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.

This study was aimed at understanding the prevalence and drug resistance status of Salmonella of waterfowl ori-gin in the Guangdong region in the past decade,to guide prevention and control efforts.The drug-sensitive paper slide method was used to conduct drug susceptibility testing on 314 waterfowl-originating Salmonella strains isolated from 238 waterfowl farms in the Guangdong region from 2013 to 2023.The isolated Salmonella strains were most resistant to penicillin,amoxicil-lin,cefradine,and cefazolin in the β-lactam group;sulphadoxine dimethylpyrimidine in the sulphonamide group;and tetracy-cline in the tetracycline group.The resistance rates ranged from 73.57％to 89.49％.The highest sensitivity was observed to amikacin,gentamicin,and kanamycin in the aminoglycoside group,and norfloxacin in the quinolone group,with susceptibility rates all exceeding 50％.The 280 strains of Salmonella showed multi-drug resistance to six classes of antimicrobial drugs and high resistance(as much as 60.83％)to five drug classes.Correlation analysis revealed the highest correlations for florfenicol with gentamicin,and for amoxicillin with penicillin(r=0.650 for both),followed by gentamicin with kanamycin(r=0.620).Salmonella resistance in waterfowl in Guangdong Province was generally severe and showed a complex pattern of drug resist-ance.Detection of waterfowl pathogens should be strengthened to prevent the spread of drug-resistant bacteria and support ra-tional use of antibiotics.This work provides a reference for Salmonella prevention and control in waterfowl farms.

Integrated metabolomic and lipidomic profiling,utilizing liquid chromatography coupled with high-resolution mass spectrometry(LC-HRMS),has emerged as a pivotal strategy for biomarker discovery.However,the inherent polarity disparity between metabolites and lipids complicates simultaneous analysis.To address this,a dual-stationary phase polarity-extended liquid chromatography(PELC)system was developed,which surpassed conventional one-dimensional LC(1D-LC)by enabling comprehensive coverage of both polar and non-polar compounds within a single injection.This system enhanced chromatographic resolution,peak capacity,and throughput while minimizing analytical variability.Extracellular vesicles(EVs),lipid bilayer-enclosed nanoparticles ubiquitously present in biofluids,had gained prominence as reservoirs of cancer biomarkers due to their cargo stability and pathophysiological relevance.Herein,the application of PELC-HRMS for concurrent metabolome-lipidome profiling in EVs was pioneered.A total of 193 metabolites were identified using this technique coupled with MS-DIAL software and Human Metabolome Database.Subsequently,this technique was employed to explore potential biomarkers for prostate cancer(PCa).Multivariate analysis identified 17 differentially abundant metabolites in PCa,implicating dysregulated pathways including purine metabolism,starch and sucrose metabolism,galactose metabolism,cysteine and methionine metabolism,and biosynthesis of unsaturated fatty acids.Notably,creatine(AUC=0.92)and DG 42:5(AUC=0.80)demonstrated robust diagnostic efficacy,attributable to their broad polarity ranges and EV-specific enrichment.This study established PELC as a high-fidelity platform for multi-omics integration in complex biospecimens,advancing mechanistic insights into metabolic rewiring and disease pathophysiology.

Albendazole-glucan particles(ABZ-GPS)and abendazole(ABZ)were used to treat secondary alveolar echinococ-cosis in mice.The therapeutic effects of ABZ-GPS on alveolar echinococcosis in vivo were evaluated,and the feasibility of using glucan particles as anti-hydatid drug carriers was further verified.Mice with echinococcosis were randomly divided into an ABZ group,glucan nanoparticle(GP)group,ABZ-GPS group,and control group.After four courses of administration with a final concentration of 50 mg/mL,the therapeutic effects of ABZ-GPS were evaluated on the basis of imaging,histopathological changes,ultrastructure,and immunology.After ABZ-GPS and ABZ administration,clear liver lesion tissue necrosis and large numbers of infiltrating lymphocytes were observed.Significant differences in the average cyst wet weight(t=7.83,P＜0.05),were observed between the ABZ-GPS group and ABZ group.Imaging revealed that ABZ-GPs were targeted to liver tissue.Pa-thology and ultrastructure analyses demonstrated that the alveolar hydatid cells in the liver in the control group and GP group grew well,and the vesicles were large,filled with cystic fluid,and translucent or transparent;the cyst wall tension was high;no calcification was observed;the stratum corneum and germinal layer were clear;and more fertile capsules and different num-bers of protocephalospora were present in the liver.In the ABZ group,the capsular cavity collapsed,and showed partial necro-sis and lymphocyte infiltration.In the ABZ-GP group,the corneum and germinal layer of echinococcus vesicles were difficult to identify,and we observed bulbous necrosis,central calcification,fibrous tissue hyperplasia,inflammatory cell infiltration,coarser,shorter or absent microvilli of the germinal layer,nuclear shrinkage,dissolution or disappearance,clear expansion of cytoplasmic microtubules,and myelin-like or vacuole-like changes.Therefore,ABZ-GPs showed good targeting and killing ac-tivity in vivo in mice with secondary alveolar coccosis.

