ObjectiveBiochemistry and Molecular Biology, a discipline that elucidates life phenomena at the molecular level, serves as a core foundational course in medical education. It provides the theoretical basis for studying other basic and clinical medical subjects, as well as for understanding pathogenesis, disease diagnosis, and treatment. However, its complex content and highly abstract concepts have posed a dual challenge to traditional teaching models: “inefficient instruction” and “inadequate learning outcomes”. Within limited classroom hours, how to engage students and stimulate their intrinsic motivation, and how to help them recognize, understand, and develop a passion for biochemistry from the perspective of the discipline’s essence, have long been key focuses of curriculum research. MethodsUsing the lipid metabolism chapter as an example, this study employs “Rain Classroom”, a generative artificial intelligence (AI)-assisted platform, to support education in four dimensions: teaching, learning, evaluation, and research. In teaching, it assists instructors through virtual experiments, lesson preparation support, knowledge mapping, and assignment design. For learning, it serves as an intelligent study assistant for students, providing automated assignment review, enabling educational resource sharing, and facilitating personalized learning pathways. In evaluation, the platform automates assignment grading, analyzes student performance data, and offers diagnostic feedback and teaching recommendations. In research, it aids educators in collecting and analyzing teaching data, as well as searching for and summarizing relevant literature. ResultsThe results indicate that an educational model integrating teacher-led instruction, student-centered learning, and generative AI assistance significantly enhances teaching quality, students’ self-directed learning abilities, and knowledge mastery. Furthermore, with the support of generative AI, curriculum-based ideological education—focusing on cutting-edge disciplinary advances and topical medical issues—helps cultivate students’ medical spirit of “honoring life and healing the wounded”, thereby fostering the establishment of appropriate professional values. Finally, while generative AI presents both opportunities and challenges for higher education, this study also analyzes potential risks in its teaching applications, emphasizing the need for both instructors and students to avoid over-reliance and to ensure that technological tools consistently serve the fundamental goals of education. ConclusionThis study demonstrates that integrating generative AI, specifically via the “Rain Classroom” platform, can effectively enhance biochemistry education. By supporting teaching, learning, evaluation, and research, this approach improves both educational effectiveness and student outcomes. It also facilitates the incorporation of cutting-edge knowledge and professional ethics, nurturing a patient-centered mindset. Additionally, the study addresses potential implementation risks to ensure that such technological tools remain aligned with the core purpose of education.

Objective To explore the impact of Shexiang Baoxin Pill(SXBXP,a traditional Chinese patent medicine,broadly applied for the treatment of cardiovascular diseases)on coronary microvascular disease(CMD)and investigate the role and underlying mechanisms of muscone,a key bioactive component of the pills,in the treatment.Methods A total of 16 ob/ob mice(8 weeks old)were randomly and equally divided into an ob/ob-SXBXP group and a ob/ob-Control group,receiving 10 mg/kg·d SXBXP or PBS via oral gavage,and another 8 wild-type mice with the same genetic background(WT group)were subjected as a negative control group.Cell model of CMD was established based on mouse coronary microvascular endothelial cells(MCMEC)under ischemia and hypoxic(HI)condition,and the cells were further treated with 20 μg/mL SXBXP(HI+SX)and 30 μmol/L muscone(HI+Muscone),respectively.Echocardiography was carried out for coronary flow reserve(CFR)and left ventricular function,and laser speckle imaging was applied to evaluate myocardial blood flow(MBF).Microvascular density in the heart was evaluated with CD31 immunofluorescence staining.The expression levels of vascular endothelial growth factor(VEGF)in cardiac microvascular endothelial cells of both mouse and cell models were detected by immunofluorescence staining and Western blot analysis.The proliferation and angiogenesis of MCMEC were observed by 5-ethynyl-2'-deoxyuridine staining and angiogenesis experiments.Results In the ob/ob-Control group of mice,the levels of CFR,MBF,and cardiac microvascular density were significantly lower than those in the WT group(P<0.05),and these indicators were significantly improved in the ob/ob-SXBXP group when compared with the ob/ob-Control group(P<0.05),which indicates that SXBXP improves the CMD phenotype.The expression level of VEGF in cardiac microvascular endothelial cells was significantly lower in the ob/ob-Control mice than the WT mice(P<0.05),while the level in the ob/ob-SXBXP group was significantly higher than that in the ob/ob-Control group(P<0.05),illustrating that SXBXP may ameliorate CMD through VEGF-mediated microvascular angiogenesis.In vitro experiments further revealed that the VEGF expression level and the proliferation and angiogenesis abilities in MCMEC were significantly lower in the HI group than the cells under the normoxia control condition(P<0.05).Both SXBXP and muscone treatment resulted in enhanced expression of VEGF and improved proliferative and angiogenesis abilities of MCMEC(P<0.05).These results suggest that muscone could improve CMD by VEGF-mediated microvascular angiogenesis.Conclusion SXBXP can improve CMD.Muscone,as a key component of SXBXP,promotes microvascular angiogenesis by inducing the expression of VEGF,then enhances myocardial perfusion,and consequently alleviates CMD.

