ObjectiveTo construct an automatic measurement model for palmar tilt and radial inclination suitable for traditional Chinese medicine （TCM） clinical scenarios， and to validate its accuracy and efficiency in TCM manipulative reduction settings. MethodsData on anteroposterior （AP） and lateral X-rays of distal radius fractures were collected from patients admitted to 18 TCM/ integrated TCM and western medicine hospitals in Jiangsu province between September 1st， 2023， and September 1st， 2024， via the Jiangsu Diagnosis and Treatment Big Data Platform for TCM Dominant Diseases. A medical image segmentation framework based on multi-scale feature fusion and edge-awareness was employed， combined with anatomical knowledge specific to TCM orthopedics， to optimize the feature extraction strategy of an artificial intelligence （AI） model. This framework enabled automatic segmentation of fracture regions and measurement of distal radius palmar tilt and radial inclination. The accuracy of the AI model in measuring radial inclination and volar tilt was validated， and the measurement time and average time gain rate of the AI model were compared to those of manual measurement. ResultsA total of 15,444 AP and lateral X-ray images of distal radius fractures were collected， and were divided into a training set （11,144 images， 5066 AP and 6078 lateral）， a validation set （3700 images， 1840 AP and 1860 lateral）， and an independent test set （600 images， 300 AP and 300 lateral） after preprocessing. In the measurement of 300 AP X-rays in the independent test set for radial inclination， when the degree error between AI measurement and manual measurement was ＜3° and ＜5°， AI measurement accuracy was 83% and 93%， respectively. In 300 lateral X-rays in the test set for palmar tilt， when AI measurements had an error of ＜3° and ＜5° compared to manual measurements， corresponding accuracy rate was 78% and 90%， respectively. For 50 X-ray images， AI measurement time was （1.37±0.05） min for radial inclination while manual measurement time was （22.57±2.52） min （P＜0.001）； in terms of palmar tilt， the AI measurement time was （1.33±0.14） min， shorter than （23.70±2.80） min for manual measurement time （P＜0.001）. Average time gain rates for manual and AI measurements were 93.93% and 94.39% respectively. ConclusionAn automatic measurement model for palmar tilt and radial inclination in distal radius fractures has been established， enabling more accurate and efficient assessment as well as providing a tool to support the quantitative evaluation of the efficacy of TCM manipulative reduction and large-sample clinical research.

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.

Chronic post-surgical pain (CPSP) is a common long-term complication following lung surgery. Its high incidence significantly impacts patients’ quality of life and functional recovery, and imposes a substantial socioeconomic burden. This consensus aims to systematically establish a standardized integrated Chinese and Western medicine diagnostic and treatment framework for chronic post-lung surgery pain (CPLSP). Based on the latest domestic and international evidence-based medical research and multidisciplinary clinical experience, the working group comprehensively elaborates on core issues regarding CPLSP, including its definition, epidemiology, pathogenesis, clinical assessment, Western medical treatment, traditional Chinese medicine (TCM) treatment, and integrated strategies. The consensus emphasizes a patient-centered approach, adhering to the principles of multimodality, individualization, and stepwise management, highlighting the synergistic advantages of integrating Chinese and Western medicine throughout the entire perioperative management cycle encompassing "perioperative anti-inflammation, acute analgesia, and chronic rehabilitation." Through systematic literature retrieval and evidence integration, a total of 9 core recommendations were established to provide scientifically sound and clinically practical guidance.

