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The antioxidant activities of 18 typical phenolic acids were investigated using 2, 2′-diphenyl-1-picrylhydrazyl (DPPH) and ferric ion reducing antioxidant power (FRAP) assays. Five thermodynamic parameters involving hydrogen atom transfer (HAT), single-electron transfer followed by proton transfer (SET-PT), and sequential proton-loss electron transfer (SPLET) mechanisms were calculated using density functional theory with the B3LYP/UB3LYP functional and 6–311++G (d, p) basis set and compared in the phenolic acids. Based on the same substituents on the benzene ring, -CH 2 COOH and -CH = CHCOOH can enhance the antioxidant activities of phenolic acids, compared with -COOH. Methoxyl (-OCH 3 ) and phenolic hydroxyl (-OH) groups can also promote the antioxidant activities of phenolic acids. These results relate to the O-H bond dissociation enthalpy of the phenolic hydroxyl group in phenolic acids and the values of proton affinity and electron transfer enthalpy (ETE) involved in the electron donation ability of functional groups. In addition, we speculated that HAT, SET-PT, and SPLET mechanisms may occur in the DPPH reaction system. Whereas SPLET was the main reaction mechanism in the FRAP system, because, except for 4-hydroxyphenyl acid, the ETE values of the phenolic acids in water were consistent with the experimental results.

This study proposes a type of trabecular–honeycomb biomimetic structures with high-efficiency energy-absorbing abilities inspired by beetle elytra. Because the trabecular structure is distributed at the ends of the honeycomb walls, the proposed structure is named an end-trabecular beetle elytron plate crash box, or EBEP crash box for simplification. A comparison between the EBEP crash box and conventional crash box (a buffering structure generally used in modern devices and vehicles) is conducted using compression experiments and finite element method. We present the following results. (1) In contrast to the fluctuation stage with a low force in a conventional crash box, the force–displacement curve of the EBEP crash box possesses a rising stage and an approximate plateau with a higher force; as a result, the absorbing energy ability and compression force efficiency are 5 and 2.6 times greater than those of a conventional crash box, respectively. (2) Experimental and numerical comparisons reveal that there is cracking failure in the conventional crash box; however, the coordinated and uniform S-typed laminated compression deformation is developed in the EBEP crash box. (3) The influences of the amplitude (A) of the sine wave deformation line on the peak force and the compression force efficiency of the EBEP crash box are investigated, thereby providing a feasible method for adjusting the peak force according to different engineering requirements. These results provide new inspiration for applying EBEP crash boxes and exploiting new buffering structures and materials in the energy-absorbing field.

Current evidence indicates that coronary microcirculation is a key target for protecting against cardiac ischemia–reperfusion (I/R) injury. Mitochondrial calcium uniporter (MCU) complex activation and mitochondrial calcium ([Ca2+]m) overload are underlying mechanisms involved in cardiovascular disease. Histidine triad nucleotide-binding 2 (HINT2) has been reported to modulate [Ca2+]m via the MCU complex, and our previous work demonstrated that HINT2 improved cardiomyocyte survival and preserved heart function in mice with cardiac ischemia. This study aimed to explore the benefits of HINT2 on cardiac microcirculation in I/R injury with a focus on mitochondria, the MCU complex, and [Ca2+]m overload in endothelial cells. The present work demonstrated that HINT2 overexpression significantly reduced the no-reflow area and improved microvascular perfusion in I/R-injured mouse hearts, potentially by promoting endothelial nitric oxide synthase (eNOS) expression and phosphorylation. Microvascular barrier function was compromised by reperfusion injury, but was repaired by HINT2 overexpression via inhibiting VE-Cadherin phosphorylation at Tyr731 and enhancing the VE-Cadherin/β-Catenin interaction. In addition, HINT2 overexpression inhibited the inflammatory response by suppressing vascular cell adhesion molecule-1 (VCAM-1) and intercellular adhesion molecule-1 (ICAM-1). Mitochondrial fission occurred in cardiac microvascular endothelial cells (CMECs) subjected to oxygen–glucose deprivation/reoxygenation (OGD/R) injury and resulted in mitochondrial dysfunction and mitochondrion-dependent apoptosis, the effects of which were largely relieved by HINT2 overexpression. Additional experiments confirmed that [Ca2+]m overload was an initiating factor for mitochondrial fission and that HINT2 suppressed [Ca2+]m overload via modulation of the MCU complex through directly interacting with MCU in CMECs. Regaining [Ca2+]m overload by spermine, an MCU agonist, abolished all the protective effects of HINT2 on OGD/R-injured CMECs and I/R-injured cardiac microcirculation. In conclusion, the present report demonstrated that HINT2 overexpression inhibited MCU complex-mitochondrial calcium overload-mitochondrial fission and apoptosis pathway, and thereby attenuated cardiac microvascular ischemia–reperfusion injury.

