RESUMO
Metabolites, as markers of phenotype at the molecular level, can regulate the function of DNA, RNA, and proteins through chemical modifications or interactions with large molecules. Citrate is an important metabolite that affects macrophage polarization and osteoporotic bone function. Therefore, a better understanding of the precise effect of citrate on macrophage polarization may provide an effective alternative strategy to reverse osteoporotic bone metabolism. In this study, a citrate functional scaffold to control the metabolic pathway during macrophage polarization based on the metabolic differences between pro-inflammatory and anti-inflammatory phenotypes for maintaining bone homeostasis, is fabricated. Mechanistically, only outside M1 macrophages are accumulated high concentrations of citrate, in contrast, M2 macrophages consume massive citrate. Therefore, citrate-functionalized scaffolds exert more sensitive inhibitory effects on metabolic enzyme activity during M1 macrophage polarization than M2 macrophage polarization. Citrate can block glycolysis-related enzymes by occupying the binding-site and ensure sufficient metabolic flux in the TCA cycle, so as to turn the metabolism of macrophages to oxidative phosphorylation of M2 macrophage, largely maintaining bone homeostasis. These studies indicate that exogenous citrate can realize metabolic control of macrophage polarization for maintaining bone homeostasis in osteoporosis.
RESUMO
BACKGROUND: Atrial fibrillation (AF) represents the most common arrhythmia worldwide, related to increased risk of ischemic stroke or systemic embolism. It is critical to screen and diagnose AF for the benefits of better cardiovascular health in lifetime. The ECG-based AF detection, the gold standard in clinical care, has been restricted by the need to attach electrodes on the body surface. Recently, ballistocardiogram (BCG) has been investigated for AF diagnosis, which is an unobstructive and convenient technique to monitor heart activity in daily life. However, here is a lack of high-dimension representation and deep learning analysis of BCG. METHOD: Therefore, this paper proposes an attention-based multi-scale features fusion method by using BCG signal. The 1-D morphology feature extracted from Bi-LSTM network and 2-D rhythm feature extracted from reconstructed phase space are integrated by means of CNN network to improve the robustness of AF detection. To the best of our knowledge, this is the first study where the phase space trajectory of BCG is conducted. RESULTS: 2000 segments (AF and NAF) of BCG signals were collected from 59 volunteers suffering from paroxysmal AF in this survey. Compared to the classical time and frequency features and the state-of-the-art energy features with the popular machine learning classifiers, AF detection performance of the proposed method is superior, which has 0.947 accuracy, 0.935 specificity, 0.959 sensitivity, and 0.937 precision, for the same BCG dataset. The experimental results show that combined feature could excavate more potential characteristics, and the attention mechanism could enhance the pertinence for AF recognition. CONCLUSIONS: The proposed method can provide an innovative solution to capture the diverse scale descriptions of BCG and explore ways to involve the deep learning method to accurately screen AF in routine life.