Multidrug-resistant (MDR) bacterial infections have emerged as a serious challenge of global public health crisis. The overuse and misuse of conventional antibiotics have dramatically accelerated the emergence, evolution and worldwide spread of drug-resistant bacterial strains, necessitating urgent exploration of novel antibacterial strategies. Bacteriophages serve as natural bacterial predators offering distinct advantages including high host specificity, autonomous self-replication capabilities and cost-effective large-scale production. However, wild-type phages present significant clinical limitations due to their narrow host ranges, susceptibility to rapid immune clearance and poor penetration of bacterial biofilms, which severely restrict their therapeutic applications. The convergence of synthetic biology, nanotechnology and advanced gene editing technologies has accelerated the development of engineered bacteriophage platforms, providing programmable, scalable and clinically translatable pathways to overcome these inherent biological constraints. Here, we systematically delineate four fundamental strategies for engineered bacteriophage development. Chemical modification utilizes reactive functional groups such as amino, carboxyl and thiol moieties on capsid proteins through esterification, amidation or click chemistry reactions to achieve precise drug conjugation and surface functionalization. In vivo editing encompasses ultraviolet or chemical mutagenesis for random mutation induction, homologous recombination for targeted genetic alterations, recombineering methodologies including electroporation-mediated bacteriophage recombination engineering, and CRISPR-Cas systems for precise genome editing to enable exact genetic reconstruction and host range reprogramming. In vitro synthesis leverages genome engineering platforms where intact phage genomes are transferred into yeast or host bacteria to facilitate highly efficient homologous recombination, enabling large DNA fragment assembly and cross-gene host range expansion without bacterial toxicity constraints. Directed evolution combines artificial selection through mutation library screening with rational design approaches involving chimeric receptor binding protein construction or site-specific mutagenesis, effectively balancing the discovery of unknown adaptive pathways with targeted host specificity modification. Moreover, we comprehensively discuss therapeutic applications across diverse clinical scenarios. Engineered bacteriophage effectively disrupt bacterial biofilms through sophisticated functionalized delivery platforms including nanozyme-conjugated phages, phage-liposome nanoconjugates and bio-responsive hydrogels, demonstrating significantly enhanced bactericidal efficiency compared to unmodified free phages. These bioengineered vectors attenuate bacterial virulence and resensitize pathogens to antibiotics by delivering CRISPR-Cas systems or base editors to disrupt critical virulence factors such as pili, capsule synthesis machineries and quorum sensing systems, or by inactivating antibiotic resistance determinants including beta-lactamase genes. As an intelligent nanomedicine delivery platform, engineered bacteriophage enable precise pathogen elimination an through photocatalytic reactive oxygen species generation, immunomodulatory interventions, or controlled release of antibacterial drugs. Furthermore, oral administration of engineered bacteriophage facilitates microbiota modulation, which selectively eliminate intestinal pathogens while preserve beneficial commensal microbiota, thereby restoring microbial community balance and preventing complications associated with dysbiosis. Finally, we critically analyze persistent challenges including host strain matching complexity, evolution of bacterial resistance mechanisms, pharmacokinetic optimization requirements, optimal administration route selection, large-scale production quality control standards and clinical dosing determination protocols. Through multidisciplinary integration of synthetic biology, infectious disease medicine and immunology, future translational medicine studies of bacteriophage should establish comprehensive technical platforms encompassing rapid phage screening, intelligent rational design, rigorous in vivo evaluation and standardized clinical validation processes, ultimately advancing engineered bacteriophage from laboratory innovations to clinically approved therapeutics for effectively combating MDR bacterial infections.

To evaluate and project the disease burden of early-onset colorectal cancer (EOCRC), and to identify its major risk factors.

Objective:To explore the clinical application value of magnetic resonance imaging (MRI)-guided wire localization to the non-palpable breast lesions (NPBL) identified on MRI only.Methods:A retrospective case series study was conducted. A total of 171 patients with NPBL identified on MRI only who underwent MRI-guided wire localization from April 2017 to May 2024 in Shaanxi Provincial Cancer Hospital were collected. All patients had breast MRI Breast Imaging Report and Data System (BI-RADS) 4a and above lesions, and underwent MRI-guided wire localization within the same menstrual cycle within 2 days to 2 months after diagnostic MRI examination. Based on postoperative pathological results, the MRI characteristics of benign and malignant lesions were compared, and the clinical application value of MRI-guided wire localization was evaluated.Results:There were 179 lesions in 171 patients, including 54 malignant lesions (30.17%) and 125 benign lesions (69.83%). There was no statistically significant difference in the enhancement morphology between pathological benign and malignant NPBL lesions ( χ2 = 0.04, P = 0.982), while there were statistically significant differences in breast background parenchymal enhancement, lesion time-signal intensity curve and BI-RADS classification ( χ2 values were 32.03, 20.72 and 37.60, respectively, all P < 0.05). Conclusions:For NPBL that is identified on MRI only and evaluated as BI-RDADS 4a or above, MRI-guided wire localization can improve the accuracy of diagnosis and treatment of intraductal carcinoma, early invasive cancer and high-risk lesions.

