Immune checkpoint inhibitors (ICIs) are widely used in the treatment of malignant tumors, and their related immune-related adverse events (irAEs) have attracted increasing attention. This study reports the diagnosis and treatment process of a case of immune cystitis in a patient with hepatobiliary tract malignant tumor after treatment with pembrolizumab. The patient was admitted to the hospital due to frequent urination, urgency of urination and dysuria for 1 month. Previous repeated anti-infection treatments were ineffective. Combined with medical history, laboratory tests, imaging findings, cystoscopy and pathological results, the patient was clinically diagnosed with ICIs-associated immune cystitis (Pembrolizumab) ultimately. The patient's symptoms significantly improved after treatment with glucocorticoids. This case reindicates that clinicians need to improve awareness of ICI-related urinary system irAEs. Early identification and timely intervention can significantly improve patient prognosis.

Pulmonary fibrosis（PF） is a progressive and life-threatening disease with limited therapeutic options， highlighting the urgent need for innovative drug discovery strategies. To address this challenge， the authors propose the formula-originated rational intelligent screening&translation（FIRST）， a systematic framework for developing anti-fibrotic monomers derived from classical traditional Chinese medicine（TCM）. The strategy integrates three key dimensions， including tissue-oriented intelligent screening of active compounds， structural optimization based on drug-target spatial interactions and plant biosynthetic pathways， and cross-scale validation of drug. We further highlight its applications in discovering tissue-oriented novel drugs from clinically validated TCM， the development and mechanistic elucidation of anti-fibrotic therapeutics， as well as the clinical translation and secondary development of candidate drugs. This strategy paves the way for first-in-class， formula-derived monomeric drugs with defined structures， clarified mechanisms， and proven safety， offering a transformative avenue to meet the urgent therapeutic needs of PF and setting a new paradigm for TCM-based drug innovation.

Pulmonary fibrosis（PF） is a progressive and life-threatening disease with limited therapeutic options， highlighting the urgent need for innovative drug discovery strategies. To address this challenge， the authors propose the formula-originated rational intelligent screening&translation（FIRST）， a systematic framework for developing anti-fibrotic monomers derived from classical traditional Chinese medicine（TCM）. The strategy integrates three key dimensions， including tissue-oriented intelligent screening of active compounds， structural optimization based on drug-target spatial interactions and plant biosynthetic pathways， and cross-scale validation of drug. We further highlight its applications in discovering tissue-oriented novel drugs from clinically validated TCM， the development and mechanistic elucidation of anti-fibrotic therapeutics， as well as the clinical translation and secondary development of candidate drugs. This strategy paves the way for first-in-class， formula-derived monomeric drugs with defined structures， clarified mechanisms， and proven safety， offering a transformative avenue to meet the urgent therapeutic needs of PF and setting a new paradigm for TCM-based drug innovation.

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.

Alzheimer’s disease (AD) is a prevalent neurodegenerative condition characterized by progressive cognitive decline and memory loss. As the incidence of AD continues to rise annually, researchers have shown keen interest in the active components found in natural plants and their neuroprotective effects against AD. Quercetin, a flavonol widely present in fruits and vegetables, has multiple biological effects including anticancer, anti-inflammatory, and antioxidant. Oxidative stress plays a central role in the pathogenesis of AD, and the antioxidant properties of quercetin are essential for its neuroprotective function. Quercetin can modulate multiple signaling pathways related to AD, such as Nrf2-ARE, JNK, p38 MAPK, PON2, PI3K/Akt, and PKC, all of which are closely related to oxidative stress. Furthermore, quercetin is capable of inhibiting the aggregation of β‑amyloid protein (Aβ) and the phosphorylation of tau protein, as well as the activity of β‑secretase 1 and acetylcholinesterase, thus slowing down the progression of the disease.The review also provides insights into the pharmacokinetic properties of quercetin, including its absorption, metabolism, and excretion, as well as its bioavailability challenges and clinical applications. To improve the bioavailability and enhance the targeting of quercetin, the potential of quercetin nanomedicine delivery systems in the treatment of AD is also discussed. In summary, the multifaceted mechanisms of quercetin against AD provide a new perspective for drug development. However, translating these findings into clinical practice requires overcoming current limitations and ongoing research. In this way, its therapeutic potential in the treatment of AD can be fully utilized.

