Objective:To explore the related risk factors of neonatal necrotizing enterocolitis (NEC) by constructing and comparing nine regression models.Methods:All NEC patients admitted to the neonatal internal medicine department, neonatal surgery department, and neonatal intensive care unit of Shanxi Provincial Children's Hospital (Shanxi Provincial Maternity and Child Health Center) from 2020 to 2022 were included as the case group. A control group consisted of children admitted during the same period based on the inclusion and exclusion criteria. The NEC data collected were used for feature selection by using the Boruta algorithm. Logistic regression, multi-decision tree gradient boosting, efficient gradient one-sided sampling, random forest, decision tree, gradient boosting decision tree (GBDT), neural network, support vector machine, and K-nearest neighbor models were constructed. The optimal model was selected through rigorous comparison and Shap explainable analysis was performed on the GBDT model.Results:Thirteen key factors were identified through screening for nine regression models construction. After strict comparison and analysis, the GBDT model showed higher stability compared with other eight regression models. In the validation set, the area under the receiver operating characteristic curve of the GBDT model was 0.958, with an accuracy of 0.925, and sensitivity and specificity of 0.827 and 0.950, respectively. Shap explainable analysis on the GBDT model revealed that suffering from anemia, non-invasive ventilator use, procalcitonin use, premature birth, and low birth weight increased the risk for NEC, while breastfeeding and probiotics decreased the risk for NEC.Conclusion:This study identified the risk factors and protective factors for NEC by using the GBDT model, which provided evidnce for the prevention and treatment of NEC.

Objective:To study the growth inhibition of Thunberg Fritillary extract on non-small cell lung cancer.Methods:The Thunberg Fritillary extract was prepared and characterized by UPLC-QE/MS.Replicated Lewis lung carcinoma ectopic tumor-bear-ing mouse model,yeast injection induced acute inflammation,compared the effect of Thunberg Fritillary extract combination with acute inflammation on the growth,tumor volume and tumor suppression rate of Lewis lung carcinoma mice,and determine the content of inflammatory factors by the flow CBA method(IL-6,IL-1β,IL-1α,IL-10,IL-27,IL-17A,IL-12p70,IL-23,TNF-α,IFN-γ,IFN-β,GM-CSF,MCP-1).Results:The inhibition of Lewis lung carcinoma mice was similar to that of cisplatin alone,and the tumor suppression rate was 35%;the tumor suppression rate of Thunberg Fritillary extract combined with acute inflammatory stimulation of yeast was 62%,1.8 times that of cisplatin alone.The decrease in the expressions of cytokines IL-23,MCP-1 after acute inflammatory stimulation in yeast was associated with tumor suppression;while the increased expressions of IL-6,IL-1β,IL-1α,IL-10,IL-27,IL-17A,IL-12p70,TNF-α,IFN-γ,IFN-β and GM-CSF cytokines were associated with tumor suppression.Conclusion:The Thun-berg Fritillary extract combination with acute inflammation can play a positive role against non-small cell lung cancer,which will pro-vide new research ideas and methods for the prevention and treatment of non-small cell lung cancer.

Objective To clone the glycosyltransferase gene UGT708Z1 in Anemarrhena asphodeloides and perform its bioinformatics analysis,prokaryotic expression analysis and functional characterization.Methods A candidate glycosyltransferase gene UGT708Z1 was mined and screened out from Anemarrhena asphodeloides based on the transcriptome data.According to its full-length open reading frame,the specific primers with homologous arms were designed.Subsequently,the UGT708Z1 gene was cloned by PCR.The prokaryotic expression recombinant vector pET-32a(+)-UGT708Z1 was constructed through homologous recombination technology,and the soluble target protein was obtained by prokaryotic expression and purified protein technology.Finally,the function of UGT708Z1 was identified through enzymatic reaction in vitro.Results Sequence analysis showed that the open reading frame of UGT708Z1 was 1377 bp,encoding 458 amino acids.The result of prokaryotic expression showed that UGT708Z1 successfully expressed the soluble target protein,and the purified recombinant protein was 70.86 kDa.The results of enzymatic reaction in vitro showed that UGT708Z1 had flavonoid 7-OH glycosylation activity and could catalyze icaritin to produce icariside I.In addition,UGT708Z1 also possessed the activities of catalyzing the 7-O-glycosylation of quercetin and apigenin.Conclusion In this study,a flavonol glycosyltransferase UGT708Z1 was successfully cloned and identified from Anemarrhena asphodeloides,which would lay a foundation for further analysis of flavonol glycosides biosynthesis.

