ObjectiveTo establish an ultra-performance liquid fingerprint and multi-components determination method for Naomaili granules. To evaluate the quality of different batches by chemometrics， and the anti-neuroinflammatory effects of water extract and main components of Naomaili granules were tested in vitro. MethodsThe similarity and common peaks of 27 batches of Naomaili granules were evaluated by using Ultra performance liquid chromatography （UPLC） fingerprint detection. Ultra-performance liquid chromatography-tandem mass spectrometry （UPLC-MS/MS） technology was used to determine the content of the index components in Naomaili granules and to evaluate the quality of different batches of Naomaili granules by chemometrics. LPS-induced BV-2 cell inflammation model was used to investigate the anti-neuroinflammatory effects of the water extract and main components of Naomaili granules. ResultsThe similarity of fingerprints of 27 batches of samples was > 0.90. A total of 32 common peaks were calibrated， and 23 of them were identified and assigned. In 27 batches of Naomaili granules， the mass fractions of 14 components that were stachydrine hydrochloride， leonurine hydrochloride， calycosin-7-O-glucoside， calycosin，tanshinoneⅠ， cryptotanshinone， tanshinoneⅡ_A， ginsenoside Rb₁， notoginsenoside R₁， ginsenoside Rg₁， paeoniflorin， albiflorin， lactiflorin， and salvianolic acid B were found to be 2.902-3.498， 0.233-0.343， 0.111-0.301， 0.07-0.152， 0.136-0.228， 0.195-0.390， 0.324-0.482， 1.056-1.435， 0.271-0.397， 1.318-1.649， 3.038-4.059， 2.263-3.455， 0.152-0.232， 2.931-3.991 mg∙g^-1， respectively. Multivariate statistical analysis showed that paeoniflorin， ginsenoside Rg₁， ginsenoside Rb₁ and staphylline hydrochloride were quality difference markers to control the stability of the preparation. The results of bioactive experiment showed that the water extract of Naomaili granules and the eight main components with high content in the prescription had a dose-dependent inhibitory effect on the release of NO in the cell supernatant. Among them， salvianolic acid B and ginsenoside Rb₁ had strong anti-inflammatory activity， with IC₅₀ values of （36.11±0.15） mg∙L^-1 and （27.24±0.54） mg∙L^-1， respectively. ConclusionThe quality evaluation method of Naomaili granules established in this study was accurate and reproducible. Four quality difference markers were screened out， and eight key pharmacodynamic substances of Naomaili granules against neuroinflammation were screened out by in vitro cell experiments.

Purpose: The aim of this study was to investigate the feasibility of an intelligent handheld ultrasound (US) device for assisting non-expert general practitioners (GPs) in detecting carotid plaques (CPs) in community populations. Methods: This prospective parallel controlled trial recruited 111 consecutive community residents. All of them underwent examinations by non-expert GPs and specialist doctors using handheld US devices (setting A, setting B, and setting C). The results of setting C with specialist doctors were considered the gold standard. Carotid intima-media thickness (CIMT) and the features of CPs were measured and recorded. The diagnostic performance of GPs in distinguishing CPs was evaluated using a receiver operating characteristic curve. Inter-observer agreement was compared using the intragroup correlation coefficient (ICC). Questionnaires were completed to evaluate clinical benefits. Results: Among the 111 community residents, 80, 96, and 112 CPs were detected in settings A, B, and C, respectively. Setting B exhibited better diagnostic performance than setting A for detecting CPs (area under the curve, 0.856 vs. 0.749; P<0.01). Setting B had better consistency with setting C than setting A in CIMT measurement and the assessment of CPs (ICC, 0.731 to 0.923). Moreover, measurements in setting B required less time than the other two settings (44.59 seconds vs. 108.87 seconds vs. 126.13 seconds, both P<0.01). Conclusion Using an intelligent handheld US device, GPs can perform CP screening and achieve a diagnostic capability comparable to that of specialist doctors.

