In view of the few studies on the influence of Armillaria spp. infection on the content of the chemical components in different parts of Polyporus umbellatus sclerotia, this study determined the biomass of P. umbellatus sclerotia and the contents of ergosterol, polyporusterone A, polyporusterone B and polysaccharide in the separated cavity wall of the sclerotia and the uninfected part of the sclerotia in different harvesting years under the conditions of A. gallica and A. mellea infection respectively. According to the difference of content and dynamic changes of the polysaccharide and the steroid substances, the superior Armillaria sp. was screened to obtain the best harvest years of P. umbellatus. Using HPLC and UV-VIS spectrophotometry methods, the contents of ergosterol, polyporusterone A, polyporusterone B and polysaccharide in P. umbellatus sclerotia infected by the two Armillaria spp. in different years were determined. In addition, the differentially expressed genes related to P. umbellatus polysaccharide synthesis were screened according to the transcriptomic data of different parts of P. umbellatus after A. mellea infection. With the increase of years, the biomass of sclerotia infected by different Armillaria spp. had significant differences, and there were significant differences in the four components of sclerotia. The four components of the separated cavity wall of the sclerotia were significantly higher than those of the uninfected part. The best harvest time was the third year after cultivation. Transcriptomic analysis showed that the infection of Armillaria spp. could significantly promote polysaccharide synthesis, which provided a basis for polysaccharide content determination at the molecular level. The study clarified the influence of different Armillaria spp. infection on the accumulation of chemical components of P. umbellatus sclerotia, laying a foundation for exploring the symbiosis mechanism and provided a scientific clue for screening superior Armillaria sp. and guiding the artificial cultivation of P. umbellatus sclerotia.

Intravitreal injection of anti-vascular endothelial growth factor(VEGF)agents has become the primary treatment for macular edema associated with retinal vascular disease such as diabetic retinopathy and retinal vein occlusion, but there are limitations such as variable treatment efficacy and insufficient durability of therapeutic effects. As the first bispecific antibody applied in ophthalmic treatment, Faricimab achieves favorable outcomes by simultaneously targeting both VEGF-A and angiopoietin-2(Ang-2)pathways. Based on evidence from recent clinical trials and real-world studies, this article reviews the research progress on Faricimab for the treatment of diabetic macular edema(DME), retinal vein occlusion-associated macular edema(RVO-ME)and refractory macular edema compared to the therapeutic effects of other agents. Additionally, based on Faricimab's safety characteristics and future potential, its therapeutic prospects for macular edema associated with retinal vascular diseases are discussed. This review aims to provide evidence-based references for optimizing clinical treatment strategies, thereby contributing to mitigating the risk of vision loss due to macular edema.

The pathogenesis of neurodegenerative diseases (NDDs) is fundamentally linked to complex and profound alterations in metabolic networks within the brain, which exhibit marked spatial heterogeneity. While conventional bulk metabolomics is powerful for detecting global metabolic shifts, it inherently lacks spatial resolution. This methodological limitation hampers the ability to interrogate critical metabolic dysregulation within discrete anatomical brain regions and specific cellular microenvironments, thereby constraining a deeper understanding of the core pathological mechanisms that initiate and drive NDDs. To address this critical gap, spatial metabolomics, with mass spectrometry imaging (MSI) at its core, has emerged as a transformative approach. It uniquely overcomes the limitations of bulk methods by enabling high-resolution, simultaneous detection and precise localization of hundreds to thousands of endogenous molecules—including primary metabolites, complex lipids, neurotransmitters, neuropeptides, and essential metal ions—directly in situ from tissue sections. This powerful capability offers an unprecedented spatial perspective for investigating the intricate and heterogeneous chemical landscape of NDD pathology, opening new avenues for discovery. Accordingly, this review provides a comprehensive overview of the field, beginning with a discussion of the technical features, optimal application scenarios, and current limitations of major MSI platforms. These include the widely adopted matrix-assisted laser desorption/ionization (MALDI)-MSI, the ultra-high-resolution technique of secondary ion mass spectrometry (SIMS)-MSI, and the ambient ionization method of desorption electrospray ionization (DESI)-MSI, along with other emerging technologies. We then highlight the pivotal applications of spatial metabolomics in NDD research, particularly its role in elucidating the profound chemical heterogeneity within distinct pathological microenvironments. These applications include mapping unique molecular signatures around amyloid β‑protein (Aβ) plaques, uncovering the metabolic consequences of neurofibrillary tangles composed of hyperphosphorylated tau protein, and characterizing the lipid and metabolite composition of Lewy bodies. Moreover, we examine how spatial metabolomics contributes to constructing detailed metabolic vulnerability maps across the brain, shedding light on the biochemical factors that render certain neuronal populations and anatomical regions selectively susceptible to degeneration while others remain resilient. Looking beyond current applications, we explore the immense potential of integrating spatial metabolomics with other advanced research methodologies. This includes its combination with three-dimensional brain organoid models to recapitulate disease-relevant metabolic processes, its linkage with multi-organ axis studies to investigate how systemic metabolic health influences neurodegeneration, and its convergence with single-cell and subcellular analyses to achieve unprecedented molecular resolution. In conclusion, this review not only summarizes the current state and critical role of spatial metabolomics in NDD research but also offers a forward-looking perspective on its transformative potential. We envision its continued impact in advancing our fundamental understanding of NDDs and accelerating translation into clinical practice—from the discovery of novel biomarkers for early diagnosis to the development of high-throughput drug screening platforms and the realization of precision medicine for individuals affected by these devastating disorders.

