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Plexiform neurofibromas arise in approximately half of all individuals with Neurofibromatosis Type I (NF1). Genetic lesions apart from NF1 pathogenic variants are not present in NF1 mutant Schwann cells, suggesting that tumorigenesis may instead require cellular rewiring. To identify putatively re-wired tumor cells, we used multiplex imaging and flow cytometry to delineate markers that distinguish tumor Schwann cell from non-tumor Schwann cells. We identified tumor Schwann cells as CD44+;CD49f+ cells that show increased transcription of epithelial to mesenchymal plasticity (EMP)- associated genes. CD44+;CD49f+ expression was induced by treating Nf1-/- Schwann cells with stressors or by direct NF-κB pathway activation via IKK2; treated Schwann cells increased secretion of NF-κB targets and immune cell chemoattractants CCL2 and CCL3. In established tumors, MEK inhibition plus NF-κB blockade diminished tumor cell proliferation, tumor Schwann cells, immune cell populations, tumor cytokines and tumor number. Notably, transient single agent NF-κB pathway blockade was sufficient to significantly delay tumor formation in mice. Thus, Nf1 loss in Schwann cells is followed by NF-κB activation and cellular reprogramming that drives tumor initiation and targeting NF-κB activation can reduce NF1-deficient tumor formation.

ABSTRACT BACKGROUND Opioids prescribed for acute pain frequently remain unused, creating opportunities for unintended use, accidental ingestion, or diversion. Little is known about counseling patients receive from prescribers or pharmacists on storage and disposal of opioids. OBJECTIVE To characterize clinician counseling on opioid disposal and patient-reported storage, use, and disposal practices among opioid-naïve patients prescribed opioids for acute pain. DESIGN Multicenter prospective cohort study with participant recruitment between September 2020 and March 2023. PARTICIPANTS Opioid-naïve patients prescribed opioids for acute pain in emergency departments, primary care or urgent care clinics, inpatient settings, and dental practices. MAIN MEASURES Patient-reported receipt of counseling on the importance of opioid disposal and appropriate disposal methods; opioid storage practices; use and disposition of prescribed opioids; disposal methods; and reasons for keeping or disposing of leftover opioids. KEY RESULTS Among 1708 participants, the median age was 38 years (IQR, 28-52); most were recruited from emergency departments (n=915; 53.6%) and primary care or urgent care clinics (n=307; 18.0%). Among 1233 participants who answered the question on counseling about the importance of opioid disposal, 811 (65.8%) reported receiving no counseling. Among 1220 participants who answered the question on counseling about appropriate disposal methods, 832 (68.2%) reported receiving no counseling. Among 812 participants who answered the question about storing opioids in a locked location, 610 (75.1%) reported never doing so. Among 929 participants providing information on opioid leftovers and disposition, 618 (66.5%) did not use all prescribed opioids. Among these 618 participants, 258 (42.8%) kept leftover opioids and 345 (57.2%) disposed of them. Among 256 participants who reported reasons for keeping leftover opioids, 159 (62.1%) wanted medication available for the same condition, 70 (27.3%) wanted medication available for a different condition, and 36 (14.1%) did not know how to properly dispose of the medication. CONCLUSIONS Among opioid-naïve patients prescribed opioids for acute pain, counseling about opioid disposal was uncommon, secure storage was infrequent, and leftover opioids were frequent, with many patients keeping leftover opioids for possible future use. These findings suggest opportunities to improve counseling on opioid storage and disposal and encourage safe disposal of unused opioids after acute pain treatment.

ABSTRACT Background Delirium affects up to one‐third of hospitalized older adults and is associated with adverse outcomes. Despite its clinical importance and the availability of validated screening tools, current care gaps extend beyond under‐recognition to inconsistent risk stratification, workforce constraints, documentation burden, fragmented intervention delivery, and practical challenges with monitoring of response. We review how artificial intelligence (AI) could bridge these gaps and distinguish model development and validation, real‐world clinical implementation, and emerging concepts in delirium care. Results Prediction models leveraging multimodal health record data, including structured variables and unstructured clinical notes, report good discrimination for delirium risk stratification, but only a few models have been integrated into clinical practice. A live multimodal machine learning model increased targeted delirium detection, yet a pre‐post comparative study found a longer length of stay; improved recognition alone therefore does not equate with improved outcomes. Natural language processing, large language models, computer vision, ambient sensing, and wearable technologies enable more continuous, objective, and less labor‐intensive assessment compared with traditional screening methods, but evidence remains limited with concerns about false positives and generalizability. AI‐supported delirium management through reinforcement learning and digital twins is currently largely conceptual. Responsible AI adoption requires a clearly defined intended use, external validation, action pathways, equitable performance, privacy and security, consent and transparency, clinician oversight, patient preferences, and post‐deployment monitoring. Conclusion AI is most defensible as an adjunct that connects evidence‐based delirium care to reliable execution rather than as a replacement for clinical assessment. Prospective comparative studies should evaluate patient outcomes, resource use, and balancing measures.