Objective To investigate the effect of endothelial cells in the perivascular niche on the stem cell-like characteristics of osteosarcoma cells and primarily explore the possible molecular mechanism.Methods A co-culture model was established in vitro using SV40T-human umbilical vein endothelial cells(HUVEC-T1)and osteosarcoma stem cells(OSCs)derived from the human osteosarcoma cell line 143B.Thus,there were 2 groups of cells,OSCs and co-cultured OSCs.The self-renewal capacity of OSCs between the 2 groups was assessed using a limiting dilution forming sphere assay.Flow cytometry and Western blotting were used to detect the expression of stem cell marker CD133 and stemness transcription factors SOX2 and NANOG.Fourteen female nude mice(4～6 weeks old,weighing 18～20 g)were randomly and equally divided into 2 groups of subcutaneous xenograft models:the control group(OSCs suspension)and the experimental group(OSCs+HUVEC-T1 mixed suspension).The tumor volume and mass were compared between the 2 xenograft groups.Immunofluorescence(IF)staining was used to verify the spatial proximity between endothelial cells and OSCs in vivo,while immunohistochemistry was employed to compare microvessel density(MVD)and CD133 expression level between the 2 groups.RNA-seq was performed to identify potential signaling pathways of endothelial cells affecting the stemness of OSCs.PCR and Western blotting were applied to confirm the RNA-seq findings.Exogenous protein treatment,IF staining,Western blotting and sphere formation assay were utilized to preliminarily validate the role of the identified pathway in regulating the stemness phenotype of OSCs.Results The in vitro co-culture model of HUVEC-T1 and OSCs was successfully established.Compared with the control group,the co-culture group exhibited significantly enhanced self-renewal ability of OSCs,laeger proportion of the stemness marker CD133+[(8.20±1.64)％vs(4.32±1.34)％,P<0.05],enhanced expression of CD117,SOX2 and NANOG(P<0.05),along with more sphere formation(P<0.05)and elevated SOX2/NANOG protein levels.The xenograft mice from the experimental group showed larger tumor volume(643.10±413.50 vs 247.90±93.66 mm3,P<0.05)and heavier tumor weight(0.52±0.27 vs 0.24±0.10 g,P<0.05)when compared the control group,correspondingly showing increased MVD(22.57±11.84 vs 11.43±5.38,P<0.05)and elevated CD133 expression(P<0.05).IF staining confirmed the adjacency of CD31-labeled endothelial cells and CD133-labeled OSCs in vivo.RNA-seq and functional experiments demonstrated that CXCR4 was highly expressed in co-cultured OSCs,CXCL12 was highly expressed in co-cultured endothelial cells,and exogenous CXCL12 promoted the sphere formation and expression levels of SOX2 and NANOG of OSCs(P<0.05).Conclusion Endothelial cells within the perivascular niche may promote the stemness phenotype of osteosarcoma cells via the CXCL12/CXCR4 pathway.

Compared to traditional machine learning algorithms,which often suffer from low feature extraction efficiency,insufficient nonlinear pattern recognition capabilities and slow training speeds,the broad learning system(BLS)enhances the learning ability and efficiency by horizontally expanding the network structure.BLS offers advantages such as a simple structure,fast training speed,and strong generalization capabilities.While BLS has demonstrated potential in various fields,but its application in identification of complex samples has not been fully explored.This research investigated the feasibility of using BLS algorithm for identification of complex samples based on physicochemical indicators.Using the iris,wine,and breast cancer datasets,the length and width of petals and sepals of iris flowers,the physicochemical properties of wine,and the nuclear characteristics of breast cancer cells were used as input variables to establish BLS models for identifying iris species,wine varieties,and benign versus malignant nucleus.The model performance was evaluated by confusion matrices,accuracy,and runtime.Compared with partial least squares-discriminant analysis(PLS-DA),soft independent modeling of class analogies(SIMCA),and artificial neural networks(ANN),the results indicated that BLS demonstrated significant advantages in computational efficiency and recognition accuracy.Thus,BLS was an efficient and reliable method for identification of complex samples.

