ObjectiveTo examine the influences of Shenfu injection on the endogenous metabolic byproducts in the myocardium of the rat model exhibiting chronic heart failure， thus deciphering the therapeutic mechanism of the Qi-reinforcing and Yang-warming method. MethodsSD rats were randomly allocated into a control group and a modeling group. Chronic heart failure with heart-Yang deficiency syndrome in rats was modeled by multi-point subcutaneous injection of isoproterenol， and the rats were fed for 14 days after modeling. The successfully modeled rats were randomized into model， Shenfu injection （6.0 mL·kg^-1）， and trimetazidine （10 mg·kg^-1） groups and treated with corresponding agents for 15 days. The control group and the model group were injected with equal doses of normal saline， and the samples were collected after the intervention was completed. Cardiac color ultrasound was performed. Hematoxylin-eosin （HE） staining was used to observe histopathological morphology， and the serum level of N-terminal pro-brain natriuretic peptide （NT-proBNP） was assessed by enzyme-linked immunosorbent assay （ELISA）. The mitochondrial morphological and structural changes of cardiomyocytes were observed by transmission electron microscopy， and the metabolic profiling was carried out by ultra high performance liquid chromatography-quantitative exactive-mass spectrometry （UHPLC-QE-MS）. Differential metabolites were screened and identified by orthogonal partial least squares-discriminant analysis （OPLS-DA） and other methods， and then the MetaboAnalyst database was used for further screening. The relevant biological pathways were obtained through pathway enrichment analysis. The receiver operating characteristic （ROC） curve was established to evaluate the diagnostic value of each potential biomarker for myocardial injury and the evaluation value for drug efficacy. ResultsThe results of color ultrasound showed that Shenfu Injection improved the cardiac function indexes of model rats （P<0.05）. The results of HE staining showed that Shenfu injection effectively alleviated the pathological phenomena such as myocardial tissue structure disorder and inflammatory cell infiltration in model rats. The results of ELISA showed that Shenfu injection effectively regulated the serum NT-proBNP level in the model rats. Transmission electron microscopy （TEM） showed that Shenfu injection effectively restored the mitochondrial morphological structure. The results of metabolomics showed that the metabolic phenotypes of myocardial samples presented markedly differences between groups. Nine differential metabolites could be significantly reversed in the Shenfu injection group， involving three metabolic pathways： pyruvate metabolism， histidine metabolism， and citric acid cycle （TCA cycle）. The results of ROC analysis showed that the area under the curve （AUC） values of all metabolites were between 0.75 and 1.0， indicating that the differential metabolites had high diagnostic accuracy for myocardial injury， and the changes in their expression levels could be used as potential markers for efficacy evaluation. ConclusionShenfu injection significantly alleviated the damage of cardiac function， myocardium， and mitochondrial structure in the rat model of chronic heart failure with heart-Yang deficiency syndrome by ameliorating energy metabolism remodeling. Reinforcing Qi and warming Yang is a key method for treating chronic heart failure with heart-Yang deficiency syndrome.

In March 2024, three male and two female specimens of the genus Lispe were intercepted on cargo ships inbound for Wan Zai Port under Gongbei Customs. Based on morphological characteristics and molecular analysis, the specimens were identified as Lispe pumila. In this study, we describe the morphological features, diagnostic characteristics, and geographical distribution of L. pumila, with the aim of providing a reference for the identification of this species when intercepted at ports.

Objective To develop an artificial intelligence (AI)-driven lung cancer database by structuring and standardizing clinical data, enabling advanced data mining for lung cancer research, and providing high-quality data for real-world studies. Methods Building on the extensive clinical data resources of the Department of Thoracic Surgery at Peking Union Medical College Hospital, this study utilized machine learning techniques, particularly natural language processing (NLP), to automatically process unstructured data from electronic medical records, examination reports, and pathology reports, converting them into structured formats. Data governance and automated cleaning methods were employed to ensure data integrity and consistency. Results As of September 2024, the database included comprehensive data from 18 811 patients, encompassing inpatient and outpatient records, examination and pathology reports, physician orders, and follow-up information, creating a well-structured, multi-dimensional dataset with rich variables. The database’s real-time querying and multi-layer filtering functions enabled researchers to efficiently retrieve study data that meet specific criteria, significantly enhancing data processing speed and advancing research progress. In a real-world application exploring the prognosis of non-small cell lung cancer, the database facilitated the rapid analysis of prognostic factors. Research findings indicated that factors such as tumor staging and comorbidities had a significant impact on patient survival rates, further demonstrating the database’s value in clinical big data mining. Conclusion The AI-driven lung cancer database enhances data management and analysis efficiency, providing strong support for large-scale clinical research, retrospective studies, and disease management. With the ongoing integration of large language models and multi-modal data, the database’s precision and analytical capabilities are expected to improve further, providing stronger support for big data mining and real-world research of lung cancer.

