Citations

Abstract Repetitive injury is hypothesized to lead to progressive tissue fibrosis and end-stage organ failure. Whether tissue-resident mesenchymal cell populations retain epigenetic memory of prior injuries that contribute to this pathological process is unknown. Here we used a genetic lineage labeling approach to mark the lung mesenchyme prior to injury, then performed multimodal analyses on isolated lung mesenchyme during the initiation, progression, and resolution of the fibrotic response. Our results demonstrate the remarkable epigenetic and transcriptional plasticity of the lung mesenchyme during fibrotic activation and de-activation. Despite this plasticity, we also find that the lung mesenchyme exhibits an enhanced fibrotic program upon reinjury. We identify RUNX1 as a critical driver of both fibrotic activation and fibrotic memory. Comparison of fresh isolated and cultured lung mesenchyme demonstrates that RUNX1 is spontaneously activated in standard culture conditions, previously masking these roles of RUNX1. Targeted knockdown of RUNX1 dampens fibrotic mesenchymal cell activation immediately after cell isolation, but with reduced efficacy after only days of culture, confirming its functional importance to both early activation and long-term memory. Collectively, our findings implicate RUNX1 in the initiation and memory of fibrotic mesenchymal cell activation that together prime enhanced mesenchymal cell responses upon repeated injury.

BACKGROUND: No large registries of patients with acute eosinophilic myocarditis (EM) are available. However, EM is perceived as a cardiac disease with high mortality, affecting mainly young and middle-aged adults according to small series and case reports. Awareness of the clinical presentation, associated systemic conditions, treatments, and outcomes of this uncommon condition is an unmet need. METHODS: In this international, multicenter, retrospective cohort study, 53 centers screened 193 patients with histologically proven acute EM between 1992 and 2023. After the exclusion of patients with insufficient data (n=10), symptoms lasting >30 days (n=19), or histological diagnosis not confirmed after review (n=8), 156 patients were included. RESULTS: Median age at presentation was 48 years (first to third quartile, 34–59 years) with male predominance (67.3%), and only 2 were pediatric cases (≤16 years of age; 1.3%). The main signs and symptoms at presentation were dyspnea (75.6%), fever (61.3%), and chest pain (53.2%). Unexpectedly, peripheral eosinophilia was reported in only 57.4% of cases, with a median cell count of 630 eosinophils/μL. The median left ventricular ejection fraction at presentation was 32% (first to third quartile, 25%–48%). The disorders most frequently associated with EM were eosinophilic granulomatosis with polyangiitis (22.4% of cases) and hypersensitivity forms (14.1%). Idiopathic/undefined forms accounted for 44.9% of cases, and miscellaneous causes accounted for 18.6%. In-hospital death or need for heart transplantation (HTx) occurred in 23 patients (14.7%; 22 deaths and 1 HTx), despite 43.6% being treated with temporary mechanical circulatory support and 92.9% being treated with immunosuppressive agents. Estimated rates of death or HTx at 1 and 3 years were 19.0% and 23.8%. Increased age, decreased left ventricular ejection fraction on admission, and no immunosuppressive therapy during hospitalization were independent predictors of death or HTx. A nonsignificant higher occurrence of deaths or HTx was observed in the hypersensitivity form (46.1%) compared with the eosinophilic granulomatosis with polyangiitis–associated form (13.1%) at 3 years ( P =0.15). CONCLUSIONS: Acute EM can often present without peripheral eosinophilia, and rates of in-hospital and midterm mortality or HTx are high. Endomyocardial biopsy is required to reach the final diagnosis of EM because relying on peripheral eosinophilia can lead to missing diagnosis. In-hospital immunosuppression is associated with HTx-free survival, although tailored immunosuppressive therapies are needed to improve outcomes. REGISTRATION: URL: https://www.clinicaltrials.gov ; Unique identifier: NCT06447935.

