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.

The sterile electrode acupuncture needle for single use is an innovative product that combines traditional acupuncture with modern electronic technology, and it has obtained Class Ⅱ medical device registration certificate. This acupuncture device consists of a needle body and a handle. The diameter of the needle body ranges from 0.16 mm to 0.55 mm, and the length from 7 mm to 150 mm. The spiral spray technology is adopted to modify the micron-level insulating coat on stainless steel needle body. The needle holder is connected to the electroacupuncture device (conductive), the micro-film insulated needle body (non-conductive) and the membrane-free needle tip (conductive) can provide a precise electrical stimulation for different tissue layers of acupoints (such as deep nerves and fascia). The intradermal stimulation test, cytotoxicity test and hypersensitivity reaction test have showed a favorable biocompatibility, laying a solid and reliable safety for clinical application. This acupuncture device is suitable for the in-depth invasive stimulation at the sites of human body surface in combination with electroacupuncture equipment in medical institutions.
Humans ; Needles ; Acupuncture Therapy/instrumentation* ; Electrodes ; Equipment Design ; Electroacupuncture/instrumentation* ; Acupuncture Points ; Animals

Recently, male reproductive health has attracted extensive attention, with the adverse effects of circadian disruption on male fertility gradually gaining recognition. However, the mechanism by which circadian disruption leads to damage to male reproductive system remains unclear. In this review, we first summarized the dual regulatory roles of circadian clock genes on the male reproductive system: (1) circadian regulation of testosterone synthesis via the hypothalamic-pituitary-testicular (HPT) and hypothalamic-pituitary-adrenal (HPA) axes; (2) non-circadian regulation of spermatogenesis. Next, we further listed the possible mechanisms by which circadian disruption impairs male fertility, including interference with the oscillatory function of the reproductive system, i.e., synchronization of the HPT axis, crosstalk between the HPT axis and the HPA axis, as well as direct damage to germ cells by disturbing the non-oscillatory function of the reproductive system. Future research using spatiotemporal omics, epigenomic assays, and neural circuit mapping in studying the male reproductive system may provide new clues to systematically unravel the mechanisms by which circadian disruption affects male reproductive system through circadian clock genes.
Male ; Humans ; Animals ; Circadian Clocks/physiology* ; Hypothalamo-Hypophyseal System/physiology* ; Circadian Rhythm/genetics* ; Spermatogenesis/physiology* ; Pituitary-Adrenal System/physiology* ; Testis/physiology* ; Testosterone/biosynthesis* ; CLOCK Proteins ; Infertility, Male/physiopathology*

To investigate the impact of preoperative serum follicle-stimulating hormone (FSH) levels on the probability of testicular sperm retrieval, we conducted a study of nonobstructive azoospermic (NOA) men with different testicular volumes (TVs) who underwent microdissection testicular sperm extraction (micro-TESE). A total of 177 NOA patients undergoing micro-TESE for the first time from April 2019 to November 2022 in Shenzhen Zhongshan Obstetrics and Gynecology Hospital (formerly Shenzhen Zhongshan Urology Hospital, Shenzhen, China) were retrospectively reviewed. The subjects were divided into four groups based on average TV quartiles. Serum hormone levels in each TV group were compared between positive and negative sperm retrieval subgroups. Overall sperm retrieval rate was 57.6%. FSH levels (median [interquartile range]) were higher in the positive sperm retrieval subgroup compared with the negative outcome subgroup when average TV was <5 ml (first quartile [Q1: TV <3 ml]: 43.32 [17.92] IU l -1 vs 32.95 [18.56] IU l -1 , P = 0.048; second quartile [Q2: 3 ml ≤ TV <5 ml]: 31.31 [15.37] IU l -1 vs 25.59 [18.40] IU l -1 , P = 0.042). Elevated serum FSH levels were associated with successful micro-TESE sperm retrieval in NOA men whose average TVs were <5 ml (adjusted odds ratio [OR]: 1.06 per unit increase; 95% confidence interval [CI]: 1.01-1.11; P = 0.011). In men with TVs ≥5 ml, larger TVs were associated with lower odds of sperm retrieval (adjusted OR: 0.84 per 1 ml increase; 95% CI: 0.71-0.98; P = 0.029). In conclusion, elevated serum FSH levels were associated with positive sperm retrieval in micro-TESE in NOA men with TVs <5 ml. In men with TV ≥5 ml, increases in average TVs were associated with lower odds of sperm retrieval.
Humans ; Male ; Azoospermia/surgery* ; Sperm Retrieval/statistics & numerical data* ; Adult ; Follicle Stimulating Hormone/blood* ; Retrospective Studies ; Testis/pathology* ; Microdissection ; Organ Size

