Oxymatrine is one of the alkaloids extracted from the Chinese herb Sophora japonica (Sophora flavescens Ait.) with anti-inflammatory, immune reaction inhibiting, antiviral, and hepatocyte and antihepatic fibrosis protective activities. However, the effect of oxymatrine on heart failure is not yet known. In this study, the effect of oxymatrine on heart failure was investigated using a Sprague-Dawley rat model of chronic heart failure. Morphological findings showed that in the group treated with 50 and 100 mg/kg of oxymatrine; intermyofibrillar lysis disappeared, myofilaments were orderly, closely and evenly arranged; and mitochondria contained tightly packed cristae compared with the heart failure group. We investigated the cytosolic Ca²⁺ transients and sarcoplasmic reticulum (SR) Ca²⁺ content, and assessed the expression of ryanodine receptor (RyR2), SR-Ca²⁺ ATPase (SERCA2a), and L-type Ca²⁺ channel (dihydropyridine receptor, DHPR). We found that the cytosolic Ca²⁺ transients were markedly increased in amplitude in the medium- (ΔF/F ₀ = 26.22 ± 2.01) and high-dose groups (ΔF/F ₀ = 29.49 ± 1.17) compared to the heart failure group (ΔF/F ₀ = 12.12 ± 1.35, P < 0.01), with changes paralleled by a significant increase in the SR Ca²⁺ content (medium-dose group: ΔF/F ₀ = 32.20 ± 1.67, high-dose group: ΔF/F ₀ = 32.57 ± 1.29, HF: ΔF/F ₀ = 17.26 ± 1.05, P < 0.01). Moreover, we demonstrated that the expression of SERCA2a and cardiac DHPR was significantly increased in the medium- and high-dose group compared with the heart failure rats. These findings suggest that oxymatrine could improve heart failure by improving the cardiac function and that this amelioration is associated with upregulation of SERCA2a and DHPR.

Lipid droplets (LDs) are dynamic lipid storage organelles that can sense and respond to changes in systemic energy balance. The size and number of LDs are controlled by complex and delicate mechanisms, among which, whether and which SNARE proteins mediate LD fusion, and the mechanisms governing this process remain poorly understood. Here we identified a SNARE complex, syntaxin 18 (STX18)–SNAP23–SEC22B, that is recruited to LDs to mediate LD fusion. STX18 targets LDs with its transmembrane domain spanning the phospholipid monolayer twice. STX18–SNAP23–SEC22B complex drives LD fusion in adiposome lipid mixing and content mixing in vitro assays. CIDEC/FSP27 directly binds STX18, SEC22B, and SNAP23, and promotes the lipid mixing of SNAREs-reconstituted adiposomes by promoting LD clustering. Knockdown of STX18 in mouse liver via AAV resulted in smaller liver and reduced LD size under high-fat diet conditions. All these results demonstrate a critical role of the SNARE complex STX18–SNAP23–SEC22B in LD fusion.

Thermoacoustic oscillation is indeed a phenomenon characterized by the symmetric coupling of thermal and acoustic waves. This paper introduces a novel approach for monitoring and predicting thermoacoustic combustion instability using a combination of recurrence quantification analysis (RQA) and an optimized deep belief network (DBN). Six samples of combustion state data were collected using two distinct types of burners to facilitate the training and validation of GA-DBN. The proposed methodology leverages RQA to extract intricate patterns and dynamic features from time series data representing combustion behavior. By quantifying the recurrence plot of specific patterns, the analysis provides valuable insights into the underlying thermoacoustic dynamics. Among three different feature extraction methods, RQA stands out remarkably in performance. These RQA-derived features serve as input to a carefully tuned DBN, which is trained to learn the complex relationships within the combustion process. The classification accuracy of deep belief network optimized by genetic algorithm (GA-DBN) reached an impressive 99.8%. Subsequent multiple comparisons were conducted between GA-DBN, DBN, and support vector machine (SVM), revealing that GA-DBN consistently demonstrated satisfactory classification results. This method holds significant importance in monitoring intricate combustion states.

The health monitoring system has been the main technological approach to extending the life of gas turbine engines and reducing maintenance costs resulting from performance degradation caused by asymmetric factors like carbon deposition, damage, or deformation. One of the most critical techniques within the health monitoring system is performance degradation prediction. At present, most research on degradation prediction is carried out using NASA’s open dataset, C-MAPSS, without considering that monitoring measurements are not always available, as in the ideal dataset. This limitation makes fault diagnosis algorithms and remaining useful life prediction methods difficult to apply to real gas turbine engines. Therefore, to solve the problem of performance degradation prediction in light-duty gas turbine engines, a prediction diagram is proposed based on Long Short-Term Memory (LSTM). Various types of onboard signals are taken into consideration among the experimental data. Only accumulated usage time, total temperature and total pressure before the inlet, low-pressure rotor speed, high-pressure rotor speed, fuel flow rate, exhaust temperature, and thrust are used in the training process, which is indispensable for an aero-engine. A genetic algorithm (GA) is introduced into the training process to optimize the hyperparameters of LSTM. The performance degradation prediction modeled with the GA-LSTM method is validated using experimental data. The maximum prediction error of thrust is 70 daN, and the mean absolute percentage error (MAPE) is less than 0.04. This study provides a practical approach to implementing performance degradation prediction in health monitoring systems to improve gas turbine engine reliability, economy, and environmental performance.