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 This study reports on the obligatory parasitism of water mites Arrenurus sp. on Anopheles messeae at the Manzhouli Port, Inner Mongolia, China. Methods Duing July 2024, a survey on the mosquito diversity was conducted at the Manzhouli Port. Captured mosquitoes and their ectoparasites were identified to species level. Results A total of 1840 adult mosquitoes were collected, representing species from three genera: Culex(Cx. modestus, Cx. pipiens pallens), Aedes(Ae. dorsalis, Ae. flavidorsalis, Ae. flavescens), and Anopheles (An. messeae). Among all the mosqutioes specimens,3 out of 150 captured An. messeae were found to carry ectoparasitic mites, with number of 2,4,27 mites separately. Morphological and molecular identification reached the same result as water mites(Hydrachnidiae, Hydracrina). COI gene sequence showed 94% similarity with the closest species Arrenurus truncatellus. Conlusions Literature review suggests water mites are host-specific parasitism of mosquito species and herein with the first record of Arrenurus sp. parasiting on An. Messeae in the most high-latitude region globally.

Immobilized enzyme-based enzyme electrode biosensors, characterized by high sensitivity and efficiency, strong specificity, and compact size, demonstrate broad application prospects in life science research, disease diagnosis and monitoring, etc. Immobilization of enzyme is a critical step in determining the performance (stability, sensitivity, and reproducibility) of the biosensors. Random immobilization (physical adsorption, covalent cross-linking, etc.) can easily bring about problems, such as decreased enzyme activity and relatively unstable immobilization. Whereas, directional immobilization utilizing amino acid residue mutation, affinity peptide fusion, or nucleotide-specific binding to restrict the orientation of the enzymes provides new possibilities to solve the problems caused by random immobilization. In this paper, the principles, advantages and disadvantages and the application progress of enzyme electrode biosensors of different directional immobilization strategies for enzyme molecular sensing elements by specific amino acids (lysine, histidine, cysteine, unnatural amino acid) with functional groups introduced based on site-specific mutation, affinity peptides (gold binding peptides, carbon binding peptides, carbohydrate binding domains) fused through genetic engineering, and specific binding between nucleotides and target enzymes (proteins) were reviewed, and the application fields, advantages and limitations of various immobilized enzyme interface characterization techniques were discussed, hoping to provide theoretical and technical guidance for the creation of high-performance enzyme sensing elements and the manufacture of enzyme electrode sensors.

ObjectiveTo evaluate the levels of physical exercise, mental health and social response in children with autism spectrum disorder (ASD), and explore the mediating effect of social response on physical exercise and mental health. MethodsFrom September, 2019 to April, 2024, 211 children with ASD from three special education schools in Haidian District and Shijingshan District of Beijing were selected. They were assessed with general data questionnaire, Physical Activity Rating Scale (PARS-3), Chinese version of Psycho-Educational Profile (C-PEP) and Social Response Scale-Short Form (SRS-SF). The correlation among physical exercise, mental health and social response was analyzed. The mediating effect of social response on physical exercise and mental health was explored. ResultsThe average physical exercise level was (58.72±3.34), the average mental health level was (14.85±1.67), and the average social response level was (24.98±3.79). Physical exercise was positively correlated with mental health (r = 0.546, P < 0.05) and negatively correlated with social response (r = -0.298, P < 0.05). Mental health was negatively correlated with social response (r = -0.397, P < 0.05). Average monthly family income, parental relationship, repeated transcranial magnetic stimulation therapy, physical exercise, social response were the influencing factors of mental health (P < 0.05). Social response was intermediary between physical exercise and mental health, accounting for 14.56%. ConclusionThe mental health level of children with ASD is poor, and there are many influencing factors. Physical exercise can directly affect the mental health of children with ASD, and can also play an indirect role through social response.

