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 investigate the effects and mechanisms of Lycium ruthenicum Murr. in improving sleep. METHODS Network pharmacology was employed to identify the active components of L. ruthenicum and their associated disease targets， followed by enrichment analysis. A caffeine‑induced zebrafish model of sleep deprivation was established ， and the zebrafish were treated with L. ruthenicum Murr. extract （LRME） at concentrations of 0.1， 0.2 and 0.4 mg/mL， respectively； 24 h later， behavioral changes of zebrafish and pathological alterations in brain neurons were subsequently observed. The levels of inflammatory factors [interleukin-6 （IL-6）， IL-1β， IL-10， tumor necrosis factor-α （TNF-α）]， oxidative stress markers [superoxide dismutase （SOD）， malondialdehyde （MDA）， glutathione peroxidase （GSH-Px）， catalase （CAT）]， and neurotransmitters [5- hydroxytryptamine （5-HT）， γ-aminobutyric acid （GABA）， glutamic acid （Glu）， dopamine （DA）， and norepinephrine （NE）] were measured. The protein expression levels of protein kinase B1 （AKT1）， phosphorylated AKT1 （p-AKT1）， epidermal growth factor receptor （EGFR）， B-cell lymphoma 2 （Bcl-2）， sarcoma proto-oncogene，non-receptor tyrosine kinase （SRC）， and heat shock protein 90α family class A member 1 （HSP90AA1） in the zebrafish were also determined. RESULTS A total of 12 active components and 176 intersecting disease targets were identified through network pharmacology analysis. Among these， apigenin， naringenin and others were recognized as core active compounds， while AKT1， EGFR and others served as key targets； EGFR tyrosine kinase inhibitor resistance signaling pathway was identified as the critical pathway. The sleep improvement rates in zebrafish of LRME low-， medium-， and high-dose groups were 54.60%， 69.03% and 77.97%， 开发。E-mail：hjp_yft@163.com respectively， while the inhibition ratios of locomotor distance were 0.57， 0.83 and 0.95， respectively. Compared with the model group， the number of resting counts， resting time and resting distance were significantly increased/extended in LRME medium- and high-dose groups （P＜0.05）. Neuronal damage in the brain was alleviated. Additionally， the levels of IL-6， IL-1β， TNF-α， MDA， Glu， DA and NE， as well as the protein expression levels of AKT1， p-AKT1， EGFR， SRC and HSP90AA1， were markedly reduced （P＜0.05）， while the levels of IL-10， SOD， GSH-Px， CAT， 5-HT and GABA， as well as Bcl-2 protein expression， were significantly elevated （P＜0.05）. CONCLUSIONS L. ruthenicum Murr. demonstrates sleep-improving effects， and its specific mechanism may be related to the regulation of inflammatory responses， oxidative stress， neurotransmitter balance， and the EGFR tyrosine kinase inhibitor resistance signaling pathway.

