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.

Objective To investigate the potential core target and its mechanism of Simiao San(SMS)in the treatment of rheumatoid arthritis(RA)using network pharmacology and animal experiments.Methods Active components and corresponding SMS targets were obtained from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform(TCMSP)and cross-referenced with the Universal Protein(UniProt)database.RA-related targets were screened from The Human Gene Database(GeneCards),Online Mendelian Inheritance in Man(OMIM),Therapeutic Target Database(TTD),DrugBank,and Disease Gene Network(DisGeNet).Protein-protein interaction(PPI)networks were constructed for shared targets between SMS and RA using Search Tool for the Retrieval of Interacting Genes/Proteins(STRING),followed by Gene Ontology(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway enrichment analyses via The Database for Annotation,Visualization and Integrated Discovery(DAVID).A"herb active component-disease target-signaling pathway"network was established to predict the mechanism of SMS in RA treatment.Molecular docking was performed between aryl hydrocarbon receptor(AHR)and the core active components of SMS to identify AHR-targeting constituents.For animal experiments,30 female SPF-grade C57/BL mice were randomly divided into normal,model,methotrexate(1.52 mg/kg,every 3 days),and SMS(12.48 g/kg,daily)groups with a 30-day intervention.Ankle diameter and arthritis index scores were measured.HE staining was used to assess joint inflammation,whereas immunohistochemistry(IHC)was used to measure cytochrome P450 1A1(CYP1A1),nuclear factor kappa B subunit p65(p65),and phosphorylated p65(p-p65)protein expression levels.Multiplex immunofluorescence(mIHC)was used to evaluate forkhead box protein P3(FOXP3)and interleukin-17A(IL-17A)protein expression.Results Forty-one active components and 228 targets of SMS were identified from TCMSP,whereas 1,207 RA-related targets were extracted from GeneCards,OMIM,TTD,DrugBank,and DisGeNet.Ninety-four overlapping targets were analyzed,yielding 612 GO terms and 143 KEGG pathways.Molecular docking of the ligand-binding domain of AHR with the top 10 Degree values of compounds of SMS(quercetin,stigmasterol,wogonin,beta-sitosterol,kaempferol,baicalein,et al.)revealed that stigmasterol,beta-sitosterol,(S)-canadine,and isocorypalmine was able to bind to AHR stably.In vivo,compared to the model group,the mice of the SMS and methotrexate groups joint swelling and arthritis index scores reduced(P<0.01).IHC indicated elevated CYP1A1 protein and decreased p65 and p-p65 protein levels in the SMS and methotrexate groups(P<0.05,P<0.01).mIHC demonstrated reduced IL-17A and increased FOXP3 protein expression in the SMS and methotrexate groups(P<0.05,P<0.01).Conclusion SMS alleviates joint inflammation in RA mice,potentially by targeting AHR,one of the core targets.SMS may suppress excessive inflammatory responses by activating AHR and inhibiting p65 phosphorylation.Additionally,SMS modulates the helper T cells 17/regulatory T cells balance by downregulating IL-17A and upregulating FOXP3.These results suggest that AHR is a key mediator in T-cell immune regulation.

Objective:To analyze the association between inflammation-related dietary patterns and the risk for cognitive impairment in older adults aged ≥65 years in longevity areas in China by using reduced rank regression (RRR) analysis.Methods:This study used cross-sectional data from the 2021 Healthy Aging and Biomarkers Cohort Study, including the information about study participants' demographic characteristics, lifestyles, daily life activities, and disease histories. Dietary intake was obtained by using a simplified food frequency questionnaire. Cognitive impairment was evaluated based on the Mini-Mental State Examination Scale combined with years of education. Fasting venous blood samples were collected to detect inflammatory markers, especially high-sensitivity C-reactive protein (hs-CRP) and the platelet-to-lymphocyte ratio (PLR). RRR analysis was used to obtain inflammation-related dietary patterns using hs-CRP and PLR as response variables. Multivariate logistic regression model was used to analyze the association between dietary pattern score and the risk for cognitive impairment. Restricted cubic spline was used to explore the dose response relationship, and mediation analysis was used to quantify the mediating effects of hs-CRP and PLR.Results:Two dietary patterns were identified with RRR. The primary pattern was characterized by higher intakes of flour, red meat, and dairy products, and lower intake of fresh vegetables, explaining 6.84% of the variance in food intake and 0.50% of the variance in inflammatory markers. Compared with the T1 group, the T3 group had significantly higher risk for cognitive impairment ( OR=1.242, 95% CI: 1.034-1.491). Each one standard deviation increase in the dietary pattern score was associated with an 8.7% increase in the risk for cognitive impairment ( OR=1.087, 95% CI: 1.008-1.172), with a significant linear trend (overall-model P<0.001, non-linear P=0.295). Mediation analysis indicated that hs-CRP mediated 6.2% of the association between the dietary pattern and the risk for cognitive impairment. Conclusion:The inflammation- related dietary pattern characterized by higher consumption of flour, red meat, and dairy products and lower consumption of fresh vegetables is associated with an increased risk for cognitive impairment in older adults, and hs-CRP partially mediates this association.

