In recent years, the deep integration of artificial intelligence (AI) into medical education has created new opportunities for teaching Biochemistry and Molecular Biology, while also offering innovative solutions to the pedagogical challenges associated with protein structure and function. Focusing on the case of anaplastic lymphoma kinase (ALK) gene mutations in non-small-cell lung cancer (NSCLC), this study integrates AI into case-based learning (CBL) to develop an AI-CBL hybrid teaching model. This model features an intelligent case-generation system that dynamically constructs ALK mutation scenarios using real-world clinical data, closely linking molecular biology concepts with clinical applications. It incorporates AI-powered protein structure prediction tools to accurately visualize the three-dimensional structures of both wild-type and mutant ALK proteins, dynamically simulating functional abnormalities resulting from conformational changes. Additionally, a virtual simulation platform replicates the ALK gene detection workflow, bridging theoretical knowledge with practical skills. As a result, a multidimensional teaching system is established—driven by clinical cases and integrating molecular structural analysis with experimental validation. Teaching outcomes indicate that the three-dimensional visualization, dynamic interactivity, and intelligent analytical capabilities provided by AI significantly enhance students’ understanding of molecular mechanisms, classroom engagement, and capacity for innovative research. This model establishes a coherent training pathway linking “fundamental theory-scientific research thinking-clinical practice”, offering an effective approach to addressing teaching challenges and advancing the intelligent transformation of medical education.

This study identified blood-entering components of Anshen Dropping Pills and explored their anti-insomnia effects and mechanisms. The main blood-entering components of Anshen Dropping Pills were detected and identified by UPLC-Q-TOF-MS/MS. The rationality of the formula was assessed by using enrichment analysis based on the relationship between drugs and symptoms, and core targets of its active components were selected as the the potential anti-insomnia targets of Anshen Dropping Pills through network pharmacology analysis. Furthermore, protein-protein interaction(PPI) network, Gene Ontology(GO) enrichment analysis, and Kyoto Encyclopedia of Genes and Genomes(KEGG) pathway analysis were performed on the core targets. An active component-core target network for Anshen Dropping Pills was constructed. Finally, the effects of low-, medium-, and high-dose groups of Anshen Dropping Pills on sleep episodes, sleep duration, and sleep latency in mice were measured by supraliminal and subliminal pentobarbital sodium experiments. Moreover, total scores of the Pittsburgh sleep quality index(PSQI) scale was used to evaluate the changes before and after the treatment with Anshen Dropping Pills in a clinical study. The enrichment analysis based on the relationship between drugs and symptoms verified the rationality of the Anshen Dropping Pills formula, and nine blood-entering components of Anshen Dropping Pills were identified by UPLC-Q-TOF-MS/MS. The network proximity revealed a significant correlation between eight components and insomnia, including magnoflorine, liquiritin, spinosin, quercitrin, jujuboside A, ginsenoside Rb_3, glycyrrhizic acid, and glycyrrhetinic acid. Network pharmacology analysis indicated that the major anti-insomnia pathways of Anshen Dropping Pills involved substance and energy metabolism, neuroprotection, immune system regulation, and endocrine regulation. Seven core genes related to insomnia were identified: APOE, ALB, BDNF, PPARG, INS, TP53, and TNF. In summary, Anshen Dropping Pills could increase sleep episodes, prolong sleep duration, and reduce sleep latency in mice. Clinical study results demonstrated that Anshen Dropping Pills could decrease total scores of PSQI scale. This study reveals the pharmacodynamic basis and potential multi-component, multi-target, and multi-pathway effects of Anshen Dropping Pills, suggesting that its anti-insomnia mechanisms may be associated with the regulation of insomnia-related signaling pathways. These findings offer a theoretical foundation for the clinical application of Anshen Dropping Pills.
Animals ; Drugs, Chinese Herbal/administration & dosage* ; Tandem Mass Spectrometry/methods* ; Sleep Initiation and Maintenance Disorders/metabolism* ; Mice ; Network Pharmacology ; Male ; Chromatography, High Pressure Liquid ; Humans ; Protein Interaction Maps/drug effects* ; Sleep/drug effects* ; Female ; Adult

