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Aims Periacetabular osteotomy (PAO) is performed in young, opioid-naive patients and is associated with postoperative pain. Optimizing perioperative analgesia while limiting opioid exposure remains a priority. This study evaluated postoperative pain trajectories, hospital length of stay (LOS), and opioid-prescribing patterns associated with an institutional transition from epidural-based to periarticular injection (PAI)-based analgesia after PAO. Secondary aims were to assess the influence of preoperative opioid exposure and compare outcomes between ropivacaine-based and bupivacaine PAI formulations. Methods The study included 724 patients who underwent PAO between January 2010 and December 2022 at a single academic institution, with epidural use replaced by PAI in 2016. Patients were stratified by postoperative analgesia strategy: epidural catheter (n = 333) or PAI (n = 391). Outcomes included daily postoperative pain scores, LOS, discharge opioid prescriptions (morphine milligram equivalents (MMEs)), total opioid exposure, and 30-day refill rates. Statistical comparisons were performed using chi-squared, Fisher’s exact, and Kruskal–Wallis tests. Results Baseline characteristics were similar between groups. Epidural analgesia was associated with lower pain scores during the first 48 hours (last pain postoperative day 0 to 1: mean 2.9 (SD 2.0) vs 4.7 (SD 2.1); p < 0.001), whereas the PAI group had a shorter LOS (mean 2.2 days (SD 1.0) vs 3.4 days (SD 0.9); p < 0.001) and higher pain scores at discharge (mean 4.8 (SD 1.9) vs 4.0 (SD 1.9); p < 0.001). Median discharge opioid prescriptions (313 (IQR 225 to 388) vs 825 (IQR 600 to 1,000) MMEs) and total 30-day opioid exposure (360 (IQR 275 to 575) vs 900 (IQR 638 to 1,100) MMEs) were lower in the PAI group (both p < 0.001), while refill rates were higher (34.0% vs 19.2%; p < 0.001). Within the PAI cohort, ropivacaine-based and bupivacaine injections showed similar pain trajectories and refill rates. Conclusion Epidural analgesia provided superior early postoperative pain control, whereas PAI-based pathways enabled earlier discharge and lower overall opioid prescribing but with higher refill rates. These findings highlight trade-offs in contemporary perioperative pathways and support individualized analgesic strategies after PAO. Cite this article: Bone Jt Open 2026;7(11):1410–1418.

Abstract Background Lower extremity compression therapy has been proposed as an adjunct to mitigate diuretic resistance during acute decompensation of heart failure (ADHF). We evaluated the effect of lower leg compression with elastic bandages on diuretic efficiency in patients with diuretic-resistant ADHF. Methods Historical cohort study enrolling adult cardiac ICU admissions with ADHF who used elastic bandages (EB) at Mayo Clinic in Minnesota from 2007 to 2017. The diuretic efficiency before and after applying EB was the primary outcome. Results A total of 176 patients with heart failure who received elastic bandages were included. The in-hospital mortality rate, the median hospital length of stay, and 30-day hospital readmission were 9.7%, 11.4 days (IQR 7.1–19.0), and 11%, respectively. When EB was applied, the average daily loop diuretic dosage was decreased by a mean difference of 1.19 mg per 40 mg furosemide equivalent (95% CI: 0.22 to 2.2; p = 0.016). The average post-EB diuretic efficiency was 1,355 mL (IQR 1,119, 1,590) per 40-mg furosemide, which was significantly higher than before EB application, with a mean difference of 340 mL per 40-mg furosemide equivalent (95% CI: -554, -125; p = 0.002). A higher diuretic efficiency led to lower diuretic doses. Average daily urine output before and after EB application was not different (2,642 pre-EB vs. 2,792 post-EB mL/day; 95% CI: -455 to 154; p = 0.33). Conclusion EB use enhances diuretic efficacy during ADHF. Further studies are needed to determine the potential clinical benefits and risks of applying EB compression treatment in ADHF.

