Background: The detection of dyskalemias—hypokalemia and hyperkalemia—currently depends on laboratory tests. Since cardiac tissue is very sensitive to dyskalemia, electrocardiography (ECG) may be able to uncover clinically important dyskalemias before laboratory results. Objective: Our study aimed to develop a deep-learning model, ECG12Net, to detect dyskalemias based on ECG presentations and to evaluate the logic and performance of this model. Methods: Spanning from May 2011 to December 2016, 66,321 ECG records with corresponding serum potassium (K+) concentrations were obtained from 40,180 patients admitted to the emergency department. ECG12Net is an 82-layer convolutional neural network that estimates serum K+ concentration. Six clinicians—three emergency physicians and three cardiologists—participated in human-machine competition. Sensitivity, specificity, and balance accuracy were used to evaluate the performance of ECG12Net with that of these physicians. Results: In a human-machine competition including 300 ECGs of different serum K+ concentrations, the area under the curve for detecting hypokalemia and hyperkalemia with ECG12Net was 0.926 and 0.958, respectively, which was significantly better than that of our best clinicians. Moreover, in detecting hypokalemia and hyperkalemia, the sensitivities were 96.7% and 83.3%, respectively, and the specificities were 93.3% and 97.8%, respectively. In a test set including 13,222 ECGs, ECG12Net had a similar performance in terms of sensitivity for severe hypokalemia (95.6%) and severe hyperkalemia (84.5%), with a mean absolute error of 0.531. The specificities for detecting hypokalemia and hyperkalemia were 81.6% and 96.0%, respectively. Conclusions: A deep-learning model based on a 12-lead ECG may help physicians promptly recognize severe dyskalemias and thereby potentially reduce cardiac events.

Diabetes mellitus is a metabolic syndrome that affects millions of people worldwide. Recent studies have demonstrated that protein kinase C (PKC) activation plays an important role in hyperglycemia-induced atherosclerosis. PKC activation is involved in several cellular responses such as the expression of various growth factors, activation of signaling pathways, and enhancement of oxidative stress in hyperglycemia. However, the role of PKC activation in pro-atherogenic and anti-atherogenic mechanisms remains controversial, especially under hyperglycemic condition. In this review, we discuss the role of different PKC isoforms in lipid regulation, oxidative stress, inflammatory response, and apoptosis. These intracellular events are linked to the pathogenesis of atherosclerosis in diabetes. PKC deletion or treatment with PKC inhibitors has been studied in the regulation of atherosclerotic plaque formation and evolution. Furthermore, some preclinical and clinical studies have indicated that PKCβ and PKCδ are potential targets for the treatment of diabetic vascular complications. The current review summarizes these multiple signaling pathways and cellular responses regulated by PKC activation and the potential therapeutic targets of PKC in diabetic complications.

New-onset atrial fibrillation (NeOAF) is a common type of tachyarrhythmia in critically ill patients and is associated with increased mortality in patients with sepsis. However, the prognostic impact of restored sinus rhythm (SR) in septic patients with NeOAF remains unclear. A total of 791 patients with sepsis, who were admitted to a medical intensive care unit from January 2011 to January 2014, were screened. NeOAF was detected by continuous electrocardiographic monitoring. Patients were categorized into three groups: no NeOAF, NeOAF with restored SR (NeOAF to SR), and NeOAF with failure to restore SR (NeOAF to atrial fibrillation (AF)). The endpoint of this study was in-hospital mortality. Patients with pre-existing AF were excluded. We reviewed the data of 503 eligible patients, including 263 patients with no NeOAF and 240 patients with NeOAF. Of these 240 patients, SR was restored in 165 patients, and SR could not be restored in 75 patients. The NeOAF to AF group had the highest in-hospital mortality rate of 61.3% compared with the NeOAF to SR and no NeOAF groups (26.1% and 17.5%, respectively). Moreover, multivariate logistic regression analysis revealed that failure of restored SR was independently associated with increased in-hospital mortality in patients with sepsis and NeOAF. Failure to restore a sinus rhythm in patients with new-onset atrial fibrillation may be associated with increased in-hospital mortality in patients with sepsis. Further prospective studies are needed to clarify the effects of restoration of sinus rhythm on survival in patients with sepsis and new-onset atrial fibrillation.

Cardiovascular disease is one of the leading causes of death and disability in the world. Atherosclerosis, characterized by lipid accumulation and chronic inflammation in the vessel wall, is the main feature of cardiovascular disease. Although the amounts of fruits and vegetables present in the diets vary by country, diets, worldwide, contain large amounts of spices; this may have positive or negative effects on the initiation and development of atherosclerosis. In this review, we focused on the potential protective effects of specific nutrients from spices, such as pepper, ginger, garlic, onion, cinnamon and chili, in atherosclerosis and atherosclerotic cardiovascular disease. The mechanisms, epidemiological analysis, and clinical studies focusing on a variety of spices are covered in this review. Based on the integrated information, we aimed to raise specific recommendations for people with different dietary styles for the prevention of atherosclerotic cardiovascular disease through dietary habit adjustments.

BACKGROUND: The ejection fraction (EF) provides critical information about heart failure (HF) and its management. Electrocardiography (ECG) is a noninvasive screening tool for cardiac electrophysiological activities that has been used to detect patients with low EF based on a deep learning model (DLM) trained via large amounts of data. However, no studies have widely investigated its clinical impacts. OBJECTIVE: This study developed a DLM to estimate EF via ECG (ECG-EF). We further investigated the relationship between ECG-EF and echo-based EF (ECHO-EF) and explored their contributions to future cardiovascular adverse events. METHODS: There were 57,206 ECGs with corresponding echocardiograms used to train our DLM. We compared a series of training strategies and selected the best DLM. The architecture of the DLM was based on ECG12Net, developed previously. Next, 10,762 ECGs were used for validation, and another 20,629 ECGs were employed to conduct the accuracy test. The changes between ECG-EF and ECHO-EF were evaluated. The primary follow-up adverse events included future ECHO-EF changes and major adverse cardiovascular events (MACEs). RESULTS: The sex-/age-matching strategy-trained DLM achieved the best area under the curve (AUC) of 0.9472 with a sensitivity of 86.9% and specificity of 89.6% in the follow-up cohort, with a correlation of 0.603 and a mean absolute error of 7.436. In patients with accurate prediction (initial difference < 10%), the change traces of ECG-EF and ECHO-EF were more consistent (R-square = 0.351) than in all patients (R-square = 0.115). Patients with lower ECG-EF (≤35%) exhibited a greater risk of cardiovascular (CV) complications, delayed ECHO-EF recovery, and earlier ECHO-EF deterioration than patients with normal ECG-EF (>50%). Importantly, ECG-EF demonstrated an independent impact on MACEs and all CV adverse outcomes, with better prediction of CV outcomes than ECHO-EF. CONCLUSIONS: The ECG-EF could be used to initially screen asymptomatic left ventricular dysfunction (LVD) and it could also independently contribute to the predictions of future CV adverse events. Although further large-scale studies are warranted, DLM-based ECG-EF could serve as a promising diagnostic supportive and management-guided tool for CV disease prediction and the care of patients with LVD.