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 To provide a reference for the establishment, development and application of the adverse drug reaction （ADR） automated monitoring system, through verifying and quantifying the research hotspots and advantages of the system by CiteSpace software and systematic review. Methods Literature on ADR automated monitoring up to December 2023 were retrieved and screened from CNKI and web of science databases. CiteSpace 6.4.R1 software was used to conduct co-occurrence, clustering and emergence analysis, and to visualize and comparatively analyze the research hotspots, rules and distribution in the field of automated monitoring of ADR at home and abroad. In compliance with the preferred reporting items for systematic reviews and Meta-analyses （PRISMA）, literature covering publications in English and Chinese including detection rates of ADR collected using Incident Reporting Systems （IRSs） and/or automated monitoring systems were retrieved and screened. The advantages and disadvantages of automated monitoring systems were analyzed by comparing the differences between these two systems in terms of the number of ADR reports and the types of positive signals. Results A total of 56 articles in English and 80 articles in Chinese were indexed by CiteSpace. The research hotspots in recent years included data mining, deep learning, text classification techniques, machine learning and so on. A total of seven studies compiled with the inclusion criteria for the systematic evaluation, all of which were completed between 1991 and 2021 in hospitals in four countries. 150 526 medical records were reviewed from 15 institutions. A total of 194 ADR reports were collected by IRSs. A total of 2 090 ADR reports were collected by the automated monitoring system over the same period, indicating a 977% increase in the number of ADR reports （P=0.0156） compared with the IRSs. Conclusion The ADR automatic monitoring system had significantly improved the level of drug risk identification and reduced costs, but it was necessary to optimize the algorithm, expand the data source and carry out standardization construction to overcome the current limitations.

Objective To investigate the mechanism by which Senegenin(SEN)alleviates microglial inflammatory response through the nuclear factor erythroid 2-related factor 2(Nrf2)/NOD-like receptor protein 3(NLRP3)pathway.Methods BV2 mouse microglia cells were randomly divided into control group,model group,SEN group and MCC950 group.Cells in control group were not treated,and cells in model group were added with 1 μg/ml lipopolysaccharide(LPS);Cells in SEN group were added with 1 μg/ml LPS+4 μmol/L SEN,and cells in MCC950 group were added with 1 μg/ml LPS+10 μmol/L MCC950 for 24 hours.CCK-8 method was used to detect the effect of different concentrations of SEN on the viability of BV2 cells.Griess method was used to determine the release amount of nitric oxide(NO)in the supernatant.Real-time fluorescent quantitative PCR was used to determine the mRNA expression levels of NLRP3,lymphocyte apoptosis-associated spect-like protein containing a CARD(ASC),caspase-1,interleukin(IL)-1β and IL-18 mRNA.Immunofluorescence staining was used to detect the expression levels of ASC,IL-1β,Nrf2 and heme oxygenase-1(HO-1).Western blotting was used to detect the expression levels of NLRP3,caspase-1,ASC,IL-1β,IL-18,Nrf2,HO-1,nuclear factor kappa B(NF-κB)and inducible nitric oxide synthase(iNOS).Results The results of CCK-8 method showed that there was no significant difference in the viability of BV2 cells treated with 2～20 μmol/L SEN compared with control group(P>0.05).Compared with control group,the viability of BV2 cells in model group decreased significantly(P<0.05).Compared with model group,the viability of BV2 cells in 4 μmol/L SEN group was significantly restored(P<0.05).Compared with control group,the results of Griess method showed that the release amount of NO in cells of model group increased significantly(P<0.05);the results of real-time PCR showed that the expression levels of NLRP3,ASC,caspase-1,IL-1β and IL-18 mRNA in cells of model group increased significantly(P<0.05);the results of Western blotting showed that the protein expression levels of NLRP3,ASC,caspase-1,IL-1β and IL-18 proteins in cells of model group increased significantly(P<0.05),and the immunofluorescence staining results showed that the expression levels of iNOS and NF-κB protein in cells of model group increased,and the expression levels of Nrf2 and HO-1 decreased,with statistically significant differences(P<0.05).Compared with model group,the release amount of NO in cells of SEN group and MCC950 group decreased,and the expression levels of NLRP3,ASC,caspase-1,IL-1β and IL-18 mRNA and proteins decreased,with statistically significant differences(P<0.05);in the SEN group,the expression levels of iNOS and NF-κB decreased,and immunofluorescence staining showed that Nrf2 was translocated into the nucleus,and the expression levels of Nrf2 and HO-1 proteins increased significantly,with statistically significant differences(P<0.05).Conclusions SEN could alleviate the inflammatory response of mouse microglia cells induced by LPS and inhibit the activation and expression of NLRP3 inflammasome,with an effect comparable to that of the inflammasome inhibitor MCC950.The mechanism may be related to the regulation of the expression of upstream factors Nrf2 and HO-1.

