Objective To provide precise,effective,and intuitive nutritional and dietary recommendations for different population groups,a multi-modal nutritional knowledge graph was constructed,which includes entities such as food,nutrition,population,and diseases.Methods Data sets in the field of nutrition were obtained using web crawling and other technical means.The OneRel model was referenced to complete the joint extraction of Chinese entity relationships and construct a text library.The RoBERTa-ResNet model were used to learn the features of text and image data separately,to align images with text,and to construct a multi-modal knowledge graph.Results The F1 value of the joint entity relationship extraction model was 0.703.The constructed multi-modal knowledge graph contains 3312 textual entities,11 259 relationships,and 1000 image entities.Conclusion The algorithms used in this study to construct the multi-modal nutritional knowledge graph achieve good results.This knowledge graph not only systematically integrates multi-modal knowledge in the field of nutrition and enables good visual query capabilities,but also serves as the underlying support for downstream tasks such as intelligent question answering and nutritional recommendation systems.

ObjectiveTo explore the biological mechanism of bone loss caused by perfluorooctanoic acid (PFOA) through transcriptomic analysis, and to provide new insights into regulating perfluoroalkyl substances (PFAS) applications and the prevention of hazards affecting bone health. MethodsMouse embryonic osteoblast precursor cells (MC3T3-E1) were exposed to 0.1, 1, 10, and 100 μmol·L^-¹ PFOA for 24 hours to assess the effects on cell viability and alkaline phosphatase (ALP) activity, and to determine the critical concentration of PFOA toxicity. The transcriptome sequencing (RNA-seq) was performed to identify differentially expressed genes (DEGs) induced by PFOA. Gene ontology (GO) analysis and gene set enrichment analysis (GSEA) were conducted to identify significantly affected gene pathways. Additionally, Seahorse XF metabolic phenotyping and reverse transcription polymerase chain reaction (RT-PCR) were used to validate the key pathways. ResultsExposure to 10 and 100 μmol·L^-¹ PFOA significantly reduced the cell viability and ALP activity of MC3T3-E1 cells. Therefore, the results of transcriptomic analysis for 10 μmol‧L^-1 PFOA exposure found that a total of 80 DEGs were identified, including 32 upregulated genes and 48 downregulated genes. According to GO analysis, PFOA mainly affected cellular components such as mitochondrion and nucleus, molecular functions involving GTPase activity and GTP binding, as well as biological process related to mRNA processing. GSEA identified the downregulation of the β-oxidation of fatty acid pathway in mitochondria. Metabolic phenotyping reserches showed that PFOA indeed reduced mitochondrial aerobic respiration capacity and adenosine triphosphate (ATP) production, and the ratio of ATP production from cellular aerobic respiration to glycolysis was significantly decreased as well. The mRNA expression of glucose metabolism-related genes (GK, G6PD, and CS), as well as fatty acid metabolism-related genes (CPT1A and CPT2), were significantly downregulated. ConclusionPFOA reduces bone formation by inhibiting energy metabolism and β-oxidation of fatty acid pathways in osteoblasts, whihc lays the foundation for revealing the mechanism of PFOA exposure induced bone loss.

Objective To explore the value of clinical-MRI radiomics combined model for differentiating borderline ovarian tumor(BOT)from epithelial ovarian cancer(EOC).Methods Totally 139 patients with BOT(BOT group)and 307 patients with EOC(EOC group)confirmed by postoperative pathology and underwent preoperative pelvic MRI were retrospectively enrolled and randomly divided into training set(n=312)and test set(n=134)at a ratio of 7∶3.Multivariable logistic regression was used to identify independent clinical predictors for differentiating BOT and EOC,then a clinical model was constructed.Radiomics features were extracted from the volumes of interest(VOI)of lesions on T2WI,diffusion weighted imaging(DWI)and apparent diffusion coefficient(ADC)images,respectively,and single-sequence and multi-sequence MRI radiomics models were built using extreme gradient boosting(XGBoost)based on data in training set.The optimal MRI radiomics model was selected according to the highest area under the curve(AUC)in test set,and a clinical-MRI radiomics combined model was constructed combined the optimal radiomics model with independent clinical predictors.The performances of clinical model,the optimal MRI radiomics model and the combined model for differentiating BOT and EOC were compared in test set.SHapley Additive exPlanations(SHAP)analysis was applied to interpret key predictive features in the best model.Results Patients' age,carbohydrate antigen 153(CA153)and carbohydrate antigen 125(CA125)were all independent predictors for differentiating BOT and EOC(all P＜0.05).Multi-sequence MRI radiomics model was the optimal MRI radiomics model.The combined model showed superior performance(AUC=0.929)for discriminating BOT and EOC compared with clinical model(AUC=0.881)and multi-sequence MRI radiomics model(AUC=0.871)(both P＜0.05).SHAP beeswarm plot revealed that the top 10 important features of combined model included age,CA153 and CA125,as well as entropy,kurtosis and gray level non-uniformity from ADC and DWI sequences.Conclusion Clinical-MRI radiomics combined model based on multi-sequence MRI radiomics features and clinical features could be used to effectively differentiate BOT from EOC.

