Animal model research on spleen and stomach diseases in traditional Chinese medicine （TCM） is of great significance for elucidating the nature of diseases and syndromes and for revealing the mechanisms of action of Chinese herbal medicinals. At present， studies on classical TCM syndrome models of spleen and stomach diseases mainly focus on spleen deficiency syndrome， liver constraint syndrome， and damp-heat syndrome. Model construction is mostly based on the etiological and pathophysiological characteristics of syndrome， and model evaluation primarily involves macroscopic manifestations and physicochemical indicators. This paper summarizes the current research status of animal models integrating disease and syndrome for seven common spleen and stomach diseases， including chronic gastritis and gastric precancerous lesions， gastroesophageal reflux disease， functional dyspepsia， inflammatory bowel disease， irritable bowel syndrome， functional constipation， and functional diarrhea. The modeling methods and characteristics of disease-syndrome combined animal models for each disease are analyzed. It is proposed that future research on disease-syndrome integration in spleen and stomach diseases should move toward syste-matic， precise， and integrative development， and that interdisciplinary and cross-disciplinary research approaches should be adopted to enhance the predictive value and application efficiency of disease-syndrome combined animal models.

The initiation of adaptive immune responses relies on the precise recognition and interpretation of antigenic information. In this process, the specific binding of T cell receptors (TCRs) to peptide-major histocompatibility complex (pMHC) molecules represents one of the key molecular events in the initiation of adaptive immune responses. Accordingly, the structural features of TCR-pMHC complexes provide a fundamental basis for dissecting antigen recognition mechanisms and support rational vaccine design, therapeutic target discovery in TCR-based immunotherapy, and TCR identification and optimization. However, experimental determination of TCR-pMHC structures remains costly, time-consuming, and limited in coverage, making computational approaches essential for rapidly obtaining reliable structural information. Computational methods for predicting the structures of TCR-pMHC complexes have advanced rapidly in recent years, driven by progress in deep learning-based modeling frameworks and the increasing availability of structural and sequence resources. Despite these developments, most existing tools do not adequately distinguish the key structural and biophysical differences between MHC class I (MHC-I) and MHC class II (MHC-II) complexes during model construction. As a consequence, their predictive performance differs substantially between class I and class II complexes. In general, structural predictions for class I complexes outperform those for class II complexes. This discrepancy may be related to several fundamental differences between the two systems, including the architecture of the peptide-binding groove, the distribution of peptide lengths, and the properties of peptide flanking residues (PFRs). Compared with MHC-I molecules, MHC-II molecules usually bind longer antigenic peptides, which typically range from 13 to 25 amino acids in length. PFRs at both termini of these peptides participate in regulating the overall conformation of TCR-pMHC class II complexes and exert a pronounced effect on the geometric and physicochemical characteristics of the TCR-pMHC binding interface. Furthermore, within the TCR recognition interface, the complementarity-determining regions (CDRs) consist of segments that differ markedly in conformational behavior. They commonly include regions that are relatively rigid and structurally stable, together with highly flexible segments exhibiting substantial conformational plasticity. These rigidity-flexibility features constitute an essential structural basis enabling TCRs to recognize diverse peptide-MHC ligands and to accommodate conformational heterogeneity at the interface. However, many current modeling tools, in an effort to enforce global conformational stability or reduce structural noise, tend to over-constrain intrinsically flexible regions. Such oversimplification may lead to inappropriate rigidification of flexible CDR loops, resulting in local structural distortions, compromised interface geometry, or even complete modeling failure for specific complexes. Against this background, the review approaches the field from the perspective of computational differences between MHC-I and MHC-II complexes. We first