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.

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.

Cyclic GMP-AMP synthase(cGAS)is a congenital immune sensor that can recognize cytoplasm abnormal dsDNA.By catalyzing the second messenger cyclic GMP-AMP(cGAMP)formation,it activates stimulator of interferon genes(STING),releases type Ⅰ interferon and inflammatory cytokines,activates the host immune response,and participates in cerebral ischemia reperfusion injury(CIRI)cascade reaction.This article reviews the research progress of the mechanism of cGAS-STING signaling pathway participation in CIRI,hoping to provide ideas for its treatment.

Objective To investigate the impact of eccentric placement for various types of artificial aortic valves on downstream flow dynamics.Methods A physiological pulsatile circulation simulation system was employed and particle image velocimetry(PIV)was utilized to analyze the downstream flow field variations for bioprosthetic and mechanical valves under two placement conditions:centralized placement(0 mm)and eccentric placement(1 mm).Hemodynamic parameters such as velocity,vorticity,and viscous shear stress were assessed to evaluate the flow field characteristics.Results By analyzing the flow field variations at four characteristic time points,namely,early systole,acceleration phase,peak systole,and deceleration phase,a significant difference in flow field distribution between bioprosthetic and mechanical valves was observed.The bioprosthetic valve exhibited a centrally symmetric jet with a higher flow velocity,whereas the mechanical valve displayed a three-jet structure with a lower central flow velocity.Under eccentric placement,the blood flow in the aortic sinus region was sluggish,with a reduction in average velocity,hindering the formation and maintenance of vortices.During the peak systolic phase,the maximum viscous shear stresses in the sinus region for the bioprosthetic and mechanical valves were 0.45 and 0.67 Pa,respectively,approaching the threshold for endothelial cell damage.Conclusions Eccentric placement of both mechanical and bioprosthetic valves resulted in reduced sinus blood flow velocity and diminished viscous shear stress,creating favorable conditions for thrombus formation.In clinical practice,careful attention should be given to the placement of valve replacement to prevent eccentric placement.

Objective:To construct a predictive model for benign and malignant colorectal lesions based on modal ultrasound parameters and clinical indicators,and evaluate the effectiveness of the predictive model.Methods:Clinical data of 198 patients with colorectal lesions treated in Hebei Petro China Central Hospital from March 2020 to March 2024 were recorded.According to pathological diagnosis,they were grouped into a benign lesion group of 102 cases and a malignant lesion group of 96 cases.All patients underwent multimodal ultrasound examination.Multivariate Logistic re-gression analysis was applied to screen the influencing factors of colorectal cancer progression.R software package was applied to build a nomogram prediction model.Hosmer-Lemeshow test,calibration curve,ROC curve,and clinical deci-sion curve were used for validation.Results:There were statistically obvious differences in internal echo,morphology,blood flow signal,rise time(RT),contrast agent of"fast in and fast out",mean Young's modulus(Emean),age,positive fecal occult blood,and polyps between the benign and malignant lesion groups(P＜0.05).Uneven internal echoes,irregu-lar shapes,abundant blood flow signals,contrast agent of"fast in and fast out",age ≥ 60 years,positive fecal occult blood,and polyps ≥ 2 were independent risk factors for colorectal cancer(P＜0.05),while RT is a protective factor for colorectal cancer(P＜0.05).The internal validation results of the nomogram prediction model showed that the Hosmer-Lemeshow test showed x2=3.661 and P=0.886.The calibration curve showed that the actual probability was basically consistent with the predicted probability,and the AUC of the ROC curve was 0.802(95％CI:0.732-0.871),indicating that the calibration and discrimination of the column chart prediction model were good.Within the high-risk threshold range of 0.28-0.98,the decision curve was above the All line and None line,indicating a high net benefit and clinical practicality.Conclusion:Internal echo,morphology,blood flow signal,contrast agent of"fast in and fast out",age,positive fecal occult blood,polyps,and RT are influencing factors for the occurrence of colorectal cancer.The column chart prediction model constructed based on this has good predictive performance and provides reference for early inter-vention by clinical physicians.

