The initiation of adaptive immune responses relies on the precise recognition and interpretation of antigenic information. In this process, the specific binding of T cell receptors (TCRs) to peptide-major histocompatibility complex (pMHC) molecules represents one of the key molecular events in the initiation of adaptive immune responses. Accordingly, the structural features of TCR-pMHC complexes provide a fundamental basis for dissecting antigen recognition mechanisms and support rational vaccine design, therapeutic target discovery in TCR-based immunotherapy, and TCR identification and optimization. However, experimental determination of TCR-pMHC structures remains costly, time-consuming, and limited in coverage, making computational approaches essential for rapidly obtaining reliable structural information. Computational methods for predicting the structures of TCR-pMHC complexes have advanced rapidly in recent years, driven by progress in deep learning-based modeling frameworks and the increasing availability of structural and sequence resources. Despite these developments, most existing tools do not adequately distinguish the key structural and biophysical differences between MHC class I (MHC-I) and MHC class II (MHC-II) complexes during model construction. As a consequence, their predictive performance differs substantially between class I and class II complexes. In general, structural predictions for class I complexes outperform those for class II complexes. This discrepancy may be related to several fundamental differences between the two systems, including the architecture of the peptide-binding groove, the distribution of peptide lengths, and the properties of peptide flanking residues (PFRs). Compared with MHC-I molecules, MHC-II molecules usually bind longer antigenic peptides, which typically range from 13 to 25 amino acids in length. PFRs at both termini of these peptides participate in regulating the overall conformation of TCR-pMHC class II complexes and exert a pronounced effect on the geometric and physicochemical characteristics of the TCR-pMHC binding interface. Furthermore, within the TCR recognition interface, the complementarity-determining regions (CDRs) consist of segments that differ markedly in conformational behavior. They commonly include regions that are relatively rigid and structurally stable, together with highly flexible segments exhibiting substantial conformational plasticity. These rigidity-flexibility features constitute an essential structural basis enabling TCRs to recognize diverse peptide-MHC ligands and to accommodate conformational heterogeneity at the interface. However, many current modeling tools, in an effort to enforce global conformational stability or reduce structural noise, tend to over-constrain intrinsically flexible regions. Such oversimplification may lead to inappropriate rigidification of flexible CDR loops, resulting in local structural distortions, compromised interface geometry, or even complete modeling failure for specific complexes. Against this background, the review approaches the field from the perspective of computational differences between MHC-I and MHC-II complexes. We first systematically organize and summarize available resources related to TCRs and pMHCs, including structural datasets, sequence databases, prediction tools, and benchmarking studies. We then focus on five representative tools capable of predicting both class I and class II complexes—AlphaFold2, AlphaFold3, TCRmodel2, tFold-TCR, and TCR-pHLA_ModellerS. After excluding structures present in the training sets of these tools, we constructed a benchmark dataset comprising 25 class I and 10 class II TCR-pMHC complexes in the bound state and conducted a systematic evaluation using this dataset. We first employ widely used general evaluation metrics, including All-Atom Root Mean Square Deviation (All-Atom RMSD), Backbone RMSD, Template Modeling score (TM-score), and DockQ, to assess the global conformational accuracy and interface modeling quality of class I and class II complexes. For class II complexes, we propose for the first time a peptide flanking residue deviation index, including the PFRs-Deviation Index (PFRs-DI), N-PFR-Deviation Index (N-PFR-DI), and C-PFR-Deviation Index (C-PFR-DI), to quantitatively characterize conformational deviations in PFRs. In addition, we propose the CDR conformational consistency index (CCC) designed to qualitatively evaluate the ability of prediction tools to capture TCR CDR conformational flexibility. These metrics collectively assess a tool’s ability to model both overall conformation and critical functional regions, thereby addressing the limitations of existing evaluation criteria that overemphasize global structure while inadequately capturing modeling quality in key functional areas. This establishes a unified analytical framework for MHC-I and MHC-II complexes to guide data resource selection, modeling strategy formulation, and evaluation system development. The framework further advances computational modeling and provides crucial support for multi-scale analysis of TCR-pMHC recognition mechanisms and their biological functions.