Coronary microembolization (CME), a common reason for periprocedural myocardial infarction (PMI), bears very important prognostic implications. However, the molecular mechanisms related to CME remain largely elusive. Statins have been shown to prevent PMI, but the underlying mechanism has not been identified. Here, we examine whether the NLRP3 inflammasome contributes to CME-induced cardiac injury and investigate the effects of statin therapy on CME. In vivo study, mice with CME were treated with 40 mg/kg/d rosuvastatin (RVS) orally or a selective NLRP3 inflammasome inhibitor MCC950 intraperitoneally (20 mg/kg/d). Mice treated with MCC950 and RVS showed improved cardiac contractile function and morphological changes, diminished fibrosis and microinfarct size, and reduced serum lactate dehydrogenase (LDH) level. Mechanistically, RVS decreased the expression of NLRP3, caspase-1, interleukin-1β, and Gasdermin D N-terminal domains. Proteomics analysis revealed that RVS restored the energy metabolism and oxidative phosphorylation in CME. Furthermore, reduced reactive oxygen species (ROS) level and alleviated mitochondrial damage were observed in RVS-treated mice. In vitro study, RVS inhibited the activation of NLRP3 inflammasome induced by tumor necrosis factor α plus hypoxia in H9c2 cells. Meanwhile, the pyroptosis was also suppressed by RVS, indicated by the increased cell viability, decreased LDH and propidium iodide uptake in H9c2 cells. RVS also reduced the level of mitochondrial ROS generation in vitro. Our results indicate the NLRP3 inflammasome-dependent cardiac pyroptosis plays an important role in CME-induced cardiac injury and its inhibitor exerts cardioprotective effect following CME. We also uncover the anti-pyroptosis role of RVS in CME, which is associated with regulating mitochondrial ROS.

To investigate the characteristics of compression, buffering and energy dissipation in beetle elytron plates (BEPs), compression experiments were performed on BEPs and honeycomb plates (HPs) with the same wall thickness in different core structures and using different molding methods. The results are as follows: 1) The compressive strength and energy dissipation capacity in the BEP are 2.44 and 5.0 times those in the HP, respectively, when the plates are prepared using the full integrated method (FIM). 2) The buckling stress is directly proportional to the square of the wall thickness (t). Thus, for core structures with equal wall thicknesses, although the core volume of the BEP is 42 percent greater than that of the HP, the mechanical properties of the BEP are several times higher than those of the HP. 3) It is also proven that even when the single integrated method (SIM) is used to prepare BEPs, the properties discussed above remain superior to those of HPs by a factor of several; this finding lays the foundation for accelerating the commercialization of BEPs based on modern manufacturing processes.

In pursuit of the development of lightweight biomimetic functional–structural materials, this study investigated the flexural properties and failure characteristics of end-trabecular beetle elytron plates (EBEPs) as well as the flexural mechanism and the role of the trabeculae. The results were as follows: (1) The EBEP specimens showed better ductility performance after the peak load was reached, and their specific elastic strength and specific flexural strength were similar to those of honeycomb plates (HPs). In an EBEP before failure, the lower skin in the same location as the load was significantly stretched, and the trabeculae in the core showed two failure modes: destruction by means of slant cracks and vertical cracks. (2) The failure mechanism of the trabeculae in an EBEP was investigated by qualitatively analyzing the load and deformation of the parts adjacent and nonadjacent to the loading point. From the macro point of view, the cores of EBEP and HP are continuous. These cores can not only bear tension with lower skins, but also divide upper skins into much smaller parts and play a role as reinforcing ribs. The equivalent trabeculae in EBEP are closed ended, the honeycomb walls are narrow, and these two parts can support and constrain each other.

Autophagy-related gene-5 (ATG-5) is one of the key regulators of autophagic cell death. It has been widely regarded as a protective molecular mechanism for tumor cells during the course of chemotherapy. In the present study, we investigated the expression pattern of ATG-5 and multidrug resistance-associated protein-1 (MRP-1) in 135 gastric cancers (GC) patients who were treated with epirubicin, cisplatin and 5-FU adjuvant chemotherapy (ECF) following surgical resection and explored their potential clinical significance. We found that both ATG-5 (77.78%) and MRP-1 (79.26%) were highly expressed in GC patients. ATG-5 expression was significantly associated with depth of wall invasion, TNM stages and distant metastasis of GC (P<0.05), whereas MRP-1 expression was significantly linked with tumor size, depth of wall invasion, lymph node metastasis, TNM stages and differentiation status (P<0.05). ATG-5 expression was positively correlated with MRP-1 (rp = 0.616, P<0.01). Increased expression of ATG-5 and MPR-1 was significantly correlated with poor overall survival (OS; P<0.01) and disease free survival (DFS; P<0.01) of our GC cohort. Furthermore, we demonstrated that ATG-5 was involved in drug resistant of GC cells, which was mainly through regulating autophagy. Our data suggest that upregulated expression of ATG-5, an important molecular feature of protective autophagy, is associated with chemoresistance in GC. Expression of ATG-5 and MRP-1 may be independent prognostic markers for GC treatment.

