According to traditional Chinese medicine （TCM） theory， impaired spleen transportation function disrupts nutrient distribution， causing metabolic accumulation of lipids that transform into pathogenic phlegm-dampness. These pathological factors disseminate through the San Jiao and obstruct meridian pathways， ultimately forming the pathogenesis described as "all disorders involve phlegm". Phlegm and dampness share common pathogenic origins but manifest distinct clinical manifestations. Dampness， as the precursor， may congeal into phlegm， while existing phlegm accumulation can further exacerbate dampness stagnation， thereby establishing a self-perpetuating pathological cycle. Modern medical research has confirmed that the essence of "phlegm-dampness" syndrome is closely associated with energy metabolism disorders， serving as a common pathological basis for metabolic syndrome， type 2 diabetes mellitus， atherosclerosis， and other major chronic diseases. As a crucial vehicle for medical experimental research， disease-syndrome combination animal models serve as an indispensable means to advance the modernization of TCM. Currently， based on classical theories such as "rich and greasy foods produce phlegm" and "physical coldness combined with cold consumption causes external pathogens to invade the skin and hair， thereby generating internal dampness"， researchers primarily employ two paradigms to construct animal models of phlegm-turbidity， dampness obstruction， and phlegm-dampness syndromes： the first involves simulating TCM etiological factors （through methods like dietary irregularities， imblanace between work and rest， and combined internal-external dampness exposure）， while the second combines disease with syndrome differentiation （inducing pathological changes through physical， chemical， or biological interventions）. Through comprehensive evaluation incorporating macroscopic observation and microscopic index detection， model animals undergo systematic biological and pathological assessment， with further syndrome type verification achieved via the "prescription-based syndrome detection" approach. However， existing models still exhibit significant deficiencies in both the standardization of modeling methodologies and the systematization of evaluation criteria. This paper reviews the strategies for constructing "phlegm-dampness" syndrome animal models and their corresponding evaluation indices， focusing on the pathological correlations among different modeling approaches. The aim is to provide methodological guidance for research on TCM syndromes related to "phlegm-dampness" syndrome and to support the development of TCM therapies for resolving phlegm and eliminating dampness. This study not only contributes to advancing the standardization of TCM syndrome research but also provides crucial technical support for the modernization of TCM.

Objective : To explore the causal relationship between 46 phenotypes ( including 15 seropositive case- control phenotypes and 31 quantitative antibody-measurement phenotypes) and ulcerative colitis( UC) using two- sample bidirectional Mendelian randomization( TSMR) . Methods: Single nucleotide polymorphisms ( SNPs) sig- nificantly associated with the relative abundance of the 46 antibody sera were extracted as instrumental variables ac- cording to preset thresholds . Summary statistics for UC were obtained from the OPEN GWAS database ( n = 47 745) . MR-Egger regression , weighted median method ( WME) , inverse variance weighting ( IVW) , the simple mode method (SM) , and weighted multitude method (WM) were used to estimate the causal relationship between antibody levels and UC , primarily using the IVW method . The results were assessed according to the effect indica- tor dominance ratios (OR) and 95% confidence intervals (CI) . Sensitivity analysis , heterogeneity test , gene plei- otropy test , and outlier test (MR-PRESSO) were combined to verify the stability and reliability of the results , and the causal association study was performed again using reverse Mendelian randomization(MR) . Results : IVW re- sults showed that Epstein-Barr( EB) virus EA-D antibody levels ( OR = 0. 806 , 95% CI = 0. 693 - 0. 939 , P < 0. 01) , Epstein-Barr virus EBNA-1 antibody levels ( OR = 1 . 870% , 95% CI = 1 . 480 - 2. 360 , P < 0. 000 1) , Anti-polyomavirus 2 IgG seropositivity (OR = 0. 570 , 95% CI = 0. 435 - 0. 746 , P < 0. 000 1) were associated with UC . The inverse MR analysis revealed a causal effect on anti-polyomavirus 2 IgG seropositivity , and none of the a- bove revealed genetic pleiotropy or significant heterogeneity of IVs . Conclusion EB virus EBNA-1 antibody levels are positively associated with the risk of UC , while EB virus EA-D antibody levels and anti-polyomavirus 2 IgG se- ropositivity are negatively associated with the risk of UC , indicating that they are protective factors for UC .

Objective:To obtain the parameters associated with hematoma morpholoy by finite element analysis(FEA) and investigated their performance on predicting and diagnosis hematoma expansion(HE) in patients with spontaneous intracrebral hemorrhage(SICH).Methods:Patients with SICH who met research criteria were retrospective enrolled between June 2015 and December 2017. Clinical parameters on admission were collected, Perform 2 independent methodology on same patient to analysis the hematoma shape base on computed tomography(CT): Clinical routine method that performed by clinical investigator to identified margin irregularity of hematoma by CT ,and calculated the volume of hematoma by simplify Tada formula(ABC/2);The FEA method performed by FEA investigator and gain the hematoma 3 dimensional morphology and variables, include Volume, Surface area, and The quantity of triangles per square milimet surface(TQOT/mm 2). The HE was defined as volume enlargement of >33% compared with that on addmission. All patients were divided into HE and none HE group ,respectively, ABC/2 and FEA generated thire own HE and none HE group as different volume calcuation. The HE risk factors of ABC/2 and FEA were assessed in univariate and multivariable Logistic regression models. and the risk fators diagnosis value for HE were determined by the receiver operating characteristic(ROC) curves. Results:Total of 127 patients were enrolled, The mean time of symptom onset to hospital admitted was 3.08±1.34 h. There were 34(26.77%) cases HE identifed by ABC/2 and 31(24.41%)by FEA. Althought there are significant different (pearson χ2=53.66, P<0.01) of HE identification between ABC/2 and FEA, the 2 methods has moderate consistency (Kappa=0.65). All patients’ hematoma 3D reconstruction were performed by FEA and general observation show that TQOT/mm 2 most likely correlate to irregularity of hematoma 3D shape. Multivariable Logistic regression models indicated that ICH score( OR=1.79, 95% CI:1.19~2.68)was independent HE risk factor for ABC/2, respectively, TQOT/mm 2≥1.95/mm 2 ( OR=16.99,95% CI:5.98~48.33)and Ultraearly Hematoma Growth,(uHG) ( OR=1.05, 95% CI:1.01~1.09)were independent HE risk factor for FEA. With ROC analysis, both the ICH score of ABC/2 and uHG of FEA have low HE predictive and diagnosis value ,the area under the curve (AUC) were 0.64 and 0.67 respectively. However, TQOT/mm 2 was found to have excellent diagnosis value (AUC:0.9), sensitivity and specificity were 77% and 83% when the cut-off value was 1.95. Panel parameter model (TQOT/mm 2+uHG) was not be found to have a significant higher AUC than single parameter on FEA and the clinical routine parameters panel model (ICH +SB P>180 mmHg on addmission) have a unacceptable AUC(<0.7) as well as single parameters. Conclusions:Hematoma shape could be reconstructed and analysis by FEA and TQOT/mm 2 was likely relevance to hematoma morphology. TQOT/mm 2≥1.95 was indicate to have a better HE predicting and diagnosis value than any other risk factors and clinical parameters panel models in our reaserch.