ABSTRACT Background: Successful use of nurse practitioner (NP) and physician assistant (PA) care models is vital to delivering optimal patient care, maintaining financial stability, and reducing staff burnout and turnover. Local Problem: A large cardiovascular surgery practice experienced substantial growth, leading to an obsolete NP and PA care model. Consequently, workplace satisfaction scores were reduced, and the NP and PA turnover rate in 2018 was unacceptably high. Methods: A multidisciplinary redesign team was formed to address gaps in the care model through a quality-improvement initiative. The team analyzed current workflows, collected external and internal survey data, conducted patient tracers, and observed staff. Seventeen collaborative care models were proposed by frontline staff members and presented to their peers for feedback. This feedback was used to select the best aspects of the proposed models to create a new model. Interventions: The updated care model consisted of several key changes, which included reducing NP- and PA-to-patient ratios, eliminating redundant tasks, shifting from location-based to surgical team–based coverage, ensuring top-of-scope practice, developing specialty service lines, and separating inpatient and outpatient practices. Results: After implementation of the new care model, NP and PA turnover rates decreased from baseline by an average of approximately 74% per year. Departmental work culture scores concomitantly increased from 68% to 85% after implementation of the new care model. Conclusions: Adapting care models in conjunction with departmental growth is crucial to enhancing staff satisfaction and reducing burnout.

Abstract Background Mitral regurgitation (MR) and tricuspid regurgitation (TR) are prevalent valvular heart diseases associated with significant morbidity and mortality. Traditional echocardiography faces limitations in availability, cost, consistency, and reliability, leading to misdiagnosis and undertreatment. The application of artificial intelligence (AI) to echocardiographic scans has the potential to address these challenges. Methods This study evaluates the performance of an AI algorithm on an external population. The algorithm utilizes deep learning networks to analyze echocardiographic exams for diagnosing atrioventricular valve disorders. We tested the algorithm on transthoracic echocardiography data collected from a single center between 2013 and 2023. The model's performance was compared to ground truth values using two classification schemes: distinguishing between normal-mild and moderate-severe regurgitation, and categorizing results into four groups: normal, mild, moderate, and severe. Results The MR cohort included 280 patients, while the TR cohort comprised 298 patients. The model demonstrated a robust ability to identify clinically significant (moderate and above) atrioventricular valve regurgitation. The MR model achieved an area under the curve (AUC) of 0.98 (95% CI: 0.97–0.99), with 91% accuracy, 95% sensitivity, and 89% specificity. In comparison, the TR model exhibited an AUC of 0.96 (95% CI: 0.94–0.98), with 84% accuracy, 91% sensitivity, and 80% specificity. Conclusion The model demonstrated high diagnostic accuracy and reliability in assessing atrioventricular valve regurgitation severity, highlighting its potential as a valuable clinical tool. The findings underscore the role of AI in complementing expert evaluations and improving access to effective diagnostics, with future applications potentially including point-of-care diagnosis and monitoring of disease progression.

PURPOSE Despite evidence of diagnostic accuracy but unclear long-term clinical benefit, national utilization patterns of prostate-specific membrane antigen positron emission tomography (PSMA-PET) are undefined. METHODS We conducted a serial cross-sectional study to evaluate the use of PET imaging among commercial insurance beneficiaries with prostate cancer using administrative claims from deidentified Blue Cross Blue Shield Axis database. Eligible patients included prevalent and incident prostate cancer cases. We calculated the proportions undergoing PET imaging in semiannual periods from January 1, 2016, through December 31, 2024. We examined the association between regional-level contextual sociodemographic and health care characteristics, and regional use of PSMA-PET imaging in 2024. RESULTS A total of 514,750 male beneficiaries age 40-89 years with prostate cancer were identified between 2016 and 2024. The proportion of individuals with prostate cancer undergoing PET imaging increased from 4.5 [95% CI, 4.1 to 4.9] per 1,000 in the first half of 2016 to 77.6 (95% CI, 76.2 to 79.1) per 1,000 in the second half of 2024, P < .001. Increases in PET were driven by uptake of PSMA-PET following approval in 2021, which increased from 0.8 (95% CI, 0.6 to 0.9) per 1,000 in the second half of 2021 to 77.0 (95% CI, 75.5 to 78.5) per 1,000 in the second half of 2024, P < .001. PSMA-PET use in 2024 was higher in regions with greater education (99.3 v 114.6 per 1,000 in lowest [Q1] v highest [Q4] educated) and income (95.4 v 112.3 per 1,000 in Q1 v Q4 income) measures, P < .001. CONCLUSION PSMA-PET was rapidly incorporated into clinical practice among commercial insurance beneficiaries with prostate cancer with higher adoption in geographic regions with higher income and education.

