This paper proposes a two-stage method integrating visual foundation models （VFM） and diffusion models. The segment anything model （SAM） as VFM is combined with the SegRefiner diffusion model to construct the SAM-SegRefiner framework for automated segmentation of edema， inflammation， and thrombus regions in histopathological images of hemorrhoidal tissue， providing a reproducible technical tool for the objective quantification of pathological morphology and its application in traditional Chinese medicine （TCM） syndrome research. Trained and validated on multi-center retrospective data， the SAM-SegRefiner model achieved an average pixel accuracy of 95.32% and a mean intersection over union （mIoU） of 66.81% on an independent test set， significantly outperfor-ming comparative models such as U-Net， MixU-Net， and SAM-Med2D， and also demonstrating robust cross-center generalization capability. Furthermore， by correlating the quantitatively segmented results from the model with the patients' TCM syndrome types， the potential associations between pathomorphological features and TCM syndrome differentiation have been explored. The analysis revealed no statistically significant differences in the degree of inflammatory infiltration and thrombus formation among different syndrome types， suggesting a complex relationship between local pathological changes and systemic syndrome manifestations.

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.

ObjectiveTo develop and validate a nomogram based on the albumin-bilirubin （ALBI） score for predicting the risk of severe perioperative complications in patients undergoing hepatectomy for hepatolithiasis. MethodsA retrospective analysis was conducted on the clinical data of 163 hepatolithiasis patients who underwent hepatectomy. Univariate and multivariate logistic regression analyses were used to identify independent risk factors for severe perioperative complications. A nomogram prediction model was constructed and its performance was evaluated. ResultsAmong the 163 patients， 66 and 97 were classified into the low-grade and high-grade ALBI groups， respectively. Significant intergroup differences were observed in gender， total bilirubin， albumin levels， and the incidence of severe complications （P0.05）. Severe complications occurred in 40 patients. Independent risk factors included age 60 years （OR=5.49， P0.001）， high-grade ALBI （OR=8.30， P0.001）， history of biliary surgery （OR=2.60， P=0.035）， hepatectomy （segmentectomy）≥3 （OR=2.75， P=0.028）， and open surgical approach （OR=4.00， P=0.009）. A nomogram for predicting severe perioperative complications was successfully established. Internal validation showed that the model had an area under the ROC curve （AUC） of 0.865， which outperformed traditional single predictors. The calibration curve closely aligned with the ideal curve， with a mean absolute error （MAE） of 0.027. Decision curve analysis （DCA） demonstrated a net clinical benefit when the threshold probability exceeded 10%， superior to that of traditional predictors. ConclusionThe ALBI score-based nomogram is successfully developed and validated to predict the risk of severe perioperative complications in hepatolithiasis patients undergoing hepatectomy. The model demonstrated favorable predictive performance and high clinical utility， serving as an effective tool for both preoperative risk assessment and postoperative risk stratification.

Background/Aims: Liver transplantation (LT) following total hepatectomy is a life-saving treatment for hepatocellular carcinoma (HCC). The HCC recurrence after LT hinders the effectiveness of the procedure. The objective of this study is to develop a pre-operative risk stratification model based on a liquid biopsy. Methods: We conducted a comprehensive multi-omics study of 260 HCC patients from three centers, including clinical data, low-coverage whole-genome sequencing of cell-free DNA (cfDNA) from plasma, as well as whole-exome, single-nucleus RNA, and spatial transcriptomics from matched tumor and non-tumor tissues. Results: We identified cfDNA-derived copy number alteration (CNA) signatures associated with post-transplant recurrence. By integrating cfDNA-derived CNA profiles with single-cell transcriptomic data, we traced recurrence-associated cfDNA to a distinct subpopulation of malignant cells within the primary tumor. These cells were embedded in a pro-metastatic microenvironment of specialized endothelial subtypes and cancer-associated fibroblasts. Notably, most recurrence-associated lesions were detectable in cfDNA prior to liver transplantation (LT). Building on these insights, we developed the ZJU Criteria based on CNA fragments and tumor markers, a pre-LT risk prediction tool that integrates conventional clinical factors with cfDNA-derived CNA signatures, and validated it using internal and independent external cohorts. Conclusion Our findings suggest that post-transplant recurrence commonly originates from advanced subclones that emerge late during tumor evolution. The ZJU Criteria provides an accurate, non-invasive strategy that significantly improves pre-LT risk stratification and clinical decision-making for patients with HCC.