Objective To explore a deep learning-based automatic bone age estimation model for elbow joint X-ray images of Chinese Han adolescents and children and evaluate its performance.Methods A total of 943(517 males and 426 females)elbow joint frontal view X-ray images of Chinese Han ado-lescents and children aged 6.00 to<16.00 years were collected from East,South,Central and North-west China.Three experimental schemes were adopted for bone age estimation.Scheme 1:Directly in-put preprocessed images into the regression model;Scheme 2:Train a segmentation network using"key elbow joint bone annotations"as labels,then input segmented images into the regression model;Scheme 3:Train a segmentation network using"full elbow joint bone annotations"as labels,then in-put segmented images into the regression model.For segmentation,the optimal model was selected from U-Net,UNet++and TransUNet.For regression,VGG16,VGG19,InceptionV2,InceptionV3,ResNet34,ResNet50,ResNet101 and DenseNet121 models were selected for bone age estimation.The dataset was randomly split into 80%(754 samples)for training and validation for model fitting and hyperparameter tuning,and 20%(189 samples)as an internal test set to test the performance of the trained model.An additional 104 elbow joint X-ray images from the same demographic and age group were col-lected and used as an external test set.Model performance was evaluated by comparing the mean ab-solute error(MAE),root mean square error(RMSE),accuracies within±0.7 years(P±0.7 years)and±1.0 years(P±1.0 years)between the estimated age and the actual age,and by drawing radar charts,scat-ter plots,and heatmaps.Results When segmented with Scheme 3,the UNet++model achieved good segmentation performance with a segmentation loss of 0.000 4 and an accuracy of 93.8%at a learning rate of 0.000 1.In the internal test set,the DenseNet121 model with Scheme 3 yielded the best results with MAE,P±0.7 years and P±1.0 years being 0.83 years,70.03%,and 84.30%,respectively.In the external test set,the DenseNet121 model with Scheme 3 also performed best,with an average MAE of 0.89 years and an average RMSE of 1.00 years.Conclusion When performing automatic bone age estima-tion using elbow joint X-ray images in Chinese Han adolescents and children,it is recommended to use the UNet++model for segmentation.The DenseNet121 model with Scheme 3 achieves optimal per-formance.Using segmentation networks,especially that trained with annotation areas encompassing the full elbow joint including the distal humerus,proximal radius,and proximal ulna,can improve the ac-curacy of bone age estimation based on elbow joint X-ray images.

Objective To explore a deep learning network model suitable for bone age estimation using shoulder joint X-ray images in Chinese Han adolescents.Methods A retrospective collection of 1 286 shoulder joint X-ray images of Chinese Han adolescents aged 12.0 to<18.0 years(708 males and 578 females)was conducted.Using random sampling,approximately 80%of the samples(1 032 cases)were selected as the training and validation sets for model learning,selection and optimization,and the other 20%samples(254 cases)were used as the test set to evaluate the model's generalization ability.The original single-channel shoulder joint X-ray images and dual-channel inputs combining original images with segmentation labels(manually annotated shoulder joint regions multiplied pixel-by-pixel with original images,followed by segmentation via the U-Net++network to retain only key shoulder joint region information)were respectively input into four network models,namely VGG16,ResNet18,ResNet50 and DenseNet121 for bone age estimation.Additionally,manual bone age estimation was con-ducted on the test set data,and the results were compared with the four network models.The mean absolute error(MAE),root mean square error(RMSE),coefficient of determination(R2),and Pear-son correlation coefficient(PCC)were used as main evaluation indicators.Results In the test set,the bone age estimation results of the four models with dual-channel input of shoulder joint X-ray images outperformed those with single-channel input in all four evaluation indicators.Among them,DenseNet121 with dual-channel input achieved best results with MAE of 0.54 years,RMSE of 0.82 years,R2 of 0.76,and PCC(r)of 0.88.Manual estimation yielded an MAE of 0.82 years,ranking second only to dual-channel DenseNet121.Conclusion The DenseNet121 model with dual-channel input combined with original images and segmentation labels is superior to manual evaluation results,and can effectively estimate the bone age of Chinese Han adolescents.

Cancer is the main cause of death,and drug therapy has greatly improved the effectiveness of anti-tumor treatment.However,there are problems such as high adverse reactions and the risk of developing drug resistance after long-term use.There is an urgent need to seek new drug targets.Human antigen R(HuR),as an RNA binding protein,promotes the whole process of tumor occurrence,development and metastasis through post transcriptional regulation of mRNA stability,and HuR is general-ly highly expressed in tumor tissue,making it a new target for an-ti-tumor therapy and a standard for prognosis evaluation.Tradi-tional Chinese medicine formulas and their various chemical components can inhibit tumor proliferation,induce tumor cell ap-optosis,inhibit angiogenesis,suppress immune escape,and re-verse tumor drug resistance by regulating HuR activity.This re-view summarizes the importance of HuR in regulation of tumor progression,as well as analyzes the mechanisms of the antitumor effects through active ingredients of Chinese medicine with the regulation of HuR.It is expected to provide new ideas for tumor therapy and guidance for the development of HuR-targeted anti-tumor drugs.

