Deep learning assessment of left ventricular hypertrophy based on electrocardiogram.

Background: Current electrocardiogram (ECG) criteria of left ventricular hypertrophy (LVH) have low sensitivity. Deep learning (DL) techniques have been widely used to detect cardiac diseases due to its ability of automatic feature extraction of ECG. However, DL was rarely applied in LVH diagnosis. Our study aimed to construct a DL model for rapid and effective detection of LVH using 12-lead ECG. Methods: We built a DL model based on convolutional neural network-long short-term memory (CNN-LSTM) to detect LVH using 12-lead ECG. The echocardiogram and ECG of 1,863 patients obtained within 1 week after hospital admission were analyzed. Patients were evenly allocated into 3 sets at 3:1:1 ratio: the training set (n = 1,120), the validation set (n = 371) and the test set 1 (n = 372). In addition, we recruited 453 hospitalized patients into the internal test set 2. Different DL model of each subgroup was developed according to gender and relative wall thickness (RWT). Results: The LVH was predicted by the CNN-LSTM model with an area under the curve (AUC) of 0.62 (sensitivity 68%, specificity 57%) in the test set 1, which outperformed Cornell voltage criteria (AUC: 0.57, sensitivity 48%, specificity 72%) and Sokolow-Lyon voltage (AUC: 0.51, sensitivity 14%, specificity 96%). In the internal test set 2, the CNN-LSTM model had a stable performance in predicting LVH with an AUC of 0.59 (sensitivity 65%, specificity 57%). In the subgroup analysis, the CNN-LSTM model predicted LVH by 12-lead ECG with an AUC of 0.66 (sensitivity 72%, specificity 60%) for male patients, which performed better than that for female patients (AUC: 0.59, sensitivity 50%, specificity 71%). Conclusion: Our study established a CNN-LSTM model to diagnose LVH by 12-lead ECG with higher sensitivity than current ECG diagnostic criteria. This CNN-LSTM model may be a simple and effective screening tool of LVH.

lncRNA NBR2 attenuates angiotensin II-induced myocardial hypertrophy through repressing ER stress via activating LKB1/AMPK/Sirt1 pathway.

Myocardial hypertrophy leads to heart failure (HF), and emerging researchers have illustrated that long noncoding RNAs (lncRNAs) modulate myocardial hypertrophy. Here, we explored the role and mechanism of a novel lncRNA, NBR2, in modulating angiotensin II (Ang II)-induced myocardial hypertrophy. First, we examined plasma NBR2 levels in 25 patients with HF and myocardial hypertrophy and ten healthy donors and analyzed the correlation between NBR2 profiles and patients' clinical indicators. In addition, the overexpression experiment of NBR2 was carried out to probe the influence of NBR2 on myocardial hypertrophy. lncRNA NBR2 was down-regulated in plasma of patients with HF and myocardial hypertrophy (vs. healthy controls), and its level was negatively correlated with cardiac function (represented by left ventricular end-diastolic diameter and left ventricular ejection fraction) and degree of myocardial hypertrophy. Besides, Ang II treatment intensified the hypertrophy of human myocardial cell lines (HCM and AC16) and curbed the NBR2 expression. Overexpressing lncRNA NBR2 alleviated Angiotension II-induced myocardial hypertrophy and declined the profiles of hypertrophic markers. Moreover, up-regulating lncRNA NBR2 weakened Ang II-mediated endoplasmic reticulum (ER) stress and activated the LKB1/AMPK/Sirt1 pathway. Interfering with the LKB1/AMPK/Sirt1 axis abated the lncRNA NBR2-mediated inhibitory effect on myocardial hypertrophy and ER stress. This study confirmed that lncRNA NBR2 dampened myocardial hypertrophy and ER stress by modulating the LKB1/AMPK/Sirt1 pathway. Our study provides the first evidence that lncRNA NBR2 is positively associated with myocardial hypertrophy.

Deep Learning Networks Accurately Detect ST-Segment Elevation Myocardial Infarction and Culprit Vessel.

Early diagnosis of acute ST-segment elevation myocardial infarction (STEMI) and early determination of the culprit vessel are associated with a better clinical outcome. We developed three deep learning (DL) models for detecting STEMIs and culprit vessels based on 12-lead electrocardiography (ECG) and compared them with conclusions of experienced doctors, including cardiologists, emergency physicians, and internists. After screening the coronary angiography (CAG) results, 883 cases (506 control and 377 STEMI) from internal and external datasets were enrolled for testing DL models. Convolutional neural network-long short-term memory (CNN-LSTM) (AUC: 0.99) performed better than CNN, LSTM, and doctors in detecting STEMI. Deep learning models (AUC: 0.96) performed similarly to experienced cardiologists and emergency physicians in discriminating the left anterior descending (LAD) artery. Regarding distinguishing RCA from LCX, DL models were comparable to doctors (AUC: 0.81). In summary, we developed ECG-based DL diagnosis systems to detect STEMI and predict culprit vessel occlusion, thus enhancing the accuracy and effectiveness of STEMI diagnosis.

Ginsenoside Rb1 Ameliorates Age-Related Myocardial Dysfunction by Regulating the NF-[Formula: see text]B Signaling Pathway.

Age-related myocardial dysfunction is a very large healthcare burden. Here, we aimed to investigate whether ginsenoside Rb1 (Rb1) improves age-related myocardial dysfunction and to identify the relevant molecular mechanism. Young mice and aged mice were injected with Rb1 or vehicle for 3 months. Then, their cardiac function was inspected by transthoracic echocardiography. Serum and myocardium tissue were collected from all mice for histological or molecular expression analyses, including aging-related proteins, markers relevant to fibrosis and inflammation, and markers indicating the activation of the nuclear factor-kappa B (NF-[Formula: see text]B) pathway. Compared with the control condition, Rb1 treatment significantly increased the ejection fraction percentage and significantly decreased the internal diameter and volume of the left ventricle at the end-systolic and end-diastolic phases in aged mice. Rb1 treatment reduced collagen deposition and collagen I, collagen III, and transforming growth factor-[Formula: see text]1 protein expression levels in aged hearts. Rb1 also decreased the aging-induced myocardial inflammatory response, as measured by serum or myocardial interleukin-6 and tumor necrosis factor-[Formula: see text] levels. Furthermore, Rb1 treatment in aged mice increased cytoplasmic NF-[Formula: see text]B but decreased nuclear NF-[Formula: see text]B, which indicated the suppression of the NF-[Formula: see text]B signaling pathway by regulating the translocation of NF-[Formula: see text]B. Rb1 could alleviate aging-related myocardial dysfunction by suppressing fibrosis and inflammation, which is potentially associated with regulation of the NF-[Formula: see text]B signaling pathway.

Tsutsugamushi disease presenting with aortic valve endocarditis: a case report and literature review.

Tsutsugamushi disease is a zoonotic disease caused by Orientia tsutsugamushi in which humans are accidental hosts. Infective endocarditis associated with Tsutsugamushi disease has not been previously reported. We are describing a case of Tsutsugamushi disease presenting with aortic valve endocarditis. The clinical data of a 67-year-old female with O. tsutsugamushi-induced aortic valve endocarditis was summarized retrospectively and analyzed with a literature review. Treatment of O. tsutsugamushi-induced aortic valve endocarditis with chloramphenicol is recommended.