Plant-derived extracellular vesicles (PDEVs) are nanoscale extracellular vesicles secreted by plant cells, characterized by a lipid bilayer structure. These vesicles carry a variety of bioactive molecules, including proteins, nucleic acids, and lipids, and play essential roles in intercellular communication and physiological regulation in plants. Compared to animal-derived extracellular vesicles, PDEVs offer several advantages, such as a broad range of sources, high biocompatibility, low immunogenicity, and low production costs. Furthermore, PDEVs have demonstrated remarkable potential as natural nanocarriers for drug delivery, due to their ability to efficiently traverse biological barriers, such as the blood-brain barrier, making them promising candidates for drug delivery systems. This review systematically elaborates on the complex composition of PDEVs, which consists of lipids, proteins, and nucleic acids, the typical structural characteristics of their lipid bilayers ranging from 30 to 150 nm, and their versatile loading capabilities as drug carriers, efficiently encapsulating various types of therapeutic agents such as hydrophilic small molecules, hydrophobic drugs, nucleic acids, and proteins. We systematically summarize the recent advancements in strategies for enhancing the loading efficiency of PDEVs, which include methods such as co-incubation, ultrasound-assisted loading, electroporation, freeze-thaw cycles, and microfluidic technology. These techniques are evaluated based on their underlying principles, suitable drug types, and their respective advantages. In addition to loading strategies, we focus on the engineered approaches to achieve targeted delivery using PDEVs, such as genetic engineering modifications, chemical ligand conjugation, membrane fusion technology, and polyethylene glycol (PEG) modification. We discuss the mechanisms of these strategies in enhancing targeting efficiency, prolonging in vivo circulation time, and improving therapeutic efficacy. Further, this review highlights the application of PDEVs in various disease models, including tumor, skin inflammation, metabolic disorders, and neurodegenerative diseases, showcasing their therapeutic potential as multifunctional delivery platforms. The ability of PDEVs to encapsulate diverse therapeutic agents and target specific tissues or cells opens up new avenues for the treatment of complex diseases, offering advantages over conventional drug delivery systems. However, despite the promising applications of PDEVs, several challenges remain in their development and clinical translation. These challenges include variability in source materials, standardization of preparation processes, quality control, scalability of production, and the need for clinical validation. To overcome these obstacles, the integration of advanced technologies such as artificial intelligence-assisted design and multi-omics analysis is proposed as a way to facilitate the precise development of PDEVs. These emerging technologies hold the potential to further enhance the precision and effectiveness of plant-based drug delivery systems, ultimately advancing the field of precision medicine. In conclusion, the use of PDEVs as a platform for drug delivery represents a promising area of research with the potential to revolutionize therapeutic strategies. Their ability to encapsulate and deliver a wide variety of bioactive molecules, along with their inherent advantages in biocompatibility and versatility, makes them a valuable tool in the development of more efficient and targeted therapeutic interventions. Continued research and innovation in this field will pave the way for the clinical implementation of PDEVs in the treatment of various diseases, offering new hope for more effective and sustainable therapeutic options.

Objective To evaluate the diagnostic efficacy of different pathogen detection methods in active tuberculosis (ATB) of kidney transplant recipients (KTRs) and analyze their diagnostic value in different types of clinical specimens. Methods A retrospective observational study was conducted, including adult KTRs with suspected ATB from the Eighth Medical Center of Chinese People's Liberation Army General Hospital from January 2020 to January 2025. Baseline data of the recipients were collected and analyzed, and the diagnostic criteria were based on a composite reference standard (including clinical diagnosis and bacteriological confirmation). The single-detection efficacy of acid-fast staining smear, mycobacterium tuberculosis culture, Xpert test for the detection of mycobacterium tuberculosis nucleic acid and resistance to rifampicin (Xpert MTB/RIF), as well as tuberculosis or non-tuberculosis mycobacterial DNA detection was compared. The differences in sensitivity among different methods were analyzed. Results A total of 95 KTRs with suspected ATB and 136 specimens were included. And 52 cases were diagnosed as ATB, among which 48 cases were confirmed by bacteriological means (92%). The sensitivity of Xpert MTB/RIF and DNA detection (0.55 and 0.52, respectively) was significantly higher than that of acid-fast staining smear (0.30) and mycobacterium tuberculosis culture (0.35) (P < 0.01). There was a statistically significant difference in the bacteriological diagnosis rate among different specimen types (P = 0.035). The detection sensitivity of each method was the highest in pus, puncture tissue and bronchoalveolar lavage fluid, followed by sputum and pleural and abdominal effusions, and the lowest in cerebrospinal fluid. Conclusions The sensitivity of nucleic acid amplification technology for immunosuppressed KTRs is slightly reduced, but it should still be recommended as the preferred initial screening tool. The comprehensive strategy of "multi-site sampling (prior to obtaining high bacterial load specimens) combined with multiple method detection" may improve the diagnostic efficacy.

