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.

ObjectiveTobacco-related diseases remain one of the leading preventable public health challenges worldwide and are among the primary causes of premature death. In recent years, accumulating evidence has supported the classification of nicotine addiction as a chronic brain disease, profoundly affecting both brain structure and function. Despite the urgency, effective diagnostic methods for smoking addiction remain lacking, posing significant challenges for early intervention and treatment. To address this issue and gain deeper insights into the neural mechanisms underlying nicotine dependence, this study proposes a novel graph neural network framework, termed TI-GNN. This model leverages functional magnetic resonance imaging (fMRI) data to identify complex and subtle abnormalities in brain connectivity patterns associated with smoking addiction. MethodsThe study utilizes fMRI data to construct functional connectivity matrices that represent interaction patterns among brain regions. These matrices are interpreted as graphs, where brain regions are nodes and the strength of functional connectivity between them serves as edges. The proposed TI-GNN model integrates a Transformer module to effectively capture global interactions across the entire brain network, enabling a comprehensive understanding of high-level connectivity patterns. Additionally, a spatial attention mechanism is employed to selectively focus on informative inter-regional connections while filtering out irrelevant or noisy features. This design enhances the model’s ability to learn meaningful neural representations crucial for classification tasks. A key innovation of TI-GNN lies in its built-in causal interpretation module, which aims to infer directional and potentially causal relationships among brain regions. This not only improves predictive performance but also enhances model interpretability—an essential attribute for clinical applications. The identification of causal links provides valuable insights into the neuropathological basis of addiction and contributes to the development of biologically plausible and trustworthy diagnostic tools. ResultsExperimental results demonstrate that the TI-GNN model achieves superior classification performance on the smoking addiction dataset, outperforming several state-of-the-art baseline models. Specifically, TI-GNN attains an accuracy of 0.91, an F1-score of 0.91, and a Matthews correlation coefficient (MCC) of 0.83, indicating strong robustness and reliability. Beyond performance metrics, TI-GNN identifies critical abnormal connectivity patterns in several brain regions implicated in addiction. Notably, it highlights dysregulations in the amygdala and the anterior cingulate cortex, consistent with prior clinical and neuroimaging findings. These regions are well known for their roles in emotional regulation, reward processing, and impulse control—functions that are frequently disrupted in nicotine dependence. ConclusionThe TI-GNN framework offers a powerful and interpretable tool for the objective diagnosis of smoking addiction. By integrating advanced graph learning techniques with causal inference capabilities, the model not only achieves high diagnostic accuracy but also elucidates the neurobiological underpinnings of addiction. The identification of specific abnormal brain networks and their causal interactions deepens our understanding of addiction pathophysiology and lays the groundwork for developing targeted intervention strategies and personalized treatment approaches in the future.

