ObjectiveThis paper aims to explore the risk factors for chronic atrophic gastritis （CAG） with turbidity toxin accumulation syndrome and establish a prediction model. MethodsClinical data of 180 patients with CAG who participated in the "clinical study of Xianglian Huazhuo Particles blocking CAG cancer transformation" of Hebei Sheng Zhong Yi Yuan from July 2021 to March 2022 were collected. After confounding factors were controlled by propensity score matching， patients were divided into a training set （namely dev） and a validation set （namely vad） in a seven to three ratio. The risk factors for CAG with turbidity toxin accumulation syndrome in the training set were investigated by using univariate Logistic regression analysis and least absolute shrinkage and selection operator （namely Lasso） regression algorithms. Subsequently， a model， named model 1se， was developed by using the training set data to predict the risk factors for CAG with turbidity toxin accumulation syndrome. The accuracy of the prediction model was assessed by using various methods， including the receiver operating characteristic （ROC） curve， Hosmer-Lemeshow test （H-L）， calibration plot， and decision curve analysis （DCA）. ResultsAge， body mass index （BMI）， family history of cancer， job and life satisfaction， yellow and greasy fur with slippery pulse， and heavy body sensation were independent risk factors of the model. The prediction model showed excellent predictive value for both the training and validation sets. ConclusionThe established prediction model for CAG with turbidity toxin accumulation syndrome has high discrimination and excellent calibration， which could provide an excellent clinical basis for disease diagnosis and individualized treatment of patients.

ObjectiveThis paper aims to explore the risk factors for chronic atrophic gastritis （CAG） with turbidity toxin accumulation syndrome and establish a prediction model. MethodsClinical data of 180 patients with CAG who participated in the "clinical study of Xianglian Huazhuo Particles blocking CAG cancer transformation" of Hebei Sheng Zhong Yi Yuan from July 2021 to March 2022 were collected. After confounding factors were controlled by propensity score matching， patients were divided into a training set （namely dev） and a validation set （namely vad） in a seven to three ratio. The risk factors for CAG with turbidity toxin accumulation syndrome in the training set were investigated by using univariate Logistic regression analysis and least absolute shrinkage and selection operator （namely Lasso） regression algorithms. Subsequently， a model， named model 1se， was developed by using the training set data to predict the risk factors for CAG with turbidity toxin accumulation syndrome. The accuracy of the prediction model was assessed by using various methods， including the receiver operating characteristic （ROC） curve， Hosmer-Lemeshow test （H-L）， calibration plot， and decision curve analysis （DCA）. ResultsAge， body mass index （BMI）， family history of cancer， job and life satisfaction， yellow and greasy fur with slippery pulse， and heavy body sensation were independent risk factors of the model. The prediction model showed excellent predictive value for both the training and validation sets. ConclusionThe established prediction model for CAG with turbidity toxin accumulation syndrome has high discrimination and excellent calibration， which could provide an excellent clinical basis for disease diagnosis and individualized treatment of patients.

ObjectiveTo explore the potential mechanism of Xiezhuo Jiedu prescription in regulating autophagy in the treatment of ulcerative colitis （UC） by bioinformatics and animal experiments. MethodsThe differentially expressed genes （DEGs） in the colonic mucosal tissue of UC patients was obtained from the Gene Expression Omnibus （GEO）， and those overlapped with autophagy genes were obtained as the differentially expressed autophagy-related genes （DEARGs）. DEARGs were imported into Metascape and STRING， respectively， for gene ontology/Kyoto Encyclopedia of Genes and Genomics （GO/KEGG） enrichment analysis and protein-protein interaction （PPI） analysis. Finally， 15 key DEARGs were obtained. The core DEARGs were obtained by least absolute shrinkage and selection operator （LASSO） regression and receiver operating characteristic curve （ROC） analysis. The CIBERSORT deconvolution algorithm was used to analyze the immunoinfiltration of UC patients and the correlations between core DEARGs and immune cells. C57BL/6J mice were assigned into a normal group and a modeling group. The mouse model of UC was established by free drinking of 2.5% dextran sulfate sodium. The modeled mice were assigned into low-， medium-， and high-dose Xiezhuo Jiedu prescription and mesalazine groups according to the random number table method and administrated with corresponding agents by gavage for 7 days. The colonic mucosal morphology was observed by hematoxylin-eosin staining. The protein and mRNA levels of cysteinyl aspartate-specific proteinase 1 （Caspase-1）， cathepsin B （CTSB）， C-C motif chemokine-2 （CCL2）， CXC motif receptor 4 （CXCR4）， and hypoxia-inducing factor-1α （HIF-1α） in the colon tissue were determined by Western blot and real-time fluorescence quantitative polymerase chain reaction， respectively. ResultsThe dataset GSE87466 was screened from GEO and interlaced with autophagy genes. After PPI analysis， LASSO regression， and ROC analysis， the core DEARGs （Caspase-1， CCL2， CTSB， and CXCR4） were obtained. The results of immunoinfiltration analysis showed that the counts of NK cells， M0 macrophages， M1 macrophages， and dendritic cells in the colonic mucosal tissue of UC patients had significant differences， and core DEARGs had significant correlations with these immune cells. This result， combined with the prediction results of network pharmacology， suggested that the HIF-1α signaling pathway may play a key role in the regulation of UC by Xiezhuo Jiedu prescription. The animal experiments showed that Xiezhuo Jiedu prescription significantly alleviated colonic mucosal inflammation in UC mice. Compared with the normal group， the model group showed up-regulated protein and mRNA levels of caspase-1， CCL2， CTSB， CXCR4， and HIF-1α， which were down-regulated after treatment with Xiezhuo Jiedu prescription or mesalazine. ConclusionCaspase-1， CCL2， CTSB， and CXCR4 are autophagy genes that are closely related to the onset of UC. Xiezhuo Jiedu prescription can down-regulate the expression of core autophagy genes to alleviate the inflammation in the colonic mucosa of mice.

