Irritable bowel syndrome （IBS） is a functional bowel disorder characterized primarily by abdominal pain and altered defecation habits. In recent years， traditional Chinese medicine （TCM） has made progress in multiple aspects of IBS research and treatment， including syndrome distribution， development of TCM formulas， clinical efficacy evaluation， external therapies， and psychosocial regulation. However， it still faces challenges such as over-reliance on symptomatic manifestations rather than biomarkers for diagnostic criteria， and the lack of high-quality evidence-based data supporting the efficacy of TCM formulas in treating IBS. This paper proposed that TCM diagnosis and treatment of IBS should adhere to the strategy of integrating the holistic concept with syndrome differentiation and treatment， combining TCM external therapies such as acupuncture， moxibustion and acupoint application）， and emphasizing individualized diagnosis and treatment for psychosomatic abnormalities. Future research should integrate multi-omics technologies， artificial intelligence and other methods to deepen the understanding of the pathogenesis of IBS and the mechanisms of TCM formulas， so as to promote the standardization and internationalization of TCM in the diagnosis and treatment of IBS.

Objective To investigate the expression of arginase Ⅱ(ArgⅡ)in kidney tissue of rats with diabetic nephropathy(DN)and its significance in the development of DN.Methods A total of 10 male SD rats were randomly divided into the control group and the model group,with 5 rats in each group.An rat model of DN was developed by feeding with high-sugar and high-fat diet combined with intra-peritoneal injection of low-dose streptozotocin(45 mg/kg),and they were sacrificed after 11 weeks of continued feeding.The body weight,and biochemical indexes of blood and urine of rats were determined.The right kidney was weighed and histopathological examination was performed.The pathological changes of kidney tissues and protein expression of ArgⅡ and CD68+were observed,and the immunofluores-cence double staining was used to observe the distribution and expression of ArgⅡand a marker of renal macrophage activation CD68+;the protein expression of ArgⅡ,NF-κB,TNF-α and IL-6 in kidney tissues was determined by Western blot.Results Compared with the control group,the ratio of kidney weight to body weight,24-hour urine volume,24-hour urine protein,fasting blood glucose,urea nitrogen and insulin level in the model group were significantly increased(P<0.05).The renal histopathology showed that the mesangial cells of the renal glomerular were necrotic with vascular dilatation,and the renal tubular epithelial cells were steatosis and congestion.Compared with the control group,the protein expression of ArgⅡ,CD68+,NF-κB,TNF-α and IL-6 in the kidney tissues of the model group were significantly increased(P<0.05).Immunofluorescence double staining demonstrated the co-expression of ArgⅡ and CD68+in renal tissue,and the fluorescence intensities of both ArgⅡ and CD68+in the model group were significantly stronger than those in the control group(P<0.01).Conclusion The expression of ArgⅡ is increased in DN,which may be participated in the occurrence of inflammatory lesions in DN.

Bronchiectasis(BE) is the third major chronic airway disease, and its incidence rate shows a continuously increasing trend. Bronchiectasis is a highly heterogeneous chronic airway disease. Due to structural alterations, airflow limitation, and mucus hypersecretion, clinical treatment faces many challenges. Particularly, problems including Pseudomonas aeruginosa-dominant drug-resistant bacterial colonization, recurrent infections, airway mucus hypersecretion, and impaired lung function are the most urgent, requiring long-term and personalized treatment and management integrating traditional Chinese and western medicine to prevent the recurrence and continuous progression of the disease. In recent years, both traditional Chinese medicine and western medicine have made certain progress in pathogenesis theories, clinical studies, and basic research regarding the therapeutic challenges of bronchiectasis. Therefore, this paper summarized relevant research from the past 10 years and explored future directions and potential advantages of integrated traditional Chinese and western medicine treatment, providing references for optimizing the clinical management strategies for bronchiectasis.
Bronchiectasis/drug therapy* ; Humans ; Drugs, Chinese Herbal/therapeutic use* ; Medicine, Chinese Traditional/methods* ; Animals