Objective: This study aimed to construct an animal model of heart failure with preserved ejection fraction (HFpEF) that integrates disease and syndrome based on the "deficiency-blood stasis-toxin" pathogenesis and to evaluate it comprehensively. Methods: The HFpEF mouse model was constructed using a combination of Nω-nitro-L-arginine methyl ester (L-NAME) and a high-fat diet. According to the random number table method, SPF-grade male C57BL/6J mice were randomly assigned to the control, L-NAME, high-fat diet, and model groups, 10 in each group. Comprehensive observations and data collection on macroscopic signs (e.g., fur condition, mental state, stool and urine, oral and nasal condition, paw and body condition, etc.) and cardiac function were performed after 10 and 16 weeks of model induction. Additionally, the syndrome evolution was elucidated based on diagnostic criteria for clinical syndromes of heart failure. Furthermore, pathological and molecular biological examinations of myocardial tissue were performed to assess the stability and reliability of the model. Results: Mice in the model group showed typical characteristics of syndrome of qi deficiency and blood stasis, as well as syndrome of internal heat accumulation, including lethargy, slow response, dull paw color and oral/nasal color, exercise intolerance, abnormal platelet activation, dry feces, and dark yellow urine. The time window for these syndromes was between 10 and 16 weeks post-modeling. Cardiac function assessments revealed severe diastolic dysfunction, concentric myocardial hypertrophy, and myocardial fibrosis in the model group. Pathological examinations showed a significantly increased collagen deposition in the myocardial interstitium, enlarged cross-sectional area of cardiomyocytes, and sparse coronary microvasculature in the model group. Molecular biological analyses indicated marked activation of the inducible nitric oxide synthase/nuclear factor kappa-light-chain-enhancer of activated B cells/NOD-like receptor family pyrin domain containing 3 inflammatory pathway and significantly elevated inflammation levels in the myocardial tissue of the model group. Although mice in the L-NAME and high-fat diet groups also showed certain manifestations of qi deficiency syndrome, the substantial cardiac damage was relatively limited compared to the control group. Conclusion This study has constructed an animal model of HFpEF that integrates disease and syndrome based on the "deficiency-blood stasis-toxin" pathogenesis. The macroscopic and microscopic characteristics of this model are consistent with the manifestations of syndrome of qi deficiency and blood stasis, toxin syndrome, and syndrome of internal heat accumulation. Moreover, it can stably simulate the HFpEF state and reflect phenotypic changes in human disease. This model provides a suitable experimental platform to explore the pathogenesis of HFpEF, evaluate the effectiveness of traditional Chinese medicine (TCM) treatment regimens, and promote in-depth research on TCM syndromes of heart failure.

Objective To evaluate the influence of the wearing position of dosimeters outside lead aprons on effective dose estimation for interventional radiology workers, analyze the differences between single and double dosimeter methods in effective dose estimation, and provide a reference for the personal dose monitoring of interventional radiology workers. Methods This study employed a combined approach of on-site monitoring and Monte Carlo simulation to evaluate the impact of the wearing position of dosimeters outside lead aprons on effective dose estimation, as well as the differences between effective doses measured using single and double dosimeters. Interventional radiology workers wore dosimeters at three positions: the neck outside the lead collar, the left chest outside the lead apron, and inside the lead apron. Effective doses were estimated using the single and double dosimeter methods specified in GBZ 128-2019 Specifications for individual monitoring of occupational external exposure, and the impact of different wearing positions on the estimation results was compared. Geant4 Monte Carlo simulations were used to model dose distributions at the neck outside the lead collar and at the left chest outside the lead apron for operators performing cardiovascular interventions under tube voltages of 70, 80, 90, and 100 kVp and exposure angles of posteroanterior (PA), anteroposterior (AP), and left anterior oblique 45° (LAO45°) positions. The study assessed the impact of dosimeter wearing position on effective dose estimation. Results Monte Carlo simulations demonstrated that neck doses consistently exceeded left chest doses across different tube voltages and exposure angles, with neck-to-chest dose ratios of 0.80-0.90. Under identical tube voltage conditions, AP showed the highest doses, followed by LAO45°, and PA demonstrated the lowest doses. The single and double dosimeter methods exhibited consistent patterns in effective dose estimation. Single dosimeter method generally yielded higher effective doses with relative deviations of 9.9% to 83%, though these deviations decreased under high tube voltages. Field monitoring data indicated that most interventional radiology workers maintained relative deviations between single and double dosimeter calculations below 6%, with neck-to-chest dose ratios of 0.95-1.1. The estimation patterns remained consistent across both methods, though single dosimeter method showed slightly higher results. Conclusion Under PA, AP, or LAO45°, the doses at the neck consistently exceeded those at the left chest. Therefore, when wearing lead protective equipment, the dosimeter should be properly positioned at the neck outside the lead collar to accurately reflect the radiation doses of surgeons. Some interventional radiology workers improperly positioned the dosimeter (intended at the neck outside the lead collar) at the left chest outside the lead apron, and this may result in an underestimation of the effective dose.

