ObjectiveTo explore the effects of Yimei Baijiang formula （YMBJF） on colitis-associated colorectal cancer （CAC） and the nuclear factor kappaB （NF-κB） signaling pathway in mice. MethodsSixty male Balb/c mice of 4-6 weeks old were randomized into 6 groups： Normal， model， capecitabine （0.83 g·kg^-1）， and low-， medium-， and high-dose （4.63， 9.25， 18.50 g·kg^-1， respectively） YMBJF. The mouse model of CAC was established by combining azoxymethane （AOM， 10 mg·kg^-1） and dextran sulfate sodium （DSS， 2%）. At the 5^th week after the start of modeling， the capecitabine group and the YMBJF groups were respectively administrated with the corresponding doses of drugs by gavage once a day for a total of 6 weeks. The body weights and general conditions of mice were measured and observed， and the disease activity index （DAI） was scored. The tumors in the colorectal tissue were counted， and the colorectal length was measured. The histological features of the colorectal tissue in each group of mice were observed by hematoxylin-eosin （HE） staining. The levels of inflammatory cytokines including interleukin-6 （IL-6）， interleukin-1β （IL-1β）， and tumor necrosis factor-α （TNF-α） in the serum were determined by enzyme-linked immunosorbent assay （ELISA）. The expression and localization of proliferating cell nuclear antigen-67 （Ki67）， proliferating cell nuclear antigen （PCNA）， and phosphorylated nuclear transcription factor-κB p65 （p-NF-κB p65） proteins were observed by immunohistochemistry （IHC）. The apoptosis of tumor cells was observed by the TUNEL assay. The mRNA levels of IL-6，IL-1β， TNF-α， nuclear transcription factor-κB p65 （NF-κB p65）， NF-κB inhibitor alpha （IκBα）， B-cell lymphoma-2 （Bcl-2）， and Bcl-2-associated X protein （Bax） in the colorectal tissue were quantified by Real-time polymerase chain reaction（Real-time PCR）. The protein levels of NF-κB p65， phosphorylated （p）-NF-κB p65， IκBα， p-IκBα， Bcl-2， and Bax in the colorectal tissue were determined by Western blot. ResultsCompared with the model group， each YMBJF group showed decreases in DAI and tumor number （P0.01）， and the medium-dose and high-dose YMBJF groups showed increases in colorectal length （P0.05）. In addition， each YMBJF group showed alleviated colorectal mucosal pathological injury， declined levels of IL-6， IL-1β， and TNF-α in the serum （P0.05）， reduced expression of Ki67 and PCNA （P0.01）， and down-regulated expression of p-NF-κB p65 （P0.05）. The apoptosis in the medium-dose and high-dose YMBJF groups increased （P0.01）. Each YMBJF group and the capecitabine group showed down-regulated mRNA levels of IL-1β， TNF-α， IL-6， NF-κB p65， and Bcl-2 （P0.05） and up-regulated mRNA levels of IκBα and Bax （P0.05）. The mRNA level of IκBα was up-regulated， and that of Bcl-2 was down-regulated （P0.05） in the high-dose YMBJF group. The protein levels of p-IκBα and Bcl-2 were down-regulated （P0.05） in each YMBJF group. The protein levels of IκBα and Bax were up-regulated in the medium-dose and high-dose YMBJF groups （P0.01）， while those of NF-κB p65 and p-NF-κB p65 were down-regulated （P0.05）. ConclusionYMBJF can reduce the number of tumors， reduce the levels of inflammatory factors， promote tumor cell apoptosis， and inhibit tumor cell proliferation by regulating the direct effector molecules of the NF-κB signaling pathway in the mouse model of CRC.