Objective:To analyze the association between moderate-to-vigorous-physical activity (MVPA) and obesity, poor sleep quality, as well as multimorbidity in 7- to 8-year-old children in Shanghai City.Methods:From September to November 2023, a cluster sampling method was used to select second-grade students from four primary schools in Jinshan District, Shanghai. Three-axis acceleration motion sensors (GT3X+, Acti-graph) were used to monitor daily physical activity for seven consecutive days. A multivariate logistic regression model was used to analyze the association between MVPA duration characteristics and obesity, poor sleep quality and multimorbidity in school-age children.Results:Of the 937 study participants, 512 (54.64%) were boys and 425 (45.36%) were girls. Among them, 89 (9.50%) were obese and 782 (83.46%) had poor sleep quality. A total of 77 cases (8.22%) were affected by obesity and poor sleep quality. The average daily MVPA time was (45.97±15.87) minutes, and the MVPA attainment rate was 17.18%. The multivariate logistic regression model analysis showed that, after adjusting for covariates, the daily average MVPA time was negatively associated with the risk of obesity ( OR=0.982, 95% CI: 0.968-0.997), as well as multimorbidity ( OR=0.981, 95% CI: 0.965-0.997). The risk of obesity, poor sleep quality and multimorbidity in <1 d was 2.228 ( OR=2.228, 95% CI: 1.398-3.549), 1.702 ( OR=1.702, 95% CI: 1.141-2.540) and 2.150 ( OR=2.150, 95% CI: 1.310-3.528) times higher than that in ≥1 d. Conclusion:Obesity, poor sleep quality and multimorbidity of school-age children are closely related to the level of moderate-to-vigorous physical activity.

Objective To investigate the clinical characteristics and prognostic factors of Staphylococcus aureus bloodstream infections in adult patients for improving clinical treatment and identifying potential interventions.Methods Clinical data of inpatients diagnosed with S.aureus bloodstream infection confirmed by blood culture in a hospital from January 2016 to December 2023 were retrospectively reviewed.The data included patient age,gender,history of hospital admission,department of admission,underlying diseases,primary infection,quick Pitt bacteremia score(qPitt),invasive treatment,empirical anti-infective treatment,and treatment outcomes.Patients were assigned to case group or control group according to whether they died in hospital in order to identify the prognostic factors of patient outcomes.Binary logistic regression analysis was used to identify independent prognostic factors.Results A total of 233 cases of S.aureus bacteremia were identified.Multivariate logistic regression analysis showed that age ≥ 70 years old(OR=4.725,95％CI:1.228-18.173,P=0.024),diabetes mellitus(OR=8.161,95％CI:1.954-34.086,P=0.004),Charlson comorbidity index(CCI)≥ 5(OR=7.672,95％CI:1.901-30.963,P=0.004),hospital infection(OR=7.853,95％CI:1.588-38.832,P=0.012),and qPitt ≥ 2(OR=23.189,95％CI:4.461-120.552,P＜0.001)were independent prognostic factors for poor outcome of patients with S.aureus bacteremia,while catheter-associated infection(OR=0.051,95％CI:0.005-0.579,P=0.016)was negatively correlated with mortality.Conclusions Advanced age,diabetes mellitus,high CCI,hospital infection,and high qPitt were independent prognostic factors for poor outcomes of patients with S.aureus bacteremia.The patients should be well managed by timely removal of eradicable lesions to improve patient outcomes.