Based on ultra-performance liquid chromatography-quadrupole-electrostatic field Orbitrap high-resolution mass spectrometry(UPLC-Q-Orbitrap HRMS) technology and molecular docking, the bitter-tasting substances(hereafter referred to as "bitter substances") in Cistanche deserticola extract were investigated, and the bitter taste and efficacy relationship was explored to lay the foundation for future research on de-bittering and taste correction. Firstly, UPLC-Q-Orbitrap HRMS was used for the qualitative analysis of the constituents of C. deserticola, and 69 chemical components were identified. These chemical components were then subjected to molecular docking with the bitter taste receptor, leading to the screening of 20 bitter substances, including 6 phenylethanol glycosides, 5 flavonoids, 3 phenolic acids, 2 cycloalkenyl ether terpenes, 2 alkaloids, and 2 other components. Nine batches of fresh C. deserticola samples were collected from the same origin but harvested at different months. These samples were divided into groups based on harvest month and plant part. The bitterness was quantified using an electronic tongue, and the content of six potential bitter-active compounds(pineconotyloside, trichothecene glycoside, tubulin A, iso-trichothecene glycoside, jinshihuaoside, and jingnipinoside) was determined by high-performance liquid chromatography(HPLC). The total content of phenylethanol glycosides, polysaccharides, alkaloids, flavonoids, and phenolic acids was determined using UV-visible spectrophotometry. Chemometric analyses were then conducted, including Pearson's correlation analysis, gray correlation analysis, and orthogonal partial least squares discriminant analysis(OPLS-DA), to identify the bitter components in C. deserticola. The results were consistent with the molecular docking findings, and the two methods mutually supported each other. Finally, network pharmacological predictions and analyses were performed to explore the relationship between the targets of bitter substances and their efficacy. The results indicated that key targets of the bitter substances included EGFR, PIK3CB, and PTK2. These substances may exert their bitter effects by acting on relevant disease targets, confirming that the bitter substances in C. deserticola are the material basis of its bitter taste efficacy. In conclusion, this study suggests that the phenylethanol glycosides, primarily pineconotyloside, mauritiana glycoside, and gibberellin, are the material basis for the "bitter taste" of C. deserticola. The molecular docking technique plays a guiding role in the screening of bitter substances in traditional Chinese medicine(TCM). The bitter substances in C. deserticola not only contribute to its bitter taste but also support the concept of the "taste-efficacy" relationship in TCM, providing valuable insights and references for future research in this area.
Molecular Docking Simulation ; Taste ; Chromatography, High Pressure Liquid ; Cistanche/chemistry* ; Drugs, Chinese Herbal/chemistry* ; Humans ; Mass Spectrometry

Medical institutions, with their clinical practice foundation and abundant human use experience data, have become important carriers for the inheritance and innovation of traditional Chinese medicine(TCM) and the "cradles" of the preparation of new TCM. To effectively promote the transformation of new TCM originating from the TCM clinical practice in medical institutions and establish an effective evaluation index system for the transformation of new TCM conforming to the characteristics of TCM, consensus experts adopted the literature research, questionnaire survey, Delphi method, etc. By focusing on the policy and technical evaluation of new TCM originating from the TCM clinical practice in medical institutions, a comprehensive evaluation from the dimensions of drug safety, efficacy, feasibility, and characteristic advantages was conducted, thus forming a comprehensive evaluation system with four primary indicators and 37 secondary indicators. The expert consensus reached aims to encourage medical institutions at all levels to continuously improve the high-quality research and development and transformation of new TCM originating from the TCM clinical practice in medical institutions and targeted at clinical needs, so as to provide a decision-making basis for the preparation, selection, cultivation, and transformation of new TCM for medical institutions, improve the development efficiency of new TCM, and precisely respond to the public medication needs.
Medicine, Chinese Traditional/standards* ; Humans ; Consensus ; Drugs, Chinese Herbal/therapeutic use* ; Surveys and Questionnaires

