ObjectiveTo compare the clinical efficacy of traditional Chinese medicine （TCM） manipulative reduction combined with small splint fixation versus surgical treatment for type A distal radius fracture （DRF） and to explore the factors influencing the choice of treatment. MethodsA multi-center retrospective study was conducted， collecting data from 1237 type A DRF patients treated in 11 hospitals in Jiangsu province from September， 2023 to April， 2025. Among them， 851 patients in the TCM group received manipulative reduction combined with small splint fixation， and 386 patients in the surgical group underwent open reduction and internal fixation. Visual analog scale （VAS） scores for pain and radiographic indicators including palmar tilt， ulnar deviation， and radial height were compared before treatment， 5-7 days after treatment， and 4-6 weeks after treatment. The wrist joint function scores including Dienst and Gartland-Werley scores at 12 weeks after treatment were recorded. Subgroup analysis was conducted for the excellent rate of Dienst and Gartland-Werley scores， stratified by age （＜50， 50-59， 60-69， ≥70 years old） and AO subtypes （A1， A2， A3）. A multivariate logistic regression model was used to identify independent factors influencing treatment choice. ResultsOn 5-7 days after treatment， the surgical group had lower VAS scores than the TCM group， while 4-6 weeks after treatment， the TCM group showed lower VAS scores than the surgical group （P＜0.01）. In terms of radiographic indicators， except for the palmar tilt before treatment being higher in the surgical group than in the TCM group （P＜0.01）， there were no significant differences in palmar tilt， ulnar deviation， and radial height at other timepoints （P＞0.05）. Twelve weeks after treatment， the surgical group had a higher average Gartland-Werley score and the excellent rate than the TCM group （P＜0.01）. Subgroup analysis showed that in patients with A2 type DRF aged 50-59 and 60-69 years old， the excellent rates of Dienst and Gartland-Werley scores in the TCM group were higher than those in the surgical group （P＜0.05）. Multivariate logistic regression analysis revealed that age， palmar tilt， ulnar deviation， and the degree of swelling on the affected side were independent factors influencing the choice of treatment （P＜0.05）. ConclusionBoth TCM manipulative reduction combined with small splint fixation and surgical treatment for type A DRF can achieve good therapeutic effects. TCM manipulative reduction combined with small splint fixation has certain advantages in medium- and long-term pain relief， especially in elderly patients， where wrist joint function recovery is more stable. Age， palmar tilt， ulnar deviation， and swelling degree are the main factors influencing the treatment choice.

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 investigate the optimal termination time for acupuncture in treating patients with acute peripheral facial paralysis and its cost-effectiveness. MethodsA total of 120 eligible patients with acute-stage peri-pheral facial paralysis were randomly assigned to either the mild dysfunction termination group and the complete recovery termination group， with 60 patients in each group. Both groups received the standard acupuncture treatment protocol. Treatment in the mild dysfunction termination group was terminated when the Sunnybrook facial grade scale （SFGS） score first reached or exceeded 83 points， while that in the complete recovery termination group was terminated when the SFGS score first reached or exceeded 95 points. Assessments were conducted before treatment， 6 and 12 months after onset. SFGS， facial disability index （FDI） including physical function （FDIp） and social function （FDIs）， self-rating anxiety scale （SAS）， and self-rating depression scale （SDS） scores were assessed before treatment， and 6 and 12 months after onset. Any acupuncture-related adverse events during treatment were recorded for safety evaluation. Treatment sessions and medical costs including direct costs， indirect costs， insurance coverage， total societal costs， and patient out-of-pocket expenses were also recorded， and an economic evaluation was conducted including cost-effectiveness ratio （CER） and incremental cost-effectiveness ratio （ICER）. ResultsUltimately， 56 patients in the mild dysfunction termination group and 55 in the complete recovery termination group completed the follow-up. At 6 and 12 months after onset， SFGS and FDIp scores in both groups improved significantly while FDIs， SAS and SDS scores decreased （P＜0.05）. Comparison of scores between groups 6 months and 12 months after onset showed no statistically significant differences （P＞0.05）. During the trial， the incidence of adverse events was 13.3% （8/60） in the mild dysfunction termination group and 18.3% （11/60） in the complete recovery termination group， with no statistically significant difference （P＞0.05）. The number of treatment sessions， total social costs， and out-of-pocket expenses in the mild dysfunction termination group were significantly lower than those in the complete recovery termination group （P＜0.05）. The CER of the mild dysfunction termination group in SFGS， FDIp， FDIs， SAS， and SDS scores was lower than that of the complete recovery termination group. The ICER analysis showed that continuing treatment until full recovery incurred an additional cost of 573.30 CNY/point in SFGS improvement， whereas 1-point improvement in FDIp， FDIs， SAS， and SDS required 21,355.25 CNY， 1779.60 CNY， 3713.96 CNY， and 2755.52 CNY， respectively. ConclusionFor acupuncture in treating acute peripheral facial palsy， terminating treatment when mild dysfunction is achieved yields long-term efficacy comparable to that of continuing treatment until complete recovery， while significantly reducing medical costs and socioeconomic burden.

