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.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

OBJECTIVE: To investigate the clinical efficacy of moxibustion (Mox) combined with low-dose tadalafil (TAD) in the treatment of diabetes mellitus-induced erectile dysfunction (DMED) with the syndrome of Qi deficiency and blood stasis. METHODS: According to the inclusion and exclusion criteria, we selected 90 patients with DMED for this trial and equally randomized them into a Mox, a TAD, and a Mox combined with TAD (Mox+TAD) group to be treated by mild Mox applied to the acupoints Zusanli, Sanyinjiao and Yinlingquan qd alt, oral medication with low-dose TAD at 5 mg per dose qd, and combination of the above two therapies, respectively, all for 4 weeks. We obtained from the patients their IIEF-5 scores, traditional Chinese medicine (TCM) symptoms scores, Erectile Hardness Scale (EHS) scores, corpus cavernosal hemodynamic indexes, and the peak systolic velocity (PSV), end diastolic velocity (EDV) and resistance index (RI) of the corpus cavernosal arteries before and after treatment, and compared them among the three groups. RESULTS: The total effectiveness rate was significantly higher in the Mox+TAD (90.0%) than in the Mox (46.7%) and TAD groups (60.0%) (P< 0.05). Compared with the baseline, the IIEF-5 and EHS scores were increased, while the TCM symptoms scores decreased in all the three groups after treatment, more significantly in the Mox+TAD group than in the other two (P< 0.05). And the PSV and RI were remarkably increased, while the EDV decreased (P< 0.05) in all the three groups (P< 0.05) after treatment, with PSV even higher in the Mox+TAD than in the Mox and TAD groups (P< 0.05). CONCLUSION Moxibustion combined with tadalafil has a definite efficacy and safety for the treatment of DMED, which can effectively improve the erectile function of the patients by increasing penile blood supply, benefiting qi and activating blood circulation.
Humans ; Male ; Tadalafil ; Erectile Dysfunction/etiology* ; Moxibustion ; Middle Aged ; Prospective Studies ; Adult ; Carbolines/administration & dosage* ; Diabetes Complications/therapy* ; Aged ; Treatment Outcome ; Combined Modality Therapy

OBJECTIVE: To examine the effectiveness of Chinese medicine (CM) Lianhua Qingwen Granule (LHQW) and Jingyin Gubiao Prescription (JYGB) in asymptomatic or mild patients with Omicron infection in the shelter hospital. METHODS: This single-center retrospective cohort study was conducted in the largest shelter hospital in Shanghai, China, from April 10, 2022 to May 30, 2022. A total of 56,244 asymptomatic and mild Omicron cases were included and divided into 4 groups, i.e., non-administration group (23,702 cases), LHQW group (11,576 cases), JYGB group (12,112 cases), and dual combination of LHQW and JYGB group (8,854 cases). The length of stay (LOS) in the hospital was used to assess the effectiveness of LHQW and JYGB treatment on Omicron infection. RESULTS: Patients aged 41-60 years, with nadir threshold cycle (C_T) value of N gene <25, or those fully vaccinated preferred to receive CM therapy. Before or after propensity score matching (PSM), the multiple linear regression showed that LHQW and JYGB treatment were independent influence factors of LOS (both P<0.001). After PSM, there were significant differences in LOS between the LHQW/JYGB combination and the other groups (P<0.01). The results of factorial design ANOVA proved that the LHQW/JYGB combination therapy synergistically shortened LOS (P=0.032). CONCLUSIONS Patients with a nadir C_T value <25 were more likely to accept CM. The LHQW/JYGB combination therapy could shorten the LOS of Omicron-infected individuals in an isolated environment.
Humans ; Drugs, Chinese Herbal/therapeutic use* ; Male ; Female ; Middle Aged ; Adult ; China/epidemiology* ; Hospitalization ; COVID-19 Drug Treatment ; COVID-19/epidemiology* ; SARS-CoV-2 ; Retrospective Studies ; Treatment Outcome ; Length of Stay ; Young Adult ; Aged

