Citations

Abstract Background Artificial intelligence (AI) models trained on 12-lead ECGs effectively detect left ventricular systolic dysfunction (LVSD; left ventricular ejection fraction [LVEF] <=40%). Continuous ECG monitoring via Holter recordings provides an opportunity for opportunistic screening for structural heart disease beyond rhythm disorders. We hypothesized that a lead-invariant version of the 12-lead AI model would enable a Holter monitor to screen for both arrhythmias and ventricular dysfunction. Methods We retrospectively analyzed continuous Holter ECGs from 17,665 patients who underwent a Holter and transthoracic echocardiogram (TTE) within 30 days of each other at Mayo Clinic. From each Holter, a random 20-minute of valid (non-flatline/lead disconnect) ECG segment was extracted and analyzed for LVSD detection using the adapted lead-invariant AI model. To evaluate stability, we examined model performance across different time points of the day, presenting results as area under the receiver operating characteristic curve (AUC) over time. Moreover, we illustrated the model’s robustness to noisy data by comparing its performance on raw ECG signals with that on bandpass-filtered inputs. Results Among 17,665 patients (mean age 59 years, 48.57% female), 4.96% had an LVEF <=40%. The AI model demonstrated strong predictive performance (20-minute segment AUC 0.90, mean prediction of 24-hour AUC 0.92). Analysis of results over time (Figure) revealed temporal patterns in predictive accuracy, with specific time periods showing greater stability. Despite modest variability, model performance remained consistently high throughout the day, confirming robustness across different physiological states. The predictions remained robust with noisy input. We did not observe performance improvement when the baseline wander and high frequency noise are removed by the bandpass filter. Conclusion Applying a 12-lead AI ECG model with a lead-invariant framework to a continuous Holter ECG enables effective screening for left ventricular dysfunction. This suggests that AI-based analysis of Holter-monitors can facilitate opportunistic screening of ventricular dysfunction and may enable assessment of an arrhythmia’s impact on LVEF, as well as the relationship between arrhythmia burden and LVEF.Figure 1.Mean prediction AUC of the day Figure 2.AUC for different time point

Abstract Enhancer is a critical epigenetic feature in head and neck squamous cell carcinoma (HNSCC), yet the functional roles of individual enhancers remain poorly understood. Here, we conducted an integrative multi-omics analysis based on publicly available ATAC-seq, H3K27ac ChIP-seq, transcriptomic profiling, and genetic association datasets to systematically map HNSCC-associated enhancers and their target genes. Integrating ATAC-seq and H3K27ac ChIP-seq, we identified 20,362 enhancers and 18,040 enhancer-associated genes, highlighting widespread regulatory complexity. Functional characterization of the TERT -associated enhancer GH05J001312 (GRCh38/hg38: chr5:1312099-1317743) revealed strong transcriptional activity in HNSCC. CRISPR-mediated deletion of a core sequence significantly reduced TERT expression, impaired cellular proliferation in vitro , and suppressed tumor growth in vivo , confirming its role as a key cis-regulatory element. RNA-seq analysis of enhancer-edited cells uncovered 742 differentially expressed genes enriched in cancer-related pathways, including MAPK and IL-17 signaling, indicating a broad transcriptional impact. Collectively, our findings establish GH05J001312 as a functional enhancer driving oncogenic programs in HNSCC and suggest enhancer-targeted strategies as a potential therapeutic avenue.

