Objective To predict the radiation impact of discharging wastewater containing uranium within the specified limit generated during the normal operation of a new production line at a nuclear fuel plant on the receiving water body and its downstream, and to provide a reference for the management of radioactive liquid effluent discharge from nuclear facilities. Methods Based on the technical guidelines for environmental impact assessment, literature on radiation environmental impact assessment, and data collected from on-site investigations, appropriate hydrological parameters and prediction models were selected to analyze and predict the variation pattern of radioactive nuclide uranium along the receiving water body and the radiation exposure of nearby residents. Results The maximum increase in uranium concentration in the receiving water body and its downstream caused by the discharge of uranium-containing wastewater was 1.14 μg/L. The maximum predicted concentration was 2.75 μg/L after adding the background data of the water body. The resulting maximum individual annual effective dose for the public was 1.49 × 10⁻⁴ mSv/a. Conclusion The maximum predicted uranium concentration in the receiving water body and its downstream is lower than the uranium concentration limit of 30 μg/L specified in the Standards for Drinking Water Quality (GB 5749-2022). The maximum individual annual effective dose for the public is much lower than the control value of 0.2 mSv/a specified in the Radiation Protection Regulations for Uranium Processing and Fuel and Fuel Manufacturing Facilities (EJ 1056-2018). The radiation impact is acceptable.

Synthetic data, generated using advanced artificial intelligence (AI) techniques, replicates the statistical properties of real-world datasets while excluding identifiable information.Although synthetic data does not consist of actual data points, it is derived from original datasets, thereby enabling analyses that yield results comparable to those obtained with real data. Synthetic datasets are evaluated based on their utility—a measure of how effectively they mirror real data for analytical purposes. This paper presents the generation of synthetic datasets through the Healthcare Big Data Showcase Project (2019–2023). The original dataset comprises comprehensive multi-omics data from 400 individuals, including cancer survivors, chronic disease patients, and healthy participants. Synthetic data facilitates efficient access and robust analyses, serving as a practical tool for research and education. It addresses privacy concerns, supports AI research, and provides a foundation for innovative applications across diverse fields, such as public health and precision medicine.

Synthetic data, generated using advanced artificial intelligence (AI) techniques, replicates the statistical properties of real-world datasets while excluding identifiable information.Although synthetic data does not consist of actual data points, it is derived from original datasets, thereby enabling analyses that yield results comparable to those obtained with real data. Synthetic datasets are evaluated based on their utility—a measure of how effectively they mirror real data for analytical purposes. This paper presents the generation of synthetic datasets through the Healthcare Big Data Showcase Project (2019–2023). The original dataset comprises comprehensive multi-omics data from 400 individuals, including cancer survivors, chronic disease patients, and healthy participants. Synthetic data facilitates efficient access and robust analyses, serving as a practical tool for research and education. It addresses privacy concerns, supports AI research, and provides a foundation for innovative applications across diverse fields, such as public health and precision medicine.

Synthetic data, generated using advanced artificial intelligence (AI) techniques, replicates the statistical properties of real-world datasets while excluding identifiable information.Although synthetic data does not consist of actual data points, it is derived from original datasets, thereby enabling analyses that yield results comparable to those obtained with real data. Synthetic datasets are evaluated based on their utility—a measure of how effectively they mirror real data for analytical purposes. This paper presents the generation of synthetic datasets through the Healthcare Big Data Showcase Project (2019–2023). The original dataset comprises comprehensive multi-omics data from 400 individuals, including cancer survivors, chronic disease patients, and healthy participants. Synthetic data facilitates efficient access and robust analyses, serving as a practical tool for research and education. It addresses privacy concerns, supports AI research, and provides a foundation for innovative applications across diverse fields, such as public health and precision medicine.

Synthetic data, generated using advanced artificial intelligence (AI) techniques, replicates the statistical properties of real-world datasets while excluding identifiable information.Although synthetic data does not consist of actual data points, it is derived from original datasets, thereby enabling analyses that yield results comparable to those obtained with real data. Synthetic datasets are evaluated based on their utility—a measure of how effectively they mirror real data for analytical purposes. This paper presents the generation of synthetic datasets through the Healthcare Big Data Showcase Project (2019–2023). The original dataset comprises comprehensive multi-omics data from 400 individuals, including cancer survivors, chronic disease patients, and healthy participants. Synthetic data facilitates efficient access and robust analyses, serving as a practical tool for research and education. It addresses privacy concerns, supports AI research, and provides a foundation for innovative applications across diverse fields, such as public health and precision medicine.