Cancer is the main cause of death,and drug therapy has greatly improved the effectiveness of anti-tumor treatment.However,there are problems such as high adverse reactions and the risk of developing drug resistance after long-term use.There is an urgent need to seek new drug targets.Human antigen R(HuR),as an RNA binding protein,promotes the whole process of tumor occurrence,development and metastasis through post transcriptional regulation of mRNA stability,and HuR is general-ly highly expressed in tumor tissue,making it a new target for an-ti-tumor therapy and a standard for prognosis evaluation.Tradi-tional Chinese medicine formulas and their various chemical components can inhibit tumor proliferation,induce tumor cell ap-optosis,inhibit angiogenesis,suppress immune escape,and re-verse tumor drug resistance by regulating HuR activity.This re-view summarizes the importance of HuR in regulation of tumor progression,as well as analyzes the mechanisms of the antitumor effects through active ingredients of Chinese medicine with the regulation of HuR.It is expected to provide new ideas for tumor therapy and guidance for the development of HuR-targeted anti-tumor drugs.

Objective:To explore the effect of self-compassion-based writing on prosocial behavior in college students with different self-construals.Methods:A total of 182 college students were recruited,and the Self-Con-structional Scales were used to determine the self-construal types(independent or interdependent).Participants were then assigned to either a self-compassion-based writing group(n=91)or a non-self-compassion daily writing group(n=91)and completed writing tasks according to the respective instructions.Prosocial behavior was evaluated with the Prosocial Tendencies Measure(PTM),situational judgment tests,and donation amounts.Results:The evaluation results from the PTM showed that prosocial behavior was significantly higher in individuals with interdependent self-construal compared to those with independent self-construal(P＜0.001),the main effect of the group type was not statistically significant(P＞0.05).The evaluation results from situational judgment tests showed that prosocial behavior was significantly higher in individuals with interdependent self-construal compared to those with independ-ent self-construal(P＜0.001),the self-compassion-based writing group demonstrated significantly more prosocial behavior than the non-self-compassion daily writing group(P＜0.05).The evaluation results from donation a-mounts showed that the main effect of self-construal type was not statistically significant(P＞0.05),the self-com-passion-based writing group demonstrated significantly more prosocial behavior than the non-self-compassion daily writing group(P＜0.05).Conclusion:Compared to daily writing,self-compassion-based writing could effectively increase prosocial behavior in college students,regardless of their self-construal type.

Objective To investigate the incidence of placental abruption in the third trimester of pregnancy in Hebei Province,identify its associated risk factors,and develop a corresponding nomogram prediction model.Methods Data from pregnant women at 22 monitored hospitals in Hebei Province,collected between 2013 and 2023,were analyzed to assess the incidence,trends,and associated risk factors of placental abruption.A prediction model was developed and visualized using R programming to generate the receiver operating characteristic(ROC)curve.The model's predictive performance was evaluated using the area under the curve(AUC)and calibration curve parameters.Results A total of 480 690 pregnant women were included in this study over the specified period.The incidence of placental abruption was 0.29%(1 395 out of 480 690),with an average annual percentage change of 2.73%,indicating a stable trend(P=0.34).Multivariate logistic regression analysis revealed that pregnancy-related anemia,preeclampsia,placenta previa,vaginal bleeding before 28 weeks'gestation,and between 28 and 31+6 weeks'gestation were significant risk factors for placental abruption(all P<0.05).In contrast,regular prenatal examinations(7～11 visits)and multiple pregnancies were identified as protective factors against placental abruption(both P<0.05).The area under the curve(AUC)for the nomogram model constructed based on independent risk factors for placental abruption was 0.79,and the calibration curve demonstrated that the predicted values closely aligned with the observed values.Conclusions Prenatal examinations should be prioritized,especially for women with preeclampsia,placenta previa,or a history of vaginal bleeding before 28 weeks of gesta-tion.Management of pregnancies between 28 and 31+6 weeks is also crucial to reduce the incidence of placental abruption and mitigate adverse maternal and neonatal outcomes.The nomogram model constructed based on these factors exhibits excellent predictive performance,providing a solid theoretical foundation for the prevention and clinical management of placental abruption.