Citations

Abstract Introduction Underwater endoscopic mucosal resection (uEMR) technique has gained popularity for removal of large non-pedunculated colorectal polyps (LNPCPs) due to high en-bloc resection rates and lower recurrence rates. However, there is no information regarding outcomes of uEMR for giant colorectal polyps. Therefore, we aimed to evaluate the outcomes of uEMR for giant colorectal polyps across multiple centers in North America. Methods We conducted a multicenter retrospective study of adults with giant colorectal polyps undergoing uEMR between January 2019 and November 2025. Giant colorectal polyps were defined as LNPCPs ≥40 mm. Primary outcomes were technical success and rate of recurrent adenoma. Secondary outcomes were severe adverse events (SAEs) and predictors of technical success, recurrence, and SAEs. Results A total of 137 polyps, with mean polyp size 47.6 mm were included. Majority of the lesions were located in right colon (61%, n = 83) with the most common histopathology being high-grade dysplasia (HGD) (31.4%, n = 43). Technical success was achieved in 90.5% (n = 124) with en-bloc resection rate of 11.7% (n = 16). Polyp fibrosis (OR: 0.08) and HGD (OR: 0.15) were independent predictors of inability to achieve technical success. Of 92 (67.1%) patients with follow-up, recurrent polyp was present in 8.7% (n = 8). SAEs were noted in 9.5%, consisting of delayed bleeding (9.5%, n = 13), while no delayed perforation or post polypectomy syndrome was noted. Discussion Our study demonstrates that uEMR is technically feasible for the removal of giant colorectal polyps with an acceptable safety profile and recurrence rate.

Abstract Purpose: Pancreatic ductal adenocarcinoma (PDAC) frequently recurs after neoadjuvant therapy (NT) and curative-intent resection. Although major pathologic response predicts favorable outcomes, most patients achieve only minor response with a heterogeneous recurrence risk. We asked whether the spatial organization of residual PDAC encodes clinically relevant biology beyond residual tumor burden. Experimental Design: In a retrospective cohort of 203 patients with resected PDAC treated with NT and restricted to minor pathologic response, routine hematoxylin and eosin (H&E) whole-slide images were segmented into cancer and stroma using an artificial intelligence–enabled pipeline. Spatial composition (patch density, edge density) and configuration (compactness/complexity, intermixing) were quantified and tested for associations with disease-free survival (DFS) using multivariable models adjusted for standard clinicopathologic factors. Results: Shorter DFS was associated with a fragmented, interface-rich tumor–stroma ecology featuring higher edge density and diversity and reduced homotypic aggregation, independent of clinicopathologic variables. Two spatial risk models were independently prognostic: (i) cancer mean shape index plus stromal shape index variability [adjusted hazard ratio (HR), 1.71; P = 0.003] and (ii) mean stromal patch area plus edge density (adjusted HR, 2.19; P = 0.002). Both models stratified outcomes in which pathologic response grading and residual cancer area did not. High-risk configurations were further associated with reduced intratumoral tumor-infiltrating lymphocyte (TIL) density and infiltration ratio, with relative TIL accumulation at the cancer periphery and within the stroma, consistent with an immune-excluded phenotype. Conclusions: Residual cancer–stroma topology quantified from standard H&E slides yields independent prognostic signals after NT in PDAC, provides a cellular immune correlate for spatial risk, and motivates prospective validation and spatially informed adjuvant strategies.

Cancer therapy has dramatically changed over the past many decades, not only in efficacy but also complexity, with targeted therapeutics that have novel mechanisms of action. As a direct result of these remarkable treatments that have extended the lives of many patients, the increased recognition of cancer therapy-related cardiac dysfunction as well as the broader description of cancer therapy-related cardiovascular toxicity (CTR-CVT) has become obvious. The discipline of cardio-oncology has accepted the challenge to provide excellent cardiovascular (CV) protection for our patients being treated for cancer in order to achieve the best overall outcomes. The successful application of optimal CV protection by cardio-oncology needs to incorporate basic principles to realise these improved overall outcomes. Critical paradigm principles include: (1) using effective CV therapy; (2) targeting the likely mechanisms of action responsible for CTR-CVT; and (3) documenting cancers and cancer therapies associated with high CTR-CVT. Such a CV protection paradigm is likely to have the biggest impact and confirm an improved overall outcome. In addition, it is important to emphasise that knowing those cancers with high CTR-CVT risk requires careful comparative study of CV endpoints during exploratory cancer trials. Furthermore, astute clinical observations of specific CTR-CVT should be routinely reported to allow the development of strategies that minimise the negative impact of CTR-CVT. Lastly, coordinated efforts that allow realistic goals of cancer treatment while minimising the impact of CTR-CVT are the end goal in order to eliminate CV disease as a barrier to effective cancer treatment.