OBJECTIVE: To investigate the anti-tumor effects of cinobufacini (CINO) on hepatocellular carcinoma (HCC) induced by des-gamma-carboxy-prothrombin (DCP) and to uncover the underlying mechanisms. METHODS: The inhibitory effect of CINO on HCC cell proliferation was evaluated using the cell counting kit-8 method, and the apoptosis rate was quantified using flow cytometry. Immunofluorescence and Western blot analyses were used to investigate the differential expression of proteins associated with cell growth, apoptosis, migration, and invasion pathways after CINO treatment. The therapeutic potential of CINO for HCC was confirmed, and the possibility of combining cinobufacini with c-Met inhibitor for the treatment of primary HCC was further validated by in vivo experiments. RESULTS: Under the induction of DCP, CINO inhibited the activity of HCC cells, induced apoptosis, and inhibited migration and invasion. Upon the induction of DCP, CINO regulated c-Met activation and the activation of the phosphatidylinositol-3 kinase/protein kinase B (PI3K/AKT) and mitogen-activated protein kinase kinase/extracellular signal-regulated kinase (MEK/ERK) pathways. In a mouse model of HCC, CINO exhibited significant antitumor effects by inhibiting the phosphorylation of c-Met and the downstream PI3K/AKT and MEK/ERK pathways in tumor tissues. CONCLUSIONS CINO inhibited HCC cell growth, promoted apoptosis, and suppressed HCC cell invasion and migration by targeting c-Met and PI3K/AKT and MEK/ERK signaling pathways under DCP induction.
Carcinoma, Hepatocellular/drug therapy* ; Proto-Oncogene Proteins c-met/metabolism* ; Liver Neoplasms/drug therapy* ; Signal Transduction/drug effects* ; Animals ; Humans ; Cell Movement/drug effects* ; Apoptosis/drug effects* ; Cell Proliferation/drug effects* ; Amphibian Venoms/therapeutic use* ; Cell Line, Tumor ; Neoplasm Metastasis ; Cell Survival/drug effects* ; Proto-Oncogene Proteins c-akt/metabolism* ; Phosphatidylinositol 3-Kinases/metabolism* ; Neoplasm Invasiveness ; Mice, Inbred BALB C ; Mice, Nude ; Mice ; Male ; Bufanolides/therapeutic use* ; Protein Precursors ; Prothrombin ; Biomarkers

OBJECTIVES: To investigate the impact of hepatitis B virus (HBV) infection on oral microbiota and metabolites in patients with metabolic dysfunction-associated fatty liver disease (MAFLD) and the underlying mechanisms. METHODS: This prospective study was conducted in 47 MAFLD patients complicated with chronic hepatitis B (CHB) and 48 MAFLD patients without CHB enrolled from November, 2023 to January, 2024. Fasting tongue coating samples were collected from the patients for analyzing microbial community structures and metabolites using high-throughput 16S rDNA sequencing and non-targeted metabolomics techniques, and their associations with clinical indicators and biological pathways were explored using correlation analysis and functional annotation. RESULTS: The levels of fasting blood glucose, total cholesterol (TC), gamma-glutamyl transferase (GGT), and severity of fatty liver were all significantly lower in MAFLD+CHB group than in MAFLD group. Microbiota analysis showed that the abundances of Patescibacteria (at the phylum level), Hydrogenophaga, and Absconditabacteriales (at the genus level) were significantly increased, while the abundance of Megasphaera was decreased in MAFLD+CHB group. The differential microbiota were significantly correlated with TC, GGT and low-density lipoprotein (r=-0.68‒0.75). Metabolomics analysis revealed that 469 metabolites (including lipids and amino acids) were upregulated and 2306 (including organic oxygen-containing compounds and phenylpropanoids) were downregulated in MAFLD+CHB group, for which KEGG enrichment analysis suggested abnormal activation of the linoleic acid metabolism and glycerophospholipid metabolism pathways. Correlation analysis between microbiota and metabolites indicated that Patescibacteria and Megasphaera, which were positively correlated with lipid metabolites and negatively with fatty acid metabolites, respectively, jointly affected glycolipid metabolism and oxidative stress pathways. CONCLUSIONS Compared to patients with MAFLD alone, MAFLD patients with concurrent chronic HBV infection showed lower levels in some lipid metabolism indicators and the degree of hepatic steatosis, accompanied by alterations in oral microbiota structure and metabolic profiles. The precise mechanisms involved require further investigation to be fully elucidated.
Humans ; Lipid Metabolism ; Prospective Studies ; Microbiota ; Hepatitis B, Chronic/microbiology* ; Male ; Female ; Adult ; Fatty Liver/microbiology* ; Middle Aged ; Mouth/microbiology* ; Metabolomics

