Objective: To explore the association between parent-child connectedness and romantic relationships of secondary vocational school students and the moderating effect of peers romantic behavior, providing scientific basis for family and school health education. Methods: From March to April 2021，2 426 students from six secondary vocational and technical schools in Shanghai and Shaanxi Province were selected to conduct the survey by combining convenience sampling and cluster sampling.Electronic questionnaires were used to collect data on students family characteristics,oneself and peer romantic behaviors, and parent-child bonding. The t-test was employed for inter group comparisons, and binary Logistic regression analysis was conducted to examine the relationship between parent-child bonding levels, peer romantic behavior, and the romantic behavior of secondary vocational students. Results: The mother-child connection (2.63±0.77) was higher than that of father-child connection (2.48±0.78), with statistically significant difference ( t =6.83, P <0.01). Multivariable Logistic regression showed that overall father-child connectedness was negatively associated with students romantic relationships( OR =0.86，95% CI =0.76-0.97， P =0.02)and was only associated to girls romantic relationships when stratified by gender( OR =0.79，95% CI =0.66-0.93， P =0.01). Peers romantic relationships were positively associated with students romantic relationships ( OR =3.19-5.12, all P <0.01), and there was a moderating effect of the association between maternal connectedness and boys romantic relationships ( OR =1.67, 95% CI =1.05-2.66, P =0.03). Among boys without romantic peers, mother-child connectedness was negatively associated with their romantic relationships ( OR = 0.60 , 95% CI =0.36-0.99, P <0.05). In the total sample of Shanghai and girls of Shaanxi, father-child connectedness was negatively correlated with the romantic relationships of secondary vocational school students ( OR =0.84,0.65,95% CI =0.71-1.00,0.50-0.85,both P <0.05). Peer romantic relationships exhibited a negative moderating effect on the influence of mother-child connectedness on the romantic relationships of males in Shanghai ( OR =1.91, 95% CI =1.03-3.57, P <0.05). Conclusions The father-daughter connectedness is negatively correlated with girls romantic behavior, and peer romantic behavior weakens the correlation between mother-child connectedness and boys romantic behavior. Efforts should be made to enhance the parent-child connectedness of secondary vocational students and their ability to cope with peer influence, providing proper guidance for adolescents heterosexual interactions.

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 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.

Fritillariae Cirrhosae Bulbus is the dried bulb of perennial herbaceous plants in the Fritillaria genus(Liliaceae family) and is a representative traditional Chinese medicinal material with distinctive regional characteristics. Clinically, it is widely used in the treatment of dry cough, bronchial asthma, and other respiratory diseases, possessing significant medicinal and economic value and being highly esteemed in TCM. Currently, Fritillariae Cirrhosae Bulbus primarily relies on wild harvesting. However, due to excessive collection, its wild resources have drastically declined, and all source species have been classified as category Ⅱ in the List of National Key Protected Wild Plants, exacerbating the supply-demand imbalance in the market. To mitigate this issue, large-scale cultivation through the domestication of wild Fritillariae Cirrhosae Bulbus has become an inevitable trend. However, its strict environmental requirements, low propagation efficiency, high seedling mortality, and immature cultivation techniques have severely hindered industrialization. This study investigates the domestication process of Fritillariae Cirrhosae Bulbus, focusing on seed propagation, seedling cultivation, and medicinal material production. It also reviews the species and distribution of wild resources, their endangered status, market supply-demand dynamics, and the historical and current development of domestication. The findings indicate that enhancing propagation efficiency, optimizing cultivation models, and distinguishing between seed propagation and medicinal material production are key measures to accelerate the industrialization of domesticated Fritillariae Cirrhosae Bulbus. This research aims to promote the industrialization of Fritillariae Cirrhosae Bulbus domestication and provide a reference model for the conservation and sustainable utilization of rare and endangered medicinal plant resources.
Fritillaria/chemistry* ; Endangered Species ; Plants, Medicinal/growth & development* ; Drugs, Chinese Herbal/economics* ; China

Objective To address personnel management challenges in large comprehensive hospitals by developing a comprehensive personnel information management system for refined multi-campus administration.Methods A centralized data-base was employed to construct a personnel information management system compatible with both"interactive management"and"independent management"modes.The system progressively implemented functions including personnel information manage-ment,meal card and subsidy administration,and shift scheduling.Results The system achieved effective interconnections be-tween subsystems,significantly improving personnel management efficiency,data governance,risk prevention capabilities,and operational decision-making.Personnel data were efficiently utilized across multiple scenarios.Conclusion The multi-campus comprehensive personnel information management system meets the refined requirements of multi-campus personnel administration and provides valuable experience for the development and expansion of subsequent hospital operation management information sys-tems.

Objective To address personnel management challenges in large comprehensive hospitals by developing a comprehensive personnel information management system for refined multi-campus administration.Methods A centralized data-base was employed to construct a personnel information management system compatible with both"interactive management"and"independent management"modes.The system progressively implemented functions including personnel information manage-ment,meal card and subsidy administration,and shift scheduling.Results The system achieved effective interconnections be-tween subsystems,significantly improving personnel management efficiency,data governance,risk prevention capabilities,and operational decision-making.Personnel data were efficiently utilized across multiple scenarios.Conclusion The multi-campus comprehensive personnel information management system meets the refined requirements of multi-campus personnel administration and provides valuable experience for the development and expansion of subsequent hospital operation management information sys-tems.

