Traditional Chinese Medicine (TCM), as a treasure of the Chinese nation, plays a significant role in maintaining public health. In 2019, the Central Committee of the Communist Party of China and the State Council proposed for the first time the establishment of a TCM registration and evaluation evidence system that integrates TCM theory, "personal experience" and clinical trials (referred to as the "Three-in-One" System) to promote the inheritance and innovation of TCM. Subsequently, the National Medical Products Administration issued several guiding principles to advance the improvement and implementation of this system. Owing to the complexity of its implementation, there are still differing understandings within the TCM industry regarding the positioning of the "Three-in-One" Registration and Evaluation Evidence System, as well as the connotation and value orientation of the "personal experience." To address this, Academician WANG Qi, President of the TCM Association, China International Exchange and Promotion Association for Medical and Healthcare and TCM master, led a group of academicians, TCM masters, TCM pharmacology experts and clinical TCM experts to convene a "Seminar on Promoting the Implementation of the ′Three-in-One′ Registration and Evaluation Evidence System for Chinese Medicinals." Through extensive discussions, an expert consensus was formed, clarifying the different roles of the TCM theory, "personal experience" and clinical trials within the system. It was further emphasized that the "personal experience" is the core of this system, and its data should be derived from clinical practice scenarios. In the future, the improvement of this system will require collaborative efforts across multiple fields to promote the high-quality development of the Chinese medicinal industry.

ObjectiveTo investigate the application value of a multimodal model integrating radiomics features， deep learning features， and clinical structured data in predicting severe acute pancreatitis （SAP）， and to provide more accurate tools for the early identification of SAP in clinical practice. MethodsThe patients with acute pancreatitis （AP） who attended The First Affiliated Hospital of Soochow University， Jintan Hospital Affiliated to Jiangsu University， and Suzhou Yongding Hospital from January 1， 2017 to December 31， 2023 were included. Related data were collected， including demographic information， previous medical history， etiology， laboratory test data， and systemic inflammatory response syndrome （SIRS） within 24 hours after admission， as well as imaging data within 72 hours after admission， while related scores were calculated， including Ranson score， modified CT severity index （MCTSI）， bedside index for severity in acute pancreatitis （BISAP）， and systemic inflammatory response syndrome， albumin， blood urea nitrogen and pleural effusion （SABP） score. The model was constructed in the following process： （1） three-dimensional CT images were used to extract and identify radiomics features， and a radiomics classification model was established based on the extreme gradient Boost （XGBoost） algorithm； （2） U-Net is used to perform semantic segmentation of three-dimensional CT images， and then the results of segmentation were imported into 3D ResNet50 to construct a deep learning classification model； （3） the predicted values of the above two models were integrated with clinical structured data to establish a multimodal model based on the XGBoost algorithm. The variable importance plot and local interpretability plot were used to perform visual interpretation of the model. The independent samples t-test was used for comparison of normally distributed continuous data between groups， and the Mann-Whitney U test was used for comparison of non-normally distributed continuous data between groups； the chi-square test or Fisher’s exact test was used for comparison of categorical data between groups. The receiver operating characteristic （ROC） curve was plotted for each model and existing scoring systems， and the area under the ROC curve （AUC） was calculated to assess their performance； the Delong test was used for comparison of AUC. ResultsA total of 609 patients who met the criteria were included， among whom 114 （18.7%） developed SAP. In this study， the data of 426 patients from The First Affiliated Hospital of Soochow University was used as the training set， and the data of 183 patients from Jintan Hospital Affiliated to Jiangsu University and Suzhou Yongding Hospital were used as the independent test set. The multimodal model had an AUC of 0.914 in the test set， which was significantly higher than the AUC of traditional scoring systems such as MCTSI （AUC=0.827）， Ranson score （AUC=0.675）， BISAP （AUC=0.791）， and SABP score （AUC=0.648）； in addition， the multimodal model showed a significant improvement in performance compared with the radiomics classification model （AUC=0.739） and the deep learning classification model （AUC=0.685） （the Delong test： Z=-3.23， -4.83， -3.48， -4.92， -4.31， and -4.59， all P <0.01）. The top 10 variables in terms of importance in the multimodal model were pleural effusion， predicted value of the deep learning model， predicted value of the radiomics model， triglycerides， calcium ions， SIRS， white blood cell count， age， platelets， and C-reactive protein， suggesting that the above variables had significant contributions to the performance of the model in predicting SAP. ConclusionBased on structured data， radiomic features， and deep learning features， this study constructs a multicenter prediction model for SAP based on the XGBoost algorithm， which has a better predictive performance than existing traditional scoring systems and unimodal models.