Objective： To investigate the relationship between the lipid metabolism-related gene sterol carrier protein 2(SCP2) and prostate cancer (PCa) from a multi-omics perspective using single-cell transcriptomes combined with Mendelian randomization. Methods： Single-cell transcriptome data of benign and malignant prostate tissues were obtained from GSE120716, GSE157703 and GSE141445 datasets, respectively. Integration, quality control and annotation were performed on the data to categorize the epithelial cells into high and low SCP2 expression groups, followed by further differential and trajectory analyses. Single nucleotide polymorphism (SNP) data for SCP2 expression quantitative trait loci (eQTL) were subsequently downloaded from Genotype-Tissue Expression (GTEx) and investigated from the PCa Society Cancer-Related Genomic Alteration Panel for the Investigation of Cancer-Related Alterations (PRACTICAL) to obtain PCa outcome data for Mendelian randomization analysis to validate the causal relationship between SCP2 and PCa. Results： High SCP2-expressing epithelial cells had higher energy metabolism and proliferation capacity with low immunotherapy response and metastatic tendency. Trajectory analysis showed that epithelial cells with high SCP2 expression may have a higher degree of malignancy, and SCP2 may be a key marker gene for differentiation of malignant epithelial cells in the prostate. Further Mendelian randomization results showed a significant causal relationship between SCP2 and PCa development (OR=1.045, 95% CI: 1.010 －1.083, P=0.011). Conclusion： By combining single-cell transcriptome and Mendelian randomization, the role of the lipid metabolism-related gene SCP2 in PCa development has been confirmed, and new targets and therapeutic directions for PCa treatment have been provided.
Humans ; Prostatic Neoplasms/genetics* ; Male ; Mendelian Randomization Analysis ; Polymorphism, Single Nucleotide ; Quantitative Trait Loci ; Single-Cell Analysis ; Sequence Analysis, RNA ; Carrier Proteins/genetics* ; Transcriptome ; Lipid Metabolism