Objective·To investigate the predictive value of the geriatric nutritional risk index(GNRI)for the occurrence of lung infection in hospitalized elderly patients with type 2 diabetes mellitus(T2DM).Methods·Elderly T2DM patients who were admitted to the Geriatric Department of Shanghai Tongren Hospital between June 2022 and June 2024 were retrospectively and consecutively enrolled.They were divided into infected and non-infected groups according to whether lung infection occurred during hospitalization.Baseline data(gender,age,height,weight,duration of diabetes,comorbidities,etc.)were collected and GNRI was calculated.A multivariate Logistic regression model was used to screen the independent risk factors for pulmonary infections,and the predictive value of GNRI for pulmonary infections in T2DM patients was analysed using receiver operating characteristic(ROC)curves.Results·A total of 264 elderly T2DM patients were enrolled,among whom 154 developed pulmonary infections.Significant differences were observed between the infected and non-infected groups in GNRI,albumin,leukocyte count,neutrophil ratio,lymphocyte ratio,glycated hemoglobin,fasting glucose,interleukin-6,C-reactive protein,and procalcitonin levels(P<0.05).Multivariate Logistic regression analysis showed that a lower GNRI was an independent risk factor for lung infection(OR=0.798,95%CI 0.712?0.894;P<0.001).Correlation analysis showed that GNRI was negatively correlated with C-reactive protein and calcitoninogen.ROC curve analysis showed that GNRI predicted pulmonary infection with an area under the curve of 0.828,a sensitivity of 77.9%,and a specificity of 76.6%.Conclusion·A lower GNRI is an independent risk factor for pulmonary infections in elderly T2DM patients,and also has a good predictive value for the occurrence of pulmonary infections.

The prevention and treatment of esophageal cancer constitute an important component of China's cancer control efforts.As the earliest established cancer prevention and control field site in China,the Linzhou Esophageal Cancer Prevention and Treatment Site has developed a sys-tematic prevention and control strategy encompassing tumor registration,etiological research,early diagnosis and treatment,and other key areas,providing valuable experience for refining China's cancer prevention system.Based on over six decades of practical experience at the Linzhou site,this paper reviews its historical development,the construction of its prevention-control system,and emerging challenges in the new era,while exploring its reference significance for China's na-tionwide cancer prevention and control initiatives.

Objective·To investigate the predictive value of the geriatric nutritional risk index(GNRI)for the occurrence of lung infection in hospitalized elderly patients with type 2 diabetes mellitus(T2DM).Methods·Elderly T2DM patients who were admitted to the Geriatric Department of Shanghai Tongren Hospital between June 2022 and June 2024 were retrospectively and consecutively enrolled.They were divided into infected and non-infected groups according to whether lung infection occurred during hospitalization.Baseline data(gender,age,height,weight,duration of diabetes,comorbidities,etc.)were collected and GNRI was calculated.A multivariate Logistic regression model was used to screen the independent risk factors for pulmonary infections,and the predictive value of GNRI for pulmonary infections in T2DM patients was analysed using receiver operating characteristic(ROC)curves.Results·A total of 264 elderly T2DM patients were enrolled,among whom 154 developed pulmonary infections.Significant differences were observed between the infected and non-infected groups in GNRI,albumin,leukocyte count,neutrophil ratio,lymphocyte ratio,glycated hemoglobin,fasting glucose,interleukin-6,C-reactive protein,and procalcitonin levels(P<0.05).Multivariate Logistic regression analysis showed that a lower GNRI was an independent risk factor for lung infection(OR=0.798,95%CI 0.712?0.894;P<0.001).Correlation analysis showed that GNRI was negatively correlated with C-reactive protein and calcitoninogen.ROC curve analysis showed that GNRI predicted pulmonary infection with an area under the curve of 0.828,a sensitivity of 77.9%,and a specificity of 76.6%.Conclusion·A lower GNRI is an independent risk factor for pulmonary infections in elderly T2DM patients,and also has a good predictive value for the occurrence of pulmonary infections.