Objective:To explore the value of serum P-selectin,Interleukin-18(IL-18),and Homocysteine(Hcy)levels in predicting the adverse outcomes of interventional therapy in elderly patients with deep vein thrombosis(DVT)in the lower extremities.Methods:A total of 165 elderly patients with DVT from January 2020 to August 2023 were se-lected.All patients underwent transcatheter intervention thrombolysis therapy.Patients were divided into good outcome group and poor outcome group according to the clinical treatment outcome.Serum P-selectin,IL-18,and Hcy levels were compared between the two groups.The correlation between serum P-selectin,IL-18,and Hcy levels and disease indicators,as well as the outcomes of interventional therapy was analyzed.The predictive value of serum P-selectin,IL-18,and Hcy levels for the outcomes of interventional therapy was evaluated.Results:The total effective rate of catheter-based interventional thrombolysis in 165 patients was 81.82%(135/165).The patients with good outcomes were included in the good outcome group,and the patients with poor outcomes were included in the poor outcome group.The difference in circumference between the affected side and the healthy side of the thigh and the difference in circumference between the affected side and the healthy side of the calf in the poor outcome group were greater than those in the good outcome group(P<0.05).The levels of serum P-selectin,IL-18,and Hcy in the poor outcome group were higher than those in the good outcome group(P<0.05).The levels of serum P-selectin,IL-18,and Hcy were posi-tively correlated with the difference in circumference between the affected and healthy thighs and the difference in cir-cumference between the affected and healthy calves,and negatively correlated with the outcome of interventional therapy(P<0.05).Before and after correcting other factors,serum P-selectin,IL-18,and Hcy were all independent risk factors for the outcome of interventional therapy in elderly patients with DVT(P<0.05).The area under the curve(AUC)of serum P-selectin,IL-18,and Hcy for predicting the outcome of interventional therapy for elderly DVT was 0.789(95%CI:0.718-0.848),0.812(95%CI:0.744-0.868),and 0.792(95%CI:0.722-0.851),respectively.The cutoff values were 44.21 ng/mL,185.73 ng/L,and 23.60 μmol/L,with sensitivities of 70.00%,70.00%,and 80.00%,and specificities of 82.22%,82.96%,and 68.15%,respectively.The AUC of the combined prediction of the outcomes of interventional therapy for elderly DVT was 0.935(95%CI:0.886-0.967),with a cutoff value of 44.08 ng/mL,185.16 ng/L,and 23.37 μmol/L for the three variables,with a sensitivity of 86.67%and a specificity of 88.15%.This was significantly better than the individual prediction values of the three variables(Z=5.817,4.753,5.206,all P<0.001).Conclusion:Serum P-selectin,IL-18,and Hcy are significantly correlated with disease indicators and interventional treatment outcomes in el-derly patients with DVT,and can effectively predict the outcome of interventional treatment,with a high combined pre-dictive value.