OBJECTIVE: To construct machine learning prediction model for sepsis-associated encephalopathy (SAE), and analyze the application value of the model on early identification of SAE risk in elderly septic patients. METHODS: Patients aged over 60 years with a primary diagnosis of sepsis admitted to intensive care unit (ICU) from 2008 to 2023 were selected from Medical Information Mart for Intensive Care-IV 2.2 (MIMIC-IV 2.2). Demographic variables, disease severity scores, comorbidities, interventions, laboratory indicators, and hospitalization details were collected. Key factors associated with SAE were identified using univariate Logistic regression analysis. The data were randomly divided into training and validation sets in a 7 : 3 ratio. Multivariable Logistic regression analysis was conducted in the training set and visualized using a nomogram model for prediction of SAE. The discrimination of the model was evaluated in the validation set using the receiver operator characteristic curve (ROC curve), and its calibration was assessed using calibration curve. Furthermore, multiple machine learning algorithms, including multi-layer perceptron (MLP), support vector machine (SVM), naive bayes (NB), gradient boosting machine (GBM), random forest (RF), and extreme gradient boosting (XGB), were constructed in the training set. Their predictive performance was subsequently evaluated on the validation set. Taking the XGB model as an example, the interpretability of the model through the SHapley Additive exPlanations (SHAP) algorithm was enhanced to identify the key predictive factors and their contributions. RESULTS: A total of 2 204 septic patients were finally enrolled, of whom 840 developed SAE (38.1%). A total of 21 variables associated with SAE were screened through univariate Logistic regression analysis. Multivariable Logistic regression analysis showed that endotracheal intubation [odds ratio (OR) = 0.40, 95% confidence interval (95%CI) was 0.19-0.88, P < 0.001], oxygen therapy (OR = 0.76, 95%CI was 0.53-0.95, P = 0.023), tracheotomy (OR = 0.20, 95%CI was 0.07-0.53, P < 0.001), continuous renal replacement therapy (CRRT; OR = 0.32, 95%CI was 0.15-0.70, P < 0.001), cerebrovascular disease (OR = 0.31, 95%CI was 0.16-0.60, P < 0.001), rheumatic disease (OR = 0.44, 95%CI was 0.19-0.99, P < 0.001), male (OR = 0.68, 95%CI was 0.54-0.86, P = 0.001), and maximum anion gap (AG; OR = 0.95, 95%CI was 0.93-0.97, P < 0.001) were associated with an decreased probability of SAE, and age (OR = 1.05, 95%CI was 1.03-1.06, P < 0.001), acute physiology score III (APSIII; OR = 1.02, 95%CI was 1.01-1.02, P < 0.001), Oxford acute severity of illness score (OASIS; OR = 1.04, 95%CI was 1.03-1.06, P < 0.001), and length of hospital stay (OR = 1.01, 95%CI was 1.01-1.02, P < 0.001) were associated with an increased probability of SAE. A nomogram model was constructed based on these variables. In the validation set, ROC curve analysis showed that the model achieved an area under the ROC curve (AUC) of 0.723, and the calibration curve showed good consistency between the predicted probability of the model and the observed probability. Among the machine learning algorithms, including MLP, SVM, NB, GBM, RF, and XGB, the SVM model and RF model demonstrated relatively good predictive performance, with AUC of 0.748 and 0.739, respectively, and the sensitivity was both exceeding 85%. The predictive performance of the XGB model was explained through SHAP analysis, and the results indicated that APSIII score (SHAP value was 0.871), age (SHAP value was 0.521), and OASIS score (SHAP value was 0.443) were important factors affecting the predictive performance of the model. CONCLUSIONS The machine learning-based SAE prediction model exhibits good predictive capability and holds significant application value for the early identification of SAE risk in elderly septic patients.
Humans ; Machine Learning ; Aged ; Sepsis-Associated Encephalopathy ; Sepsis/complications* ; Intensive Care Units ; Logistic Models ; Middle Aged ; Male ; ROC Curve ; Female ; Bayes Theorem ; Nomograms ; Support Vector Machine ; Algorithms

Functional dyspepsia (FD), characterized by persistent or recurrent dyspeptic symptoms without identifiable organic, systemic or metabolic causes, is an increasingly recognized global health issue. The objective of this guideline is to equip clinicians and nursing professionals with evidence-based strategies for the management and treatment of adult patients with FD using traditional Chinese medicine (TCM). The Guideline Development Group consulted existing TCM consensus documents on FD and convened a panel of 35 clinicians to generate initial clinical queries. To address these queries, a systematic literature search was conducted across PubMed, EMBASE, the Cochrane Library, China National Knowledge Infrastructure (CNKI), VIP Database, China Biology Medicine (SinoMed) Database, Wanfang Database, Traditional Medicine Research Data Expanded (TMRDE), and the Traditional Chinese Medical Literature Analysis and Retrieval System (TCMLARS). The evidence from the literature was critically appraised using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach. The strength of the recommendations was ascertained through a consensus-building process involving TCM and allopathic medicine experts, methodologists, pharmacologists, nursing specialists, and health economists, leveraging their collective expertise and empirical knowledge. The guideline comprises a total of 43 evidence-informed recommendations that span a range of clinical aspects, including the pathogenesis according to TCM, diagnostic approaches, therapeutic interventions, efficacy assessments, and prognostic considerations. Please cite this article as: Zhang SS, Zhao LQ, Hou XH, Bian ZX, Zheng JH, Tian HH, Yang GH, Hong WS, He YY, Liu L, Shen H, Li YP, Xie S, Shu J, Zeng BF, Li JX, Liu Z, Xiao ZH, Xiao JD, Zheng PY, Huang SG, Chen SL, Fei GJ. International clinical practice guideline on the use of traditional Chinese medicine for functional dyspepsia (2025). J Integr Med. 2025; 23(5):502-518.
Dyspepsia/drug therapy* ; Humans ; Medicine, Chinese Traditional/methods* ; Practice Guidelines as Topic ; Drugs, Chinese Herbal/therapeutic use*