systematically organize and summarize available resources related to TCRs and pMHCs, including structural datasets, sequence databases, prediction tools, and benchmarking studies. We then focus on five representative tools capable of predicting both class I and class II complexes—AlphaFold2, AlphaFold3, TCRmodel2, tFold-TCR, and TCR-pHLA_ModellerS. After excluding structures present in the training sets of these tools, we constructed a benchmark dataset comprising 25 class I and 10 class II TCR-pMHC complexes in the bound state and conducted a systematic evaluation using this dataset. We first employ widely used general evaluation metrics, including All-Atom Root Mean Square Deviation (All-Atom RMSD), Backbone RMSD, Template Modeling score (TM-score), and DockQ, to assess the global conformational accuracy and interface modeling quality of class I and class II complexes. For class II complexes, we propose for the first time a peptide flanking residue deviation index, including the PFRs-Deviation Index (PFRs-DI), N-PFR-Deviation Index (N-PFR-DI), and C-PFR-Deviation Index (C-PFR-DI), to quantitatively characterize conformational deviations in PFRs. In addition, we propose the CDR conformational consistency index (CCC) designed to qualitatively evaluate the ability of prediction tools to capture TCR CDR conformational flexibility. These metrics collectively assess a tool’s ability to model both overall conformation and critical functional regions, thereby addressing the limitations of existing evaluation criteria that overemphasize global structure while inadequately capturing modeling quality in key functional areas. This establishes a unified analytical framework for MHC-I and MHC-II complexes to guide data resource selection, modeling strategy formulation, and evaluation system development. The framework further advances computational modeling and provides crucial support for multi-scale analysis of TCR-pMHC recognition mechanisms and their biological functions.

The initiation of adaptive immune responses relies on the precise recognition and interpretation of antigenic information. In this process, the specific binding of T cell receptors (TCRs) to peptide-major histocompatibility complex (pMHC) molecules represents one of the key molecular events in the initiation of adaptive immune responses. Accordingly, the structural features of TCR-pMHC complexes provide a fundamental basis for dissecting antigen recognition mechanisms and support rational vaccine design, therapeutic target discovery in TCR-based immunotherapy, and TCR identification and optimization. However, experimental determination of TCR-pMHC structures remains costly, time-consuming, and limited in coverage, making computational approaches essential for rapidly obtaining reliable structural information. Computational methods for predicting the structures of TCR-pMHC complexes have advanced rapidly in recent years, driven by progress in deep learning-based modeling frameworks and the increasing availability of structural and sequence resources. Despite these developments, most existing tools do not adequately distinguish the key structural and biophysical differences between MHC class I (MHC-I) and MHC class II (MHC-II) complexes during model construction. As a consequence, their predictive performance differs substantially between class I and class II complexes. In general, structural predictions for class I complexes outperform those for class II complexes. This discrepancy may be related to several fundamental differences between the two systems, including the architecture of the peptide-binding groove, the distribution of peptide lengths, and the properties of peptide flanking residues (PFRs). Compared with MHC-I molecules, MHC-II molecules usually bind longer antigenic peptides, which typically range from 13 to 25 amino acids in length. PFRs at both termini of these peptides participate in regulating the overall conformation of TCR-pMHC class II complexes and exert a pronounced effect on the geometric and physicochemical characteristics of the TCR-pMHC binding interface. Furthermore, within the TCR recognition interface, the complementarity-determining regions (CDRs) consist of segments that differ markedly in conformational behavior. They commonly include regions that are relatively rigid and structurally stable, together with highly flexible segments exhibiting substantial conformational plasticity. These rigidity-flexibility features constitute an essential structural basis enabling TCRs to recognize diverse peptide-MHC ligands and to accommodate conformational heterogeneity at the interface. However, many current modeling