Abstract Background Fibroblast growth factor-inducible 14 (Fn14) belongs to the TNFR superfamily. Fn14 overexpression can drive receptor-autonomous signaling, increase both cell invasion and tumor-associated macrophages/microglia (TAMMs) recruitment, and correlates with reduced survival in glioblastoma (GBM) patients and rat gliomas. While prior studies report Fn14 expression in non-tumor cells within the GBM tumor microenvironment (TME), their relative contributions to glioma pathobiology remain unclear. Methods Using tumor-host pairings of Fn14-positive and -knockout (-KO) cells and mice, we examined the role of Fn14 in glioma biology. Mouse glioma and human GBM datasets were analyzed at the cellular, protein, and transcriptomic levels to assess Fn14-associated changes in the glioma TME and survival outcomes. Results Fn14 was found to be highly expressed in tumor cells and TAMMs in human GBM and 2 well-characterized murine glioma models. Fn14 KO in both tumor and host cells increased overall survival. Notably, this survival benefit was greater in the glioma model characterized by a more immunologically activated TME. Immunophenotyping revealed that Fn14 loss reshapes the tumor-immune landscape, reducing the presence of immunosuppressive macrophages and exhausted T-cells, suggesting that Fn14 modulates both innate and adaptive immune responses. These findings were supported by analyses of human GBM datasets, where high Fn14 expression correlated with immunosuppressive shifts and poor patient responses to immune checkpoint inhibitor therapy. Conclusions This study provides the first description of the contributions of both tumor- and host-derived Fn14 expression to tumor immunity and survival and identifies Fn14 as an important mediator of innate and adaptive immune responses in gliomas.

Abstract Background Right ventricular (RV) systolic function is an independent predictor of outcome, but a robust automated method of quantitation is lacking. Purpose To develop and validate a deep learning model for quantifying RV systolic function from 2D echo clips and assess its prognostic value in various loading states and left-heart disease. Methods A deep learning convolutional neural network (CNN) was developed to quantitate RV systolic function using all available apical 4 chamber (A4C) views from 13,039 patients (61,187 A4C video clips) in a US institution. Videos were converted to frames of 256x256 grayscale pixels, with graphics, including the ECG, removed. The model used the S3D architecture, a 3D-CNN with many of the spatial and temporal convolutions separated. RV free wall longitudinal strain (RVFWLS) was used to define RV function: normal (≤-25%); mild (-20% to –24%); moderate (-15% to –19%) and severe RV dysfunction (0% to –14%). The AI-estimate was the average of multiple 32-frame segments for each clip identified as an A4C, including both standard and RV-focused variations. The model was validated in four distinct clinical cohorts from independent data sources: 1) 1365 patients referred to an external diagnostic echocardiography lab in a different US state; 2) 569 patients recruited prospectively with known or suspected pulmonary hypertension (PH); 3) 416 patients with moderate or greater tricuspid valve regurgitation (TR); 4) 872 patients imaged at an Eastern European University with left-sided cardiac diseases (valvular disease, cardiomyopathies, etc.), athletes, or healthy volunteers. Results Model output was strongly correlated with the presence of RV dysfunction by manual measurement [AUC 0.907 (95% CI: 0.888-0.926)] in the internal validation set. Results were similar in an external validation US cohort [N=1365; AUC 0.919 (95% CI: 0.899-0.939)]. When the model was applied to 569 patients with known or suspected PH, presence and severity of the AI measures of RV dysfunction were strongly associated with functional class, 6 min walk distance, NT-proBNP levels and right heart failure (p&lt;0.001). The presence and severity of RV dysfunction (as categorized by the AI model) was strongly associated with all-cause mortality in patients with PH (Figure 1A) and in patients with significant TR (Figure 1B). The European cohort was a geographically and institutionally distinct cohort with a low prevalence of &gt;mild TR (10.8%) or PH (6.4%). In this cohort the AI model predicted a reduced RVEF (&lt; 45%) with an AUC of 0.864 (95% CI: 0.831-0.898) and associated with both all-cause mortality (Figure 2A) and a composite endpoint of heart failure hospitalization or death (Figure 2B). Conclusions A fully automated, AI-based assessment of RV systolic function, using 2D apical 4 chamber echo views alone, is highly feasible and can accurately categorize disease severity and predict outcome across a wide spectrum of cardiovascular conditions.Figure 1 Figure 2