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

As a new type of model organism， zebrafish is gradually gaining prominence in the field of scientific research. The unique biological characteristics and advantages of zebrafish make them play an increasingly important role in the quality evaluation of traditional Chinese medicine. Compared with other common experimental animals， zebrafish have a fast reproductive and growth speed and high embryo transparency， making them an ideal model for evaluating the quality of traditional Chinese medicine. This provides a new perspective and method for research on traditional Chinese medicine. With the growing global interest in traditional Chinese medicine， it has become crucial to find scientific and accurate methods to evaluate the quality and effectiveness of traditional Chinese medicine. The introduction of the zebrafish model has brought new breakthroughs in the quality evaluation of traditional Chinese medicine. To further promote the application of zebrafish in evaluating the quality of traditional Chinese medicine， this article systematically searched and sorted out the previous studies related to the application of zebrafish for this purpose since 2023. The commonly used disease models and indicators of zebrafish in evaluating the effectiveness of traditional Chinese medicine， as well as the mechanism of zebrafish in exploring the active ingredients of traditional Chinese medicine， were primarily reviewed. The application of zebrafish in evaluating the safety of traditional Chinese medicine and the typical examples in ensuring the quality of traditional Chinese medicine were demonstrated. The limitations encountered by zebrafish models in evaluating the quality of traditional Chinese medicine were highlighted. The resolution of these problems will help further improve the accuracy and reliability of zebrafish in evaluating the quality of traditional Chinese medicine. The article discussed the evaluation of effectiveness， safety， and quality control of zebrafish applied in traditional Chinese medicine， so as to provide a reference for establishing standards for traditional Chinese medicine and promoting its modernization in the future.

Objective:To discuss the clinical value of pan-immune inflammation index(PIV)in predicting the major adverse cardiovascular events(MACE)within 1 year after percutaneous coronary intervention(PCI)in the elderly patients with coronary heart disease,and to clarify the role of inflammatory response in postoperative recovery and prognosis of the patients with coronary heart disease.Methods:A total of 150 elderly patients with coronary heart disease who underwent PCI from July 2020 to August 2023 were selected as the research subjects;according to the occurrence of MACE within 1 year after operation,they were divided into MACE group(n=28)and non-MACE group(n=122);the baseline data and biochemical indicators of the patients were collected,and PIV was calculated;multivariate Logistic regression was used to analyze the influencing factors of MACE within 1 year after PCI in the elderly patients with coronary heart disease;receiver operating characteristic(ROC)curve was used to analyze the predictive value of PIV for MACE within 1 year after PCI in the elderly patients with coronary heart disease.Results:Compared with non-MACE group,the levels of total cholesterol(TC)and low-density lipoprotein cholesterol(LDL-C),neutrophils(NEUT),platelets(PLT)counting and PIV in the patients in MACE group were significantly increased(P<0.05);there were no significant differences in other data between two groups(P>0.05).The multivariate Logistic regression analysis results showed that the levels of TC(OR=1.571,95％CI:1.088-2.270)and LDL-C(OR=32.506,95％CI:8.880-118.994)and PIV(OR=1.014,95％CI:1.010-1.019)were the influencing factors of MACE within 1 year after PCI in the elderly patients with coronary heart disease(P<0.05).The ROC curve analysis results showed that the area under the ROC curve(AUC)of PIV for predicting MACE was 0.857(95％CI:0.762-0.951),the sensitivity was 0.821,the specificity was 0.959,the maximum Youden index was 0.780,and the best cut-off value was 778.805(P<0.01).Conclusion:PIV has important predictive value for MACE within 1 year after PCI in elderly patients with coronary heart disease.

Emotion is defined as a physiological and psychological state that encompasses human thoughts, behaviors, and feelings. This phenomenon is also regarded as a spontaneous physiological and psychological response generated by the human body to external stimuli. Given the established correlation between electroencephalogram signal and cerebral activity, it is possible to extrapolate the emotional state of subjects by means of electroencephalogram signal analysis. In this review, emotion models, datasets, and popular machine learning and deep learning methods in recent years used in emotion recognition research were summarized. In addition, the research progress of emotion recognition based on electroencephalogram signal was reviewed, with the aim of assisting subsequent researchers in understanding developments in electroencephalogram signal domain and offering insights for addressing clinical challenges in emotion recognition.

