ObjectiveTo understand the surveillance of influenza-like illness (ILI) and influenza vaccination status in Jing’an District, Shanghai, and to provide a basis for optimizing influenza prevention and control strategies. MethodsThe sentinel surveillance data for ILI and virological surveillance data of influenza viruses in Jing’an District were collected from the Chinese influenza surveillance information system, and data for influenza vaccination were collected from Shanghai immunization information system from September 2017 to August 2023. Epidemiological characteristics of ILI, influenza etiology, and the temporal and population distributions of influenza vaccination were analyzed using descriptive epidemiological methods. ResultsILI as a percentage of total visit surveillance units (ILI%) reported by sentinel hospital was increased in Jing’an District of Shanghai from September 2017 to August 2023 (F=18.841, P=0.012). The peak of the influenza cases mainly appeared in winter-spring, but there were two peaks in winter-spring and summer from September 2019 to August 2020, from September 2020 to August 2021, and from September 2021 to August 2022. In particular, there were two peaks in winter-spring from September 2022 to August 2023, with a rebound during the descending process. The average positive rate of ILI was 21.64% (2 421/11 189) during the 6 years. There was a peak in winter-spring during every year with the exception of the period from September 2020 to August 2021. The dominant strains were B/Yamagata and A/H1N1 in winter-spring from September 2017 to August 2018. The dominant strain was A/H1N1 in winter-spring from September 2018 to August 2019 and from September 2022 to August 2023. The dominant strain was B/Victoria in winter-spring from September 2019 to August 2020 and from September 2021 to August 2022. Different subtype strains occurred alternately, and the dominant strains were A/H1N1 and A/H3N2 in recent years. The influenza vaccination coverage was 2.94% from September 2017 to August 2023, and the vaccination coverage was highest in young children. The vaccination coverage for females was higher than that for males (χ²=546.963, P<0.001), and the vaccination coverage for registered residents was higher compared to that for migrants (χ²=123.141, P<0.001). ConclusionILI% exhibits an upward trend in Jing’an District of Shanghai, and the dominant strain is A subtype. The influenza vaccination coverage is still low, which is insufficient to have an impact on the spread of influenza. It is recommended that the surveillance of ILI and variations of influenza virus strains should be improved continuously, and effective steps should be taken to promote influenza vaccination.

Objective To systematically evaluate the radiation impact of radioactive sources used in online elemental analyzers in cement enterprises on the surrounding environment, and to provide a scientific basis for radiation monitoring and safety management at the application sites of this type of radioactive sources. Methods A statistical analysis was conducted on 15 cement enterprises in Guangxi Province using online elemental analyzers with ²⁵²Cf as the radioactive source. On-site investigation of radiation safety management and on-site monitoring of radiation environment were performed, followed by an evaluation based on the collected data. Results Although the gamma radiation ambient dose equivalent rate and neutron ambient dose equivalent rate increased around the sites using online elemental analyzers with ²⁵²Cf as the radioactive source, they all met the requirements of the Radiological Health Protection Requirements for Instruments with Sealed Sources (GBZ 125—2009). Conclusion Under the current usage and management conditions, the application of this type of radioactive sources has controllable radiation impact on the surrounding environment, and will not pose a threat to public health and environmental safety. However, continuous strengthening of radiation safety management measures and regular radiation monitoring work are still needed to ensure the safe use of radioactive sources, further reducing potential radiation risks and providing strong guarantees for the safe application of radioactive sources in online elemental analyzers in cement enterprises.

