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.

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.

The study aims to examine the effects and potential mechanisms of disulfiram (DSF) on cardiac hypertrophic injury, focusing on the role of transforming growth factor-β-activated kinase 1 (TAK1)-mediated pan-apoptosis (PANoptosis). H9C2 cardiomyocytes were treated with angiotensin II (Ang II, 1 µmol/L) to establish an in vitro model of myocardial hypertrophy. DSF (40 µmol/L) was used to treat cardiomyocyte hypertrophic injury models, either along or in combination with the TAK1 inhibitor, 5z-7-oxozeaenol (5z-7, 0.1 µmol/L). We assessed cell damage using propidium iodide (PI) staining, measured cell viability with CCK8 assay, quantified inflammatory factor levels in cell culture media via ELISA, detected TAK1 and RIPK1 binding rates using immunoprecipitation, and analyzed the protein expression levels of key proteins in the TAK1-mediated PANoptosis pathway using Western blot. In addition, the surface area of cardiomyocytes was measured with Phalloidin staining. The results showed that Ang II significantly reduced the cellular viability of H9C2 cardiomyocytes and the binding rate of TAK1 and RIPK1, significantly increased the surface area of H9C2 cardiomyocytes, PI staining positive rate, levels of inflammatory factors [interleukin-1β (IL-1β), IL-18, and tumor necrosis factor α (TNF-α)] in cell culture media and p-TAK1/TAK1 ratio, and significantly up-regulated key proteins in the PANoptosis pathway [pyroptosis-related proteins NLRP3, Caspase-1 (p20), and GSDMD-N (p30), apoptosis-related proteins Caspase-3 (p17), Caspase-7 (p20), and Caspase-8 (p18), as well as necroptosis-related proteins p-MLKL, RIPK1, and RIPK3]. DSF significantly reversed the above changes induced by Ang II. Both 5z-7 and exogenous IL-1β weakened these cardioprotective effects of DSF. These results suggest that DSF may alleviate cardiac hypertrophic injury by inhibiting TAK1-mediated PANoptosis.
Animals ; MAP Kinase Kinase Kinases/physiology* ; Rats ; Myocytes, Cardiac/pathology* ; Disulfiram/pharmacology* ; Cardiomegaly ; Apoptosis/drug effects* ; Cell Line ; Angiotensin II ; Necroptosis/drug effects* ; Interleukin-1beta/metabolism* ; Receptor-Interacting Protein Serine-Threonine Kinases/metabolism* ; Lactones ; Resorcinols ; Zearalenone/administration & dosage*

A pyrene-phenol-based fluorescent probe PyP which showed typical intramolecular charge transfer(ICT)and monomer-excimer activities was synthesized by using pyrene carboxaldehyde hydrazone and 4-tert-butyl-2,6-diformylphenol as the raw materials.The effects of solvents on PyP were studied,and the results showed that the color of protic polar solvents(Ethanol,N,N-dimethylformamide,methanol and H2O)were successfully identified.Based on the solvent polarity-regulated PyP monomer-excimer switching,the rapid and highly sensitive ratiometric probe,"Turn-off"and"Turn-on"multimodal probes were established for detection of trace water content in organic solvents(Dimethyl sulfoxide,N,N-dimethylformamide,ethanol and methanol),with detection limits(3σ/k)of 0.0021％,0.046％,0.062％and 0.024％.The method was successfully used to detect water content in dimethyl sulfoxide,N,N-dimethy lformamide,ethanol and methanol commercial organic solvents,with recoveries ranging from 97.2％to 108.0％.The developed method showed good accuracy and stability,and had good application prospect.

Lentiviral vectors(LVs)are key gene delivery tools for integrating target genes into the host genome,but they may also pose risks of insertional mutagenesis.The vector copy number(VCN)in cells is critical for determining the safety of gene modification.However,the reliability and accuracy of its quantification process are influenced by multiple factors.Developing cell reference materials with specific vector copy numbers represents a viable approach to enhance the reliability and consistency of measurement results,enabling quality control of the quantification process and traceability of outcomes.However,the preparation of such reference materials faces challenges in cell sample design,preparation protocols,and advanced quantification techniques.In this study,T lymphocyte cell line Jurkat-based reference materials with LV gene copy numbers of 1 and 2 copy/cell were developed.A high-precision duplex digital polymerase chain reaction(dPCR)method was established to quantify the LV gene and endogenous genes simultaneously.Additionally,the results of dPCR were cross-validated through next-generation sequencing and flow cytometric analysis.Ultimately,confocal microscopy characterization results showed that the developed cell reference materials had intact morphology.The quantification result of VCN-1 was(1.07±0.11)copy/cell,and that of VCN-2 was(2.09±0.21)copy/cell.These cell reference materials demonstrated compliance with stability and homogeneity requirements,and could be applied for quality control throughout the VCN measurement workflow and metrological traceability,improving the accuracy,comparability,and validity of copy number measurements.

The objective assessment of hearing loss is one of the critical components in forensic clini-cal research.Auditory brainstem response(ABR)is an important method for objectively assessing hearing levels.It is divided into various types based on different stimulus signals,each with its own characteris-tics and applications.Among them,narrow-band Chirp ABR,due to its frequency specificity,fulfills the basic requirements for objective assessment of forensic audiology,promising to be an important method of objective hearing assessment in forensic medicine.This article reviews the development history,charac-teristics and clinical applications of Chirp ABR,and envisions its application prospects in forensic audi-tory identification.

