Activation of Autophagic Flux Blunts Cardiac Ischemia/Reperfusion Injury.

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Serial analysis of coronary artery disease progression by artificial intelligence assisted coronary computed tomography angiography: early clinical experience.

BACKGROUND: Studies have shown that quantitative evaluation of coronary artery plaque on Coronary Computed Tomography Angiography (CCTA) can identify patients at risk of cardiac events. Recent demonstration of artificial intelligence (AI) assisted CCTA shows that it allows for evaluation of CAD and plaque characteristics. Based on publications to date, we are the first group to perform AI augmented CCTA serial analysis of changes in coronary plaque characteristics over 13 years. We evaluated whether AI assisted CCTA can accurately assess changes in coronary plaque progression, which has potential clinical prognostic value in CAD management. CASE PRESENTATION: 51-year-old male with hypertension, hyperlipidemia and family history of myocardial infarction, underwent CCTA exams for anginal symptom evaluation and CAD assessment. 5 CCTAs were performed between 2008 and 2021. Quantitative atherosclerosis plaque characterization (APC) using an AI platform (Cleerly), was performed to assess CAD burden. Total plaque volume (TPV) change-over-time demonstrated decreasing low-density non-calcified plaque (LD-NCP) with increasing overall NCP and calcified-plaque (CP). Examination of individual segments revealed a proximal-LAD lesion with decreasing NCP over-time and increasing CP. In contrast, although the D2/D1/ramus lesions showed increasing stenosis, CP, and total plaque, there were no significant differences in NCP over-time, with stable NCP and increased CP. Remarkably, we also consistently visualized small plaques, which typically readers may interpret as false positives due to artifacts. But in this case, they reappeared each study in the same locations, generally progressing in size and demonstrating expected plaque transformation over-time. CONCLUSIONS: We performed the first AI augmented CCTA based serial analysis of changes in coronary plaque characteristics over 13 years. We were able to consistently assess progression of plaque volumes, stenosis, and APCs with this novel methodology. We found a significant increase in TPV composed of decreasing LD-NCP, and increasing NCP and CP, with variations in the evolution of APCs between vessels. Although the significance of evolving APCs needs to be investigated, this case demonstrates AI-based CCTA analysis can serve as valuable clinical tool to accurately define unique CAD characteristics over time. Prospective trails are needed to assess whether quantification of APCs provides prognostic capabilities to improve clinical care.

Lipoprotein lipase is active as a monomer.

Lipoprotein lipase (LPL), the enzyme that hydrolyzes triglycerides in plasma lipoproteins, is assumed to be active only as a homodimer. In support of this idea, several groups have reported that the size of LPL, as measured by density gradient ultracentrifugation, is ∼110 kDa, twice the size of LPL monomers (∼55 kDa). Of note, however, in those studies the LPL had been incubated with heparin, a polyanionic substance that binds and stabilizes LPL. Here we revisited the assumption that LPL is active only as a homodimer. When freshly secreted human LPL (or purified preparations of LPL) was subjected to density gradient ultracentrifugation (in the absence of heparin), LPL mass and activity peaks exhibited the size expected of monomers (near the 66-kDa albumin standard). GPIHBP1-bound LPL also exhibited the size expected for a monomer. In the presence of heparin, LPL size increased, overlapping with a 97.2-kDa standard. We also used density gradient ultracentrifugation to characterize the LPL within the high-salt and low-salt peaks from a heparin-Sepharose column. The catalytically active LPL within the high-salt peak exhibited the size of monomers, whereas most of the inactive LPL in the low-salt peak was at the bottom of the tube (in aggregates). Consistent with those findings, the LPL in the low-salt peak, but not that in the high-salt peak, was easily detectable with single mAb sandwich ELISAs, in which LPL is captured and detected with the same antibody. We conclude that catalytically active LPL can exist in a monomeric state.

Chronic heart failure: Ca(2+), catabolism, and catastrophic cell death.

