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 analyze the clinical and epidemiological characteristics of influenza A(H3N2) in Tangshan City, Hebei Province, from 2023 to 2025, and to provide a scientific basis for precise prevention and control of local influenza. Methods The influenza-like cases and influenza outbreak cases in the influenza surveillance sentinel hospitals in Tangshan City from January 2023 to December 2025 were included in this study. The clinical characteristics and epidemic characteristics of H3N2 influenza patients were analyzed by descriptive epidemiological methods and chi-square test. Results A total of 11 567 influenza case samples were tested in the study, with a positive rate of 9.34% (1 080/11 567). Influenza A (H3N2) remained the predominant circulating subtype throughout the study period. January 2023 to February 2023 marked the tailing end of the previous season’s H3N2. Then, a minor peak dominated by H1N1 occurred from March to June, and H3N2 circulated again from October to December, peaking in December. From September 2024 to December 2024, H1N1 and H3N2 circulated concurrently, with H3N2 as the dominant strain, peaking in January of the following year. From March 2025 to September 2025, influenza activity remained at low level, and activity began to rise in October, with H1N1 peaking in November before declining, and H3N2 becoming the dominant strain in December. H3N2 mainly occurred in the 5-14 years old group. Cases in the 0–4-year-old group were primarily characterized by gastrointestinal symptoms, with high fever being common. Cases in the 5–59-year-old group were primarily characterized by systemic and respiratory symptoms, with better clinical outcomes. Cases aged 60 and older exhibited the highest rates of hospitalization, severe illness, and incidence of complications. Conclusion From 2023 to 2025, the influenza virus in Tangshan City, Hebei Province is dominated by influenza A (H3N2). The epidemic is characterized by a peak incidence during autumn and winter, co-circulation with H1N1, and fluctuations during the peak period. It is necessary to pay attention to the clinical management and prevention and control of influenza A (H3N2) in infants and the elderly.

Background: The surgical neck of the humerus is located approximately 2–3 cm distal to the anatomical neck (collum anatomicum) at the junction of the greater and lesser tubercles and the humeral shaft. Owing to the transition between cortical and cancellous bones, this region is biomechanically vulnerable and prone to fractures, particularly in older adults. Fractures of the surgical neck of the humerus are classified under the International Classification of Diseases, 10th Revision (ICD-10), with diagnostic codes S42.21.3, S42.22, and S42.23. National statistics indicate that 210,763 new trauma cases were reported in Mongolia in 2023, representing an 11.6% increase compared to the previous year, of which 11,452 cases (5.4%) involved shoulder and humeral injuries. Aim: This study aimed to compare the clinical effects of traditional Mongolian bone-setting therapy and surgical treatment on pain intensity, shoulder function, range of motion, and activities of daily living in patients with surgical neck of the humerus fractures. Materials and Methods: Based on the Department of Traditional Therapies of the Mongolian National University of Medical Sciences (MNUMS) and the Department of Orthopedic Trauma of the Inner Mongolia International Hospital, Inner Mongolia Autonomous Region, China, a total of 60 patients diagnosed with surgical neck fractures of the humerus were enrolled in this study between January 2024 and December 2025. The patients were randomly assigned into two groups: a Mongolian traditional bone-setting therapy group (n=30) and a surgical treatment group (n=30). Assessments were conducted before treatment and at 1 month and 3 months after treatment. Shoulder joint range of motion, activities of daily living, and muscle strength were evaluated using the Constant–Murley score, while pain intensity was assessed using the Visual Analog Scale (VAS). The collected data were systematically analyzed, and the therapeutic outcomes of the two treatment modalities were comprehensively compared and summarized. Result: The baseline demographic and clinical characteristics were comparable between the groups. In the traditional bone-setting group, VAS scores decreased significantly, whereas Constant–Murley scores improved markedly at both follow-up points. The overall outcomes in the traditional bone-setting group were significantly better than those in the surgical group (P<0.05). Conclusion Traditional Mongolian bone-setting therapy offers rapid pain relief, superior long-term functional recovery, and fewer complications, making it a valuable treatment option, particularly for elderly patients and those unsuitable for surgery.

