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The supply chain optimization of e-commerce is the optimization of the integrated linear management model for e-commerce enterprises based on manufacturers, suppliers, warehouses, distribution centers, and channels. The application of Internet of Things (IoT) technology to the optimization of e-commerce supply chain can help e-commerce enterprises to monitor each product in real time and manage logistics system reasonably; thus, the circulation process of e-commerce product in the supply chain can be monitored and shared, and the product information at each stage of the supply chain can be analyzed and predicted, helping e-commerce companies to estimate the future trends or accident probability. On the analysis of summarizing and analyzing previous works, this article expounded the research status and significance of e-commerce supply chain optimization, elaborated the development background, current status, and future challenges of the IoT technology, introduced the methods and principles of the node distribution model and constraint parameter analysis of logistics supply chain, constructed the IoT-based e-commerce supply chain linear and nonlinear model including competition and risk-control modules, performed the design of IoT-based e-commerce supply chain optimization, and discussed the solutions to procurement robustness optimization and management process optimization. The final simulation analysis showed that the proposed optimization model can effectively predict e-commerce product information in each stage of the supply chain; the IoT-based e-commerce supply chain optimization can enable each node company in the supply chain to improve the quality of information transmission and reduce the risk of information asymmetry in the process of e-commerce exchanges and can quickly and easily respond to the sudden changes of market and environment, thereby promoting them to cooperate with each other for establishing a supply chain network structure with closer business relationships, smoother logistics transportation, more reasonable benefit sharing, and stronger market competitiveness.

At present, bone nonunion and delayed union are still difficult problems in orthopaedics. Since the discovery of bone morphogenetic protein (BMP), it has been widely used in various studies due to its powerful role in promoting osteogenesis and chondrogenesis. Current results show that BMPs can promote healing of bone defects and reduce the occurrence of complications. However, the mechanism of BMP in vivo still needs to be explored, and application of BMP alone to a bone defect site cannot achieve good therapeutic effects. It is particularly important to modify implants to carry BMP to achieve slow and sustained release effects by taking advantage of the nature of the implant. This review aims to explain the mechanism of BMP action in vivo , its biological function, and how BMP can be applied to orthopaedic implants to effectively stimulate bone healing in the long term. Notably, implantation of a system that allows sustained release of BMP can provide an effective method to treat bone nonunion and delayed bone healing in the clinic.

Accurate risk prediction remains a critical challenge in reliability engineering and system safety, particularly in complex systems characterized by interdependent temporal progression and spatial co-occurrence patterns. While existing approaches predominantly focus on either temporal dynamics or co-occurrence relationships, this study introduces a novel spatio-temporal graph learning architecture. First, a dual-matrix graph construction mechanism simultaneously captures spatial risk correlations through co-occurrence frequency analysis and temporal progression patterns using transition probability modeling. Second, an adaptive subgraph extraction module generates system-specific topological representations that preserve both localized risk clusters and directed temporal pathways. Third, a dual-channel graph convolutional network with bilinear interaction fusion facilitates synergistic processing of spatial coexistence features and temporal progression patterns while preserving modality-specific characteristics. Empirical validation across medical diagnosis and vehicular risk domains demonstrates the model’s effectiveness in handling multi-risk coexistence scenarios and long-term progression patterns, significantly outperforming conventional single-modality approaches. The proposed methodology offers a generalizable solution for cross-domain risk prediction tasks. The source code is available at https://github.com/FanghuaX/HS-TGN .

Multimodal phototheranostics utilizing single molecules offer a “one-and-done” approach, presenting a convenient and effective strategy for cancer therapy. However, therapies based on conventional photosensitizers often suffer from limitations such as a single photosensitizing mechanism, restricted tumor penetration and retention, and the requirement for multiple irradiations, which significantly constrain their application. In this report, we present an aggregation-induced emission luminogen (AIEgen) bacteria hybrid bionic robot to address above issues. This bionic robot is composed of multifunctional AIEgen (INX-2) and Escherichia coli Nissle 1917 (EcN), i.e., EcN@INX-2. The EcN@INX-2 bionic robot exhibits near-infrared II (NIR-II) fluorescence emission and demonstrates efficient photodynamic and photothermal effects, as well as tumor-targeting capabilities. These features are facilitated by the complementary roles of INX-2 and EcN. The robot successfully enables in vivo multimodal imaging and therapy of colon cancer models in female mice through various mechanisms, including the activation of anti-tumor immunity, as well as photodynamic and photothermal therapy. Our study paves an avenue for designing multifunctional diagnostic agents for targeted colon cancer therapy through image-guided combinational immunotherapy. The utility of photosensitizers for cancer therapies is often hindered by a single photosensitizing mechanism, the need of multiple irradiations, and limited tumor penetration and retention. Here, the authors address these issues by developing an aggregation-induced emission luminogen bacteria hybrid bionic robot that enables multimodal imaging and therapy and activates anti-tumor immunity.

The stem cell is the foundation of regenerative medicine and tissue engineering. Regulating specific stem cell fate, such as cell attachment, proliferation, differentiation, and even death, undergoes continuous development. Cold atmospheric plasma (CAP), the core technology of plasma medicine, is attracting tremendous attention due to its ability and versatility to manipulate various types of cells, including stem cells. Specifically, the direct and indirect applications of CAP in controlling cell fate are best exemplified by upfront irradiation of the stem cells and modification of the stem cell niche, respectively. This review will describe the recent advances in various CAP strategies, both direct and indirect, and their influence on the fate of healthy and cancer stem cells. Particular emphasis will be placed on the mechanism of connecting the physical and chemical cues carried by the plasma and biological changes presented by the cells, especially at the transcriptomic level. The ultimate goal is to exploit CAP’s potential in regenerative medicine.