BACKGROUND: Type 2 long-QT syndrome results from KCNH2 -encoded Kv11.1 loss-of-function, but whether secondary inward-current dysfunction amplifies dysfunction remains unclear. METHODS: We characterized sodium ( SCN5A , I Na ) and L-type calcium channel ( CACNA1C , I Ca ) function in KCNH2-G604S and variant-corrected isogenic control induced pluripotent stem cell–derived cardiomyocytes (iPSC-CMs). Whole-cell patch-clamp measured action potential duration at 90% repolarization (APD90), and potassium (I Kr ), sodium (I Na ), and calcium (I Ca, L ) currents. Fluo-4 imaging quantified calcium transients. A modified Kernik-Clancy iPSC-CM model incorporated experimentally measured persistent I Na,L and I Ca,L fractions and simulated isolated and combined current defects. RESULTS: KCNH2-G604S caused severe I Kr loss-of-function (72.5% reduction). APD90 was prolonged in G604S-iPSC-CMs (765±58 ms) compared with isogenic controls (368±37 ms; P <0.005), with early afterdepolarizations in 50% of G604S cells. Nifedipine, verapamil, and mexiletine successfully shortened APD90 and abolished early afterdepolarizations. Compared with isogenic control, I Na,L (0.60±0.1% versus 0.26±0.06%; P <0.01), persistent I Ca,L (2.88±0.55% versus 1.26±0.35%; P <0.05), and calcium transient decay times ( P <0.0001) were greater in G604S iPSC-CMs. Increased persistent I Ca,L was phenocopied by E-4031 in isogenic controls and reduced by nifedipine in G604S iPSC-CMs. In silico modeling revealed that the I Kr defect alone prolonged APD90 to only 620 ms. The addition of the observed I Na,L defect had a small additive effect (+20 ms); however, incorporating persistent I Ca,L synergistically extended APD90 to 758 ms, replicating the experimental phenotype. Flux dynamics analysis indicated that this persistent Ca 2+ entry suppresses the forward-mode sodium-calcium exchanger, forcing a compensatory reliance on sarcoplasmic-endoplasmic reticulum Ca-ATPase that primes for calcium instability. CONCLUSIONS: The KCNH2-G604S variant drives a severe multihit phenotype via primary I Kr loss-of-function and secondary state-dependent gain-of-function in persistent I Ca,L . These comprehensive validations demonstrate that the remodeled persistent I Ca,L acts as the essential amplifier driving extreme action potential duration prolongation and Ca 2+ mishandling.

Type 1 diabetes (T1D) is a complex autoimmune disease characterized by the destruction of insulin-producing pancreatic β cells. Both genetic susceptibility and epigenetic dysregulation contribute to T1D risk. Recent studies have reported β-cell dysfunction before disease onset, suggesting that β-cell-intrinsic mechanisms contribute to pathogenesis. However, the mechanistic link between disease-associated variants and β-cell dysfunction remains poorly understood. We hypothesized that a subset of T1D-associated variants directly influences cell type-specific candidate cis-regulatory elements (cCREs) by disrupting transcription factor (TF) binding and altering gene expression. To define cell type-specific cCREs in human islets, we integrated publicly available epigenomic datasets, including bulk histone modification profiles and single-cell multiomic data from human pancreatic islets. We combined these data with T1D genome-wide association study (GWAS) variants to identify disease-associated SNPs located within enhancer regions. Using multiomic datasets from PANC-DB, we characterized cell type-specific chromatin accessibility in nondiabetic and T1D islets and identified regulatory regions that may control gene expression in β cells and other islet cell types. We then applied ChromBPNet, a deep learning model that predicts base-pair-resolution chromatin accessibility from scATAC-seq data, to evaluate how specific variants may alter local regulatory activity. In parallel, we used TF footprinting to nominate TFs likely to bind these variant-containing regions. These analyses identified several T1D-associated SNPs predicted to alter chromatin accessibility at candidate TF binding sites, suggesting mechanisms by which noncoding variants may contribute to β-cell dysfunction and T1D susceptibility. Our findings link T1D-associated variants to cell type-specific enhancer activity and regulatory pathways and provide candidates for future mechanistic studies.