ObjectiveTo explore the effects of Kaixuan Jiedu core prescription （KXJD） on sphingolipid metabolism in the mouse model of imiquimod-induced psoriasis-like skin lesions. MethodsThirty-seven male C57BL/6J mice were randomly assigned into five groups： healthy control （n=11）， model （n=11）， methotrexate （MTX， n=5）， low-dose （15.21 g·kg^-1） KXJD （n=5）， and high-dose （30.42 g·kg^-1） KXJD （n=5）. Psoriasis-like skin lesions were induced in mice with 62.5 mg 5% imiquimod cream applied on the back. The KXJD groups and MTX group were treated with 0.2 mL corresponding decoction and MTX， respectively， by gavage daily， while the other groups were given an equal volume of normal saline by the same way. After 5 days of treatment， back skin lesions were collected. Firstly， healthy control and model mice were selected for tandem mass tag （TMT） quantitative proteomics （control vs model=3 vs 3） and targeted lipid metabolomics （control vs model=11 vs 11）. Then， the binding degree between core components and target proteins was predicted via network pharmacology and molecular docking. Finally， an animal experiment was performed to decipher the specific regulation mechanism of KXJD on sphingolipid metabolism. Immunohistochemistry was employed to determine the expression level of sphingosine-1-phosphate （S1P）， and Western blot was employed to determine the expression levels of sphingosine kinase 2 （SPHK2） and monocyte chemotactic protein-1 （MCP-1）. ResultsTMT proteomics and targeted lipid metabolomics suggested that sphingolipid metabolism was active in the psoriatic skin， and key proteases ［serine palmitoyltransferase， long chain base subunit 2 （SPTLC2）， SPHK2， delta（4）-desaturase sphingolipid 1 （Degs1）， and ceramide synthase 4 （CerS4）］ and 8 sphingolipid metabolites （including ceramides， sphingol， sphingomyelin， and glycosphingolipid） expressed abnormally （P<0.05） compared with those in the healthy skin. The molecular docking results indicated that the binding energy between the active components （quercetin， kaempferol， and luteolin） in KXJD and key proteins involved in sphingolipid metabolism was less than-8 kal·mol^-1. Further experimental verification showed elevated expression levels of SPHK2， S1P， and MCP-1 in psoriatic skin compared with healthy skin （P<0.05）， and KXJD down-regulated the expression levels of SPHK2， S1P， and MCP-1 compared with the model group （P<0.05）. ConclusionThis study indicates that there is an imbalance in sphingolipid metabolism in psoriatic skin lesions. KXJD may reduce psoriasis-like lesions in mice by regulating sphingolipid metabolism via the SPHK2/S1P/MCP-1 pathway.