Introduction: Uneven rainfall distribution alters tree water use patterns, ultimately influencing plantation establishment.Methods: Based on monthly rainfall, six drought levels were classified. Whole-tree sap flux and meteorological variables were monitored across these levels from 2010 to 2013 in a pure Schima superba plantation in South China. The relationships between daily transpiration (Tt) and the influencing factors were modeled using the Support vector regression (SVR) method. Shapley additive explanations (SHAP) values were employed to characterize the sensitivity and contributions of four environmental variables to Tt.Results: The results indicate that monthly rainfall (RFt) significantly influences the sensitivity of these four environmental variables to Tt when RFt exceeds 300 mm (Level 6). Furthermore, when RFt is 300 mm or less (Levels 1–5), the sensitivity of these factors and their total contributions to Tt are independent of tree size.Discussion: Our findings indicate that the decoupling between Tt and environmental factors may be a significant characteristic of ongoing water stress during high rainfall months. Additionally, these findings enhance the predictive capability of machine learning models in assessing tree water use.

Alterations in myocardial energy substrate metabolism and mitochondrial injury following myocardial infarction (MI) lead to structural and functional abnormalities of the heart. The fatty acid translocase CD36 (CD36) plays a pivotal role in regulating lipid homeostasis and mitochondrial metabolism. Here, we demonstrate that inhibiting the palmitoylation of CD36 and the resulting alteration in its subcellular localization alleviates lipid metabolism disorders and mitochondrial dysfunction in cardiomyocytes of male mice post-MI. Mechanistically, the inhibition of CD36 palmitoylation enhances cardiac function through a dual mechanism: first, by alleviating fatty acid overload mediated by plasma membrane CD36, thereby restoring lipid metabolic balance; second, by augmenting the activity of the mitochondrial CD36-PGAM5 signaling axis and modulating Fundc1 and Drp1 Dephosphorylation, which subsequently improves mitophagy efficiency. Overall, our study highlights the significant role of CD36 palmitoylation in preserving heart function by regulating downstream metabolic signaling pathways, suggesting that targeting CD36 palmitoylation could be a promising therapeutic strategy for MI. Alterations in myocardial mitochondrial metabolism following myocardial infarction (MI) lead to structural and functional abnormalities of the heart. Here, the author shows that inhibiting the palmitoylation of CD36 alleviates lipid metabolism disorders and mitochondrial dysfunction in cardiomyocytes post-MI.

Guben Xiezhuo decoction (GBXZD), a traditional Chinese medicine (TCM), shows promise in treating chronic kidney disease (CKD) by reducing renal fibrosis (RF). This study seeks to uncover the exact mechanism behind GBXZD’s therapeutic effects. This study identified 14 active components and 18 specific metabolites in the serum of GBXZD-treated rats via mass spectrometry. Potential target proteins were predicted using PubChem, TCMSP, and SwissTargetPrediction databases and refined through OMIM and Genecards databases. A total of 276 proteins were filtered from these potential target proteins to develop a protein-protein interaction (PPI) network to explore the interactions between GBXZD and RF target proteins, revealing significant correlations for proteins such as SRC, EGFR, and MAPK3. Subsequently, the active components and specific metabolites were employed for molecule docking simulation to assess their interactions with EGFR protein. The effects and mechanisms of GBXZD on RF were validated in a unilateral ureteral obstruction (UUO) rat model by assessing changes in RF-related protein expressions identified from the PPI network. GBXZD showed a reduction in the phosphorylation expression of SRC, EGFR, ERK1, JNK, and STAT3. In vitro, LPS stimulated HK 2 cells treated with the identified GBXZD bioactive components trans-3-Indoleacrylic acid and Cuminaldehyde exhibited significantly enhanced viability and reduced fibrotic marker expression and p-EGFR levels. KEGG pathway analysis suggested that GBXZD’s anti-fibrotic effects might be mediated by inhibiting the EGFR tyrosine kinase inhibitor resistance and MAPK signaling pathways.

This study aims to analyze how the built environment influences urban residents’ physical activity in tropical coastal regions, and to identify the relative weights of key environmental factors. Through semi-structured interviews with 31 residents in Hainan, China, and qualitative analysis using NVivo 14, five core categories influencing physical activity were identified. A conceptual model with the built environment as its central node was then developed to elucidate the interrelationships among these categories. To further weight the sub-categories, a follow-up Analytic Hierarchy Process survey was conducted with 12 experts. Integrating the two stages, it was found that the safety and site conditions are prerequisite conditions to ensure residents’ physical activity. On this basis, residents have the strongest perception of the incentive effect of site conditions and landscape. The findings provide a theoretical basis and practical reference for systematically evaluating the impact of the built environment on residents’ health and well-being, and offer guidance for planning and designing health-promoting places in tropical regions.

This study focuses on the impact mechanism of the natural landscape of parks in tropical coastal areas on residents' physical activities. Taking Haikou Baishamen Park and its adjacent beach as a case study, it integrates methods such as on-site observation, in-depth interviews, and online text investigation to study the characteristics of residents' physical activities in typical natural landscape areas (beach, lake, lawn, grove, and stream). Based on the coding and analysis of textual data, this study reveals significant differences in the activity patterns of residents within different landscape types, and explores the promoting and restricting effects of various landscapes on physical activities and their internal mechanisms. The results clarify the site effect of different natural landscapes, and then put forward targeted suggestions for optimizing the landscape design of tropical coastal parks to improve the residents’ physical activity levels.