tools, in an effort to enforce global conformational stability or reduce structural noise, tend to over-constrain intrinsically flexible regions. Such oversimplification may lead to inappropriate rigidification of flexible CDR loops, resulting in local structural distortions, compromised interface geometry, or even complete modeling failure for specific complexes. Against this background, the review approaches the field from the perspective of computational differences between MHC-I and MHC-II complexes. We first systematically organize and summarize available resources related to TCRs and pMHCs, including structural datasets, sequence databases, prediction tools, and benchmarking studies. We then focus on five representative tools capable of predicting both class I and class II complexes—AlphaFold2, AlphaFold3, TCRmodel2, tFold-TCR, and TCR-pHLA_ModellerS. After excluding structures present in the training sets of these tools, we constructed a benchmark dataset comprising 25 class I and 10 class II TCR-pMHC complexes in the bound state and conducted a systematic evaluation using this dataset. We first employ widely used general evaluation metrics, including All-Atom Root Mean Square Deviation (All-Atom RMSD), Backbone RMSD, Template Modeling score (TM-score), and DockQ, to assess the global conformational accuracy and interface modeling quality of class I and class II complexes. For class II complexes, we propose for the first time a peptide flanking residue deviation index, including the PFRs-Deviation Index (PFRs-DI), N-PFR-Deviation Index (N-PFR-DI), and C-PFR-Deviation Index (C-PFR-DI), to quantitatively characterize conformational deviations in PFRs. In addition, we propose the CDR conformational consistency index (CCC) designed to qualitatively evaluate the ability of prediction tools to capture TCR CDR conformational flexibility. These metrics collectively assess a tool’s ability to model both overall conformation and critical functional regions, thereby addressing the limitations of existing evaluation criteria that overemphasize global structure while inadequately capturing modeling quality in key functional areas. This establishes a unified analytical framework for MHC-I and MHC-II complexes to guide data resource selection, modeling strategy formulation, and evaluation system development. The framework further advances computational modeling and provides crucial support for multi-scale analysis of TCR-pMHC recognition mechanisms and their biological functions.

Objective: This prospective study evaluated the feasibility of chemical exchange saturation transfer (CEST) MRI for assessing disease activity in thyroid-associated ophthalmopathy (TAO). Materials and Methods: A total of 88 patients with active TAO, 76 with inactive TAO, and 30 healthy controls were enrolled. CEST MRI-derived magnetization transfer ratio (MTR) and MTR asymmetry (MTRasym) at 1 ppm, 2 ppm, and 3.5 ppm were calculated. Clinical data, MTR, and MTRasym values for the extraocular muscles (one representative muscle per eye, yielding two measurements per participant) were compared among the groups. Spearman’s correlation was used to examine associations between imaging parameters and the clinical activity score (CAS) in patients with TAO. Logistic regression analysis was used to identify independent associations between imaging parameters and disease activity in patients with TAO (active vs. inactive). Receiver operating characteristic (ROC) analysis was conducted to evaluate the diagnostic performance for discriminating active from inactive TAO. Results: Patients with active TAO showed lower MTR values (P < 0.001) and higher MTRasym (1 ppm), MTRasym (2 ppm), and MTRasym (3.5 ppm) (all P < 0.001) compared with those with inactive TAO. MTR was negatively correlated with CAS (r = -0.402; P < 0.001), while MTRasym (1 ppm), MTRasym (2 ppm), and MTRasym (3.5 ppm) were positively correlated with CAS (r = 0.369, 0.350, and 0.349, respectively;all P < 0.001). MTR and MTRasym (1 ppm) were independently associated with TAO activity. The areas under the ROC curve (AUCs) for MTR and MTRasym (1 ppm) in discriminating active from inactive TAO were 0.772 and 0.730, respectively. Combining MTR with MTRasym (1 ppm) significantly improved diagnostic performance compared with either parameter alone, achieving an AUC of 0.805 (P = 0.029 and 0.001). Conclusion MTR and MTRasym (1 ppm) were independently associated with TAO activity. Their combination further enhanced diagnostic performance in distinguishing active from inactive TAO, suggesting their potential as quantitative imaging biomarkers to guide treatment in patients with TAO.