Currently, the drug delivery system (DDS) based on nanomaterials has become a hot interdisciplinary research topic. One of the core issues is drug loading and controlled release, in which the key lever is carriers. Vaterite, as an inorganic porous nano-material, is one metastable structure of calcium carbonate, full of micro or nano porous. Recently, vaterite has attracted more and more attention, due to its significant advantages, such as rich resources, easy preparations, low cost, simple loading procedures, good biocompatibility and many other good points. Vaterite, gained from suitable preparation strategies, can not only possess the good drug carrying performance, like high loading capacity and stable loading efficiency, but also improve the drug release ability, showing the better drug delivery effects, such as targeting release, pH sensitive release, photothermal controlled release, magnetic assistant release, optothermal controlled release. At the same time, the vaterite carriers, with good safety itself, can protect proteins, enzymes, or other drugs from degradation or inactivation, help imaging or visualization with loading fluorescent drugs in vitro and in vivo, and play synergistic effects with other therapy approaches, like photodynamic therapy, sonodynamic therapy, and thermochemotherapy. Latterly, some renewed reports in drug loading and controlled release have led to their widespread applications in diverse fields, from cell level to clinical studies. This review introduces the basic characteristics of vaterite and briefly summarizes its research history, followed by synthesis strategies. We subsequently highlight recent developments in drug loading and controlled release, with an emphasis on the advantages, quantity capacity, and comparations. Furthermore, new opportunities for using vaterite in cell level and animal level are detailed. Finally, the possible problems and development trends are discussed.

This study aimed to investigate the effect of Dahuang Zhechong Pills(DHZCP) in delaying heart aging(HA) and explore the potential mechanism. Network pharmacology and molecular docking were employed to explore the targets and potential mechanisms of DHZCP in delaying HA. Furthermore, in vitro experiments were conducted with the DHZCP-containing serum to verify key targets and pathways in D-galactose(D-gal)-induced aging of cardiomyocytes. Active components of DHZCP were searched against the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform(TCSMP), and relevant targets were predicted. HA-related targets were screened from the GeneCards, Online Mendelian Inheritance in Man(OMIM), and DisGeNET. The common targets shared by the active components of DHZCP and HA were used to construct a protein-protein interaction network in STRING 12.0, and core targets were screened based on degree in Cytoscape 3.9.1. Metaspace was used for Gene Ontology(GO) and Kyoto Encyclopedia of Genes and Genomes(KEGG) enrichment analyses of the core targets to predict the mechanisms. Molecular docking was performed in AutoDock Vina. The results indicated that a total of 774 targets of the active components of DHZCP and 4 520 targets related to HA were screened out, including 510 common targets. Core targets included B-cell lymphoma 2(BCL-2), serine/threonine kinase 1(AKT1), and hypoxia-inducible factor 1 subunit A(HIF1A). The GO and KEGG enrichment analyses suggested that DHZCP mainly exerted its effects via the phosphatidylinositol 3-kinase(PI3K)/AKT signaling pathway, HIF-1α signaling pathway, longevity signaling pathway, and apoptosis signaling pathway. Among the pathways predicted by GO and KEGG enrichment analyses, the PI3K/AKT/HIF-1α signaling pathway was selected for verification. The cell-counting kit 8(CCK-8) assay showed that D-gal significantly inhibited the proliferation of H9c2 cells, while DHZCP-containing serum increased the viability of H9c2 cells. SA-β-gal staining revealed a significant increase in the number of blue-green positive cells in the D-gal group, which was reduced by DHZCP-containing serum. TUNEL staining showed that DHZCP-containing serum decreased the number of apoptotic cells. After treatment with DHZCP-containing serum, the protein levels of Klotho, BCL-2, p-PI3K/PI3K, p-AKT1/AKT1, and HIF-1α were up-regulated, while those of P21, P16, BCL-2 associated X protein(Bax), and cleaved caspase-3 were down-regulated. The results indicated that DHZCP delayed HA via multiple components, targets, and pathways. Specifically, DHZCP may delay HA by reducing apoptosis via activating the PI3K/AKT/HIF-1α signaling pathway.
Proto-Oncogene Proteins c-akt/genetics* ; Drugs, Chinese Herbal/pharmacology* ; Signal Transduction/drug effects* ; Apoptosis/drug effects* ; Myocytes, Cardiac/cytology* ; Hypoxia-Inducible Factor 1, alpha Subunit/genetics* ; Phosphatidylinositol 3-Kinases/genetics* ; Animals ; Rats ; Humans ; Molecular Docking Simulation ; Aging/metabolism* ; Protein Interaction Maps/drug effects* ; Heart/drug effects* ; Network Pharmacology

