BACKGROUND:Because adult cardiomyocytes have little regenerative capacity, resident cardiac fibroblasts (CFs) synthesize extracellular matrix after myocardial infarction (MI) to form fibrosis, leading to cardiac dysfunction and heart failure. Therapies that can regenerate the myocardium and reverse fibrosis in chronic MI are lacking. The overexpression of cardiac transcription factors, including (MGTH), can directly reprogram CFs into induced cardiomyocytes (iCMs) and improve cardiac function under acute MI. However, the ability of in vivo cardiac reprogramming to repair chronic MI with established scars is undetermined. METHODS:We generated a novel Tcf21/reporter/MGTH2A transgenic mouse system in which tamoxifen treatment could induce both MGTH and reporter expression in the resident CFs for cardiac reprogramming and fibroblast lineage tracing. We first tested the efficacy of this transgenic system in vitro and in vivo for acute MI. Next, we analyzed in vivo cardiac reprogramming and fusion events under chronic MI using Tcf21/Tomato/MGTH2A and Tcf21/mTmG/MGTH2A mice, respectively. Microarray and single-cell RNA sequencing were performed to determine the mechanism of cardiac repair by in vivo reprogramming. RESULTS:We confirmed the efficacy of transgenic in vitro and in vivo cardiac reprogramming for acute MI. In chronic MI, in vivo cardiac reprogramming converted ≈2% of resident CFs into iCMs, in which a majority of iCMs were generated by means of bona fide cardiac reprogramming rather than by fusion with cardiomyocytes. Cardiac reprogramming significantly improved myocardial contraction and reduced fibrosis in chronic MI. Microarray analyses revealed that the overexpression of MGTH activated cardiac program and concomitantly suppressed fibroblast and inflammatory signatures in chronic MI. Single-cell RNA sequencing demonstrated that resident CFs consisted of 7 subclusters, in which the profibrotic CF population increased under chronic MI. Cardiac reprogramming suppressed fibroblastic gene expression in chronic MI by means of conversion of profibrotic CFs to a quiescent antifibrotic state. MGTH overexpression induced antifibrotic effects partly by suppression of Meox1, a central regulator of fibroblast activation. CONCLUSIONS:These results demonstrate that cardiac reprogramming could repair chronic MI by means of myocardial regeneration and reduction of fibrosis. These findings present opportunities for the development of new therapies for chronic MI and heart failure.

Acute myocardial infarction is a condition in which a part of the heart muscle cannot receive enough blood due to the narrowing and blockage of the vessels feeding the heart over time. Noticing this situation lately and failing to intervene immediately may cause death and some permanent damage to individuals. The ST-segment elevation MI (STEMI) is one of the most serious and fatal types of acute myocardial infarction which requires urgent diagnosis and intervention. Artificial intelligence-based applications used in health have become widespread, paving the way for early diagnosis and treatment. In modern medicine, it is vital that STEMI patients are identified and treated accurately and quickly. Determining the risk of death of patients in advance plays a major role in making clinical decisions. Traditional risk assessment methods are often time-consuming and subjective processes and rely on manual analysis of clinical data. In this respect, this study is expected to provide clinical decision support in the management of STEMI patients and contribute to improving the quality of healthcare services. In the proposed work, death risk analysis and in-hospital mortality risk prediction are carried out using some selected machine learning (ML) algorithms, such as SVM, RF, RT, k-NN, LMT, and MLP, that are proven to be effective in medical classification tasks. The conducted test results indicate that the proposed method outperforms similar studies in the literature, achieving a superior performance of over 99 % in all metrics, i.e., accuracy, recall, precision, sensitivity, F-score, and AUC. Moreover, the same competitive results have been obtained with even much fewer predictors.