Objective:To explore the interrelationship between brain functional networks and features in functional magnetic resonance imaging(fMRI)of patients with Alzheimer's disease(AD),and to construct mixed-function networks(MFN),and apply them in machine learning classification models to improve the accuracy of AD classification.Methods:102 AD patients and 227 healthy subjects in the Alzheimer's Neuroimaging Initiative(ADNI)dataset were retrospectively analyzed.The partial correlation brain network of the blood oxygen level dependent(BOLD)signal was calculated and fused with low-frequency wave amplitude(ALFF),fractional low-frequency wave amplitude(fALFF)and local consistency(ReHo)features to construct MFN.Network topology parameters were extracted,and a variety of machine learning classification models were constructed based on MFN topological parameters,accuracy,precision,recall and area under the curve(AUC)were used to evaluate the predictive efficiency of the models.Results:By constructed MFN and calculated intra group to inter group ratio(IIGR),35 features could be obtained from ALFF,fALFF and ReHo feature topological parameter analysis,after rank sum test and FDR correction,there were statistical differences among 28 features(P＜0.05).The classification results show that,all the five classifiers have high classification performance on the test data set.The accuracy,precision and recall rates of random forest(RF),adaptive lifting algorithm(AdaBoost),guided aggregation algorithm(Bagging)and support vector machine(SVM)were all 99.7％,and the AUC values were up to 100％,99.5％,99.1％and 99.5％,respectively.The accuracy(98.5％),precision(98.5％),recall(98.5％),and AUC(99.1％)of the multi-layer perceptron(MLP)were slightly lower than other models,but remained excellent.It was worth noting that RF has the highest AUC value of all models at 100.0％,while Bagging has the lowest AUC value(99.1％)in the integrated approach.The results of performance comparison show that,MFN classification model can significantly improve the recognition and classification of AD disease,and greatly improve the performance of various indicators of the classifier.The results showed that,MFN classification model was superior to intelligent classification based fusion,DBN-based multitask learning,PVT-TSVM,unsupervised learning and clustering,SVM and SVM of degree 3 polynomial kernel function in key indicators such as accuracy(99.13％),AUC(99.42％),recall rate(99.46％)and specificity(99.42％)with plasma proteins,machine learning algorithms.It was further proved that MFN classification model has good generalization ability and robustness in AD disease classification.Conclusion:The AD classification model constructed based on brain mixed function network topology parameters and machine learning can improve the accuracy of AD classification.

Aim To investigate the intervention effect of Rehmannia radix water extract on bleomycin(BLM)-induced pulmonary fibrosis in mice combined with metabolomics and to reveal the potential mechanism,in order to provide new ideas for clinical treatment of pul-monary fibrosis.Methods Male C57BL/6N mice were randomly divided into the control group,model group,pirfenidone group(positive control,PFD,270 mg·kg-1),and low dose(DH-L,4.55 g·kg-1)group,medium dose(DH-M,9.1 g·kg-1)group and high dose(DH-H,18.2 g·kg-1)group of Rehman-nia.Except for the control group,BLM(5 mg·kg-1)was instilled into the trachea to establish the model of pulmonary fibrosis in the other groups.The survival rate,lung index and blood oxygen saturation of mice in each group were evaluated.HE and Masson staining were used to observe the pathological changes of lung tissue.WBP was used to detect lung function.Flow cytometry was used to detect the apoptosis of primary lung cells,ROS and immune cells.ELISA was used to detect the levels of fibrosis markers and inflammatory factors(α-SMA,collagen Ⅰ,collagen Ⅲ,TGF-β1,TNF-α,IL-1 β,and IL-6).Biochemical method was employed to detect the contents of GSH-Px,T-SOD and MDA.Liquid chromatograph mass spectrometer(LC-MS)metabolomics was used to analyze the changes of serum metabolic profile.Results Water extract of Re-hmannia significantly increased the survival rate,oxy-gen saturation and lung function of mice with pulmona-ry fibrosis,reduced the lung coefficient,ameliorated pathological damage and collagen deposition in lung tissue,reduced the levels of apoptosis and oxidative stress,and down-regulated the levels of inflammatory factors in lung tissue.It regulated the levels of metabo-lites such as bile acid metabolism,sphingolipid metabo-lism,and unsaturated fatty acid metabolism.Conclu-sions Water extract of Rehmannia inhibits lung injury and collagen deposition in mice with pulmonary fibrosis by inhibiting inflammatory response,which may be a-chieved by regulating the levels of inflammatory factors through the metabolic pathways of bile acid and sphin-golipid.