Objective To investigate the application value of quantitative parameters MRFDGmax and SUVmax in the stages of hepatitis,liver fibrosis and cirrhosis in rats by whole-body dynamic 18 F-FDG PET/CT Patlak imaging.Methods Twenty-four SD rats were randomly divided into four groups of six rats each,which were the normal group,hepatitis group,liver fibrosis group and cirrhosis group.According to the experimental grouping,rats in each group were induced by the CC14 oil solution complex method.Whole-body dynamic 18 F-FDG PET/CT patlak imaging was performed on each group of rats separately at the completion of induction.After the imaging was com-pleted,the MRFDGmax,SUVmax and CT values of the livers of each group were analyzed;subsequently,the serum of rats in each group was extracted for the detection of liver function indexes(AST,ALT and ALP),and HE staining was performed on the livers of rats in the normal,hepatitis and cirrhosis groups,and Masson staining was performed on those in the liver fibrosis group;the α-SMA expression in the liver tissues of each group was analyzed by immu-nohistochemical method.The data were analyzed by one-way ANOVA,two independent samples t-test and Pearson correlation analysis.Results MRFDGmax,SUVmax values were statistically significant differences among normal,hep-atitis,liver fibrosis and cirrhosis groups(F=84.54,38.35,P＜0.001).The difference in CT values between liver fibrosis and cirrhosis groups was not statistically significant(t=-0.407,P=0.693),and the difference was statistically significant when compared between the rest of the groups(F=112.25,P＜0.001).Compared with the normal group,AST,ALT and ALP of the experimental group showed a staged increase,and the differences were statistically significant(F=93.32,64.63,145.03,P＜0.001).HE staining showed that hepatocytes of the normal group were neatly arranged and structurally intact;a large number of inflammatory cells infiltrated the hepa-titis group with steatosis;pseudo lobe formation was observed in the cirrhosis group.Masson staining of the liver fi-brosis group showed collagen fiber proliferation and thickening of the peritoneum.Immunohistochemistry test results showed that α-SMA expression increased in hepatitis group,liver fibrosis group and cirrhosis group,with a staged increase,and the difference was statistically significant(F=80.57,P＜0.001).Correlation analysis showed a positive correlation between SUVmax and MRFDGmax(r=0.967,P＜0.01).α-SMA was positively correlated with AST,ALT and ALP in the hepatitis,liver fibrosis and cirrhosis groups,respectively(r=0.924,0.756,0.934,P＜0.01).Conclusion Whole-body dynamic 18F-FDG PET/CT Patlak imaging has application value in monitoring hepatitis,liver fibrosis and cirrhosis stages through quantitative parameters MRFDGmax and SUVmax changes.

