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INTRODUCTION:Cirrhosis is associated with cardiac dysfunction and distinct electrocardiogram (ECG) abnormalities. This study aimed to develop a proof-of-concept deep learning-based artificial intelligence (AI) model that could detect cirrhosis-related signals on ECG and generate an AI-Cirrhosis-ECG (ACE) score that would correlate with disease severity.METHODS:A review of Mayo Clinic's electronic health records identified 5,212 patients with advanced cirrhosis ≥18 years who underwent liver transplantation at the 3 Mayo Clinic transplant centers between 1988 and 2019. The patients were matched by age and sex in a 1:4 ratio to controls without liver disease and then divided into training, validation, and test sets using a 70%-10%-20% split. The primary outcome was the performance of the model in distinguishing patients with cirrhosis from controls using their ECGs. In addition, the association between the ACE score and the severity of patients' liver disease was assessed.RESULTS:The model's area under the curve in the test set was 0.908 with 84.9% sensitivity and 83.2% specificity, and this performance remained consistent after additional matching for medical comorbidities. Significant elevations in the ACE scores were seen with increasing model for end-stage liver disease-sodium score. Longitudinal trends in the ACE scores before and after liver transplantation mirrored the progression and resolution of liver disease.DISCUSSION:The ACE score, a deep learning model, can accurately discriminate ECGs from patients with and without cirrhosis. This novel relationship between AI-enabled ECG analysis and cirrhosis holds promise as the basis for future low-cost tools and applications in the care of patients with liver disease.

amilial hypobetalipoproteinemia (FHBL) is an autosomal codominant genetic disorder characterized by over 60 different mutations in the apolipoprotein B (APOB) gene, consequently leading to translation of truncated apolipoprotein B proteins apoB-100 and apoB-48. (1) These mutations attenuate the liver's ability to export lipoproteins, thereby resulting in triglyceride accumulation. (1) As a result, FHBL is associated with the development of NAFLD as well as increased risk of NASH, hepatic cirrhosis, and HCC. (1) The estimated prevalence of FHBL in the general population is between 1:1,000 and 1:3,000. (1) The prevalence of FHBL in NAFLD populations is unclear. A recent retrospective study of patients with NASH demonstrated ~10% of patients presented with LDL-cholesterol (C) levels below the 5th percentile; a key surrogate indicator of FHBL, although not diagnostic in nature. (2) Given FHBL's predisposition toward liver disease, (1,2) increasing awareness is necessary to ensure early diagnosis and proper care for these patients. Here, we present the findings of 3 patients with FHBL, confirmed by genetic testing, and two other probable diagnoses of FHBL, based on clinical and laboratory features and family history.

Background/Aim: Head and neck squamous cell carcinoma affects nearly 500,000 people annually. Augmenting PPARγ functional activation is linked with multiple anticarcinogenic processes in aerodigestive cell lines and animal models. PPARγ/RXRα heterodimers may be key partners in this activation. Materials and Methods: CA 9-22 and NA cell lines were treated with the PPARγ agonist ciglitazone and/or the RXRα agonist 9-cis-retinoic acid. PPARγ functional activation, cellular proliferation, apoptosis activity, and phenotype were subsequently analyzed. Results: Ciglitazone and 9-cis-retinoic acid independently activated PPARγ and down-regulated the carcinogenic phenotype in vitro. Combination treatment significantly augmented these effects, further decreasing proliferation (p<0.0001), and increasing PPARγ functional activation (p<0.0001), apoptosis (p<0.05), and adipocyte differentiation markers (p<0.0001). Conclusion: The efficacy of the combination of ciglitazone and 9-cis-retinoic acid afforded lowering treatment concentrations while maintaining desired therapeutic outcomes, optimistically supporting the feasibility and practicality of this novel treatment option.Head and neck squamous cell carcinoma (HNSCC) affects nearly 500,000 individuals worldwide every year (1). The 20-50% survival rate of stage III and IV disease has remained stagnant over multiple decades despite advances in research and multi-modality treatment protocols (1, 2). This illuminates the imperative need for novel treatment options. Nuclear hormone receptor targeting has revealed its therapeutic potential in a variety of cancers (3). A class of nuclear receptors, peroxisome proliferator-activated receptors (PPAR), were originally recognized for their role in regulating lipid and glucose metabolism (4, 5). These are therapeutic targets of interest with respect to HNSCC. An isoform of the PPAR family, PPARγ, is an intriguing target; initially described as an adipocyte differentiation transcription factor (6, 7). Cancer cells are characterized by the lack of differentiation, thus, attempting to induce differentiation may be exploitable as a treatment strategy. Differentiation therapy has previously demonstrated value as a novel treatment strategy for non-small cell lung cancer (8, 9), as well as for acute promyelocytic leukemia (10,11). Research has shown that PPARγ can form a heterodimer with retinoic X receptor alpha (RXRα) and transcriptionally activate down-stream genes (5, 7). In lipid biology, this process has directed malignant precursor cells into non-malignant adipocytes (12). Once activated, it has been postulated that the PPARγ/RXRα heterodimer transcription factor may regulate multiple pathways with the following downstream anticarcinogenic effects: decreased proliferation, decreased angiogenesis, and increased apoptosis (13). Research has demonstrated that cellular dysfunction involving this pathway, adversely stunting PPARγ expression, is linked to multiple cancerous etiologies including colorectal carcinomas ( 14) and poorly differenti...