Objective To investigate necroptosis-related diagnostic biomarkers of bronchopulmo-nary dysplasia(BPD)and their relationships with the immune microenvironment through the analysis of necroptosis-related genes(NRGs)in BPD.Methods The dataset GSE32472 was downloaded from the Gene Expression Omnibus(GEO)database,and NRGs were obtained from the Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway and Gene Cards databases.Differentially expressed necroptosis-related genes(DE-NRGs)were screened,and their biological functions and pathways were explored through functional enrichment analysis.Machine learning algorithms,inclu-ding least absolute shrinkage and selection operator(LASSO)and support vector machine-recursive feature elimination(SVM-RFE),were applied to screen feature genes.The Cell-type Ⅰdentification By Estimating Relative Subsets of RNA Transcripts(CIBERSORT)algorithm and the Estimation of Stromal and Immune Cells in Malignant Tumor Tissues using Expression Data(ESTIMATE)algo-rithm were used to explore the immune infiltration characteristics of BPD.Spearman correlation anal-ysis between feature genes and immune cells was performed using the"corrplot"package in R lan-guage.Results A total of 19 DE-NRGs were identified.The main biological functions and path-ways of DE-NRGs included the regulation of necroptosis and inflammatory responses.Three feature genes,namely flotillin-2(FLOT2),CASP8 and FADD-like apoptosis regulators(CFLAR),and charged multivesicular body protein 7(CHMP7),were further screened to construct a nomogram.In the validation sets GSE8586 and GSE188944,the area under the curve(AUC)values were all greater than 0.7.CIBERSORT analysis revealed that BPD group presented a higher proportion of naive B cells,neutrophils,eosinophils and resting mast cells compared to control group(P＜0.05).Meanwhile,the proportion of CD8+T cells,CD4+naive T cells,CD4+resting memory T cells,regulatory T cells,resting natural killer(NK)cells,M0 macrophages,M2 macrophages and activated dendritic cells was lower than that in the control group(P＜0.05).ESTIMATE analysis showed that the stromal score in the BPD group was higher than that in the control group,while the immune score was lower,with statistically significant differences(P＜0.05).Correlation analysis between the three feature genes and ESTIMATE scores indicated that FLOT2 and CFLAR were posi-tively correlated with the stromal score and negatively correlated with the immune score,whereas CHMP7 was positively correlated with the immune score and negatively correlated with the stromal score.Conclusion The three necroptosis-related feature genes can serve as diagnostic biomarkers for BPD-related necroptosis,with high diagnostic efficacy.They may play an important roles through immune mechanisms,providing new insights and theoretical references for the early diagnosis and immune intervention of BPD.

Objective To explore an assessment model for multi-level integrated training in health service based on advanced intelligent trauma simulators in order to innovate health service training.Methods An assessment model for multi-level integrated training that involved advanced trauma simulators was adopted to assess chains of treatment and rescue that were composed of multi-hierarchy medical institutions.The assessment focused on trauma emergency response capabilities at each level and the overall therapeutic effect.Results In terms of capabilities for treatment and rescue,group B was the best one,followed by group C and group A.As for the overall therapeutic effect,group A was outstanding,followed by group B and group C.Based on the priorities of combat casualty care,the final results of assessment were as follows:group A was the best one,followed by group B and group C.Conclusion The analysis of processes and outcomes of assessment reveals the edge of this model,as evidenced by the continuity of treatment and rescue,integrity of overall effectiveness,and adaptability of this assessment model.

Objective To evaluate the role and potential mechanism of apolipoprotein E(APOE)in drug resistance of colon cancer by bioinformatic tools and cellular experiments.Methods After downloading the microarray dataset GSE196900 from the GEO database,the online tool GEO2R was used to identify genes that were expressed differently in the drug-resistant and control groups.The differently expressed genes were then examined for Gene Ontology(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway enrichment.The STRING database and Cytoscape software were used to build protein-protein interaction(PPI)networks and find hub genes.Hub genes'predictive significance in colon cancer was further assessed.Western blod and qRT-PCR were used to identify changes in APOE expression,whereas Transwell was used to identify changes in the colon cancer cells'capacity for invasion and migration.Results The analysis of GO and KEGG enrichment revealed that the differential genes derived from the GSE196900 dataset were primarily focused on receptor-ligand activity and cytokine-cytokine receptor interaction pathways.Using the CytoNCA plug-in in Cytoscape software,ten hub genes were obtained through PPI construction.Of these,the prognosis of the patients with colon cancer was negatively correlated with the expression of the APOE gene(P<0.05)and the overexpression of the APOE gene might significantly increase the migration and nvasivenessability of colon cancer cells(P<0.05).Conclusion The increased expression of APOE significantly promotes the migration and invasion ability of colon cancer cells,which may be one of the mechanisms by which APOE gene promotes tumor progression in the patients with colon cancer.