ObjectiveTo explore the effects of Kaixuan Jiedu core prescription （KXJD） on sphingolipid metabolism in the mouse model of imiquimod-induced psoriasis-like skin lesions. MethodsThirty-seven male C57BL/6J mice were randomly assigned into five groups： healthy control （n=11）， model （n=11）， methotrexate （MTX， n=5）， low-dose （15.21 g·kg^-1） KXJD （n=5）， and high-dose （30.42 g·kg^-1） KXJD （n=5）. Psoriasis-like skin lesions were induced in mice with 62.5 mg 5% imiquimod cream applied on the back. The KXJD groups and MTX group were treated with 0.2 mL corresponding decoction and MTX， respectively， by gavage daily， while the other groups were given an equal volume of normal saline by the same way. After 5 days of treatment， back skin lesions were collected. Firstly， healthy control and model mice were selected for tandem mass tag （TMT） quantitative proteomics （control vs model=3 vs 3） and targeted lipid metabolomics （control vs model=11 vs 11）. Then， the binding degree between core components and target proteins was predicted via network pharmacology and molecular docking. Finally， an animal experiment was performed to decipher the specific regulation mechanism of KXJD on sphingolipid metabolism. Immunohistochemistry was employed to determine the expression level of sphingosine-1-phosphate （S1P）， and Western blot was employed to determine the expression levels of sphingosine kinase 2 （SPHK2） and monocyte chemotactic protein-1 （MCP-1）. ResultsTMT proteomics and targeted lipid metabolomics suggested that sphingolipid metabolism was active in the psoriatic skin， and key proteases ［serine palmitoyltransferase， long chain base subunit 2 （SPTLC2）， SPHK2， delta（4）-desaturase sphingolipid 1 （Degs1）， and ceramide synthase 4 （CerS4）］ and 8 sphingolipid metabolites （including ceramides， sphingol， sphingomyelin， and glycosphingolipid） expressed abnormally （P<0.05） compared with those in the healthy skin. The molecular docking results indicated that the binding energy between the active components （quercetin， kaempferol， and luteolin） in KXJD and key proteins involved in sphingolipid metabolism was less than-8 kal·mol^-1. Further experimental verification showed elevated expression levels of SPHK2， S1P， and MCP-1 in psoriatic skin compared with healthy skin （P<0.05）， and KXJD down-regulated the expression levels of SPHK2， S1P， and MCP-1 compared with the model group （P<0.05）. ConclusionThis study indicates that there is an imbalance in sphingolipid metabolism in psoriatic skin lesions. KXJD may reduce psoriasis-like lesions in mice by regulating sphingolipid metabolism via the SPHK2/S1P/MCP-1 pathway.

Acute lateral ankle sprain (ALAS) is one of the most common sport injuries, with high incidence, recurrence and disability rates. Currently, exercise rehabilitation-based non-surgical treatment is the primary management approach for ALAS. However, there remain improper practices such as excessive immobilization or uncontrolled activity, which contribute to recurrent sprains and chronic ankle instability, significantly impairing patients′ athletic function and quality of life. To standardize the non-surgical management of ALAS, improve the cure rates, and reduce the recurrence and disability rates, Chinese Sports Rehabilitation Medicine Training Project of Chinese Medical Association, Foot and Ankle Basics and Orthopedics Group, Orthopedic Branch of Chinese Medical Doctor Association, and Sports Medicine Branch of Jiangsu Medical Association organized relevant experts to formulate Expert consensus on non-surgical treatment for acute lateral ankle sprain ( version 2025), following the principles of scientific vigor, practicality, and innovation. Thirteen recommendations were proposed for standardized treatment protocols across different healing phases, aiming to provide references for standard management of ALAS and improve the therapeutic outcomes.