Cinnabar(HgS) was widely used in ancient times for medicinal purposes, religious rituals, and pigments. A group of bright red powdery clumps was excavated from Mawangdui Tomb No.3 of the Han Dynasty. Early studies considered the clumps as evidence of cinnabar's medicinal use during the Qin-Han period. This study employed a range of archaeometric techniques, including extended-depth-of-field stereo imaging, micro-CT, scanning electron microscopy-energy dispersive spectroscopy, Raman spectroscopy, and Fourier transform infrared spectrometry FTIR, to systematically analyze the material composition and structural characteristics of these remains. The results revealed that the cinnabar particles were granular, finely ground, and tightly bound to silk matrix, with no detectable excipients typically associated with medicinal formulations. Micro-CT imaging indicated a well-preserved textile structure, with clear signs of sedimentary accumulation and mechanical damage. Based on historical and archaeological studies, this study suggested that these remains were more likely degraded accumulations of cinnabar-colored silk textiles rather than medicinal cinnabar. By clarifying the diversity of ancient cinnabar applications and preservation states, this study provides new insights for the archaeological identification of mineral medicinal materials and contributes to the standardized study of Chinese medicinal materials and understanding of the historical use of cinnabar.
History, Ancient ; China ; Humans ; Medicine, Chinese Traditional/history* ; Archaeology ; Drugs, Chinese Herbal/history* ; Spectroscopy, Fourier Transform Infrared ; Spectrum Analysis, Raman ; Mercury Compounds

Motor imagery (MI) is a mental process that can be recognized by electroencephalography (EEG) without actual movement. It has significant research value and application potential in the field of brain-computer interface (BCI) technology. To address the challenges posed by the non-stationary nature and low signal-to-noise ratio of MI-EEG signals, this study proposed a Riemannian spatial filtering and domain adaptation (RSFDA) method for improving the accuracy and efficiency of cross-session MI-BCI classification tasks. The approach addressed the issue of inconsistent data distribution between source and target domains through a multi-module collaborative framework, which enhanced the generalization capability of cross-session MI-EEG classification models. Comparative experiments were conducted on three public datasets to evaluate RSFDA against eight existing methods in terms of classification accuracy and computational efficiency. The experimental results demonstrated that RSFDA achieved an average classification accuracy of 79.37%, outperforming the state-of-the-art deep learning method Tensor-CSPNet (76.46%) by 2.91% ( P < 0.01). Furthermore, the proposed method showed significantly lower computational costs, requiring only approximately 3 minutes of average training time compared to Tensor-CSPNet's 25 minutes, representing a reduction of 22 minutes. These findings indicate that the RSFDA method demonstrates superior performance in cross-session MI-EEG classification tasks by effectively balancing accuracy and efficiency. However, its applicability in complex transfer learning scenarios remains to be further investigated.
Electroencephalography/methods* ; Brain-Computer Interfaces ; Humans ; Imagination/physiology* ; Signal Processing, Computer-Assisted ; Movement/physiology* ; Signal-To-Noise Ratio ; Deep Learning ; Algorithms