Objective To investigate the risk factors for pneumonia complicating infectious mononucleosis(IM)in adults and construct a nomogram prediction model.Methods A retrospective analysis was conducted on 198 IM patients admitted to the Second Affiliated Hospital of Air Force Medical University from January 2015 to December 2021.Patients were divided into pneumonia group(n=52)and non-pneumonia group(n=146)based on whether pulmonary infection occurred during hospitalization.The baseline data(age,gender,place of onset,etc.),clinical manifestations(maximum body temperature,lymph node enlargement,splenomegaly,etc.),and inflammatory indicators[white blood cell count(WBC),C-reactive protein(CRP),etc.]were compared between the two groups.Kaplan-Meier curves were plotted to analyze the key indicators affecting the hospital stay of IM patients.Multivariate logistic regression was used to analyze the independent risk factors for pneumonia complicating IM in adults and construct a nomogram prediction model based on the identified risk factors.The predictive efficacy of the model was evaluated using the receiver operating characteristic(ROC)curve and the consistency of the model was assessed using the calibration curve.The fit of the model was evaluated using the Hosmer-Lemeshow test.Additionally,the sensitivity,specificity,and accuracy of the model were assessed using confusion matrix.Results Compared with non-pneumonia group,the pneumonia group had a significantly higher proportion of patients from rural areas,with body mass index(BMI)≥24 kg/m2,smoking history,hepatomegaly,fever duration of≥7 d,as well as increased total hospitalization costs and average daily hospitalization costs,and prolonged hospital stay(P<0.05).The proportion of patients with a history of antibiotic use was lower in the pneumonia group(P<0.05).Kaplan-Meier survival analysis showed that patients from rural areas,with BMI≥24 kg/m2,smoking history,no prophylactic use of antibiotics,fever duration≥7 d,and hepatomegaly had significantly prolonged hospital stays(P<0.05).Multivariate logistic regression analysis revealed that living in a rural area(OR=4.089,P<0.05),hepatomegaly(OR=4.082,P<0.05),and elevated WBC(OR=1.205,P<0.05)were independent risk factors for pneumonia complicating IM in adults,while the prophylactic use of antibiotics(OR=0.142,P<0.05)was an independent protective factor.The area under the ROC curve of the constructed nomogram prediction model was 0.827(95%CI 0.762-0.892),and the slope of the calibration curve was close to 1,and the Hosmer-Lemeshow test showed χ2=5.299,P=0.725,indicating good consistency and fit of the prediction model.The results of the confusion matrix assessment showed that the sensitivity of the model was 0.669(0.624-0.773),the specificity was 0.827(0.724-0.930),and the accuracy was 0.732(0.665-0.793).Conclusion The nomogram prediction model based on place of onset,hepatomegaly,the prophylactic use of antibiotics and WBC has excellent fit and discrimination,providing an effective quantitative tool for prognosis assessment of IM.

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.

Objective To investigate the inhibitory effects of Wumeiwan on oxidative stress injury of ulcerative colitis mice induced by dextran sulfate sodium(DSS)by regulating Kelch-like ECH related protein 1(Keap-1)-nuclear factor E2 related factor 2(Nrf2)/heme oxygenase-1(HO-1)signaling pathwayand.Methods Forty C57BL/6 mice were randomly divided into five groups:normal group,model group,positive control group,experimental-L,-H groups.UC mice model were induced by free access to 2％DSS water.Mice in normal and model group were orally administered with 0.9％NaCl,mice in positive control group were orally treated with Mesalazine solution(0.005 g·10 g-1·d-1),while mice in experimental groups were orally administered with Wumeiwan decoction at the dose of 0.13 and 0.26 g·10 g-1·d-1,respectively.All the drugs were administered for consecutive 7 days,1 times a day.The levels of disease activity index(DAI)and the colon length were scored.The levels of superoxide dismutase(SOD),catalase(CAT),cyclooxygenase-2(COX-2)and inducible nitric oxide synthase(iNOS)in colon tissue of mice were determined by real-time fluorescence quantitative polymerase chain reaction(qRT-PCR)method.The level of Keap-1,Nrf2,HO-1 proteins in colon tissue were determined by Western blot method.Results The levels of DAI of seventh day in normal group,positive control group,experimental-L,-H groups were 0、(2.62±0.33),(1.87±0.35),(1.87±0.35)and(1.58±0.35);the colon lengths were(8.16±0.47)、(5.98±0.24),(7.58±0.38),(7.33±0.24)and(7.48±0.51)cm;the SOD mRNA were 1.01±0.16、0.40±0.01,1.43±0.45,0.65±0.01 and 0.83±0.02;the CAT mRNA were 1.01±0.20、0.45±0.01,0.84±0.02,0.68±0.07 and 0.87±0.05;the COX-2 mRNA were 1.03±0.33、16.65±0.60,4.78±0.25,14.07±0.60 and 7.39±0.15;the iNOS mRNA were 1.04±0.40、20.71±0.66,8.09±0.93,15.44±0.68 and 11.66±0.06;the levels of Keap-1 were 1.22±0.16、1.10±0.05,1.18±0.05,1.94±0.08 and 1.17±0.08;the levels of Nrf2 were 1.12±0.16、0.76±0.15,0.65±0.13,0.70±0.16 and 0.82±0.18;the levels of HO-1 were 1.34±0.15、1.00±0.12,0.89±0.10,1.50±0.18 and 1.40±0.13,respectively.Significant difference was found between normal group and model group(P＜0.01,P＜0.05);significant difference was also found between the experimental-L,-H groups and model group(P＜0.01,P＜0.05).Conclusion Wumeiwan can inhibit oxidative stress in mice with UC,the mechanisms may be related to adjusted the expression of Keap-1-Nrf2/HO-1 signaling pathway protein in colon.