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Background: Cellular senescence is pivotal in the occurrence and progression of atrial fibrillation (AF). This study aimed to identify senescence-related genes that could be potential therapeutic biomarkers for AF. Methods: AF-related differentially expressed genes (DEGs) were identified using the Gene Expression Omnibus dataset. Weighted gene co-expression network analysis (WGCNA) was used to analyze important modules and potential hub genes. Integrating senescence-related genes, potential biomarkers were identified. Their differential expression levels were then validated in human atrial tissue, HL-1 cells, and Angiotensin II-infused mice. Finally, molecular docking analysis was conducted to predict potential interactions between potential biomarkers and the senolytic drug Navitoclax. Results: We identified seven genes common to AF-related DEGs and senescence-related genes. Three significant modules were selected from WGCNA analysis. Taken together, three senescence-related genes (ETS1, SP1, and WT1) were found to be significantly associated with AF. Protein-protein interaction network analysis revealed biological connections among the predicted target genes of ETS1, SP1, and WT1. Notably, ETS1, SP1, and WT1 exhibited significant differential expression in clinical samples as well as in vitro and in vivo models. Molecular docking revealed favorable binding affinity between senolytic Navitoclax and these potential biomarkers. Conclusions: This study highlights ETS1, SP1, and WT1 as crucial senescence-related genes associated with AF, offering potential therapeutic targets, with supportive evidence of binding affinity with senolytic Navitoclax. These findings provide novel insights into AF pathogenesis from a senescence perspective.

Abstract Background Impaired collateral formation is a major factor contributing to poor prognosis in type 2 diabetes mellitus (T2DM) patients with atherosclerotic cardiovascular disease. However, the current pharmacological treatments for improving collateral formation remain unsatisfactory. The induction of endothelial autophagy and the elimination of reactive oxygen species (ROS) represent potential therapeutic targets for enhancing endothelial angiogenesis and facilitating collateral formation. This study investigates the potential of molybdenum disulfide nanodots (MoS2 NDs) for enhancing collateral formation and improving prognosis. Results Our study shows that MoS2 NDs significantly enhance collateral formation in ischemic tissues of diabetic mice, improving effective blood resupply. Additionally, MoS2 NDs boost the proliferation, migration, and tube formation of endothelial cells under high glucose/hypoxia conditions in vitro. Mechanistically, the beneficial effects of MoS2 NDs on collateral formation not only depend on their known scavenging properties of ROS (H2O2, •O2 -, and •OH) but also primarily involve a molecular pathway, cAMP/PKA-NR4A2, which promotes autophagy and contributes to mitigating damage in diabetic endothelial cells. Conclusions Overall, this study investigated the specific mechanism by which MoS2 NDs mediated autophagy activation and highlighted the synergy between autophagy activation and antioxidation, thus suggesting that an economic and biocompatible nano-agent with dual therapeutic functions is highly preferable for promoting collateral formation in a diabetic context, thus, highlighting their therapeutic potential. Graphical Abstract

Deep learning methods have been widely explored in motor imagery (MI)-based brain computer interface (BCI) systems to decode electroencephalography (EEG) signals. However, most studies fail to fully explore temporal dependencies among MI-related patterns generated in different stages during MI tasks, resulting in limited MI-EEG decoding performance. Apart from feature extraction, learning temporal dependencies is equally important to develop a subject-specific MI-based BCI because every subject has their own way of performing MI tasks. In this paper, a novel temporal dependency learning convolutional neural network (CNN) with attention mechanism is proposed to address MI-EEG decoding. The network first learns spatial and spectral information from multi-view EEG data via the spatial convolution block. Then, a series of non-overlapped time windows is employed to segment the output data, and the discriminative feature is further extracted from each time window to capture MI-related patterns generated in different stages. Furthermore, to explore temporal dependencies among discriminative features in different time windows, we design a temporal attention module that assigns different weights to features in various time windows and fuses them into more discriminative features. The experimental results on the BCI Competition IV-2a (BCIC-IV-2a) and OpenBMI datasets show that our proposed network outperforms the state-of-the-art algorithms and achieves the average accuracy of 79.48%, improved by 2.30% on the BCIC-IV-2a dataset. We demonstrate that learning temporal dependencies effectively improves MI-EEG decoding performance. The code is available at https://github.com/Ma-Xinzhi/LightConvNet