Background: Several breast cancer (BC) risk prediction models have been developed to provide personal risk assessments. Though individually validated, their performance has not been systematically evaluated across a wide range of populations or ages. Methods: We harmonized individual-level baseline questionnaire data and incident BC diagnoses from 21 cohorts from North America, Europe, and Australia participating in the Breast Cancer Risk Prediction Project. Five-year absolute risk of invasive BC was estimated for five established risk prediction models using classical risk factors only. Discrimination was evaluated by area under the curve (AUC). Calibration was assessed using average and risk-decile specific expected to observed (E/O) ratios. Performance metrics were meta-analyzed across cohorts and models. Metaregression tested associations between cohort characteristics and performance metrics. Results: This analysis included 1,595,977 women aged 20-75 years, enrolled in studies between 1976-2015, with 19,062 (1.2%) invasive BC cases ascertained within 5 years from exposure assessment. Age-adjusted AUCs were similar across models and cohorts (pooled AUCs by model: 0.57-0.58), while E/O ratios varied substantially (pooled E/O ratios by model: 0.83-1.25). Overestimation was common among predicted high-risk individuals (>3%). No appreciable differences in model performance by cohort age, birth year, race, and variable missingness emerged. Calibration improved after assigning race-specific incidence rates. Conclusion: Existing BC risk prediction models provided similar risk discrimination across multiple cohorts, although there was overestimation of risk for high-risk individuals. Performance variation across cohorts was not driven by specific characteristics, which supports development of a unified risk model for diverse populations that leverages appropriate incidence rates.

ABSTRACT Extracellular vesicles (EVs) are increasingly recognized as mediators of intercellular communication in cancer. We previously demonstrated that re‐expression of the leucine‐rich domain (LRD) of neurofibromin (NF1) suppresses glioblastoma (GBM) invasion and limits microglia/macrophage infiltration into the tumor microenvironment (TME). Given the central role of tumor‐associated microglia/macrophages (TAMs) in GBM progression, we investigated whether NF1‐LRD‐containing EVs (NF1‐LRD‐EVs) could modulate TAM function and remodel the TME. Our results showed that NF1‐LRD‐EVs attenuated microglia and macrophage recruitment in migration assays, consistent with reduced microglia/macrophage recruitment observed in vivo . In parallel, treatment with NF1‐LRD‐EVs enhanced phagocytic activity of both microglia and iPSC‐derived macrophages, accompanied by induction of pro‐inflammatory cytokines TNF‐α, IL‐6, and IL‐1β, and downregulation of immunosuppressive mediators such as Arginase‐1 and IL‐10. Mechanistically, NF1‐LRD‐EVs induced ADAM17‐associated inflammatory signaling, accompanied by NF‐κB activation. Pharmacological inhibition of ADAM17 reduced TNF‐α release and attenuated NF‐κB activation, supporting a role for ADAM17‐dependent signaling in amplifying this inflammatory response. Together, these findings show that NF1‐LRD‐EVs reprogram TAMs toward a pro‐inflammatory phenotype and modulate the GBM immune microenvironment. These results provide a framework for understanding NF1‐LRD‐EV‐mediated immune regulation and support further investigation of EV‐mediated immune modulation of the TME.

Abstract Anthracycline-induced cardiotoxicity (AIC) remains a dose-limiting complication of cancer treatment, often leading to irreversible heart failure, yet effective cardioprotective therapies remain limited. Graph-driven drug repurposing offers a rapid pathway to identify cardioprotective therapies, but different computational models often generate divergent candidate lists that exceed experimental validation capacity. We applied a novel "Union-then-Filter" framework to prioritize therapeutics for AIC using AI-based approaches and evaluated candidates in zebrafish models. Three state-of-the-art graph learning models (TxGNN, CompGCN, and RLR) were trained on the PrimeKG to generate an initial candidate pool. A Large Language Model (LLM) agent automated evidence synthesis across 4,286 PubMed abstracts for the top 50 drugs from each model. Candidates were stratified using dual decision criteria and filtered against existing clinical trials. The pipeline narrowed 150 initial candidates to four novel candidates: Cysteine, Dasatinib, Tranilast, and Tretinoin. Prospective in vivo validation was subsequently conducted using an adult AIC (aAIC) zebrafish model. Phenotypic screening via echocardiography revealed that Cysteine, Tranilast, and Tretinoin significantly restored ejection fraction (EF) (overall one-way ANOVA: \(\:F\left(\text{4,95}\right)=47.66,\:\:p<0.0001\); Tukey's post hoc: \(\:\:\:p<0.01\) for all three treatments vs. DOX+ control) and normalized cardiac remodeling markers in doxorubicin-treated subjects (Kruskal-Wallis: \(\:{\chi\:}^{2}\left(4\right)=17.21,\:\:p=0.0018\); Dunn’s post hoc: \(\:\:\:p<0.01\) for all three comparisons vs. DOX control). This study demonstrates the translational utility of an LLM-integrated AI pipeline for drug repurposing and highlights zebrafish AIC as an efficient bridge between in silico prediction and in vivo phenotypic validation in cardio-oncology.