ObjectiveTo investigate the effect of honokiol on proliferation， apoptosis， migration， and invasion of gastric cancer cells and its underlying mechanistic. MethodsHuman gastric cancer cell lines HGC-27 and AGS were treated with Honokiol at concentrations of 0， 15， and 25 μmol/L. CCK-8 assays were conducted to determine the half maximal inhibitory concentration （IC₅₀） for both cell lines. Cell viability， proliferation， migration， and invasion capabilities were assessed using CCK-8， colony formation， wound healing， Transwell migration and Transwell invasion assays. Apoptosis rates were measured via flow cytometry. Western blot analysis examined proteins related to proliferation， apoptosis， migration， invasion， and the endoplasmic reticulum stress pathway ATF4-CHOP-TRIB3. ResultsCompared with the control group， treatment with 15 and 25 μmol/L Honokiol significantly reduced the proliferation， colony formation， migration， and invasion capabilities of the two gastric cancer cell lines， while significantly increasing the apoptosis rate （P<0.05）. Additionally， compared to the control group， the protein expression levels of neural cadherin（N-cadherin）， Vimentin， proliferating cell nuclear antigen（PCNA）， and B-cell lymphoma/leukemia-2 protein（Bcl-2）decreased in the two gastric cancer cell lines after treatment with 15 and 25 μmol/L Honokiol， while the protein expression levels of epithelial cadherin（E-cadherin）， Bcl-2-associated X protein（Bax）， activating transcription factor 4（ATF4）， endoplasmic reticulum stress-related protein（CHOP）， and tribbles homolog 3（TRIB3）increased（P<0.05）. ConclusionHonokiol promotes apoptosis and inhibits proliferation， migration， and invasion of gastric cancer HGC-27 and AGS cells by regulating the ERS signaling pathway ATF4/CHOP/TRIB3.

Gastric cancer remains a highly prevalent malignancy worldwide, with surgical resection currently constituting the cornerstone of treatment aimed at improving long-term patient survival. Owing to their notable advantages, including reduced surgical trauma and accelerated postoperative recovery, minimally invasive procedures are progressively supplanting conventional open surgery and have become the mainstream approach in gastric cancer management. Concurrently, the rapid advancement of artificial intelligence (AI) technologies has enabled real-time intraoperative monitoring of surgical scenes, thereby furnishing novel technical support for adjunctive decision-making, surgical navigation, and skill assessment during gastrectomy. This article provides a systematic review of the current status of AI applications in minimally invasive gastric cancer surgery, with a particular focus on research progress pertaining to instrument recognition, surgical phase identification, delineation of normal anatomical structures, detection of metastatic foci, and early warning of intraoperative adverse events. Furthermore, we discuss the potential value of AI in enhancing surgical efficiency, ensuring patient safety, and optimizing surgical education. On this basis, we further analyze the principal challenges and inherent risks confronting current AI systems, with the aim of informing future technological innovation and facilitating clinical translation.

Objective: To investigate the association between mobile phone addiction and career maturity among senior high school students, and to examine the mediating role of learning motivation problems and the moderating effect of grade level, so as to provide evidence for targeted interventions to promote career development. Methods: From August to November 2025, a total of 1 591 students from grade 10 to grade 12 were selected from two senior high schools in Haibei Prefecture,Qinghai Province, using cluster sampling. Data were collected using the Mobile Phone Addiction Index, Middle School Students Motivation Scale and Career Maturity Scale. Pearson correlation analysis was conducted to examine the relationships among variables. Hierarchical linear regression was used to explore the association between mobile phone addiction and career maturity. The PROCESS macro (version 5.0 ) was used to test mediation and moderation effects. Results: Mobile phone addiction was negatively correlated with career maturity ( r =-0.21) and positively correlated with learning motivation problems ( r =0.12), while learning motivation problems were negatively correlated with career maturity ( r =-0.23) (all P <0.01). Mobile phone addiction was negatively associated with career maturity（ β =-0.21，95% CI =-0.27 to -0.15）, and learning motivation problems partially mediated the association (indirect effect= -0.03, 95% CI =-0.04 to -0.01)(both P <0.05). The interaction between mobile phone addiction and grade significantly predicted career maturity ( β =0.07, P <0.05). Simple slope analysis showed that the effect of mobile phone addiction on career maturity was strongest among first year senior high school students ( β =-0.26), followed by second year students ( β =-0.19), and was weaker among third year students ( β =-0.12)(all P <0.05). Conclusions Mobile phone addiction is negatively associated with career maturity among senior high school students. Learning motivation problems partially mediate the association, and the association between mobile phone addiction and career maturity is stronger among first and second year students than among third year students.