Objective To establish a stable,reliable,and clinically relevant porcine model of endotoxin-induced acute respiratory distress syndrome(ARDS).Methods Ten 8-month-old male Bama minipigs were deeply sedated,followed by invasive mechanical ventilation and electrocardiographic monitoring.Lipopolysaccharide(LPS)was intravenously pumped at 600 μg/(kg·h)for 3 hours,then maintained at 15 μg/(kg·h)thereafter.Dynamic monitoring was performed at five time points after LPS injection(LPS 0,1,3,5,and 8 h),including arterial blood gas analysis and chest computed tomography(CT)scans.Pathological examination of lung tissues obtained via bronchoscopic biopsy(HE staining and transmission electron microscopy)was conducted.These indicators were comprehensively used to evaluate the success of the animal model.Results At 5 hours after LPS administration,8 minipigs developed symptoms such as skin cyanosis,elevated body temperature,and respiratory distress.The oxygenation index decreased to<300 mmHg.Chest CT scans showed diffuse pulmonary infiltrates.Histopathology revealed alveolar edema and hyaline membrane formation.Transmission electron microscopy demonstrated disruption of pulmonary blood-air barrier,depletion of lamellar bodies in type Ⅱ pneumocytes,inflammatory cell infiltration,and exudation of plasma proteins and fibrin.Compared with LPS 0 h,at LPS 8 h,the oxygenation index and arterial blood pH were significantly decreased(P<0.001),while blood lactic acid and serum potassium were significantly increased(P<0.05);serum calcium and base excess were significantly decreased(P<0.05),and the lung injury score based on HE-stained lung sections was significantly increased(P<0.01).Conclusion The porcine ARDS model established by continuous LPS injection can dynamically simulate the pathophysiological characteristics and typical pathological manifestations of clinical septic ARDS,making it an effective tool to study the pathogenesis,prevention,and treatment strategies of septic ARDS.

In the central nervous system (CNS), the myelin sheath, a specialized membrane structure that wraps around axons, is formed by oligodendrocytes through a highly coordinated spatiotemporal developmental program. The process begins with the directed differentiation of neural precursor cells into oligodendrocyte precursor cells (OPCs), followed by their migration, proliferation, differentiation, and maturation, ultimately leading to the formation of a multi-segmental myelin sheath structure. Recent single-cell sequencing research has revealed that this process involves the temporal regulation of over 200 key genes, with a regulatory network composed of transcription factors such as Sox10 and Olig2 playing a central role. The primary function of the myelin sheath is to accelerate nerve signal transmission and protect nerve fibers from damage. Its insulating properties not only increase nerve conduction speed by 50-100 times but also ensure the long-term functional integrity of the nervous system by maintaining axonal metabolic homeostasis and providing mechanical protection. The pathological effects of myelin sheath injury exhibit a cascade amplification pattern: acute demyelination leads to action potential conduction block, while chronic lesions may cause axonal damage and neuronal death in severe or long-term cases, ultimately resulting in irreversible neurological dysfunction with neurodegenerative characteristics. Multiple sclerosis (MS) is a neurodegenerative disease characterized by chronic inflammatory demyelination of the CNS. Clinically, the distribution of lesions in MS exhibits spatial heterogeneity, which is closely related to differences in the regenerative capacity of oligodendrocytes within the local microenvironment. Emerging evidence suggests that astrocytes form a dynamic “neural-immune-metabolic interface” and play a multidimensional regulatory role in myelin development and regeneration by forming heterogeneous populations composed of different subtypes. During embryonic development, astrocytes induce the targeted differentiation of OPCs in the ventricular region through the Wnt/β-catenin pathway. In the mature stage, they secrete platelet-derived growth factor AA (PDGF-AA) to establish a chemical gradient that guides the precise migration of OPCs along axonal bundles. Notably, astrocytes also provide crucial metabolic support by supplying energy substrates for high-energy myelin formation through the lactate shuttle mechanism. In addition, astrocytes play a dual role in myelin regulation. During the acute injury phase, reactive astrocytes establish a triple defense system within 72 h: upregulating glial fibrillary acidic protein (GFAP) to form scars that isolate lesions, activating the JAK-STAT3 regeneration pathway in oligodendrocytes via leukemia inhibitory factor (LIF), and releasing tumor necrosis factor-stimulated gene-6 (TSG-6) to inhibit excessive microglial activation. However, in chronic neurodegenerative diseases, the phenotypic transformation of astrocytes contributes to microenvironmental deterioration. The secretion of chondroitin sulfate proteoglycans (CSPGs) inhibits OPC migration via the RhoA/ROCK pathway, while the persistent release of reactive oxygen species (ROS) leads to mitochondrial dysfunction and the upregulation of complement C3-mediated synaptic pruning. This article reviews the mechanisms by which astrocytes regulate the development and regeneration of myelin sheaths in the CNS, with a focus on analyzing the multifaceted roles of astrocytes in this process. It emphasizes that astrocytes serve as central hubs in maintaining myelin homeostasis by establishing a metabolic microenvironment and signaling network, aiming to provide new therapeutic strategies for neurodegenerative diseases such as multiple sclerosis.