ObjectiveIn traditional Chinese medicine (TCM), the foundational doctrine that the eyes reflect the essence of the internal viscera establishes ocular observation as a cornerstone of diagnostic practice. Specifically, the morphological characteristics and coloration variations of the scleral microvasculature serve as critical clinical indicators for assessing the dynamic balance of Qi and Blood, as well as the pathological status of internal organs. Historically, however, TCM eye diagnosis has relied predominantly on the subjective clinical experience and visual acuity of individual practitioners, leading to inherent challenges in standardization and reproducibility. While automated computer-aided diagnostic systems offer a promising solution, existing vessel segmentation algorithms encounter significant domain-specific bottlenecks when applied to scleral imagery. These challenges primarily stem from the highly reflective and moist nature of the ocular surface, which generates severe reflective interference. Furthermore, the inherent low contrast of fine capillary networks against complex background textures, compounded by non-uniform illumination, frequently results in high false-positive rates, misdetections, and severe vessel fragmentation. To address these critical limitations and advance the objective quantification of TCM diagnostics, this paper proposes a novel, highly robust sclera vessel segmentation framework that innovatively integrates Frangi-Sato dual-filter adaptive enhancement with pixel-level reflection detection. MethodsThe proposed methodology systematically addresses the segmentation pipeline through three synergistic stages. First, to overcome the structural limitations of single-filter approaches, a multi-scale weighted fusion strategy is meticulously designed to harness the complementary extraction capabilities of both Frangi and Sato filters. This adaptive enhancement optimally balances the preservation of main vessel trunk continuity with the heightened sensitivity required for delineating delicate, low-contrast peripheral capillaries. Second, to tackle the persistent issue of reflective highlights, a sophisticated multi-feature synergistic reflection detection module is introduced. By jointly analyzing local information entropy, gradient field variations, and intensity statistical distributions, this module achieves precise, pixel-level identification and elimination of reflective artifacts without compromising the underlying vascular structures. Finally, a dual-level adaptive thresholding strategy, featuring an innovative “core protection” mechanism, is implemented. This critical step effectively suppresses complex background noise while rigorously preserving the structural and topological integrity of the intricate vessel network, preventing the structural breaks often seen in conventional binarization methods. ResultsThe efficacy of the proposed framework was rigorously evaluated using both self-constructed clinical datasets specifically acquired for TCM research and standardized public datasets. Extensive experimental results demonstrate that the proposed method consistently outperforms state-of-the-art traditional approaches and contemporary deep learning models. Specifically, the proposed method achieves a Dice similarity coefficient of approximately 0.71 on the private clinical dataset, and secures the best performance across the majority of quantitative metrics on both datasets. Notably, the framework exhibits exceptional robustness and generalization capabilities in highly challenging scenarios characterized by intense reflective interference, low signal-to-noise ratios, and cross-domain image variations. ConclusionThis study successfully realizes the high-integrity, automated segmentation of scleral vessel networks under complex clinical imaging conditions. By overcoming the fundamental algorithmic challenges of reflection interference and micro-vessel loss, the proposed methodology provides potential support for the digitization, objective standardization, and intelligent advancement of modern TCM eye diagnosis systems.

