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.

ObjectiveBased on ultra-performance liquid chromatography-quadrupole-electrostatic field orbitrap high resolution mass spectrometry（UPLC-Q-Orbitrap-MS）， the chemical constituents of Qili Qiangxin capsules was identified， and their distribution in vivo was analyzed. MethodsUPLC-Q-Orbitrap-MS was used to detect the sample solution of Qili Qiangxin capsules， as well as the serum， brain， heart， lung， spleen， liver and kidney tissues of mice after oral administration. Using the Thermo Xcalibur 2.2 software， the compound information database was constructed， and the molecular formulas of compounds corresponding to the quasi-molecular ions were fitted. Based on the information of retention time， accurate relative molecular mass and fragments， the compounds and their distribution in vivo were analyzed by comparing with the data of reference substances and literature. ResultsA total of 233 compounds， including 70 terpenoids， 60 flavonoids， 23 organic acids， 17 alkaloids， 20 steroids， 7 coumarins and 36 others， were identified or predicted from Qili Qiangxin capsules， 73 of which were identified matching with standard substances. Tissue distribution results showed that 71， 17， 38， 33， 32， 58 and 43 migrating components were detected in blood， brain， heart， lung， spleen， liver and kidney， respectively. Thirty-seven components were absorbed into the blood and heart， including quinic acid， benzoylaconitine benzoylmesaconine and so on. Fourteen components were absorbed into the blood and six tissues， including calycosin， methylnissolin， formononetin， alisol B， alisol A and so on. ConclusionThis study comprehensively analyzes the chemical components of Qili Qiangxin capsules and their distribution in vivo. Among them， astragaloside Ⅳ， salvianolic acid B， ginsenoside Rb₁， ginsenoside Rb₃， ginsenoside Rd， ginsenoside Rg₃， calycosin-7-glucoside， and sinapine may be the important components for the treatment of heart failure， which can provide useful reference for its quality control and research on pharmacodynamic material basis.

Bone repair remains an important target in tissue engineering, making the development of bioactive scaffolds for effective bone defect repair a critical objective. In this study, β-tricalcium phosphate (β-TCP) scaffolds incorporated with processed pyritum decoction (PPD) were fabricated using three-dimensional (3D) printing-assisted freeze-casting. The produced composite scaffolds were evaluated for their mechanical strength, physicochemical properties, biocompatibility, in vitro pro-angiogenic activity, and in vivo efficacy in repairing rabbit femoral defects. They not only demonstrated excellent physicochemical properties, enhanced mechanical strength, and good biosafety but also significantly promoted the proliferation, migration, and aggregation of pro-angiogenic human umbilical vein endothelial cells (HUVECs). In vivo studies revealed that all scaffold groups facilitated osteogenesis at the bone defect site, with the β-TCP scaffolds loaded with PPD markedly enhancing the expression of neurogenic locus Notch homolog protein 1 (Notch1), vascular endothelial growth factor (VEGF), bone morphogenetic protein-2 (BMP-2), and osteopontin (OPN). Overall, the scaffolds developed in this study exhibited strong angiogenic and osteogenic capabilities both in vitro and in vivo. The incorporation of PPD notably promoted the angiogenic-osteogenic coupling, thereby accelerating bone repair, which suggests that PPD is a promising material for bone repair and that the PPD/β-TCP scaffolds hold great potential as a bone graft alternative.
Calcium Phosphates/chemistry* ; Animals ; Bone Regeneration ; Rabbits ; Tissue Scaffolds ; Printing, Three-Dimensional ; Humans ; Human Umbilical Vein Endothelial Cells ; Neovascularization, Physiologic ; Osteogenesis ; Tissue Engineering/methods* ; Biomimetic Materials ; Cell Proliferation ; Angiogenesis

Hearing loss is the most prevalent disabling disease. Cochlear implantation（CI） serves as the primary intervention for severe to profound hearing loss. This consensus systematically explores the value of genetic diagnosis in the pre-operative assessment and efficacy prognosis for CI. Drawing upon domestic and international research and clinical experience, it proposes an evidence-based medicine three-tiered prognostic classification system（Favorable, Marginal, Poor）. The consensus focuses on common hereditary non-syndromic hearing loss（such as that caused by mutations in genes like GJB2, SLC26A4, OTOF, LOXHD1） and syndromic hereditary hearing loss（such as Jervell & Lange-Nielsen syndrome and Waardenburg syndrome）, which are closely associated with congenital hearing loss, analyzing the impact of their pathological mechanisms on CI outcomes. The consensus provides recommendations based on multiple round of expert discussion and voting. It emphasizes that genetic diagnosis can optimize patient selection, predict prognosis, guide post-operative rehabilitation, offer stratified management strategies for patients with different genotypes, and advance the application of precision medicine in the field of CI.
Humans ; Cochlear Implantation ; Prognosis ; Hearing Loss/surgery* ; Consensus ; Connexin 26 ; Mutation ; Sulfate Transporters ; Connexins/genetics*