AIM: To provide references for the non-clinical evaluation of therapeutic targets or drugs for retinoblastoma, fluorescently labeled Y79 cells are injected into the vitreous body of BALB/c-nu mice to establish a retinoblastoma model, and the Melphalan treatment group is used as a positive control, which is verified by fluorescence imaging technology.METHODS: BALB/c-nu mice were intravitreous injected with GFP transfected Y79 cells(1.0×107 cell/mL, 3 μL)to establish the model. On the 27th day, the mice were randomly divided into model control group and different doses of Melphalan groups(1, 3, 10 μg/eye groups)according to the fluorescence value of in vivo imaging, with vitreous body single administrated and ocular symptoms observed daily. Slit-lamp examination was performed at 12, 20, 29, 35, 42, 48, 55, 76, and 83 d after modeling. In vivo imaging was performed on 12, 20, 27, 41, 48, 55, 62, 69, 76, and 83 d. At the last treatment, the eyeball, brain and cerebellum tissues were removed for histopathological examination.RESULTS: From the sixth day of modeling, cloud-like substances could be seen in the eyes of the animals, and the cloud-like substances occupied the whole eyeball of the mice in the model control group at the later stage, accompanied by irregular growth of blood vessels. After 27 days of modeling, the fluorescence value was detected in all the animals, and the fluorescence value continued to increase with the extension of modeling time. The fluorescence value of the tumor reached the peak after 69-83 days of modeling. Histological examination showed severe proliferation of intraocular tumor cells in the model control group, and tumor cells were observed in the brain of 1 model animal. In the 10 μg/eye Melphalan group, the fluorescence value was significantly decreased at 17 d after administration. The fluorescence value of the 3 μg/eye Melphalan group was significantly inhibited at 59 d after administration. No tumor cells were found in the brain tissue of animals in all Melphalan groups.CONCLUSION: After vitreous injection of Y79/pCDH-LUC-copGFP cells in BALB/c-nu mice, significant ocular lesions and proliferation of tumor cells were observed in the eyes. Meanwhile, Melphalan intervention significantly inhibited tumor cells in a dose-dependent manner, indicating that the mouse model of retinoblastoma was successfully constructed.

ObjectiveThe theory of homotherapy for heteropathy is one of the classical rules in traditional Chinese medicine. Taking this theory as a breakthrough point， this study employed gas chromatography-mass spectrometry （GC-MS） to elucidate the mechanism underlying the therapeutic effects of Zhuyuwan on both intrahepatic cholestasis （IC） and ulcerative colitis （UC） from the viewpoint of serum metabolic homeostasis. MethodsThe rat models of α-naphthylisothiocyanate （ANIT）-induced cholestasis and 2，4，6-trinitro-benzenesulfonic acid （TNBS）-induced UC were treated with low （0.6 g·kg^-1） and high （1.2 g·kg^-1） doses of Zhuyuwan by gavage. In the experiment regarding IC， 24 Sprague-Dawley （SD） rats were randomly assigned into four groups： normal， ANIT model， low-dose Zhuyuwan， and high-dose Zhuyuwan. In the experiment regarding UC， 24 SD rats were randomly allocated into four groups： normal， TNBS model， low-dose Zhuyuwan， and high-dose Zhuyuwan. Firstly， the two disease models and the intervention effects of Zhuyuwan on the two diseases were evaluated based on serum levels of biochemical indicators ［alanine aminotransferase （ALT）， aspartate transaminase （AST）， γ-glutamyltranspeptidase （γ-GT）， and total bile acid （TBA）］， colon damage score， colon weight index， disease activity index， and histopathological changes in rats. Secondly， the rat serum samples were analyzed by gas chromatography-mass spectrometry （GC-MS） to screen the common core pathways of the two disease models， and the expression of core genes in the pathways was determined by Real-time PCR， on the basis of which the biological mechanism of the treatment of the two disease models by Zhuyuwan was ultimately elucidated. ResultsThe results of the experiment regarding IC showed that the ANIT model group had higher ALT， AST， γ-GT， and TBA levels than the normal group （P<0.01）. Compared with the ANIT model group， the low-dose Zhuyuwan group showed declined ALT and TBA levels （P<0.01） and the high-dose Zhuyuwan group showed lowered ALT， TBA， AST， and γ-GT levels （P<0.01）. The results of the experiment regarding UC showed that compared with the normal group， the TNBS model group presented increases in the colonic damage score， colon weight index， and disease activity index （P<0.01）. Compared with the TNBS model group， the low-dose Zhuyuwan group showcased declines in colon weight index （P<0.01） and disease activity index （P<0.05）， and the high-dose Zhuyuwan group showed reductions in the colon damage score， colon weight index， and disease activity index （P<0.01）. GC-MS metabolomics analysis combined with qRT-PCR demonstrated that Zhuyuwan had a similar inverse regulatory effect on arginine metabolism disruption in the above two disease models. ConclusionZhuyuwan exhibited definite therapeutic effects on both IC and UC， and the regulation of arginine biosynthesis pathway is the core mechanism for the treatment of both diseases by Zhuyuwan.

