The research of damp-heat syndrome in modern TCM mainly focuses on inflammatory response, water metabolism, lipid metabolism, hemorheology, intestinal flora and so on. Modern omics techniques such as metabolomics and genomics provide a new perspective for the exploration of the micro-mechanism of damp-heat syndrome. The study found that the abnormal expression of aquaporin is closely related to the formation of "dampness" in damp-heat syndrome, and the release of inflammatory factors reflects the pathological characteristics of "heat". Damp-heat syndrome is often accompanied by dyslipidemia, hemorheological changes and intestinal flora imbalance, showing characteristic changes in urine, blood and saliva metabolomics, and there are differences in gene expression between damp-heat constitution and gentleness constitution. In the future, the pertinence and systematicness of research should be strengthened, the relationship between indicators should be deeply explored, build a biomarker system should be built, the immune-metabolic regulation mechanism should be explored, the multi-target mechanism of heat-clearing and dampness-removing Chinese materia medica should be clarified to further improve the damp-heat syndrome system, and provide theoretical support for clinical treatment.

Objective:To construct and evaluate the predictive performance of a multiphase ultrasound radiomics model for microvascular invasion（MVI）in hepatocellular carcinoma（HCC）.Methods:A total of 126 patients with pathologically confirmed HCC were retrospectively enrolled from 4 medical centers between May 2018 and July 2025，including the First Hospital of Shanxi Medical University，Shanxi Province Third People's Hospital，Changzhi People's Hospital，and the Organ Transplant Center of the Affiliated Hospital of Qingdao University. A total of 630 ultrasound images of the lesions in different phases were collected，from which 1 561 radiomic features were extracted. The patients from medical institutions in Shanxi Province were chosen as the training set（ n=91），and the patients from the Organ Transplant Center of the Affiliated Hospital of Qingdao University were chosen as the validation set（ n=35）. In the training set，37.4%（34/91）patients presented MVI（+），whereas in the validation set，54.3%（19/35）patients presented MVI（+）. Radiomics features were extracted from ultrasound images，and features related to the MVI（+）were selected through dimensionality reduction analysis. Five multiple machine learning algorithms were used to construct predictive models，which were then evaluated using an external validation set. The Radscore was calculated，and a nomogram was constructed combining Radscore with ultrasound and clinical characteristics to predict MVI. Results:The model combining radiomics features from the portal venous phase and the delay phase showed the best predictive performance in both the training and validation sets，with area under curve（AUC）values of 0.835 and 0.727，respectively. The prediction model developed using radiomics Radscore and clinical indicators could be represented and presented as a nomogram.Conclusions:The radiomics model based on multi-phase ultrasound offers a novel approach for non-invasive preoperative prediction of MVI in liver cancer. Furthermore，its integration with clinical features aids in optimizing clinical treatment strategies.

Objective:To construct and evaluate the predictive performance of a multiphase ultrasound radiomics model for microvascular invasion（MVI）in hepatocellular carcinoma（HCC）.Methods:A total of 126 patients with pathologically confirmed HCC were retrospectively enrolled from 4 medical centers between May 2018 and July 2025，including the First Hospital of Shanxi Medical University，Shanxi Province Third People's Hospital，Changzhi People's Hospital，and the Organ Transplant Center of the Affiliated Hospital of Qingdao University. A total of 630 ultrasound images of the lesions in different phases were collected，from which 1 561 radiomic features were extracted. The patients from medical institutions in Shanxi Province were chosen as the training set（ n=91），and the patients from the Organ Transplant Center of the Affiliated Hospital of Qingdao University were chosen as the validation set（ n=35）. In the training set，37.4%（34/91）patients presented MVI（+），whereas in the validation set，54.3%（19/35）patients presented MVI（+）. Radiomics features were extracted from ultrasound images，and features related to the MVI（+）were selected through dimensionality reduction analysis. Five multiple machine learning algorithms were used to construct predictive models，which were then evaluated using an external validation set. The Radscore was calculated，and a nomogram was constructed combining Radscore with ultrasound and clinical characteristics to predict MVI. Results:The model combining radiomics features from the portal venous phase and the delay phase showed the best predictive performance in both the training and validation sets，with area under curve（AUC）values of 0.835 and 0.727，respectively. The prediction model developed using radiomics Radscore and clinical indicators could be represented and presented as a nomogram.Conclusions:The radiomics model based on multi-phase ultrasound offers a novel approach for non-invasive preoperative prediction of MVI in liver cancer. Furthermore，its integration with clinical features aids in optimizing clinical treatment strategies.

Infl uidfl owfi eld analysis, the common simulation method is 1-D simulation or 3-D simulation, In order to analyze bloodfl uid system more quickly and accurately and choose the appropriate sample tube at the beginning of design for the system, this paper adopts a recently emerging 1D-3D simulation method to make simulations offl ow chamber subsystem. Respectively using Flowmaster and ANSYS system modeling, use MpCCI connects the two parameter coupling, realize the sheathfl uid velocity of research. The software can meet the needs of design and analysis of the Hematology Analyzerfl uid system. In this paper, the method of co-simulation for medical apparatus and instruments of Hematology Analyzerfl uid system development provides a new method, has important signifi cance on the subsequent simulation.

Objective To assess the effect of continuous renal replacement therapy (CRRT) on severe acute pancre-atitis (SAP). Methods According to the collaborative search strategy, MEDLINE (1966-2013.9), Embase(1974-2013.9), Chinese Wanfang database(1996-2013.9), CNKI(1979-2013.9) and the clinical control test database of Cochrane Library were searched.Published and unpublished literature and meeting summary were searched manually.Quality assessment and data extraction were conducted by two independent investigators. Meta-analysis was performed using RevMan 5.2. Results A total of 15 reports were identified which met the inclusion criteria, in which include data of 652 patients.Com-pared with control group, serum amylase decreased in CRRT group (WMD=-140.76, 95%CI was-212.42 to-69.11, P=0.000 1). Mediators of inflammatory mediators were scavenged effectively and APACHEⅡscore decreased in CRRT group (WMD=-2.97, 95%CI was-4.01 to-1.93, P<0.001). The overall survival rate was significantly higher in CRRT group than that in control group (RR=1.35, 95%CI 1.19-1.52, P<0.001). Conclusion Continuous renal replacement therapy could ef-fectively improve the survival rate of patients with severe acute pancreatitis.