Alzheimer's disease (AD) is the major form of dementia in the elderly and is closely related to the toxic effects of microglia sustained activation. In AD, sustained microglial activation triggers impaired synaptic pruning, neuroinflammation, neurotoxicity, and cognitive deficits. Accumulating evidence has demonstrated that aberrant expression of deubiquitinating enzymes is associated with regulating microglia function. Here, we use RNA sequencing to identify a deubiquitinase YOD1 as a regulator of microglial function and AD pathology. Further study showed that YOD1 knockout significantly improved the migration, phagocytosis, and inflammatory response of microglia, thereby improving the cognitive impairment of AD model mice. Through LC-MS/MS analysis combined with Co-IP, we found that Myosin heavy chain 9 (MYH9), a key regulator maintaining microglia homeostasis, is an interacting protein of YOD1. Mechanistically, YOD1 binds to MYH9 and maintains its stability by removing the K48 ubiquitin chain from MYH9, thereby mediating the microglia polarization signaling pathway to mediate microglia homeostasis. Taken together, our study reveals a specific role of microglial YOD1 in mediating microglia homeostasis and AD pathology, which provides a potential strategy for targeting microglia to treat AD.

BACKGROUND: The primary limitation to kidney transplantation is organ shortage. Recent progress in gene editing and immunosuppressive regimens has made xenotransplantation with porcine organs a possibility. However, evidence in pig-to-human xenotransplantation remains scarce, and antibody-mediated rejection (AMR) is a major obstacle to clinical applications of xenotransplantation. METHODS: We conducted a kidney xenotransplantation in a brain-dead human recipient using a porcine kidney with five gene edits (5GE) on March 25, 2024 at Xijing Hospital, China. Clinical-grade immunosuppressive regimens were employed, and the observation period lasted 22 days. We collected and analyzed the xenograft function, ultrasound findings, sequential protocol biopsies, and immune surveillance of the recipient during the observation. RESULTS: The combination of 5GE in the porcine kidney and clinical-grade immunosuppressive regimens prevented hyperacute rejection. The xenograft kidney underwent delayed graft function in the first week, but urine output increased later and the single xenograft kidney maintained electrolyte and pH homeostasis from postoperative day (POD) 12 to 19. We observed AMR at 24 h post-transplantation, due to the presence of pre-existing anti-porcine antibodies and cytotoxicity before transplantation; this AMR persisted throughout the observation period. Plasma exchange and intravenous immunoglobulin treatment mitigated the AMR. We observed activation of latent porcine cytomegalovirus toward the end of the study, which might have contributed to coagulation disorder in the recipient. CONCLUSIONS 5GE and clinical-grade immunosuppressive regimens were sufficient to prevent hyperacute rejection during pig-to-human kidney xenotransplantation. Pre-existing anti-porcine antibodies predisposed the xenograft to AMR. Plasma exchange and intravenous immunoglobulin were safe and effective in the treatment of AMR after kidney xenotransplantation.
Transplantation, Heterologous/methods* ; Kidney Transplantation/methods* ; Heterografts/pathology* ; Immunoglobulins, Intravenous/administration & dosage* ; Graft Survival/immunology* ; Humans ; Animals ; Sus scrofa ; Graft Rejection/prevention & control* ; Kidney/pathology* ; Gene Editing ; Species Specificity ; Immunosuppression Therapy/methods* ; Plasma Exchange ; Brain Death ; Biopsy ; Male ; Aged

BACKGROUND: The STAR (Scientific, Transparent, and Applicable Rankings) working group conducts regular evaluations of Chinese guidelines and consensus statements. This study gathered insights from STAR working group members using qualitative interviews. METHODS: From March to August 2023, members of the STAR specialist committees were interviewed using semi-structured interview outline. The interviewees were selected through purpose-based sampling. Subject analysis was employed to summarize the findings. RESULTS: We conducted interviews with 37 members from 36 committees and summarized the contents into four main themes and 16 specific topics. The value of STAR in enhancing the development and selection of high-quality guidelines in China was commonly mentioned. Challenges identified included the lack of resources and suboptimal organizational structures, collaboration, and evaluation efficiency. Suggestions for the STAR tool included developing extensions for different guideline types, adjusting certain items, and better covering guideline applicability. The promotion of STAR and the consideration of an international committee for global outreach were also highlighted. CONCLUSION STAR has exerted a substantial influence on the evaluation of Chinese guidelines, and the insights gained from interviews offer valuable directions for its further enhancement.
Humans ; China ; Qualitative Research ; Practice Guidelines as Topic ; Interviews as Topic