Fangcang shelter hospitals are modular, rapidly deployable facilities that play a vital role in pandemic response by providing centralized isolation and basic medical care for large patient populations. Artificial intelligence (AI) has the potential to transform Fangcang shelter hospitals into intelligent, responsive systems that are capable of significantly improving emergency preparedness, operational efficiency, and patient outcomes. Key application areas include site selection and design optimization, clinical decision support, AI-assisted clinical documentation and patient engagement, intelligent robotics, and operational management. However, realizing AI's full potential requires overcoming several challenges, including limited data accessibility, privacy and governance concerns, inadequate algorithmic adaptability in dynamic emergency settings, insufficient transparency and accountability in AI-driven decisions, fragmented system architectures due to proprietary formats, high costs disproportionate to the temporary nature of Fangcang shelter hospitals, and hardware reliability in austere environments. Addressing these challenges demands standardized data-sharing frameworks, development of explainable and robust AI algorithms, clear ethical and legal oversight, interoperable modular system designs, and active collaboration among multidisciplinary stakeholders.
Artificial Intelligence ; Humans ; Emergency Shelter ; China ; Hospitals ; COVID-19

OBJECTIVE: To explore the clinical characteristics, therapeutic responses and prognostic features of E2A-PBX1 fusion gene for childhood acute lymphoblastic leukemia (ALL). METHODS: A total of 837 pediatric patients with ALL who were initially diagnosed in our hospital from July 2010 to November 2017 were retrospectively analyzed, 48 children with positive E2A-PBX1 fusion gene were detected by the real-time quantitative PCR techniques and their data were retrospectively collected for analysis. RESULTS: Among 48 cases with positive E2A-PBX1 fusion gene, there were 26 males and 22 females, with onset ages ranging from 9 months to 13 years old. There were 2 cases (4.2%) in the low-risk group, 32 cases (66.7%) in the intermediate-risk group, and 14 cases (29.1%) in the high-risk group at initial diagnosis. The white blood cell (WBC) counts of 25 cases (53.2%) at initial diagnosis were <50×10⁹/L, 11 cases (23.4%) were (50-100)×10⁹/L, and 11 cases (23.4%) ≥100×10⁹/L. The main immunophenotype was common-B ALL (44 cases, 91.7%). Other leukemia fusion genes such as BCR-ABL1, MLL-AF4, and TEL-AML1 were not observed in this cohort of patients. All patients received the treatment of NPCLC-ALL2008 protocol, and 5 cases (10.4%) occurred poor prednisone response. All the 48 cases achieved complete remission (CR) after the induction treatments. The last follow-up date was April 30, 2023. A total of 5 children relapsed, including 1 case with intermediate risk and 4 cases with high risk. The recurrence rate in the high-risk group was significantly higher than that in the intermediate- and low-risk groups (both P < 0.05). Most relapsed children had elevated WBC counts at initial diagnosis. Among them, WBC counts ≥100×10⁹/L was observed in 4 cases. The recurrence rate among children with WBC counts ≥100×10⁹/L was significantly higher than that with WBC counts <100×10⁹/L (P < 0.01). Four deaths occurred in this cohort, of which 3 died of leukemia recurrence. The 10-year event-free survival rate and 10-year overall survival rate of the 48 children with positive E2A-PBX1 fusion gene were 87.5%±4.8% and 91.7%±4.0%, respectively. CONCLUSION In ALL children with positive E2A-PBX1 fusion gene, those with elevated WBC counts and high risk stratification at initial diagnosis are more likely to experience recurrence. Recurrence is the main cause of death in this group. It is suggested that such kind of children receive more intensive chemotherapy or undergo hematopoietic stem cell transplantation as early as possible to further improve prognosis.
Humans ; Precursor Cell Lymphoblastic Leukemia-Lymphoma/diagnosis* ; Oncogene Proteins, Fusion/genetics* ; Prognosis ; Child ; Male ; Female ; Child, Preschool ; Adolescent ; Retrospective Studies ; Infant ; Homeodomain Proteins