Objective To provide precise,effective,and intuitive nutritional and dietary recommendations for different population groups,a multi-modal nutritional knowledge graph was constructed,which includes entities such as food,nutrition,population,and diseases.Methods Data sets in the field of nutrition were obtained using web crawling and other technical means.The OneRel model was referenced to complete the joint extraction of Chinese entity relationships and construct a text library.The RoBERTa-ResNet model were used to learn the features of text and image data separately,to align images with text,and to construct a multi-modal knowledge graph.Results The F1 value of the joint entity relationship extraction model was 0.703.The constructed multi-modal knowledge graph contains 3312 textual entities,11 259 relationships,and 1000 image entities.Conclusion The algorithms used in this study to construct the multi-modal nutritional knowledge graph achieve good results.This knowledge graph not only systematically integrates multi-modal knowledge in the field of nutrition and enables good visual query capabilities,but also serves as the underlying support for downstream tasks such as intelligent question answering and nutritional recommendation systems.

Objective To explore the value of clinical-MRI radiomics combined model for differentiating borderline ovarian tumor(BOT)from epithelial ovarian cancer(EOC).Methods Totally 139 patients with BOT(BOT group)and 307 patients with EOC(EOC group)confirmed by postoperative pathology and underwent preoperative pelvic MRI were retrospectively enrolled and randomly divided into training set(n=312)and test set(n=134)at a ratio of 7∶3.Multivariable logistic regression was used to identify independent clinical predictors for differentiating BOT and EOC,then a clinical model was constructed.Radiomics features were extracted from the volumes of interest(VOI)of lesions on T2WI,diffusion weighted imaging(DWI)and apparent diffusion coefficient(ADC)images,respectively,and single-sequence and multi-sequence MRI radiomics models were built using extreme gradient boosting(XGBoost)based on data in training set.The optimal MRI radiomics model was selected according to the highest area under the curve(AUC)in test set,and a clinical-MRI radiomics combined model was constructed combined the optimal radiomics model with independent clinical predictors.The performances of clinical model,the optimal MRI radiomics model and the combined model for differentiating BOT and EOC were compared in test set.SHapley Additive exPlanations(SHAP)analysis was applied to interpret key predictive features in the best model.Results Patients' age,carbohydrate antigen 153(CA153)and carbohydrate antigen 125(CA125)were all independent predictors for differentiating BOT and EOC(all P＜0.05).Multi-sequence MRI radiomics model was the optimal MRI radiomics model.The combined model showed superior performance(AUC=0.929)for discriminating BOT and EOC compared with clinical model(AUC=0.881)and multi-sequence MRI radiomics model(AUC=0.871)(both P＜0.05).SHAP beeswarm plot revealed that the top 10 important features of combined model included age,CA153 and CA125,as well as entropy,kurtosis and gray level non-uniformity from ADC and DWI sequences.Conclusion Clinical-MRI radiomics combined model based on multi-sequence MRI radiomics features and clinical features could be used to effectively differentiate BOT from EOC.

In the process of achieving the"Healthy China 2030"goal,the importance of health knowledge popularization behavior is increasingly prominent.Medical communication has emerged to meet the growing demand for medical popularization and improve the shortage of professional medical staff in medical knowledge popularization.At present,literature is relatively scarce on the development process of medical communication in the academic community.By analyzing the development process of this discipline,this paper divided the development process of the knowledge system of medical communication into three stages,including the"initial stage",the"practical exploration stage",and the"rapid development stage".After summarizing the different characteristics exhibited in the three stages,it can be concluded that the number of colleges and universities offering medical communication courses is gradually increasing,and social organizations such as the Society of Medical Communication have been established one after another,expanding the influence of the discipline.The development of medical communication not only helps to enhance the popularization and communication abilities of medical students,but also accelerates the development of China's health industry through its extensive influence at the practical level.

With the rapid development of the biopharmaceutical industry in China, the laboratory animal sector has entered a stage of rapid growth, and the construction of facility operation systems has become increasingly automated and intelligent. Compared to traditional laboratory animal facilities, new facilities require a more specialized technical team for the maintenance of air supply and exhaust systems, air conditioning, automated control, and the entire barrier system. The Lingang Laboratory’s animal facilities accommodate both large and small animals for feeding and experimental purposes. The facility management team has summarized daily maintenance experiences and explored various operational and maintenance modes based on the characteristics of laboratory operation. After analyzing the advantages and disadvantages of three common modes, this paper provides new ideas for the management of these laboratory animal facilities, and offers guidance for peers in choosing the most appropriate professional maintenance mode.