Background: Mongolian traditional bone-setting is a non-surgical treatment method for fractures; however, there is a lack of evidencebased research evaluating its effectiveness, which forms the basis of our study. Aim: To study the therapeutic efficacy of traditional bonesetting treatment for surgical neck fractures of the humerus. Materials and Methods: From June 2023 to June 2025, 120 patients diagnosed with surgical neck fractures of the humerus and treated at the Inner Mongolia International Mongolian Hospital were randomly divided into a traditional bone-setting group (n=64) and a surgical treatment group (n=56). Shoulder and upper limb function were evaluated at 1, 3, and 6 months after treatment using the Constant–Murley score, Oxford Shoulder Score (OSS), and Visual Analog Scale (VAS) for pain. Result: A total of 120 patients with surgical neck fractures of the humerus were divided into two groups, with no significant differences in the baseline characteristics (p>0.05).In the traditional bone-setting group, pain (VAS) decreased more rapidly at 1–3 months (p<0.01), while activities of daily living and functional outcomes improved significantly at 3–6 months (p<0.01). At 6 months, shoulder function (Constant–Murley score) was significantly improved (p=0.049).In contrast, the surgical group showed faster recovery in joint range of motion and muscle strength in the early stage; however, by 6 months, the outcomes between the two groups were comparable. Conclusion In the treatment of surgical neck fractures of the humerus, no significant difference was observed between traditional bone-setting and surgical treatment in terms of shoulder range of motion and muscle strength recovery. However, the advantages of traditional bone-setting lie in its ability to relieve pain more rapidly, promote faster functional recovery, improve activities of daily living, and achieve better long-term outcomes than modern bone-setting. Therefore, traditional bone setting is a safe and effective treatment method that aligns with modern theories of bone healing and rehabilitation principles and holds significant value for wider clinical application and further research in the management of surgical neck fractures of the humerus.

Surgical resection remains the primary option for achieving radical cure and long-term survival in the treatment of liver cancer.In recent years,profound changes have taken place in the field of liver surgery:the surgical concept of liver resection for liver cancer has been constantly updated;Significant progress has also been made in many aspects such as liver imaging technology,liver resection techniques and equipment,and perioperative management.Liver resection for liver cancer has gradually developed into a more precise,minimally invasive and safer treatment model.However,liver resection for liver cancer still faces many new challenges up to now:the prevention strategies for recurrence and metastasis are limited;There is a lack of predictive indicators for the efficacy of targeted immunotherapy.Insufficient precision in individualized treatment,etc.

OBJECTIVE: Gastric cancer (GC) is one of the most common malignancies seen in clinic and requires novel treatment options. Morin is a natural flavonoid extracted from the flower stalk of a highly valuable medicinal plant Prunella vulgaris L., which exhibits an anti-cancer effect in multiple types of tumors. However, the therapeutic effect and underlying mechanism of morin in treating GC remains elusive. The study aims to explore the therapeutic effect and underlying molecular mechanisms of morin in GC. METHODS: For in vitro experiments, the proliferation inhibition of morin was measured by cell counting kit-8 assay and colony formation assay in human GC cell line MKN45, human gastric adenocarcinoma cell line AGS, and human gastric epithelial cell line GES-1; for apoptosis analysis, microscopic photography, Western blotting, ubiquitination analysis, quantitative polymerase chain reaction analysis, flow cytometry, and RNA interference technology were employed. For in vivo studies, immunohistochemistry, biomedical analysis, and Western blotting were used to assess the efficacy and safety of morin in a xenograft mouse model of GC. RESULTS: Morin significantly inhibited the proliferation of GC cells MKN45 and AGS in a dose- and time-dependent manner, but did not inhibit human gastric epithelial cells GES-1. Only the caspase inhibitor Z-VAD-FMK was able to significantly reverse the inhibition of proliferation by morin in both GC cells, suggesting that apoptosis was the main type of cell death during the treatment. Morin induced intrinsic apoptosis in a dose-dependent manner in GC cells, which mainly relied on B cell leukemia/lymphoma 2 (BCL-2) associated agonist of cell death (BAD) but not phorbol-12-myristate-13-acetate-induced protein 1. The upregulation of BAD by morin was due to blocking the ubiquitination degradation of BAD, rather than the transcription regulation and the phosphorylation of BAD. Furthermore, the combination of morin and BCL-2 inhibitor navitoclax (also known as ABT-737) produced a synergistic inhibitory effect in GC cells through amplifying apoptotic signals. In addition, morin treatment significantly suppressed the growth of GC in vivo by upregulating BAD and the subsequent activation of its downstream apoptosis pathway. CONCLUSION Morin suppressed GC by inducing apoptosis, which was mainly due to blocking the ubiquitination-based degradation of the pro-apoptotic protein BAD. The combination of morin and the BCL-2 inhibitor ABT-737 synergistically amplified apoptotic signals in GC cells, which may overcome the drug resistance of the BCL-2 inhibitor. These findings indicated that morin was a potent and promising agent for GC treatment. Please cite this article as: Wang Y, Sun XY, Ma FQ, Ren MM, Zhao RH, Qin MM, Zhu XH, Xu Y, Cao ND, Chen YY, Dong TG, Pan YF, Zhao AG. Morin inhibits ubiquitination degradation of BCL-2 associated agonist of cell death and synergizes with BCL-2 inhibitor in gastric cancer cells. J Integr Med. 2025; 23(3): 320-332.
Humans ; Flavonoids/therapeutic use* ; Stomach Neoplasms/pathology* ; Animals ; Proto-Oncogene Proteins c-bcl-2/metabolism* ; Cell Line, Tumor ; Apoptosis/drug effects* ; Cell Proliferation/drug effects* ; Ubiquitination/drug effects* ; Mice ; Drug Synergism ; Mice, Inbred BALB C ; Mice, Nude ; Xenograft Model Antitumor Assays ; Flavones