ObjectiveTobacco-related diseases remain one of the leading preventable public health challenges worldwide and are among the primary causes of premature death. In recent years, accumulating evidence has supported the classification of nicotine addiction as a chronic brain disease, profoundly affecting both brain structure and function. Despite the urgency, effective diagnostic methods for smoking addiction remain lacking, posing significant challenges for early intervention and treatment. To address this issue and gain deeper insights into the neural mechanisms underlying nicotine dependence, this study proposes a novel graph neural network framework, termed TI-GNN. This model leverages functional magnetic resonance imaging (fMRI) data to identify complex and subtle abnormalities in brain connectivity patterns associated with smoking addiction. MethodsThe study utilizes fMRI data to construct functional connectivity matrices that represent interaction patterns among brain regions. These matrices are interpreted as graphs, where brain regions are nodes and the strength of functional connectivity between them serves as edges. The proposed TI-GNN model integrates a Transformer module to effectively capture global interactions across the entire brain network, enabling a comprehensive understanding of high-level connectivity patterns. Additionally, a spatial attention mechanism is employed to selectively focus on informative inter-regional connections while filtering out irrelevant or noisy features. This design enhances the model’s ability to learn meaningful neural representations crucial for classification tasks. A key innovation of TI-GNN lies in its built-in causal interpretation module, which aims to infer directional and potentially causal relationships among brain regions. This not only improves predictive performance but also enhances model interpretability—an essential attribute for clinical applications. The identification of causal links provides valuable insights into the neuropathological basis of addiction and contributes to the development of biologically plausible and trustworthy diagnostic tools. ResultsExperimental results demonstrate that the TI-GNN model achieves superior classification performance on the smoking addiction dataset, outperforming several state-of-the-art baseline models. Specifically, TI-GNN attains an accuracy of 0.91, an F1-score of 0.91, and a Matthews correlation coefficient (MCC) of 0.83, indicating strong robustness and reliability. Beyond performance metrics, TI-GNN identifies critical abnormal connectivity patterns in several brain regions implicated in addiction. Notably, it highlights dysregulations in the amygdala and the anterior cingulate cortex, consistent with prior clinical and neuroimaging findings. These regions are well known for their roles in emotional regulation, reward processing, and impulse control—functions that are frequently disrupted in nicotine dependence. ConclusionThe TI-GNN framework offers a powerful and interpretable tool for the objective diagnosis of smoking addiction. By integrating advanced graph learning techniques with causal inference capabilities, the model not only achieves high diagnostic accuracy but also elucidates the neurobiological underpinnings of addiction. The identification of specific abnormal brain networks and their causal interactions deepens our understanding of addiction pathophysiology and lays the groundwork for developing targeted intervention strategies and personalized treatment approaches in the future.

ObjectiveThis study aims to develop and validate a novel multimodal predictive model， termed criss-cross network（CCNet）-directed graph neural network（DGNN）（CGN）， for accurate assessment of pulmonary nodule progression in high-risk individuals for lung cancer， by integrating longitudinal chest computed tomography（CT） imaging with both traditional Chinese and western clinical evaluation data. MethodsA cohort of 4 432 patients with pulmonary nodules was retrospectively analyzed. A twin CCNet was employed to extract spatiotemporal representations from paired sequential CT scans. Structured clinical assessment and imaging-derived features were encoded via a multilayer perceptron， and a similarity-based alignment strategy was adopted to harmonize multimodal imaging features across temporal dimensions. Subsequently， a DGNN was constructed to integrate heterogeneous features， where nodes represented modality-specific embeddings and edges denoted inter-modal information flow. Finally， model optimization was performed using a joint loss function combining cross-entropy and cosine similarity loss， facilitating robust classification of nodule progression status. ResultsThe proposed CGN model demonstrated superior predictive performance on the held-out test set， achieving an area under the receiver operating characteristic curve（AUC） of 0.830， accuracy of 0.843， sensitivity of 0.657， specificity of 0.712， Cohen's Kappa of 0.417， and F₁ score of 0.544. Compared with unimodal baselines， the CGN model yielded a 36%-48% relative improvement in AUC. Ablation studies revealed a 2%-22% increase in AUC when compared to simplified architectures lacking key components， substantiating the efficacy of the proposed multimodal fusion strategy and modular design. Incorporation of traditional Chinese medicine （TCM）-specific symptomatology led to an additional 5% improvement in AUC， underscoring the complementary value of integrating TCM and western clinical data. Through gradient-weighted activation mapping visualization analysis， it was found that the model's attention predominantly focused on nodule regions and effectively captured dynamic associations between clinical data and imaging-derived features. ConclusionThe CGN model， by synergistically combining cross-attention encoding with directed graph-based feature integration， enables effective alignment and fusion of heterogeneous multimodal data. The incorporation of both TCM and western clinical information facilitates complementary feature enrichment， thereby enhancing predictive accuracy for pulmonary nodule progression. This approach holds significant potential for supporting intelligent risk stratification and personalized surveillance strategies in lung cancer prevention.

