Citations

ABSTRACT The insulin‐like growth factor‐1 (IGF‐1) signaling pathway is known as a potent aging modifier, disruption of which consistently associates with lifespan extension across diverse species. Despite this established association, the mechanisms by which IGF‐1 signaling modulates organ aging remain poorly understood. In this study, we assessed age‐related changes in IGF‐1 expression across multiple organs in mice and identified a more prominent increase in skin IGF‐1 levels with aging—a phenomenon also observed in human skin. To explore the consequences of elevated IGF‐1, we developed transgenic mice ectopically expressing human IGF‐1 in the epidermis, driven by the bovine keratin 5 promoter (IGF‐1 Tg). These mice exhibited premature aging of hair follicles, as evidenced by accelerated hair graying and loss. Single‐cell RNA sequencing analyses of dorsal skin highlighted an upsurge in cellular senescence markers and the senescence‐associated secretory phenotype (SASP) in hair follicle stem cells (HFSCs), alongside a decline in hair growth and HFSC exhaustion. Our findings indicate that excessive IGF‐1 triggers HFSC senescence, thereby disrupting hair follicle homeostasis. Remarkably, interventions in IGF‐1 signaling via downstream mechanisms—specifically blocking Ac‐p53 activation via SIRT1 overexpression or senolytic treatment for senescent cell clearance, or reducing IGF‐1 through dietary restriction—significantly reduced senescence markers, mitigated premature hair follicle aging phenotypes, and restored the stem cell pool. Our findings provide fundamental insights into the biological processes of hair aging and highlight the therapeutic promise of targeted interventions to rejuvenate aged HFSCs and promote hair follicle health.

AbstractA deeper understanding of pathophysiology, from the initiation of tumors of epithelial cells to the outcomes associated with it, is imperative to test potential interventions for these diseases. However, few models can accomplish this because of the complexity of origin cells and their transformation. We established an epithelial cell original tumor mouse model by conditional KrasG12D activation and tumor suppressor gene p53 deletion specifically in epithelial cells of the whole body using Cytokeratin 19 (CK19) promoter via Cre‐ERT mediation. Multiple tumors derived from epithelial cells developed in this novel model. This model can recapitulate the progress of intestinal, pancreatic, liver, lung, and subcutaneous tumors which derive from epithelial cells. This model is suitable for studying the pathophysiology of tumors derived from epithelial cells and may have the potential to test interventions.

INTRODUCTION: Building on prior results, we hypothesized that an electrocardiogram (ECG)-enabled machine learning (ML) model could be used to detect advanced chronic liver disease (CLD). METHODS: A cohort with CLD and 12-lead ECGs was matched with controls from electronic health records. A ML model was trained as a binary classifier. RESULTS: There are 12,930 patients with CLD and 64,577 controls in the cohort. The model's discriminative ability to classify CLD showed an area under the receiver-operating characteristic curve 0.858 (95% confidence interval: 0.850–0.866), and at the chosen threshold, CLD ECGs had 12 times higher odds of being classified as CLD (diagnostic odds ratio 12.33, 95% confidence interval: 11.16–13.63). DISCUSSION: An ECG-enabled ML model affords great promise in identifying advanced CLD in low resource areas.

Cardiac fibrosis, a hallmark of heart failure and various cardiomyopathies, represents a complex pathological process that has long challenged therapeutic intervention. High-throughput omics technologies have begun revolutionizing our understanding of the molecular mechanisms driving cardiac fibrosis and are providing unprecedented insights into its heterogeneity and progression. This review provides a comprehensive analysis of how techniques—encompassing genomics, epigenomics, transcriptomics, proteomics, and metabolomics—are providing insight into our understanding of cardiac fibrosis. Genomic studies have identified novel genetic variants and regulatory networks associated with fibrosis susceptibility and progression, and single-cell transcriptomics has unveiled distinct cardiac fibroblast subpopulations with unique molecular signatures. Epigenomic profiling has revealed dynamic chromatin modifications controlling fibroblast activation states, and proteomic analyses have identified novel biomarkers and potential therapeutic targets. Metabolomic studies have uncovered important alterations in cardiac energetics and substrate utilization during fibrotic remodeling. The integration of these multi-omic data sets has led to the identification of previously unrecognized pathogenic mechanisms and potential therapeutic targets, including cell-type-specific interventions and metabolic modulators. We discuss how these advances are driving the development of precision medicine approaches for cardiac fibrosis while highlighting current challenges and future directions in translating multi-omic insights into effective therapeutic strategies. This review provides a systems-level perspective on cardiac fibrosis that may inform the development of more effective, personalized therapeutic approaches for heart failure and related cardiovascular diseases.

