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.

Background::Computer-aided detection (CAD) software has been introduced to automatically interpret digital chest X-rays. This study aimed to evaluate the performance of CAD software (JF CXR-1 v3.0, which was developed by a domestic Hi-tech enterprise) in tuberculosis (TB) case finding in China.Methods::In 2019, we conducted an internal evaluation of the performance of JF CXR-1 v3.0 by reading standard images annotated by a panel of experts. In 2020, using the reading results of chest X-rays by a panel of experts as the reference standard, we conducted an on-site prospective study to evaluate the performance of JF CXR-1 v3.0 and local radiologists in TB case finding in 13 township health centers in Zhongmu County, Henan Province.Results::Internal assessment results based on 277 standard images showed that JF CXR-1 v3.0 had a sensitivity of 85.94% (95% confidence interval [CI]: 77.42%, 94.45%) and a specificity of 74.65% (95% CI: 68.81%, 80.49%) to distinguish active TB from other imaging conditions. In the on-site evaluation phase, images from 3705 outpatients who underwent chest X-ray detection were read by JF CXR-1 v3.0 and local radiologists in parallel. The imaging diagnosis of local radiologists for active TB had a sensitivity of 32.89% (95% CI: 22.33%, 43.46%) and a specificity of 99.28% (95% CI: 99.01%, 99.56%), while JF CXR-1 v3.0 showed a significantly higher sensitivity of 92.11% (95% CI: 86.04%, 98.17%) ( p < 0.05) and maintained high specificity at 94.54% (95% CI: 93.81%, 95.28%). Conclusions::CAD software could play a positive role in improving the TB case finding capability of township health centers.

Objective To explore the effects of different concentrations of simvastatin on nerve regeneration after sciatic nerve injury.Methods Rats were randomly divided into the normal group,the control group,the low-dose group and the high-dose group,with 3 rats in each group.Except for the normal group,adult rat sciatic nerve crush injury models were established in the other groups.Rats in the normal group and the control group were orally administered with water,while those in the low-dose group and high-dose group were orally administered with 98%simvastatin at dosages of 4 mg/mL and 40 mg/mL,respectively.The sciatic nerve regeneration in rats was evaluated by sciatic function index(SFI),HE staining,luxol fast blue(LFB)staining and immunofluorescence staining,etc.Results The SFI of rats in the high-dose group 7 days and 14 days after surgery were higher than those in the control group(P<0.05);there was no significant difference in SFI of rats between the low-dose group and the control group 7 days and 14 days after surgery(P>0.05).HE staining and LFB staining results showed that compared with the control group,the number of neurons of rats in the high-dose group increased,the nerve fibers and myelin were clearer and denser,and the nerve function was significantly restored;while no significant improvement was observed in the sciatic nerve of rats in the low-dose group.The immunofluorescence staining results showed that compared with the control group,the immunofluorescence intensity in the high-dose group increased,while that in the low-dose group decreased,the differences were statistically significant(P<0.05).Conclusion High-dose simvastatin can promote peripheral nerve regeneration by regulating the expression of M2 macrophages.

Intervertebral disc herniation is a highly prevalent orthopedic disorder,and intervertebral disc degeneration(IDD),the key pathological basis,is a complex pathological process characterized by progressive degradation of extracellular matrix,structural failure,and loss of biomechanical function,which not only shows higher prevalence in the population,but is also the primary cause of chronic low back pain and dysfunction worldwide,causing a huge socioeconomic burden.Although constructing IDD animal models is important for exploring the pathological mechanisms and promoting translational research of this disease,the etiology and pathophysiological mechanisms of IDD have not been fully elucidated.There are significant differences between humans and common laboratory animals in spinal anatomy,biomechanics,and degenerative course,coupled with the diversity and lack of unified standards of existing IDD animal models.This guide systematically reviews IDD animal models of rodents,non-human primates,as well as different species such as rabbits,goats/sheep,pigs,and dogs,focusing on the modeling principles of three main types of models:inducible models(such as annulus fibrosus/nucleus pulposus/endplate injury and mechanical injury)are suitable for simulating acute injury and rapid screening of therapies due to their high controllability and short cycle;spontaneous models can better simulate the age-related natural degeneration process in humans;genetically modified models provide powerful tools for analyzing specific molecular pathways.The guideline deeply analyzes the key technical points,reproducibility,and clinical relevance of these models.It also compares their advantages,limitations,and applicable research scenarios to guide researchers to conduct"scientific question-driven"precise model selection.Meanwhile,to improve the depth and comparability of research results,this guideline proposes a multidimensional endpoint evaluation system for IDD animal model experiments covering imaging,histology,biochemistry/molecular biology,biomechanics,and pain-related behavior,with recommended observation time windows.It also clarifies the"3Rs(replacement,reduction,and refinement)"ethical principles and animal welfare requirements throughout the experiment.In addition,the guideline outlines future research directions such as integrating single-cell omics,multiscale mechanical analysis,and strengthening pain-related phenotype assessment.This guideline aims to provide researchers with a systematic and standardized methodological framework for the rational selection and application of IDD animal models under specific scientific questions and resource constraints,in order to reduce inter-study heterogeneity,enhance the translation efficiency of preclinical findings,promote high-quality development in the field,and ultimately provide a solid scientific foundation for developing innovative therapies to delay or even reverse IDD.