OBJECTIVE: To explore the effect mechanism of electroacupuncture (EA) for ameliorating diabetic peripheral neuropathy (DPN) based on the analysis of gut microbiota and metabolomics. METHODS: Thirty SPF-grade male SD rats were randomly divided into a normal group, a model group, and an EA group, with 10 rats in each one. Except in the normal group, the intraperitoneally injection with streptozotocin was used to induce diabetes mellitus model in the rest groups. In the EA group, acupuncture was delivered at bilateral "Zusanli" (ST36), "Sanyinjiao" (SP6), "Pishu" (BL20) and "Shenshu" (BL23), and electric stimulation was attached to "Zusanli" (ST36)-"Sanyinjiao" (SP6) and "Pishu" (BL20)-"Shenshu" (BL23), on the same side, with continuous wave and a frequency of 2 Hz, for 10 min in each intervention. The intervention measure of each group was delivered once every 2 days, 3 times a week, for 8 consecutive weeks. Body weight, random blood glucose (RBG), thermal withdrawal latency (TWL), and mechanical withdrawal threshold (MWT) before intervention, and in 4 and 8 weeks of intervention, separately, as well as sensory nerve conduction velocity (SCV) and motor nerve conduction velocity (MCV) of the sciatic nerve after intervention were measured. Metagenomic sequencing (MS) was used to analyze gut microbiota and screen for differential species. Liquid chromatography-mass spectrometry (LC-MS) was employed to detect the differential metabolites in plasma, and the metabolic pathway enrichment analysis was performed on the differential metabolites. Spearman correlation analysis was adopted to assess the relationship between gut microbiota and metabolomics. RESULTS: After 4 and 8 weeks of intervention, when compared with the model group, the EA group showed the increase in body weight, TWL, MWT (P<0.01), and the decrease in RBG (P<0.01). Compared with the normal group, SCV and MCV, as well as Chao1 index were dropped in the model group (P<0.01), and those were elevated in the EA group when compared with those in the model group (P<0.01). The dominant bacterial phyla of each group were Firmicutes (F) and Bacteroidota (B), the ratio of them (F/B) in the model group was lower than that of the normal group (P<0.05), and F/B in the EA group was higher when compared with that in the model group (P<0.05). In comparison with the normal group, the relative abundance increased in Prevotella, Segatella, Prevotella-hominis and Segatella-copri (P<0.05); and it decreased in Ligilactobacillus, Eubacterium, Pseudoflavonifractor, Ligilactobacillus-murinus (P<0.05) in the model group. Compared with the model group, the relevant abundance of the above mentioned gut bacteria was all ameliorated in the EA group (P<0.05, P<0.01). Among the three groups, 120 differential metabolites were identified and enriched in 28 key metabolic pathways, such as glycerophospholipid and linoleic acid, of which, glycerophospholipid was the most significantly affected pathway in EA intervention. Spearman correlation analysis showed that 6 phosphatidylcholine metabolites were significantly positively correlated with Pseudoflavonifractor and were negatively with Prevotella, Segatella, Prevotella-hominis, Segatella-copri; 5 phosphatidylethanolamine metabolites were significantly negatively correlated with Pseudoflavonifractor and positively correlated with Prevotella, Segatella, Prevotella-hominis, Segatella-copri. CONCLUSION EA may regulate metabolic pathways such as glycerophospholipid, modulate specific gut microbiota such as Pseudoflavonifractor, Prevotella, and Segatella, and the co-expressed differential metabolites like phosphatidylcholine and phosphatidylethanolamine, thereby reducing blood glucose and protecting nerve function, so as to relieve the symptoms of DPN of rats.
Animals ; Electroacupuncture ; Male ; Gastrointestinal Microbiome ; Diabetic Neuropathies/microbiology* ; Rats, Sprague-Dawley ; Rats ; Metabolomics ; Humans ; Acupuncture Points