Objective To establish and evaluate a nomogram prediction model for spontaneous peritonitis in HBV-related primary liver cancer.Methods A retrospective study was conducted on 1298 patients with HBV-related primary liver cancer hospitalized in the Kunming Third People's Hospital from January 2012 to December 2022.General data and serological indicators were collected,and patients were divided into infection group(n=262)and control group(n=1036)based on the occurrence of spontaneous peritonitis.Univariate and LASSO regression analyses were used to screen variables,followed by binary logistic regression to analyze the influencing factors of spontaneous peritonitis in HBV-related primary liver cancer patients,leading to the establishment of a nomogram prediction model.Finally,the Hosmer-lemeshow(H-L)goodness of fit test,receiver operating characteristic(ROC)curve,calibration curve,decision curve analysis(DCA)and clinical impact curve(CIC)were utilized to evaluate the fit degree,accuracy,calibration,and clinical practicability of the nomogram prediction model.Results Single factor analysis revealed significant differences between infection group and control group in portal vein cancer thrombus(PVTT),Child-Pugh grade,China Liver Cancer Staging(CNLC)stage,alcohol consumption history,smoking history,white blood cell count(WBC),neutrophil count(NE),hemoglobin(Hb),fibrinogen(FIB),abnormal prothrombin(PIVKA-Ⅱ),aspartate aminotransferase(AST),alanine aminotransferase(ALT),total protein(TP),prealbumin(PA),γ-glutamyltransferase(GGT),alkaline phosphatase(ALP),cholinesterase(CHE),total bile acid(TBA),total cholesterol(TC),low density lipoprotein(LDL),creatinine(Cr),HBV DNA,CD3+T cells count,CD4+T cells count,CD8+T cells count,CD4+T cells/CD8+T cells ratio,procalcitonin(PCT),serum amyloid A(SAA),interleukin-6(IL-6),high-sensitivity C-reactive protein(hs-CRP),alpha-fetoprotein(AFP),and IL-4(P＜0.05).LASSO regression analysis identified 5 variables:Child-Pugh grade,PVTT,WBC,CHE and hs-CRP.Binary logistic regression analysis indicated that Child-Pugh grade(Grade B:OR=5.780,95％CI 3.271-10.213,P＜0.001;Grade C:OR=14.818,95％CI 7.697-28.526,P＜0.001),PVTT(OR=2.893,95％CI 2.037-4.108,P＜0.001),WBC(OR=1.088,95％CI 1.031-1.148,P=0.002),and hs-CRP(OR=1.005,95％CI 1.001-1.010,P=0.026)were the independent risk factors of spontaneous peritonitis in HBV-related primary liver cancer patients.Using these 4 variables,a nomogram prediction model was constructed and evaluated.The P-value of the H-L goodness of fit test was 0.760.Moreover,the area under ROC curve(AUC)was 0.866,with a sensitivity of 0.870 and a specificity of 0.716.The average absolute error of the calibration curve is 0.022.DCA and CIC analyses demonstrated that the nomogram prediction model possessed some clinical utility.Conclusion The nomogram prediction model for spontaneous peritonitis in HBV-related primary liver cancer patients,constructed using Child-Pugh grade,PVTT,WBC and hs-CRP,exhibits a high fitting degree and accuracy,with the prediction probability highly consistent with the actual occurrence probability,and possesses certain clinical practicability.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

The lab module of exploratory experiment is newly designed in the practical course of bio-chemistry.Here we describe one of the experimental projects,and it originates from new scientific re-search results on the dynamic structure of ATP synthase.This exploratory experiment is organized in the form of real scientific research,which would fully mobilize the initiative and creativity of students in learning theoretical knowledge and experimental technology.Students work in groups and start with refer-ence reading.Through cooperation,they must develop certain experimental plan,handle samples with photocrosslinking technique and utilize the high-throughput electrophoresis method to analyze the dynamic structural change of ε subunit in ATP synthase under different physiological conditions.High quality re-sults from high-throughput electrophoresis can only be obtained through optimized operation and treat-ment,from which students would experience the process of technological innovation.The teaching process of this lab module embodies the student-centered teaching concept and is widely approved and supported by students.The project of ATP synthase closely combines the content of lab course with cut-ting-edge technology.Students can deeply experience the importance of experimental technology innova-tion in solving scientific problems.The practical ability of students would be comprehensively improved through this lab module.