BackgroundMyeloperoxidase (MPO), released by activated neutrophils, is significantly increased in atrial fibrillation (AF). MPO may play a role in the progression of atrial fibrillation and further involved in AF recurrence after catheter ablation. We compared plasma MPO levels in paroxysmal and persistent AF and explored their role in AF recurrence after catheter ablation.MethodsPlasma MPO levels were measured in consecutive patients with paroxysmal AF (n = 225) and persistent AF (n = 106). Samples of patients were collected from the femoral vein during catheter ablation and all patients included were followed up after catheter ablation.ResultsPlasma MPO levels increased from paroxysmal AF to persistent AF patients (56.31 [40.33–73.51] vs. 64.11 [48.65–81.11] ng/ml, p

Background: We investigated whether serum levels of immunoglobin (Ig) E and Nε-carboxymethyl-lysine (CML) are related to in-stent restenosis (ISR) in patients with stable coronary artery disease and type 2 diabetes mellitus (T2DM). Methods: Serum levels of IgE and CML were measured in 196 ISR patients and 220 non-ISR patients with stable angina and T2DM who received angiographic follow-up 12 months after percutaneous coronary intervention (PCI) with third-generation drug-eluting stent (DES) implantation for de novo lesions. Multivariate logistic regression analysis was performed to assess the association between IgE or CML and ISR. Results: Both IgE and CML levels were higher in patients with ISR compared with non-ISR patients (IgE: 187.10 (63.75–489.65) vs. 80.25 (30.65–202.50), p < 0.001; CML: 203.26 (164.50–266.84) vs. 174.26 (130.85–215.56), p < 0.001). The rate of ISR increased stepwise with increasing tertiles of IgE and CML levels (p for all trends < 0.001), and IgE correlated significantly with CML. After adjusting for potential confounders, IgE and CML levels remained independently associated with ISR. Moreover, IgE and CML levels improved the predictive capability of traditional risk factors for ISR, and there existed an interaction between IgE and CML in relation to ISR (p for interaction < 0.01). Conclusion: Elevated circulating IgE and CML levels confer an increased risk for ISR after DES-based PCI in type 2 diabetic patients with stable coronary artery disease.

The introduction of Ethernet makes the distributed network system more flexible and efficient, but it also makes nodes which are far apart from each other unable to work in the same time basis due to the long distance. This is not allowed for the high performance requirements of the system synchronization, such as high-precision multiaxis machining system. This paper presents a high-precision network clock synchronization algorithm, namely, optimal PI clock servo, which imposes a PI controller in order to compensate for the clock drift of each network node. Then a simulation platform established by the toolbox TrueTime is used to verify the stability of the algorithm and compare it with the clock synchronization algorithm of EtherCAT. The results show that the new synchronization algorithm has higher synchronization precision and faster convergence rate.

A smart electricity monitoring system of building is presented using ZigBee and internet to establish the network. This system consists of three hardware layers: the host PC, the router, and the sensor nodes. A hierarchical ant colony algorithm is developed for data transmission among the wireless sensor nodes. The wireless communication protocol is also designed based on IPv6 protocol on IEEE 802.15.4 wireless network. All-IP approach and peer-to-peer mode are integrated to optimize the network building. Each node measures the power, current, and voltage and transmits them to the host PC through the router. The host software is designed for building test characteristics, having a tree hierarchy and a friendly interface for the user. The reliability and accuracy of this monitoring system are verified in the experiment and application.