Objective:To observe any effects of repetitive high-frequency transcranial magnetic stimulation (rTMS) on the upper limb motor function of stroke survivors with right hemiplegia.Methods:Forty stroke survivors with right hemiplegia were divided at random into a high-frequency rTMS group and a sham stimulation group, each of 20. In addition to routine rehabilitation, the high-frequency rTMS group was given daily high-frequency rTMS 5d per week for 2 weeks, while the sham stimulation group was provided with sham rTMS. Before and after the treatment, both groups were evaluated using the Fugl-Meyer Upper Extremity motor function evaluation scale (FMA-UE), surface electromyography (sEMG), and electroencephalographic microstatus testing. Any adverse reactions in the course of the treatment were recorded.Results:After the treatment, the average FMA-UE scores of both groups had improved significantly, with the average of the high-frequency rTMS group significantly higher than the other group′s average. After the treatment the peak-to-peak sEMG value of the radial long extensor carpi radialis longus muscle in the high-frequency rTMS group was significantly higher than before the treatment and significantly higher than that of the other group. The temporal coverage of microstate B, the average duration and temporal coverage of microstate C, and the temporal coverage and frequency of occurrence of microstate D after treatment of both groups were also significantly improved. The mean duration of electroencephalographic (EEG) microstate A was negatively correlated with the FMA-UE scale scores ( r=-0.57) and its temporal coverage was positively correlated with the peak-to-peak sEMG value of the ulnar lateral wrist flexor. The mean duration of EEG microstate B was positively correlated with the peak-to-peak sEMG value of the triceps brachii and deltoid, and the mean duration of EEG microstate C was also positively correlated with the peak-to-peak sEMG value of the deltoid muscle. Conclusions:High-frequency rTMS can effectively improve the upper limb motor functioning of stroke survivors with right hemiparesis. After high-frequency rTMS, the functional network activity related to EEG microstate B increases significantly, while that related to microstates C and D decreases significantly.

ObjectiveBased on network pharmacology and molecular docking techniques， the mechanism of essential oil from Chimonanthus nitens leaves （CLO） in the treatment of acute lung injury （ALI） was predicted， and a rat model of ALI was established to verify the mechanism of CLO. MethodThe composition of CLO was determined by gas chromatography-mass spectrometry （GC-MS）. The component targets were obtained from PharmMapper and SwissTargetPrediction databases， ALI-related targets were obtained from GeneCards， Online Mendelian Inheritance in Man （OMIM） and DisGeNET， cross-over analysis with differential expressed genes （DEGs） of ALI obtained from Gene Expression Omnibus （GEO） on the Venny 2.1.0 platform yielded potential anti-ALI targets of CLO. Protein-protein interaction （PPI） analysis of potential targets was carried out by STRING 11.5. The tissue expression profiles of potential targets were obtained from the National Center for Biotechnology Information （NCBI） and the target tissue distribution maps were constructed. Potential targets were analyzed for Gene Ontology （GO） function and Kyoto Encyclopedia of Genes and Genomes （KEGG） pathway enrichment by RStudio 4.0.0 software. Composition-target-pathway network was constructed by Cytoscape 3.9.1 software， and key components and pathways were screened out and verified by molecular docking. ALI model was established by lipopolysaccharide （LPS） induction， levels of interleukin （IL）-6 and tumor necrosis factor （TNF）-α in serum of rats were measured， the expression levels of IL-17 protein in the lung tissue of ALI rats were analyzed by immunohistochemistry. ResultA total of 19 components of CLO were identified by GC-MS， and 18 potential targets were obtained by target screening. After PPI analysis， 15 target proteins with interaction relationship were obtained， further analysis showed that they were highly expressed in lung and thymus. The network diagram of component-target-pathway was analyzed to obtain the key components， including bornyl acetate， linalool， elemol， geranyl isobutyrate， and the core targets of matrix metalloproteinase 13 （MMP13）， S100 calcium binding protein A9 （S100A9）， spleen tyrosine kinase （SYK）， as well as the main signaling pathways， such as IL-17 and TNF. The results of molecular docking showed that the key components were stably bound to MMP13 and S100A9 of IL-17 signaling pathway. The results of pharmacological experiment confirmed that CLO could significantly inhibit the expression of IL-6 and TNF-α in serum of ALI rats， and decrease the expression of IL-17 protein in lung tissue of ALI rats. ConclusionCLO can achieve the therapeutic effect on ALI and protect lung tissue， the mechanism may be related to the decrease of the expression of IL-6 and TNF-α in serum and the inhibition of the activation of IL-17 signaling pathway in lung tissue of ALI rats.

With the arrival of aging, various industries in society are facing great pressure, especially higher requirements for the medical and nursing industries. The integrated eldercare services with medical care breaks through the traditional endowment pattern and integrates medical treatment, elderly care, rehabilitation, and nursing. However, many factors hinder the development of this model at present, and it requires the cooperation of the country and various departments to improve. From the perspective of a Healthy China, based on the release and implementation of policies such as the "Healthy China 2030" Plan Outline and the "Healthy China Action (2019-2030)" , this paper briefly summarized the current status and existing problems of the development of integrated eldercare services with medical care in China, and put forward rectification suggestions, aiming to provide reference to build a new endowment pattern of integrated eldercare services with medical care with Chinese characteristics.