ObjectiveIn traditional Chinese medicine (TCM), the foundational doctrine that the eyes reflect the essence of the internal viscera establishes ocular observation as a cornerstone of diagnostic practice. Specifically, the morphological characteristics and coloration variations of the scleral microvasculature serve as critical clinical indicators for assessing the dynamic balance of Qi and Blood, as well as the pathological status of internal organs. Historically, however, TCM eye diagnosis has relied predominantly on the subjective clinical experience and visual acuity of individual practitioners, leading to inherent challenges in standardization and reproducibility. While automated computer-aided diagnostic systems offer a promising solution, existing vessel segmentation algorithms encounter significant domain-specific bottlenecks when applied to scleral imagery. These challenges primarily stem from the highly reflective and moist nature of the ocular surface, which generates severe reflective interference. Furthermore, the inherent low contrast of fine capillary networks against complex background textures, compounded by non-uniform illumination, frequently results in high false-positive rates, misdetections, and severe vessel fragmentation. To address these critical limitations and advance the objective quantification of TCM diagnostics, this paper proposes a novel, highly robust sclera vessel segmentation framework that innovatively integrates Frangi-Sato dual-filter adaptive enhancement with pixel-level reflection detection. MethodsThe proposed methodology systematically addresses the segmentation pipeline through three synergistic stages. First, to overcome the structural limitations of single-filter approaches, a multi-scale weighted fusion strategy is meticulously designed to harness the complementary extraction capabilities of both Frangi and Sato filters. This adaptive enhancement optimally balances the preservation of main vessel trunk continuity with the heightened sensitivity required for delineating delicate, low-contrast peripheral capillaries. Second, to tackle the persistent issue of reflective highlights, a sophisticated multi-feature synergistic reflection detection module is introduced. By jointly analyzing local information entropy, gradient field variations, and intensity statistical distributions, this module achieves precise, pixel-level identification and elimination of reflective artifacts without compromising the underlying vascular structures. Finally, a dual-level adaptive thresholding strategy, featuring an innovative “core protection” mechanism, is implemented. This critical step effectively suppresses complex background noise while rigorously preserving the structural and topological integrity of the intricate vessel network, preventing the structural breaks often seen in conventional binarization methods. ResultsThe efficacy of the proposed framework was rigorously evaluated using both self-constructed clinical datasets specifically acquired for TCM research and standardized public datasets. Extensive experimental results demonstrate that the proposed method consistently outperforms state-of-the-art traditional approaches and contemporary deep learning models. Specifically, the proposed method achieves a Dice similarity coefficient of approximately 0.71 on the private clinical dataset, and secures the best performance across the majority of quantitative metrics on both datasets. Notably, the framework exhibits exceptional robustness and generalization capabilities in highly challenging scenarios characterized by intense reflective interference, low signal-to-noise ratios, and cross-domain image variations. ConclusionThis study successfully realizes the high-integrity, automated segmentation of scleral vessel networks under complex clinical imaging conditions. By overcoming the fundamental algorithmic challenges of reflection interference and micro-vessel loss, the proposed methodology provides potential support for the digitization, objective standardization, and intelligent advancement of modern TCM eye diagnosis systems.

This study investigates the material basis and mechanism of Arisaematis Rhizoma Preparatum in the treatment of chronic obstructive pulmonary disease(COPD) using animal experiments, component analysis, network pharmacology, and molecular docking. A mouse model of COPD was constructed by cigarette smoke and lipopolysaccharide(LPS). Blood gas analysis was performed to measure the pH and partial pressure of carbon dioxide(PCO_2) in the blood of the mice. Lung tissue sections were analyzed using HE staining, and the effects of Arisaematis Rhizoma Preparatum water extract on inflammatory factors(TNF-α, IL-6, and IL-1β) and the PI3K/AKT signaling pathway in the lung tissue of COPD model mice were studied by qPCR and Western blot. The composition of the Arisaematis Rhizoma Preparatum water extract was analyzed using UPLC Q-Exactive Orbitrap MS. The SwissTargetPrediction database was used to predict the targets of the chemical components in Arisaematis Rhizoma Preparatum. GeneCards, OMIM, TTD, PharmGKB and DrugBank disease databases were used to screen for COPD targets, and the potential targets of Arisaematis Rhizoma Preparatum in treating COPD were identified. A protein-protein interaction(PPI) network of intersection targets was constructed and analyzed using the STRING database and Cytoscape 3.9.0, and core genes were screened. GO functional analysis and KEGG pathway enrichment analysis were performed using R language, and molecular docking verification was conducted using AutoDock Vina software. The results of the animal experiments showed that Arisaematis Rhizoma Preparatum water extract improved pulmonary ventilation function in COPD model mice, reduced lung inflammatory cells, decreased alveolar cavities, and improved lung tissue condition. The levels of inflammatory factors TNF-α, IL-6 and IL-1β were decreased, and the phosphorylation levels of PI3K and AKT were inhibited. Fifty-two chemical components were identified from Arisaematis Rhizoma Preparatum, and 440 intersection targets related to COPD were found. Nine key components were screened, including hydroxyphenylethylamine, L-tyrosine, L-tyrosyl-L-alanine, 3,4,5-trihydroxy-1-cyclohexene-1-carboxylic acid, methyl azelate, zingerone, 6-gingerol, linoleamide, and linoleoyl ethanolamine. Five core targets were identified, including AKT1, TNF, STAT3, ESR1, and IL1B. The PI3K/AKT pathway was identified as the key pathway for the treatment of COPD with Arisaematis Rhizoma Preparatum. Molecular docking results showed that 75% of the binding energies of key components and core targets were less than-5 kcal·mol~(-1), indicating good binding affinity. In conclusion, Arisaematis Rhizoma Preparatum may improve pulmonary ventilation function, enhance lung pathological morphology, and reduce pulmonary inflammation in COPD model mice by inhibiting the PI3K/AKT signaling pathway and downregulating TNF-α, IL-6, and IL-1β inflammatory factors. The material basis may be associated with L-tyrosyl-L-alanine, 3,4,5-trihydroxy-1-cyclohexene-1-carboxylic acid, zingerone and 6-gingerol, and AKT1 and TNF may be the primary targets.
Animals ; Pulmonary Disease, Chronic Obstructive/metabolism* ; Network Pharmacology ; Mice ; Drugs, Chinese Herbal/administration & dosage* ; Male ; Rhizome/chemistry* ; Humans ; Molecular Docking Simulation ; Chromatography, High Pressure Liquid ; Disease Models, Animal ; Signal Transduction/drug effects* ; Lung/metabolism* ; Phosphatidylinositol 3-Kinases/metabolism* ; Tumor Necrosis Factor-alpha/metabolism* ; Proto-Oncogene Proteins c-akt/metabolism* ; Interleukin-6/immunology*

