Antibodies play a critical role in adaptive immune responses and serve as key components in disease diagnosis and treatment. These molecules exhibit dynamic post-translational modifications (PTMs), such as glycosylation and phosphorylation, which regulate their effector functions. To date, nearly all of our knowledge about antibody repertoires has come from B cell receptor (BCR) sequencing (BCR-seq), which facilitates the profiling of clonal composition and the tracing of maturation trajectories within B-cell repertoires. However, circulating antibodies found in bodily fluids—such as serum, saliva, milk, mucosal secretions, and cerebrospinal fluid—exhibit diversities and specificities beyond what BCR-seq alone can predict. Therefore, identifying and quantifying antibody clonotypes at the protein level could enhance diagnosis, prognosis, and treatment strategies in personalized medicine. The critical gap between genotype and phenotype necessitates complementary methodologies that enable the direct characterization of antibody proteins in their native functional states. Mass spectrometry (MS)-based antibody repertoire sequencing (Ab-seq) is currently the only feasible approach for this task and primarily includes database-dependent methods—such as bottom-up, middle-down, and top-down approaches—as well as database-independent de novo sequencing technology. These strategies enable multi-level, high-precision characterization ranging from peptides and domains to intact antibody molecules. Unlike the shotgun strategy commonly used in routine proteomics, obtaining full sequences of all antibodies presents unique challenges. It requires specialized methodological adaptations to address issues related to dynamic range, sequence variation, and sample complexity. This review introduces the technical principles, methodological workflows, and recent applications of various mass spectrometry-based antibody repertoire sequencing (Ab-seq) strategies, with a focus on approaches designed to improve sequence coverage and identification accuracy. These include multi-enzyme digestion, hybrid fragmentation methods, and artificial intelligence-assisted de novo sequencing. By systematically comparing database-dependent techniques—such as bottom-up, middle-down, and top-down approaches—with database-independent de novo sequencing, this review outlines their respective advantages and limitations in terms of sample throughput, sequence coverage, post-translational modification characterization, and data analysis complexity. In addition, this review discusses emerging technological trends, including the integration of ion mobility separation, native mass spectrometry, and artificial intelligence-driven data interpretation, which are expected to enhance the depth and accuracy of antibody characterization. Although current methods continue to face challenges related to sample complexity, dynamic range, and unambiguous sequence variant assignment, we emphasize the importance of integrating BCR-seq and Ab-seq data to construct gene-protein association maps. These maps help validate sequence accuracy and facilitate epitope discovery. This dual-platform strategy helps bridge the gap between genotype and phenotype, thereby enhancing both the resolution and scope of antibody repertoire studies. Such an integrative approach also offers a valuable tool for therapeutic antibody development, structure-function analysis, and precise evaluation of vaccine efficacy.

To explore the advantages of traditional Chinese medicine （TCM） and integrative TCM-Western medicine approaches in the treatment of diabetic microvascular complications （DMC）， refine key pathophysiological insights and treatment principles， and promote academic innovation and strategic research planning in the prevention and treatment of DMC. The 38th session of the Expert Salon on Diseases Responding Specifically to Traditional Chinese Medicine， hosted by the China Association of Chinese Medicine， was held in Beijing， 2024. Experts in TCM， Western medicine， and interdisciplinary fields convened to conduct a systematic discussion on the pathogenesis， diagnostic and treatment challenges， and mechanism research related to DMC， ultimately forming a consensus on key directions. Four major research recommendations were proposed. The first is addressing clinical bottlenecks in the prevention and control of DMC by optimizing TCM-based evidence evaluation systems. The second is refining TCM core pathogenesis across DMC stages and establishing corresponding "disease-pattern-time" framework. The third is innovating mechanism research strategies to facilitate a shift from holistic regulation to targeted intervention in TCM. The fourth is advancing interdisciplinary collaboration to enhance the role of TCM in new drug development， research prioritization， and guideline formulation. TCM and integrative approaches offer distinct advantages in managing DMC. With a focus on the diseases responding specifically to TCM， strengthening evidence-based support and mechanism interpretation and promoting the integration of clinical care and research innovation will provide strong momentum for the modernization of TCM and the advancement of national health strategies.