ObjectiveTo investigate the influencing factors for rebleeding after endoscopic therapy and the effect of the number of sequential treatment sessions on postoperative rebleeding in patients with liver cirrhosis receiving secondary prevention of gastroesophageal varices （GOV）. MethodsA total of 1 717 patients with liver cirrhosis who received secondary prevention of GOV and attended The 960th Hospital of the PLA Joint Logistice Support Force from January 2017 to December 2021 were enrolled， and according to the presence or absence of bleeding after endoscopic therapy， they were divided into non-bleeding group and rebleeding group. The influencing factors for rebleeding were analyzed， as well as the association between the number of endoscopic treatment sessions and rebleeding. The chi-square test was used for comparison of categorical data between groups； the independent-samples t test or the Mann-Whitney U test was used for comparison of continuous data between the two groups； the Kruskal-Wallis H test was used for comparison bertween multiple groups， and the Wilcoxon test was used for further comparison between two groups. The Cox regression model was used to investigate the influencing factors for rebleeding， and the Kaplan-Meier method was used to plot survival curves， while the Log-rank test was used for comparison between groups. ResultsOf all patients， 286 （16.7%） experienced rebleeding after endoscopic therapy， and 1 431 （83.3%） did not experience bleeding. There were significant differences between the two groups in history of smoking and drinking， etiology of liver cirrhosis， hemoglobin （Hb）， prothrombin time （PT）， prothrombin activity （PTA）， international normalized ratio （INR）， albumin （Alb）， fasting blood glucose， blood urea nitrogen， Child-Pugh class， aspartate aminotransferase-to-platelet ratio index （APRI） score， albumin-bilirubin （ALBI） score， use of non-selective beta-blocker （NSBB） before surgery， treatment modality， type of varices， and maximal varicose vein diameter （all P<0.05）. The univariate Cox regression analysis showed that in the patients with liver cirrhosis who received secondary prevention of GOV， rebleeding was associated with history of smoking and drinking， etiology of liver cirrhosis， use of NSBB before surgery， treatment modality， maximal varicose vein diameter， Hb， platelet count， PT， PTA， INR， Alb， total bilirubin （TBil）， alkaline phosphatase （ALP）， gamma-glutamyl transpeptidase， blood glucose， Child-Pugh class， and ALBI score （all P<0.05）. The multivariate Cox regression analysis showed that Hb （hazard ratio ［HR］=0.989， 95% confidence interval ［CI］： 0.983‍ ‍—‍ ‍0.994， P<0.001）， TBil （HR=1.020， 95%CI： 1.006‍ ‍—‍ ‍1.034， P=0.005）， Alb （HR=0.868， 95%CI： 0.758‍ ‍—‍ ‍0.994， P=0.041）， treatment modality （sclerosing agent： HR=2.158， 95%CI： 1.342‍ ‍—‍ ‍3.470， P=0.002； tissue adhesive： HR=2.709， 95%CI： 1.343‍ ‍—‍ ‍5.462， P=0.005； ligation+sclerosing agent： HR=3.181， 95%CI： 1.522‍ ‍—‍ ‍6.645， P=0.002； sclerosing agent+tissue adhesive： HR=1.851， 95%CI： 1.100‍ ‍—‍ ‍3.113， P=0.020）， ALP （HR=1.003， 95%CI： 1.001‍ ‍—‍ ‍1.004， P=0.002）， and maximal varicose vein diameter （HR=1.346， 95%CI： 1.119‍ ‍—‍ ‍1.618， P=0.002） were independent influencing factors for rebleeding after endoscopic therapy. Comparison of rebleeding rate after different numbers of sequential treatment sessions showed that the patients treated for three sessions had a significantly lower rebleeding rate than those treated for one or two sessions （χ²=8.643 and 5.277， P=0.003 and 0.022）. The survival analysis showed that with the increase in the number of treatment sessions， there was a significantly longer interval between rebleeding （P=0.006） and a significantly lower mortality rate （P<0.001）. ConclusionThe levels of TBil， ALP， Hb， and Alb on admission， endoscopic treatment modality， and maximal varicose vein diameter were the main predictive factors for rebleeding after endoscopic therapy for GOV in liver cirrhosis， and such predictive factors should be closely monitored in clinical practice. Regular endoscopic therapy can reduce the rebleeding and mortality rates of patients with liver cirrhosis and GOV and prolonmg the interval between rebleeding.

Objective To monitor and evaluate the synergistic antitumor effects of programmed death-1(PD-1)checkpoint inhibitor combined with radiation therapy through whole-body dynamic 18 F-Fluorodeoxy glucose positron emission computed tomography(18F-FDG PET/CT)and Patlak multi-parametric analysis.Methods B16F10 mel-anoma dual-tumor mouse model was established and randomly divided into control,PD-1 monoclonal antibody,ra-diation-only,and combination groups(n=6).Whole-body 18F-FDG PET/CT imaging was performed before and 24 hours post-treatment.The changes of maximum standardized uptake value(SUVmax)and metabolic rate of FDG(MRFDG)changes were analyzed and compared.Mice were then euthanized,tumors excised and underwent histo-pathology with HE,CD8,Ki-67 staining to assess immune infiltration and proliferation.Distal tumor volumes were monitored during treatment.Results At 24 hours post-treatment,in the primary tumors,SUVmax and MRFDG values increased compared to pre-treatment in the control group(P＜0.000 1),while they decreased in the combination treatment group(P＜0.000 1),with statistically significant differences.In the distal tumors,SUVmax and MRFDG values increased compared to pre-treatment in the control group,PD-1 monoclonal antibody group,and radiothera-py-alone group.The SUVmax differences were statistically significant in the control group before and after treatment(P＜0.000 1).MRFDG values in the distal tumors showed statistically significant differences in all three groups(P＜0.01 or P＜0.000 1).In the combination treatment group,SUVmax and MRFDG values in the distal tumors de-creased significantly compared to pre-treatment(P＜0.000 1).Post-treatment comparison of SUVmax and MRFDG values in the distal tumors showed that statistically significant differences in SUVmax and MRFDG values were observed among all groups except between the radiotherapy-alone and PD-1 monoclonal antibody groups(all P＜0.05).Im-munohistochemistry results showed that the mean absorbance value of CD8 T lymphocytes in the distal tumor was significantly higher than that in the other three groups(P＜0.001);the mean absorbance value of Ki-67 immuno-histochemistry in the distal tumor proliferation index was significantly lower than that in the other three groups(P＜0.001).Conclusion The synergistic effects of combined treatment reduced distal tumor growth.Whole-body 18F-FDG PET/CT Patlak multi-parametric imaging can monitor the synergistic effects of PD-1 antibody and radiotherapy in B16F10 melanoma,providing reliable imaging parameters for optimizing combinatorial therapies.