The initiation of adaptive immune responses relies on the precise recognition and interpretation of antigenic information. In this process, the specific binding of T cell receptors (TCRs) to peptide-major histocompatibility complex (pMHC) molecules represents one of the key molecular events in the initiation of adaptive immune responses. Accordingly, the structural features of TCR-pMHC complexes provide a fundamental basis for dissecting antigen recognition mechanisms and support rational vaccine design, therapeutic target discovery in TCR-based immunotherapy, and TCR identification and optimization. However, experimental determination of TCR-pMHC structures remains costly, time-consuming, and limited in coverage, making computational approaches essential for rapidly obtaining reliable structural information. Computational methods for predicting the structures of TCR-pMHC complexes have advanced rapidly in recent years, driven by progress in deep learning-based modeling frameworks and the increasing availability of structural and sequence resources. Despite these developments, most existing tools do not adequately distinguish the key structural and biophysical differences between MHC class I (MHC-I) and MHC class II (MHC-II) complexes during model construction. As a consequence, their predictive performance differs substantially between class I and class II complexes. In general, structural predictions for class I complexes outperform those for class II complexes. This discrepancy may be related to several fundamental differences between the two systems, including the architecture of the peptide-binding groove, the distribution of peptide lengths, and the properties of peptide flanking residues (PFRs). Compared with MHC-I molecules, MHC-II molecules usually bind longer antigenic peptides, which typically range from 13 to 25 amino acids in length. PFRs at both termini of these peptides participate in regulating the overall conformation of TCR-pMHC class II complexes and exert a pronounced effect on the geometric and physicochemical characteristics of the TCR-pMHC binding interface. Furthermore, within the TCR recognition interface, the complementarity-determining regions (CDRs) consist of segments that differ markedly in conformational behavior. They commonly include regions that are relatively rigid and structurally stable, together with highly flexible segments exhibiting substantial conformational plasticity. These rigidity-flexibility features constitute an essential structural basis enabling TCRs to recognize diverse peptide-MHC ligands and to accommodate conformational heterogeneity at the interface. However, many current modeling tools, in an effort to enforce global conformational stability or reduce structural noise, tend to over-constrain intrinsically flexible regions. Such oversimplification may lead to inappropriate rigidification of flexible CDR loops, resulting in local structural distortions, compromised interface geometry, or even complete modeling failure for specific complexes. Against this background, the review approaches the field from the perspective of computational differences between MHC-I and MHC-II complexes. We first systematically organize and summarize available resources related to TCRs and pMHCs, including structural datasets, sequence databases, prediction tools, and benchmarking studies. We then focus on five representative tools capable of predicting both class I and class II complexes—AlphaFold2, AlphaFold3, TCRmodel2, tFold-TCR, and TCR-pHLA_ModellerS. After excluding structures present in the training sets of these tools, we constructed a benchmark dataset comprising 25 class I and 10 class II TCR-pMHC complexes in the bound state and conducted a systematic evaluation using this dataset. We first employ widely used general evaluation metrics, including All-Atom Root Mean Square Deviation (All-Atom RMSD), Backbone RMSD, Template Modeling score (TM-score), and DockQ, to assess the global conformational accuracy and interface modeling quality of class I and class II complexes. For class II complexes, we propose for the first time a peptide flanking residue deviation index, including the PFRs-Deviation Index (PFRs-DI), N-PFR-Deviation Index (N-PFR-DI), and C-PFR-Deviation Index (C-PFR-DI), to quantitatively characterize conformational deviations in PFRs. In addition, we propose the CDR conformational consistency index (CCC) designed to qualitatively evaluate the ability of prediction tools to capture TCR CDR conformational flexibility. These metrics collectively assess a tool’s ability to model both overall conformation and critical functional regions, thereby addressing the limitations of existing evaluation criteria that overemphasize global structure while inadequately capturing modeling quality in key functional areas. This establishes a unified analytical framework for MHC-I and MHC-II complexes to guide data resource selection, modeling strategy formulation, and evaluation system development. The framework further advances computational modeling and provides crucial support for multi-scale analysis of TCR-pMHC recognition mechanisms and their biological functions.

Body weight abnormalities, including overweight, obesity, and underweight, have become a dual public health challenge in Chinese adults: overweight and obesity lead to a variety of chronic complications, while underweight increases the risks of malnutrition, sarcopenia, and organ dysfunction. To systematically address these issues, multidisciplinary experts in endocrinology, sports science, nutrition, and psychiatry from various regions have held multiple weight management seminars. Based on the latest epidemiological data and clinical evidence, they expanded the guideline to include assessment and intervention strategies for underweight, in addition to the core content of obesity management. This guideline outlines the etiological mechanisms, evaluation methods, and multidimensional management strategies for overweight and obesity, covering key areas such as diagnosis and assessment, medical nutrition therapy, exercise prescription, pharmacological intervention, and psychological support. It is intended to provide a scientific and standardized approach to weight management across the adult population, aiming to curb the rising prevalence of obesity, mitigate complications associated with abnormal body weight, and improve nutritional status and overall quality of life.