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 address the growing challenge of an increasing number of elderly lung cancer patients amidst China's aging population and to fill the gap in quality control standards for surgical treatment in this special population, this study aimed to develop a standardized surgical evaluation system for elderly lung cancer patients tailored to China's national conditions. The system was established through a literature review, integrated the pathophysiological characteristics of elderly patients, and was constructed following review, feedback, and revision by experts from multiple thoracic surgery centers. Employing a 100-point scoring system, it comprises three primary domains: physical infrastructure and geriatric adaptability foundational conditions (10 points); management level and perioperative care models (20 points); and technical proficiency and clinical outcomes (70 points). The system places a strong emphasis on geriatric adaptability, proposing specific, quantifiable indicators for age-friendly facility modifications, control of elderly-specific complications, multidisciplinary collaboration, and standardized perioperative management. It provides a convenient and measurable assessment tool for quality control in the surgical treatment of elderly lung cancer in China, which is expected to promote the standardization and homogenization of diagnosis and treatment.

ObjectiveTo evaluate the efficacy and safety of Janus kinase （JAK） inhibitors combined with Chinese herbal medicine （CHM） in treating rheumatoid arthritis （RA）. MethodsClinical data from 169 RA patients were retrospectively collected. Among them， 71 cases received JAK inhibitors as the control group， while 98 cases received JAK inhibitors plus CHM as the observation group， both treated for 24 weeks. The rheumatoid factor （RF）， cyclic citic peptide antibody （anti-CCP）， erythrocyte sedimentation rate （ESR）， C-reactive protein （CRP）， and white blood cell count （WBC） were recorded before and after treatment. Databases including CNKI， Wanfang， VIP， PubMed and Web of Science were searched from inception till August 31st， 2025 for randomized controlled trials （RCTs） on the combined use of JAK inhibitors and CHM for RA. The methodological quality of the included studies was evaluated using the risk of bias assessment tool. Meta-analyses were performed for RF， anti-CCP， ESR， CRP， 28-joint disease activity score （DAS28）， overall clinical effective rate， and incidence of adverse events. Sensitivity analysis were also performed. ResultsThe retrospective study demonstrated that after treatment， ESR， CRP， and anti-CCP levels decreased in the observation group， while ESR and CRP levels decreased in the control group （P＜0.05）. Moreover， ESR and RF levels in the observation group were lower than those in the control group （P＜0.05）. A total of 9 RCTs involving 770 patients were included in the meta-analysis. The results indicated that the JAK inhibitors plus CHM group was superior to the JAK inhibitors group in reducing RF （MD=-8.97， 95%CI -15.01 to -2.94， P=0.004）， CRP （MD=-3.34， 95%CI -3.82 to -2.86， P＜0.001）， ESR （MD=-5.33， 95%CI -7.98 to -2.69， P＜0.001）， and DAS28 score （MD=-0.54， 95%CI -0.74 to -0.34， P＜0.001）， as well as in improving the overall clinical effective rate （OR=4.53， 95%CI 2.55 to 8.03， P＜0.001）. No statistically significant differences were observed between groups in anti-CCP levels （SMD=-2.08， 95%CI -4.41 to 0.24， P=0.080） or incidence of adverse events （OR=0.93， 95%CI 0.55 to 1.57， P=0.790）. ConclusionThe combination of JAK inhibitors and CHM demonstrates remarkable efficacy in treating RA， contributing to improved disease activity and reduced inflammatory markers with a favorable safety profile.

