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.

There is a complex biological mechanism in the phosphatidylinositol-3-kinase(PI3K)/protein kinase B(PKB/Akt)/mammalian target of rapamycin(mTOR)signaling pathway,which plays a key role in the development and development of lymphoma.In this review,the relevant literature of PI3K inhibitor research in the past five years summarized and analyzed,and found that the research and development,application,efficacy,and adverse reactions of PI3K inhibitors are the current research hotspots,and the positive results of PI3K inhibitors in clinical trials and basic research have strongly demonstrated the potential of PI3K inhibitors in personalized treatment of lymphoma.In order to maximize the clinical benefits,a variety of strategies need to be explored,including novel drugs with better selectivity and safety,and related combination therapies.

Objective:To investigate the method and clinical efficacy of simultaneous correction of sunken upper eyelid during double eyelid plasty.Methods:A retrospective analysis was conducted on patients with single eyelids and sunken upper eyelid treated at the Department of Plastic Surgery, the First People’s Hospital of Zhengzhou, from October 2022 to February 2024. All patients underwent incisional double eyelid plasty with individualized correction based on depression severity. For mild depression, a combination of pretarsal orbicularis oculi muscle flap folding and orbital septum fat flap transposition was performed for correction. For moderate depression, additional autologous stromal vascular fraction (SVF)-gel grafting would be performed if residual depression persisted after the aforementioned approach. For severe depression, if the orbital fat was sufficient, the orbicularis oculi muscle flap folding, orbital fat flap repositioning and autologous SVF-gel transplantation were used. If there was insufficient orbital fat, the correction involved the use of a orbicularis oculi muscle flap and SVF-gel transplantation. Postoperative follow-up was conducted regularly, and related complications were statistically analyzed. Six months after the surgery, two blinded plastic surgeons evaluated outcomes via visual analog scale (VAS), including five aspects: symmetry of palpebral folds(including width and curvature of the double eyelid fold), fold contour, depression improvement, scar quality, overall eyelid aesthetics. Each aspect was rated on a scale from 1 to 10, with higher scores indicating better outcomes. Patient satisfaction was scored separately (1-5 per item), with higher scores indicating greater satisfaction.Results:A total of 64 patients were enrolled, including 4 males and 60 females; the age ranged from 19 to 66 years[ (32.0±9.7) years]. The degree of sunken upper eyelid was mild in 33 cases, moderate in 19 cases and severe in 12 cases. All patients underwent double eyelid plasty and orbicularis oculi flap folding. Based on this, 45 cases (33 mild and 12 moderate cases) underwent orbital septum fat flap transposition, 11 severe cases underwent SVF-gel grafting, and 8 cases (7 moderate and 1 severe cases) required combined approaches to correct depression. During the procedure, 19 cases (38 sides) received (1.8±0.8) ml of SVF-gel injection. Among these cases, one case with severe sunken upper eyelid who did not receive orbital fat transfer was injected with high density fat (left side: 0.6 ml, right side: 0.8 ml) due to insufficient SVF-gel volume. Two cases with severe sunken upper eyelid who only had autologous SVF-gel transplantation did not fully correct the depression within 3 months post-surgery received a second SVF-gel injection[(1.0±0.1) ml]. Follow-up was conducted for 6 to 12 months after surgery. All patients experienced varying degrees of eyelid edema, which typically lasted 2 to 3 months. Ecchymosis (34 sides), conjunctival congestion (3 sides), and temporary ptosis (5 sides) all resolved within 2 weeks. Pigmentation (14 sides) and scar hyperplasia (3 sides) disappeared or stabilized within 6 months. No patients developed complications such as incision infection, hematoma, fat liquefaction, local skin unevenness, or induration. The surgeons’ VAS scores of the above five indicators were all > 8 points, and the satisfaction scores of patients for the five indicators were all > 4 points. In both scoring, the improvement of sunken upper eyelid scored the highest, which were (9.2 ± 0.9) points and (4.8 ± 0.6) points respectively.Conclusion:For patients with single eyelids and varying degrees of sunken upper eyelid, performing double eyelid plasty and orbicularis oculi muscle flap folding, individualized correction is achieved through autologous fat redistribution techniques, based on the severity of the sunken upper eyelid and the amount of orbital fat. This single procedure can restore upper eyelid volume and rejuvenate the appearance, resulting in a natural and aesthetically pleasing double eyelid with minimal complications and high patient satisfaction.

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 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.