Surgical resection remains the primary option for achieving radical cure and long-term survival in the treatment of liver cancer.In recent years,profound changes have taken place in the field of liver surgery:the surgical concept of liver resection for liver cancer has been constantly updated;Significant progress has also been made in many aspects such as liver imaging technology,liver resection techniques and equipment,and perioperative management.Liver resection for liver cancer has gradually developed into a more precise,minimally invasive and safer treatment model.However,liver resection for liver cancer still faces many new challenges up to now:the prevention strategies for recurrence and metastasis are limited;There is a lack of predictive indicators for the efficacy of targeted immunotherapy.Insufficient precision in individualized treatment,etc.

Objective To design a training device of the flight crew for plateau normobaric low-oxygen acclimatization so as to enhance the flight crew's ability to adapt to the low oxygen environment after rushing into the plateau and reduce the incidence of acute plateau reaction.Methods The training device comprised a plateau environment simulation controller,a multimodal physiological acquisition system and hypoxia exercise training evaluation software.The plateau environment simulation controller was composed of an environment monitor for plateau acclimatization,two composite sensor sets,a control valve and an alarm device;the multimodal physiological acquisition system was made up of 20 groups of vital signs acquisi-tion devices,with a wearable dynamic ECG and respiration recorder,a wrist oximeter and an arm sphygmomano-meter included in each group.The hypoxia exercise training evaluation software was developed with a B/S architecture,Java language and JetBrains 2020.3.Results The training device proved to have the simulation altitude ranging from 0 to 6 000 m and facilitated simultaneous training of 20 persons for normobaric low-oxygen acclimatization,screening for hypoxia endurance,real-time monitoring of physiological parameters and assessment of training effect,with none of the trainees having acute plateau reaction.Conclusion The training device assists the flight crew for plateau normobaric low-oxygen acclimatization,and can be used for acclimatization training before plateau missions.[Chinese Medical Equipment Journal,2025,46(8):18-24]