Instrument separation is a critical complication during root canal therapy, impacting treatment success and long-term tooth preservation. The etiology of instrument separation is multifactorial, involving the intricate anatomy of the root canal system, instrument-related factors, and instrumentation techniques. Instrument separation can hinder thorough cleaning, shaping, and obturation of the root canal, posing challenges to successful treatment outcomes. Although retrieval of separated instrument is often feasible, it carries risks including perforation, excessive removal of tooth structure and root fractures. Effective management of separated instruments requires a comprehensive understanding of the contributing factors, meticulous preoperative assessment, and precise evaluation of the retrieval difficulty. The application of appropriate retrieval techniques is essential to minimize complications and optimize clinical outcomes. The current manuscript provides a framework for understanding the causes, risk factors, and clinical management principles of instrument separation. By integrating effective strategies, endodontists can enhance decision-making, improve endodontic treatment success and ensure the preservation of natural dentition.
Humans ; Root Canal Therapy/adverse effects* ; Consensus ; Root Canal Preparation/adverse effects*

Sustained inflammatory responses are closely related to various severe diseases, and inhibiting the excessive activation of inflammasomes and pyroptosis has significant implications for clinical treatment. Natural products have garnered considerable concern for the treatment of inflammation. Huanglian-Wumei decoction (HLWMD) is a classic prescription used for treating inflammatory diseases, but the necessity of their combination and the exact underlying anti-inflammatory mechanism have not yet been elucidated. Inspired by the supramolecular self-assembly strategy and natural drug compatibility theory, we successfully obtained berberine (BBR)-chlorogenic acid (CGA) supramolecular (BCS), which is an herbal pair from HLWMD. Using a series of characterization methods, we confirmed the self-assembly mechanism of BCS. BBR and CGA were self-assembled and stacked into amphiphilic spherical supramolecules in a 2:1 molar ratio, driven by electrostatic interactions, hydrophobic interactions, and π-π stacking; the hydrophilic fragments of CGA were outside, and the hydrophobic fragments of BBR were inside. This stacking pattern significantly improved the anti-inflammatory performance of BCS compared with that of single free molecules. Compared with free molecules, BCS significantly attenuated the release of multiple inflammatory mediators and lipopolysaccharide (LPS)-induced pyroptosis. Its anti-inflammatory mechanism is closely related to the inhibition of intracellular nuclear factor-kappaB (NF-κB) p65 phosphorylation and the noncanonical pyroptosis signalling pathway mediated by caspase-11.

Computational approaches for predicting drug-target interactions (DTIs) are pivotal in advancing drug discovery. Current methodologies leveraging heterogeneous networks often fall short in fully integrating both local and global network information. To comprehensively consider network information, we propose DHGT-DTI, a novel deep learning-based approach for DTI prediction. Specifically, we capture the local and global structural information of the network from both neighborhood and meta-path perspectives. In the neighborhood perspective, we employ a heterogeneous graph neural network (HGNN), which extends Graph Sample and Aggregate (GraphSAGE) to handle diverse node and edge types, effectively learning local network structures. In the meta-path perspective, we introduce a Graph Transformer with residual connections to model higher-order relationships defined by meta-paths, such as "drug-disease-drug", and use an attention mechanism to fuse information across multiple meta-paths. The learned features from these dual perspectives are synergistically integrated for DTI prediction via a matrix decomposition method. Furthermore, DHGT-DTI reconstructs not only the DTI network but also auxiliary networks to bolster prediction accuracy. Comprehensive experiments on two benchmark datasets validate the superiority of DHGT-DTI over existing baseline methods. Additionally, case studies on six drugs used to treat Parkinson's disease not only validate the practical utility of DHGT-DTI but also highlight its broader potential in accelerating drug discovery for other diseases.