To characterize the genomes of different emm-type group A Streptococcus (GAS), their virulence genes and drug resistance profiles in Tianjin City from 2011 to 2024. After PCR, a total of 42 strains with different years and emm types were selected for whole genome sequencing and multi-locus sequence typing (MLST), and the core genomes were used to generate a phylogenetic tree, after which the virulence genes and resistance genes were identified and analyzed, followed by the drug susceptibility test. In this study, the GAS strains were dominated by emm1 (50.0%) and emm12 (40.4%), and the MLST phenotypes were categorized into six types: ST36 (40.4%), ST1274 (26.1%), ST28 (23.8%), ST921 (4.7%), ST46 (2.3%), and ST403 (2.3%). There was a high consistency between their emm-types and ST types. A total of 68 virulence genes were detected in the genomes of 42 GAS strains, involving functional genes encoding exotoxin, bacterial adhesion, extracellular enzymes, etc. The virulence genes they carried were significantly different between emm1-type and emm12-type strains, such as speA. At the same time, the carrying rates of some virulence genes in the same emm-type strains changed with time, such as hyl. The resistance genes were basically the same among different emm-type strains except for the vanSE gene detected in all emm12 strains. The results of drug sensitivity showed that the GAS strains isolated in Tianjin City from 2011 to 2024 were sensitive to penicillin, cefazolin, chloramphenicol, vancomycin, and levofloxacin, while the resistance rates to erythromycin, azithromycin, clarithromycin, and clindamycin ranged from 88.5% to 100.0%, and there was a certain degree of consistency between the resistance phenotypes and the detected resistance genes. Overall, the main emm types and evolutionary features of GAS in Tianjin City from 2011 to 2024 were consistent with the dominant types in China, and the carrying rate of virulence genes and drug resistance genes differed significantly among different emm-type strains, and there were continuous evolution and variation in the prevalence of virulence genes in GAS.

Objective:To investigate the feasibility and safety of intracardiac echocardiography(ICE)combined with total three-dimensional(T3D)technique in zero-fluoroscopy individualized transseptal puncture.Methods:A total of 112 patients with atrial fibrillation who underwent radiofrequency ablation in Yongchuan Hospital Affiliated to Chongqing Medical University from April 2021 to March 2024 were enrolled,and according to the method for transseptal puncture,they were randomly divided into ICE+T3D group with 56 patients and ICE group with 56 patients.The two groups were analyzed in terms of baseline data,time to atrial reconstruc-tion,time to coronary sinus electrode placement,frequency of ICE probe adjustment during transseptal puncture,duration of transsep-tal puncture,pretreatment time before ablation,incidence rate of complications,and the duration and dosage of X-ray exposure.Results:There were no significant differences in baseline data between the two groups.Compared with the ICE group,the ICE+T3D group had a significantly lower frequency of ICE probe adjustment during transseptal puncture(1.70±0.63 vs.5.34±1.71,P<0.001)and the duration of transseptal puncture(3.66±1.09 min vs.4.90±1.92 min,P<0.001).Compared with the ICE group,the ICE+T3D group had significantly longer time to atrial reconstruction(22.44±3.13 min vs.12.34±2.12 min,P<0.001)and pretreatment time be-fore ablation(49.41±3.52 min vs.37.65±4.04 min,P<0.001).In the ICE+T3D group,43(76.8%)patients achieved zero radiation during pretreatment before ablation,and 13 patients received X-ray due to the difficulty in catheter placement;compared with the ICE group,the ICE+T3D group had a significantly shorter duration of X-ray exposure(1.68±0.72 min vs.3.14±1.95 min,P=0.010)and a significantly lower dosage of X-ray exposure(6.28±2.78 mGy vs.23.85±21.32 mGy,P=0.004).During the stage of transseptal punc-ture,all patients in the ICE+T3D group achieved zero radiation,while 45 patients(80.4%)in the ICE patients received X-ray.In terms of complications,there were no life-threatening complications such as cardiac tamponade,perforation of the aorta by mistake,and embolization in either group,while there was one case(1.8%)of vascular complications in each group.Conclusions:ICE combined with T3D after integration and improvement is a safe and reliable procedure for zero-fluoroscopy individualized transseptal puncture.