Primary liver cancer is one of the most common causes of cancer-related deaths in China,with hepatocellular carcinoma(HCC)accounting for 75%-85%.Approximately 70%of HCC patients are in the advanced stage at the time of diagnosis and miss the opportunity for radical surgery,leading to a poor prognosis.Yttrium-90 microsphere selective internal radiation therapy(90Y-SIRT),an emerging therapeutic modality,delivers radioactive microspheres via the hepatic artery to target tumors and uses beta radiation for localized tumor ablation.Compared to conventional transarterial chemoembolization and pharmacotherapy,90Y-SIRT shows the advan-tages of significant clinical benefits,good safety profiles,and broad applicability across diverse patient populations.This article re-views the advances in the application of 90Y-SIRT in HCC treatment.

Primary liver cancer is a malignant tumor with high incidence and mortality rates worldwide,and the early diagnosis of pri-mary liver cancer and the optimization of precise treatment strategies have become critical issues in the healthcare field.Due to the in-sufficient capabilities for molecular characterization,it is increasingly difficult for traditional imaging techniques to meet clinical needs in the era of precision medicine.Multimodal molecular imaging technology integrates the advantages of imaging modalities such as ul-trasound imaging,magnetic resonance imaging,and optical imaging,thereby achieving synergistic enhancement between molecular bio-logical information of liver cancer and precise anatomical localization and demonstrating a significant value in the diagnosis and treat-ment of liver cancer.This article reviews the advances in the application of multimodal molecular imaging in the early diagnosis,pre-cise treatment,and therapeutic efficacy monitoring of liver cancer.

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.

Objective:To analyze the association between inflammation-related dietary patterns and the risk for cognitive impairment in older adults aged ≥65 years in longevity areas in China by using reduced rank regression (RRR) analysis.Methods:This study used cross-sectional data from the 2021 Healthy Aging and Biomarkers Cohort Study, including the information about study participants' demographic characteristics, lifestyles, daily life activities, and disease histories. Dietary intake was obtained by using a simplified food frequency questionnaire. Cognitive impairment was evaluated based on the Mini-Mental State Examination Scale combined with years of education. Fasting venous blood samples were collected to detect inflammatory markers, especially high-sensitivity C-reactive protein (hs-CRP) and the platelet-to-lymphocyte ratio (PLR). RRR analysis was used to obtain inflammation-related dietary patterns using hs-CRP and PLR as response variables. Multivariate logistic regression model was used to analyze the association between dietary pattern score and the risk for cognitive impairment. Restricted cubic spline was used to explore the dose response relationship, and mediation analysis was used to quantify the mediating effects of hs-CRP and PLR.Results:Two dietary patterns were identified with RRR. The primary pattern was characterized by higher intakes of flour, red meat, and dairy products, and lower intake of fresh vegetables, explaining 6.84% of the variance in food intake and 0.50% of the variance in inflammatory markers. Compared with the T1 group, the T3 group had significantly higher risk for cognitive impairment ( OR=1.242, 95% CI: 1.034-1.491). Each one standard deviation increase in the dietary pattern score was associated with an 8.7% increase in the risk for cognitive impairment ( OR=1.087, 95% CI: 1.008-1.172), with a significant linear trend (overall-model P<0.001, non-linear P=0.295). Mediation analysis indicated that hs-CRP mediated 6.2% of the association between the dietary pattern and the risk for cognitive impairment. Conclusion:The inflammation- related dietary pattern characterized by higher consumption of flour, red meat, and dairy products and lower consumption of fresh vegetables is associated with an increased risk for cognitive impairment in older adults, and hs-CRP partially mediates this association.

Objective To observe the value of machine learning model based on MR T2WI and diffusion weighted imaging(DWI)radiomics for predicting perineural invasion(PNI)of rectal cancer.Methods Totally 343 patients with rectal cancer were retrospectively collected and divided into training set(n=275,92 PNI[+]and 183 PNI[-])and test set(n=68,23 PNI[+]and 45 PNI[-])at the ratio of 8∶2.Univariate and multivariate logistic regression(LR)were used to analyze clinical data and screen the independent predictors of PNI in rectal cancer,so as to construct a clinical model.The best radiomics features were extracted and screened based on preoperative T2WI and DWI.Then extremely randomized trees,multilayer perceptron,light gradient boosting machine,extreme gradient boosting,support vector machine(SVM),LR,K-nearest neighbor and random forest algorithms were used to construct ML models,respectively,and the optimal ML model was selected to establish a clinical-radiomics ML model combined with clinical relevant independent predictors.The predictive efficacy and clinical value of each model were evaluated.Results Patients' age was the independent predictor of PNI of rectal cancer(OR=0.988,P＜0.001),and the area under the curve(AUC)of the clinical model constructed based on it was 0.435 and 0.458 in training and test sets,respectively.SVM model was the best one among 8 ML models,with AUC in training and test set of 0.887 and 0.854,respectively.The AUC of clinical-radiomics ML model in training and test sets was 0.887 and 0.860,respectively,not different with AUC of SVM model(both P＞0.05).Decision curve analysis showed that when the threshold value was 0.20-0.45,clinical net benefit of SVM model was higher than that of other models.Conclusion SVM model based on T2WI and DWI radiomics could effectively predict PNI of rectal cancer.