OBJECTIVE To analyze the status of targeted monitoring of the health care-associated infections in in-tensive care unit(ICU)of a three-A hospital of northwest China in recent 7 years so as to provide bases for formu-lating effective prevention and control measures for the health care-associated infections.METHODS The related data were successively collected from the ICU hospitalized patients of the Second Affiliated Hospital of Air Force Medical University who were under the targeted monitoring by nosocomial infection real-time monitoring system from Jan.2017 to Dec.2023.The data included the incidence of infections,infection sites,use of catheters,inci-dence of catheter-related infections,and distribution and prevalence trend of pathogens.RESULTS A total of 49,137 hospitalized patients from five ICU wards of respiratory medicine department,neurosurgery department,neurology department,thoracic surgery department and critical care medicine department were under the monito-ring,1446(1681 case-times)of whom had health care-associated infections,with the infection rate 2.94％,the case-time infection rate 3.42％.The respiratory system was the major infection site of the patients with the health care-associated infections.Among the patients with instrument-associated infections,20.70％had ventilator-asso-ciated pneumonia(VAP),5.71％had urinary catheter-associated urinary tract infection(CAUTI),and 1.96％had catheter-related bloodstream infection(CRBSI).Totally 405 strains of non-repetitive pathogens were isolated from 477 patients with instruments-associated infections,78.77％of which were gram-negative bacteria.The iso-lation rate of multidrug-resistant organisms(MDROs)was 44.12％,and Acinetobacter baumannii was the pre-dominant species of pathogen isolated from the patients with VAP.CONCLUSIONS The targeted monitoring of health care-associated infections may facilitate the awareness of dynamic changes of the infections in a accurate and timely manner so as to provide bases for developing effective prevention and control measures for the health care-associated infection.It is necessary to strengthen the prevention and control of the infections in critical care medi-cine department,neurosurgery department as well as the lower respiratory tract infections and prevent the MDROs infection in the meantime.

Near infrared spectroscopy(NIRS)analysis technology has become an important process analysis tool in industrial and agricultural production,and has been widely used for qualitative and quantitative analysis in the fields of tobacco,agriculture,and pharmaceuticals.To address issues such as poor generalization ability and low prediction accuracy in NIRS modeling,a two-dimensional convolutional neural network(2DCNN)quantitative analysis model based on competitive adaptive reweighted sampling(CARS)and Gramian angular difference field(GADF)(CARS-GADF-2DCNN)was proposed.CARS-GADF-2DCNN used the CARS method to select an optimal wavelength set from the full spectrum,then employed GADF to encode the selection results into two-dimensional images,and finally used 2DCNN for prediction analysis.The 2DCNN model consisted of convolutional layers,parallel convolution modules,flattening layer,and fully connected layers.Simulation experiments were conducted on three public near-infrared(NIR)spectral datasets encompassing soil,tablet,and grain datasets to evaluate the CARS-GADF-2DCNN model.The results demonstrated that,compared to the one-dimensional convolutional neural network(1DCNN),the GADF-2DCNN model achieved 16.74％,23.40％,and 7.13％improvement in prediction accuracy for the soil,tablet,and grain datasets,respectively.Compared to GADF-2DCNN,VCPA-GADF-2DCNN,and IRIV-GADF-2DCNN models,the CARS-GADF-2DCNN model further improved prediction accuracy.For the soil dataset,prediction accuracy improved by 39.00％,30.78％and 4.13％;for the tablet dataset,the improvements were 9.52％,6.94％and 2.56％;for the grain dataset,the improvements were 20.57％,9.85％and 15.66％.In conclusion,CARS-GADF-2DCNN effectively selected the optimal wavelength subset from near infrared spectra,and revealed the latent features between different wavelengths.CARS-GADF-2DCNN addresses the issues of high complexity in prediction models and low prediction accuracy in near infrared spectral modeling,and could be effectively applied to near infrared spectral prediction analysis of different substances.

Body weight abnormalities, including overweight, obesity, and underweight, have become a dual public health challenge in Chinese adults: overweight and obesity lead to a variety of chronic complications, while underweight increases the risks of malnutrition, sarcopenia, and organ dysfunction. To systematically address these issues, multidisciplinary experts in endocrinology, sports science, nutrition, and psychiatry from various regions have held multiple weight management seminars. Based on the latest epidemiological data and clinical evidence, they expanded the guideline to include assessment and intervention strategies for underweight, in addition to the core content of obesity management. This guideline outlines the etiological mechanisms, evaluation methods, and multidimensional management strategies for overweight and obesity, covering key areas such as diagnosis and assessment, medical nutrition therapy, exercise prescription, pharmacological intervention, and psychological support. It is intended to provide a scientific and standardized approach to weight management across the adult population, aiming to curb the rising prevalence of obesity, mitigate complications associated with abnormal body weight, and improve nutritional status and overall quality of life.