Objective:To explore the related risk factors of neonatal necrotizing enterocolitis (NEC) by constructing and comparing nine regression models.Methods:All NEC patients admitted to the neonatal internal medicine department, neonatal surgery department, and neonatal intensive care unit of Shanxi Provincial Children's Hospital (Shanxi Provincial Maternity and Child Health Center) from 2020 to 2022 were included as the case group. A control group consisted of children admitted during the same period based on the inclusion and exclusion criteria. The NEC data collected were used for feature selection by using the Boruta algorithm. Logistic regression, multi-decision tree gradient boosting, efficient gradient one-sided sampling, random forest, decision tree, gradient boosting decision tree (GBDT), neural network, support vector machine, and K-nearest neighbor models were constructed. The optimal model was selected through rigorous comparison and Shap explainable analysis was performed on the GBDT model.Results:Thirteen key factors were identified through screening for nine regression models construction. After strict comparison and analysis, the GBDT model showed higher stability compared with other eight regression models. In the validation set, the area under the receiver operating characteristic curve of the GBDT model was 0.958, with an accuracy of 0.925, and sensitivity and specificity of 0.827 and 0.950, respectively. Shap explainable analysis on the GBDT model revealed that suffering from anemia, non-invasive ventilator use, procalcitonin use, premature birth, and low birth weight increased the risk for NEC, while breastfeeding and probiotics decreased the risk for NEC.Conclusion:This study identified the risk factors and protective factors for NEC by using the GBDT model, which provided evidnce for the prevention and treatment of NEC.

Objective To clone the glycosyltransferase gene UGT708Z1 in Anemarrhena asphodeloides and perform its bioinformatics analysis,prokaryotic expression analysis and functional characterization.Methods A candidate glycosyltransferase gene UGT708Z1 was mined and screened out from Anemarrhena asphodeloides based on the transcriptome data.According to its full-length open reading frame,the specific primers with homologous arms were designed.Subsequently,the UGT708Z1 gene was cloned by PCR.The prokaryotic expression recombinant vector pET-32a(+)-UGT708Z1 was constructed through homologous recombination technology,and the soluble target protein was obtained by prokaryotic expression and purified protein technology.Finally,the function of UGT708Z1 was identified through enzymatic reaction in vitro.Results Sequence analysis showed that the open reading frame of UGT708Z1 was 1377 bp,encoding 458 amino acids.The result of prokaryotic expression showed that UGT708Z1 successfully expressed the soluble target protein,and the purified recombinant protein was 70.86 kDa.The results of enzymatic reaction in vitro showed that UGT708Z1 had flavonoid 7-OH glycosylation activity and could catalyze icaritin to produce icariside I.In addition,UGT708Z1 also possessed the activities of catalyzing the 7-O-glycosylation of quercetin and apigenin.Conclusion In this study,a flavonol glycosyltransferase UGT708Z1 was successfully cloned and identified from Anemarrhena asphodeloides,which would lay a foundation for further analysis of flavonol glycosides biosynthesis.