Background: The tight junction protein claudin18.2 (CLDN18.2) has been implicated in poor prognosis and suboptimal immunotherapy response in gastric cancer (GC). This study investigates the clinicopathological relevance of CLDN18.2 expression and its association with molecular subtypes in GC patients from a high-incidence region, combining transcriptomic and proteomic approaches to explore how CLDN18.2 contributes to progression and metastasis. Methods: A retrospective cohort of 494 GC patients (2019–2024) underwent immunohistochemical analysis for CLDN18.2, Epstein-Barr virus (Epstein–Barr virus–encoded RNA), p53, human epidermal growth factor receptor 2 (HER2), and mismatch repair proteins (MLH1, MSH2, PMS2, and MSH6). CLDN18.2 positivity was defined as moderate to strong (2+/3+) membranous staining in ≥75% of tumor cells. Clinicopathological correlations, biomarker associations, and survival outcomes were evaluated. Transcriptomic and proteomic sequencing was performed to explore molecular mechanisms. Results: CLDN18.2 positivity was observed in 26.9% (133/494) of gastric adenocarcinomas. CLDN18.2-positive tumors correlated with TNM stage (p = .003) and shorter overall survival (p = .018). No associations were identified with age, sex, HER2 status, microsatellite instability, or Epstein-Barr virus infection. Transcriptomic profiling revealed CLDN18.2-high tumors enriched in pathways involving cell junction disruption, signaling regulation, and immune modulation. Proteomic profiling showed that tumors with high CLDN18.2 were enriched in multiple mechanism-related pathways such as integrated metabolic reprogramming, cytoskeletal recombination, immune microenvironment dysregulation, and pro-survival signaling. These mechanisms may collectively contribute to tumor progression and metastasis. Conclusions CLDN18.2 overexpression is associated with poor prognosis in GC patients. Transcriptomic and proteomic analyses demonstrate that CLDN18.2 promotes tumor progression and metastasis, underscoring its potential as an independent prognostic factor in regions with a high incidence of GC.

Tumor drug resistance is an important problem in the failure of chemotherapy and targeted drug therapy, which is a complex process involving chromatin remodeling. SWI/SNF is one of the most studied ATP-dependent chromatin remodeling complexes in tumorigenesis, which plays an important role in the coordination of chromatin structural stability, gene expression, and post-translation modification. However, its mechanism in tumor drug resistance has not been systematically combed. SWI/SNF can be divided into 3 types according to its subunit composition: BAF, PBAF, and ncBAF. These 3 subtypes all contain two mutually exclusive ATPase catalytic subunits (SMARCA2 or SMARCA4), core subunits (SMARCC1 and SMARCD1), and regulatory subunits (ARID1A, PBRM1, and ACTB, etc.), which can control gene expression by regulating chromatin structure. The change of SWI/SNF complex subunits is one of the important factors of tumor drug resistance and progress. SMARCA4 and ARID1A are the most widely studied subunits in tumor drug resistance. Low expression of SMARCA4 can lead to the deletion of the transcription inhibitor of the BCL2L1 gene in mantle cell lymphoma, which will result in transcription up-regulation and significant resistance to the combination therapy of ibrutinib and venetoclax. Low expression of SMARCA4 and high expression of SMARCA2 can activate the FGFR1-pERK1/2 signaling pathway in ovarian high-grade serous carcinoma cells, which induces the overexpression of anti-apoptosis gene BCL2 and results in carboplatin resistance. SMARCA4 deletion can up-regulate epithelial-mesenchymal transition (EMT) by activating YAP1 gene expression in triple-negative breast cancer. It can also reduce the expression of Ca²⁺ channel IP3R3 in ovarian and lung cancer, resulting in the transfer of Ca²⁺ needed to induce apoptosis from endoplasmic reticulum to mitochondria damage. Thus, these two tumors are resistant to cisplatin. It has been found that verteporfin can overcome the drug resistance induced by SMARCA4 deletion. However, this inhibitor has not been applied in clinical practice. Therefore, it is a promising research direction to develop SWI/SNF ATPase targeted drugs with high oral bioavailability to treat patients with tumor resistance induced by low expression or deletion of SMARCA4. ARID1A deletion can activate the expression of ANXA1 protein in HER2+ breast cancer cells or down-regulate the expression of progesterone receptor B protein in endometrial cancer cells. The drug resistance of these two tumor cells to trastuzumab or progesterone is induced by activating AKT pathway. ARID1A deletion in ovarian cancer can increase the expression of MRP2 protein and make it resistant to carboplatin and paclitaxel. ARID1A deletion also can up-regulate the phosphorylation levels of EGFR, ErbB2, and RAF1 oncogene proteins.The ErbB and VEGF pathway are activated and EMT is increased. As a result, lung adenocarcinoma is resistant to epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs). Although great progress has been made in the research on the mechanism of SWI/SNF complex inducing tumor drug resistance, most of the research is still at the protein level. It is necessary to comprehensively and deeply explore the detailed mechanism of drug resistance from gene, transcription, protein, and metabolite levels by using multi-omics techniques, which can provide sufficient theoretical basis for the diagnosis and treatment of poor tumor prognosis caused by mutation or abnormal expression of SWI/SNF subunits in clinical practice.