OBJECTIVES:The aim of this study was to test whether texture analysis and machine learning enable the detection of myocardial infarction (MI) on non-contrast-enhanced low radiation dose cardiac computed tomography (CCT) images. MATERIALS AND METHODS:In this institutional review board-approved retrospective study, we included non-contrast-enhanced electrocardiography-gated low radiation dose CCT image data (effective dose, 0.5 mSv) acquired for the purpose of calcium scoring of 27 patients with acute MI (9 female patients; mean age, 60 ± 12 years), 30 patients with chronic MI (8 female patients; mean age, 68 ± 13 years), and in 30 subjects (9 female patients; mean age, 44 ± 6 years) without cardiac abnormality, hereafter termed controls. Texture analysis of the left ventricle was performed using free-hand regions of interest, and texture features were classified twice (Model I: controls versus acute MI versus chronic MI; Model II: controls versus acute and chronic MI). For both classifications, 6 commonly used machine learning classifiers were used: decision tree C4.5 (J48), k-nearest neighbors, locally weighted learning, RandomForest, sequential minimal optimization, and an artificial neural network employing deep learning. In addition, 2 blinded, independent readers visually assessed noncontrast CCT images for the presence or absence of MI. RESULTS:In Model I, best classification results were obtained using the k-nearest neighbors classifier (sensitivity, 69%; specificity, 85%; false-positive rate, 0.15). In Model II, the best classification results were found with the locally weighted learning classification (sensitivity, 86%; specificity, 81%; false-positive rate, 0.19) with an area under the curve from receiver operating characteristics analysis of 0.78. In comparison, both readers were not able to identify MI in any of the noncontrast, low radiation dose CCT images. CONCLUSIONS:This study indicates the ability of texture analysis and machine learning in detecting MI on noncontrast low radiation dose CCT images being not visible for the radiologists' eye.

Coronary F-sodium fluoride (F-NaF) PET and CT angiography-based quantitative plaque analysis have shown promise in refining risk stratification in patients with coronary artery disease. We combined both of these novel imaging approaches to develop an optimal machine-learning model for the future risk of myocardial infarction in patients with stable coronary disease. Patients with known coronary artery disease underwent coronary F-NaF PET and CT angiography on a hybrid PET/CT scanner. Machine-learning by extreme gradient boosting was trained using clinical data, CT quantitative plaque analysis, measures and F-NaF PET, and it was tested using repeated 10-fold hold-out testing. Among 293 study participants (65 ± 9 y; 84% male), 22 subjects experienced a myocardial infarction over the 53 (40-59) months of follow-up. On univariable receiver-operator-curve analysis, only F-NaF coronary uptake emerged as a predictor of myocardial infarction (c-statistic 0.76, 95% CI 0.68-0.83). When incorporated into machine-learning models, clinical characteristics showed limited predictive performance (c-statistic 0.64, 95% CI 0.53-0.76) and were outperformed by a quantitative plaque analysis-based machine-learning model (c-statistic 0.72, 95% CI 0.60-0.84). After inclusion of all available data (clinical, quantitative plaque and F-NaF PET), we achieved a substantial improvement ( = 0.008 versus F-NaF PET alone) in the model performance (c-statistic 0.85, 95% CI 0.79-0.91). Both F-NaF uptake and quantitative plaque analysis measures are additive and strong predictors of outcome in patients with established coronary artery disease. Optimal risk stratification can be achieved by combining clinical data with these approaches in a machine-learning model.

