Lianyungang Runzhong Pharmaceutical (China)

To improve the accuracy of deformation prediction in concrete buildings and large-scale infrastructures such as dams, this study proposes an Attention-BiLSTM model integrated with a parameter-optimized Variational Mode Decomposition (VMD). Specifically, the Pelican Optimization Algorithm (POA) is employed to optimize VMD parameters, enhancing signal decomposition efficiency for structural deformation time series. The optimized VMD is then coupled with a BiLSTM network embedded with an attention mechanism, forming a hybrid prediction framework that captures both temporal dependencies and key feature weights in monitoring data. Using three sets of engineering-measured deformation datasets, the proposed model is validated through comparative analyses with conventional single models (e.g., standalone BiLSTM and VMD-BiLSTM without attention). Results demonstrate that the developed model achieves superior accuracy and stability, significantly outperforming all comparative methods, with the highest R2 reaching 0.996, while reducing MAE and RMSE by over 60% and 30%, respectively. Quantitative evaluation indicators (e.g., RMSE, MAE, and R2) confirm that the approach effectively captures both short-term fluctuations and long-term trends of structural deformation. These findings verify its reliability and applicability for intelligent safety monitoring of concrete buildings and infrastructures.

This study investigated the therapeutic mechanism of Baojing Tablets(BJT) in the treatment of chronic prostatitis through the signal transducer and activator of transcription 3(STAT3)/hypoxia-inducible factor 1α(HIF-1α)/glycolysis axis. A rat model of experimental autoimmune prostatitis was established to evaluate the efficacy of Baojing Tablets. Male Wistar rats were randomly divided into six groups(7 per group): control group, model group, low-, medium-, and high-dose Baojing Tablets groups(197.2, 591.5, 1 774.4 mg·kg~(-1), respectively), and a positive control group(Pusitai, 34.2 mg·kg~(-1)). After model induction, all treatments were administered continuously for four weeks. Hematoxylin and eosin(HE) staining was used to observe inflammatory infiltration and glandular damage in the prostate. Serum levels of interleukin-8(IL-8), interleukin-1β(IL-1β), and immunoglobulin G(IgG) were measured by enzyme-linked immunosorbent assay(ELISA). The expression levels of phosphorylated STAT3(p-STAT3), HIF-1α, multiple inositol polyphosphate phosphatase 1(MINPP1), and hexokinase(HK) in prostate tissues were detected by Western blot and immunohistochemistry. Untargeted metabolomics was employed to screen differential metabolites and analyze relevant metabolic pathways, thereby investigating the potential mechanism of Baojing Tablets in treating chronic prostatitis. HE staining results revealed that Baojing Tablets significantly ameliorated pathological damage to the prostate. ELISA results demonstrated that Baojing Tablets reduced serum levels of IL-8, IL-1β, and IgG. Western blot and immunohistochemistry analyses revealed that Baojing Tablets downregulated the expression levels of p-STAT3, HK, and HIF-1α in prostate tissues, while upregulating MINPP1 expression. Metabolomics results suggested that Baojing Tablets may exert its therapeutic effect by modulating 96 differential metabolites and participating in metabolic pathways such as glycolysis and arachidonic acid metabolism. In conclusion, Baojing Tablets may alleviate inflammation and inhibit the progression of chronic prostatitis by targeting STAT3 and suppressing the HIF-1 signaling pathway, thereby modulating glycolytic metabolism.

The quality of traditional Chinese medicine(TCM) is a critical foundation for ensuring the stability of its efficacy, as well as the safety and effectiveness of its clinical use. The identification of critical quality attributes(CQAs) is one of the core components of TCM preparation quality control. This study focuses on Jianwei Xiaoshi Tablets and explores their CQAs related to property and flavor from the perspective of taste receptor proteins. Three taste receptor proteins, T1R2, T1R3, and TRPV1, were selected, and a biosensor based on high-electron-mobility transistor(HEMT) was constructed to detect the interactions between Jianwei Xiaoshi Tablets and taste receptor proteins. Simultaneously, liquid chromatography-mass spectrometry(LC-MS) technology was used to analyze the chemical composition of Jianwei Xiaoshi Tablets. In examining the interaction strength, the results indicated that the interaction between Jianwei Xiaoshi Tablets and TRPV1 protein was the strongest, followed by T1R3, with the interaction with T1R2 being relatively weaker. By combining biosensing technology with LC-MS, 16 chemical components were identified from Jianwei Xiaoshi Tablets, among which six were selected as CQAs for sweetness and seven for pungency. Further validation experiments demonstrated that CQAs such as hesperidin and hesperetin had strong interactions with their corresponding taste receptor proteins. Through the combined use of multiple technological approaches, this study successfully determined the property and flavor-related CQAs of Jianwei Xiaoshi Tablets. It provides novel ideas and approach for the identification of CQAs in TCM preparations and offers comprehensive theoretical support for TCM quality control, contributing to the improvement and development of TCM preparation quality control systems.

Abstract C39H53Cl2NO4, monoclinic, P21 (no. 4), a = 7.55730(10) Å, b = 11.0989(2) Å, c = 20.9222(3) Å, β = 91.426(1)°, V = 1754.36(5) Å3, Z = 2, R gt (F) = 0.0360, wR ref (F 2) = 0.1028, T = 169.98(10) K.

Abstract Transition metal dichalcogenides (TMDs) have many excellent properties as promising class of two-dimensional materials. In this study, we conducted rigorous calculations utilizing density functional theory to evaluate the potential of Janus ReSSe monolayers, doped with transition metals such as Fe, Ru, and Os, in gas-sensitive applications specifically targeting CO detection. Three stable structures of X-Re15S16Se16 Janus doped with X elements (X = Fe, Os, Ru) were designed. Our findings indicate that the C atom of the CO molecule exhibits a higher affinity for adsorbing onto the X (X = Fe, Os, Ru) transition metal atoms, forming robust X–C bonds, rather than the O atom. Among these bonds, the Os-C bond exhibits the strongest bonding states, followed by the Ru-C bond, while the Fe-C bond behaves the weakest. Notably, the d-orbital peaks of the X (X = Fe, Os, Ru) transition metals display distinct bonding strengths with the C atom. This research may provide a theoretical foundation for the development of new gas sensors based on two-dimensional materials.

Recent advancements in organoid technology have heralded a transformative era in biomedical research, characterized by the emergence of gut organoids that replicate the structural and functional complexity of the human intestines. These stem cell-derived structures provide a dynamic platform for investigating intestinal physiology, disease pathogenesis, and therapeutic interventions. This model outperforms traditional two-dimensional cell cultures in replicating cell interactions and tissue dynamics. Gut organoids represent a significant leap towards personalized medicine. They provide a predictive model for human drug responses, thereby minimizing reliance on animal models and paving the path for more ethical and relevant research approaches. However, the transition from basic organoid models to more sophisticated, biomimetic systems that encapsulate the gut's multifaceted environment-including its interactions with microbial communities, immune cells, and neural networks-presents significant scientific challenges. This review concentrates on recent technological strides in overcoming these barriers, emphasizing innovative engineering approaches for integrating diverse cell types to replicate the gut's immune and neural components. It also explores the application of advanced fabrication techniques, such as 3D bioprinting and microfluidics, to construct organoids that more accurately replicate human tissue architecture. They provide insights into the intricate workings of the human gut, fostering the development of targeted, effective treatments. These advancements hold promise in revolutionizing disease modeling and drug discovery. Future research directions aim at refining these models further, making them more accessible and scalable for wider applications in scientific inquiry and clinical practice, thus heralding a new era of personalized and predictive medicine.