Abstract Colorectal cancer develops through a normal-adenoma-carcinoma sequence, yet only 5-10% of adenomas progress to malignancy, and the cellular programs governing that sequence remain poorly defined. Here we generate a spatial multi-omics atlas of human colon adenomas, combining Visium CytAssist and protein co-detection across 24 nonadvanced and advanced tubular adenomas with single-cell resolution Xenium Prime 5K profiling of 101 patient-matched normal, adenoma, and carcinoma cores from 16 patients. Integrating whole-transcriptome and 31-plex protein data identifies nine spatial clusters and two dysplastic epithelial populations that co-express stemness, proliferation, and senescence programs. These programs occupy a shared, spatially confined epithelial niche that expands from adenoma to carcinoma. Spatial analysis revealed GDF15, a senescence-associated secretory factor, mediated the coupling between senescence and stemness in advanced adenomas, and that GDF15-high epithelium locally excludes CD8+ T cells in adenoma and, more broadly, in carcinoma. These findings position senescence as a spatially instructive rather than merely tumor-suppressive program during colorectal carcinogenesis and suggest GDF15 might be a potential candidate target for cancer prevention and interception in the colon.

Abstract INTRODUCTION Associations of arterial stiffness, measured through pulse wave velocity (PWV), with incident dementia may be driven by age‐related structural changes to the arterial wall or by elevated blood pressure. METHODS Dementia‐free White or Black participants in the community‐based Atherosclerosis Risk in Communities (ARIC) cohort study with measures of PWV were examined. The associations of carotid‐femoral PWV (cfPWV) and total, structural, and load‐dependent PWV with dementia were estimated using Poisson regression models and Cox proportional hazards models. Dementia cases were defined by physician‐adjudicated review, telephone interviews, hospitalization records, and death certificates. RESULTS Of 4818 participants included in the study population (mean age: 75 years; 41% male, 21% Black), 1067 (22%) developed dementia over a median follow‐up of 9.0 years. Higher cfPWV, total PWV, structural PWV, and load‐dependent PWV were associated with greater dementia risk. DISCUSSION Both structural and pressure‐related arterial stiffness mechanisms may underlie the link between PWV and dementia.

Abstract Generative AI (Gen AI), Foundation Models (FMs), and Large Language Models (LLMs) are powerful emerging technologies that demonstrate exceptional capabilities in processing vast amounts of unstructured and structured data, including text, voice, images, video and other formats, and adapting to a wide range of specific tasks. Their immense potential to drive meaningful improvements in treatment outcomes is increasingly evident. The advent of these technologies has marked a transformative era in healthcare, including the fields of radiation oncology and medical physics. Specifically, these powerful technologies offer unprecedented opportunities to analyze domain‐specific data, process and synthesize medical images, automate routine tasks, support clinical decision‐making, optimize and streamline clinical workflows, and enhance the quality of clinical trials. While these emerging technologies present new opportunities to revolutionize radiation therapy practice, their implementation also raises important educational, ethical, and regulatory considerations. This scoping review highlights benefits, promises, risks, and challenges, such as interpretability, data privacy, regulatory compliance, reproducibility, hallucination, and integration into existing clinical workflow. Finally, emerging opportunities are outlined to guide future research directions. This review paper provides a timely overview of Gen AI, FMs and LLMs, aiming to inform medical physicists, clinicians, and researchers of the evolving role of these disruptive technologies in shaping the future of radiation therapy.