Background: Discharge summaries are essential for safe transitions of care, but writing the hospital course narrative is time intensive. Large language models can generate plausible clinical narratives, yet prior approaches often rely on static inputs and remain vulnerable to omissions and factual inconsistencies. Methods: We developed an electronic health record (EHR)-integrated agentic artificial intelligence (AI) system that uses Gemini 2.5 Pro and Model Context Protocol tools to retrieve clinical notes, diagnostic reports, diagnoses, medications, and appointments on demand. In this retrospective, blinded, paired study, 50 inpatient cardiology encounters were randomly selected from admissions July 13 to 27, 2024, at Mayo Clinic, Rochester, Minnesota. Five physicians rated AI-generated and clinician-authored hospital course narratives for each encounter (250 ratings) on 0-to-10 scales for overall quality, clarity, completeness, accuracy, and readability and indicated a preference. The primary outcome was the paired difference in overall quality (linear mixed-effects model with crossed random intercepts for encounter and reviewer). Secondary domains were analyzed similarly with Bonferroni adjustment, and preference with a crossed random-effects logistic model. Results: Clinician-authored narratives were preferred in 132 ratings (52.8%) and AI-generated narratives in 118 (47.2%) (odds ratio for clinician-authored preference, 1.12; 95% CI, 0.71 to 1.78). Overall quality did not differ significantly (mean, 7.27 AI-generated versus 7.42 clinician-authored; difference, -0.15; 95% CI, -0.86 to 0.56; p=0.627). AI-generated narratives were rated higher for clarity (0.36; 95% CI, 0.07 to 0.65) and readability (0.72; 95% CI, 0.20 to 1.24) but lower for completeness (-0.84; 95% CI, -1.36 to -0.32) and accuracy (-0.35; 95% CI, -0.66 to -0.05). After Bonferroni correction, only lower completeness remained significant (adjusted p=0.012). Conclusions: Hospital course narratives generated by the EHR-integrated agentic AI system did not differ significantly from clinician-authored narratives in physician-rated overall quality or preference. Lower completeness supports continued clinician review before use in the medical record.

Importance Exposure to fine particulate matter with an aerodynamic diameter of 2.5 µm or less (PM 2.5 ) has been associated with multiple malignant neoplasms, but its relationship with sinonasal malignant neoplasm (SNM) remains poorly characterized. Objective To evaluate the association between regional PM 2.5 exposure and first observed SNM diagnosis. Design, Setting, and Participants This national case-control study used commercial outpatient claims with eligible index dates from January 2007 to December 2020. Cases were defined by the first eligible SNM diagnosis after a 1-year enrollment washout period. Controls without any SNM diagnosis throughout claims history were randomly sampled at an approximate 1:5 case to control ratio. Regional PM 2.5 exposure was derived from NASA 1-km gridded models integrating satellite, meteorologic, land-use, and atmospheric data, aggregated to each participant’s residential metropolitan statistical area and index date. Associations were evaluated using mixed-effects logistic regression with a metropolitan statistical area–level random intercept, adjusted for demographic characteristics, comorbidities, regional smoking prevalence, and socioeconomic deprivation. The analysis was conducted from March 2025 to August 2026. Exposures Mean regional PM 2.5 exposure during the 6-month, 1-year, and 5-year periods preceding the index date. Main Outcome and Measure First observed SNM diagnosis after a 1-year enrollment washout period. Results The analytic cohort included 21 295 individuals: 3600 patients with SNM (mean [SD] age, 51.5 [10.6] years; 1656 [46.0%] female and 1944 [54.0%] male participants) and 17 695 controls (41.5 [14.0] years; 9663[54.6%] female and 8032 [45.39%] male participants). Mean (SD) PM 2.5 concentrations were higher among cases than controls across the 6-month (8.57 [2.52] vs 8.36 [2.50] μg/m 3 ), 1-year (8.69 [2.45] vs 8.45 [2.43] μg/m 3 ), and 5-year (9.36 [2.67] vs 9.08 [2.67] μg/m 3 ) windows. In fully adjusted models, each SD increase in PM 2.5 was associated with higher odds of first observed SNM diagnosis (adjusted odds ratio, 1.25 [95% CI, 1.05-1.49] for 6-month average exposure, 1.31 [95% CI, 1.08-1.59] for 1-year average exposure, and 1.36 [95% CI, 1.11-1.66] for 5-year average exposure). Conclusions and Relevance In this case-control study, greater regional PM 2.5 exposure was associated with higher odds of a first observed SNM diagnosis, with larger estimates for longer averaging windows. Further epidemiologic and mechanistic investigation is warranted.