Robust successes have been achieved in recent years in conquering the acutely lethal manifestations of heart disease. Many patients who previously would have died now survive to enjoy happy and productive lives. Nevertheless, the devastating impact of heart disease continues unabated, as the spectrum of disease has evolved with new manifestations. In light of this ever-evolving challenge, insights that culminate in novel therapeutic targets are urgently needed. Here, we review fundamental mechanisms of heart failure, both with reduced (HFrEF) and preserved (HFpEF) ejection fraction. We discuss pathways that regulate cardiomyocyte remodeling and turnover, focusing on Ca(2+) signaling, autophagy, and apoptosis. In particular, we highlight recent insights pointing to novel connections among these events. We also explore mechanisms whereby potential therapeutic approaches targeting these processes may improve morbidity and mortality in the devastating syndrome of heart failure.

Artificial intelligence using a deep learning versus expert computed tomography human reading in calcium score and coronary artery calcium data and reporting system classification.

BACKGROUND: Artificial intelligence (AI) applied to cardiac imaging may provide improved processing, reading precision and advantages of automation. Coronary artery calcium (CAC) score testing is a standard stratification tool that is rapid and highly reproducible. We analyzed CAC results of 100 studies in order to determine the accuracy and correlation between the AI software (Coreline AVIEW, Seoul, South Korea) and expert level-3 computed tomography (CT) human CAC interpretation and its performance when coronary artery disease data and reporting system (coronary artery calcium data and reporting system) classification is applied. METHODS: A total of 100 non-contrast calcium score images were selected by blinded randomization and processed with the AI software versus human level-3 CT reading. The results were compared and the Pearson correlation index was calculated. The CAC-DRS classification system was applied, and the cause of category reclassification was determined using an anatomical qualitative description by the readers. RESULTS: The mean age was age 64.5 years, with 48% female. The absolute CAC scores between AI versus human reading demonstrated a highly significant correlation (Pearson coefficient R  = 0.996); however, despite these minimal CAC score differences, 14% of the patients had their CAC-DRS category reclassified. The main source of reclassification was observed in CAC-DRS 0-1, where 13 were recategorized, particularly between studies having a CAC Agatston score of 0 versus 1. Qualitative description of the errors showed that the main cause of misclassification was AI underestimation of right coronary calcium, AI overestimation of right ventricle densities and human underestimation of right coronary artery calcium. CONCLUSION: Correlation between AI and human values is excellent with absolute numbers. When the CAC-DRS classification system was adopted, there was a strong correlation in the respective categories. Misclassified were predominantly in the category of CAC = 0, most often with minimal values of calcium volume. Additional algorithm optimization with enhanced sensitivity and specificity for low values of calcium volume will be required to enhance AI CAC score utilization for minimal disease. Over a broad range of calcium scores, AI software for calcium scoring had an excellent correlation compared to human expert reading and in rare cases determined calcium missed by human interpretation.

Performance and Integration of Smartphone Wireless ECG Monitoring into the Enterprise Electronic Health Record: First Clinical Experience.

INTRODUCTION: Patient initiated, remote cardiac monitoring has proved to be a significant advance in the diagnosis and management of arrhythmias. Further improvements in ease of use and access to results will further improve health outcomes and cost-effectiveness. Here we describe a proof-of-concept evaluation to assess the feasibility of successfully implementing a cloud-based management system using KardiaPro (KP) for remote electrocardiogram (ECG) monitoring to interface into EPIC, an enterprise electronic health record (EHR) system. METHODS: The KP management system was embedded using hypertext markup language (HTML) code directly into the EHR. Encrypted credentials and patient data were bundled with an application programming interface key allowing linkage of remote monitoring from patients' smartphones. During the time of implementation, a total of 322 patients and 32 179 ECGs were recorded. RESULTS: The KP-EHR interface provided full functionality, allowing detection, interpretation and documentation of atrial fibrillation (AF), flutter events, ventricular tachycardia, and complete heart block. Our study focused on KP's detection of AF, and 16.7% of tracings were classified as probable AF with only 2.3% of tracings not analyzed by the KP algorithm because of tracings that were too noisy or truncated. Enhanced management was facilitated with clinical information immediately accessible. Blinded physician ECG review validated the KP proprietary algorithm interpretation and ECGs. CONCLUSIONS: Direct integration of KP into EHR was successful and practical. It allows for historical, point of care and immediate retrieval of remote ambulatory monitoring data and documentation into the electronic health record. KP EHR integration warrants further study as it has the potential to improve cost-effectiveness and clinical diagnostic value, leading to improvements in delivery of patient care.