INTRODUCTION Recent work has identified unique cognitive profiles for early‐onset Alzheimer's disease (EOAD) relative to late‐onset Alzheimer's disease (LOAD), however, examination has been limited in determining whether the association between age and cognitive severity at presentation also differs across conditions. METHODS A series of linear spline regression models was conducted across baseline cognitive data from 325 EOAD and 314 LOAD participants, after accounting for education, sex, and apolipoprotein ε4 status. RESULTS Significant differences existed in the relationship between baseline age and cognitive performance between EOAD and LOAD samples for Processing Speed/Attention, Executive Functioning, and Episodic Immediate Memory. Younger participants from both EOAD and LOAD groups performed disproportionately worse on non‐amnestic cognitive domains, with this occurring to a greater extent in EOAD than LOAD. DISCUSSION In the age of disease‐modifying treatments, results highlight the importance of assessing for cognitive declines in individuals starting much earlier than age 65. Highlights Early‐onset Alzheimer's disease (EOAD) and late‐onset Alzheimer's disease (LOAD) participants each displayed cognitive impairments relative to same‐aged peers across most domains. Both groups displayed positive relationships between impairment among non‐amnestic cognitive domains and baseline age. This relationship displayed a significantly greater effect in EOAD than LOAD, with domains of Processing Speed/Attention and Executive Functioning skills being the most pronounced. Of those participants developing AD, age displayed a disproportionate impact on their symptom onset.

BACKGROUND: Myocardial flow reserve (MFR), measured by positron emission tomography (PET) myocardial perfusion imaging, provides valuable information on epicardial coronary disease, diffuse atherosclerosis, and microvascular function. Despite its routine use, the prognostic efficacy of 13 N-ammonia PET MFR remains unconfirmed in larger multicenter cohorts of patients with suspected or known coronary artery disease. METHODS: We considered patients from 5 sites in the REFINE PET (Registry of Flow and Perfusion Imaging for Artificial Intelligence with PET) registry who underwent 13 N-ammonia PET myocardial perfusion imaging for coronary artery disease. Clinical and imaging data were collected at the time of myocardial perfusion imaging. MFR was quantified as the ratio of stress to rest myocardial blood flow, using QPET software (Cedars-Sinai Medical Center, Los Angeles, CA). The primary outcome was all-cause mortality. Survival analyses were performed using Kaplan-Meier and Cox regression models adjusted for clinical and imaging covariates. RESULTS: In total, 6277 patients were included (median age of 65 years, 56% male). Median follow-up time was 3.8 years. There were 1895 patients with MFR ≤2 and 4382 with MFR >2. Patients with MFR ≤2 had significantly higher mortality than those with MFR >2 (n=701 [37.0%] versus n=537 [12.3%], respectively; P <0.001). Annualized all-cause mortality rates by MFR and summed stress score ranged from 1.7 to 15.8. In multivariable analysis, MFR ≤2 was independently associated with increased all-cause mortality in the overall population (hazard ratio, 2.70 [95% CI, 2.41–3.03]; P <0.001), even among patients with no perfusion defects (hazard ratio, 2.36 [95% CI, 1.93–2.89]; P <0.001). Mortality risk decreased across increasing MFR deciles, ranging from hazard ratio, 2.73 (95% CI, 2.39–3.11) to hazard ratio, 0.35 (95% CI, 0.25–0.49). CONCLUSIONS: In this large multicenter cohort, MFR derived from 13 N-ammonia PET myocardial perfusion imaging is a strong, independent predictor of all-cause mortality, even in patients with normal perfusion. An MFR of ≤2 identifies elevated risk, while higher values are associated with improved survival. These findings support the routine integration of MFR to enhance risk stratification in patients with suspected or known coronary artery disease.