Abstract INTRODUCTION Combining Alzheimer's disease blood biomarkers may improve prediction of amyloid positivity (A+) compared with single biomarkers. METHODS Logistic regression models using plasma phosphorylated tau 217 (p‐tau217), amyloid beta (Aβ) 42, and Aβ40 were developed in cognitively impaired (CI) participants from Mayo Clinic Rochester (MCR) and validated in Alzheimer's Disease Neuroimaging Initiative (ADNI) and Mayo Clinic Florida (MCF) cohorts. Performance was measured by area under the curve (AUC) and Probability of Amyloid Positivity Score (PAPS). RESULTS Plasma p‐tau217 alone showed high AUC (95% confidence intervals) across cohorts MCR, 0.928 (0.900–0.953); ADNI, 0.938 (0.915–0.959); MCF, 0.935 (0.893–0.970). Adding Aβ42 to regression models yielded marginal model improvement MCR, 0.940 (0.915–0.963); ADNI, 0.945 (0.922–0.965); MCF, 0.960 (0.925–0.986). Utilization of two cutpoints (≥ 90% sensitivity and specificity), yielded ≤ 10% intermediate results for p‐tau217, and ≤ 8% for p‐tau217+Aβ42. Addition of Aβ40 did not improve models’ performance. DISCUSSION An immunoassay‐based p‐tau217 PAPS model accurately predicted amyloid positivity across independent CI cohorts and achieved confirmatory‐test performance with two cutpoints. The added value of Aβ42 was modest and cohort dependent, supporting the potential of use of p‐tau217–based PAPS in clinical workflows.

Objectives: Metastasis to the thyroid gland (MTT) is uncommon, and its presentation, diagnosis, and management remain incompletely characterized. We described a contemporary institutional cohort and examined detection, diagnostic evaluation, treatment, and survival after MTT diagnosis. Methods: We conducted a retrospective descriptive cohort study of patients with pathologically confirmed MTT who were evaluated at the Mayo Clinic in Rochester, Minnesota, from 2010 through 2025. Clinical, imaging, cytologic, pathologic, treatment, and outcome data were abstracted from the electronic health record. Survival was estimated using the Kaplan-Meier method. Results: Among 42 patients, the most common primary malignancies were kidney (40.5%) and lung (23.8%). MTT was detected incidentally on staging or surveillance imaging in 66.7% and was symptomatic in 23.8%. The median interval from primary cancer diagnosis to MTT was 3.67 years and was longer for kidney cancer (8.7 y) than lung cancer (0.94 y). Most lesions were unifocal (81.0%); common ultrasound features were solid composition (50.0%) and hypoechogenicity (47.6%), but findings were nonspecific. Among 23 patients evaluated with a single fine-needle aspiration (FNA) before diagnosis, metastasis was confirmed on the first procedure in 56.5% of cases. Eighteen patients underwent surgery. The median overall survival after MTT diagnosis was ∼1.75 years; outcomes differed by surgical status, although treatment selection and disease extent preclude causal interpretation. Conclusions: MTT should be considered in patients with a new thyroid lesion and a history of malignancy. Tissue diagnosis may require repeat FNA, core-needle biopsy, and immunohistochemistry. Management should be individualized based on symptoms, primary tumor biology, extent of systemic disease, and goals of care.

Background: Accurate preoperative characterization of axillary lymph nodes (ALNs) is important in breast cancer, but conventional ultrasound has variable diagnostic performance. We evaluated quantitative shear wave elastography (SWE) for distinguishing reactive from metastatic ALNs, and we explored associations between nodal SWE parameters, breast cancer subtypes, and histological grades. Methods: We prospectively enrolled women with suspicious breast lesions and ALNs identified by clinical ultrasound who were scheduled for biopsy. After exclusions, 141 patients (57 reactive and 84 metastatic ALNs) were included. ElasticNet-regularized logistic regression with nested cross-validation compared clinical ultrasound alone with ultrasound plus SWE. Among 70 patients with metastatic ALNs and immunohistochemical characterization, SWE parameters were compared across surrogate subtypes and histological grades using Kruskal–Wallis tests with Holm adjustments. Results: The combined ultrasound–SWE model achieved an area under the receiver operating characteristic curve of 0.911 (95% CI, 0.859–0.961) compared to 0.798 (95% CI, 0.723–0.873) for ultrasound alone. At the Youden-derived threshold, the combined model achieved 88.1% sensitivity, 82.5% specificity, and 85.8% accuracy. Among metastatic ALNs, SWE parameters, namely Vmean, Vsd, and Vmax, differed significantly across surrogate subtypes, with the most pronounced differences observed between Luminal A and Luminal B tumors, (p = 0.0250, p = 0.0069, and p = 0.0065). No statistically significant differences were detected with histological grades in the five SWE parameters. Conclusions: Quantitative SWE complemented conventional ultrasound for assessing axillary nodal metastasis, with higher cross-validated discrimination for the combined model. Exploratory analyses identified differences in several SWE stiffness metrics between Luminal A and Luminal B tumors; these subtype-associated biomechanical differences require validation in larger cohorts.