Objective:To study the growth inhibition of Thunberg Fritillary extract on non-small cell lung cancer.Methods:The Thunberg Fritillary extract was prepared and characterized by UPLC-QE/MS.Replicated Lewis lung carcinoma ectopic tumor-bear-ing mouse model,yeast injection induced acute inflammation,compared the effect of Thunberg Fritillary extract combination with acute inflammation on the growth,tumor volume and tumor suppression rate of Lewis lung carcinoma mice,and determine the content of inflammatory factors by the flow CBA method(IL-6,IL-1β,IL-1α,IL-10,IL-27,IL-17A,IL-12p70,IL-23,TNF-α,IFN-γ,IFN-β,GM-CSF,MCP-1).Results:The inhibition of Lewis lung carcinoma mice was similar to that of cisplatin alone,and the tumor suppression rate was 35%;the tumor suppression rate of Thunberg Fritillary extract combined with acute inflammatory stimulation of yeast was 62%,1.8 times that of cisplatin alone.The decrease in the expressions of cytokines IL-23,MCP-1 after acute inflammatory stimulation in yeast was associated with tumor suppression;while the increased expressions of IL-6,IL-1β,IL-1α,IL-10,IL-27,IL-17A,IL-12p70,TNF-α,IFN-γ,IFN-β and GM-CSF cytokines were associated with tumor suppression.Conclusion:The Thun-berg Fritillary extract combination with acute inflammation can play a positive role against non-small cell lung cancer,which will pro-vide new research ideas and methods for the prevention and treatment of non-small cell lung cancer.

Objective To analyze the factors influencing early-onset sepsis in preterm infants and construct nomogram model.Methods A total of 124 neonates with premature sepsis admitted to Shanxi Children's Hospital(Shanxi Maternal and Child Health Hos-pital)from January 2020 to December 2021 were collected.According to gestational age,the neonates were divided into premature group(n=33)and full-term group(n=91),and the clinical characteristics of the two groups were compared,and nomogram model was es-tablished to internally validate the predictiveness and accuracy of the model.Results Compared with the full-term group,the proportion of females in premature group was higher(x2=7.147,P＜0.05),the 1min Apgarscore in premature group was lower(x2=-3.398,P＜0.05),the proportion of perinatal mothers with pregnancy complications in premature group was higher(x2=7.846,P＜0.05),the incidence of pneumonia and poor response in preterm infants of premature group were higher(x2=18.210,P＜0.05;x2=14.814,P＜0.05),but the incidence of jaundice in premature group was lower(x2=10.400,P＜0.05).Multivariate Logistic regression analysis showed that female and pneumonia were risk factors for early-onset sepsis in preterm infants(P＜0.05).The results of the nomogram model showed that the C-index of the model was 0.886.The predicted incidence was generally consistent with the actual incidence,the area under the receiver operator characteristic curve was 0.886,and the decision curve showed a high net benefit value at threshold proba-bilities of 4％-100％.Conclusion Female,preterm infants with pneumonia have a higher risk of early-onset sepsis.The nomogram model of premature sepsis constructed in this study has high clinical value and can provide a reference basis for clinical prevention of early-onset sepsis in preterm infants.

ObjectiveTo investigate the prevalence of polycystic ovary syndrome （PCOS） among female college students at a university in Fuzhou City， Jiangxi Province， and to facilitate early detection and intervention of PCOS. MethodsUsing a stratified sampling method， a total of 450 female freshmen were randomly selected for PCOS screening. A self-designed questionnaire was used for data collection， covering menstrual status， high androgen signs， lifestyle， dietary habits， and awareness of PCOS. Sample t test and χ² test were used to compare the basic information and dietary habits between PCOS and non-PCOS cases. The correlation between various indicators and the prevalence of PCOS was analyzed by a logistic regression model. ResultsA total of 12 PCOS cases were identified， with a prevalence rate of 2.99%. PCOS cases exhibited statistically significant differences compared to non-PCOS cases in terms of waist-to-hip ratio， waist circumference， abdominal obesity， the proportion of overweight or obese individuals， and a preference for sweet food （all P<0.05）. Multivariate logistic regression analysis revealed a significant correlation between preference for sweet food and the occurrence of PCOS （OR=4.858， 95%CI=1.199‒19.675，P=0.027）， as well as a significant correlation with PCOS accompanied by abdominal obesity （OR=7.083， 95%CI=0.773‒64.937， P=0.048）. Among the female college students surveyed， 37.90% had never heard of PCOS， 51.62% were only familiar with the name of the disease， and 10.47% had attempted to search for PCOS-related information. ConclusionThe prevalence of PCOS among female college students should not be overlooked and unhealthy dietary habits may be a crucial factor contributing to the occurrence of PCOS during this period. Early screening for PCOS during puberty is crucial.