Objective:To investigate the effects of acute sleep deprivation (ASD) on hippocampal glucose metabolism and neuroinflammation in rat models.Methods:Twenty SD rats (10 males and 10 females) were divided into four groups (five in each group) by random sampling method: female ASD group, male ASD group, female control group, and male control group. Among them, the ASD group constructed the ASD model. After 72h sleep deprivation, all rats underwent 18F-FDG and N, N-diethyl-2-(2-(4-(2- 18F-fluoroethoxy)phenyl)-5, 7-dimethylpyrazolo[1, 5-a]pyrimidin-3-yl)acetamide ( 18F-DPA-714) microPET/CT brain imaging in 2d to compare the changes of 18F-FDG and 18F-DPA-714 SUV mean in the hippocampus of rats. Brain histopathology, immunohistochemistry and immunofluorescence staining were detected in rats. Independent-sample t test was used to analyze the data. Results:18F-FDG imaging showed the hippocampal SUV mean between ASD group and control group (female: 4.11±0.35 vs 1.89±0.28; male: 3.43±0.47 vs 2.02±0.54) were statistically significant ( t values: 9.65, 3.92, P values: <0.001, 0.002). 18F-DPA-714 imaging showed the hippocampal SUV mean between ASD group and control group (females: 0.28±0.01 vs 0.28±0.02; male: 0.26±0.02 vs 0.31±0.04) were not statistically significant ( t values: -0.18, -2.24, P values: 0.859, 0.056). The 18×10 3 translocator protein (TSPO) immunohistochemistry showed the expression in the hippocampal region of the brain between ASD group and control group (female: 0.19±0.02 vs 0.19±0.01; male: 0.21±0.01 vs 0.20±0.01) were not statistically different ( t values: -0.48, -1.67, P values: 0.651, 0.139). Immunofluorescence staining showed that microglial cytosol in the hippocampal region of the brain decreased after 72h of ASD, and the protrusion points and surrounding branches were significantly reduced. Conclusion:Increased hippocampal glucose metabolism in rats is observed after 72 h of ASD without significant neuroinflammation.

Objective:To evaluate the effect of cathepsin B(CTSB)/NOD-like receptor pyrin domain containing 3(NLRP3)pathway on the polarization of macrophages induced by LPS.Methods:The well-growing RAW264.7 mouse mononuclear macrophage lines were cultured in vitro and divided into 3 groups(n=6)according to the random number table method:control group(C group),LPS group(L group)and LPS+CA074-me(CTSB inhibitors)group(B group).C group was cultured normally for 24 h,L group was cultured with LPS concentration of 1 μg/ml medium for 24 h.B group was pretreated with CTSB inhibitor CA074-me 30 μmol/L for 1 h before LPS induction,and co-cultured with LPS concentration of 1 μg/ml medium for 24 h.After 24 hours,the morphological changes of the cells were observed by microscope,the concentrations of IL-1β and IL-18 in the supernatant were determined by ELISA.The ex-pressions of cathepsin B precursor(pro-CTSB),mature cathepsin B(mature-CTSB),NLRP3,apoptosis-related speck protein(ASC)and apoptosis-related speck protein-1(caspase-1)were detected by Western blot.The mRNA expression levels of CD32,inducible ni-tric oxide synthase(iNOS),arginase 1(Arg-1)and CD206 were detected by qRT-PCR.The positive expression rates of M1 macro-phage surface marker CD86 and M2 macrophage surface marker CD206 were detected by flow cytometry.Results:Compared with group C,the morphology of cells in groups L and B became larger and pseudopodia appeared.The concentrations of IL-1β and IL-18 in cell supernatant were increased,the expressions of pro-CTSB,mature-CTSB,NLRP3,ASC and caspase-1 were increased,and the expressions of CD32,iNOS mRNA were up-regulated and the positive rates of CD86 and CD206 were increased(P<0.01).Arg-1 and CD206 mRNA in group B were up-regulated(P<0.01).Compared with group L,the pseudopodia of group B were reduced,and the morphology was closer to group C.The concentration of IL-1β and IL-18 in the supernatant,the expression of mature-CTSB,NLRP3,ASC and caspase-1,CD32 and iNOS mRNA and the positive rate of CD86 were down-regulated in group B.The expression of pro-CTSB,Arg-1 and CD206 mRNA and the positive rate of CD206 were increased(P<0.01).Conclusion:Inhibition of CTSB/NLRP3 pathway can reduce the inflammatory response,reduce the LPS-induced polarization of RAW264.7 cells to M1 macrophages,and pro-mote their polarization to M2 macrophages.