Objective:This study aimed to validated the diagnostic accuracy of Global Leadership Initiative on Malnutrition(GLIM)criteria for malnutrition in pancreatic cancer patients undergoing pancreaticoduodenectomy and to evaluated its prognostic value for postoperative outcome.Methods:A retrospective analysis was conducted on 230 consecutive pancreatic cancer patients who underwent pancreaticoduodenectomy at the Department of Pancreatobiliary Surgery,Sun Yat-sen University Cancer Center,between January 2018 to January 2024.Patients were stratified into malnutrition group and non-malnutrition group using Nutritional Risk Screening 2002(NRS 2002)and GLIM criteria.Multivariable logistic regression identified independent risk factors for postoperative morbidity.Results:GLIM criteria identified malnutrition in 96 patients(41.7％).Compared with the non-malnourished group,the number of preoperative nutritional support(t=20.038,P<0.001),the number of preoperative enteral nutrition support(t=8.377,P=0.004),the number of preoperative parenteral nutrition support(t=22.302,P<0.001),the number of anemia(t=8.037,P=0.005)and preoperative parenteral nutrition use days(t=-2.898,P=0.009),the difference was statistically significant.There were statistically significant differences in C-reactive protein(t=10.944,P=0.008),NLR(t=-2.523,P=0.012)and PNI(t=-2.397,P=0.017)between the two groups before surgery.Preoperative BMI(t=-4.410,P<0.001)was significantly lower in the malnourished group.The number of postoperative parenteral nutrition days(Z=-2.283,P=0.022)and amino acid supplementation during postoperative hospitalization were significantly higher in the malnourished group(Z=-2.309,P=0.021).The incidence of malnutrition was higher in patients with Clavien-Dindo grade≥Ⅲ(P=0.030)and intra-abdominal infections(P=0.049).Multivariable analysis identified preoperative weight loss(OR=2.154,95％CI:1.158～4.005;P=0.015)and BMI reduction(OR=0.175,95％CI:0.040～0.775;P=0.022)as independent predictors of postoperative complications.Conclusions:The GLIM standard effectively characterize malnutrition status in pancreatic cancer patients after pancreaticoduodenectomy patients and demonstrate superior predictive performance for postoperative morbidity.It has good predictive performance and clinical application value.

Gene regulation relies on the precise binding of transcription factors (TFs) at regulatory elements, but simultaneously detecting hundreds of TFs on chromatin is challenging. We developed cFOOT-seq, a cytosine deaminase-based TF footprinting assay, for high-resolution, quantitative genome-wide assessment of TF binding in both open and closed chromatin regions, even with small cell numbers. By utilizing the dsDNA deaminase SsdAtox, cFOOT-seq converts accessible cytosines to uracil while preserving genomic integrity, making it compatible with techniques like ATAC-seq for sensitive and cost-effective detection of TF occupancy at the single-molecule and single-cell level. Our approach enables the delineation of TF footprints, quantification of occupancy, and examination of chromatin influences on TF binding. Notably, cFOOT-seq, combined with FootTrack analysis, enables de novo prediction of TF binding sites and tracking of TF occupancy dynamics. We demonstrate its application in capturing cell type-specific TFs, analyzing TF dynamics during reprogramming, and revealing TF dependencies on chromatin remodelers. Overall, cFOOT-seq represents a robust approach for investigating the genome-wide dynamics of TF occupancy and elucidating the cis-regulatory architecture underlying gene regulation.
Transcription Factors/genetics* ; Humans ; Chromatin/genetics* ; Animals ; Binding Sites ; Mice ; DNA Footprinting/methods*

Lung cancer is the most common malignant tumor worldwide, ranking first in both incidence and mortality rates. According to the latest statistics from the International Agency for Research on Cancer (IARC), approximately 2.5 million new cases and around 1.8 million deaths from lung cancer occurred in 2022, placing a tremendous burden on global healthcare systems. The high mortality rate of lung cancer is closely linked to its subtle early symptoms, which often lead to diagnosis at advanced stages. This not only complicates treatment but also results in substantial economic losses. Current treatment options for lung cancer include surgery, radiotherapy, chemotherapy, targeted drug therapy, and immunotherapy. Among these, immunotherapy has emerged as the most groundbreaking advancement in recent years, owing to its unique antitumor mechanisms and impressive clinical benefits. Unlike traditional therapies such as radiotherapy and chemotherapy, immunotherapy activates or enhances the patient’s immune system to recognize and eliminate tumor cells. It offers advantages such as more durable therapeutic effects and relatively fewer toxic side effects. The main approaches to lung cancer immunotherapy include immune checkpoint inhibitors, tumor-specific antigen-targeted therapies, adoptive cell therapies, cancer vaccines, and oncolytic virus therapies. Among these, immune checkpoint inhibitors and tumor-specific antigen-targeted therapies have received approval from the U.S. Food and Drug Administration (FDA) for clinical use in lung cancer, significantly improving outcomes for patients with advanced non-small cell lung cancer. Although other immunotherapy strategies are still in clinical trials, they show great potential in improving treatment precision and efficacy. This article systematically reviews the latest research progress in lung cancer immunotherapy, including the development of novel immune checkpoint molecules, optimization of treatment strategies, identification of predictive biomarkers, and findings from recent clinical trials. It also discusses the current challenges in the field and outlines future directions, such as the development of next-generation immunotherapeutic agents, exploration of more effective combination regimens, and the establishment of precise efficacy prediction systems. The aim is to provide a valuable reference for the continued advancement of lung cancer immunotherapy.

