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.

This paper summarizes professor LIU Zhibin's clinical experience in treating Parkinson's disease with festinating gait by acupuncture combined with tuina based on the theory of the eight hollow spaces. Professor LIU holds that the disease involve the channel sinews and joints， and that the key pathogenesis lies in dysfunction of the eight hollow spaces， impairment of mechanical pivots and contraction of channel sinews. Clinically， these manifestations are characterized by impaired function of the hip and popliteal regions with dysregulation of gait initiation and termination， as well as impaired function of the axillary and elbow regions with discoordination of postural balance and movement. The therapeutic principle is regulating the spirit， unblocking the collaterals， and facilitating the eight hollow spaces. Acupuncture focuses on awakening the brain， regulating the spirit， unblocking the eight hollow spaces and dredging the sinew channels， whereas tuina focuses on releasing sinew knots around the hip and popliteal regions and restoring the movement of mechanical pivots. Particular emphasis is placed on the sequential application of acupuncture and tuina and the integration of static and dynamic approaches， with physical movement used to facilitate regulation of the spirit and gait training employed to consolidate therapeutic effects. This approach provides a new perspective for the treatment of festinating gait in Parkinson's disease with traditional Chinese medicine.

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.

Targeting T-cell is a strategy to control allogeneic response disorders, such as acute graft-versus-host disease (GVHD) which is an important cause of therapy-failure after allogeneic hematopoietic cell transplants. Free fatty acid receptor-4 (FFAR4) is a regulator of obesity but its role in T-cell and allogeneic reactions is unknown. Here, we found knockout of Ffar4 in donor T-cells in a mouse allograft model increased acute GVHD whereas the natural FFAR4 ligands and the synthetic FFAR4 agonists decreased it. FFAR4 agonist-mediated anti-acute GVHD effects depended on FFAR4-expression in donor T-cells. The FFAR4 agonist CpdA suppressed donor T-cell-mediated alloreaction by activating an aryl hydrocarbon receptor (AhR) pathway. CpdA recruited β-Arrestin2 to FFAR4 which facilitated nuclear translocation of AhR and upregulation of IL-22. The CpdA-mediated anti-acute GVHD effect was absent in mice receiving Ahr-knockout or Il22-knockout T-cells. Recipient-expressing Ffar4 was also important for the anti-acute GVHD effect of CpdA which inhibited activation of antigen presenting cells. Importantly, CpdA decreased acute GVHD in obese mice, an effect also depended on Ffar4-expression in donor T-cells and recipients. Our study shows the immunoregulatory effect of FFAR4 in T-cell, and targeting FFAR4 might be a relative option for controlling allogeneic reactions in obese patients.

Nitrogen narcosis is a neurological syndrome that manifests when humans or animals encounter hyperbaric nitrogen, resulting in a range of motor, emotional, and cognitive abnormalities. The anterior cingulate cortex (ACC) is known for its significant involvement in regulating motivation, cognition, and action. However, its specific contribution to nitrogen narcosis-induced hyperlocomotion and the underlying mechanisms remain poorly understood. Here we report that exposure to hyperbaric nitrogen notably increased the locomotor activity of mice in a pressure-dependent manner. Concurrently, this exposure induced heightened activation among neurons in both the ACC and dorsal medial striatum (DMS). Notably, chemogenetic inhibition of ACC neurons effectively suppressed hyperlocomotion. Conversely, chemogenetic excitation lowered the hyperbaric pressure threshold required to induce hyperlocomotion. Moreover, both chemogenetic inhibition and genetic ablation of activity-dependent neurons within the ACC reduced the hyperlocomotion. Further investigation revealed that ACC neurons project to the DMS, and chemogenetic inhibition of ACC-DMS projections resulted in a reduction in hyperlocomotion. Finally, nitrogen narcosis led to an increase in local field potentials in the theta frequency band and a decrease in the alpha frequency band in both the ACC and DMS. These results collectively suggest that excitatory neurons within the ACC, along with their projections to the DMS, play a pivotal role in regulating the hyperlocomotion induced by exposure to hyperbaric nitrogen.
Animals ; Gyrus Cinguli/drug effects* ; Male ; Mice, Inbred C57BL ; Locomotion/drug effects* ; Neurons/drug effects* ; Mice ; Nitrogen/toxicity* ; Inert Gas Narcosis/physiopathology* ; Corpus Striatum/physiopathology*

The prevalence of Class III malocclusion varies among different countries and regions. The populations from Southeast Asian countries (Chinese and Malaysian) showed the highest prevalence rate of 15.8%, which can seriously affect oral function, facial appearance, and mental health. As anterior crossbite tends to worsen with growth, early orthodontic treatment can harness growth potential to normalize maxillofacial development or reduce skeletal malformation severity, thereby reducing the difficulty and shortening the treatment cycle of later-stage treatment. This is beneficial for the physical and mental growth of children. Therefore, early orthodontic treatment for Class III malocclusion is particularly important. Determining the optimal timing for early orthodontic treatment requires a comprehensive assessment of clinical manifestations, dental age, and skeletal age, and can lead to better results with less effort. Currently, standardized treatment guidelines for early orthodontic treatment of Class III malocclusion are lacking. This review provides a comprehensive summary of the etiology, clinical manifestations, classification, and early orthodontic techniques for Class III malocclusion, along with systematic discussions on selecting early treatment plans. The purpose of this expert consensus is to standardize clinical practices and improve the treatment outcomes of Class III malocclusion through early orthodontic treatment.
Humans ; Malocclusion, Angle Class III/classification* ; Orthodontics, Corrective/methods* ; Consensus ; Child