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Importance The prevalence of cardiovascular disease differs substantially across neighborhoods. However, few studies have examined the contribution of neighborhood-level structural racism to inequities in cardiovascular health. Objective To investigate the association between neighborhood-level manifestations of structural racism and the prevalence of cardiovascular clinical and behavioral risk factors and cardiovascular diseases. Design, Setting, and Participants This cross-sectional study consisted of 71 915 US census tracts. The Structural Racism Effect Index (SREI) was appended to the 2020 Centers for Disease Control and Prevention’s PLACES dataset. Race and ethnicity were self-reported and obtained from the 2019 American Community Survey 5-year estimates. Data analysis was conducted from June 2024 to October 2024. Exposure The SREI is a composite score of census tract measures within 9 domains affected by structural racism (built environment, criminal justice, education, employment, housing, income and poverty, social cohesion, transportation, and wealth). The SREI scores were categorized into distribution-based quintiles, where higher quintiles represented greater neighborhood structural racism. Main Outcomes and Measures Neighborhood-level prevalence of cardiovascular clinical (high blood pressure, high cholesterol, obesity, and diabetes) and behavioral (current cigarette smoking and no leisure-time physical activity) risk factors and cardiovascular disease (coronary heart disease and stroke). Multilevel-linear mixed models, adjusting for confounders, were used to estimate adjusted prevalence rate ratios (aPRR). Results Among 71 915 census tracts, neighborhoods in the highest quintiles of structural racism were more likely than neighborhoods in the lowest quintile to be in the southern region (quintile 1, 19.9% vs quintile 5, 56.8%). The proportion of the population that was Black was largest in neighborhoods in the highest quintile of structural racism (quintile 1, 2.2% vs quintile 5, 23.0%). After adjustment, compared with the lowest quintile of structural racism, the highest quintile had significantly higher prevalence rates of cardiovascular clinical risk factors, including high blood pressure (aPRR, 1.37; 95% CI, 1.37-1.38), obesity (aPRR, 1.40; 95% CI, 1.39-1.40), and diabetes (aPRR, 1.80; 95% CI, 1.79-1.81). Similarly, for cardiovascular behavioral risk factors, the highest quintile of structural racism had significantly higher prevalence rates of current cigarette smoking (aPRR, 1.84; 95% CI, 1.83-1.85) and no leisure-time physical activity (aPRR, 1.88; 95% CI, 1.87-1.89). Additionally, for cardiovascular diseases, the highest quintile of structural racism had higher prevalence rates of coronary heart disease (aPRR, 1.80; 95% CI, 1.79-1.82) and stroke (aPRR, 1.99; 95% CI, 1.98-2.00). Conclusions and Relevance In this cross-sectional study, structural racism was associated with inequities in neighborhood cardiovascular health, highlighting opportunities for place-based prevention efforts.

Objective: Progressive apraxia of speech (PAOS) is a neurodegenerative syndrome characterized by impaired motor speech planning and programming, whereas behavioral variant frontotemporal dementia (bvFTD) is characterized by deviant behavioral (e.g., personality and social) features. Clinical and anatomic characteristics of bvFTD in the context of PAOS are understudied. Methods: We identified 12 participants with PAOS and features that were consistent with bvFTD at baseline or follow-up. Eleven completed a head MRI scan. We compared clinical features and anatomical patterns of atrophy in these 11 PAOS-bvFTD participants to 11 matched PAOS participants without bvFTD and 22 age- and sex-matched healthy controls. Statistical Parametric Mapping (SPM) was applied to visualize gray matter volume across both groups compared to controls and each other. Medians and 25th and 75th percentiles were assessed in patients and across groups; Fisher’s Exact Test and Mann–Whitney U tests were applied using BlueSky software, version 10.3.1-Pro. Results: As expected, PAOS-bvFTD participants performed worse on the Frontal Behavioral Inventory (median: 33/72 vs. 10/72), 20-item behavioral assessment scale (4.5/20 vs. 1.0/20), and the Neuropsychiatry inventory (4/36 vs. 1.5/36) compared to the PAOS group (p < 0.01 for all), with no differences in other demographic, neurological, or language tests. Seven of the eleven PAOS-bvFTD participants had bvFTD features develop within three years of symptom onset. The PAOS-bvFTD and PAOS groups showed volume loss in frontal lobe regions compared to controls, with PAOS-bvFTD participants having more prefrontal volume loss than PAOS participants. Conclusions: Behavioral features consistent with bvFTD can co-occur in patients with PAOS and are related to greater atrophy of the prefrontal cortex.