ObjectiveTo investigate the effect of sorafenib and donafenib on the pharmacokinetics of ertugliflozin in rats， and to provide a theoretical basis for drug combination in clinical practice. MethodsA total of 24 male Sprague-Dawley rats were randomly divided into groups A， B， C， and D， with 6 rats in each group. The rats in groups A and B were given sorafenib control solvent and sorafenib （100 mg/kg）， respectively， by gavage for 7 consecutive days， followed by ertugliflozin （1.5 mg/kg） by gavage on day 7. Blood samples were collected from the angular vein plexus at different time points， and ultra-performance liquid chromatography-tandem mass spectrometry was used to determine the mass concentration of ertugliflozin and plot the plasma concentration-time curves， while the non-compartment model in DAS 2.1.1 software was used to calculate related pharmacokinetic parameters. The independent-samples t test was used for comparison of normally distributed continuous data between two groups， and the Mann-Whitney U test was used for comparison of non-normally distributed continuous data between two groups. ResultsCompared with group A， group B had significant increases in the AUC_0-t and AUC_0-∞ of the plasma concentration-time curve of ertugliflozin （both P<0.05）， significant prolongation of t_1/2， MRT_0-t， and MRT_0-∞ （all P<0.05）， and a significant reduction in CL_Z/F （P<0.05）. Compared with group C， group D had significant increases in the AUC_0-t and AUC_0-∞ of ertugliflozin （both P<0.05）， significant prolongation of T_max， t_1/2， MRT_0-t， and MRT_0-∞ （all P<0.01）， and significant reductions in V_Z/F and CL_Z/F （both P<0.05）. ConclusionBoth sorafenib and donafenib can affect the pharmacokinetics of ertugliflozin in rats and significantly increase the plasma exposure of ertugliflozin. The efficacy and adverse drug reactions of ertugliflozin should be closely monitored during combined use in clinical practice and the dose should be adjusted when necessary to avoid the potential risk of drug interaction.

OBJECTIVE To investigate the effects of ertugliflozin on pharmacokinetic of sorafenib and donafenib in rats and explore the mechanism. METHODS Twenty-four male SD rats were randomly divided into four groups， with 6 rats in each group. Groups A and B were respectively gavaged with 0.5% sodium carboxymethyl cellulose solution and ertugliflozin （1.5 mg/kg） for 7 consecutive days， and both were given sorafenib （100 mg/kg） on the 7th day. Groups C and D were administered intragastrically in the same way as those in Groups A and B， respectively， for the first 7 days； after the drug administration on the 7th day， all rats in Groups C and D were further gavaged with donafenib （40 mg/kg）. Blood samples were collected at different time points before and after administration of sorafenib or donafenib， the concentrations of sorafenib in plasma of rats in groups A and B and donafenib in groups C and D were determined by UPLC-MS/MS method. The pharmacokinetic parameters were calculated by DAS 2.1.1 software. Six additional rats were randomly divided into blank control group and ertugliflozin group， with three rats in each group. Blank control group was given 0.5% sodium carboxymethyl cellulose intragastrically， while rats in ertugliflozin group were given ertugliflozin （1.5 mg/kg） once a day for 7 consecutive days. After the last administration， the mRNA expression levels of uridine diphosphate glucuronosyl transferase 1A7 （UGT1A7）， breast cancer resistance protein （BCRP）， and P-glycoprotein （P-gp） in the liver and small intestine tissues of the rats were detected. RESULTS Compared with group A， the AUC0-t， AUC0-∞， cmax， tmax， MRT0-t and MRT0-∞ of sorafenib in group B were decreased significantly， while CL and V were increased significantly. Compared with group C， the AUC0-t， AUC0-∞ ， tmax， cmax and MRT0-t of Δ donafenib in group D were decreased significantly， while V and CL were increased significantly （P＜0.05）. mRNA expression of UGT1A7， P-gp and BCRP in the liver tissue and small intestine of rats were not significantly affected after intragastric administration of ertugliflozin for 7 consecutive days. CONCLUSIONS Ertugliflozin can affect the pharmacokinetics of sorafenib and donafenib in rats and decrease the plasma exposure of them significantly. However， its mechanism of action may not be through the regulation of related metabolic enzymes and transporters. When using drugs in combination clinically， one should be vigilant about the potential for disease progression due to poor therapeutic effects.