Objective:To establish and evaluated a logistic regression model for predicting the acute biliary pancreatitis (ABP) based on liver-injury related indexes.Methods:Clinical data of 210 patients diagnosed with acute pancreatitis (AP) at the Affiliated Hospital of Jiangnan University from October 2020 to December 2022 were retrospectively analyzed, including 113 males and 97 females, with a median age of 52 years (range, 43 to 58). Among these, 88 were diagnosed with ABP and 122 with acute non-biliary pancreatitis (ANBP). Additionally, a test cohort was created using data from 101 AP patients diagnosed between January and December 2023, including 60 males and 41 females, with a median age of 53 years (range, 43 to 63). Based on the original dataset, univariate and multivariate logistic regression analyses were conducted to identify the factors influencing ABP. A prediction probability formula (Pre) was then established based on the multivariate results. The effectiveness of each indicator in predicting ABP was evaluated using the receiver operating characteristic (ROC) curve. The ROC curve analysis determined the optimal cutoff value of Pre, which was subsequently used to diagnose ABP and ANBP in the test cohort.Results:Multivariate logistic regression analysis showed the factors influencing ABP include direct bilirubin (DBIL), alanine aminotransferase (ALT), aspartate aminotransferase (AST), cholinesterase (CHE), and fibrinogen (FIB). Based on the multivariate analysis results, the prediction probability formula (Pre) for ABP was established as follows: P=1/{1+ exp[-(4.807+ 0.134×DBIL-1.859×AST/ALT-0.0003×CHE-0.387×FIB)]}. ROC curve analysis revealed that the area under the curve (AUC) for Pre in predicting ABP was 0.858, with an optimal cutoff value of 0.56, at which the sensitivity was 69.3% and the specificity was 91.0%. Using the cutoff value of 0.56 for Pre, ABP was diagnosed when Pre≥0.56 and ANBP was diagnosed when Pre<0.56. This criterion was applied to diagnose patients in the test cohort, where the sensitivity and specificity of Pre for diagnosing ABP were 86.1% and 92.3%, respectively.Conclusion:The logistic regression model based on liver injury-related indicators is a valuable tool for clinically assessing the incidence of ABP.

Objective:To analyze the changes of serum metabolites in patients with acute pancreatitis (AP) by non-targeted metabolomics method.Methods:Serum samples and clinical data of 15 AP patients hospitalized in the Affiliated Hospital of Jiangnan University from August to September 2024 were collected and included in the AP group, including 9 males and 6 females, aged (55.4±15.3) years. The serum and clinical data of 25 patients with colon polyps in the same hospital during the same period of time were collected, including 15 males and 10 females, aged (61.2±11.5) years, and were included in the control group. Serum metabolomic detection was performed using the ultra-high performance liquid chromatography tandem Fourier transform mass spectrometer. The modeling method was orthogonal partial least square discriminant analysis, and principal component analysis was performed on the data matrix to screen the differential metabolites in serum of AP patients. The Kyoto Encyclopedia database of Genes and Genomes was used to annotate differential metabolites, and the pathway of differential metabolite enrichment was analyzed by software.Results:The principal component analysis showed that the contribution ratio of the first principal component was 15.1%, the proportion of the second principal component was 10.8%, and the total proportion of the two was 25.9%. In principal component analysis, two groups of samples can be clearly distinguished and show obvious clustering characteristics. According to the analysis of OPLS-DA model, there were significant differences in serum metabolic profiles between AP group and control group. There were 683 differentially expressed metabolites between the two groups, with 367 differentially expressed metabolites up-regulated compared with the control group and 316 differentially expressed metabolites down-regulated compared with the control group. It is mainly Phosphatidic Acid (Lte4/8: 0) (+ 218%), Omeprazole Sulphone (-38%), and 2-(Propylthio) Nicotinic Acid (2-propyl thionicotinic acid) (-58%), Gein (salicyricetin) (-47%) and so on. Pathway enrichment analysis showed that the differential metabolites in AP patients were mainly concentrated in citric acid cycle, arginine biosynthesis and glycerophospholipid metabolism pathways.Conclusion:Serum metabolites in AP patients change significantly, including citric acid cycle, arginine biosynthesis, glycerophospholipid metabolism.