Objective:To evaluate the value of spectral CT quantitative parameters in predicting microvascular invasion (MVI) of hepatocellular carcinoma (HCC).Methods:A total of 100 HCC patients who underwent surgical resection and were pathologically diagnosed in the Affiliated Cancer Hospital of Xiangya Medical College of Central South University from January 2020 to January 2023 were retrospectively enrolled. According to pathological grading, the patients were divided into the microvascular invasion group (invasion group, n=60) and the non-vascular invasion group (non-invasion group, n=40). Serological indicators and spectral CT quantitative parameters were compared between the two groups. Receiver operating characteristic (ROC) curve was used to analyze the value of spectral CT quantitative parameters in predicting MVI of HCC. Results:The serum alpha-fetoprotein (AFP) level in the invasion group was higher than that in the non-invasion group, with a statistically significant difference ( P<0.05). There were no statistically significant differences in serum carcinoembryonic antigen (CEA) and carbohydrate antigen 199 (CA-199) levels between the two groups (all P>0.05). In the invasion group, arterial phase iodine concentration, arterial phase normalized iodine concentration, venous phase iodine uptake reduction rate, arterial phase effective atomic number, and energy spectrum curve slope were all higher than those in the non-invasion group, with statistically significant differences (all P<0.05); there were no statistically significant differences in venous phase iodine concentration, venous phase normalized iodine concentration, and venous phase effective atomic number between the two groups (all P>0.05). The rates of peritumoral enhancement in the arterial phase and irregular tumor margin in the invasion group were higher than those in the non-invasion group, with statistically significant differences (all P<0.05); there was no statistically significant difference in tumor capsule between the two groups ( P>0.05). ROC curve analysis showed that the areas under the curve (AUC) of arterial phase iodine concentration, arterial phase normalized iodine concentration, venous phase iodine uptake reduction rate, arterial phase effective atomic number, and energy spectrum curve slope for predicting MVI in HCC were 0.812, 0.885, 0.726, 0.823, and 0.788, respectively. Conclusions:Spectral CT quantitative parameters are helpful to improve the preoperative diagnostic efficiency of MVI in HCC and can effectively predict MVI in HCC. Especially, arterial phase normalized iodine concentration has high application value in judging whether there is MVI in HCC.

Objective:To investigate the impact of deep learning image reconstruction (DLIR), adaptive statistical iterative reconstruction-veo (ASiR-V) and filtered back projection (FBP) on the histogram and wavelet features of portal venous phase abdominal CT in patients with hepatic tumor.Methods:The CT data of 68 patients with hepatic tumor who underwent enhanced CT scans were retrospectively collected. FBP, 30%ASiR-V, DLIR-L, DLIR-M and DLIR-H images were reconstructed. The images of portal venous phase were reconstructed with five algorithms, including FBP, ASIR-V at a level of 30% (ASiR-V 30%), DLIR at low (DLIR-L), medium (DLIR-M), and high (DLIR-H). Histogram and wavelet features were extracted from hepatic lesion, liver, spleen, kidney and erector spinae muscle, and compared using one-way analysis of variance or Kruskal-Wallis test. Two radiologists delineated the three-dimensional lesions independently and one of them repeated the delineation after one month. Intra-class correlation coefficients ( ICC) among five sets of images were calculated to evaluate the consistency of radiomics features of hepatic lesion. P<0.05 was considered to indicate statistical significance. Results:Most histogram and wavelet features extracted from hepatic lesion, liver, spleen, kidney and erector spinae muscle showed significant differences among five groups (all P<0.05). The number of features without significant differences decreased with the intensity of DLIR reconstruction increased. For histogram features, there were no significant differences of energy, mean, median, and total energy among five sets of images ( P>0.05). For wavelet features, there were no significant differences of mean and median among five sets of images ( P>0.05). The consistency of all histogram features was high except for the mean value of wavelet feature. The intra-and inter-observer ICC ranged from 0.756 to 1 and 0.767 to 1, respectively. Conclusion:Both 30%ASiR-V and DLIR at three levels algorithms had influence on the histogram and wavelet features of abdominal organs and hepatic tumors extracted from CT images in portal venous phase, and the effects expanded with the strengthening of levels. Median can be a reliable quantitative parameter for CT texture analysis of hepatic tumor.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.