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.

Objective To design a pairs of detection gloves for orthopedic manipulation to solve the problems of orthopedic manipulation in accurate,visual and quantitative description and reproduction in teaching.Methods The orthopedic manipulation detection gloves were mainly composed of a detection body worn on the hands,a main control module and a data visualization system.The detection body was made of multi-layer flexible materials such as rubber,cotton and non-woven fabrics,which integrated inertial sensors,pressure sensors and a data acquisition device;the main control module consisted of a filter circuit,an A/D converter(MCP3008)and a main controller(ARM-STM32 microcontroller);the data visualization system was designed based on the Unity 3D platform.Results The orthopedic manipulation detection gloves effectively detected the rotation angle of the knuckle during the orthopedic process with a high accuracy rate.Conclusion The orthopedic manipulation detection gloves can quantitatively display the abstract orthopedic manipulation,and can provide support for intelligent orthopedic teaching and orthopedic manipulation optimization.[Chinese Medical Equipment Journal,2025,46(7):27-33]

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.

Objective To conduct a bibliometric analysis of relevant literature on liver failure caused by viral hepatitis from the past five years,and to help researchers understand the current status and hotspots in this field,and to provide insights into future research trends.Methods Based on the Science Citation Index Expanded(SCI-Expanded)data from Web of Science Core Collection,visualization analysis and mapping were conducted through VOSviewer and CiteSpace software to generate visual representations of international research collaboration networks,keyword co-occurrence clustering,and keyword bursts.Results From 2019 to 2023,a total of 873 relevant literature were included,with a total citation frequency of 7 364 and an average citation frequency of 8.44.Among them,China had the highest number of publications(458 articles,52.46%)and had the most cooperation with the United States.The research hotspots of viral hepatitis induced liver failure were mainly divided into three categories:basic and clinical research on liver failure caused by non-hepatitis B virus(HBV),the pathogenesis of HBV related liver failure,and treatment and prediction models of liver failure.The keyword time overlay map and burst map showed that the research hotspots had gradually shifted from the prevention and control of new infections to the treatment and prognosis assessment of patients with chronic infection.Conclusion China is a major international research entity in liver failure caused by viral hepatitis and actively participates in international scientific collaborations.The research hotspots on liver failure caused by viral hepatitis have gradually shifted from preventing viral hepatitis infections and expanding treatment options to the treatment of chronic infection patients and prognostic prediction.

Cervical spinal cord injury without fracture-dislocation (CSCIWFD) is referred to as a special type of cervical spinal cord injury characterized by traumatic spinal cord dysfunction and no significant bony structural abnormalities on imagines. Duo to the high risk of missed diagnosis during the initial consultation, CSCIWFD may lead to progressive neurological deterioration or even complete paralysis, severely impacting patients′ prognosis. Currently, there are no established consensuses over the diagnosis and treatment of CSCIWFD, such as the lack of evidence-based standards for indications of non-surgical treatment and risk of secondary neurological injury, as well as debates over the optimal timing for surgical intervention and indications for different surgical approaches. To address these issues, the Spine Trauma Group of the Orthopedic Branch of the Chinese Medical Doctor Association organized experts in the relevant fields to formulate Diagnosis and treatment guideline for acute cervical spinal cord injury without fracture- dislocation in adults ( version 2025) . Based on evidence-based medicine and the principles of scientific rigor and clinical applicability, the guidelines proposed 11 recommendations covering terminology, diagnosis, evaluation treatment, and rehabilitation, etc., aiming to standardize the management of CSCIWFD.

Postoperative infection of internal fixation of closed fractures the lower limbs in adults represents a devastating complication, characterized by diagnostic challenges, prolonged treatment duration and high disability rates. Current management of these infections faces multiple challenges, such as difficulties in early accurate diagnosis, and various controversies about the treatment plan, leading to poor overall diagnosis and treatment results. To address these issues, based on evidence-based medicine and principles with emphasis on scientific rigor, clinical applicability and innovation, the Trauma Branch of the Chinese Medical Association, Orthopedic Branch of the Chinese Medical Doctor Association, Orthopedics Branch of the Chinese Medical Association, and Trauma Orthopedics and Polytrauma Group of the Resuscitation and Emergency Committee of the Chinese Medical Doctor Association have collaboratively organized a panel of relevant experts to develop the Guideline for diagnosis and treatment of infection after internal fixation of closed lower limb fractures in adults ( version 2025). The guideline proposed 10 recommendations, aiming to provide a foundation for standardized diagnosis and treatment of postoperative infection in adults with closed lower limb fractures.