Quantitative plaque analysis with A.I.-augmented CCTA in end-stage renal disease and complex CAD.

BACKGROUND: Adverse cardiovascular events are a significant cause of mortality in end-stage renal disease (ESRD) patients. High-risk plaque anatomy may be a significant contributor. However, their atherosclerotic phenotypes have not been described. We sought to define atherosclerotic plaque characteristics (APC) in dialysis patients using artificial-intelligence augmented CCTA. METHODS: We retrospectively analyzed ESRD patients referred for CCTA using an FDA approved artificial-intelligence augmented-CCTA program (Cleerly). Coronary lesions were evaluated for APCs by CCTA. APCs included percent atheroma volume(PAV), low-density non-calcified-plaque (LD-NCP), non-calcified-plaque (NCP), calcified-plaque (CP), length, and high-risk-plaque (HRP), defined by LD-NCP and positive arterial remodeling >1.10 (PR). RESULTS: 79 ESRD patients were enrolled, mean age 65.3 years, 32.9% female. Disease distribution was non-obstructive (65.8%), 1-vessel disease (21.5%), 2-vessel disease (7.6%), and 3-vessel disease (5.1%). Mean total plaque volume (TPV) was 810.0 mm3, LD-NCP 16.8 mm3, NCP 403.1 mm3, and CP 390.1 mm3. HRP was present in 81.0% patients. Patients with at least one >50% stenosis, or obstructive lesions, had significantly higher TPV, LD-NCP, NCP, and CP. Patients >65 years had more CP and higher PAV. CONCLUSION: Our study provides novel insight into ESRD plaque phenotypes and demonstrates that artificial-intelligence augmented CCTA analysis is feasible for CAD characterization despite severe calcification. We demonstrate elevated plaque burden and stenosis caused by predominantly non-calcified-plaque. Furthermore, the quantity of calcified-plaques increased with age, with men exhibiting increased number of 2-feature plaques and higher plaque volumes. Artificial-intelligence augmented CCTA analysis of APCs may be a promising metric for cardiac risk stratification and warrants further prospective investigation.

Inhibition of class I histone deacetylases blunts cardiac hypertrophy through TSC2-dependent mTOR repression.

Altering chromatin structure through histone posttranslational modifications has emerged as a key driver of transcriptional responses in cells. Modulation of these transcriptional responses by pharmacological inhibition of class I histone deacetylases (HDACs), a group of chromatin remodeling enzymes, has been successful in blocking the growth of some cancer cell types. These inhibitors also attenuate the pathogenesis of pathological cardiac remodeling by blunting and even reversing pathological hypertrophy. The mechanistic target of rapamycin (mTOR) is a critical sensor and regulator of cell growth that, as part of mTOR complex 1 (mTORC1), drives changes in protein synthesis and metabolism in both pathological and physiological hypertrophy. We demonstrated through pharmacological and genetic methods that inhibition of class I HDACs suppressed pathological cardiac hypertrophy through inhibition of mTOR activity. Mice genetically silenced for HDAC1 and HDAC2 had a reduced hypertrophic response to thoracic aortic constriction (TAC) and showed reduced mTOR activity. We determined that the abundance of tuberous sclerosis complex 2 (TSC2), an mTOR inhibitor, was increased through a transcriptional mechanism in cardiomyocytes when class I HDACs were inhibited. In neonatal rat cardiomyocytes, loss of TSC2 abolished HDAC-dependent inhibition of mTOR activity, and increased expression of TSC2 was sufficient to reduce hypertrophy in response to phenylephrine. These findings point to mTOR and TSC2-dependent control of mTOR as critical components of the mechanism by which HDAC inhibitors blunt pathological cardiac growth. These results also suggest a strategy to modulate mTOR activity and facilitate the translational exploitation of HDAC inhibitors in heart disease.