BACKGROUND: Our understanding of the correlation between postdischarge cancer and mortality in patients with coronary artery disease (CAD) remains incomplete. The aim of this study was to investigate the relationships between postdischarge cancers and all-cause mortality and cardiovascular mortality in CAD patients. METHODS: In this retrospective cohort study, 25% of CAD patients without prior cancer history who underwent coronary artery angiography between January 1, 2011 and December 31, 2015, were randomly enrolled using SPSS 26.0. Patients were monitored for the incidence of postdischarge cancer, which was defined as cancer diagnosed after the index hospitalization, survival status and cause of death. Cox regression analysis was used to explore the association between postdischarge cancer and all-cause mortality and cardiovascular mortality in CAD patients. RESULTS: A total of 4085 patients were included in the final analysis. During a median follow-up period of 8 years, 174 patients (4.3%) developed postdischarge cancer, and 343 patients (8.4%) died. A total of 173 patients died from cardiovascular diseases. Postdischarge cancer was associated with increased all-cause mortality risk (HR = 2.653, 95% CI: 1.727-4.076, P < 0.001) and cardiovascular mortality risk (HR = 2.756, 95% CI: 1.470-5.167, P = 0.002). Postdischarge lung cancer (HR = 5.497, 95% CI: 2.922-10.343, P < 0.001) and gastrointestinal cancer (HR = 1.984, 95% CI: 1.049-3.750, P = 0.035) were associated with all-cause mortality in CAD patients. Postdischarge lung cancer was significantly associated with cardiovascular death in CAD patients (HR = 4.979, 95% CI: 2.114-11.728, P < 0.001), and cardiovascular death was not significantly correlated with gastrointestinal cancer or other types of cancer. CONCLUSIONS Postdischarge cancer was associated with all-cause mortality and cardiovascular mortality in CAD patients. Compared with other cancers, postdischarge lung cancer had a more significant effect on all-cause mortality and cardiovascular mortality in CAD patients.

OBJECTIVE: As an age-related neurodegenerative disease, the prevalence of mild cognitive impairment (MCI) increases with age. Within the framework of traditional Chinese medicine, spleen-kidney deficiency syndrome (SKDS) is recognized as the most frequent MCI subtype. Due to the covert and gradual onset of MCI, in community settings it poses a significant challenge for patients and their families to discern between typical aging and pathological changes. There exists an urgent need to devise a preliminary diagnostic tool designed for community-residing older adults with MCI attributed to SKDS (MCI-SKDS). METHODS: This investigation enrolled 312 elderly individuals diagnosed with MCI, who were randomly distributed into training and test datasets at a 3:1 ratio. Five machine learning methods, including logistic regression (LR), decision tree (DT), naive Bayes (NB), support vector machine (SVM), and gradient boosting (GB), were used to build a diagnostic prediction model for MCI-SKDS. Accuracy, sensitivity, specificity, precision, F1 score, and area under the curve were used to evaluate model performance. Furthermore, the clinical applicability of the model was evaluated through decision curve analysis (DCA). RESULTS: The accuracy, precision, specificity and F1 score of the DT model performed best in the training set (test set), with scores of 0.904 (0.845), 0.875 (0.795), 0.973 (0.875) and 0.973 (0.875). The sensitivity of the training set (test set) of the SVM model performed best among the five models with a score of 0.865 (0.821). The area under the curve of all five models was greater than 0.9 for the training dataset and greater than 0.8 for the test dataset. The DCA of all models showed good clinical application value. The study identified ten indicators that were significant predictors of MCI-SKDS. CONCLUSION The risk prediction index derived from machine learning for the MCI-SKDS prediction model is simple and practical; the model demonstrates good predictive value and clinical applicability, and the DT model had the best performance. Please cite this article as: Ai YT, Zhou S, Wang M, Zheng TY, Hu H, Wang YC, Li YC, Wang XT, Zhou PJ. Development of a machine learning-based risk prediction model for mild cognitive impairment with spleen-kidney deficiency syndrome in the elderly. J Integr Med. 2025; 23(4): 390-397.
Humans ; Cognitive Dysfunction/diagnosis* ; Aged ; Male ; Female ; Machine Learning ; Spleen ; Aged, 80 and over ; Kidney ; Medicine, Chinese Traditional

