ObjectiveThe rapid advancement of bioanalytical technologies has heightened the demand for high-throughput, label-free, and real-time cellular analysis. Electrochemical impedance spectroscopy (EIS) operating in the GHz frequency range (GHz-EIS) has emerged as a promising tool for characterizing cell suspensions due to its ability to rapidly and non-invasively capture the dielectric properties of cells and their microenvironment. Although GHz-EIS enables rapid and label-free detection of cell suspensions, significant challenges remain in interpreting GHz impedance data for complex samples, limiting the broader application of this technique in cellular research. To address these challenges, this study presents a novel method that integrates GHz-EIS with deep learning algorithms, aiming to improve the precision of cell suspension concentration identification and quantification. This method provides a more efficient and accurate solution for the analysis of GHz impedance data. MethodsThe proposed method comprises two key components: dielectric property dataset construction and backpropagation (BP) neural network modeling. Yeast cell suspensions at varying concentrations were prepared and separately introduced into a coaxial sensor for impedance measurement. The dielectric properties of these suspensions were extracted using a GHz-EIS dielectric property extraction method applied to the measured impedance data. A dielectric properties dataset incorporating concentration labels was subsequently established and divided into training and testing subsets. A BP neural network model employing specific activation functions (ReLU and Leaky ReLU) was then designed. The model was trained and tested using the constructed dataset, and optimal model parameters were obtained through this process. This BP neural network enables automated extraction and analytical processing of dielectric properties, facilitating precise recognition of cell suspension concentrations through data-driven training. ResultsThrough comparative analysis with conventional centrifugal methods, the recognized concentration values of cell suspensions showed high consistency, with relative errors consistently below 5%. Notably, high-concentration samples exhibited even smaller deviations, further validating the precision and reliability of the proposed methodology. To benchmark the recognition performance against different algorithms, two typical approaches—support vector machines (SVM) and K-nearest neighbor (KNN)—were selected for comparison. The proposed method demonstrated superior performance in quantifying cell concentrations. Specifically, the BP neural network achieved a mean absolute percentage error (MAPE) of 2.06% and an R² value of 0.997 across the entire concentration range, demonstrating both high predictive accuracy and excellent model fit. ConclusionThis study demonstrates that the proposed method enables accurate and rapid determination of unknown sample concentrations. By combining GHz-EIS with BP neural network algorithms, efficient identification of cell concentrations is achieved, laying the foundation for the development of a convenient online cell analysis platform and showing significant application prospects. Compared to typical recognition approaches, the proposed method exhibits superior capabilities in recognizing cell suspension concentrations. Furthermore, this methodology not only accelerates research in cell biology and precision medicine but also paves the way for future EIS biosensors capable of intelligent, adaptive analysis in dynamic biological research.

Objective: To investigate the current status of screen exposure among preschool children in Shanghai and its association with family screen environment, so as to provide a scientific basis for family screen management. Methods: Using a convenient sampling method, a total of 349 preschool children aged 4-6 years were selected from 36 kindergarten classes in Xuhui District and Pudong New Area in Shanghai during April to June in 2023. Demographic characteristics and family screen environment were surveyed through an online questionnaire. Screen exposure of children was assessed using a diary method, with parents recording the activities over a 7day period. Multiple Logistic regression analysis was employed to identify factors influencing childrens screen content. Results: The average daily screen exposure time for children was (61.2±40.2) minutes, with an average of (12.4±17.6) minutes spent on educational screen content, 80.8% predominantly watched noneducational screen content. The percentages of time spent on educational screen content for 4yearold boys, 4yearold girls, 5yearold boys, 5yearold girls, 6yearold boys, and 6yearold girls were 20.1%, 14.7%, 21.3%, 21.9%, 20.6%, and 26.9%, respectively. Multivariate Logistic regression showed that children aged 5yearold (OR=0.49, 95%CI=0.25-0.96) and 6yearold (OR=0.45, 95%CI=0.21-0.95) were negatively associated with more noneducational screen content (P<0.05). However, occasional (OR=2.02, 95%CI=1.09-3.75) and sometimes (OR=4.50, 95%CI=1.70-11.90) using electronic devices to calm young child when crying, as well as children using electronic devices without adult supervision (OR=1.81, 95%CI=1.01-3.24) were positively associated with more noneducational screen content (P<0.05). Conclusions Preschool children in Shanghai exhibit high exposure to noneducational screen content, and family screen environment and parentchild interaction are associated with noneducational screen exposure. Strategies for family screen management should be developed to regulate childrens screen exposure behaviors, allowing electronic devices to play a positive role in their developmental process.