Objective:To investigate the efficacy of laparoscopic versus open hepatectomy for intrahepatic bile duct stones based on propensity score matching. Methods:This study used a case-control design to retrospectively analyze 163 patients with intrahepatic bile duct stones who were treated at The Second Hospital of Anhui Medical University between February 2014 and February 2024. Based on the surgical approach, the patients were divided into two groups: the laparoscopic hepatectomy group ( n = 72) and the open hepatectomy group ( n = 91). Using 1:1 PSM, two groups with similar baseline clinical characteristics were created to compare perioperative outcomes, stone residual rates, and recurrence rates. Results:After PSM, a total of 52 matched pairs were successfully obtained. Compared with the laparoscopic hepatectomy group, the open hepatectomy group demonstrated a significantly shorter operative time [233.00 (180.00, 315.00) minutes vs. 313.00 (222.25, 405.75) minutes, Z = 3.41, P = 0.01]. However, no statistically significant differences were observed between the open hepatectomy and laparoscopic hepatectomy groups in terms of pre- to postoperative hemoglobin change [(22.69 ± 14.27) g/L vs. (20.63 ± 14.36) g/L, t = 0.73, P = 0.465], postoperative bile leakage [5.77% (3/52) vs. 11.54% (6/52), χ2 = 1.10, P = 0.25], hypoalbuminemia [82.69% (43/52) vs. 84.62% (44/52), χ2 = 0.07, P = 0.791], pulmonary infection [28.85% (15/52) vs. 40.38% (21/52), χ2 = 1.53, P = 0.216], surgical site infection [5.77% (3/52) vs. 1.92% (1/52), χ2 = 1.04, P = 0.308], intra-abdominal infection [1.92% (1/52) vs. 5.77% (3/52), χ2 = 1.04, P = 0.308], postoperative drainage tube removal time [8.00 (6.00, 11.75) days vs. 8.00 (6.25, 10.00) days, t = 0.05, P = 0.958], postoperative hospital stay [8.00 (9.00, 15.00) days vs. 9.00 (7.00, 12.50) days, t = -1.22, P = 0.222], residual stone rate [11.54% (6/52) vs. 9.62% (5/52), χ2 = 0.10, P = 0.750], and stone recurrence rate [13.46% (7/52) vs. 3.85% (2/52), χ2 = 3.04, P = 0.081]. All differences were statistically significant (all P > 0.05). Conclusions:Laparoscopic hepatectomy and open hepatectomy have comparable efficacy in the treatment of intrahepatic bile duct stones.

Objective To analyze the global literature related to functional cure of hepatitis B from 2019 to 2023 by using bibliometric analysis methods,so as to help researchers understand the research hotspots and trends in this field.Methods The literature related to the topic of functional cure of hepatitis B included in the Science Citation Index Expanded(SCI-Expanded)of the Web of Science Core Collection from 2019 to 2023 was searched.By using VOSviewer and CiteSpace visual analysis tools,analyses were conducted from the perspectives of publication trends,international research cooperation networks,and keyword emergence,and were elaborated with the specific contents of the related literature to elucidate research hotspots and trends.Results A total of 600 eligible papers in this field were included.Keyword co-occurrence and thematic clustering suggested that the main research directions of functional cure were:serum biomarkers for prediction and monitoring of functional cure,functional cure and immunity,nucleoside analog discontinuation,interferon therapy,and long-term prognosis of functional cure.The research contents of the ESI highly cited original research papers were similar to the clustering of the above,but showed more attention on the novel agents for functional cure.The content of the keyword emergence map showed that hotspots of interest changed from virologic mechanisms and serum markers,to nucleoside analog discontinuation and interferon therapy,and finally to immunologic mechanisms and new drug.Conclusion The research hotspots and trends of functional cure of hepatitis B were focused on virological mechanism,serum markers,immunological mechanism,nucleoside analog discontinuation,interferon therapy,and long-term prognosis after cure.