Objective To address the difficulty in collecting sufficient endoscopic images of colorectal submucosal tumors for traditional deep learning model training,a few-shot learning based model(FSL model)is proposed for classifying colorectal submucosal tumors and polyps on endoscopic images.Methods A total of 172 endoscopic images of colorectal submucosal tumors were collected from different centers,including 43 each of colorectal lipomas(CRLs),neuroendocrine tumors(NETs),serrated lesions and polyps(SLPs),and traditional adenomas.A support set and a query set were constructed using these endoscopic images.ResNet50 which was pre-trained on ImageNet and esophageal endoscopic images was used to extract image features.Subsequently,K-nearest neighbors algorithm was used for classification based on the calculated Euclidean distance.The classification performance of FSL model was evaluated through the comparison with the original model and endoscopists.Results FSL model had a 4-class classification accuracy of 0.831,Macro AUC of 0.925,Macro F1-score of 0.831;moreover,the proposed model achieved diagnostic accuracies of 0.925 and 0.906 for CRLs and NETs,with F1 score of 0.850 and 0.805.Additionally,the proposed model exhibited high classification consistency(Kappa=0.775)and interpretability.Conclusion The established FSL model performs well in distinguishing CRLs,NETs,SLPs and traditional adenomas on endoscopic images,indicating its potential utility in assisting the identification of colorectal submucosal tumors under endoscopy.

AIM: To elucidate the relationship between childhood asthma susceptibility and clinical efficacy of inhaled glucocorticoids (ICS) in children with different genotypes of asthma by exploring rs776746 and rs15524 single nucleotide polymorphisms (SNPs) of cytochrome P450 enzyme 3A5 (CYP3A5) gene in asthmatic children and healthy children. METHODS: The CYP3A5 gene rs776746 and rs15524 polymorphic sites were detected in 79 children (Case group) with asthma of Han nationality and 100 healthy children (Control group) who met the inclusion criteria admitted to the Northern Theater General Hospital in Northeast China from October 2016 to October 2020, and genotype, allele and linkage analysis were performed. The case group was given inhaled glucocorticoids by nebulised inhalation for 3 months, and lung function and exhaled breath nitric oxide (FeNO) were measured at entry and after treatment, and asthma control score C-ACT/ACT was done after treatment, so as to compare the prevalence of different genotypes and the differences in the above test index scores. RESULTS: There was complete linkage disequilibrium at rs776746 and rs15524 loci. There were three genotypes of T/T, T/C and C/C at rs776746 locus of CYP3A5 gene. There were significant differences in the frequency of different genotypes between the case group and the control group (χ

Acute pancreatitis （AP） is a gastrointestinal disease that requires early intervention， and when it progresses to moderate-severe AP （MSAP） or severe AP （SAP）， there will be a significant increase in the mortality rate of patients. Machine learning （ML） has achieved great success in the early prediction of AP using clinical data with the help of its powerful computational and learning capabilities. This article reviews the research advances in ML in predicting the severity， complications， and death of AP， so as to provide a theoretical basis and new insights for clinical diagnosis and treatment of AP through artificial intelligence.