The compound (E)-1-(4-(3-(5-chloro-6-oxo-3,6-dihydropyridin-1(2H)-yl)-3-oxo-propyl-1-ene-1-yl) phenyl)-3-(4-fluorophenyl) urea (C12), a novel derivative of piperlongumine previously synthesized by our research group, was investigated in this study to examine its effects on human non-small cell lung cancer cell line H1299 in vitro and elucidate its potential mechanism of action. The impact of C12 on the proliferation, migration, and invasion abilities of H1299 cells were assessed using methyl thiazolyl tetrazolium (MTT) assay, wound healing assay, cloning formation assay, and Transwell assay. Flow cytometry was employed to evaluate the influence of C12 on cell cycle progression, reactive oxygen species (ROS) production, mitochondrial membrane potential (MMP), and apoptosis induction in H1299 cells. Western blot analysis was conducted to investigate the expression levels of p21, Cyclin B1, CDK1, Bax, Bcl-2, JNK, p-JNK, Erk1/2, p-Erk1/2, p38 and p-p38 proteins for exploring the anti-tumor mechanism underlying C12's actions. The results demonstrated that C12 exerted inhibitory effects on the proliferation, migration, and invasion capacities of H1299 cells in a time-dependent and concentration-dependent manner. Moreover, C12 induced G2/M phase arrest in the cell cycle, reduced MMP levels, elevated ROS production, and triggered apoptotic processes. Flow cytometry analysis revealed that C12 downregulated Cyclin B1 and CDK1 protein expressions, resulting in G2/M phase arrest. C12 also upregulated Bax/Bcl-2 ratio, promoting apoptosis. Furthermore, C12 activated MAPK signaling pathway by enhancing phosphorylation levels of JNK, Erk1/2, and p38 proteins. In conclusion, C12 significantly suppressed proliferation, migration, and invasion capabilities while inducing cell cycle arrest and apoptosis in H1299 cells. These effects may be attributed to activation of the MAPK signaling pathway.

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.

Objective To estimate adolescents and children age using stepwise regression and machine learning methods based on the pulp and tooth volumes of the left maxillary central incisor and cuspid on cone beam computed tomography(CBCT)images,and to compare and analyze the estimation re-sults.Methods A total of 498 Shanghai Han adolescents and children CBCT images of the oral and maxillofacial regions were collected.The pulp and tooth volumes of the left maxillary central incisor and cuspid were measured and calculated.Three machine learning algorithms(K-nearest neighbor,ridge regression,and decision tree)and stepwise regression were used to establish four age estimation models.The coefficient of determination,mean error,root mean square error,mean square error and mean ab-solute error were computed and compared.A correlation heatmap was drawn to visualize and the monotonic relationship between parameters was visually analyzed.Results The K-nearest neighbor model(R2=0.779)and the ridge regression model(R2=0.729)outperformed stepwise regression(R2=0.617),while the decision tree model(R2=0.494)showed poor fitting.The correlation heatmap demon-strated a monotonically negative correlation between age and the parameters including pulp volume,the ratio of pulp volume to hard tissue volume,and the ratio of pulp volume to tooth volume.Con-clusion Pulp volume and pulp volume proportion are closely related to age.The application of CBCT-based machine learning methods can provide more accurate age estimation results,which lays a founda-tion for further CBCT-based deep learning dental age estimation research.

Objective To explore the medication rules of Professor HUANG Li for the treatment of recurrent angina pectoris after percutaneous coronary intervention(PCI)for coronary heart disease by data mining method.Methods The prescriptions for effective cases of recurrent angina pectoris after PCI for coronary heart disease treated by Professor HUANG Li in the outpatient department of China-Japan Friendship Hospital were collected.SPSS Statistics 26.0 software and SPSS Modeler 18.0 software were used for frequency statistics,analysis of the therapeutic actions,properties,flavors and meridian tropism of the prescribed herbs as well as association rule analysis,cluster analysis and factor analysis of the herbs.Results A total of 344 Chinese medicine prescriptions were obtained,involving 209 herbs,with a cumulative frequency of 5 874 times.The top 30 Chinese medicinals were named as the high-frequency Chinese medicines,and the herbs with the frequency over 100 times in descending order were Astragali Radix,Chuanxiong Rhizoma,Puerariae Lobatae Radix,Rhodiolae Crenulatae Radix et Rhizoma,Notoginseng Radix et Rhizoma,Poria,Dalbergiae Odoriferae Lignum,Atractylodis Macrocephalae Rhizoma,Curcumae Rhizoma,Sparganii Rhizoma,Dioscoreae Rhizoma,Citri Reticulatae Pericarpium,Pinelliae Rhizoma Praeparatum,Codonopsis Radix,and Glycyrrhizae Radix et Rhizoma.The high-frequency Chinese medicinals were mostly classified as blood-activating and stasis-resolving drugs and qi-replenishing drugs.The medicinal properties of the drugs were characterized by being warm,mild,or cold,the flavors were predominated by being sweet,pungent or bitter,and the medicinals usually had the meridian tropism of the spleen,lung and liver meridians.A total of 30 association rules were mined out,cluster analysis yielded 5 herbal groups,and factor analysis yielded 11 groups of common factors.Conclusion For the treatment of cardiovascular diseases,Professor HUANG Li follows the theory of qi,blood and water,and especially pays more attention to the ascending and descending of qi movement.For qi deficiency and blood stasis contribute to the basic pathogenesis of recurrent angina pectoris after PCI,the therapy of benefiting qi,activating blood and removing stasis is recommended.Moreover,the simultaneous regulation of five zang-organs and simultaneous use of the cold and warm herbs are performed,and the herbs of benefiting qi and invigorating spleen,resolving phlegm and inducing diuresis,tranquilizing mind,promoting qi and dissipating masses,and activating blood to eliminate stasis are used for adjuvant therapy.