To understand the background of Escherichia coli(E.coli)carried by artificially bred sika deer and the biological characteristics of the isolated strains,such as drug resistance and pathogenicity,in April 2024,we collected 184 fresh deer fecal samples from four deer farms in Luxiang Township,Shuangyang District,Changchun City,Jilin Province,for isolation and cultivation of E.coli.The isolates were tested for drug resistance and biochemical identification with a BD PhoenixTM-100 Automated Microbiology System.The virulence genes were detected with PCR,and the strains were molecularly typed with ERIC-PCR.A total of 165 E.coli strains were isolated from 184 samples of deer feces,with an isolation rate of 89.67%.Twenty strains had a drug resistance phenotype,and the drug resistance rate was 12.12%;these strains included 15 strains of multi-drug resistant bacteria and 11 strains of ESBL-producing bacteria.Virulence gene detection indicated that the sika deer isolates carried multiple diarrhea-associated virulence genes,such as EAST-1(12.12%),eae(1.21%),stx1(7.88%),stx2(7.27%),and STa(1.82%).ERIC-PCR demonstrated that the isolates showed high polymorphism.The ESBL-producing E.coli carried by sika deer are likely to spread drug resistance in the community and livestock population.Some isolates carried multiple diarrhea-associated virulence genes,thus posing a human transmission risk.Therefore,monitoring of drug resistance and virulence genes must be strengthened,and antibiotics must be used reasonably during the breeding process to avoid excessive use and misuse.

Objective:To compare the therapeutic efficacy and safety of local anesthesia and spinal anesthesia for the patients with varicocele(VC)who underwent microsurgical varicocelectomy(MV).Methods:We retrospectively analyzed the data of VC patients who underwent MV treatment at the Andrology Department of the Affiliated Ruikang Hospital of Guangxi University of Chinese Medicine from May 2020 to March 2023.Cases with complete clinical data and follow-up evaluation were selected and divided into a control group(spinal anesthesia)and an observation group(local anesthesia)according to different anesthesia methods.The surgical time(including anesthesia time),visual analogue scale(VAS)score for pain,hospital stay,treatment cost,sperm concentration,for-ward motile sperm rate,and normal sperm morphology rate after three months of surgery,as well as postoperative complications and re-currence rate were compared between the two groups.Results:A total of 107 eligible cases were included,with 56 cases in the con-trol group and 51 cases in the observation group.There was no significant difference in the VAS score for pain during and after four hours of surgery,as well as postoperative complications,and recurrence rate between the two groups(P＞0.05).There was an signif-icant increase in sperm concentration,forward motile sperm rate,and normal sperm morphology rate in both of two groups after three months of surgery(P＜0.05).However,there was no significant difference between the two groups three months after surgery(P＞0.05).The surgical time and hospital stay were shorter than those of the control group(P＜0.05).And the treatment cost in observa-tion group was lower than that of the control group(P＜0.05).Conclusion:Both local anesthesia and lumbar anesthesia for MV treatment of VC have good efficacy and safety.However,patients treated with MV under local anesthesia for VC have obvious advanta-ges in terms of operation time(including anesthesia time),hospital stay,and treatment cost,which is worthy of clinical promotion and application.

To understand the background of Escherichia coli(E.coli)carried by artificially bred sika deer and the biological characteristics of the isolated strains,such as drug resistance and pathogenicity,in April 2024,we collected 184 fresh deer fecal samples from four deer farms in Luxiang Township,Shuangyang District,Changchun City,Jilin Province,for isolation and cultivation of E.coli.The isolates were tested for drug resistance and biochemical identification with a BD PhoenixTM-100 Automated Microbiology System.The virulence genes were detected with PCR,and the strains were molecularly typed with ERIC-PCR.A total of 165 E.coli strains were isolated from 184 samples of deer feces,with an isolation rate of 89.67%.Twenty strains had a drug resistance phenotype,and the drug resistance rate was 12.12%;these strains included 15 strains of multi-drug resistant bacteria and 11 strains of ESBL-producing bacteria.Virulence gene detection indicated that the sika deer isolates carried multiple diarrhea-associated virulence genes,such as EAST-1(12.12%),eae(1.21%),stx1(7.88%),stx2(7.27%),and STa(1.82%).ERIC-PCR demonstrated that the isolates showed high polymorphism.The ESBL-producing E.coli carried by sika deer are likely to spread drug resistance in the community and livestock population.Some isolates carried multiple diarrhea-associated virulence genes,thus posing a human transmission risk.Therefore,monitoring of drug resistance and virulence genes must be strengthened,and antibiotics must be used reasonably during the breeding process to avoid excessive use and misuse.