Trust management in shared vehicle data systems presents significant challenges, necessitating innovative approaches. A data analysis system integrating blockchain-based distributed trust management with deep reinforcement learning (DRL) is introduced to address these issues. The proposed system includes two core components: (1) User Trust Evaluation Model: Bayesian statistical methods are employed to estimate user credibility, utilizing historical interaction records as prior information. Blockchain technology separates the data chain and trust chain, enabling a distributed architecture that enhances data storage security and trust management robustness. (2) Behavioral Modeling and Defensive Strategies: The shared vehicle service process and user behavior are conceptualized as a Markov Decision Process. Using the Deep Q-Network (DQN) algorithm, the system identifies optimal defensive strategies through multidimensional data interactions. Performance evaluation is conducted using the Autonomous Driving Dataset ( https://github.com/DRL-CASIA/Autonomous-Driving-Dataset-Open ), with the following key metrics: (1) Trust Evaluation Accuracy: Assesses the precision of the system in evaluating user trust. The blockchain-based approach enhances accuracy by approximately 16% compared to centralized methods, demonstrating its reliability. (2) Average System Reward: Indicates the expected return from implementing defensive strategies. The DQN-based system achieves a performance increase exceeding 20% compared to Q-learning, highlighting its decision-making efficacy. (3) Malicious Behavior Detection Rate: Measures the system’s ability to detect and address malicious activities. The proposed model attains a detection rate of approximately 93%, an improvement of over 15%, reflecting its advanced defensive capabilities. (4) Service Response Time: Evaluates the system’s efficiency in responding to user requests. A reduction of more than 11% in response time underscores the enhanced operational speed. Experimental results validate the effectiveness of the proposed system in addressing trust management and decision-making challenges. By combining blockchain’s decentralized storage capabilities with DRL’s dynamic optimization potential, the system demonstrates a scalable and efficient approach for distributed data analysis in complex scenarios.

Background Interactions of microbes and diseases are of great importance for biomedical research. However, large-scale of microbe–disease interactions are hidden in the biomedical literature. The structured databases for microbe–disease interactions are in limited amounts. In this paper, we aim to construct a large-scale database for microbe–disease interactions automatically. We attained this goal via applying text mining methods based on a deep learning model with a moderate curation cost. We also built a user-friendly web interface that allows researchers to navigate and query required information. Results Firstly, we manually constructed a golden-standard corpus and a sliver-standard corpus (SSC) for microbe–disease interactions for curation. Moreover, we proposed a text mining framework for microbe–disease interaction extraction based on a pretrained model BERE. We applied named entity recognition tools to detect microbe and disease mentions from the free biomedical texts. After that, we fine-tuned the pretrained model BERE to recognize relations between targeted entities, which was originally built for drug–target interactions or drug–drug interactions. The introduction of SSC for model fine-tuning greatly improved detection performance for microbe–disease interactions, with an average reduction in error of approximately 10%. The MDIDB website offers data browsing, custom searching for specific diseases or microbes, and batch downloading. Conclusions Evaluation results demonstrate that our method outperform the baseline model (rule-based PKDE4J) with an average F 1 -score of 73.81%. For further validation, we randomly sampled nearly 1000 predicted interactions by our model, and manually checked the correctness of each interaction, which gives a 73% accuracy. The MDIDB webiste is freely avaliable throuth http://dbmdi.com/index/

To address the inefficiency and high error rates in traditional methods for handling complex design processes in modern mold design, this study proposes a Knowledge Graph-driven Intelligent Data-enabled Design System for molds. Initially, deep semantic understanding techniques are employed to intelligently parse a large volume of mold design documents and data. Using Bidirectional Encoder Representations from Transformers (BERT) and Random Forest (RF) algorithms, key information and knowledge points are accurately extracted from the design documents, laying a solid foundation for constructing the knowledge graph. The study collects a significant number of representative mold design documents, followed by detailed data preprocessing and cleaning. Subsequently, the BERT model is utilized for semantic analysis to precisely extract various entities (such as components, materials, and process parameters) and their complex relationships during the design process. Research findings show that the system significantly reduces the error rate in mold design processes, decreasing from 0.15 to 0.0975. Regarding design efficiency, the average completion time per design task reduces from 20 h to 12 h. Compared to traditional design methods, the system shortens the average design cycle from 30 days to 22.5 days, achieving a reduction of 0.25. Validation through examples further demonstrates that the system exhibits notable advantages in intelligence and automation during mold design processes, effectively enhancing design quality and efficiency. Additionally, it reduces related labor costs by 0.2. In summary, the proposed Knowledge Graph-based mold design system not only demonstrates significant innovation and application prospects theoretically but also shows substantial effectiveness and value in practical applications. Future research directions include further optimizing system performance, expanding application domains, and exploring integration with other intelligent manufacturing technologies to elevate the overall level of smart manufacturing.