Objective: To design and establish a new method for flow cytometry-based detection of commonly observed highly expressed antigens on red blood cells, and to further evaluate the differences and distribution characteristics of antigen expression levels between ABO blood type homozygotes and heterozygotes in healthy individuals. Methods: Residual blood samples after donor blood type identification by Shanghai Blood Center in April 2024 were collected. Among them, samples of 19 homozygous and 19 heterozygous individuals of type A and type B were selected. Then the expression level of ABO antigen on red blood cells were detected using the new method established in this study and the traditional aldehyde fixed red blood cell method. Both methods were tested independently three times and the results were compared. Results: The mean values of the three detection results of the new method was (×10 /RBC): AA homozygous 3.3±0.5, AO heterozygous 2.8±0.3, BB homozygous 3.6±0.3, BO heterozygous 3.1±2.8. The mean values of the three detection results of the aldehyde fixation method were AA homozygous 5.9±0.9, AO heterozygous 5.0±1.4, BB homozygous 3.8±0.6, and BO heterozygous 3.3±0.4. The average antigen distribution of each genotype followed a normal distribution. Comparing the average antigen expression levels of homozygotes and heterozygotes, both methods showed that A/B homozygotes had higher antigen levels than heterozygotes, with AA being 1.17 to 1.18 times that of AO and BB being 1.15 to 1.16 times that of BO. Comparing the inter batch differences in the three test results of two methods, the new method showed no significant difference in the three test results for four genotypes (P>0.05). The aldehyde fixation method showed significant differences in the test results for all three genotypes (P<0.01) except for BB homozygotes (P>0.05). The reliability and reproducibility of the new method were better than those of the traditional aldehyde fixation method. Conclusion: The antigen expression level of ABO homozygotes is higher than that of heterozygotes, and the difference in antigen level between type A homozygotes and heterozygotes is slightly higher than that of type B. The new method is superior to traditional aldolization fixation methods.

Objective: This study aims to investigate the therapeutic effects and underlying mechanisms of Xuanfei Baidu Decoction (XFBD) in a mouse model of dampness-heat toxin pneumonia. By exploring how XFBD exerts its effects, we seek to deepen our understanding of its role in treating pulmonary diseases and to address the current knowledge gap regarding its mechanisms of action, thereby supporting its clinical application. Methods: Ultra-high-performance liquid chromatography and high-resolution mass spectrometry (HRMS) were employed to analyze the chemical constituents of XFBD. The protective effects of XFBD were evaluated using a dampness-heat toxin-induced mouse model, established through dampness-heat exposure and HCoV-229E infection. XFBD was administered orally, followed by assessments including lung index measurement, micro-CT imaging, viral load quantification, cytokine analysis, and histological evaluation via hematoxylin-eosin staining. Proteomics and single-cell transcriptomic analyses were conducted to explore the potential mechanisms underlying XFBD’s pharmacological effects. A cellular model of HCoV-229E infection was developed to investigate changes in the cAMP/PKA signaling pathway. Molecular docking and surface plasmon resonance (SPR) experiments confirmed the strong binding affinity between key XFBD components and PKA. Finally, PKA activators and inhibitors were applied in vitro to validate these mechanistic findings. Results: In vivo studies demonstrated that XFBD significantly reduced the lung index, improved the structural integrity of lung and tongue tissues, and decreased levels of proinflammatory mediators, including IL-6, IL-8, and TNF-α. Proteomic and single-cell transcriptomic analyses showed that the differentially expressed proteins after XFBD treatment were primarily associated with inflammatory responses and immune regulation. The cAMP/PKA signaling pathway was identified as a key mechanism underlying these therapeutic effects. Notably, Western blot, ELISA, molecular docking, and SPR analyses confirmed that XFBD elevated cAMP levels and p-PKA expression, thereby activating the cAMP/PKA signaling pathway in vitro. Conclusion: This study demonstrated that XFBD significantly alleviates symptoms in mice with dampness-heat toxin pneumonia. Its therapeutic effects are mediated, at least in part, through activation of the cAMP/PKA signaling pathway. These findings provide compelling evidence that XFBD is an effective herbal remedy against HCoV-229E infection.