ObjectiveTo explore the effects of Yimei Baijiang formula （YMBJF） on colitis-associated colorectal cancer （CAC） and the nuclear factor kappaB （NF-κB） signaling pathway in mice. MethodsSixty male Balb/c mice of 4-6 weeks old were randomized into 6 groups： Normal， model， capecitabine （0.83 g·kg^-1）， and low-， medium-， and high-dose （4.63， 9.25， 18.50 g·kg^-1， respectively） YMBJF. The mouse model of CAC was established by combining azoxymethane （AOM， 10 mg·kg^-1） and dextran sulfate sodium （DSS， 2%）. At the 5^th week after the start of modeling， the capecitabine group and the YMBJF groups were respectively administrated with the corresponding doses of drugs by gavage once a day for a total of 6 weeks. The body weights and general conditions of mice were measured and observed， and the disease activity index （DAI） was scored. The tumors in the colorectal tissue were counted， and the colorectal length was measured. The histological features of the colorectal tissue in each group of mice were observed by hematoxylin-eosin （HE） staining. The levels of inflammatory cytokines including interleukin-6 （IL-6）， interleukin-1β （IL-1β）， and tumor necrosis factor-α （TNF-α） in the serum were determined by enzyme-linked immunosorbent assay （ELISA）. The expression and localization of proliferating cell nuclear antigen-67 （Ki67）， proliferating cell nuclear antigen （PCNA）， and phosphorylated nuclear transcription factor-κB p65 （p-NF-κB p65） proteins were observed by immunohistochemistry （IHC）. The apoptosis of tumor cells was observed by the TUNEL assay. The mRNA levels of IL-6，IL-1β， TNF-α， nuclear transcription factor-κB p65 （NF-κB p65）， NF-κB inhibitor alpha （IκBα）， B-cell lymphoma-2 （Bcl-2）， and Bcl-2-associated X protein （Bax） in the colorectal tissue were quantified by Real-time polymerase chain reaction（Real-time PCR）. The protein levels of NF-κB p65， phosphorylated （p）-NF-κB p65， IκBα， p-IκBα， Bcl-2， and Bax in the colorectal tissue were determined by Western blot. ResultsCompared with the model group， each YMBJF group showed decreases in DAI and tumor number （P0.01）， and the medium-dose and high-dose YMBJF groups showed increases in colorectal length （P0.05）. In addition， each YMBJF group showed alleviated colorectal mucosal pathological injury， declined levels of IL-6， IL-1β， and TNF-α in the serum （P0.05）， reduced expression of Ki67 and PCNA （P0.01）， and down-regulated expression of p-NF-κB p65 （P0.05）. The apoptosis in the medium-dose and high-dose YMBJF groups increased （P0.01）. Each YMBJF group and the capecitabine group showed down-regulated mRNA levels of IL-1β， TNF-α， IL-6， NF-κB p65， and Bcl-2 （P0.05） and up-regulated mRNA levels of IκBα and Bax （P0.05）. The mRNA level of IκBα was up-regulated， and that of Bcl-2 was down-regulated （P0.05） in the high-dose YMBJF group. The protein levels of p-IκBα and Bcl-2 were down-regulated （P0.05） in each YMBJF group. The protein levels of IκBα and Bax were up-regulated in the medium-dose and high-dose YMBJF groups （P0.01）， while those of NF-κB p65 and p-NF-κB p65 were down-regulated （P0.05）. ConclusionYMBJF can reduce the number of tumors， reduce the levels of inflammatory factors， promote tumor cell apoptosis， and inhibit tumor cell proliferation by regulating the direct effector molecules of the NF-κB signaling pathway in the mouse model of CRC.

Objective: To develop a depression recognition model by integrating the spirit-expression diagnostic framework of traditional Chinese medicine (TCM) with machine learning algorithms. The proposed model seeks to establish a TCM-informed tool for early depression screening, thereby bridging traditional diagnostic principles with modern computational approaches. Methods: The study included patients with depression who visited the Shanghai Pudong New Area Mental Health Center from October 1, 2022 to October 1, 2023, as well as students and teachers from Shanghai University of Traditional Chinese Medicine during the same period as the healthy control group. Videos of 3 – 10 s were captured using a Xiaomi Pad 5, and the TCM spirit and expressions were determined by TCM experts (at least 3 out of 5 experts agreed to determine the category of TCM spirit and expressions). Basic information, facial images, and interview information were collected through a portable TCM intelligent analysis and diagnosis device, and facial diagnosis features were extracted using the Open CV computer vision library technology. Statistical analysis methods such as parametric and non-parametric tests were used to analyze the baseline data, TCM spirit and expression features, and facial diagnosis feature parameters of the two groups, to compare the differences in TCM spirit and expression and facial features. Five machine learning algorithms, including extreme gradient boosting (XGBoost), decision tree (DT), Bernoulli naive Bayes (BernoulliNB), support vector machine (SVM), and k-nearest neighbor (KNN) classification, were used to construct a depression recognition model based on the fusion of TCM spirit and expression features. The performance of the model was evaluated using metrics such as accuracy, precision, and the area under the receiver operating characteristic (ROC) curve (AUC). The model results were explained using the Shapley Additive exPlanations (SHAP). Results: A total of 93 depression patients and 87 healthy individuals were ultimately included in this study. There was no statistically significant difference in the baseline characteristics between the two groups (P > 0.05). The differences in the characteristics of the spirit and expressions in TCM and facial features between the two groups were shown as follows. (i) Quantispirit facial analysis revealed that depression patients exhibited significantly reduced facial spirit and luminance compared with healthy controls (P < 0.05), with characteristic features such as sad expressions, facial erythema, and changes in the lip color ranging from erythematous to cyanotic. (ii) Depressed patients exhibited significantly lower values in facial complexion L, lip L, and a values, and gloss index, but higher values in facial complexion a and b, lip b, low gloss index, and matte index (all P < 0.05). (iii) The results of multiple models show that the XGBoost-based depression recognition model, integrating the TCM “spirit-expression” diagnostic framework, achieved an accuracy of 98.61% and significantly outperformed four benchmark algorithms—DT, BernoulliNB, SVM, and KNN (P < 0.01). (iv) The SHAP visualization results show that in the recognition model constructed by the XGBoost algorithm, the complexion b value, categories of facial spirit, high gloss index, low gloss index, categories of facial expression and texture features have significant contribution to the model. Conclusion This study demonstrates that integrating TCM spirit-expression diagnostic features with machine learning enables the construction of a high-precision depression detection model, offering a novel paradigm for objective depression diagnosis.

Objective: Patients with atherosclerotic cardiovascular disease (ASCVD) following percutaneous coronary intervention (PCI) are classified as very-high-risk individuals in cardiovascular disease (CVD) risk stratification. The distribution pattern of traditional Chinese medicine (TCM) syndromes in this patient population, as well as its association with blood lipid profiles and clinical prognosis, remains unclear. The present prospective cohort study aims to investigate these correlations, thereby providing insights to enrich the research fields. Methods: We enrolled consecutive patients with ASCVD who underwent PCI at the Integrated Cardiology Unit of China-Japan Friendship Hospital between September 1, 2020 and December 31, 2022. Demographics and clinical characteristics, signs and symptoms defining each TCM syndrome, and fasting venous blood samples were collected at baseline and follow up or upon major adverse cardiovascular events (MACEs). We analyzed the correlation between TCM syndromes, blood lipid profiles, and MACEs, and developed a new joint prognostic model incorporating both TCM syndromes and blood lipids using logistic regression. The analyses were based on detailed baseline and one-year follow-up data. Results: A per-protocol analysis was performed on 586 patients with complete data ultimately. During the one-year follow-up, 174 patients (29.69%) experienced a MACE. We performed statistical analyses on comorbidities, medication, and biochemical indicators across groups defined by TCM syndrome differentiation. When comparing different TCM syndromes, no significant differences were found in age, body mass index (BMI), history of revascularization, comorbidities, family history of CVD, smoking or drinking, or statin intensity (P > 0.05). Patients with intertwined phlegm and blood stasis syndrome exhibited significantly higher levels of total cholesterol (TC, 5.27 ± 1.18 mmol/L, P < 0.001), triglyceride (TG, 1.96 ± 1.33 mmol/L, P = 0.008), low-density lipoprotein cholesterol (LDL-C, 3.35 ± 0.79 mmol/L, P < 0.001), and high-density lipoprotein cholesterol (HDL-C, 1.24 ± 0.81 mmol/L, P < 0.001) compared with those with other TCM syndromes combined. A multivariable logistic regression model was constructed to predict MACEs. The model included TCM syndrome type [with intertwined phlegm and blood stasis as a predictor, adjusted odds ratio (OR) = 1.413, 95% confidence interval (CI): 0.517 – 3.864, P = 0.501], age (adjusted OR = 0.97, 95% CI: 0.955 – 1.001, P = 0.057), male gender (adjusted OR = 0.698, 95% CI: 0.416 – 1.170, P = 0.173), TC (adjusted OR = 1.004, 95% CI: 0.513 – 1.965, P = 0.990), and LDL-C (adjusted OR = 5.825, 95% CI: 2.214 – 15.326, P < 0.001). This model demonstrated good discriminatory ability for MACEs in post-PCI ASCVD patients [the area under the receiver operating characteristic (ROC) curve (AUC) = 0.865, 95% CI: 0.816 – 0.914]. Conclusion The intertwined phlegm and blood stasis TCM syndrome is associated with a distinct atherogenic lipid profile characterized by elevated levels of TC and LDL-C. The prognostic model that incorporates this TCM syndrome type along with conventional lipid parameters (TC and LDL-C) shows good discriminatory ability for predicting MACEs in ASCVD patients after PCI, underscoring the potential clinical utility of integrating TCM syndrome differentiation into CVD risk assessment.

