Macrophage MicroRNA-155 Promotes Cardiac Hypertrophy and Failure

BACKGROUND: Cardiac hypertrophy and subsequent heart failure triggered by chronic hypertension represent major challenges for cardiovascular research. Beyond neurohormonal and myocyte signaling pathways, growing evidence suggests inflammatory signaling pathways as therapeutically targetable contributors to this process. We recently reported that microRNA-155 is a key mediator of cardiac inflammation and injury in infectious myocarditis. Here, we investigated the impact of microRNA-155 manipulation in hypertensive heart disease. METHODS AND RESULTS: Genetic loss or pharmacological inhibition of the leukocyte-expressed microRNA-155 in mice markedly reduced cardiac inflammation, hypertrophy, and dysfunction on pressure overload. These alterations were macrophage dependent because in vivo cardiomyocyte-specific microRNA-155 manipulation did not affect cardiac hypertrophy or dysfunction, whereas bone marrow transplantation from wild-type mice into microRNA-155 knockout animals rescued the hypertrophic response of the cardiomyocytes and vice versa. In vitro, media from microRNA-155 knockout macrophages blocked the hypertrophic growth of stimulated cardiomyocytes, confirming that macrophages influence myocyte growth in a microRNA-155-dependent paracrine manner. These effects were at least partly mediated by the direct microRNA-155 target suppressor of cytokine signaling 1 (Socs1) because Socs1 knockdown in microRNA-155 knockout macrophages largely restored their hypertrophy-stimulating potency. CONCLUSIONS: Our findings reveal that microRNA-155 expression in macrophages promotes cardiac inflammation, hypertrophy, and failure in response to pressure overload. These data support the causative significance of inflammatory signaling in hypertrophic heart disease and demonstrate the feasibility of therapeutic microRNA targeting of inflammation in heart failure.status: publishe

Automated Assessment of COVID-19 Reporting and Data System and Chest CT Severity Scores in Patients Suspected of Having COVID-19 Using Artificial Intelligence

Background: The coronavirus disease 2019 (COVID-19) pandemic has spread across the globe with alarming speed, morbidity, and mortality. Immediate triage of patients with chest infections suspected to be caused by COVID-19 using chest CT may be of assistance when results from definitive viral testing are delayed. Purpose: To develop and validate an artificial intelligence (AI) system to score the likelihood and extent of pulmonary COVID-19 on chest CT scans using the COVID-19 Reporting and Data System (CO-RADS) and CT severity scoring systems. Materials and Methods: The CO-RADS AI system consists of three deep-learning algorithms that automatically segment the five pulmonary lobes, assign a CO-RADS score for the suspicion of COVID-19, and assign a CT severity score for the degree of parenchymal involvement per lobe. This study retrospectively included patients who underwent a nonenhanced chest CT examination because of clinical suspicion of COVID-19 at two medical centers. The system was trained, validated, and tested with data from one of the centers. Data from the second center served as an external test set. Diagnostic performance and agreement with scores assigned by eight independent observers were measured using receiver operating characteristic analysis, linearly weighted kappa values, and classification accuracy. Results: A total of 105 patients (mean age, 62 years +/- 16 [standard deviation]; 61 men) and 262 patients (mean age, 64 years +/- 16; 154 men) were evaluated in the internal and external test sets, respectively. The system discriminated between patients with COVID-19 and those without COVID-19, with areas under the receiver operating characteristic curve of 0.95 (95% CI: 0.91, 0.98) and 0.88 (95% CI: 0.84, 0.93), for the internal and external test sets, respectively. Agreement with the eight human observers was moderate to substantial, with mean linearly weighted k values of 0.60 +/- 0.01 for CO-RADS scores and 0.54 +/- 0.01 for CT severity scores. Conclusion: With high diagnostic performance, the CO-RADS AI system correctly identified patients with COVID-19 using chest CT scans and assigned standardized CO-RADS and CT severity scores that demonstrated good agreement with findings from eight independent observers and generalized well to external data. (C) RSNA, 202