Objective To systematically evaluate the advance care planning readiness assessment tools applicable to the elderly and to provide a reliable assessment tool for the development of advance care planning.Methods PubMed,Web of Science,Cochrane Library,Embase,CINAHL,Yimaitong,Wanfang Database,China National Know-ledge Network,VIP Database,and China Biomedical Literature Database were systematically searched.The search time limit is from database establishment to April 30,2024.Pre-established medical care plan readiness assessment tools that meet the inclusion criteria were screened,and the"consensus based standards for the selection of health measurement instruments"was used to evaluate the methodological quality and measurement attributes of the inclusion assessment tools.The improved version of the evidence quality evaluation and recommendation strength grading method is used to grade the evidence of the assessment tool and form the final recommendation opinion.Results A total of 14 articles were included,including 10 advance care planning readiness assessment tools suitable for the elderly.None of the articles reported cross-cultural validity,measurement error,and responsiveness.The content validity and internal consistency of the Chinese Advance Care Planning Readiness Scale and the Advance Care Planning Readiness Scale for Chronic Disease Patients were both"adequate",with A-level recommendations,and the remaining assessment tools were all B-level recommendations.Conclusion There are various types of advance care planning readiness assessment tools for the elderly,but the methodological quality needs to be improved and the measurement properties need to be further evaluated.It is temporarily recommended to use the Chinese Advance Care Planning Readiness Scale and the Advance Care Planning Readiness Scale for Chronic Disease Patients.

Nonunion of osteoporotic vertebral fractures (OVF), predominantly affecting the elderly, can lead to intractable pain, vertebral collapse, progressive kyphotic deformity, and neurological impairment, significantly compromising patients′ quality of life. There exists considerable debate on diagnosis and management of OVF, encompassing key issues such as clinical diagnosis and staging criteria for nonunion, surgical indications and procedure selection, and postoperative rehabilitation planning. Currently, there lacks standardized clinical guideline and expert consensus on the diagnosis and management of OVF nonunion in China. To address this gap, Minimally Invasive Surgery Group of Chinese Orthopedic Association, Osteoporosis Committee of Chinese Association of Orthopedic Surgeons, Prevention and Rehabilitation Committee for Osteoporosis of Chinese Association of Rehabilitation Medicine and Minimally Invasive Orthopedic Surgery Branch of China Association for Geriatric Care jointly organized domestic experts in spinal surgery, endocrinology, and rehabilitation to formulate the Clinical guideline for the diagnosis and treatment for nonunion of osteoporotic vertebral fractures ( version 2025), based on existing literature and clinical experience and adhering to principles of scientific rigor and practicality. The guideline provided 13 evidence-based recommendations encompassing diagnosis and treatment of OVF nonunion, aiming to standardize its clinical management.