Abstract Polygenic risk scores (PRSs), which quantify inherited susceptibility to complex traits and diseases, have emerged as valuable tools for risk stratification and precision medicine. Despite their promise, PRS developed on European cohorts often demonstrate substantially reduced predictive accuracy in non-European populations, due to differences in genetic architecture. The disproportionate representation of European ancestry cohorts in genome-wide association studies (GWAS) leads to inequitable deployment of PRS technologies across diverse populations. Here, we introduce PRANA (Polygenic Risk Adaptation via Neural-network Architecture), a deep learning framework that adapts an existing PRS developed on one population to other ancestries. Unlike methods that require large-scale GWAS in the target population, PRANA leverages pre-trained PRS models derived from European cohorts and adapts them using modestly sized cohorts from the target population. We evaluated PRANA on seven complex traits in South Asian, East Asian and Ashkenazi Jewish populations, as well as in selected smaller East Asian subpopulations where the scarcity of training data poses a particular challenge. PRANA mostly improved predictive performance of the baseline PRS models by 5%-20% in terms of effect size (β) and Nagelkerke’s R², and, in most cases, outperformed existing cross-ancestry multi-PRS approaches. These results highlight PRANA as a scalable and practical strategy to reduce disparities in genomic risk prediction and advance the equitable application of PRS in diverse populations.

Background and Objectives: We developed the Mayo Outpatient Precepting Evaluation Tool (MOPET) to assess and improve clinical teaching for family medicine residents. Given the scheduling challenges of peer evaluation, we sought to improve the MOPET’s usefulness by evaluating the reliability of self- and resident-reported teaching behaviors during clinical precepting compared to peer observation. Methods: Forty-eight half-day sessions in a family medicine residency clinic were assessed with parallel observer, preceptor, and resident MOPET forms. Interrater reliability was calculated using the κ (kappa) statistic. Results: Residents reported that preceptors used 14 of 20 teaching behaviors more often than observers. Observers and preceptors significantly agreed on the use of five teaching behaviors, while residents agreed with observers on two teaching behaviors. Conclusions: Although self-assessment and resident-based feedback of preceptor teaching behavior may improve practicality, they are less reliable compared to direct peer observation with the validated MOPET.

Importance Ischemic heart disease (IHD), the leading cause of death in the US, is predominantly due to modifiable risk factors. Estimates of IHD mortality attributable to risk factors provide evidence for health policy decision-making. Objective To estimate the burden of IHD death attributable to risk factors in the US from 1990-2023. Design, Setting, and Participants The Global Burden of Disease Study 2023 (GBD 2023) used vital records and a broad set of epidemiologic data to estimate IHD death rates, risk factor exposure, and relative risk curves for risk-outcome pairs for 1990-2023 for the general population. Data analysis was performed from October 2024 to December 2025. Exposure Twelve metabolic, behavioral, and environmental risk factors. Main Outcomes and Measures The primary outcomes were IHD death rates per 100 000 persons, counts, and attributable risks from 1990-2023 by age, sex, and US state. Estimates include 95% uncertainty intervals (UI). IHD death rates were estimated using ensemble modeling methods. Risk exposures were estimated using bayesian meta-regression methods. Relative risks were estimated following the Burden of Proof framework. Results In 2023, there were 473 000 IHD deaths (95% UI, 414 000-510 000) in the US, a decrease of 58.7% (95% UI, 56.8%-61.0%) in age-standardized rate since 1990. Between 2010 and 2023, there was a 19.0% decrease (95% UI, 15.0%-22.9%) for males and a 24.5% decrease (95% UI, 20.3%-29.7%) for females in IHD death rates. High systolic blood pressure (SBP), dietary risks, and high low-density lipoprotein cholesterol (LDL-C) were the leading risk factors for IHD deaths in 2023, accounting for 47.2% (95% UI, 36.4%-57.0%), 38.6% (95% UI, 17.2%-56.8%), and 28.5% (95% UI, 19.3%-39.6%) of IHD deaths, respectively. Increased exposure to several risk factors substantially increased their attributable burden for IHD deaths in 2023, with high fasting plasma glucose (FPG) increasing 38.8% (95% UI, 11.5%-81.1%) and high body mass index (BMI) increasing 54.5% (95% UI, 41.8%-66.3%) since 1990. Smoking and particulate matter pollution had the greatest decrease in attributable IHD mortality since 1990, at 33.3% (95% UI, 23.6%-41.7%) and 74.9% (95% UI, 46.7%-88.8%), respectively. Conclusion and Relevance Per the results of this systematic analysis of GBD 2023, a total of 88.7% (95% UI, 83.4%-92.5%) of IHD deaths were attributable to modifiable risk factors in the US in 2023, with high SBP, dietary risks, and high LDL-C being the greatest contributors. High BMI and high FPG showed the largest attribution increases, while exposure to other risks did not increase significantly for the population.