Objective:To explore the diagnostic value of total bilirubin to albumin ratio(B/A ratio)combined with alpha-fetoprotein(AFP)and abnormal prothrombin induced by vitamin K absence-Ⅱ(PIVKA-Ⅱ)for hepatocellular carcinoma(HCC).Methods:35 HCC patients(HCC group),35 cirrhosis patients(cirrhosis group),35 HCC patients post-transcatheter arterial chemoembolization(TACE)(TACE postoperative group),and 35 healthy volunteers(healthy control group)were selected in our hospital from October 2023 to October 2024.The serum B/A ratio,AFP,and PIVKA-Ⅱ levles were measured and compared across the groups.The correlations between serum B/A ratio,AFP,and PIVKA-Ⅱ in the HCC group were analyzed.The B/A ratio,AFP,PIVKA-Ⅱ were compared across different clinical and pathological features in HCC patients.The serum B/A ratio,AFP,and PIVKA-Ⅱ levels were compared pre and post operation in the HCC group.The diagnostic value of B/A ratio,AFP,PIVKA-Ⅱ alone and in combination for HCC were analyzed by receiver operating characteristic(ROC)curves.Results:The serum B/A ratio,AFP,and PIVKA-Ⅱ levels in HCC group and TACE postoperative group were significantly higher than those in the cirrhosis group and healthy control group,and the HCC group was higher than that in the TACE postoperative group(P＜0.05).Pearson correlation analysis results showed that,B/A ratio in the HCC group was positively correlated with AFP(r=0.352,P=0.001),B/A ratio was positively correlated with PIVKA-Ⅱ(r=0.327,P=0.003),and AFP was positively correlated with PIVKA-Ⅱ(r=0.285,P=0.008).Higher TNM stage,larger tumor diameter,presence of vascular invasion,and lower differentiation degree of HCC patients,who had higher B/A ratio,AFP,and PIVKA-Ⅱ levels(P＜0.05).Serum B/A ratio,AFP,and PIVKA-Ⅱ levels in the HCC group post operation were significantly lower than those in pre operation(P＜0.05).ROC curve analysis results showed that,when B/A ratio,AFP,and PIVKA-Ⅱ were detected separately,the area under the curve(AUC)was 0.785,0.756,and 0.802,respectively.The AUC for joint detection was 0.925.The AUC in combination was greater than that of individual detection of each indicator.Conclusion:The combination of B/A ratio,AFP,and PIVKA-Ⅱ testing significantly improves the diagnostic efficiency for HCC,which is worthy of clinical application.

Objective To investigate the clinical efficacy and safety of facilitated percutaneous coronary intervention(PCI)with half-dose recombinant staphylokinase(r-SAK)in patients with ST-segment elevation myocardial infarction(STEMI)who are expected to undergo PCI within 120 minutes.Methods From October 2021 to August 2022,a total of 200 STEMI patients in eight centers were included and randomly assigned in a 1﹕1 ratio to either r-SAK group or control group.Patients received loading doses of aspirin and ticagrelor and intravenous heparin and were randomized to receive an intravenous bolus of either 5 mg r-SAK or normal saline prior to PCI.The outcomes were set as ST-segment resolution(STR)at 60-90 minutes after PCI,the proportion and transition of pathological Q waves on the 5th day after PCI,and the proportion of high-sensitivity cardiac troponin T(hs-cTnT)peaking within 12 hours of onset.The safety outcome was major bleeding events defined as Bleeding Academic Research Consortium(BARC)≥type 3 bleeding during hospitalization.Results Compared with the control group,the r-SAK group had a higher proportion of STR≥70%within 60-90 minutes after PCI(58.3%vs.40.3%,P=0.009);a lower proportion of pathological Q waves(59.1%vs.74.1%,P=0.040);a lower rate of Q wave progression(14.8%vs.43.2%,P＜0.001);a higher rate of Q wave disappearance(12.5%vs.3.7%,P=0.027);and a higher proportion of hs-cTnT peaking within 12 hours of symptom onset[31/40(77.5%)vs.17/33(51.5%),P=0.027].Regarding the safety outcome,no significant difference in BARC≥type 3 bleeding was found between the two groups during hospitalization(P＞0.05).Conclusions For STEMI patients who were expected to undergo primary PCI within 120 minutes of symptom onset,the facilitated PCI with half-dose r-SAK significantly increased the proportion of STR≥70%at 60-90 minutes after PCI,reduced the formation of pathological Q waves,and shortened the time to peak hs-cTnT,without increasing the risk of bleeding,which should be an alternative reperfusion strategy worthy of further study.