Abstract Purpose: AXL, a receptor tyrosine kinase related to oncogenic processes, is aberrantly expressed in various cancers and associated with treatment resistance. Enapotamab vedotin (EnaV), a novel anti-AXL human IgG1 and monomethyl auristatin E antibody–drug conjugate, demonstrated antitumor activity in preclinical models, including non–small cell lung cancer (NSCLC). This phase 1/2 study assessed the safety and preliminary efficacy of EnaV in solid tumors. Patients and Methods: This study comprised dose-escalation and dose-expansion phases; both phases investigated EnaV once every 3 weeks (Q3W) and EnaV on days 1, 8, and 15 of a 28-day cycle (3Q4W). Primary objectives determined the maximum tolerated dose (dose escalation) and safety (dose expansion). Pharmacokinetic profile, antitumor activity, and AXL expression were also assessed. Results: During dose escalation, 32 patients received EnaV Q3W; 15 received EnaV 3Q4W. The maximum tolerated dose and recommended phase 2 dose were 2.2 mg/kg in Q3W and 1.0 mg/kg in 3Q4W schedules. In dose expansion, 189 patients received EnaV Q3W; 70 received EnaV 3Q4W. Common adverse events in dose expansion included fatigue, constipation, nausea, decreased appetite, and diarrhea. Overall response rates ranged from 4.5% to 12.5% with Q3W dose schedule and from 9.1% to 11.5% with 3Q4W dose schedule. Disease control rates for NSCLC cohorts were 40.9% to 50.0%. NSCLC subset analysis demonstrated correlation between radiomics signature and disease control. The relationship between clinical activity and AXL expression was not apparent. Conclusions: EnaV had an acceptable safety profile; however, because the evaluation of antitumor activity did not show clinically meaningful responses, clinical development of EnaV was discontinued. Significance: EnaV, an anti-AXL human IgG1 and monomethyl auristatin E antibody–drug conjugate, showed single-agent antitumor activity in preclinical models. This phase 1/2 study of EnaV demonstrated a manageable safety profile and antitumor activity in selected tumor types. Further studies exploring alternative targeting modalities, patient selection, and/or combinations are needed.

Abstract Understanding tumor cell dynamics can improve prognosis and treatment but remains limited for lung adenocarcinoma in people who have never smoked (NS-LUAD). With RNA sequencing data from 684 NS-LUAD cases and validation in an independent dataset, we identified three subtypes with distinct phenotypic traits and cell compositions. Additional genomic and histologic data further characterized the subtypes. “Steady,” marked by low proliferation, high alveolar cell fraction, moderate-to-well differentiation, and fewer driver gene alterations, is linked to prolonged survival and low immune evasion. “Proliferative” shows high proliferation markers, TP53 mutations, and gene fusions. “Chaotic,” with high epithelial-to-mesenchymal transition markers, has the worst prognosis, even within stage I tumors. Lacking known molecular or histologic characteristics, this aggressive subtype is solely identified by transcriptomic data. A 60-gene signature recapitulates the classification and predicts survival even within subgroups based on tumor stage or known genomic features, emphasizing its potential for improving early-stage NS-LUAD prognostication in clinical settings. Significance: The transcriptome of 684 NS-LUAD identifies three subtypes with different cellular dynamics and genomic and morphologic features. A 60-gene signature accurately stratifies subjects for mortality risk, even in stage I, offering a potential clinically applicable tool for treatment decision-making in patients with NS-LUAD. See related commentary by Azizi et al., p. 423

