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.

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.

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.

Objective To investigate the effect of postoperative reduction quality in femoral neck fracture internal fixation on mechanical properties of the femoral head from the perspective of trabecular bone biomechanics.Methods From patients who underwent hip replacement surgery for femoral neck fractures,a total of 26 femoral head slice specimens were obtained.The central axis of the primary compressive trabeculae was defined as the 0° group,with the intersection point of the primary compressive trabeculae and the femoral calcar serving as the center.By rotating the specimens to simulate different reduction angles,the cut femoral head slice specimens were randomly divided into five groups:-10°,-5°,0°,5°,and 10°,representing femoral heads with varying reduction qualities.The specimens were subjected to single compression load tests and fatigue load tests.The load was set from 70 N to 1 400 N,at a frequency of 1 Hz,with 10 000 cycles.Axial stiffness,displacement,and the number of collapse cycles were measured,to compare the biomechanical properties of femoral head specimens under different reduction qualities.Results There were differences in the axial stiffness,displacement,and number of collapse cycles among the femoral head specimens in different groups.Under 800 N load,the axial stiffness of 0° group was significantly greater than that of±10° groups(P＜0.05).The axial stiffness of 0° group was also greater than that of the±5° groups,but the differences were not statistically significant(P＞0.05).The axial stiffness of±5° groups was greater than that of±10° groups(P＜0.05).0° group had a lower displacement than±5° groups and±10° groups.However,the differences in displacement between 0° group and±5° groups were not statistically significant(P＞0.05),while the differences between the 0° group and±10° groups were statistically significant(P＜0.05).The differences in displacement between±5° groups and±10° groups were also statistically significant(P＜0.05).0° group had a significantly higher number of collapse cycles than±10° groups(P＜0.05).The number of collapse cycles in 0° group was also higher than that in±5° groups,but the differences were not statistically significant(P＞0.05).The number of collapse cycles in±5° groups was significantly higher than that±10° groups(P＜0.05).Conclusions The quality of reduction after internal fixation of femoral neck fractures significantly affects the biomechanical properties of the femoral head.This study provides a scientific basis for optimizing treatment and postoperative management,aiming to improve clinical outcomes and patients' quality of life.

Objective To assess the efficacy of an innovative operational management model in enhancing the refined operational management of a public hospital.Methods An innovative operational management model,"One Body,Two Wings and Three Drives",was developed,which involved establishing a systematic operational management system,strengthening per-formance and cost control,and reinforcing the supporting roles of discipline construction,scientific and technological innovation,and smart hospital initiatives.This comprehensive approach aimed to systematically promote hospital operational management re-forms and improve overall efficiency and quality.Results After using this model,the hospital presented continuous improve-ments in operational efficiency and medical quality,with key performance indicators trending positively.Over the past three years,the average annual growth rate of outpatient and emergency service visits reached 6.6％,inpatient service visits increased by 5.7％,and the Case Mix Index(CMI)rose by 0.22 over two consecutive years.Conclusion This model is highly systemat-ic,practical,and policy-compatible,providing a replicable path for the high-quality development of public hospitals.

Objective To establish a liver disease refined management platform encompassing hepatocellular carcinoma(HCC)screening for high-risk populations,longitudinal patient follow-up management,multidisciplinary team(MDT)consulta-tions for complex cases,and intelligent data analysis.This platform aims to achieve early detection and standardized management of individuals at risk of HCC,thereby improving the rates of curative treatment and patient survival.Methods Standardized data interfaces were utilized to integrate diverse medical data,including electronic health records,laboratory test results,and imaging reports.Multiple HCC risk assessment models were applied to assist clinicians in patient risk stratification and diagnosis.For un-diagnosed patients,personalized follow-up programs were implemented according to their risk categories.Clinical information of patients requiring MDT discussions was systematically collated to support comprehensive case evaluations.Results After the platform's introduction,a total of 6 187 patients were enrolled,accounting for 23.2％of outpatient attendees.The number of MDT discussions reached 351,representing a 46.3％year-on-year increase.The outcomes demonstrated significant improvements in the quality of liver disease management and diagnostic processes.Conclusion The platform can effectively achieve precise stratification of HCC risk groups,providing significant support for the early detection,diagnosis,and treatment of high-risk indi-viduals.Additionally,it offers a digital solution for facilitating the full-cycle precision management of liver disease patients,from screening to diagnosis treatment and follow-up.

Objective To establish the preliminary reference intervals for the percentage and fluorescence intensity ratio(FIR)for 12 platelet-leukocyte aggregates(PLAs)in peripheral blood circulation.Methods A total of 124 healthy individuals(61 males and 63 females)aged 18-90 years were selected from Beijing Jishuitan Hospital.Venous blood samples were collected in EDTA-K2 tubes for anticoagulation,and flow cytometry was used to measure the percentage and FIR of 12 types of PLAs.All the participants were grouped by gender for comparative analysis.Reference intervals for each parameter were calculated according to the WS/T 402-2024 standard.Results The percentages of platelet-T lymphocyte aggregates,platelet-CD4+T lymphocyte aggregates,and platelet-CD8+T lymphocyte aggregates in males were significantly lower than those in females(all P＜0.05).The FIRs of platelet-T lymphocyte aggregates,platelet CD8+T lymphocyte aggregates,platelet-B lymphocyte aggregates,and platelet-NK cell aggregates in males were significantly higher than those in females(all P＜0.05).Conclusion The preliminary reference intervals for the percentage and FIR of 12 types of PLAs in healthy adults have been established.Different gender may influence the detection results of platelet-leukocyte aggregates,especially platelet-lymphocyte aggregates,and the corresponding FIR values in healthy adults.Further large-scale studies should be necessary to confirm these findings.

Objective To mine and analyze signals of acute kidney injury(AKI)related to drugs,comprehensively summarize the potential risk drugs,and provide a reference for clinically safe medication.Methods The AKI reports from January 2004 to September 2023 in the US FDA Adverse Event Reporting System(FAERS)were retrieved.Disproportionality methods were used to explore the relationship between drugs and AKI,and demographic information,time to onset,and patient outcomes were analyzed.Results Out of 1 253 drugs,159 were identified as AKI signal drugs.Among these,there were 49 antimicrobial agents(30.82%),including 35 antibiotics and 14 antiviral agents;33 antineoplastic agents(20.75%);and 25 hypotensive agents(15.72%).Drug-related AKI occurred mostly in the elderly,and the male-to-female ratio was 124∶100.The median time to onset for AKI related to antibiotics was≤8 d,with the third quartile≤21 d.Rivaroxaban and aspirin had higher proportions of death reports,with 33.03%and 31.44%respectively.Conclusions A multitude of drugs pose a risk for acute kidney injury,necessitating caution in their clinical application and the implementation of monitoring of renal function.The elderly are a high-risk group for drug-related AKI,and there are more males than females.For antibiotics,the first 21 days are the key monitoring period.For drugs that require long-term use,regular monitoring is necessary.