OBJECTIVES: To investigate the epidemiological characteristics of human metapneumovirus (hMPV) and the risk factors for severe pneumonia in hospitalized children. METHODS: The epidemiological characteristics of hMPV in hospitalized children at Hebei Children's Hospital from January 2019 to December 2023 were retrospectively analyzed. The clinical data of hospitalized children with hMPV infection from April to December 2023 were included, and independent risk factors for severe pneumonia were identified through logistic regression. RESULTS: A total of 44 092 children were tested, with an hMPV positive rate of 7.30% (3 220/44 092). Children aged 3-6 years constituted the largest proportion (40.93%, 1 318/3 220) among hMPV-positive cases. The detection rate varied significantly by year (P<0.001), peaking in 2022 (12.35%, 978/7 919). The peak season of the epidemic was winter and spring from 2019 to 2021, but shifted to spring and summer from 2022 to 2023. The proportion of co-infection was 38.70% (1 246/3 220), primarily with rhinovirus (600/1 246, 48.15%), Mycoplasma pneumoniae (217/1 246, 17.42%), and respiratory syncytial virus (182/1 246, 14.61%). The main manifestations of hMPV pneumonia were cough, expectoration, and fever. Children with severe pneumonia were significantly younger (P<0.05). Wheezing, underlying diseases, co-infection, and younger age were identified as independent risk factors for severe pneumonia (P<0.05). CONCLUSIONS There are significant annual and seasonal differences in the epidemiological characteristics of hMPV in hospitalized children. Young age, underlying diseases, wheezing, and co-infection are independent risk factors for severe pneumonia.
Humans ; Risk Factors ; Metapneumovirus ; Child, Preschool ; Child ; Male ; Female ; Paramyxoviridae Infections/complications* ; Pneumonia/epidemiology* ; Retrospective Studies ; Child, Hospitalized ; Infant ; Logistic Models ; Seasons ; Hospitalization

Readiness for Hospital Discharge(RHD)refers to a multidimensional assessment of a patient's ability to transition safely from hospital to home,encompassing physiological stability,psychological preparedness,and social support adequacy.For patients requiring Home Nutrition Support(HNS),discharge readiness is particularly critical due to their heightened need for post-discharge specialized care,which significantly influences long-term recovery and quality of life.This paper reviews the concept,influencing factors,and unmet needs of RHD in patients with HNS and proposes targeted strategies to enhance discharge preparedness.By addressing gaps in current practices,we aim to optimize RHD in this vulnerable population and provide clinicians with evidence-based guidance for developing effective discharge plans.