Objective:To investigate the differences in electrophysiological brain development of neonates in Tibet and Beijing.Methods:This prospective cohort study included neonates with gestational ages of 28 to 40 weeks and 6 days, without asphyxia, hypoxia, or brain injury, who were born between January 2022 and June 2024 at the Tibet Autonomous Region People's Hospital and Peking University First Hospital. The first electroencephalographic (EEG) monitoring was completed within 48 hours to 7 days after birth, which included a 4-channel amplitude-integrated EEG (aEEG) and a 12-channel continuous EEG (cEEG). Two electrophysiology experts scored the EEG results according to a rating scale, and the intraclass correlation coefficient (ICC) was used to explore the consistency between different evaluators. Preterm infants with gestational ages of 32 to 36 weeks and 6 days and post-menstrual age (PMA) less than full-term at the first EEG monitoring were re-examined with aEEG and cEEG at PMA of 37 to 40 weeks and 6 days. Infants were grouped based on PMA at the first EEG monitoring. Spearman rank correlation was used to analyze the correlations between total aEEG+cEEG scores, individual aEEG and cEEG scores, and PMA, gestational age, birth weight, and head circumference at the first EEG monitoring. Mann-Whitney U test, Kruskal-Wallis H test, and Bonferroni correction were used to compare the differences in total aEEG+cEEG scores, individual aEEG and cEEG scores between Tibet and Beijing, among adjacent PMA groups, and for premature infants at full-term PMA. Results:(1) A total of 341 neonates were included in this study, including 154 cases from Tibet (nine cases in the PMA of 28-29 weeks and 6 days group, 13 cases in the PMA of 30-31 weeks and 6 days group, 28 cases in the PMA of 32-33 weeks and 6 days group, 38 cases in the PMA of 34-36 weeks and 6 days group, and 66 cases in the PMA of 37-40 weeks and 6 days group) and 187 cases from Beijing (10 cases in the PMA of 28-29 weeks and 6 days group, 10 cases in the PMA of 30-31 weeks and 6 days group, 16 cases in the PMA of 32-33 weeks and 6 days group, 91 cases in the PMA of 34-36 weeks and 6 days group, and 60 cases in the PMA of 37-40 weeks and 6 days group). (2) Inter-rater consistency:the consistency of PMA inferred based on the total aEEC+CEEC score and actual PMA was high in two raters ( ICCrater one=0.96, ICCrater two=0.94, both P<0.01). (3) The correlation between total aEEG+cEEG score and PMA ( r=0.80) was stronger than that between the aEEG alone or cEEG scores and PMA ( r were 0.79 and 0.66, respectively). The total aEEG+cEEG score also correlated with gestational age at birth ( r=0.74), birth weight ( r=0.69), and head circumference at first EEG monitoring ( r=0.69) (all P<0.01). (4) Regardless of whether in Tibet or Beijing, the total aEEG+cEEG score increased sequentially in the PMA of 30- 31 weeks and 6 days, 32-33 weeks and 6 days, 34-36 weeks and 6 days, and 37-40 weeks and 6 days groups; the cEEG score increased sequentially in the PMA of 32-33 weeks and 6 days group, 34-36 weeks and 6 days group, and 37-40 weeks and 6 days groups; the aEEG score in the PMA 32- 33 weeks and 6 days group was higher than that in the 30-31 weeks and 6 days group, and the score in the PMA 37-40 weeks and 6 days group was higher than that in the 34-36 weeks and 6 days group (Bonferroni correction, all P<0.05). (5) At PMA of 34-36 weeks and 6 days, the total aEEG+cEEG score [25 points (22-26 points) vs. 26 points (24-28 points), Z=－2.62, P=0.009] and cEEG score [12 points (12-14 points) vs. 15 points (13-16 points), Z=－4.77, P<0.001] of newborns in Tibet were lower than those in Beijing, while the aEEG score was higher than those in Beijing [12 points (10-13 points) vs. 11 points (10-12 points), Z=2.17, P=0.030]; at PMA of 37-40 weeks and 6 days, the cEEG score of newborns in Tibet was lower than those in Beijing [16 points (15-17 points) vs. 17 points (15-18 points), Z=－2.27, P=0.023]. (6) The total aEEG+cEEG score of preterm infants born at 32 to 33 weeks and 6 days in Tibet was lower at PMA full-term compared to those in Beijing [27 points (26-28 points) vs. 29 points (28 -30 points), Z=－2.94], and also lower compared to the total aEEG+cEEG score of full-term gestational age newborns in Tibet during their first EEG monitoring [29 points (27-30 points)] (both P<0.05). Conclusions:In the high-altitude hypobaric hypoxic environment, the electroencephalographic development of newborns, especially premature infants, maybe lag behind of plain areas. The combined use of aEEG+cEEG may provide a better evaluation of neonatal brain development than using cEEG or aEEG alone.