Objective To develop a deep learning model based on fundus retinal images to improve the detection rate of mild cognitive impairment(MCI)in patients with coronary heart disease,achieve early intervention and improve prognosis.Methods The study was a single-center cross-sectional study that retrospectively included patients diagnosed with coronary heart disease(CHD)by coronary angiography(≥50％ stenosis of at least one coronary vessel)from Beijing Anzhen Hospital between November 2021 and December 2022.The whole data set was randomly divided into the training set and the testing set according to the ratio of 8∶2 for model development.After that,the patient data of the same center from January 2023 to April 2023 were included in the time verification method to verify the model.The diagnostic criteria for MCI were MMSE＜27 or MoCA＜26.Four kinds of convolutional neural network(CNN)architectures were used to train fundus images,and a comprehensive vision model of MCI detection was established through model integration.The area under the curve(AUC),sensitivity and specificity of the receiver operating curve(ROC)were used to evaluate the performance of the AI model.Results We collected 5 880 eligible fundus images from 3 368 CHD patients.Based on the results of the MMSE scale,the algorithm was labeled,including 2 898 males and 527 MCI patients.The AUC of the deep learning model in the test group is 0.733(95％CI 0.688-0.778),and the sensitivity of the algorithm in the test group is 0.577(95％CI 0.528-0.625)by using the operating point with the maximum sum of sensitivity and specificity.With a specificity of 0.758(95％CI 0.714-0.802),corresponding to a validated AUC of 0.710(95％CI 0.601-0.818).Based on the results of the MoCA scale,the algorithm labels 2 437 males and 1 626 MCI patients.The AUC of the deep learning model in the test group was 0.702(95％CI 0.671-0.733).The operating point with the maximum sum of sensitivity and specificity was selected,and the sensitivity of the algorithm was 0.749(95％CI 0.719-0.778)and the specificity was 0.561(95％CI 0.527-0.595),corresponding to the AUC value of the verification group was 0.674(95％CI 0.622-0.726).Conclusions The deep learning algorithm model based on fundus images has good diagnostic performance,and may be used as a new non-invasive,convenient and rapid screening method for MCI in CHD population.

Objective:This study aimed to integrate RNA-seq data to identify key genes associated with macrophage-ferroptosis and develop a prognostic model for hepatocellular carcinoma(HCC).Methods:We retrieved a dataset from the GEO database containing transcriptome data and clinical information for 163 samples.Macrophage-related genes were identified using WGCNA and immune infiltration analyses.Ferroptosis-related genes from the FerrDbV2 database were intersected with differentially expressed genes to obtain significant macrophage-ferroptosis-related genes.Hub genes were screened via protein interaction network construction,and key genes were identified using four machine learning algorithms.These genes exhibited significant expression differences between cancer and normal tissues and were closely linked to patient prognosis.ROC curve and KM survival analyses were performed,and expression levels were validated at the transcriptome and proteome levels.Results:Four key genes RRM2,KIF20A,PCK2,and PDK4 were identified and evaluated.RRM2 and KIF20A demonstrated high importance in classification prediction models and reliable performance in ROC and KM analyses(AUC＞0.9,P＜0.05).These genes regulate cancer cell proliferation/survival and macrophage polarization/function,influencing the tumor microenvironment and HCC progression.Conclusion:RRM2 and KIF20A regulate cancer cell proliferation and survival,modulate macrophage polarization and function,and influence the immune response in the tumor microenvironment.They can serve as prognostic biomarkers and potential immunotherapy targets for hepatocellular carcinoma.

Objective To investigate the expression of arginase Ⅱ(ArgⅡ)in kidney tissue of rats with diabetic nephropathy(DN)and its significance in the development of DN.Methods A total of 10 male SD rats were randomly divided into the control group and the model group,with 5 rats in each group.An rat model of DN was developed by feeding with high-sugar and high-fat diet combined with intra-peritoneal injection of low-dose streptozotocin(45 mg/kg),and they were sacrificed after 11 weeks of continued feeding.The body weight,and biochemical indexes of blood and urine of rats were determined.The right kidney was weighed and histopathological examination was performed.The pathological changes of kidney tissues and protein expression of ArgⅡ and CD68+were observed,and the immunofluores-cence double staining was used to observe the distribution and expression of ArgⅡand a marker of renal macrophage activation CD68+;the protein expression of ArgⅡ,NF-κB,TNF-α and IL-6 in kidney tissues was determined by Western blot.Results Compared with the control group,the ratio of kidney weight to body weight,24-hour urine volume,24-hour urine protein,fasting blood glucose,urea nitrogen and insulin level in the model group were significantly increased(P<0.05).The renal histopathology showed that the mesangial cells of the renal glomerular were necrotic with vascular dilatation,and the renal tubular epithelial cells were steatosis and congestion.Compared with the control group,the protein expression of ArgⅡ,CD68+,NF-κB,TNF-α and IL-6 in the kidney tissues of the model group were significantly increased(P<0.05).Immunofluorescence double staining demonstrated the co-expression of ArgⅡ and CD68+in renal tissue,and the fluorescence intensities of both ArgⅡ and CD68+in the model group were significantly stronger than those in the control group(P<0.01).Conclusion The expression of ArgⅡ is increased in DN,which may be participated in the occurrence of inflammatory lesions in DN.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.