Objective:To construct a prediction model for postoperative poor in-hospital prognosis in patients with traumatic brain injury (TBI) and evaluate its predictive performance.Methods:A retrospective case control study was conducted to analyze the clinical data of 1 120 TBI patients admitted to Changde Hospital Affiliated to Xiangya Medical College of Central South University from May 2019 to December 2024. The patients were divided into the training set ( n=784) and verification set ( n=336) at a ratio of 7∶3. Based on the Glasgow outcome scale-extended (GOS-E) at discharge, the training set was stratified into favorable prognosis group ( n=335, GOS-E 5-8 points) and poor prognosis group ( n=449, GOS-E 1-4 points). The two groups in the training set were compared in terms of general baseline indicators, TBI-related clinical indicators, and admission laboratory blood test results. Univariate analysis and Lasso regression analysis were employed to screen risk factors associated with postoperative poor in-hospital prognosis in TBI patients. Multivariate Logistic regression analysis was used to determine independent risk factors and construct a regression equation. The regression equation was presented using R language to create a visual nomogram for predicting postoperative poor in-hospital prognosis in TBI patients. In both the training set and verification set, the predictive performance of the model was evaluated by calculating the area under the receiver operating characteristic (ROC) curve (AUC), plotting calibration curves, and performing decision curve analysis (DCA). Results:The results of the univariate analysis indicated that the age, Charlson complication index (CCI), time from trauma to admission, time from trauma to operation, cause of injury, abbreviated injury scale (AIS) (head and neck), injury severity score (ISS), admission Glasgow coma scale (GCS), admission pupil responsiveness, multiple craniocerebral injuries, subdural hematoma, intracerebral hematoma, intraventricular hemorrhage, subarachnoid hemorrhage, decompressive craniotomy, intraoperative blood loss, intraoperative blood transfusion, traumatic cerebral infarction, postoperative delayed bleeding, epilepsy seizures, as well as the following admission tested results including red blood cell count, white blood cell count, platelet count, neutrophil percentage, percentage of lymphocytes, albumin, total bilirubin, urea nitrogen, thrombin time (TT), prothrombin time (PT), international standardized ratio (INR), glutamic aminotransferase, alanine aminotransferase, creatinine, and blood glucose were statistically different between the two groups in the training set ( P<0.05). Lasso regression analysis suggested 14 risk factors of age, CCI, cause of injury, head and neck AIS, ISS, admission GCS, admission pupil responsiveness, multiple craniocerebral injuries, subdural hematoma, intracerebral hematoma, intraoperative blood loss, admission platelet count, admission albumin, admission blood glucose for postoperative poor in-hospital prognosis. The results of the multivariate Logistic regression analysis showed that age ( OR=1.02, 95% CI 1.00, 1.03, P<0.01), CCI ( OR=1.46, 95% CI 1.02, 2.09, P<0.05), head and neck AIS ( OR=1.43, 95% CI 1.11, 1.85, P<0.01), ISS ( OR=2.16, 95% CI 1.39, 3.35, P<0.01), admission GCS ( OR=1.59, 95% CI 1.19, 2.13, P<0.01), intracerebral hematoma ( OR=4.41, 95% CI 2.15, 9.44, P<0.01), intraoperative blood loss ( OR=1.05, 95% CI 1.00, 1.09, P<0.05), admission platelet count ( OR=0.98, 95% CI 0.97, 0.99, P<0.01), admission blood glucose ( OR=1.08, 95% CI 1.02, 1.15, P<0.05) could be the main risk factors to construct a prediction model for postoperative poor in-hospital prognosis in TBI patients. Meanwhile, a regression equation was constructed: Logit[ P/(1- P)]=-2.4+ 0.02×"age"+0.38×"CCI"+0.36×"head and neck AIS"+0.77×"ISS"+0.47×"admission GCS"+1.48×"intracerebral hematoma"+0.05×intraoperative blood loss-0.02×admission platelet count+0.08×admission blood glucose. In the training set, the predictive model for poor postoperative in-hospital prognosis in TBI patients achieved an AUC of 0.87 (95% CI 0.84, 0.89), with a Youden′s index of 0.57, sensitivity of 73.70%, and specificity of 83.00%. In the verification set, the model showed an AUC of 0.80 (95% CI 0.76, 0.85), with a Youden′s index of 0.63, sensitivity of 65.20%, and specificity of 77.90%. In the training set, the Brier score for the calibration curve was 0.14 (95% CI 0.13, 0.16). In the verification set, the Brier score for the calibration curve was 0.18 (95% CI 0.15, 0.20). The DCA diagram indicated that the nomogram prediction model provided high clinical net benefit for predicting postoperative poor in-hospital prognosis in TBI patients. Conclusion:The prediction model for postoperative poor in-hospital prognosis in TBI patients, constructed based on age, CCI, head and neck AIS, ISS, admission GCS, intracerebral hematoma, intraoperative blood loss, admission platelet count, and admission blood glucose, exhibits good predictive performance.