Objective:To explore the relationship between maternal rejection and internet addiction in college students, as well as the chain mediating role of rejection sensitivity and adaptability.Methods:From March to May 2024, a total of 1 119 college students were surveyed using the short-form Egna Minnen av Barndoms Uppforstran for Chinese(s-EMBU-C), internet addiction test(IAT), rejection sensitivity questionnaire(RSQ), and the China college student adjustment scale(CCSAS).SPSS 26.0 statistical software was used for independent sample t-test, one-way ANOVA, Pearson product moment correlation, and multiple linear regression analysis, and PROCESS 4.0 macro program was used for chain mediation analysis. Results:(1)Maternal rejection (11.19±2.97) was positively correlated with internet addiction (44.89±9.74)( r=0.60, P<0.01) and rejection sensitivity (102.93±55.63)( r=0.63, P<0.01), while negatively correlated with adaptability (200.19±14.18)( r=-0.56, P<0.01) among college students. Rejection sensitivity was positively correlated with internet addiction ( r=0.75, P<0.01) and negatively correlated with adaptability ( r=-0.76, P<0.01). Adaptability was negatively correlated with internet addiction ( r=-0.68, P<0.01). (2)Maternal rejection had a significant direct effect on internet addiction among college students (effect value=0.193, 95% CI=0.145-0.241), accounting for 32.06%(0.193/0.602) of the total effect. Rejection sensitivity mediated the relationship between maternal rejection and internet addiction (effect value=0.290, 95% CI=0.232-0.357), accounting for 48.17%(0.290/0.602) of the total effect. Adaptability also mediated this relationship (effect value=0.028, 95% CI=0.009-0.053), accounting for 4.65%(0.028/0.602) of the total effect. Additionally, there was a chain mediation effect of rejection sensitivity and adaptability on the relationship between maternal rejection and internet addiction (effect value=0.091, 95% CI=0.052-0.130), accounting for 15.12%(0.091/0.602) of the total effect. Conclusion:Maternal rejection can directly influence internet addiction in college students, and it can also indirectly influence internet addiction through the independent mediating effects of rejection sensitivity and adaptability, as well as through the chain mediating effects of both rejection sensitivity and adaptability.

