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.

With the continuous rise in the incidence of cardiovascular disease (CVD) and the prolongation of life expectancy of patients living with the disease, improving the quality of life of patients has become an important management goal. Sexual life, as a basic physiological need in human survival, is also a key component of the quality of life for CVD patients. However, due to the impact of the disease, sexual health issues are common among CVD patients and have adverse effects on their quality of life, mental health, and marital relationships, which should be addressed promptly. This article reviews the definitions related to sexual health, the current research on sexual health in CVD patients, research tools related to sexual health, influencing factors of sexual health in CVD patients, and sexual health interventions, aiming to provide a reference for clinical research in this area.

This article aims to explore the effect and mechanism of Liujunzi Pills on the intestinal microbiota of rats with spleen Qi deficiency syndrome. The raw Rhei Radix et Rhizoma water extract(1 g·mL~(-1)) was used to prepare spleen Qi deficiency rat models. A total of 44 SD male rats were randomly divided into a control group, a model group, Liujunzi Pills groups at high(3.24 g·kg~(-1)), medium(1.62 g·kg~(-1)), low(0.81 g·kg~(-1)) doses, and Shenling Baizhu San(2.50 g·kg~(-1)) group. The drug effect was evaluated by observing the following aspects: spleen index, fecal water content, body weight, and intestinal propulsion index. Gut microbiota analysis and 16S rRNA gene sequencing were conducted on feces. Enzyme-linked immunosorbent assay(ELISA) and UV spectrophotometry were used to detect interleukin-1β(IL-1β) and adenosine triphosphate(ATP) levels in small intestine tissues. Hematoxylin-eosin staining and transmission electron microscopy were employed to observe changes in intestinal pathology and microstructure. The results show that, compared with the control group, fecal moisture content is significantly increased while spleen index, body weight, and intestinal propulsion index are significantly reduced in rats of the model group, indicating the successful establishment of the model. The above symptoms can be improved by both Shenling Baizhu San and Liujunzi Pills. Compared with the control group, in the model group, the gut microbiota abundance is changed with an unbalanced development: the abundance of beneficial bacteria within the Bacteroidetes phylum is reduced, accompanied by a significantly decreased Shannon index, and reduced signal levels of nicotinamide adenine dinucleotide phosphate(NADPH)-related enzymes relevant to mitochondria. However, Liujunzi Pills and Shenling Baizhu San can significantly improve the Bacteroidetes phylum abundance in gut microbiota, microbial diversity, and NADPH activity in the model group. Additionally, compared with the control group, the ATP level is decreased and the IL-1β level is increased in small intestinal tissues of the model group, with shorter small intestinal epithelial villi and decreased mitochondrial number. The above symptoms can be improved by Liujunzi Pills and Shenling Baizhu San. In conclusion, Liujunzi Pills can treat spleen Qi deficiency syndrome by enhancing mitochondrial function to regulate gut microbiota balance and diversity.
Animals ; Gastrointestinal Microbiome/drug effects* ; Drugs, Chinese Herbal/pharmacology* ; Male ; Rats, Sprague-Dawley ; Rats ; Qi ; Spleen/metabolism* ; Splenic Diseases/metabolism* ; Humans ; Interleukin-1beta/genetics* ; Bacteria/drug effects* ; Feces/microbiology* ; Adenosine Triphosphate/metabolism*

Objective To explore the characteristics of latent profiles of ego depletion in patients with heart failure,and analyze the impacts of these profiles on patients' self-management behavior.Methods 280 heart failure patients who were hospitalized in a tertiary grade A hospital were conveniently selected in Nanjing from April to October 2024 as study subjects.The investigation utilized a general information questionnaire,the Ego-Depletion Scale for Heart Failure Patients,the Self-Management Scale of Heart Failure Patients,and the Chronic Disease Self-Efficacy Scale.The latent profile categories of ego depletion was explored in heart failure patients.Univariate analysis and multiple Logistic regression analysis were used to investigate the influencing factors of different latent profile categories and the impact of each category on patients' self-management levels.Results The ego depletion of 255 heart failure patients could be classified into 3 latent profiles:self-adaptive low depletion group(35.29％),symptom distressed moderate depletion group(43.53％),and social life change high depletion group(21.18％).Multiple Logistic regression analysis showed that residence,primary caregiver,education level,New York Heart Association cardiac function grade,and self-efficacy score were factors influencing the potential profile of ego depletion in heart failure patients(P＜0.05).The comparison of self-management scores among patients in different latent profiles of ego depletion showed statistically significant differences(P＜0.05).Conclusion The level of ego depletion in patients with heart failure is high with significant heterogeneity.Medical staff should pay attention to the heterogeneity and influencing factors of ego depletion in patients with heart failure in time,and develop personalized comprehensive intervention strategies to alleviate ego depletion and improve their self-management levels.

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.

With the continuous rise in the incidence of cardiovascular disease (CVD) and the prolongation of life expectancy of patients living with the disease, improving the quality of life of patients has become an important management goal. Sexual life, as a basic physiological need in human survival, is also a key component of the quality of life for CVD patients. However, due to the impact of the disease, sexual health issues are common among CVD patients and have adverse effects on their quality of life, mental health, and marital relationships, which should be addressed promptly. This article reviews the definitions related to sexual health, the current research on sexual health in CVD patients, research tools related to sexual health, influencing factors of sexual health in CVD patients, and sexual health interventions, aiming to provide a reference for clinical research in this area.

Objective To explore the characteristics of latent profiles of ego depletion in patients with heart failure,and analyze the impacts of these profiles on patients' self-management behavior.Methods 280 heart failure patients who were hospitalized in a tertiary grade A hospital were conveniently selected in Nanjing from April to October 2024 as study subjects.The investigation utilized a general information questionnaire,the Ego-Depletion Scale for Heart Failure Patients,the Self-Management Scale of Heart Failure Patients,and the Chronic Disease Self-Efficacy Scale.The latent profile categories of ego depletion was explored in heart failure patients.Univariate analysis and multiple Logistic regression analysis were used to investigate the influencing factors of different latent profile categories and the impact of each category on patients' self-management levels.Results The ego depletion of 255 heart failure patients could be classified into 3 latent profiles:self-adaptive low depletion group(35.29％),symptom distressed moderate depletion group(43.53％),and social life change high depletion group(21.18％).Multiple Logistic regression analysis showed that residence,primary caregiver,education level,New York Heart Association cardiac function grade,and self-efficacy score were factors influencing the potential profile of ego depletion in heart failure patients(P＜0.05).The comparison of self-management scores among patients in different latent profiles of ego depletion showed statistically significant differences(P＜0.05).Conclusion The level of ego depletion in patients with heart failure is high with significant heterogeneity.Medical staff should pay attention to the heterogeneity and influencing factors of ego depletion in patients with heart failure in time,and develop personalized comprehensive intervention strategies to alleviate ego depletion and improve their self-management levels.

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.