BACKGROUND: Patients presenting with cervical artery dissection (CAD) are at risk for subsequent ischemic events. We aimed to identify characteristics that are associated with increased risk of ischemic stroke after initial presentation of CAD and to evaluate the differential impact of anticoagulant versus antiplatelet therapy in these high-risk individuals. METHODS: This was a preplanned secondary analysis of the STOP-CAD study (Antithrombotic Treatment for Stroke Prevention in Cervical Artery Dissection), a multicenter international retrospective observational study (63 sites from 16 countries in North America, South America, Europe, Asia, and Africa) that included patients with CAD predominantly between January 2015 and June 2022. The primary outcome was subsequent ischemic stroke by day 180 after diagnosis. Clinical and imaging variables were compared between those with versus without subsequent ischemic stroke. Significant factors associated with subsequent stroke risk were identified using stepwise Cox regression. Associations between subsequent ischemic stroke risk and antithrombotic therapy type (anticoagulation versus antiplatelets) among patients with identified risk factors were explored using adjusted Cox regression. RESULTS: In all, 4023 patients (mean age was 47.4 years; 44.5% were women) were included. By day 180, subsequent ischemic stroke occurred in 5.3% of the cohort. In adjusted Cox regression, factors associated with increased risk of subsequent ischemic stroke were prior history of ischemic stroke (adjusted hazard ratio [aHR], 7.31 [95% CI, 1.61–33.13]; P =0.010), presentation within 7 days from first symptoms (aHR, 3.04 [95% CI, 1.04–8.91]; P =0.043), infarct on baseline imaging (aHR, 9.85 [95% CI, 3.65–26.58]; P <0.001), and occlusive dissection (aHR, 2.34 [95% CI, 1.03–5.34]; P =0.043). Only patients with occlusive dissection demonstrated a reduced risk of subsequent ischemic stroke when treated with anticoagulation versus antiplatelets (aHR, 0.36 [95% CI, 0.16–0.80]; P =0.01). CONCLUSIONS: In this post hoc analysis of the STOP-CAD study, several factors associated with subsequent ischemic stroke were identified among patients with CAD. Furthermore, we identified a potential benefit of anticoagulation in patients with CAD with occlusive dissection. These findings require validation by meta-analyses of prior studies to formulate optimal treatment strategies for specific high-risk CAD subgroups.

Objectives/Goals: Our study’s objective is to evaluate RadOnc-GPT, a GPT-4o powered LLM, in generating responses to in-basket messages related to prostate cancer treatment in the Radiation Oncology department. By integrating it with electronic health record (EHR) systems, the goal is to assess its impact on clinician workload, response quality, and efficiency in healthcare communication. Methods/Study Population: RadOnc-GPT was integrated with patient EHRs from both hospital-wide and radiation-oncology-specific databases. The study examined 158 pre-recorded in-basket message interactions from 90 non-metastatic prostate cancer patients. Quantitative natural language processing analysis and two randomized single-blinded grading studies, involving four clinicians and four nurses, were conducted to evaluate RadOnc-GPT’s response quality in completeness, correctness, clarity, empathy, and estimated editing time. Response times were measured to estimate the time saved for clinicians and nurses. The study population included patient messages across all phases of care (pre-, during, and post-treatment) for those undergoing radiotherapy. Results/Anticipated Results: In the single-blinded grader study, clinician graders evaluated 316 responses (158 from human care teams and 158 from RadOnc-GPT). Results showed RadOnc-GPT outperformed human responses in empathy and clarity, while humans excelled in completeness and correctness. Sentiment analyses using TextBlob and VADER revealed RadOnc-GPT responses had a positive mean score of 0.25, whereas human responses clustered around neutral. VADER analysis indicated a high median score for RadOnc-GPT, nearing 1.0, reflecting predominantly positive sentiment, while human responses displayed a broader sentiment range, indicating sensitivity to context. Clinicians averaged 3.60 minutes (SD 1.44) to respond, compared to 6.39 minutes (SD 4.05) for nurses, highlighting RadOnc-GPT’s efficiency in generating timely responses. Discussion/Significance of Impact: RadOnc-GPT effectively generated responses to individualized patient in-basket messages, comparable to those from radiation oncologists and nurses. While human oversight is still necessary to avoid errors, RadOnc-GPT can speed up response times and reduce pressure on care teams, shifting their role from drafting to reviewing responses.