Objective To draw the genome-wide distribution and remodeling characteristics of H3K27me3 silencers in signet-ring cell carcinoma of the stomach(SRCC)through epigenetic sequencing technology,and to investigate their roles in transcriptional regulation in order to elucidate the regulatory mechanism of SRCC malignant progression.Methods The study was conducted on 35 gastric samples obtained by gastroendoscopic biopsy(15 normal and 20 SRCC tissues)from Department of Gastroenterology of Army Medical Center of PLA between January 2021 and December 2023.Multi-omics analyses,including assay for transposase-accessible chromatin with high-throughput sequencing(ATAC-seq),cleavage under targets and tagmentation(CUT&Tag)and transcriptome sequencing(RNA-seq),were performed to identify chromatin accessibility,H3K27me3 silencer regions,and transcriptional changes,with aid of Illumina NovaSeq 6000.H3K27me3 related differentially expressed genes(|Log2FC|>1,FDR<0.05)were screened using DESeq2.Gene Ontology(GO)analysis and Kyoto Encyclopedia of Genes and Genomes(KEGG)analysis were employed to analyze the enrichment function,and Homer was employed to identify transcription factor motifs.A regulatory network was constructed using Cytoscape,and then validated using immunohistochemistry to explore its regulatory mechanism.Results H3K27me3 silencers were primarily located in distal intergenic regions(37.06%)in SRCC.Compared with the normal tissues,SRCC showed a significant reduction in H3K27me3 silencer signals(95%CI:1.34～2.30,P=0.007)with 6 257 lost sites(FDR<0.01).Integrating CUT&Tag and RNA-seq revealed 380 up-regulated immune-related genes,particularly in T cell receptor signaling(OR=4.2,95%CI:2.8～6.3,P=0.002).Immunohistochemistry confirmed elevated expression of transcription factor EHF(P<0.05).Conclusion There is the remodeling of H3K27me3 silencers in SRCC,and EHF may potentially play a crucial role in the SRCC malignant progression.

Objective To identify the enhancer profile marked by histone H3K27ac modification in high-grade intraepithelial neoplasia(HGIN)in order to reveal the novel regulatory mechanism of HGIN pathogensis.Methods Gastric tissue samples were collected from Department of Gastroenterology of Army Medical Center of PLA between June 2022 and June 2023,including 14 normal gastric tissues(Nor group),31 HGIN tissues(HGIN group)and 17 gastric cancer tissues(GC group).Cleavage under targets and tagmentation(CUT&Tag)technique was employed to capture enhancer regions modified by histone H3K27ac.Multi-omics analysis was performed to identify HGIN-specific active enhancers and their potentially regulated genes.Immunohistochemical profiling was performed to assess differential expression of the gene of interest across clinically stratified specimens,combined with CRISPR-dCas9-mediated ablation of active enhancers to monitor the gene of interest transcriptional dynamics and validate enhancer-mediated regulatory mechanisms.Results Epigenomic sequencing obtained the data with excellent quality,and indicated that obvious remodeling was observed in H3K27ac enhancers in HGIN and GC groups(P<0.05),though no significant difference in the genome-wide distribution of H3K27ac modification among the 3 groups.Combining transcriptome data revealed that enhancer remodeling may up-regulate the expression of the proliferation-related target gene,CD24,in the HGIN tissue;while,inhibiting enhancer activity can notably reduce CD24 expression level(P<0.05).Immunohistochemical assay displayed a positive correlation between the expression levels of CD24 and Ki-67(P<0.001).Conclusion The remodeling of H3K27ac enhancer represents a significant epigenetic feature of the transformation from normal condition to HGIN.Remodeling of H3K27ac enhancer up-regulates CD24,which may facilitate the abnormal proliferation of gastric epithelial cells.

Objective To analyze clinical characteristics of mesenchymal-subtype gastric cancer(Mes-GC)by integrating multi-omics data and explore the characteristics of tumor cells and microenvironment associated with the risk for recurrence.Methods Gastric tumor tissue samples were collected from the patients who visited Department of Gastroenterology of Army Medical Center of PLA from January 2022 to December 2023.Transcriptome and genome sequencing were applied for these tissue samples,including 19 cases of diffuse-type gastric cancer,22 cases of intestinal-type gastric cancer,and 23 cases of mixed-type gastric cancer patients.Bioinformatics analysis was employed to investigate the differences in clinical characteristics and tumor microenvironment between Mes-GC and non-mesenchymal-subtype gastric cancer(non-Mes-GC)by integrating data resources including The Cancer Genome Atlas(TCGA),Gene Expression Omnibus(GEO),and National Genomics Data Center(NGDC).Results Compared to non-Mes-GC patients,Mes-GC ones were characterized by later clinical stages,deeper tumor infiltration,and higher rates of lymph node metastasis.Kaplan-Meier survival analysis confirmed that Mes-GC patients were associated with shorter survival time,poor prognosis as well as increased risk of cancer recurrence(P<0.05).Single-cell RNA sequencing data revealed that tumor cells in Mes-GC showed higher expression levels of the genes related to stemness,metastasis(P<0.05),and epithelial-mesenchymal transition(EMT).And in the tumor microenvironment,there were significant more myeloid cells,smooth muscle cells,endothelial cells and fibroblasts,with the most pronounced elevation in the proportion of fibroblasts(P<0.05).Moreover,the patients with larger proportion of fibroblasts were associated with poorer prognosis.Conclusion Mes-GC tumor cells exhibit higher stemness and EMT characteristics,and stromal cells such as myeloid cells,endothelial cells,and fibroblasts are enriched in the tumor microenvironment.These features may be key factors contributing to poor prognosis and high recurrence rate of Mes-GC.