Objective To analyze the current status of medical services in secondary and tertiary hospitals in Shandong Province based on DRG,and to provide effective support for the implementation strategies of bidirectional referral.Methods The DRG group was used to analyze 42.348 3 million cases from 75 secondary hospitals and 69 tertiary hospitals in Shandong Province from 2019 to 2023.Experts were organized to establish standards for bidirectional referral of diseases.Results(1)35 high-frequency DRG disease groups with diagnosis,treatment ability and medical resources in secondary hospitals were selected,(2)The medical expenses and medical quality in the high-frequency DRG disease groups within secondary hospitals were lower than those in tertiary hospitals,(3)To develop standardized referral standards and programs with esophageal cancer as an example.Conclusion It is urgent to triage patients gradually and accurately through disease classification management,and formulate disease diagnosis and treatment plans and bidirectional referral standards to improve the medical quality of secondary hospitals.

The application of sensors to the monitoring of spinal morphology and motion was reviewed in terms of the research object and monitoring index.The present situation of the application of sensors was introduced,such as inertial sensor,stretchable strain sensor and electromagnetic sensor.The deficiencies of sensors applied to the monitoring of spinal morphology and motion were analyzed,and the future directions of the application were pointed out.[Chinese Medical Equipment Journal,2025,46(6):105-110]

Objective To explore the protective effect of resveratrol on myocardial damage caused by sepsis in rats.Methods Forty SD rats were randomly divided into control group,model group,experimental group and combined group,with 10 rats in each group.Except the control group,the other rats were intraperitoneally injected with 20 mg·kg-1 lipopolysaccharide,while the control group was injected with the same amount of normal saline.Two hours before modeling,the experimental group and combinated group were given 50 mg·kg-1 resveratrol by gavage,and the control group and model group were given the same amount of normal saline by gavage.The combined group was injected with 10 nmol of micro RNA-155(miR-155)agomir through the tail vein,and the other group was injected with equal volume of normal saline through the tail vein.Left ventricular function parameters of rats were measured by echocardiography.The level of myocardial injury markers was detected by colorimetry.Quantitative reverse transcription polymerase chain reaction was used to detect the expression of miR-155.The expression of sirtuin 1(SIRT1)and related proteins of nuclear factor κB signaling pathway were detected by Western blot.Results The left ventricular ejection fraction of control group,model group and experimental group were(80.78±12.85)％,(55.92±7.86)％and(71.55±10.71)％,respectively;left ventricular fractional shortening were(34.08±5.75)％,(22.92±2.96)％,(28.72±4.25)％,respectively;left ventricular end disatolic diameter were(3.12±0.46),(6.34±0.69),(4.95±0.57)mm,respectively;the left ventricular end systolic diameter were(5.98±0.65),(7.24±0.80),(6.16±0.78)mm,respectively;the fractional shortening were(38.91±5.38)％,(22.67±3.53)％,(30.74±3.97)％,and the expression levels of creatine kinase-MB were(661.56±85.44),(1181.41±142.14),(915.02±105.19)U·L-1,respectively;the expressions levels of cardiac troponin Ⅰ were(148.17±28.48),(448.17±60.34)and(375.44±49.01)ng·mL-1,respectively.The expression of miR-155 in control group,model group,experimental group and combined group were 1.00±0.12,3.79±0.45,1.87±0.23 and 4.03±0.49,respectively;the protein relative expression levels of nuclear factor κB(NF-κB)were 1.00±0.08,5.04±0.59,2.73±0.35,5.58±0.63,respectively;the protein relative expression levels of inhibitor of NF-κB-β were 1.00±0.11,3.03±0.37,1.35±0.15 and 2.89±0.34,respectively;the protein relative expressions of inhibitor of NF-κB-α were 1.00±0.13,0.86±0.08,1.21±0.18,0.77±0.09,respectively;the protein relative expression levels of SIRT1 were 1.00±0.16,0.66±0.07,0.93±0.14,0.54±0.06,respectively.The above indicators of the model group were compared with the control group,the experimental group were compared with the model group,and the above indicators of the combined group were compared with the experimental group,and the differences were statistically significant(all P＜0.05).Conclusion Resveratrol can alleviate myocardial injury and improve cardiac function in sepsis rats,which may be achieved by down-regulating the expression of miR-155,up-regulating the level of SIRT1,and inhibiting the nuclear factor κB signaling pathway.