Adequate inflammation can effectively eliminate harmful substances and prevent disease as a self-protective measure to prevent further damage to the body,while abnormally activated inflammation is detrimental to the body.Nucleotide binding and oligomerization domain-like receptor protein 3(NLRP3)inflammasome that participates in inflammatory responses are closely related to many physiological and pathological processes and play an important role in the occurrence and development of pulmonary diseases.This article mainly reviewed the activation mechanism and hypothesis of NLRP3 inflammasome,as well as the research on treating respiratory diseases by interfering with NLRP3 inflammasome.

The purpose of this study was to enrich the genomic information and provide a basis for further development and utilization of Polygonatum kingianum. The fresh rhizome of P. kingianum was used as the experimental material, and the full-length transcriptome was sequenced by PacBio Sequel platform. The final measured polymerase reads were 1 120 485, with a total of 77.73 GB of data. In NR database, 41 864 homologous sequence alignments were aligned to 5 species; 40 506 were annotated in the KOG database and classified into 26 categories based on their functionality; 69 060 GO annotated 32 functional groups divided into 3 categories: cellular components, molecular functions, and biological processes; 45 779 annotations were added to 145 metabolic pathways in the KEGG database. Among them, there are 127 identified transcripts related to polysaccharide synthesis in the glycolysis/gluconeogenesis metabolic pathway, 144 in the starch and sucrose metabolic pathway, 85 in the amino acid sugar and nucleotide sugar metabolic pathway, and 69 in the fructose and mannose metabolic pathway. In addition, 1 781 transcription factors were detected, distributed in 37 transcription factor families; 180 293 SSR loci were detected, among which single base repeats accounted for the most, accounting for 30.48% of all base repeats, and at least five base repeats, accounting for only 0.14% of single base repeats. The results of this study provide important data supporting for enriching the genomic information of P. kingianum and exploring its effective biosynthetic pathway.

The incidence of intrahepatic cholangiocarcinoma (ICC) continues to rise, and there are no effective drugs to treat it. The immune microenvironment plays an important role in the development of ICC and is currently a research hotspot. Icaritin (ICA) is an innovative traditional Chinese medicine for the treatment of advanced hepatocellular carcinoma. It is considered to have potential immunoregulatory and anti-tumor effects, which is potentially consistent with the understanding of "Fuzheng" in the treatment of tumor in traditional Chinese medicine. However, whether ICA can be used to treat ICC has not been reported. Therefore, in this study, sgp19/kRas, an in situ ICC mouse model with intact immune system, was selected to evaluate the efficacy of ICA in the treatment of ICC in vivo for the first time, and the effects of ICA on the tumor immune microenvironment of ICC mice were analyzed by flow cytometry. This experiment was approved by the Experimental Animal Ethics Committee of Capital Medical University (approval number: AEEI-2023-138). In this study, sgp19/kRas ICC mouse model was treated with oral gavage of 100 mg·kg^-1 ICA. We found that ICA significantly inhibited tumor growth and tumor cell proliferation in the sgp19/kRas ICC mouse model after 3 weeks of treatment. Flow cytometry analysis indicated that ICA treatment markedly reduced the proportion of M2 macrophages and increased the number of CD3⁺CD4⁺ T cells. In vitro experiments demonstrated that ICA promoted macrophage polarization towards the M1 phenotype while inhibiting polarization towards the M2 phenotype. Furthermore, transcriptomic analysis suggested that ICA enhanced Toll-like receptor 9 (TLR9) expression, influencing macrophage nitric oxide metabolism synthesis pathways and receptor-related activities, thereby regulating macrophage polarization. In summary, this study demonstrates that ICA treatment significantly delays tumor progression in sgp19/kRas ICC mouse models. Mechanistically, ICA may achieve its anti-ICC effects by upregulating TLR9 receptor expression levels, promoting macrophage polarization towards the M1 phenotype, and altering the tumor immune microenvironment.