[摘 要] 目的：探讨鼠尾草酸（CA）通过调节CXC基序趋化因子受体7（CXCR7）/CXC基序趋化因子配体（CXCL12）轴对胃癌AGS细胞增殖、迁移和侵袭的影响。方法：用不同浓度（0、5、10、20、40、80 µg/mL））的CA处理胃癌AGS细胞，采用CCK-8法筛选合适的CA浓度；将AGS细胞分为对照组（未经处理的AGS细胞）、CA组（20 µg/mL CA处理）、CA+siCXCR7组（转染siCXCR7+20 µg/mL CA处理）、CA+siNC组（转染siNC+20 µg/mL CA处理）、CA+vectorNC组（转染vectorNC+20 µg/mL CA处理）、CA+vectorCXCR7组（转染vectorCXCR7+20 µg/mL CA处理），采用CCK-8法检测AGS细胞增殖的变化，qPCR法检测细胞中CXCR7、CXCL12 mRNA表达水平的变化，Transwell实验检测细胞侵袭能力的变化，划痕实验检测细胞迁移能力的变化，WB法检测周期蛋白D1、Bcl-2、CXCR7、CXCL12、MMP-2蛋白表达的变化。结果：不同浓度CA均可抑制AGS细胞存活率，且浓度为20 µg/mL时，细胞存活率接近50%，故选择20 µg/mL CA用于后续研究。与对照组相比，CA组增殖率、侵袭数、迁移率、周期蛋白D1、MMP-2、Bcl-2、CXCR7、CXCL12 mRNA及蛋白表达显著降低（均P<0.05）；与CA+siNC组相比，CA+siCXCR7组增殖率、侵袭数、迁移率、周期蛋白D1、MMP-2、Bcl-2、CXCR7、CXCL12 mRNA及蛋白表达显著降低（均P<0.05）；与CA+vectorNC组相比，CA+vectorCXCR7组增殖率、侵袭数、迁移率、周期蛋白D1、MMP-2、Bcl-2、CXCR7、CXCL12 mRNA及蛋白表达显著增加（均P<0.05）。结论：CA可抑制AGS细胞增殖、迁移和侵袭，其机制可能与抑制CXCR7/CXCL12轴有关。

OBJECTIVE: To establish a machine learning model based on extreme gradient boosting (XGBoost) algorithm for early prediction of severe acute pancreatitis (SAP), and explore its predictive efficiency. METHODS: A retrospective cohort study was conducted. The patients with acute pancreatitis (AP) who admitted to the First Affiliated Hospital of Soochow University, the Second Affiliated Hospital of Soochow University and Changshu Hospital Affiliated to Soochow University from January 1, 2020 to December 31, 2021 were enrolled. Demography information, etiology, past history, and clinical indicators and imaging data within 48 hours of admission were collected according to the medical record system and image system, and the modified CT severity index (MCTSI), Ranson score, bedside index for severity in acute pancreatitis (BISAP) and acute pancreatitis risk score (SABP) were calculated. The data sets of the First Affiliated Hospital of Soochow University and Changshu Hospital Affiliated to Soochow University were randomly divided into training set and validation set according to 8 : 2. Based on XGBoost algorithm, the SAP prediction model was constructed on the basis of hyperparameter adjustment by 5-fold cross validation and loss function. The data set of the Second Affiliated Hospital of Soochow University was served as independent test set. The predictive efficacy of the XGBoost model was evaluated by drawing the receiver operator characteristic curve (ROC curve), and compared it with the traditional AP related severity score; variable importance ranking diagram and Shapley additive explanation (SHAP) diagram were drawn to visually explain the model. RESULTS: A total of 1 183 AP patients were enrolled finally, of which 129 (10.9%) developed SAP. Among the patients from the First Affiliated Hospital of Soochow University and Changshu Hospital Affiliated to Soochow University, there were 786 patients in the training set and 197 in the validation set; 200 patients from the Second Affiliated Hospital of Soochow University were used as the test set. Analysis of all three datasets showed that patients who advanced to SAP exhibited pathological manifestation such as abnormal respiratory function, coagulation function, liver and kidney function, and lipid metabolism. Based on the XGBoost algorithm, an SAP prediction model was constructed, and ROC curve analysis showed that the accuracy for prediction of SAP reached 0.830, the area under the ROC curve (AUC) was 0.927, which was significantly improved compared with the traditional scoring systems including MCTSI, Ranson, BISAP and SABP, the accuracy was 0.610, 0.690, 0.763, 0.625, and the AUC was 0.689, 0.631, 0.875, and 0.770, respectively. The feature importance analysis based on the XGBoost model showed that the top ten items ranked by the importance of model features were admission pleural effusion (0.119), albumin (Alb, 0.049), triglycerides (TG, 0.036), Ca²⁺ (0.034), prothrombin time (PT, 0.031), systemic inflammatory response syndrome (SIRS, 0.031), C-reactive protein (CRP, 0.031), platelet count (PLT, 0.030), lactate dehydrogenase (LDH, 0.029), and alkaline phosphatase (ALP, 0.028). The above indicators were of great significance for the XGBoost model to predict SAP. The SHAP contribution analysis based on the XGBoost model showed that the risk of SAP increased significantly when patients had pleural effusion and decreased Alb. CONCLUSIONS A SAP prediction scoring system was established based on the machine automatic learning XGBoost algorithm, which can predict the SAP risk of patients within 48 hours of admission with good accuracy.
Humans ; Pancreatitis ; Acute Disease ; Retrospective Studies ; Hospitalization ; Algorithms