OBJECTIVE To analyze the status of targeted monitoring of the health care-associated infections in in-tensive care unit(ICU)of a three-A hospital of northwest China in recent 7 years so as to provide bases for formu-lating effective prevention and control measures for the health care-associated infections.METHODS The related data were successively collected from the ICU hospitalized patients of the Second Affiliated Hospital of Air Force Medical University who were under the targeted monitoring by nosocomial infection real-time monitoring system from Jan.2017 to Dec.2023.The data included the incidence of infections,infection sites,use of catheters,inci-dence of catheter-related infections,and distribution and prevalence trend of pathogens.RESULTS A total of 49,137 hospitalized patients from five ICU wards of respiratory medicine department,neurosurgery department,neurology department,thoracic surgery department and critical care medicine department were under the monito-ring,1446(1681 case-times)of whom had health care-associated infections,with the infection rate 2.94％,the case-time infection rate 3.42％.The respiratory system was the major infection site of the patients with the health care-associated infections.Among the patients with instrument-associated infections,20.70％had ventilator-asso-ciated pneumonia(VAP),5.71％had urinary catheter-associated urinary tract infection(CAUTI),and 1.96％had catheter-related bloodstream infection(CRBSI).Totally 405 strains of non-repetitive pathogens were isolated from 477 patients with instruments-associated infections,78.77％of which were gram-negative bacteria.The iso-lation rate of multidrug-resistant organisms(MDROs)was 44.12％,and Acinetobacter baumannii was the pre-dominant species of pathogen isolated from the patients with VAP.CONCLUSIONS The targeted monitoring of health care-associated infections may facilitate the awareness of dynamic changes of the infections in a accurate and timely manner so as to provide bases for developing effective prevention and control measures for the health care-associated infection.It is necessary to strengthen the prevention and control of the infections in critical care medi-cine department,neurosurgery department as well as the lower respiratory tract infections and prevent the MDROs infection in the meantime.

Extensive epigenetic reprogramming involves in memory CD8+ T-cell differentiation. The elaborate epigenetic rewiring underlying the heterogeneous functional states of CD8+ T cells remains hidden. Here, we profile single-cell chromatin accessibility and map enhancer-promoter interactomes to characterize the differentiation trajectory of memory CD8+ T cells. We reveal that under distinct epigenetic regulations, the early activated CD8+ T cells divergently originated for short-lived effector and memory precursor effector cells. We also uncover a defined epigenetic rewiring leading to the conversion from effector memory to central memory cells during memory formation. Additionally, we illustrate chromatin regulatory mechanisms underlying long-lasting versus transient transcription regulation during memory differentiation. Finally, we confirm the essential roles of Sox4 and Nrf2 in developing memory precursor effector and effector memory cells, respectively, and validate cell state-specific enhancers in regulating Il7r using CRISPR-Cas9. Our data pave the way for understanding the mechanism underlying epigenetic memory formation in CD8+ T-cell differentiation.
CD8-Positive T-Lymphocytes/metabolism* ; Cell Differentiation ; Chromatin/immunology* ; Animals ; Mice ; Immunologic Memory ; Epigenesis, Genetic ; SOXC Transcription Factors/immunology* ; NF-E2-Related Factor 2/immunology* ; Mice, Inbred C57BL ; Gene Regulatory Networks ; Enhancer Elements, Genetic

Dysregulated RNA splicing is a well-recognized characteristic of colorectal cancer (CRC); however, its intricacies remain obscure, partly due to challenges in profiling full-length transcript variants at the single-cell level. Here, we employ high-depth long-read scRNA-seq to define the full-length transcriptome of colorectal epithelial cells in 12 CRC patients, revealing extensive isoform diversities and splicing alterations. Cancer cells exhibited increased transcript complexity, with widespread 3'-UTR shortening and reduced intron retention. Distinct splicing regulation patterns were observed between intrinsic-consensus molecular subtypes (iCMS), with iCMS3 displaying even higher splicing factor activities and more pronounced 3'-UTR shortening. Furthermore, we revealed substantial shifts in isoform usage that result in alterations of protein sequences from the same gene with distinct carcinogenic effects during tumorigenesis of CRC. Allele-specific expression analysis revealed dominant mutant allele expression in key oncogenes and tumor suppressors. Moreover, mutated PPIG was linked to widespread splicing dysregulation, and functional validation experiments confirmed its critical role in modulating RNA splicing and tumor-associated processes. Our findings highlight the transcriptomic plasticity in CRC and suggest novel candidate targets for splicing-based therapeutic strategies.
Humans ; Colorectal Neoplasms/metabolism* ; RNA Isoforms/metabolism* ; Single-Cell Analysis ; RNA Splicing ; Gene Expression Regulation, Neoplastic ; RNA, Neoplasm/metabolism* ; Transcriptome