PURPOSE: End-stage knee osteoarthritis (OA) patients are the primary candidates for total knee arthroplasty (TKA). However, most morphological refinements of TKA prosthesis are based on anatomical data from the knees of healthy individuals. This study aimed to determine whether differences exist in key bony morphological characteristics of the distal femur and proximal tibia between osteoarthritic knees and healthy knees. METHODS: This was a retrospective cross-sectional observational study with a case-control design. Patients who were aged ≥ 50 years, had no history of trauma, fracture, or surgery in the studied knee, and had no obvious knee flexion contracture were included in this study by CT scans. Patients who met the American College of Rheumatology clinical criteria for knee OA were included in the study group. Kellgren-Lawrence grade III or IV knees were studied (for bilateral cases, the more severely affected knee was chosen). Patients who presented with unilateral knee pain or trauma were included in the control group, with CT scans from the opposite (asymptomatic) knee used for analyzing. The studied knee had a Kellgren-Lawrence grade of 0 or I and showed no abnormalities upon physical examination. Archived knee CT scans from 160 patients were divided into 2 groups: the study group (80 moderate-to-severe OA knees) and the control group (80 healthy knees). After 3-dimensional reconstruction and virtual cutting using a CT workstation, 13 morphological parameters of the distal femur and proximal tibia were compared between the 2 groups using independent-samples t-tests. RESULTS: No significant group differences in the femoral anteroposterior dimension (p = 0.797), height of the lateral femoral condyle (p = 0.268), posterior condylar angle (p = 0.240), tibial anteroposterior dimension (p = 0.536), or tibial lateral anteroposterior dimension (p = 0.702) were observed. However, the femoral mediolateral dimension (p = 0.002), distal femoral aspect ratio (femoral mediolateral dimension/femoral anteroposterior dimension) (p < 0.001), height of the femoral trochlear groove (p < 0.001), height of the medial femoral condyle (p < 0.001), tibial mediolateral dimension (p = 0.001), proximal tibial aspect ratio (tibial mediolateral dimension/tibial anteroposterior dimension) (p = 0.004), tibial medial anteroposterior dimension (p = 0.005), and tibial asymmetry ratio (tibial medial anteroposterior dimension/tibial lateral anteroposterior dimension) (p = 0.006) were all significantly greater in the study group. CONCLUSION Knees with moderate-to-severe OA are significantly wider than healthy knees, and OA is a risk factor for increased tibial platform asymmetry. When refining the morphological parameters of TKA prostheses, the specific bony morphological characteristics of OA knees should be taken into account to reduce the potential risk of femoral or tibial component underhang and facilitate optimal balance between tibial component fit and rotational alignment.
Humans ; Osteoarthritis, Knee/pathology* ; Male ; Female ; Cross-Sectional Studies ; Retrospective Studies ; Arthroplasty, Replacement, Knee ; Middle Aged ; Aged ; Case-Control Studies ; Prosthesis Design ; Knee Prosthesis ; Femur/anatomy & histology* ; Tibia/anatomy & histology* ; Tomography, X-Ray Computed ; Knee Joint/diagnostic imaging*