Importance Although the Martin-Hopkins equation is widely validated and used to estimate low-density lipoprotein cholesterol (LDL-C) for clinical care, a simplified equation could streamline implementation and enable broader adoption. Objective To develop a simplified machine learning–based alternative to the Martin-Hopkins equation using multivariate adaptive regression splines (LDL-C–MH-MARS) and compare its performance with the Friedewald (LDL-C–F), Sampson–National Institutes of Health (LDL-C–S), Modified Sampson (LDL-C–MS), and Martin-Hopkins (LDL-C–MH) equations. Design, Setting, and Participants The Very Large Database of Lipids is a cross-sectional database of clinical lipid measurements from a population-representative convenience sample of adult and pediatric patients. Lipid measurements were performed by Vertical Auto Profile ultracentrifugation between October 1, 2015, and June 30, 2019. The study included patients with complete lipid panel data who were randomly assigned to a training set and a test set. External validation against preparative ultracentrifugation-based LDL-C measurements was performed in reference laboratory datasets from the Mayo Clinic (broad LDL-C concentrations) and the Further Cardiovascular Outcomes Research With Proprotein Convertase Subtilisin/Kexin Type 9 (PCSK9) Inhibition in Subjects with Elevated Risk (FOURIER) trial (observations from patients with low LDL-C concentrations while taking evolocumab). Study data were analyzed from January to October 2025. Exposures LDL-C–F, LDL-C–S, LDL-C–MS, LDL-C–MH, and LDL-C–MH-MARS equations. Main Outcomes and Measures Accuracy of estimated vs ultracentrifugation-measured LDL-C concentration based on bias, root mean square error (RMSE), and concordance by guideline-based categories. Results The study included 4 939 528 patients (mean [SD] age, 56 [16] years; 2 635 486 female [53%]) with complete lipid panel data who were randomly assigned to a training set (n = 3 292 889) and a test set (n = 1 646 639). LDL-C–MH-MARS equation demonstrated a very low median (IQR) bias of −0.1 (−2.1 to 1.8) mg/dL (to convert to millimoles per liter, multiply by 0.0259), comparable with LDL-C–MH. The median (IQR) difference between the MARS and original Martin-Hopkins equations (ie, LDL-C–MH-MARS – LDL-C–MH) was −0.5 (−1.2 to 0) mg/dL, further suggesting comparability between the 2 methods. The RMSE was smallest for LDL-C–MH-MARS (4.7 mg/dL) and LDL-C–MH (4.9 mg/dL), followed by LDL-C–S (5.8 mg/dL), LDL-C-MS (6.0 mg/dL), and LDL-C–F (7.2 mg/dL). The proportion of patients correctly classified according to clinical categories was nearly identical for LDL-C–MH-MARS (89.7%) and LDL-C–MH (89.6%) but lower for LDL-C–S (86.3%), LDL-C–MS (84.7%), and LDL-C–F (83.1%). The LDL-C–MH-MARS, LDL-C–MH, LDL-C–MS, LDL-C–S, and LDL-C–F equations underestimated LDL-C concentration in 16%, 17%, 28%, 39%, and 60%, respectively, when classifying LDL-C less than 70 mg/dL in patients with triglyceride concentrations of 200 to 399 mg/dL (to convert to millimoles per liter, multiply by 0.0113). The patterns of results were similar in external validation datasets, with the LDL-C–MH-MARS and LDL-C–MH equations demonstrating the highest accuracy. Conclusions and Relevance Study results suggest that the new machine learning–based LDL-C equation provided results comparable with those of the original Martin-Hopkins method while simplifying implementation to a single equation.