Synthetic data, generated using advanced artificial intelligence (AI) techniques, replicates the statistical properties of real-world datasets while excluding identifiable information.Although synthetic data does not consist of actual data points, it is derived from original datasets, thereby enabling analyses that yield results comparable to those obtained with real data. Synthetic datasets are evaluated based on their utility—a measure of how effectively they mirror real data for analytical purposes. This paper presents the generation of synthetic datasets through the Healthcare Big Data Showcase Project (2019–2023). The original dataset comprises comprehensive multi-omics data from 400 individuals, including cancer survivors, chronic disease patients, and healthy participants. Synthetic data facilitates efficient access and robust analyses, serving as a practical tool for research and education. It addresses privacy concerns, supports AI research, and provides a foundation for innovative applications across diverse fields, such as public health and precision medicine.

This paper applied gas chromatography-mass spectrometry (GC-MS), network pharmacology and nuclear magnetic resonance hydrogen spectroscopy (¹H NMR) metabolomics techniques to study the material basis and mechanism of action of Ning Shen Essential Oil in anti-insomnia. The main volatile components of Ning Shen Essential Oil were analyzed by gas chromatography-mass spectrometry (GC-MS), and the insomnia-related targets were predicted using the Traditional Chinese Medicine Systematic Pharmacology Database and Analytical Platform (TCMSP) and the databases of GeneCards, OMIM and Drugbank. The insomnia model of rats was replicated by intraperitoneal injection of 4-chloro-DL-phenylalanine (PCPA). Animal experiments were approved by the Animal Ethics Committee of Shandong University of Traditional Chinese Medicine (Ethics No.: SDUTCM20221025010). The modulating effect of Compound Ning Shen Essential Oil on anxiety behavior of rats was evaluated by behavioral related indexes. The serum levels of corticotropin-releasing hormone (CRH), adrenotropic corticotropic hormone (ACTH) and melatonin (MT) were measured by enzyme-linked immunoassay (ELISA). Rat serum and hippocampus were taken for nuclear magnetic resonance (¹H NMR) metabolomics to detect the changes of endogenous metabolites in rat serum hippocampus, to designate the differential metabolites and to construct metabolic pathways. The results showed that the exercise distance in the open field experiment and the number of times and time to enter the open arm in the elevated cross maze experiment of the rats in the model group were significantly reduced (P < 0.05, P < 0.01). The behavioral indexes of rats improved to different degrees after the administration of Ning Shen Essential Oil. The serum CRH and ACTH levels of rats in the model group increased significantly (P < 0.05, P < 0.01), and the MT level decreased significantly (P < 0.01); After the intervention, serum CRH and ACTH levels were reduced to different degrees, and MT levels could be regressed. ¹H NMR metabolomics screened 10 potential biomarkers related to insomnia, which involved in 6 potential metabolic pathways. A total of 35 components of Ning Shen Essential Oil were detected by GC-MS, the main component targets of Ning Shen Essential Oil and insomnia disease targets were intersected, a total of 172 intersecting genes were screened, and 26 core targets were identified. The study demonstrated that Ning Shen Essential Oil had protective effects against PCPA-induced insomnia in rats, which was probably correlated with regulation of the hypothalamic-pituitary-adrenal axis (HPA) related hormones and metabolism of amino acids, lipids and choline.

