Single institution system spanning community clinics and tertiary/quaternary centers reported via Mayo Clinic Platform. Patients Adults with hypertension, International Classification of Disease (ICD) diagnosis of PA, or had plasma renin/aldosterone testing. Main Outcome Measures Predicting PA diagnosis (disease) or negative workup (control) using age, sex, ICD diagnoses, vitals, labs, and medications by area under receiver operating characteristic (AUROC), estimated calibration error (ECE), and specificity. Results Of 22,264 patients (1,833 disease and 20,431 controls), median time from initial encounter to screening was 5.0 years (IQR 0.5-12.6). The model demonstrated AUROC 0.709 (95% CI 0.688-0.730) with ECE 0.037. At a 0.5 cut-off threshold, the model had high specificity (71.6%) and low false positivity (28.4%). In a test cohort of 1,279,455 adults with hypertension, the model identified 5,295 (0.4%) for first-pass screening. Conclusions A high-specificity model can provide a pre-screening PA pathway using routine EHR data to identify and prioritize patients for PA screening. This AI-based approach can risk-stratify patients, guide sequencing of testing/referrals, and pragmatically facilitate adoption of current guidelines into practice.

Background Microsurgery is a critical component of plastic and reconstructive surgery. However, the high cost of training and equipment pose significant barriers, particularly in resource-limited settings. To address this challenge, the Microsurgery and Regenerative Medicine Lab within the Division of Plastic Surgery at the University of Wisconsin developed a low-cost, 3D-printed surgical microscope. This study qualitatively appraised the use of this novel microscope for microsurgery training by surveying surgeons about their experiences using this tool relative to traditional magnification methods. Methods In this prospective, multi-institutional appraisal study, surgical residents, fellows, and attending surgeons with varying microsurgical experience performed a single end-to-end microsurgical anastomosis on chicken thigh femoral arteries using the 3D-printed microscope. Participants then completed a survey assessing ease of use, adequacy for microsurgical training, educational value, and perceived impact on teaching capacity in local and global settings. Results Over 80% of participants reported that performing an end-to-end anastomosis was easier or equivalent using the 3D-printed microscope compared to surgical loupes. All respondents expressed willingness to use the device for microsurgery teaching and practice. Additionally, 91.7% (33/36) believed access to the microscope would enhance microsurgical teaching at their home institutions, and 97.1% (34/35) felt it would improve teaching capacity in globally. Qualitative feedback was overwhelmingly positive, with suggestions primarily related to ergonomics and lighting. Conclusion This study supports that this low-cost, 3D-printed microscope is an effective microsurgical training tool. Ongoing design refinements informed by user feedback may further optimize its role in expanding access to microsurgery education worldwide.

Abstract Backgrounds Isolated REM sleep behaviour disorder (iRBD) is a prodromal marker of α-synucleinopathies. Although cognitive impairment is consistently reported in iRBD, its assessment varies considerably across studies. This study characterized mild cognitive impairment (MCI) patterns in iRBD using machine learning and identified the minimal neuropsychological test battery needed for accurate diagnosis. Methods Comprehensive neuropsychological data from 216 assessments (162 with MCI, 54 without) in 110 iRBD patients were analysed. Four supervised classifiers (logistic regression, support vector machine, random forest, gradient boosting) were trained across three frameworks (raw scores, raw scores plus demographics, binarized scores) to distinguish MCI status. A multi-objective hyperparameter optimization approach then identified the minimal test subset preserving diagnostic accuracy. Results Of MCI cases, 30% were single-domain and 70% multi-domain, with attention/executive functions and memory most frequently impaired. Binarized neuropsychological scores yielded the best classification performance, with support vector machine achieving 94.7% accuracy. Feature importance analyses consistently highlighted episodic memory, executive, and visuospatial measures. The optimization framework identified a mean of 8 informative tests spanning these domains, achieving comparable diagnostic accuracy to the full battery. Conclusions Cognitive impairment is common in iRBD and predominantly multidomain. Machine learning approaches applied to neuropsychological data can accurately classify MCI status and identify a concise, clinically feasible set of tests with high diagnostic value. These findings support comprehensive, multidomain neuropsychological assessment in iRBD and may inform optimized risk stratification in prodromal α-synucleinopathies.

The chemotherapeutic 5‐fluorouracil (5‐FU) is commonly used to treat solid tumors, especially colorectal cancer. Deleterious variants in DPYD, which encodes dihydropyrimidine dehydrogenase (DPD), are strongly associated with severe and potentially lethal 5‐FU toxicity. Phenotypic assays for DPD function have been proposed as alternative or complementary approaches to identify DPD deficiencies with the rationale that they may capture unrecognized causes. These assays include the direct measurement of DPD activity in peripheral blood mononuclear cell (PBMC) lysates and measuring plasma metabolites uracil (U), dihydrouracil (UH 2 ), and the dihydrouracil/uracil (UH 2 :U) ratio. We evaluated correlations between PBMC DPD activity and plasma metabolite measures in matched samples from 203 patients with available DPYD exome sequencing data. We also compared the ability of phenotypic measures to identify carriers of deleterious DPYD variants. In addition to established high‐risk variants, we identified three rare DPYD variants predicted to be deleterious: c.274C>G, c.966C>A, and c.1435G>A. Collectively, these variants were associated with impaired DPD function with effect sizes comparable to well‐studied toxicity‐associated variants. At the population level and within carriers of specific genotypes, weak but significant correlations were observed between PBMC DPD activity and either UH 2 :U ratio or U level. These findings suggest that although phenotypic assays can detect severe DPD deficiency, high variability limits their utility as standalone diagnostic tools. Integration with genetic testing may provide a more robust pretreatment risk assessment strategy.