Objective:To investigate the value of biparametric-MRI (bpMRI) based peritumoral radiomics for preoperative prediction of extraprostatic extension (EPE) in prostate cancer (PCa).Methods:In this cross-sectional study, consecutive bpMRI of patients undergoing prostatectomy for PCa were retrospectively collected from the First Medical Center (center 1) and the Third Medical Center (center 2) of Chinese PLA General Hospital. A total of 274 patients were finally enrolled. Patients at center 1 from January 2020 to December 2022 were randomly divided into a training set (149 cases) and an internal validation set (63 cases) by stratified random sampling. Patients at center 2 from January 2023 to March 2024 were assigned to the external test set (62 cases). Patients were categorized into EPE-positive group and EPE-negative group according to pathological assessment postoperatively. In the training set, there were 49 cases in EPE-positive group and 100 cases in EPE-negative group. In the internal validation set, there were 26 cases in EPE-positive group and 37 cases in EPE-negative group. In the external test set, there were 22 cases in EPE-positive group and 40 cases in EPE-negative group. Axial T 2WI and apparent diffusion coefficient (ADC) images were manually annotated to obtain index lesion regions of interest (ROIs), with the peritumoral ROIs subsequently delineated by semi-automatic segmentation technique. Radiomics features were extracted from intra-tumoral, peri-tumoral, and intra-tumoral plus peri-tumoral ROIs. The training set data was employed to select and optimize features to build the radiomics models. The logistic regression analysis was used to develop radiomics, clinical, and integrated models. The predictive performance was assessed by the area under the receiver operating characteristic curve (AUC) in the external test set, and compared by the DeLong test. The sensitivity and specificity were compared by the exact McNemar test. Results:In the external test set, the peri-tumoral radiomics model based on bpMRI showed the highest performance in evaluating EPE, with an AUC of 0.739 (95% CI 0.611-0.842), which was identified as the optimal radiomics model. EPE grade ( OR=6.151, 95% CI 3.371-11.226, P<0.001) was incorporated into the clinical model, with an AUC of 0.780 (95% CI 0.657-0.875) in the external test set. The integrated model had an AUC of 0.817 (95% CI 0.698-0.904) in the external test set. There was no statistically significant difference in comparisons of AUCs among the three models (all P>0.05). The sensitivity of the integrated model (68.2%) showed no significant difference from those of the clinical model and the optimal radiomics model (77.3% and 86.4%, respectively; P=0.500 and P=0.289). However, the specificity of the integrated model (85.0%) was significantly higher than those of the clinical model (67.5%, P=0.016) and the optimal radiomics model (50.0%, P<0.001). Conclusion:A bpMRI-based peritumoral radiomics integrating clinical model demonstrates high performance for preoperative prediction of EPE in PCa.

Objective To observe the value of machine learning(ML)models based on ultrasound radiomics for preoperatively distinguishing atypical parathyroid tumor(APT)/parathyroid carcinoma(PC)and parathyroid adenoma(PA).Methods Totally 330 primary hyperparathyroidism patients who underwent surgical treatments were retrospectively enrolled and categorized into APT/PC group(n=78)and PA group(n=252)according to surgical pathology and clinical follow-up results,also divided into training set(n=231)and test set(n=99)at the ratio of 7∶3.Based on preoperative ultrasound,545 radiomics features were extracted,and recursive feature elimination(RFE),Kruskal-Wallis or analysis of variance methods were used to screen the features,respectively.Support vector machine(SVM),linear discriminant analysis(LDA),least absolute shrinkage and selection operator logistic regression(LRLASSO),also random forest(RF)and decision tree(DT)algorithms were adopted to construct ML models for differentiating APT/PC and PA,respectively.Then the models were trained in training set,their performance were verified in test set,and a 5-fold cross-validation was adopted to screen out the better combinations.Results Compared with Kruskal-Wallis and analysis of variance methods,the distinguishing efficacy of SVM,LDA,LRLASSO,RF and DT models constructed based on features screened out using RFE method in training set(area under the curve[AUC]=0.870,0.878,0.850,0.847,1.000)and test set(AUC=0.856,0.842,0.827,0.847 and 0.704)were all relatively higher.In test set,the AUC of SVM,LDA,LRLASSO and RF models constructed based on the features screened out using RFE method(included 25,23,17 and 23 features)were all higher than that of DT model(8 features)(all P＜0.001).No significant difference of AUC was found between SVM,LRLASSO or RF models and LDA model(all P＞0.05).The AUC of SVM and RF models were higher than that of LRLASSO model(both P＜0.05),while of SVM and RF models were not significantly different(P＞0.05),indicating that SVM,LDA and RF models were better ones.Conclusion SVM,LDA,LRLASSO,RF and DT models based on ultrasound radiomics could effectively distinguish APT/PC and PA preoperatively,among which SVM,LDA and RF models had better diagnostic efficacy.