Abstract Glioblastoma (GBM) remains the most aggressive primary adult brain cancer with limited therapeutic options. Our prior research demonstrated that androgen receptor antagonists (ARAs) enhance survival in GBM mouse models by preferentially suppressing glioma stem cells. This study investigates the potential of ARAs as radiosensitizers in combination with radiotherapy (RT). Combined effects of ARAs and RT were evaluated using an orthotopic GBM mouse model. RNA-Seq and TCGA analyses delineated AR-associated regulatory networks, while integrated cellular and molecular approaches utilizing human and mouse GBM cell lines, as well as in patient-derived primary high-grade glioma cultures, interrogated signaling and immune paradigms. ARA treatment induced G2/M cell cycle arrest, apoptosis, and downregulated DNA repair genes, with AR expression correlating with the DNA repair and TGF-β pathway. In both immortalized GBM cell lines and primary high-grade glioma cultured cells, ARAs in combination with RT showed only a modest enhancement of radiosensitivity. However, in an orthotopic GBM mouse model, ARA + RT demonstrated a strong synergy, achieving 100% long-term survival, compared to less than 50% with ARA alone and 0% with RT alone. Mechanistically, ARA modulated the TGF-β pathway, durably switching from Smad3 linker to c-terminal phosphorylation (pSmad3C) and inhibiting LIF/STAT3 axis. Distinct TGF-βs ligand expression patterns were observed in ARA-treated GBM cells. A protein–protein interaction noted between AR and Smad3, which was disrupted following ARA treatment, leading to elevated protein levels and nuclear localization of pSmad3C (S423/425), indicating normalization/activation of the TGF-β/pSmad3C-dependent anti-tumorigenic cascade. Comprehensive analyses in GBM cell lines and mouse model tissues demonstrated pathway reprogramming characterized by elevated TGF-β2 and increased pSmad3C. Tissue analysis revealed immune activation in the tumor microenvironment, while peripheral blood and spleen showed systemic immune responses following ARA + RT. Our study provides novel insights into how ARAs enhance RT efficacy through immunomodulation involving TGF-β/pSmad3C cascade, offering therapeutic implications in GBM.

Abstract Cerebrospinal fluid (CSF) proteomics offers insights into molecular changes in aging and Alzheimer’s disease (AD). Key AD biomarkers, in particular amyloid-β (Aβ) and tau, in CSF are strongly associated with APOE genotype, the strongest genetic risk determinant of AD. To investigate how APOE genotype influences CSF proteome across AD pathology and age, we analyzed 362 neurology-related proteins and established AD biomarkers in CSF from 145 cognitively unimpaired participants in the Mayo Clinic Study of Aging. Importantly, our cohort is uniquely balanced across APOE genotypes, with similar representation of APOE2 carriers, APOE3/3 genotype, and APOE4 carriers. We identified several proteins, including lipid metabolism-related Lp-PLA2 and immune-related ITGAM, with strong APOE genotype-specific association. Notably, meta-analysis confirmed that ITGAM levels were consistently higher in APOE4 compared to APOE2 carriers across multiple cohorts and proteomic platforms. In addition, with increasing amyloid deposition, APOE4 carriers exhibited stronger immune responses, reflected by elevated ITGAM, TNF-α receptors, and IL-6, whereas APOE2 carriers showed attenuated responses. We further observed sex-specific effects among APOE2 carriers, characterized by distinct patterns in amyloid and CXCL11 levels. These findings suggest distinct mechanisms underlying APOE2’s protective and APOE4’s detrimental effects in brain aging and AD, paving for personalized diagnostics and interventions.

Abstract Background Relapsed medulloblastoma remains a significant therapeutic challenge as it is near universally fatal. The tumor microenvironment of medulloblastoma plays a critical role in tumor progression, influencing tumor growth, immune evasion, and therapeutic resistance. We hypothesized that defining tumor-immune interactions in diagnostic and relapsed medulloblastoma may uncover mechanisms of immune evasion and identify novel therapeutic targets. Methods We analyzed paired primary and recurrent RNA-sequencing data from 140 medulloblastoma patients to profile immune cell composition and validate spatial relationships within the TME. To identify key tumor-immune interactions, we developed a novel algorithm to detect receptor-ligand pairs using single-cell RNA-sequencing data. These interactions were validated across RNA and proteomic datasets. Their functional significance was empirically demonstrated in newly developed immunocompetent models of recurrent medulloblastoma that closely recapitulate the human disease. Results We observed a shift toward a heightened immunosuppressive TME at relapse. Using our algorithm, we identified biologically significant receptor-ligand interactions, most notably MIF-CD74, constitutively expressed at RNA and protein levels across medulloblastoma subgroups, at diagnosis and relapse. Disrupting MIF-CD74 interactions led to significant alterations in the tumor microenvironment, highlighting its functional significance. Conclusions Our multifaceted approach identified key tumor-immune interactions in medulloblastoma. Among these, MIF-CD74 was validated as a targetable interaction, demonstrating the utility of our integrative approach for identifying novel therapeutic targets across multiple tumor types.

This study uncovers a previously unrecognized role for NAV3 in TGF-β1-driven cardiac fibroblast activation. We show that NAV3 facilitates profibrotic remodeling through noncanonical signaling and cell cycle arrest, independently of SMAD2/3. These findings position NAV3 as a novel regulator of fibroblast phenotype and a potential modulator of cardiac fibrosis.