Objective:To investigate the prognostic differences between endobronchial and invasive bronchial characteristics in patients with non-small cell lung cancer (NSCLC) undergoing sleeve lobectomy.Methods:This is a retrospective cohort study. A retrospective analysis was conducted on clinical data of 200 NSCLC patients who underwent sleeve lobectomy at the Department of Thoracic Surgery, Shanghai Pulmonary Hospital between January 2014 and December 2016. There were 181 males and 19 females, aged (61.3±8.1) years (range: 30 to 81 years). Based on imaging data, they were divided into the endobronchial group ( n=71) and the invasive bronchial group ( n=129). Clinical data and prognosis were collected and analyzed. Sex, age, hypertension, diabetes, smoking history, pathology type, tumor size, staging, surgical method, surgical site, and sleeve technique were included as covariates. Propensity score matching was performed with a 1∶1 ratio between the endobronchial and invasive bronchial groups using the nearest-neighbor matching method. The Kaplan-Meier method was used to plot survival curves, and the Log-rank test was applied to compare differences between groups. Independent sample t tests, Mann-Whitney U tests, χ2 tests, or Fisher′s exact tests was used for group comparisons. Results:After propensity score matching, 71 cases from each group (endobronchial and invasive bronchial) were included, with no significant differences in baseline characteristics between the two groups (all P>0.05). Endobronchial group showed significantly better overall survival (OS) and disease-free survival (DFS) compared to invasive bronchial group(Log-rank test: P=0.014,0.027). Further subgroup analysis revealed that in the minimally invasive surgery subgroup, the DFS of the endobronchial group was significantly better than that of the invasive bronchial group (Log-rank test: P=0.002), while in the open thoracotomy subgroup, there was no significant difference in DFS between the two groups (Log-rank test: P=0.290). In the right upper lobe sleeve resection subgroup, the endobronchial group had significantly better DFS than the invasive bronchial group (Log-rank test: P=0.039). For patients in the stage Ⅱ to Ⅲ subgroup, the endobronchial group had significantly better DFS than the invasive bronchial group (Log-rank test: P=0.005). Conclusion:In sleeve lobectomy, patients with endobronchial NSCLC have better OS and DFS compared to those with invasive bronchial type, particularly in patients undergoing minimally invasive surgery, right upper lobe sleeve lobectomy, and stage Ⅱ to Ⅲ patients.

Objective:To investigate the clinical characteristics of children with cervicothoracic hemivertebra at different ages and evaluate the surgical outcomes of hemivertebra resection.Methods:This retrospective cohort study analyzed clinical and radiographic data of 35 children (under 18 years old) with cervicothoracic hemivertebra who underwent one-stage posterior hemivertebra resection at the Department of Spine Surgery, Second Xiangya Hospital of Central South University, from June 2005 to June 2022. The cohort included 19 males and 16 females, with a surgical age of (9.7±4.1) years (range: 3 to <18 years). Patients were divided into three groups based on initial surgical age: preschool group (≤5 years, n=10), school-age group (6 to 10 years, n=10), and adolescent group (11 to <18 years, n=15). The intraoperative blood loss, operative time, number of fixed vertebrae, and perioperative and postoperative complications were recorded. Radiographic measurements were taken preoperatively, postoperatively, and at final follow-up, including the Cobb angle of the primary curve, curve length, distal curve Cobb angle, neck tilt, shoulder balance, coronal balance, trunk tilt, clavicle angle, head deviation, and local kyphosis. Statistical analyses included one-way ANOVA, Kruskal-Wallis H test, chi-square test, and Fisher′s exact test. Results:The operative time was (333±74) minutes (range:200 to 450 minutes), the intraoperative blood loss was (419±132) ml (range:200 to 650 ml), and the number of fixed vertebrae was (6.0±2.7) segments (range:2 to 12 segments). Preoperatively, the preschool group had a smaller primary curve Cobb angle compared to the adolescent group (33.6°±8.4° vs. 43.0°±9.4°, F=3.394, P=0.046) and distal curve Cobb angle (6.4°±2.4° vs.11.9°±4.5°, F=6.550, P=0.038). The preschool group had better coronal balance ((8.8±4.3) mm vs. (20.2±11.7)mm, F=9.448, P=0.009) and trunk tilt (4.0°±2.0° vs.7.0°±3.0°, F=4.343, P=0.029) than the school-age group. The preschool group had fewer fused vertebrae than the school-age and adolescent groups (3.8±1.6 vs. 7.0±2.6 vs. 6.8±2.6, F=10.480, P=0.005). The preschool group also had less intraoperative blood loss than the adolescent groups ((320±125) ml vs. (480±113) ml, F=8.666, P=0.013). However, no significant differences were found in postoperative and final follow-up measurements of primary and distal curve Cobb angles, neck tilt, shoulder balance, coronal balance, trunk tilt, clavicle angle, head deviation, local kyphosis, or complication rates (all P>0.05). Conclusions:Children with cervicothoracic hemivertebra across different age groups exhibit similar surgical outcomes and complication rates. But the preschool group had fewer fixed segments and less intraoperative blood loss, suggesting that this age group is an ideal time for surgery.