Objective: To investigate the prevalence of undernutrition and its associated factors among left behind and non left behind primary and secondary school students in the Nutrition Improvement Program for Rural Compulsory Education Students (NIPRCES) areas of central and western China, so as to provide evidence for improving the nutritional status of children and adolescents. Methods: A survey was conducted among 123 782 students selected by random cluster sampling method in grades 3-9 from NIPRCES in central (Hebei, Shanxi, Heilongjiang, Jilin, Anhui, Jiangxi, Henan, Hunan, Hubei, and Hainan) and western (Gansu, Guangxi, Inner Mongolia, Ningxia, Tibet, Shaanxi, Guizhou, Sichuan, Xinjiang, the Xinjiang Production and Construction Corps, Yunnan, Qinghai, and Chongqing) China in 2023. Anthropometric measurements and questionnaires were used to assess nutritional and dietary status. The prevalence of undernutrition was compared between left behind and non left behind students by Chi square test, and associated factors were analyzed by three level Logistic mixed effects model. Results: The prevalence of undernutrition was 8.5% (4 326) in left behind students and 8.1% (5 905) in non left behind students. Three level Logistic mixed effect model analysis showed that whether left behind or non left behind, the undernutrition rates of primary and secondary students in western regions were higher than those of students in central regions [ OR (95% CI )=1.72(1.57-1.87),2.25(2.07- 2.43 )]; the undernutrition risk was lower for those whose fathers had a cultural level of high school or above [ OR (95% CI )=0.69(0.62-0.77),0.90(0.82-0.98)] or junior high school [ OR (95% CI )=0.72(0.66-0.79),0.92(0.85-0.99)] compared to those with primary school or below; picky eating or selective eating increased the risk of undernutrition [ OR (95% CI )=2.36(2.07-2.68),2.28(2.04-2.55)], and primary and secondary school students without nutritional content in health education classes had higher rates of undernutrition [ OR (95% CI )=1.12(1.03-1.23),1.09(1.01-1.17)](all P <0.05). Conclusion The prevalence of undernutrition is slightly higher in left behind primary and secondary students than in non left behind primary and secondary students in central and western NIPRCES areas, with variations across different characteristics.

In the context of the modernization of traditional Chinese medicine （TCM）， the inheritance of the experiences of famous doctors faces significant challenges due to its complex nonlinear characteristics and dynamic evolution. There are still issues in the current inheritance system， such as the homogenization of talent cultivation models， lack of standardized mentoring practices， and monotonous evaluation method， which hinder the systematic inheritance and innovative development of famous doctors' experiences. Based on a systematic review of the current state of artificial intelligence （AI）-assisted inheritance of famous doctors' experiences， this study explores innovative pathways for deep integration of modern information technologies with famous doctors' experiences from key dimensions， including data authenticity assurance， interdisciplinary collaboration mechanisms， and the establishment of dynamic inheritance standards. It proposes a paradigm shift in the inheritance of TCM famous doctors' experiences in the AI era， aiming to build a new TCM inheritance system of "digital intelligence empowerment and cross-disciplinary innovation"， providing theoretical support and practical pathways for the inheritance of famous doctors' experiences in TCM.

