Ulcerative colitis(UC)is a chronic,progressive inflammatory bowel disease associated with an increased risk of color-ectal cancer.In recent years,traditional Chinese medicine(TCM)has been increasingly utilized in the treatment of UC,and its thera-peutic efficacy has gained widespread recognition.Nevertheless,several challenges remain in optimizing TCM's role in UC manage-ment,which hinder the full realization of its potential benefits.This paper reviews the current status of TCM in treating UC,identifies existing challenges,and proposes targeted strategies to promote the advancement of TCM in this field.

Ulcerative colitis(UC)is a chronic,progressive inflammatory bowel disease associated with an increased risk of color-ectal cancer.In recent years,traditional Chinese medicine(TCM)has been increasingly utilized in the treatment of UC,and its thera-peutic efficacy has gained widespread recognition.Nevertheless,several challenges remain in optimizing TCM's role in UC manage-ment,which hinder the full realization of its potential benefits.This paper reviews the current status of TCM in treating UC,identifies existing challenges,and proposes targeted strategies to promote the advancement of TCM in this field.

OBJECTIVE To analyze the plasma-entering components of the ethanol extract of Yu-Ping-Feng-San by ultra-per-formance liquid chromatography-quadrupole-time of flight tandem mass spectrometry(UPLC-Q-TOF-MS)technology,so as to pro-vide a reference for the study of the pharmacological substances of Yu-Ping-Feng-San.METHODS Male BALB/c mice were ga-vaged with the ethanol extract of Yu-Ping-Feng-San(6.5 g·kg-1),and plasma samples were collected before administration(0 h)and 1,2,4,6,8,and 12 h after administration.The UPLC-Q-TOF-MS technology was used to analyze and identify the components absorbed into the plasma.RESULTS A total of 73 chemical components in the ethanol extract of Yu-Ping-Feng-San were identi-fied,and 26 plasma-entering components were identified in all plasma samples.Among the plasma-entering components,11 compo-nents were from Saposhnikoviae Radix,and 15 components were from Radix Astragali.CONCLUSION The chemical components and plasma-entering components of the ethanol extract of Yu-Ping-Feng-San were analyzed comprehensively,which provides a reference for further clarifying the pharmacological substance basis and mechanism of action of Yu-Ping-Feng-San.

Objective:To explore the effect of learning stress on weight and the mediating effect abnormal eating behavior in junior high school students.Methods:By multi-stage stratified cluster sampling,2 686 junior high school students were selected and investigated the learning stress and abnormal eating behavior with the middle school students' learning stress questionnaire and the Chinese version of the Dutch eating behavior questionnaire(C-DEBQ).The height and weight of the students were measured and the body mass index(BMI)was calculat-ed.Bootstrap method was used to analyze the mediating effect by using Process 4.1 software.Results:The preva-lence of overweight and obesity among 2 686 junior high school students was 19.8％.The score of middle school students'learning stress questionnaire was(57.6±11.3),the score of restricted eating was(20.0±7.3),the score of emotional eating was(21.3±9.5),and the score of exogenous eating was(25.8±7.7).There was a positive correlation between learning stress,restricted eating,emotional eating,overweight and obesity(r=0.08-0.24,Ps＜0.001).In the relationship between learning stress and overweight and obesity,the total indirect effect coefficient of restrictive eating behavior and emotional eating behavior was 0.013,indicating a partial mediating effect(95％CI:0.010-0.015,0.001-0.002).Conclusion:Learning stress could directly or indirectly affect the weight of junior high school students through abnormal eating behavior,increasing the risk of overweight and obesity.