Objective To screen variables through LASSO regression,conduct multifactor Logistic regression analysis based on the screening results,and construct a Bayesian network model using max-min hill-climbing(MMHC)algorithm to explore the related fac-tors of necrotizing enterocolitis(NEC)in newborns and the complex network relationships among factors.The study also aimed to compare the two models to find the optimal modeling tool.Methods All NEC patients admitted to the Department of Neonatology,Department of Neonatal Surgery,and NICU of Shanxi Children's Hospital(Shanxi Maternal and Child Health Hospital)from January 2020 to December 2023 were retrospectively studied.NEC investigation data were collected and variable screening was conducted using LASSO regression.Multifactor Logistic regression analysis was performed based on the screening results.The MMHC mixed algorithm was employed for struc-ture learning,and the maximum likelihood estimation method was used for parameter learning to construct the NEC Bayesian network model.Results After variable screening,10 factors including prematurity,low birth weight,feeding method,intrauter distress and post-natal asphyxia history,anemia,non-invasive ventilator,probiotics,gestational diabetes,C-reactive protein(CRP),and procalcitonin(PCT)were included in the model construction.The area under the receiver operating characteristic(ROC)curve of the Bayesian net-work model in the modeling group and validation group were 0.825 and 0.817,respectively,with accuracies of 89.78％and 90.43％,respectively.The AUC of the multifactor Logistic regression analysis in the modeling group and validation group were 0.777 and 0.741,respectively,with accuracies of 70.01％and 69.44％,respectively.The performance of the Bayesian network model was superior to that of multifactor Logistic regression analysis.Furthermore,the Bayesian network model showed that low birth weight,feeding method,probi-otics,and PCT were directly related to NEC,prematurity and non-invasive ventilator were indirectly related to NEC through low birth weight,and CRP was indirectly related to NEC through PCT.Conclusion By comparing the two models,it was found that the Bayesian network model is an effective tool for in-depth study of NEC and the network relationships among related factors.Through this model,the association strength between NEC and various factors can be accurately evaluated,providing a scientific basis for the prevention and treat-ment of NEC.

Objective To screen variables through LASSO regression,conduct multifactor Logistic regression analysis based on the screening results,and construct a Bayesian network model using max-min hill-climbing(MMHC)algorithm to explore the related fac-tors of necrotizing enterocolitis(NEC)in newborns and the complex network relationships among factors.The study also aimed to compare the two models to find the optimal modeling tool.Methods All NEC patients admitted to the Department of Neonatology,Department of Neonatal Surgery,and NICU of Shanxi Children's Hospital(Shanxi Maternal and Child Health Hospital)from January 2020 to December 2023 were retrospectively studied.NEC investigation data were collected and variable screening was conducted using LASSO regression.Multifactor Logistic regression analysis was performed based on the screening results.The MMHC mixed algorithm was employed for struc-ture learning,and the maximum likelihood estimation method was used for parameter learning to construct the NEC Bayesian network model.Results After variable screening,10 factors including prematurity,low birth weight,feeding method,intrauter distress and post-natal asphyxia history,anemia,non-invasive ventilator,probiotics,gestational diabetes,C-reactive protein(CRP),and procalcitonin(PCT)were included in the model construction.The area under the receiver operating characteristic(ROC)curve of the Bayesian net-work model in the modeling group and validation group were 0.825 and 0.817,respectively,with accuracies of 89.78％and 90.43％,respectively.The AUC of the multifactor Logistic regression analysis in the modeling group and validation group were 0.777 and 0.741,respectively,with accuracies of 70.01％and 69.44％,respectively.The performance of the Bayesian network model was superior to that of multifactor Logistic regression analysis.Furthermore,the Bayesian network model showed that low birth weight,feeding method,probi-otics,and PCT were directly related to NEC,prematurity and non-invasive ventilator were indirectly related to NEC through low birth weight,and CRP was indirectly related to NEC through PCT.Conclusion By comparing the two models,it was found that the Bayesian network model is an effective tool for in-depth study of NEC and the network relationships among related factors.Through this model,the association strength between NEC and various factors can be accurately evaluated,providing a scientific basis for the prevention and treat-ment of NEC.