Objective To investigate the potential core target and its mechanism of Simiao San(SMS)in the treatment of rheumatoid arthritis(RA)using network pharmacology and animal experiments.Methods Active components and corresponding SMS targets were obtained from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform(TCMSP)and cross-referenced with the Universal Protein(UniProt)database.RA-related targets were screened from The Human Gene Database(GeneCards),Online Mendelian Inheritance in Man(OMIM),Therapeutic Target Database(TTD),DrugBank,and Disease Gene Network(DisGeNet).Protein-protein interaction(PPI)networks were constructed for shared targets between SMS and RA using Search Tool for the Retrieval of Interacting Genes/Proteins(STRING),followed by Gene Ontology(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway enrichment analyses via The Database for Annotation,Visualization and Integrated Discovery(DAVID).A"herb active component-disease target-signaling pathway"network was established to predict the mechanism of SMS in RA treatment.Molecular docking was performed between aryl hydrocarbon receptor(AHR)and the core active components of SMS to identify AHR-targeting constituents.For animal experiments,30 female SPF-grade C57/BL mice were randomly divided into normal,model,methotrexate(1.52 mg/kg,every 3 days),and SMS(12.48 g/kg,daily)groups with a 30-day intervention.Ankle diameter and arthritis index scores were measured.HE staining was used to assess joint inflammation,whereas immunohistochemistry(IHC)was used to measure cytochrome P450 1A1(CYP1A1),nuclear factor kappa B subunit p65(p65),and phosphorylated p65(p-p65)protein expression levels.Multiplex immunofluorescence(mIHC)was used to evaluate forkhead box protein P3(FOXP3)and interleukin-17A(IL-17A)protein expression.Results Forty-one active components and 228 targets of SMS were identified from TCMSP,whereas 1,207 RA-related targets were extracted from GeneCards,OMIM,TTD,DrugBank,and DisGeNet.Ninety-four overlapping targets were analyzed,yielding 612 GO terms and 143 KEGG pathways.Molecular docking of the ligand-binding domain of AHR with the top 10 Degree values of compounds of SMS(quercetin,stigmasterol,wogonin,beta-sitosterol,kaempferol,baicalein,et al.)revealed that stigmasterol,beta-sitosterol,(S)-canadine,and isocorypalmine was able to bind to AHR stably.In vivo,compared to the model group,the mice of the SMS and methotrexate groups joint swelling and arthritis index scores reduced(P<0.01).IHC indicated elevated CYP1A1 protein and decreased p65 and p-p65 protein levels in the SMS and methotrexate groups(P<0.05,P<0.01).mIHC demonstrated reduced IL-17A and increased FOXP3 protein expression in the SMS and methotrexate groups(P<0.05,P<0.01).Conclusion SMS alleviates joint inflammation in RA mice,potentially by targeting AHR,one of the core targets.SMS may suppress excessive inflammatory responses by activating AHR and inhibiting p65 phosphorylation.Additionally,SMS modulates the helper T cells 17/regulatory T cells balance by downregulating IL-17A and upregulating FOXP3.These results suggest that AHR is a key mediator in T-cell immune regulation.

Objective To observe the value of machine learning model based on MR T2WI and diffusion weighted imaging(DWI)radiomics for predicting perineural invasion(PNI)of rectal cancer.Methods Totally 343 patients with rectal cancer were retrospectively collected and divided into training set(n=275,92 PNI[+]and 183 PNI[-])and test set(n=68,23 PNI[+]and 45 PNI[-])at the ratio of 8∶2.Univariate and multivariate logistic regression(LR)were used to analyze clinical data and screen the independent predictors of PNI in rectal cancer,so as to construct a clinical model.The best radiomics features were extracted and screened based on preoperative T2WI and DWI.Then extremely randomized trees,multilayer perceptron,light gradient boosting machine,extreme gradient boosting,support vector machine(SVM),LR,K-nearest neighbor and random forest algorithms were used to construct ML models,respectively,and the optimal ML model was selected to establish a clinical-radiomics ML model combined with clinical relevant independent predictors.The predictive efficacy and clinical value of each model were evaluated.Results Patients' age was the independent predictor of PNI of rectal cancer(OR=0.988,P＜0.001),and the area under the curve(AUC)of the clinical model constructed based on it was 0.435 and 0.458 in training and test sets,respectively.SVM model was the best one among 8 ML models,with AUC in training and test set of 0.887 and 0.854,respectively.The AUC of clinical-radiomics ML model in training and test sets was 0.887 and 0.860,respectively,not different with AUC of SVM model(both P＞0.05).Decision curve analysis showed that when the threshold value was 0.20-0.45,clinical net benefit of SVM model was higher than that of other models.Conclusion SVM model based on T2WI and DWI radiomics could effectively predict PNI of rectal cancer.