Abstract INTRODUCTION The Genetic Frontotemporal Initiative (GENFI) and Advancing Research and Treatment in Frontotemporal Lobar Degeneration (ARTFL)‐Longitudinal Evaluation of Familial Frontotemporal Dementia Subjects (LEFFTDS) Longitudinal Frontotemporal Lobar Degeneration Study (ALLFTD) consortia developed Clinical Dementia Rating (CDR)‐derived scales with a motor domain to overcome systematic underestimation of disease severity by the CDR. We calculated disease severity scores using these scales in a mixed neurodegenerative cohort and correlated them with objective motor measures. METHODS The CDR plus National Alzheimer's Coordinating Center (NACC) Frontotemporal Lobar Degeneration (FTLD), CDR + NACC FTLD‐M (Motor), and Multidomain Impairment Rating (MIR) scores and motor measures were determined and correlated in 242 participants. RESULTS Both CDR + NACC FTLD‐M and MIR showed increased disease severity scores and correlated with motor measures. These findings were held in 81 amyotrophic lateral sclerosis (ALS) participants and correlated with the Revised Amyotrophic Lateral Sclerosis Functional Rating Scale. Including a motor domain required fewer study participants in a simulated clinical trial sample size calculation. DISCUSSION With a motor domain, CDR + NACC FTLD‐M and MIR improve disease severity classification and correlate with quantitative motor assessments. This addition more fully captures the extent of symptoms across the FTD‐ALS spectrum and improves clinical trial efficiency. Highlights CDR + NACC FTLD‐M and MIR strongly correlate with objective motor measures. The enhanced scales improve disease severity classification in FTD and ALS. Greater clinical trial efficiency is achieved using these enhanced scales.

Abstract The highest risk for liver allograft rejection is within the first weeks to months after a liver transplant; however, while later-stage mechanisms are well described, the initial window at the moment of alloantigen influx (e.g., ~ 1 h post-reperfusion) remains unmapped. T-cell priming is initiated in lymph nodes, yet these sites are rarely profiled in humans and their relationship to rejection risk is unclear. To address this gap, we profiled paired pre- and ~ 1 h post-reperfusion hepatic draining lymph nodes from liver transplant recipients using multiplexed immunofluorescence and GeoMx whole-transcriptome profiling to identify features that predict rejection versus non-rejection. Early after reperfusion, CD4 + and CD8 + T cells became proximal to macrophages in rejection compared to dendritic cells in non-rejection, which also displayed an immunoregulatory-leaning profile marked by PD-1, IDO1, and CD39, suggesting that macrophages and dendritic cells differentially influence early T-cell alloimmunity. These results provide nodal targets for precise immune modulation.

ABSTRACT As artificial intelligence (AI) continues to rapidly reshape health care, there is a critical need for clear frameworks for clinicians to ensure ethical, equitable, and effective integration and use of AI in patient care. Key integrations of AI include enhancing health communications, patient engagement, clinicians' training, pharmaceutical advertising, clinical decision‐making, and automation of clinical operations and workflow. However, there are growing concerns related to regulatory gaps, the spread of misinformation, security threats, patient and data privacy leaks, and widening health disparities gaps. These concerns are exacerbated by limited institutional infrastructure and limited AI literacy of clinicians and patients. Recent policy developments reflect efforts to guide responsible AI development and use. While progress has been made, the lack of standardized human oversight remains a critical gap, particularly as policies may not fully consider the challenges and complexities at institutional, societal, technical, and individual levels. Thus, herein, the Drug Information Practice and Research Network (DI PRN) of the American College of Clinical Pharmacy (ACCP) : (1) explores current and emerging multifaceted challenges, a call to action, and opportunities of AI integration in health care including examination of the regulatory, ethical, operational, and health equity implications; and (2) provides practical recommendations for responsible use of AI through DI PRN‐developed example case‐based approaches and best practices infographics to enhance AI literacy for diverse learners including clinicians, trainees, and patients. This DI PRN opinion paper highlights the importance of proactive governance frameworks and equips and empowers diverse learners with practical AI literacy tools to confidently engage with AI technologies in patient care.