Hepatocellular carcinoma(HCC)expresses abundant glycolytic enzymes and displays comprehensive glucose metabolism reprogramming.Aldolase A(ALDOA)plays a prominent role in glycolysis;however,little is known about its role in HCC development.In the present study,we aim to explore how ALDOA is involved in HCC proliferation.HCC proliferation was markedly suppressed both in vitro and in vivo following ALDOA knockout,which is consistent with ALDOA overexpression encouraging HCC prolifera-tion.Mechanistically,ALDOA knockout partially limits the glycolytic flux in HCC cells.Meanwhile,ALDOA translocated to nuclei and directly interacted with c-Jun to facilitate its Thr93 phosphorylation by P21-activated protein kinase;ALDOA knockout markedly diminished c-Jun Thr93 phosphorylation and then dampened c-Jun transcription function.A crucial site Y364 mutation in ALDOA disrupted its interaction with c-Jun,and Y364S ALDOA expression failed to rescue cell proliferation in ALDOA deletion cells.In HCC patients,the expression level of ALDOA was correlated with the phosphorylation level of c-Jun(Thr93)and poor prognosis.Remarkably,hepatic ALDOA was significantly upregulated in the promotion and progression stages of diethylnitrosamine-induced HCC models,and the knockdown of Aldoa strikingly decreased HCC development in vivo.Our study demonstrated that ALDOA is a vital driver for HCC development by activating c-Jun-mediated oncogene transcription,opening additional avenues for anti-cancer therapies.

Objective To obtain differential spectral characteristics of etomidate and its structural ana-logues,and to establish a rapid identification method using surface-enhanced Raman spectroscopy(SERS)combined with machine learning algorithms for distinguishing etomidate and its analogues.Methods Silver nanoparticles(AgNPs)were used as the SERS substrate to collect SERS spectra of etomidate,metomidate,propoxate,and isopropoxate at two concentrations of 1×10-4 and 1×10-5 mol/L.SERS spectra were also obtained from blood and urine samples containing 1×10-5 mol/L of etomidate,metomidate,propoxate,and isopropoxate,as well as from confiscated e-cigarette oil containing etomi-date.Uniform manifold approximation and projection(UMAP)was employed for nonlinear dimensiona-lity reduction and visualization,and a classification model based on the XGBoost algorithm was con-structed to enable discriminant analysis of these four structurally highly similar compounds.Results Mi-nor characteristic peak shifts(5-3 cm-1)were identified in the range of 1 398-811 cm-1.Qualitative identification of the compounds in serum,urine and e-cigarette oil samples was achieved without pre-treatment.After UMAP dimensionality reduction,distinct clustering separation among different sub-stances was observed.The XGBoost model achieved 100%classification accuracy on the test set.Feature weight analysis revealed that C-N stretching vibration(841 cm-1),C=O stretching vibration(1 367 cm-1),and C-O-C asymmetric vibration(1 049 cm-1)were the key spectral bands for discrimination.Conclu-sion The combination of SERS and machine learning can effectively amplify subtle differences in mo-lecular structures,enabling rapid and accurate identification of etomidate and its analogues.This ap-proach is suitable for on-site rapid screening in forensic toxicology.

OBJECTIVE: Peritoneal fibrosis (PF) is an adverse event that occurs during long-term peritoneal dialysis, significantly impairing treatment efficiency and adversely affecting patient outcomes. Astragaloside IV (AS-IV), a principal active component derived from Astragalus membranaceus (Fisch.) Bunge, has exhibited anti-inflammatory and antifibrotic effects in various settings. This study aims to investigate the potential therapeutic efficacy and mechanism of AS-IV in the treatment of PF. METHODS: The PF mouse model was established by intraperitoneal injection of 4.25% peritoneal dialysis fluid (100 mL/kg). The epithelial-mesenchymal transition (EMT) of HMrSV5 cells was induced by the addition of 10 ng/mL transforming growth factor β (TGF-β). The differentially expressed genes in HMrSV5 cells treated with AS-IV were screened using transcriptome sequencing analysis. The potential targets of AS-IV were screened using network pharmacology and analyzed using molecular docking and molecular dynamics simulations. RESULTS: Administration of AS-IV at doses of 20, 40, or 80 mg/kg effectively mitigated the increase in peritoneal thickness and the development of fibrosis in mice with PF. The expression of the fibrosis marker α-smooth muscle actin in the peritoneum was significantly decreased in AS-IV-treated mice. The treatment of AS-IV (10, 20, and 40 μmol/L) significantly delayed the EMT of HMrSV5 cells induced by TGF-β, as demonstrated by the decreased number of 5-ethynyl-2'-deoxyuridine-positive cells, reduced migrated area, and decreased expression of fibrosis markers. A total of 460 differentially expressed genes were detected in AS-IV-treated HMrSV5 cells through transcriptome sequencing, with notable enrichment in the phosphatidylinositol-4,5-bisphosphate 3-kinase (PI3K)-AKT serine/threonine kinase 1 (AKT) signaling pathway. The reduced levels of phosphorylated PI3K (p-PI3K) and p-AKT were detected in HMrSV5 cells with AS-IV treatment. Epidermal growth factor receptor (EGFR) was predicted as a direct target of AS-IV, exhibiting strong hydrogen bond interactions. The activation of the PI3K-AKT pathway by the compound 740Y-P, and the activation of the EGFR pathway by NSC 228155 each partially counteracted the inhibitory effect of AS-IV on the EMT of HMrSV5 cells. CONCLUSION AS-IV delayed the EMT process in peritoneal mesothelial cells and slowed the progression of PF, potentially serving as a therapeutic agent for the early prevention and treatment of PF. Please cite this article as: Huang Y, Chu CL, Qiu WH, Chen JY, Cao LX, Ji SY, Zhu B, Wang GK, Shen QQ. Astragaloside IV delayed the epithelial-mesenchymal transition in peritoneal fibrosis by inhibiting the activation of EGFR and PI3K-AKT pathways. J Integr Med. 2025; 23(6):694-705.
Epithelial-Mesenchymal Transition/drug effects* ; Animals ; Saponins/pharmacology* ; Triterpenes/pharmacology* ; Mice ; Peritoneal Fibrosis/pathology* ; Proto-Oncogene Proteins c-akt/metabolism* ; ErbB Receptors/metabolism* ; Phosphatidylinositol 3-Kinases/metabolism* ; Signal Transduction/drug effects* ; Male ; Humans ; Molecular Docking Simulation ; Cell Line ; Mice, Inbred C57BL