Myocardial infarction (MI) is a significant global health issue. Despite the advances in genome-wide association studies, a complete genetic and molecular understanding of MI is elusive and needs to be fully explored. This study aimed to elucidate the genetic framework of MI and explore the potential health benefits of natural compounds (NCs). The genetic architecture of MI was explored using data from the Korean Genome and Epidemiology Study. We pinpointed crucial protein-coding genes related to MI by multi-marker analysis of genomic annotation for gene-based analysis. The bioinformatics-integrated deep neural analysis of NCs (BioDeepNat), a novel disease discovery application, was utilized to assess the influence of NCs on MI-related target proteins and validated with molecular docking analysis. The BioDeepNat application revealed significant NCs on MI-related target proteins, such as -resveratrol, epicatechin 3-gallate, and kaempferol. The E3 region of RNF213 protein with a point mutation (Arg4810Lys) had different binding energies with NCs, such as ursolic acid and olean-12-en-28-oic acid, compared to the wild type. However, ginsenosides, eleutheroside, oleanolic acid, and hederagenin showed similar binding energies to wild and mutated types of protein. The predicted NCs were primarily sourced from foods such as common grapes and teas. Aromatic hydrocarbons are frequently observed as the prevalent functional groups with high binding affinity for NCs in a molecular docking analysis. In conclusion, the proteins encoded by these genes identified by gene-based analysis interacted with several NCs with health promotion found in day-to-day foods, particularly -resveratrol and kaempferol. This understanding offers promising directions for precision nutrition strategies in MI.

Background Precise assessment of myocardial ischemia burden and cardiovascular risk stratification based on dynamic CT myocardial perfusion imaging (MPI) is lacking. Purpose To develop and validate a deep learning (DL) model for automated quantification of myocardial blood flow (MBF) and ischemic myocardial volume (IMV) percentage and to explore the prognostic value for major adverse cardiovascular events (MACE). Materials and Methods This multicenter study comprised three cohorts of patients with clinically indicated CT MPI and coronary CT angiography (CCTA). Cohorts 1 and 2 were retrospective cohorts (May 2021 to June 2023 and January 2018 to December 2022, respectively). Cohort 3 was prospectively included (November 2016 to December 2021). The DL model was developed in cohort 1 (training set: 211 patients, validation set: 57 patients, test set: 90 patients). The diagnostic performance of MBF derived from the DL model (MBF) for myocardial ischemia was evaluated in cohort 2 based on the area under the receiver operating characteristic curve (AUC). The prognostic value of the DL model-derived IMV percentage was assessed in cohort 3 using multivariable Cox regression analyses. Results Across three cohorts, 1108 patients (mean age: 61 years ± 12 [SD]; 667 men) were included. MBF showed excellent agreement with manual measurements in the test set (segment-level intraclass correlation coefficient = 0.928; 95% CI: 0.921, 0.935). MBF showed higher diagnostic performance (vessel-based AUC: 0.97) over CT-derived fractional flow reserve (FFR) (vessel-based AUC: 0.87; = .006) and CCTA-derived diameter stenosis (vessel-based AUC: 0.79; < .001) for hemodynamically significant lesions, compared with invasive FFR. Over a mean follow-up of 39 months, MACE occurred in 94 (14.2%) of 660 patients. IMV percentage was an independent predictor of MACE (hazard ratio = 1.12, = .003), with incremental prognostic value (C index: 0.86; 95% CI: 0.84, 0.88) over conventional risk factors and CCTA parameters (C index: 0.84; 95% CI: 0.82, 0.86; = .02). Conclusion A DL model enabled automated CT MBF quantification and accurate diagnosis of myocardial ischemia. DL model-derived IMV percentage was an independent predictor of MACE and mildly improved cardiovascular risk stratification. © RSNA, 2025 See also the editorial by Zhu and Xu in this issue.