OBJECTIVE: The relationship between fish consumption and stroke is inconsistent, and it is uncertain whether this association varies across predicted stroke risks. METHODS: A cohort study comprising 95,800 participants from the Prediction for Atherosclerotic Cardiovascular Disease Risk in China project was conducted. A standardized questionnaire was used to collect data on fish consumption. Participants were stratified into low- and moderate-to-high-risk categories based on their 10-year stroke risk prediction scores. Hazard ratios ( HRs) and 95% confidence intervals ( CIs) were estimated using Cox proportional hazard models and additive interaction by relative excess risk due to interaction (RERI), attributable proportion (AP), and synergy index (SI). RESULTS: During 703,869 person-years of follow-up, 2,773 incident stroke events were identified. Higher fish consumption was associated with a lower risk of stroke, particularly among moderate-to-high-risk individuals ( HR = 0.53, 95% CI: 0.47-0.60) than among low-risk individuals ( HR = 0.64, 95% CI: 0.49-0.85). A significant additive interaction between fish consumption and predicted stroke risk was observed (RERI = 4.08, 95% CI: 2.80-5.36; SI = 1.64, 95% CI: 1.42-1.89; AP = 0.36, 95% CI: 0.28-0.43). CONCLUSION Higher fish consumption was associated with a lower risk of stroke, and this beneficial association was more pronounced in individuals with moderate-to-high stroke risk.
Humans ; China/epidemiology* ; Male ; Female ; Stroke/etiology* ; Middle Aged ; Prospective Studies ; Incidence ; Aged ; Animals ; Fishes ; Risk Factors ; Diet ; Seafood ; Adult ; Cohort Studies

BACKGROUND: Durvalumab after chemoradiotherapy (CRT) failed to bring survival benefits to patients with epidermal growth factor receptor ( EGFR ) mutations in PACIFIC study (evaluating durvalumab in patients with stage III, unresectable NSCLC who did not have disease progression after concurrent chemoradiotherapy). We aimed to explore whether locally advanced inoperable patients with EGFR mutations benefit from tyrosine kinase inhibitors (TKIs) and the optimal treatment regimen. METHODS: We searched the PubMed, Embase, the Cochrane Central Register of Controlled Trials, and ClinicalTrials.gov databases from inception to December 31, 2022 and performed a meta-analysis based on a Bayesian framework, with progression-free survival (PFS) and overall survival (OS) as the primary endpoints. RESULTS: A total of 1156 patients were identified in 16 studies that included 6 treatment measures, including CRT, CRT followed by durvalumab (CRT-Durva), TKI monotherapy, radiotherapy combined with TKI (RT-TKI), CRT combined with TKI (CRT-TKI), and TKI combined with durvalumab (TKI-Durva). The PFS of patients treated with TKI-containing regimens was significantly longer than that of patients treated with TKI-free regimens (hazard ratio [HR] = 0.37, 95% confidence interval [CI], 0.20-0.66). The PFS of TKI monotherapy was significantly longer than that of CRT (HR = 0.66, 95% CI, 0.50-0.87) but shorter than RT-TKI (HR = 1.78, 95% CI, 1.17-2.67). Furthermore, the PFS of RT-TKI or CRT-TKI were both significantly longer than that of CRT or CRT-Durva. RT-TKI ranked first in the Bayesian ranking, with the longest OS (60.8 months, 95% CI = 37.2-84.3 months) and the longest PFS (21.5 months, 95% CI, 15.4-27.5 months) in integrated analysis. CONCLUSIONS: For unresectable stage III EGFR mutant NSCLC, RT and TKI are both essential. Based on the current evidence, RT-TKI brings a superior survival advantage, while CRT-TKI needs further estimation. Large randomized clinical trials are urgently needed to explore the appropriate application sequences of TKI, radiotherapy, and chemotherapy. REGISTRATION PROSPERO; https://www.crd.york.ac.uk/PROSPERO/ ; No. CRD42022298490.
Humans ; Carcinoma, Non-Small-Cell Lung/therapy* ; ErbB Receptors/genetics* ; Lung Neoplasms/drug therapy* ; Mutation/genetics* ; Protein Kinase Inhibitors/therapeutic use* ; Chemoradiotherapy ; Antibodies, Monoclonal/therapeutic use*