AIM:To analyze differential proteins associated with the pathogenesis of dry eye(DE)using bioinformatics methods, in order to reveal their potential molecular mechanisms.METHODS: Articles published in PubMed and EMBASE databases from the inception of the database to August 31, 2023, that used proteomic methods to detect protein expression in clinical samples of dry eye were searched. Differential proteins were selected and further analyzed using the STRING database and Cytoscape software for hub gene screening and module analysis. Protein-protein interaction(PPI)analysis, gene ontology(GO)functional annotation, and Kyoto encyclopedia of genes and genomes(KEGG)pathway enrichment analysis were performed.RESULTS: A total of 21 articles were included, identifying 74 differentially expressed proteins. The most frequently occurring differential proteins were calgranulin A(SA1008), lipocalin-1(LCN1), lysozyme C(LYZ), mammaglobin-B(SCGB2A1), proline-rich protein 4(PRR4), transferrin(TF), and calgranulinB(S100A9). The top 10 hub genes were serum albumin(ALB), tumor necrosis factor(TNF), interleukin 6(IL6), IL1B, IL8, matrix metalloproteinase 9(MMP9), alpha-1-antitrypsin(SERPINA1), IL10, complement component 3(C3), and lactotransferrin(LTF). Module analysis suggested MMP9 and PRR4 as seed genes. KEGG analysis showed that differential proteins were mainly enriched in the IL17 signaling pathway(61.9%).CONCLUSION: The results reveal potential molecular targets and pathways for DE and confirm the association between the pathogenesis of DE and inflammation. Further in-depth research is needed to confirm the significance of these biomarkers in clinical practice.

Objective:To explore the quantitative indexes of dual energy spectrum CT and related clinical data to establish a predictive model for predicting pneumoconiosis.Methods:In April 2024, the information of 203 pneumoconiosis patients diagnosed by the occupational disease appraisal expert group in the Third People's Hospital of Xinjiang Uygur Autonomous Region (Occupational Disease Hospital of Xinjiang Autonomous Region) from January 2022 to December 2023 was retrospectively analyzed. Another 207 non-pneumoconiosis patients with dust exposure history were selected as control group. The measurement data between the two groups were compared using T test or Wilcoxon in dependent quality test, count date asing chi-square or Fishers test, the energy spectrum related indicators and clinical indicators of the patients were compared between groups, and potential factors for diagnosis of pneumoconiosis were screened through univariate analysis, and independent risk factors were further determined by multivariate logistic regression. Based on the results of regression analysis, the machine learning model was constructed, and the reciver operating characteristic curve (ROC) was drawn to evaluate the efficiency of the model, and the Area under cruve (AUC) value, sensitivity and specificity were calculated.Results:Smoking, lung tissue mass, silicon dioxide (SiO 2) equivalent total mass and SiO 2 equivalent concentration were the risk factors for pneumoconiosis ( P<0.05) . Multivariate logistic regression analysis showed that smoking, lung tissue mass, total lung SiO 2 equivalent total volume and total lung SiO 2 equivalent total mass were independent predicators of the diagnosis of pneumoconiosis ( OR=0.53, 0.99, 1.13, 0.85, P<0.05) . Logistic regression machine learning was used to establish a predictive model, and the training set AUC was 0.74, and the verification set AUC was 0.72, indicating that the model had good accuracy and certain ability to diagnose pneumoconiosis. Conclusion:The machine learning prediction model established by the quantitative analysis index of dual energy spectrum CT and clinical related indexes has a good diagnostic performance for the diagnosis of pneumoconiosis.

To explore the interaction between ASFV capsid protein M1249L and host from the host cellular perspective,M1249L was selected for constructing the bait plasmid(pGBKT7-M1249L)to screen the bone marrow-derived macrophages(BMDMs)cDNA library.After again co-transform and sequence alignment,20 candidate interacting host proteins were screened,such as IL-1β,CTSB and DNAJA3.And then,co-immunoprecipitation assay was performed to verify the interaction be-tween M1249L and host proteins.GO ontology(GO)and KEGG pathway enrichment analyses re-vealed that biological regulation,cellular communication and response to stimulus and others were enriched in biological processes.And these host proteins could share some pathways,including toll-like receptor signaling pathway and Nod-like receptor signaling pathway.Therefore,the results provides the theoretical basis for further research on the mechanism of ASFV M1249L in viral in-fection and immune regulation.