BACKGROUND:Preliminary research by our group suggests that targeting fibroblast growth factor receptor 1(FGFR1)may be an effective strategy for treating RA. OBJECTIVE:To investigate the effects of an FGFR1 inhibitor(PD173074)on bone destruction in rats with collagen-induced arthritis. METHODS:Twenty-five female Sprague-Dawley rats were randomly divided into five groups:normal control group,model group,methotrexate group,low-dose PD173074 group,and high-dose PD173074 group.Except for the normal control group,rat models of type Ⅱ collagen-induced arthritis were made in each group.After successful modeling,rats were injected intraperitoneally with sterile PBS in the normal and model groups,1.04 mg/kg methotrexate in the methotrexate group,and 5 and 20 mg/kg in the low-dose group and high-dose PD173074 groups,once a week.After 4 weeks of drug administration,clinical symptoms and joint swelling in rats were observed.Micro-CT was used for three-dimensional reconstruction and analysis of the ankle joints.Pathological changes in the ankle joints were observed.Periarticular angiogenesis and the expression of receptor activator of nuclear factor-Κb ligand were detected.The expression levels of p-FGFR1,vascular endothelial growth factor A,and tartrate-resistant acid phosphatase in the synovial membrane were measured.Pathological changes in the liver,spleen,and kidney were observed and liver,spleen,and kidney indices were calculated. RESULTS AND CONCLUSION:PD173074 could alleviate clinical symptoms and joint swelling,delay bone loss,improve bone structure,reduce synovial invasion and cartilage bone erosion,reduce the number of periarticular osteoclasts,inhibit angiogenesis in synovial tissues,reduce the expression of receptor activator of nuclear factor-Κb ligand,and inhibit the expression of FGFR1 phosphorylated protein,tartrate-resistant acid phosphatase and vascular endothelial growth factor A.Pathologic observation of the liver,spleen and kidney in rats showed no obvious toxic side effects after PD173074 treatment.To conclude,the FGFR1 inhibitor can delay the progression of joint inflammation and bone destruction and inhibit angiogenesis in the rat model of type Ⅱ collagen-induced arthritis.The therapeutic effect of PD173074 has been preliminarily validated in the type Ⅱ collagen-induced arthritis model and may act by inhibiting FGFR1 phosphorylation,which provides a direction for the search of new therapeutic targets for rheumatoid arthritis.

BACKGROUND:Although researchers have noted that fibroblast growth factor receptor 1 shows great potential in rheumatoid arthritis bone destruction,there is a lack of reviews related to the potential mechanisms of fibroblast growth factor receptor 1 in rheumatoid arthritis bone destruction. OBJECTIVE:To comprehensively analyze the mechanism of fibroblast growth factor receptor 1 in bone destruction in rheumatoid arthritis by reviewing the relevant literature at both home and abroad. METHODS:We searched the CNKI database using the Chinese search terms"fibroblast growth factor receptor 1,rheumatoid arthritis,bone destruction,bone cells,osteoblasts,osteoclasts,chondrocytes,macrophages,synovial fibroblasts,T cells,vascular endothelial cells."PubMed database was searched using the English search terms"fibroblast growth factor receptor 1,rheumatoid arthritis,bone destruction,osteocytes,osteoblasts,osteoclasts,chondrocytes,macrophages,synovial fibroblasts,T cells,endothelial cells."The search period focused on April 1992 to January 2024.After screening the literature by reading titles,abstracts,and full texts,a total of 82 articles were finally included for review according to inclusion and exclusion criteria. RESULTS AND CONCLUSION:Fibroblast growth factor receptor 1 was found to be widely expressed in bone tissue-associated cells,including osteoblasts,osteoclasts,and osteoclasts.Fibroblast growth factor receptor 1 affects bone remodeling and homeostasis by regulating the function of these cells,as well as promoting the onset and progression of bone destruction in rheumatoid arthritis.Fibroblast growth factor receptor 1 is involved in the inflammatory response of synovial fibroblasts and macrophages and regulates angiogenesis of endothelial cells in synovial tissues.Fibroblast growth factor receptor 1 promotes bone destruction in several ways.Fibroblast growth factor receptor 1 may be a potential causative agent of bone destruction in rheumatoid arthritis and provides a reference for further research on its therapeutic targets.

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.

Objective To investigate the effect of dorsal repulsive axon guidance protein(Draxin)on the migration of trunk neural crest cells during the early development of embryonic mouse spinal cord.Methods Immunohistochemistry and in situ hybridization were used to detect the expression characteristics of Draxin in early embryonic spinal cord(8 mice each group);In situ hybridization was used to detect the change of migration characteristics of trunk neural crest cells in early embryonic spinal cord of different types of mouse(5 mice each group);in vitro culture method was used to check the effect of Draxin on the migration characteristics of embryonic mouse trunk neural crest cells(16 mice each group).Resultsβ-galactosidase gene Z(LacZ)gene was introduced when Draxin gene was knocked out to produce Draxin gene knockout mice.β-galactosidase staining was used to detect LacZ gene expression in Draxin knockout embryonic mice,and the result showed that Draxin expression was observed in the spinal cord of early embryonic mice since 9.5 days(E9.5).Draxin expression was obvious in the embryonic mice spinal cord in E10.5 period.In situ hybridization was used to detect the expression of Draxin gene in the spinal cord of wild type embryonic mice,and the result further verified the obvious expression of Draxin in the early embryonic mice spinal cord in El0.5 period.Sox10 in situ hybridization was used to detect neural crest cell migration in the spinal cord of embryonic mice in E10.5 period.The result showed that segmental migration of neural crest cells in the early embryonic spinal cord of some Draxin knockout mice was delayed compared with the wild type mice.The effect of Draxin on the migration of wild type early embryonic mice trunk neural crest cells in vitro was tested.The result showed that Draxin reduced the migration distance of neural crest cells in vitro.Conclusion In the early developmental stage of embryonic spinal cord(E9.5-E10.5),neural crest cells migrated exuberant.At the same time,Draxin plays an important inhibitory function in the formation of the specific migration pathways of trunk neural crest cells by promoting neural crest cells migrating away from Draxin expressing regions.