This study aims to explore the pharmacodynamic material basis of Jinbei Oral Liquid(JBOL) against idiopathic pulmonary fibrosis(IPF) based on serum pharmacochemistry and network pharmacology. The ultra-high performance liquid chromatography-quadrupole time-of-flight tandem mass spectrometry(UHPLC-Q-TOF-MS/MS) technology was employed to analyze and identify the components absorbed into rat blood after oral administration of JBOL. Combined with network pharmacology, the study explored the pharmacodynamic material basis and potential mechanism of JBOL against IPF through protein-protein interaction(PPI) network construction, "component-target-pathway" analysis, Gene Ontology(GO) functional enrichment, and Kyoto Encyclopedia of Genes and Genomes(KEGG) pathway enrichment analysis. First, a total of 114 compounds were rapidly identified in JBOL extract according to the exact relative molecular mass, fragment ions, and other information of the compounds with the use of reference substances and a self-built compound database. Second, on this basis, 70 prototype components in blood were recognized by comparing blank serum with drug-containing serum samples, including 28 flavonoids, 25 organic acids, 4 saponins, 4 alkaloids, and 9 others. Finally, using these components absorbed into blood as candidates, the study obtained 212 potential targets of JBOL against IPF. The anti-IPF mechanism might involve the action of active ingredients such as glycyrrhetinic acid, cryptotanshinone, salvianolic acid B, and forsythoside A on core targets like AKT1, TNF, and ALB and thereby the regulation of multiple signaling pathways including PI3K/AKT, HIF-1, and TNF. In conclusion, JBOL exerts the anti-IPF effect through multiple components, targets, and pathways. The results would provide a reference for further study on pharmacodynamic material basis and pharmacological mechanism of JBOL.
Drugs, Chinese Herbal/pharmacokinetics* ; Animals ; Tandem Mass Spectrometry ; Network Pharmacology ; Rats ; Chromatography, High Pressure Liquid ; Rats, Sprague-Dawley ; Male ; Idiopathic Pulmonary Fibrosis/metabolism* ; Humans ; Administration, Oral ; Protein Interaction Maps/drug effects* ; Signal Transduction/drug effects*

Objective To develop a predictive model for postoperative mortality risk in patients with acute aortic dissection(AAD)using the Extreme Gradient Boosting(XGBoost)algorithm combined with Shapley Additive Explanation(SHAP),and to establish a prediction website to serve as a diagnostic and therapeutic support platform for clinicians and patients.Methods A retrospective cohort study design was adopted.Data from 782 AAD patients who underwent surgical treatment at the Fourth Hospital of Hebei Medical University from January 2013 to December 2023 were collected,including basic information and initial serum biomarker test results.Patients were randomly divided into training and test sets at a 7:3 ratio.An external validation set consisting of 313 AAD patients admitted to the Second Hospital of Hebei Medical University from January 2020 to December 2023 was also established for further model validation.Variables were screened using LASSO regression,and an XGBoost machine learning model was constructed and interpreted using SHAP.The predictive performance of the model was evaluated using receiver operating characteristic(ROC)curve analysis.Using the Shiny package,the XGBoost model was deployed to shinyapps.io to create a prediction website for postoperative mortality risk in AAD patients.One patient was selected by simple random sampling from the test set and the external validation set respectively for the prediction example on the Shiny webpage.Results The XGBoost model demonstrated high predictive performance for postoperative mortality in AAD patients,with area under the ROC curve(AUC)values of 0.928(95%CI 0.901-0.956)in the training set,0.919(95%CI 0.891-0.949)in the test set,and 0.941(95%CI 0.915-0.967)in the external validation set.SHAP values indicated the following order of variable importance in the model(from highest to lowest):"lactate dehydrogenase""blood chlorine""multiple organ injury""carbon dioxide combining power""prothrombin time""α-hydroxybutyric acid""creatine kinase isoenzyme""Stanford classification""combined use of bedside blood purification""gender""acute kidney injury""gastrointestinal bleeding""brain injury"and"shock".A risk prediction website for adverse postoperative outcomes in AAD patients was developed using XGBoost-SHAP method(https://dun-dunxiaolu.shinyapps.io/document/)and validated with examples.One randomly selected patient from each of the test and external validation sets was applied:the predicted mortality risk value for patient 1(who died postoperatively)was 0.9539,and that for patient 2(who survived postoperatively)was 0.0206.Conclusions The XGBoost-SHAP model demonstrates high accuracy in predicting postoperative mortality risk for AAD patients.The online prediction tool established based on this model enhances the identification efficiency of high-risk postoperative mortality patients.