Objective This study aimed to explore the correlation between abnormal hand features and coronary atherosclerotic heart disease(CHD)to provide clinical data support for digitalized traditional Chinese medicine(TCM)hand diagnosis.Methods A palm key point prediction algorithm was used to automatically capture and collect detailed features of the palm and nails through image analysis and data mining using the hand diagnosis information collection technology based on the NVIDIA Jetson platform and Qt framework.A total of 438 cardiac patients who underwent coronary artery computed tomography angiography(CACTA)in the Department of Cardiology,Dongzhimen Hospital,Beijing University of Chinese Medicine,from December 2023 to April 2024 were included and divided into CHD(148 cases)and non-CHD groups(290 cases)based on the CACTA results.The hand features of the two groups were compared,and abnormal hand features associated with CHD were screened using random forest analysis as well as univariate and multivariate logistic regression analyses.Results Based on the results of univariate logistic regression and random forest analyses,a set of hand-related features associated with CHD were identified and subsequently included in the multivariate logistic regression analysis.These features included the morphology of the thenar eminence,the wrinkles of the thenar eminence,nail shape,nail texture,and the length of the blood vessel in the middle finger.Multivariate logistic regression analysis revealed that hypertrophic thenar eminence[odds ratio(OR):3.049,95%confidence interval(CI):1.430-6.503,P=0.004],presence of wrinkles on the thenar eminence(OR:2.206,95%CI:1.119-4.348,P=0.022),presence of gray-black vertical stripes on the nails(OR:1.981,95%CI:1.173-3.347,P=0.011),uneven nail surface(OR:3.130,95%CI:1.822-5.378,P<0.001),and inward-bending nail surface(OR:5.727,95%CI:1.812-18.102,P=0.003)were positively associated with CHD.In contrast,the blood vessel in the middle finger longer than 1/3 of the phalanx was negatively associated with CHD(OR:0.405,95%CI:0.234-0.702,P=0.001).Conclusion Certain hand features are significantly associated with CHD,providing the valuable clinical data to support for the digitalization of TCM hand diagnosis.

Objective To develop dynamic prediction models based on multiple postnatal time points to support early diagnosis and individualized intervention for bronchopulmonary dysplasia(BPD)in preterm infants with gestational age<32 weeks.Methods Clinical data of 472 preterm infants with gestational age<32 weeks admitted to the Second Xiangya Hospital of Central South University between January 2016 and November 2020 were retrospectively analyzed.Multivariable logistic regression was applied to develop five independent prediction models at postnatal days 1,7,14,21,and 28.The performance of the models was assessed using the area under the receiver operating characteristic curve(AUC)and the Hosmer-Lemeshow test.Results Baseline characteristics such as gestational age and birth weight differed significantly between the BPD group(n=147)and the non-BPD group(n=325)(P<0.05).Predictors of BPD evolved across time points:on day 1,key predictors included gestational age,birth weight,Score for Neonatal Acute Physiology II(SNAP-II),invasive mechanical ventilation,and fraction of inspired oxygen>30%;by day 7,additional variables emerged,including fasting duration>2 days,mean feeding advancement rate<8.5 mL/(kg·d),neonatal respiratory distress syndrome,apnea of prematurity,and positive sputum culture;from day 14 onward,nutrition-and treatment-related indicators were incorporated additionally.The models demonstrated good discrimination at postnatal days 1,7,14,21,and 28,with AUCs of 0.917,0.927,0.939,0.944,and 0.968,respectively,and good calibration(Hosmer-Lemeshow P>0.05).Internal validation showed AUCs ranging from 0.899 to 0.958,indicating robust performance.Conclusions Dynamic postnatal prediction models incorporating indicators spanning perinatal factors,respiratory support,nutritional management,and therapeutic interventions demonstrate high predictive performance and facilitate dynamic risk assessment for BPD in preterm infants with gestational age<32 weeks.