ObjectiveThe theory of homotherapy for heteropathy is one of the classical rules in traditional Chinese medicine. Taking this theory as a breakthrough point， this study employed gas chromatography-mass spectrometry （GC-MS） to elucidate the mechanism underlying the therapeutic effects of Zhuyuwan on both intrahepatic cholestasis （IC） and ulcerative colitis （UC） from the viewpoint of serum metabolic homeostasis. MethodsThe rat models of α-naphthylisothiocyanate （ANIT）-induced cholestasis and 2，4，6-trinitro-benzenesulfonic acid （TNBS）-induced UC were treated with low （0.6 g·kg^-1） and high （1.2 g·kg^-1） doses of Zhuyuwan by gavage. In the experiment regarding IC， 24 Sprague-Dawley （SD） rats were randomly assigned into four groups： normal， ANIT model， low-dose Zhuyuwan， and high-dose Zhuyuwan. In the experiment regarding UC， 24 SD rats were randomly allocated into four groups： normal， TNBS model， low-dose Zhuyuwan， and high-dose Zhuyuwan. Firstly， the two disease models and the intervention effects of Zhuyuwan on the two diseases were evaluated based on serum levels of biochemical indicators ［alanine aminotransferase （ALT）， aspartate transaminase （AST）， γ-glutamyltranspeptidase （γ-GT）， and total bile acid （TBA）］， colon damage score， colon weight index， disease activity index， and histopathological changes in rats. Secondly， the rat serum samples were analyzed by gas chromatography-mass spectrometry （GC-MS） to screen the common core pathways of the two disease models， and the expression of core genes in the pathways was determined by Real-time PCR， on the basis of which the biological mechanism of the treatment of the two disease models by Zhuyuwan was ultimately elucidated. ResultsThe results of the experiment regarding IC showed that the ANIT model group had higher ALT， AST， γ-GT， and TBA levels than the normal group （P<0.01）. Compared with the ANIT model group， the low-dose Zhuyuwan group showed declined ALT and TBA levels （P<0.01） and the high-dose Zhuyuwan group showed lowered ALT， TBA， AST， and γ-GT levels （P<0.01）. The results of the experiment regarding UC showed that compared with the normal group， the TNBS model group presented increases in the colonic damage score， colon weight index， and disease activity index （P<0.01）. Compared with the TNBS model group， the low-dose Zhuyuwan group showcased declines in colon weight index （P<0.01） and disease activity index （P<0.05）， and the high-dose Zhuyuwan group showed reductions in the colon damage score， colon weight index， and disease activity index （P<0.01）. GC-MS metabolomics analysis combined with qRT-PCR demonstrated that Zhuyuwan had a similar inverse regulatory effect on arginine metabolism disruption in the above two disease models. ConclusionZhuyuwan exhibited definite therapeutic effects on both IC and UC， and the regulation of arginine biosynthesis pathway is the core mechanism for the treatment of both diseases by Zhuyuwan.