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 evaluate the antitumor activity of Periplaneta americana extract（PAE） against human-derived lung adenocarcinoma organoids（LUAD-PDOs） and to elucidate its potential mechanism based on transcriptomics. MethodsFresh tumor and adjacent normal tissues from patients with LUAD were collected to construct LUAD-PDOs and normal lung organoid（Nor-PDOs） models using 3D organoid culture technology. The effective intervention concentration of PAE was determined using the cell counting kit-8（CCK-8） assay. Experimental groups included the model group（LUAD-PDOs）， normal group， model administration group（LUAD-PDOs+PAE）， and normal administration group（Nor-PDOs+PAE）. Hematoxylin-eosin（HE） staining was used to observe the pathological structures of PDOs， immunohistochemistry（IHC） was performed to detect the expressions of the proliferation marker Ki-67 and lung adenocarcinoma differentiation markers cytokeratin-7（CK-7） and Napsin A， TUNEL staining was applied to detect cell apoptosis. RNA sequencing（RNA-Seq） was conducted to identify differentially expressed genes（DEGs）， followed by Gene Ontology（GO）， Kyoto Encyclopedia of Genes and Genomes（KEGG）， and Gene Set Enrichment Analysis（GSEA）， alongside protein-protein interaction（PPI） network analysis to screen core mechanisms. Finally， key targets were validated by integrating external database analysis with immunofluorescence（IF）. ResultsNor-PDOs and LUAD-PDOs that highly recapitulated the pathological characteristics of the primary tissues were successfully established. The CCK-8 assay determined that the effective intervention concentration of PAE was 16 g·L^-1. Morphological observation showed that Nor-PDOs exhibited lumen-forming structures， whereas LUAD-PDOs displayed dense， solid structures. CCK-8 and TUNEL assays revealed that， compared with the model group， PAE intervention inhibited the proliferation of LUAD-PDOs and promoted apoptosis in LUAD cells， while showing no significant effect on the viability of Nor-PDOs. Transcriptomic analysis identified 719 DEGs that were significantly reversed after PAE intervention（347 up-regulated and 372 down-regulated）（P<0.05）. GO enrichment analysis indicated that DEGs in the model administration group were significantly enriched in biological processes related to cell cycle regulation compared to the model group. KEGG pathway analysis revealed that PAE affected pathways related to proliferation and metabolism， including pathways in cancer and the p53 signaling pathway. GSEA further confirmed that PAE significantly enhanced the activity of the p53 signaling pathway（P<0.05）. PPI network analysis indicated that breast cancer type 1 susceptibility protein（BRCA1） and checkpoint kinase 1（CHEK1） were the core down-regulated targets in the p53 pathway. IF verified the high expression of BRCA1 and CHEK1 in LUAD-PDOs and their significant downregulation after PAE intervention（P<0.05）. Furthermore， survival analysis based on The Cancer Genome Atlas（TCGA） database indicated that low expression of BRCA1 and CHEK1 was significantly associated with prolonged overall survival in patients with LUAD（P<0.05）. ConclusionPAE effectively inhibits proliferation of LUAD-PDOs and promotes their apoptosis， its anti-tumor mechanism is potentially associated with the activation of the p53 signaling pathway， with BRCA1 and CHEK1 genes likely serving as key downstream targets for the effects of PAE.

Objective To establish a stable and reliable sepsis model of rat after liver transplantation (LT) for clinical translational research and analyze its characteristics. Methods The "two-sleeve method" was used to establish the in situ LT model of SD rats, and the sepsis model was constructed through cecal ligation and puncture (CLP) at 3 d after the operation. SD rats were randomly divided into 3 groups: sham operation group (Sham group), LT group, and LT + CLP group, with 6 rats in each group. The changes in body weight, rectal temperature and survival rate were compared, and the sepsis score was used for evaluation. The levels of blood biochemical indicators [alanine aminotransferase (ALT), aspartate aminotransferase (AST), urea (Urea), creatinine (Cr), creatine kinase (CK), lactate dehydrogenase (LDH)] and inflammatory factors [interleukin (IL)-1β, IL-6, IL-10, tumor necrosis factor (TNF)-α] in each group were detected, and the pathological changes and cell apoptosis in different organs were observed. Results Compared with the Sham group, the body weight of the LT group and LT + CLP group decreased (all P<0.05). The rectal temperature of the LT + CLP group showed a continuous downward trend after the operation, the sepsis score increased sharply after the operation, and the survival rate dropped to 16.7%, and the differences between the Sham group, LT group and LT + CLP group were statistically significant (all P<0.05). The levels of ALT, AST, Urea, Cr, CK, LDH, and serum IL-1β, IL-6, IL-10 and TNF-α in the LT + CLP group were higher than those in the Sham group and LT group rats within 72 hours after the operation(all P<0.05). The pathological examination of the LT + CLP group showed severe tissue structure destruction, necrosis and infiltration of inflammatory cells in multiple organs, and terminal deoxynucleotidyl transferase-mediated dUTP nick-end labeling (TUNEL) staining showed an increased level of cell apoptosis in multiple organs. Conclusions Using liver transplantation combined with CLP, a stable animal model of liver transplantation infection is successfully established, which exhibits a high mortality rate, significant multi-organ damage and intense inflammatory response, providing an ideal animal model for transplantation infection research.