Inflammatory bowel disease (IBD) is characterized by chronic relapsing intestinal inflammation and encompasses ulcerative colitis (UC) and Crohn's disease (CD). IBD has emerged as a global healthcare problem. Clinically efficacious therapeutic agents are deficient. This study concentrates on models of ulcerative colitis with the objective of discovering novel therapeutic strategies. Previous investigations have established that schisandrin A demonstrates anti-inflammatory effects in vitro, concurrently enhancing the transcriptional activity of farnesoid X receptor (FXR). FXR inversely modulates the transcriptional activity of NF-κB, which has an important role in regulating inflammatory responses. Consequently, the current study was to explore the safeguarding influence of schisandrin A on ulcerative colitis and delineate the mechanism by which it regulates this effect through the FXR signaling pathway. The effect of schisandrin A on the mRNA levels of inflammatory factors was evaluated in RAW264.7 cells. The dual luciferase reporter gene assay was used to verify the relationship between schisandrin A and FXR targeting. The animal experiments were performed in accordance with the regulations of the Animal Ethics Committee of Shanghai University of Traditional Chinese Medicine (approval No. PZSHUTCM2304250005). Acute ulcerative colitis was induced in wild-type or FXR knockout C57BL/6 mice by drinking 3% dextran sodium sulfate (DSS) for 7 days, and schisandrin A was administered via gavage for a continuous treatment period of 7 days. The body weight and faecal were monitored daily. The mRNA levels of inflammatory factors in colon tissue and FXR target genes were measured by RT-qPCR. The findings revealed that schisandrin A, in vitro, impeded the lipopolysaccharide (LPS)-induced elevation in mRNA levels of inflammatory factors and schisandrin A could augment transcriptional activity of FXR. In wild-type mice, schisandrin A significantly improved weight loss, colon shortening, loose stools and blood in stools in mice with acute ulcerative colitis, and schisandrin A significantly reduced the expression of pro-inflammatory factors genes and significantly increased the expression of FXR target genes in colon tissues. In FXR knockout mice, the administration of schisandrin A failed to yield ameliorative effect on acute ulcerative colitis in mice. In conclusion, schisandrin A can reduce intestinal inflammation through the FXR signaling pathway to alleviate acute ulcerative colitis in mice. Implications arise that Schisandra lignans could serve as lead compounds for drug development aimed at inflammatory bowel disease.

Studies on chemical constituents in the rhizome of Dalbergia rimosa Roxb. The chemical constituents from the ethyl acetate part of D. rimosa were isolated and purified by silica gel, MCI gel, Sephadex LH-20 gel and semi-preparative HPLC, and the stuctures were identified by spectral method. Thirteen compounds were isolated from the ethyl acetate part of the rhizome of D. rimosa and identified as dalbergiaisoflavones A, B (1, 2), formononetin (3), 7,4′-dimethoxyisoflavone (4), 4′,6,7-trimethoxyisoflavone (5), biochanin A (6), prunetin (7), 7-O-methyltectorigenin (8), 3′-hydroxydaidzein (9), orobol 7,3′-dimethyl ether (10), 2′,7-dihydroxy-4′,5′- dimethoxyisoflavone (11), pruinosanone E (12), caviunin (13). Compounds 1 and 2 are new compounds, and compounds 3-13 were isolated from this plant for the first time. Compounds 9 and 11 had remarkable scavenging effect on DPPH free radicals.

Twelve compounds were isolated from the ethyl acetate fraction of the 80% aqueous ethanol extract of the roots and stems of Dalbergia rimosa Roxb. by silica gel, MCI, Sephadex LH-20 column chromatography, and semi-preparative HPLC. Their structures were identified by spectral analysis such as UV, IR, MS, 1D/2D NMR and by comparison with literature information as dalbergiquinol A (1), dalbergiquinol B (2), R-(-)-3′-hydroxy-2,4,5-trimethoxydalbergiquinol (3), neokhriol A (4), mucronulatol (5), (3R)-7,2′,3′-trihydroxy-4′-methoxy-isoflavane (6), isomucronulatol (7), (3S)-violanone (8), 3′-O-methylviolanone (9), eryvarin M (10), (±)-α,3,4,2′,4′-pentahydroxydihydrochalcone (11) and (-)-butin (12). Compound 1 and 2 are new compounds, and compounds 3-12 were isolated from this plant for the first time. Compounds 1, 2, 4, 6, 8, 11, 12 showed good scavenging effect on DPPH free radical.