Objective To observe the intervention effect and mechanism of Chinese herbal compound(Zhenyuan Granules)on overweight in mice caused by estrogen deficiency.Methods An ovariectomized female mouse model was established to observe the effects of intragastric 14 week administration of Zhenyuan Granules on body mass and fat accumulation in mice.The 3T3-L1 mouse preadipocyte model was constructed to observe the effect of Zhenyuan Granules intervention on adipogenic differentiation.The expressions of key genes/proteins regulating adipocyte formation and fat synthesis in mouse adipose tissue and 3T3-L1 cells was detected to explore the mechanism of lipid-lowering effect of Zhenyuan Granules in vivo and in vitro.Results Zhenyuan Granules significantly decreased the body mass gain(P＜0.01),perigonadal and inguinal fat indexes(P＜0.05),significantly inhibited the differentiation of 3T3-L1 preadipocytes into mature adipocytes(P＜0.01),significantly downregulated the expression levels of transcription factors peroxisome proliferators activated receptor γ(PPARγ)and sterol-regulatory element binding protein 1c(SREBP-1c)and enzymes acetyl-CoA carboxylase 1(ACC1)and fatty acid synthase(FAS)in adipose tissue and 3T3-L1 preadipocytes(P＜0.05),significantly enhanced the phosphorylation of AMP-activated protein kinase(AMPK)and ACC1(P＜0.05).Conclusion Zhenyuan Granules can inhibit the formation of adipocytes and improve the overweight of female mice caused by estrogen deficiency.The mechanism is related to the activation of AMPK signaling pathway.It is suggested that Bushen Jianpi Chinese herbal compound Zhenyuan Granules is helpful for body mass control in postmenopausal women.

With the in-depth study of molecular biology,non-small cell lung cancer(NSCLC)has opened the era of precision medicine based on mutation-based molecular targeting therapy.Epidermal growth factor receptor(EGFR)driver mutations are closely related to the progression of NSCLC,and EGFR-tyrosine kinase inhibitors(TKIs)developed based on this have achieved significant therapeutic effects,but acquired drug resistance is still one of the major factors limiting their long-term use.As resistance mechanisms are further investigated,in addition to secondary EGFR mutation,MET amplification,HER2 amplification,histologic transformation,etc.,receptor tyrosine kinase(RTK)fusion mutation have been shown to be a targetable mechanism of acquired resistance.Among the acquired RTK fusion mutations,rearranged during transfection(RET)fusion mutations are the accessible targets of our concern.As the RET molecule continues to be explored,drugs targeting RET fusions have been approved and marketed.There are different clinical strategies to deal with acquired RET fusion mutation mediating resistance to EGFR-TKIs treatment.In this review,the structure and function of RET,its relationship with EGFR-TKIs resistance,and treatment strategies are reviewed to further improve patient survival outcomes.

Objective To develop a health belief model(HBM)based adolescent alcohol-related cognition scale to measure adolescent alcohol-related cognition and test its reliability and validity.Methods The adolescents'alcohol-related cognitive scale was developed based on HBM model.By using purposive sampling,three general high schools in Qingpu District,Shanghai were selected.One-third of the classes from grades 10 and 11 in each school were randomly selected,and the students from these classes were surveyed as the research subjects.Exploratory factor analysis and confirmatory factor analysis were used to analyze its reliability(internal consistency reliability and combination reliability)and validity(structural validity,convergent validity,discriminative validity and criterion validity).Results A total of 970 questionnaires were collected,of which 948 were valid,with an effective rate of 97.7%.The adolescents'alcohol-related cognitive scale contained 22 items.Five common factors were extracted from exploratory factor analysis,including perceived susceptibility,perceived severity,perceived benefits,perceived obstacles,and self-efficacy.The cumulative variance contribution rate reached 83.89%.The results of confirmatory factor analysis confirmed the overall fit of the model.The average variance extracted value(AVE)of each dimension was greater than 0.5,and the convergent validity of the model was ideal.The AVE square root of each dimension of the scale was greater than its correlation coefficient,indicating good discrimination validity.Cronbach's α coefficient of the total volume table was 0.892,indicating good overall reliability.Conclusion The adolescents'alcohol-related cognitive scale developed in this study has good reliability and validity,which can be used to measure adolescents'alcohol-related perceptions.

Purpose/Significance To deeply analyze the research progress on the application of medical knowledge graph in the field of stroke,to discuss the problems of the development of stroke knowledge graph in China,and to put forward suggestions for the construc-tion of stroke knowledge graph.Method/Process By reviewing and analyzing the relevant literature,the application of medical knowledge graph in the field of stroke is sorted out and summarized.Result/Conclusion There are still many deficiencies in the development of stroke knowledge graph in China,and in the future,in-depth research can be carried out from four aspects,namely,expanding the ap-plication scope of knowledge graph,promoting the fusion of knowledge graph,developing more efficient algorithms,and upgrading to cog-nitive graph by joint artificial intelligence(AI).