Objective:To compare the outcomes of simple hysterectomy(SH)and radical hysterectomy(RH)in elderly patients with cervical cancer.Methods:A retrospective cohort study was conducted, including 633 elderly patients with cervical cancer who underwent surgical treatment at Shangqiu First People's Hospital from January 2016 to December 2020.Among them, 247 patients underwent SH, and 215 patients underwent RH.Propensity score matching was applied, resulting in two groups: 125 patients in the SH group and 124 patients in the RH group.The primary outcome was the pelvic recurrence rate at 2 years of follow-up.Secondary outcomes included the incidence of urinary incontinence and urinary retention.Kaplan-Meier survival analysis was used to compare the recurrence rates between the two groups, and a Cox proportional hazards regression model was used to analyze the factors influencing recurrence rates.Results:After matching, the baseline characteristics of the two groups were similar and comparable(all P>0.05).Kaplan-Meier results showed that, although the pelvic recurrence rate in the SH group was higher than that in the RH group before matching(4.5% vs.2.3%, P<0.05), the pelvic recurrence rates were similar between the two groups after matching(3.2% vs.2.4%, P>0.05).The SH group had significantly lower postoperative complications, including urinary retention(1.6% vs.8.1%, P<0.05), compared to the RH group, while there was no significant difference in the incidence of urinary incontinence(4.0% vs.9.7%)and the risk of cervical cancer-related death(1.6% vs.0.8%)between the two groups( P>0.05).Multivariate Cox regression analysis showed that age( HR=1.254), tumor grade( HR=1.315), and FIGO stage( HR=1.203)were important factors influencing pelvic tumor recurrence during follow-up(all P<0.05). Conclusions:elderly patients with low-risk cervical cancer, the 2-year pelvic recurrence rates for SH and RH are similar.However, SH is associated with a lower risk of urinary incontinence and urinary retention.

Purpose Based on a variety of combinations of cranial MR Dixon images,the deep learning method is used to generate CT images,and the reconstruction efficiency is evaluated by comparing with the corresponding CT images.Materials and Methods A total of 77 cranial CT and MR images were collected retrospectively in Ruijin Hospital,Shanghai Jiaotong University School of Medicine from June to December 2021.The U-Net neural network was used for network training,with 62 cases in the training set and 15 cases in the test set.CT image reconstruction was performed using four kinds of Dixon images and a total of seven models among the various combinations.Mean absolute error,mean squared error,Pearson correlation coefficient and skull area Dice similarity coefficient were used to evaluate the image reconstruction efficiency.Results The generated CT images of the various Dixon image combination models showed strong correlation with the corresponding CT images(R>0.75,P<0.05),and the CT images reconstructed by the four-channel model had the closest value to the actual CT images[mean absolute error=147.516±30.802,mean squared error=(8.648±3.403)×104],the highest correlation coefficient(R=0.796±0.055),and the highest similarity coefficient in the cranial region(Dice similarity coefficient=0.800±0.036).Conclusion Deep learning training through Dixon images can be used to generate CT images,and the combination of four kinds of Dixon contrast images can improve the CT image reconstruction efficiency.

Objective To investigate the value of CT radiomics for predicting effect of neoadjuvant chemotherapy(NACT)for locally advanced gastric cancer(LAGC).Methods Totally 325 LAGC patients who received NACT were retrospectively enrolled,among them 247 were taken as training set,while the rest 78 were taken as validation set.Tumor regression scale(TRG)was evaluated according to postoperation pathology after NACT,and the efficacy of NACT was evaluated.Univariate logistic regression was used to analyze and screen clinical predictors of effect of NACT,and clinical model was constructed.Radiomics features were extracted based on venous phase enhanced CT pre-and post-NACT,and Delta radiomics features(i.e.the ratio of the difference of pre-and post-NACT radiomics features and pre-NACT radiomics features)were calculated.The best features were screened based on pre-NACT,post-NACT and Delta radiomics features to construct radiomics labels,the optimal label was screened and used to construct combined model through combining clinical model.Receiver operating characteristic(ROC)curve was plotted,and the area under the curve(AUC)was calculated to evaluate predicting efficiency of the above models.Decision curve analysis(DCA)was performed to explore the clinical value of each model.Results In training set,significant effect was found in 67 cases,but not in 180 cases,while in validation set,significant effect was found in 18 cases but not in 60 cases.Borrmann classification of LAGC before NACT was the clinical predictor(P=0.031),and clinical model was constructed,which had AUC of 0.577 and 0.520 in training and validation sets,respectively.Based on pre-NACT,post-NACT and Delta radiomics features,19,14 and 17 best features were selected,and AUC of the established radiomics labels of Pre-Rad,Post-Rad and Delta-Rad in training set was 0.672,0.796 and 0.789,while in validation set was 0.558,0.805 and 0.666,respectively.Post-Rad was the optimal label,which was used to construct combined model.AUC of the obtained combined model in training and validation sets was 0.824 and 0.818,respectively,both higher than that of clinical model(both P＜0.001)but not different with that of Post-Rad(both P＞0.05).Taken 0.4 to 0.7 as the threshold,the combined model had higher clinical net benefit than the other two.Conclusion Venous CT radiomics could effectively predict effect of NACT for LAGC.Combining with clinical features could improve its predictive efficacy.