BACKGROUND:In suspected non-ST-segment elevation myocardial infarction (NSTEMI), this presumed diagnosis may not hold true in all cases, particularly in patients with nonobstructive coronary arteries (NOCA). Additionally, in multivessel coronary artery disease, the presumed infarct-related artery may be incorrect. OBJECTIVES:This study sought to assess the diagnostic utility of cardiac magnetic resonance (CMR) before invasive coronary angiogram (ICA) in suspected NSTEMI. METHODS:A total of 100 consecutive stable patients with suspected acute NSTEMI (70% male, age 62 ± 11 years) prospectively underwent CMR pre-ICA to assess cardiac function (cine), edema (T-weighted imaging, T mapping), and necrosis/scar (late gadolinium enhancement). CMR images were interpreted blinded to ICA findings. The clinical care and ICA teams were blinded to CMR findings until post-ICA. RESULTS:Early CMR (median 33 hours postadmission and 4 hours pre-ICA) confirmed only 52% (52 of 100) of patients had subendocardial infarction, 15% transmural infarction, 18% nonischemic pathologies (myocarditis, takotsubo, and other forms of cardiomyopathies), and 11% normal CMR; 4% were nondiagnostic. Subanalyses according to ICA findings showed that, in patients with obstructive coronary artery disease (73 of 100), CMR confirmed only 84% (61 of 73) had MI, 10% (7 of 73) nonischemic pathologies, and 5% (4 of 73) normal. In patients with NOCA (27 of 100), CMR found MI in only 22% (6 of 27 true MI with NOCA), and reclassified the presumed diagnosis of NSTEMI in 67% (18 of 27: 11 nonischemic pathologies, 7 normal). In patients with CMR-MI and obstructive coronary artery disease (61 of 100), CMR identified a different infarct-related artery in 11% (7 of 61). CONCLUSIONS:In patients presenting with suspected NSTEMI, a CMR-first strategy identified MI in 67%, nonischemic pathologies in 18%, and normal findings in 11%. Accordingly, CMR has the potential to affect at least 50% of all patients by reclassifying their diagnosis or altering their potential management.

BACKGROUND:Randomized trials in obstructive coronary artery disease (CAD) have largely shown no prognostic benefit from coronary revascularization. Although there are several potential reasons for the lack of benefit, an underexplored possible reason is the presence of coincidental nonischemic cardiomyopathy (NICM). We investigated the prevalence and prognostic significance of NICM in patients with CAD (CAD-NICM). METHODS:We conducted a registry study of consecutive patients with obstructive CAD on coronary angiography who underwent contrast-enhanced cardiovascular magnetic resonance imaging for the assessment of ventricular function and scar at 4 hospitals from 2004 to 2020. We identified the presence and cause of cardiomyopathy using cardiovascular magnetic resonance imaging and coronary angiography data, blinded to clinical outcomes. The primary outcome was a composite of all-cause death or heart failure hospitalization, and secondary outcomes were all-cause death, heart failure hospitalization, and cardiovascular death. RESULTS:Among 3023 patients (median age, 66 years; 76% men), 18.2% had no cardiomyopathy, 64.8% had ischemic cardiomyopathy (CAD+ICM), 9.3% had CAD+NICM, and 7.7% had dual cardiomyopathy (CAD+dualCM), defined as both ICM and NICM. Thus, 16.9% had CAD+NICM or dualCM. During a median follow-up of 4.8 years (interquartile range, 2.9, 7.6), 1116 patients experienced the primary outcome. In Cox multivariable analysis, CAD+NICM or dualCM was independently associated with a higher risk of the primary outcome compared with CAD+ICM (adjusted hazard ratio, 1.23 [95% CI, 1.06-1.43]; =0.007) after adjustment for potential confounders. The risks of the secondary outcomes of all-cause death and heart failure hospitalization were also higher with CAD+NICM or dualCM (hazard ratio, 1.21 [95% CI, 1.02-1.43]; =0.032; and hazard ratio, 1.37 [95% CI, 1.11-1.69]; =0.003, respectively), whereas the risk of cardiovascular death did not differ from that of CAD+ICM (hazard ratio, 1.15 [95% CI, 0.89-1.48]; =0.28). CONCLUSIONS:In patients with CAD referred for clinical cardiovascular magnetic resonance imaging, NICM or dualCM was identified in 1 of every 6 patients and was associated with worse long-term outcomes compared with ICM. In patients with obstructive CAD, coincidental NICM or dualCM may contribute to the lack of prognostic benefit from coronary revascularization.