Objective:To explore the quantitative indexes of dual energy spectrum CT and related clinical data to establish a predictive model for predicting pneumoconiosis.Methods:In April 2024, the information of 203 pneumoconiosis patients diagnosed by the occupational disease appraisal expert group in the Third People's Hospital of Xinjiang Uygur Autonomous Region (Occupational Disease Hospital of Xinjiang Autonomous Region) from January 2022 to December 2023 was retrospectively analyzed. Another 207 non-pneumoconiosis patients with dust exposure history were selected as control group. The measurement data between the two groups were compared using T test or Wilcoxon in dependent quality test, count date asing chi-square or Fishers test, the energy spectrum related indicators and clinical indicators of the patients were compared between groups, and potential factors for diagnosis of pneumoconiosis were screened through univariate analysis, and independent risk factors were further determined by multivariate logistic regression. Based on the results of regression analysis, the machine learning model was constructed, and the reciver operating characteristic curve (ROC) was drawn to evaluate the efficiency of the model, and the Area under cruve (AUC) value, sensitivity and specificity were calculated.Results:Smoking, lung tissue mass, silicon dioxide (SiO 2) equivalent total mass and SiO 2 equivalent concentration were the risk factors for pneumoconiosis ( P<0.05) . Multivariate logistic regression analysis showed that smoking, lung tissue mass, total lung SiO 2 equivalent total volume and total lung SiO 2 equivalent total mass were independent predicators of the diagnosis of pneumoconiosis ( OR=0.53, 0.99, 1.13, 0.85, P<0.05) . Logistic regression machine learning was used to establish a predictive model, and the training set AUC was 0.74, and the verification set AUC was 0.72, indicating that the model had good accuracy and certain ability to diagnose pneumoconiosis. Conclusion:The machine learning prediction model established by the quantitative analysis index of dual energy spectrum CT and clinical related indexes has a good diagnostic performance for the diagnosis of pneumoconiosis.

Abstract In recent years, the prevalence of overweight and obesity among children and adolescents has risen rapidly, posing a serious threat to their physical and mental health. To provide scientific, systematic, and standardized weight management guidance for overweight and obese children and adolescents, the study focuses on the core concept of healthy lifestyle intervention, integrates multidisciplinary expert opinions and research findings,and proposes a comprehensive multidisciplinary intervention framework covering scientific exercise intervention, precise nutrition and diet, optimized sleep management, and standardized psychological support. It calls for the establishment of a multi agent collaborative management mechanism led by the government, implemented by families, fostered by schools, initiated by individuals, optimized by communities, reinforced by healthcare, and coordinated by multiple stakeholders. Emphasizing a child and adolescent centered approach, the consensus advocates for comprehensive, multi level, and personalized guidance strategies to promote the internalization and maintenance of a healthy lifestyle. It serves as a reference and provides recommendations for the effective prevention and control of overweight and obesity, and enhancing the health level of children and adolescents.

OBJECTIVE To analyze the plasma-entering components of the ethanol extract of Yu-Ping-Feng-San by ultra-per-formance liquid chromatography-quadrupole-time of flight tandem mass spectrometry(UPLC-Q-TOF-MS)technology,so as to pro-vide a reference for the study of the pharmacological substances of Yu-Ping-Feng-San.METHODS Male BALB/c mice were ga-vaged with the ethanol extract of Yu-Ping-Feng-San(6.5 g·kg-1),and plasma samples were collected before administration(0 h)and 1,2,4,6,8,and 12 h after administration.The UPLC-Q-TOF-MS technology was used to analyze and identify the components absorbed into the plasma.RESULTS A total of 73 chemical components in the ethanol extract of Yu-Ping-Feng-San were identi-fied,and 26 plasma-entering components were identified in all plasma samples.Among the plasma-entering components,11 compo-nents were from Saposhnikoviae Radix,and 15 components were from Radix Astragali.CONCLUSION The chemical components and plasma-entering components of the ethanol extract of Yu-Ping-Feng-San were analyzed comprehensively,which provides a reference for further clarifying the pharmacological substance basis and mechanism of action of Yu-Ping-Feng-San.