OBJECTIVE: To investigate chronic hepatitis C virus (HCV) infection's effect on gestational liver function, pregnancy and delivery complications, and neonatal development. METHODS: A total of 157 HCV antibody-positive (anti-HCV[+]) and HCV RNA(+) patients (Group C) and 121 anti-HCV(+) and HCV RNA(-) patients (Group B) were included as study participants, while 142 anti-HCV(-) and HCV RNA(-) patients (Group A) were the control group. Data on biochemical indices during pregnancy, pregnancy complications, delivery-related information, and neonatal complications were also collected. RESULTS: Elevated alanine aminotransferase (ALT) rates in Group C during early, middle, and late pregnancy were 59.87%, 43.95%, and 42.04%, respectively-significantly higher than Groups B (26.45%, 15.70%, 10.74%) and A (23.94%, 19.01%, 6.34%) ( P < 0.05). Median ALT levels in Group C were significantly higher than in Groups A and B at all pregnancy stages ( P < 0.05). No significant differences were found in neonatal malformation rates across groups ( P > 0.05). However, neonatal jaundice incidence was significantly greater in Group C (75.16%) compared to Groups A (42.25%) and B (57.02%) ( χ ² = 33.552, P < 0.001). HCV RNA positivity during pregnancy was an independent risk factor for neonatal jaundice ( OR = 2.111, 95% CI 1.242-3.588, P = 0.006). CONCLUSIONS Chronic HCV infection can affect the liver function of pregnant women, but does not increase the pregnancy or delivery complication risks. HCV RNA(+) is an independent risk factor for neonatal jaundice.
Humans ; Female ; Pregnancy ; Adult ; Pregnancy Complications, Infectious/epidemiology* ; Retrospective Studies ; Pregnancy Outcome ; Infant, Newborn ; Viremia/virology* ; Hepatitis C ; Hepacivirus/physiology* ; Hepatitis C, Chronic/virology* ; Young Adult ; Alanine Transaminase/blood*

Primary ciliary dyskinesia (PCD) is a genetically heterogeneous disorder characterized by impaired motility of cilia and flagella. Mutations in cilia- and flagella-associated protein 300 ( CFAP300 ) are associated with human PCD and male infertility; however, the underlying pathogenic mechanisms remain poorly understood. In a consanguineous Chinese family, we identified a homozygous CFAP300 loss-of-function variant (c.304delC) in a proband presenting with classical PCD symptoms and severe sperm abnormalities, including dynein arm deficiency and acrosomal malformation, as confirmed by transmission electron microscopy (TEM). Histological analysis revealed multiple morphological abnormalities of the sperm flagella in CFAP300 -mutant individual, whereas immunofluorescence demonstrated markedly reduced CFAP300 expression in the spermatozoa of the proband. Furthermore, tandem mass tag (TMT)-based quantitative proteomics showed that the CFAP300 mutation reduced key spermatogenesis proteins (e.g., sperm flagellar 2 [SPEF2], solute carrier family 25 member 31 [SLC25A31], and A-kinase anchoring protein 3 [AKAP3]) and mitochondrial ATP synthesis factors (e.g., SLC25A31, cation channel sperm-associated 3 [CATSPER3]). It also triggered abnormal increases in autophagy-related proteins and signaling mediator phosphorylation. These molecular alterations are likely to contribute to progressive deterioration of sperm ultrastructure and function. Notably, successful pregnancy was achieved via intracytoplasmic sperm injection (ICSI) using the proband's sperm. Overall, this study expands the known CFAP300 mutational spectrum and offers novel mechanistic insights into its role in spermatogenesis.
Humans ; Male ; Infertility, Male/pathology* ; Acrosome/pathology* ; Sperm Tail/pathology* ; Pedigree ; Spermatozoa ; Adult ; Loss of Function Mutation ; Ciliary Motility Disorders/genetics* ; Spermatogenesis/genetics* ; Female