Cognitive impairment is the main clinical manifestation of many nervous system diseases such as stroke,multiple sclerosis,and neurodegeneration,and neuroinflammation is one of the key mechanisms for the onset of cognitive disorders.Chemokines are a class of highly conserved small-molecule secretory proteins that bind to the corresponding chemokine receptors located on cell mem-brane,activating downstream signaling pathways and playing an important role in cell migration,proliferation,differentiation,and sur-vival.In the central nervous system,chemokines and their receptors are involved in immune response and can exert a certain regulatory effect on neuroinflammation.This article reviews the research advances in chemokines and their receptors in cognitive disorders,in or-der to provide new insights and targets for the early diagnosis and treatment of related diseases.

Objective To compare the differences of endoscopic ultrasound (EUS)-guided non-invasive measurement of portal venous pressure and EUS-guided portal pressure gradient(EUS-PPG) in measurement of portal venous pressure on animals and their correlation. Methods Twenty-four miniature pigs were selected and fed with carbon tetrachloride and phenobarbital sodium combined with high-fat,low-protein and low-choline diet for 16 weeks to establish a liver cirrhotic portal hypertension model. The changes of biochemical indexes of liver function and liver pathology in the experimental pigs were observed to evaluate whether the model was successful. After the model was successfully established,the hemodynamic parameters of the portal venous trunk were measured non-invasively under EUS guidance,including portal venous blood flow and splenic artery pulsatility index,thereby calculating portal venous pressure. Then,taking EUS-PPG,the portal vein,hepatic vein,and inferior vena cava were punctured with an 18G puncture needle under general anesthesia guided by the translinear endoscopic ultrasound,and the PPG was calculated through the central venous pressure monitoring system.The Pearson correlation analysis,Kappa test,ICC intraclass correlation coefficient and Bland-Altman plot were used for consistency analysis. Results All the 24 pigs survived 16 weeks after modeling.The serum levels of alanine transaminase (ALT),aspartate transaminase (AST),albumin (ALB),globulin (GLB),total bilirubin (TBIL) and indirect bilirubin (IBIL)after modeling were higher than those before modeling(P<0.05). HE staining and Sirius red staining showed abnormal liver morphology and increased collagen fibers after modeling,suggesting that the experimental pig model of liver cirrhotic portal hypertension was successfully established. The results of EUS-guided non-invasive measurement of portal venous pressure showed that the mean splenic artery pulsatility index was (2.03±0.68),the mean portal vein flow was (17.27±4.31)cm/s,and the mean portal venous pressure was (15.97±3.65)mmHg. The measurement results of the mean portal venous pressure,hepatic venous pres-sure and PPG of EUS-PPG were (20.68±4.71)mmHg,(4.07±2.14)mmHg and (16.38±4.28)mmHg respectively. Pearson correlation analysis showed that there was a significant positive correlation between the portal venous pressures measured by the two methods (r=0.902,P<0.001);the consistency tests of Kappa test and ICC intraclass correlation coefficient showed that the measurement results of the two methods were highly consistent (Kappa=0.699,P<0.001;ICC=0.945);Bland-Altman plot analysis showed that most of the points fell within 95％ limits of agreement. Conclusion EUS-guided non-invasive measurement of portal venous pressure has a high correlation and consistency with the measurement results by EUS-PPG,which has high success rate,and accurate reflection of portal venous pressure,with low cost and good safety.