BACKGROUND:Osteonecrosis due to drugs is a serious adverse reaction occurring after the application of such drugs.Increasing evidence suggests that the gut microbiota composition is associated with osteonecrosis due to drugs.However,the causal relationship of the gut microbiota to osteonecrosis due to drugs is still unclear. OBJECTIVE:To evaluate the potential causal relationship between the gut microbiota and the risk of osteonecrosis due to drugs using the Mendelian randomization method. METHODS:A two-sample Mendelian randomization study was performed using the summary statistics of gut microbiota from the largest available genome-wide association study meta-analysis(n=13 266)conducted by the MiBioGen consortium as well as the summary statistics of osteonecrosis due to drugs obtained from the FinnGen consortium R9 release data(264 cases and 377 013 controls).Inverse variance weighted,MR-Egger,weighted median,weighted model and simple model were used to examine the causal association between gut microbiota and osteonecrosis due to drugs.Sensitivity analysis was used to test whether the results of the Mendelian randomization analysis were reliable.Reverse Mendelian randomization analysis was performed on all the bacteria as an outcome for effect analysis and sensitivity analysis. RESULTS AND CONCLUSION:Inverse variance weighted estimates suggested that Lentisphaerae(phylum),Lentisphaeria(class),Melainabacteria(class),Gastranaerophilales(order),Rhodospirillales(order),Victivallales(order)and Bifidobacterium(genus)had protective causal effects on osteonecrosis due to drugs.Methanobacteria(class),Bacillales(order),Methanobacteriaceae(family),Lachnospiraceae(family),Methanobacteriales(order),Holdemania(genus),Holdemania(UCG010 group)(genus),Odoribacter(genus)and Tyzzerella3(genus)had negative causal effects on osteonecrosis due to drugs.According to the results of reverse Mendelian randomization analysis,Clostridiaceae1(family),Peptostreptococcaceae(family),Streptococcaceae(family),Clostridiumsensustricto1(genus)and Streptococcus(genus)showed negative causal effects on osteonecrosis due to drugs.However,Eisenbergiella(genus)showed protective causal effects on osteonecrosis due to drugs.None of the bidirectional sensitivity analysis revealed heterogeneity or horizontal pleiotropy.When gut microbiota were used as exposure and osteonecrosis due to drugs as the outcome,Mendelian randomization analysis found that seven bacterial traits were positively correlated to osteonecrosis due to drugs,nine bacterial traits were negatively related to osteonecrosis due to drugs.When osteonecrosis due to drugs were used as exposure and gut microbiota as the outcome,reverse Mendelian randomization analysis found a negative correlated relationship with five bacterial traits and a positive causal relationship with one bacterial trait.By changing the diversity and composition of gut microbiota,it is expected to improve the incidence and prognosis of osteonecrosis due to drugs,providing new ideas for the study of orthopedic diseases.

Objective To investigate the efficacy and safety of intensity-modulated radiation therapy combined with camrelizumab in the treatment of advanced hepatocellular carcinoma(HCC).Methods A total of 84 patients with advanced HCC admitted to our hospital from January to December 2020 were selected as the study objects,and were randomly divided into the observation group and the control group,with 42 cases in each group.Patients in the observation group received intensity-modulated radiation therapy combined with carrelli-zumab,and patients in the control group received intensity-modulated radiation therapy.The short-term efficacy,immune function and long-term survival rate of patietns in the two groups were compared,and the incidence of adverse reactions was recorded.Results The total effec-tive rates of the observation group 1 month and 3 months after treatment were significantly higher than those of the control group(P<0.05).The levels of CD3+,CD4+ and CD4+/CD8+ 1 month and 3 months after treatment were all increased in the two groups,while the levels of CD8+ in both two groups were decreased(P<0.05),and the levels of CD3+,CD4+ and CD4+/CD8+ in the observation group were higher than those in the control group(P<0.05),and the levels of CD8+ in the observation group were lower than those in the control group(P<0.05).The median survival time of patients in the observation group was significantly longer than that of patients in the control group(P<0.05).The incidence of cutaneous capillary hyperplasia in the observation group was higher than that in the control group(P<0.001),and there was no significant difference in the incidence of other adverse reactions between the two groups(P>0.05),and all of adverse reactions were grades 1 to 2.Conclusion Intensity-modulated radiation therapy combined with camrelizumab has a good effect on HCC,it can improve the immune function of the body,and control the development of the disease,with good safety.

Objective Analyzes the grouping effect and its influencing factors under DRG payment,provides reference for the reform of DRG payment.Methods Evaluates the effectiveness of DRG grouping using Coefficient of Variation(CV)and Reduction in Variance;using Value of Structure of Variation and Degree of Structure Variation,analyzes hospitalization costs structure changes of different DRG groups,and calculates the degree of correlation between average hospitalization costs through grey relational analysis;using non parametric tests and multiple regression to analyze the influencing factors of hospitalization cost.Results DRG grouping effect was not good,inter-group heterogeneity was not obvious;the structure of hospitalization expenses is unreasonable,and the proportion of consumables expenses is too high,ranking first in the grey correlation degree of hospitalization expenses,comprehensive medical service fees and treatment fees rank third and fifth respectively;the main factors affecting hospitalization costs are treatment methods,length of stay,presence of complications,and first hospitalization,the difference is statistically significant(P＜0.05).Conclusion More grouping nodes or higher CV value standards should be added to enhance the grouping effect of gastric cancer DRG;optimize the structure of hospitalization costs to reflect the labor and technical value of medical personnel;strengthen internal management and control the unreasonable use of drugs and consumables.