Objective:To investigate the value of Sequential Organ Failure (SOFA) score and its dynamics (ΔSOFA) in predicting mortality in hematology care unit (HCU) .Methods:A retrospective clinical study was conducted on 79 critically ill hematologic patients admitted to the Center for Critical Care Medicine, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences, between May and June 2024. SOFA scores and ΔSOFA were calculated within 2 days before and after HCU admission. The predictive value of SOFA and ΔSOFA in mortality was assessed using receiver operating characteristic (ROC) curve analysis.Results:Among the 79 patients, the HCU mortality rate was 54.4%. The SOFA scores on days 1–3 (D1, D2, and D3) and ΔSOFA on day 1 (ΔD_1) of all patients, leukemia patients and hematopoietic stem cell transplantation (HSCT) patients were significantly higher in the death group compared with the non-death group (all P<0.05). ROC curve analysis revealed that the D_1, D_2, D_3 scores, and ΔD_1 significantly predicted mortality ( P<0.001), with areas under the curve (AUCs) of 0.786, 0.866, 0.901, and 0.843, respectively. The sensitivity values were 74.36%, 57.89%, 62.85%, and 86.84%, while specificity values were 70%, 100%, 100%, and 67.65%, respectively. In the HSCT group, the D_-1, D_1, D_2, D_ 3, scores and ΔD_1 were predictive of HCU mortality, with AUCs of 0.833, 0.794, 0.871, 0.846, and 0.795, respectively. Sensitivity values for these scores were 100%, 85.71%, 71.43%, 57.14%, and 57.14%, while specificity values were 73.33%, 70.59%, 91.33%, 100%, and 100%, respectively. In the leukemia group, the D_1, D_2, D_3 scores, and ΔD_1 were predictive of HCU mortality, with AUCs of 0.760, 0.829, 0.846, and 0.756, respectively. Sensitivity values were 71.43%, 78.57%, 53.85%, and 71.43%, while specificity values were 76.19%, 78.95%, 100%, and 63.16%, respectively. For all patients, the D_3 score exhibited the highest specificity, while the ΔD_1 demonstrated the highest sensitivity. For patients in both the HSCT and leukemia groups, the sensitivity and specificity values of the D_1 and D_3 scores exceeded those of the ΔD_1. Conclusion:For patients with hematologic critical illness, including leukemia and those undergoing HSCT hospitalized in the HCU, D_1, D_2, D_ 3 scores and ΔD_1 are significantly associated with HCU mortality.

Objective To design an 8-channel gene analyzer to take the place of the widely used gene analyzer with problems in inconvenient consumable replacement and short storage time of electrophoresis polymer.Methods The 8-channel gene analyzer had its mechanical components composed of an automatic sample loading table,a polymer injection module,a high-voltage temperature control module,an optical module and an integrated U box,its electrical control system made up of a host computer(an embedded computer)and three slave computers(a sampling control board,a polymer injection control board and a high-voltage temperature control board).The automatic sample loading table involved in four motors and transmission systems for x,y,z directions and optical alignment,the transmission systems adopted mainly belt drive mode and the optical alignment motor had its threads with an anti-backlash structure;the polymer injuection module was manipulated by the polymer injection control board,and the polymer block was made of highly transparent acrylic material;the high-voltage temperature control module realized the regulation of electrophoresis voltage and the detection of electrophoresis current by the low-ripple precision high-voltage power supply,and controlled the temperature of the heating furnace by the proportional-integral-differential(PID)algorithm;the optical module consisted of an excitation module and a light-receiving module,which had the base of the reflector made of low expansion coefficient alloy material;the integrated U box had the electrophoresis polymer,capillary array,polymer block and anode buffer in a plastic housing;the host computer had the data acquisition software programmed with C# and C++,and the slave computers were controlled by STM32 SCM.Results The 8-channel gene analyzer had no significant differences with the widely used ABI3500 gene analyzer in resolution,precision accuracy and clinical results.Conclusion The 8-channel gene analyzer gains advantages in consumable replacement and storage time of electrophoresis polymer,and can meet the requirements for gene sequencing.[Chinese Medical Equipment Journal,2025,46(2):24-30]