Objective:To investigate immunomodulatory effects of methionine enkephalin(MENK)on macrophages,and to explore effect of opsonizing receptors in anti-influenza virus infection of macrophages.Methods:Potential targets for antiviral effects of MENK on macrophages were explored by network pharmacology.Proteomics analysis was used to identify differentially expressed pro-teins(DEPs)in macrophages of MENK-PR8 and PR8 groups.DEPs were analyzed by bioinformatics,and key factors were verified by qPCR and Western blot.Results:MENK had 85 intersection targets with macrophages and influenza viruses,of which 7 were related to phagosome pathway(mmu04145).A total of 215 DEPs were identified by mass spectrometry,which were highly enriched in phago-some(mmu04145)and interaction of viral proteins with cytokines and cytokine receptors(mmu04061)pathways.qPCR and Western blot showed that Fc gamma receptor(FcγR)and complement receptor(CR3)related to phagosome were highly expressed.Conclu-sion:MENK enhances function of phagocytosis and killing virus by upregulating opsonizing receptors via opioid receptor,suggesting that MENK can serve as an immune modulator or a novel preventive drug for influenza viruses.

Objective To evaluate the value of biparametric MRI(bpMRI)radiomics for predicting postoperation Gleason score(GS)upgrade of prostate cancer(PCa).Methods Totally 344 PCa patients who underwent radical prostatectomy(RP)were retrospectively enrolled and divided into training set(n=241)and test set(n=103)at a ratio of 7∶3.T2WI,diffusion weighted imaging(DWI)and apparent diffusion coefficient(ADC)map radiomics signatures were constructed based on preoperative bpMRI,respectively,then logistic regression(LR)algorithm was used to establish bpMRI radiomics model.Univariate and multivariate logistic regression analyses were performed to screen independent risk factors for postoperation GS upgrade of PCa,and a clinical model was constructed.Then a clinical-radiomics combined model was established based on clinical model and bpMRI radiomics model.Receiver operating characteristic curves were drawn,the area under the curves(AUC)were calculated to evaluate the efficacy of each model for predicting postoperation GS upgrade of PCa.Results Elevated preoperative prostate imaging reporting and data system(PI-RADS)score and reduced biopsy Gleason grade group(GG)were both independent risk factors of postoperation GS upgrade of PCa(both P＜0.05).The AUC of bpMRI radiomics model and clinical-radiomics combined model for predicting postoperation GS upgrade of PCa were higher than that of single-sequence radiomics signatures and clinical model(all P＜0.05),while no significant difference was found between the former two(P＞0.05).The clinical-radiomics combined model demonstrated good efficacy for predicting postoperation GS upgrade of PCa with different biopsy GG before operation,with AUC ranging from 0.835 to 0.949 in training set and 0.803 to 0.948 in test set.Conclusion bpMRI radiomics model could effectively predict postoperation GS upgrade of PCa.

OBJECTIVE To investigate the characteristics of influenza A and influenza B viruses infections in the children aged between 0 and 14 years old after COVID-19 was downgraded to category B management of infectious diseases.METHODS From Jan.2023 to Feb.2024,a total of 2349 children aged between 0 and 14 years old who were treated in Beijing Hospital of Traditional Chinese Medicine,Capital Medical University due to influenza-like symptoms of infection and received nucleic acid testing for influenza A and influenza B viruses were recruited as the research subjects.The gender and age of the children as well as the seasons were observed by chi-square test.RESULTS Totally 2349 children were included in the study,and the total positive rate of influenza was 49.85％(1171/2349);the positive rate of influenza A virus was 36.36％(854/2349),the positive rate of influenza B virus was 13.92％(327/2349),and the positive rate of the mixed infections of influenza A virus and influenza B virus was 0.43％(10/2349).The positive rate of influenza A of the girls was the highest(44.17％)(x2=8.980,P=0.011)among the children aged less than 5 years old;the positive rate of influenza B of the boys was the highest(17.19％)(x2=8.378,P=0.015)among the children aged between 5 and 10 years old.There was significant difference in the positive rate of influenza A virus among the seasons in 2023 to 2024(x2=268.12,P＜0.001);the prevalence rate was 60.93％in spring,44.40％in autumn,22.01％in winter.There was significant difference in the positive rate of influenza B virus among the seasons in 2023 to 2024(x2=373.16,P＜0.001),and the preva-lence rate was 25.44％in winter.CONCLUSIONS The influenza viruses are prevalent in spring,autumn and winter from 2023 to 2024,and the influenza A is dominant.The positive rate of influenza viruses shows an upward trend among the children aged between 0 and 14 years old after the COVID-19 is downgraded to category B management of infectious diseases,with the peak of prevalence lagging behind.