Objective To identify the enhancer landscape marked by histone H3K27ac modifications in diffuse-type gastric cancer(DGC)tissues,and to elucidate the epigenetic remodeling mechanisms by which active enhancers regulate cachexia-related genes.Methods Gastric mucosal tissue samples were collected from Department of Gastroenterology of Army Medical Center of PLA during January 2022 to March 2023,including 10 normal gastric mucosa tissues(Normal group),10 DGC tissues diagnosed with cachexia(DGC group),and 10 organoids derived from DGC tissues(Organoid group).Using H3K27ac chromatin targeting cleavage and tagmentation(CUT&Tag)technology,genomic modification regions were captured to screen specific active enhancers and their potential target genes in DGC tissues.CRISPR-dCas9 gene editing technology was used to intervene with the enhancers,and the expression of target genes was detected with Western blotting and qRT-PCR.Sixteen female SPF-grade BALB/c Nude mice(6～8 weeks old,weighing 18～21 g)were utilized to establish an orthotopic xenograft tumor model using the human diffuse-type gastric cancer cell line MKN45.Cachexia-related phenotypes were evaluated in 3 groups:normal group(n=4),silencing group(n=6),and control group(n=6).Results Significant differential enhancer regions were identified between DGC and normal gastric mucosa tissues.DGC tissues exhibited a marked increase in enhancer abundance(P<0.05)and signal intensity when compared with the normal counterparts.Integrated analysis of transcriptome data revealed that some of these active enhancers up-regulated the expression of GDF15,a cachexia-associated target gene in DGC.Targeted silencing of the active enhancer of GDF15 using CRISPR/dCas9-KRAB plasmid technology resulted in a significant reduction in GDF15 expression at both mRNA levels(P<0.05)and protein.Results from orthotopic transplantation experiments of DGC demonstrated that silencing of active enhancers alleviated the cachexia phenotype in nude mice(P<0.05).Conclusion DGC exhibits enhancer remodeling,which regulates the expression of the cachexia-associated gene GDF15,and thereby contributes to the pathogenesis and progression of cancer cachexia.

Objective To explore the effect of motor imagery brain-computer interface(BCI)combined with upper limb rehabilita-tion robot on upper limb function of stroke patients.Methods From May to December,2024,45 stroke inpatients from Xuzhou Rehabilitation Hospital and Xuzhou Central Hospital were recruited and randomly divided into BCI group(n=15),robot group(n=15)and combined group(n=15).All the groups received conventional rehabilitation therapy.On this basis,the BCI group received motor imagery based BCI training,the robot group received upper extremity rehabilitation robot training,and the com-bined group received BCI combined with upper extremity rehabilitation robot training.They were assessed with Fugl-Meyer Assessment-Upper Extremities(FMA-UE),Action Research Arm Test(ARAT)and modified Barthel Index(MBI)before and four weeks after treatment,while they were measured delta-alpha ratio(DAR)using the quantitative electroencephalogram.Results The intra-group effect(F>101.870,P<0.001)and interaction effect(F>7.891,P<0.001)were significant in the scores of FMA-UE,ARAT and MBI,and DAR.Post Hoc test showed that all the indexes were better in the combined group than in BCI group and the robot group(P<0.05),and there was no significant difference in the indexes between BCI group and the robot group(P>0.05).Conclusion BCI combined with upper limb rehabilitation robot can promote the recovery of upper limb function and ac-tivities of daily living of stroke patients.