OBJECTIVE: This study aimed to identify high-risk areas for type 2 diabetes mellitus (T2DM) mortality to provide relevant evidence for interventions in emerging economies. METHODS: Empirical Bayesian Kriging and a discrete Poisson space-time scan statistic were applied to identify the spatiotemporal clusters of T2DM mortality. The relationships between economic factors, air pollutants, and the mortality risk of T2DM were assessed using regression analysis and the Poisson Log-linear Model. RESULTS: A coastal district in East Guangdong, China, had the highest risk (Relative Risk [RR] = 4.58, P < 0.01), followed by the 10 coastal districts/counties in West Guangdong, China (RR = 2.88, P < 0.01). The coastal county in the Pearl River Delta, China (RR = 2.24, P < 0.01), had the third-highest risk. The remaining risk areas were two coastal counties in East Guangdong, 16 districts/counties in the Pearl River Delta, and two counties in North Guangdong, China. Mortality due to T2DM was associated with gross domestic product per capita (GDP per capita). In pilot assessments, T2DM mortality was significantly associated with carbon monoxide. CONCLUSION High mortality from T2DM occurred in the coastal areas of East and West Guangdong, especially where the economy was progressing towards the upper middle-income level.
Diabetes Mellitus, Type 2/epidemiology* ; China/epidemiology* ; Humans ; Risk Factors ; Spatio-Temporal Analysis ; Air Pollutants/analysis* ; Socioeconomic Factors ; Bayes Theorem ; Female ; Male ; Middle Aged

Objective To analyze the prognostic significance and biological effects of cytochrome P450 family 27 subfamily A member 1(CYP27A1)in hepatocellular carcinoma(HCC),and to preliminarily explore its molecular mechanism of regulating the malignant growth of HCC.Methods The Cance Genome Atlas(TCGA)database was used to analyze the expression level of CYP27A1 and its prognostic effect on HCC patients.The samples were divided into CYP27A1 high-expression group(n=170)and low-expression group(n=170)based on the median expression of CYP27A1 in HCC,gene set enrichment analysis(GSEA)was performed to investigate gene sets associated with CYP27A1 expression.The subcellular localization of CYP27A1 was detected by immunofluorescence staining and search database.The over-expression plasmid of CYP27A1 was constructed and then transfected into the HCC cells MHCC-97H and HCCLM3 cell lines,including two groups,namely control group(transfecting empty vector)and CYP27A1 over-expression group(transfecting CYP27A1 over-expressed vector).CCK-8,flow cytometer,and reactive oxygen species(ROS)fluorescence probe were applied to detect the effects of CYP27A1 over-expression on cell viability,apoptosis and ROS levels in HCC cells.Combining bioinformatics to analyze the correlation between CYP27A1 and the expression of ROS generation-related genes and HCC proliferation-related genes.Results Compared with the normal liver tissue,the expression level of CYP27A1 mRNA in HCC tissue was significantly reduced(P<0.01).The expression of CYP27A1 was significantly correlated with sex,T stage,tumor grade and tumor stage of HCC patients(P<0.05).Compared to the CYP27A1 high-expression group,patients in CYP27A1 low-expression group had lower survival rate(P<0.01).GSEA enrichment analysis revealed that the levels of HCC stem cell-related gene clusters and HCC proliferation gene clusters were remarkably increased in CYP27A1 low-expression group.The immunofluorescence showed that CYP27A1 was mainly located in nucleus in MHCC-97H and HCCLM3,whereas CYP27A1 was mainly located in mitochondria in HepG2.CYP27A1 over-expression attenuated cell viability(P<0.01),and reduced the ROS levels(P<0.05),whereas it had no effects on the apoptosis in HCC cells(P>0.05).The expression of CYP27A1 and the expression of inhibiting ROS generation-related genes were positively correlated(P<0.05),while the expression of inhibiting ROS generation-related genes and the expression of HCC proliferation-related genes were negatively correlated(P<0.05).Conclusions The expression of CYP27A1 was decreased in HCC,and down-regulated CYP27A1 promoted cell growth by enhancing ROS generation,although the precise mechanism requires future educidation.