Objective:To explore the dosimetric characteristics of intensity modulated proton therapy (IMPT), intensity modulated radiation therapy (IMRT) and tomotherapy (TOMO) techniques applied in the irradiation of pediatric whole central nervous system tumors.Methods:Taking the target area of a 14-year-old pediatric patient clinically diagnosed with atypical teratoid/rhabdomyoid tumor, meningeal metastasis by Shandong Cancer Hospital and Institute, and undergoing craniospinal irradiation (CSI) as an example, IMPT, IMRT and TOMO plans were designed respectively based on the clinical prescription of the target area and the limit requirements of organs at risk (OARs). The conformal index (CI), homogeneity index (HI) and gradient index (GI) of each planning target volume, as well as the dose volume index of normal tissues, were evaluated to compare the dosimetric characteristics of the three types of plans.Results:The CI (0.71), HI (0.05) and GI (3.13) of the IMPT plan were comparable to those of IMRT plan (0.80, 0.08, 3.14). The HI (0.03) and GI (2.54) of the TOMO plan were excellent, which were all within the clinically acceptable range. The irradiation dose to parallel organs in the IMPT plan was lower than that in the IMRT and TOMO plan. In the IMPT plan, V 5 of lungs was 2.9%, IMRT plan was 37.6%, and TOMO plan was 43.5%. The D mean of liver in the IMPT plan was 0.01 Gy (RBE), IMRT plan was 6.12 Gy, and TOMO plan was 6.39 Gy. In the IMPT plan, none of the bladder, rectum, and femoral head received the dose, while there was low-dose radiation in both IMRT and TOMO plan. For serial organs adjacent to and within the target area, the D max of spinal cord and brainstem in IMPT plan was 39.89 and 39.88 Gy (RBE), respectively; in IMRT plan, they were 39.43 and 38.59 Gy, respectively; and in TOMO plan, they were 38.41 and 37.69 Gy, respectively. The low-dose area in the IMPT plan was significantly better than the photon radiotherapy plans. Among them, the absolute volume IMPT plan occupied by 10% of the prescribed dose area in the patient's body was reduced by 70.10% compared with IMRT plan and 76.96% compared with TOMO plan; the 30% prescribed dose volume IMPT plan was reduced by 53.49% compared with IMRT plan and 62.51% compared with TOMO plan; the 50% prescribed dose volume IMPT plan was reduced by 39.06% compared with IMRT plan and 42.23% compared with TOMO plan. Conclusions:The IMPT plan demonstrated significantly reduced low-dose exposure and lower doses to parallel OARs compared to both IMRT and TOMO plans in pediatric CSI. The CI, HI and GI of the three plans can all meet the clinical requirements. However, for serial organs adjacent to and within the target area, the D max of the IMPT plan may be higher than that of IMRT and TOMO plans.

Objective:To explore the dosimetric characteristics of proton and photon radiotherapy in the treatment of breast cancer.Methods:Four female breast cancer patients who needed radiotherapy at Shandong Cancer Hospital and Institute from January 2024 to May 2024 were selected as the research subjects. The target area ranges of 4 patients were left-sided breast cancer with lymph node involvement, left-sided breast cancer with lymph node involvement and internal mammary node, right-sided breast cancer with lymph node involvement and internal mammary node and synchronous bilateral breast cancer. Intensity modulated proton therapy (IMPT) and intensity modulated radiation therapy (IMRT) plans were designed respectively based on the prescribed dose in the target area and the limits of organs at risk (tomotherapy plan for bilateral breasts). The conformity index (CI), homogeneity index (HI), gradient index (GI) and organs at risk doses were evaluated. The dosimetric characteristics of IMPT and photon radiotherapy were compared.Results:Both IMPT and photon radiotherapy plans of the 4 breast cancer cases met the clinical dose requirements. The HI value of IMPT plans (0.10-0.14) was comparable to that of photon radiotherapy plans (0.10-0.12), and the average CI of the photon radiotherapy plans was 0.10 higher than that of the IMPT plans, and the average GI was 0.55 lower than that of the IMPT plans. The D mean of ipsilateral lung and heart of IMPT was lower, especially in the low-dose area (V 0-3), which was significantly lower than the photon radiotherapy plans, D mean of ipsilateral lung was reduced by 12.2%, 6.1%, 16.1% and 34.8%, respectively, D mean of heart was reduced by 47.2%, 57.0%, 72.4% and 83.0%, respectively. The ipsilateral lung V 20 of IMPT was not lower than photon radiotherapy plans (unilateral breast: IMPT was 30.0%-34.0%, IMRT was 29.0%-35.9%) . Conclusions:IMPT significantly reduces the D mean to the ipsilateral lung and heart while ensuring dose coverage of the target in breast cancer, preventing more volume of surrounding normal tissues from being irradiated. However, IMPT does not show much more advantage than photon radiotherapy plans in the ipsilateral lung V 20.