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.

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 study the pathogenic bacteria infection in hospitalized diabetic foot patients in the Third People's Hospital of Yunnan Province and its correlation with different Wagner grades,to understand the the characteristics of pathogenic bacteria and related risk factors in hospitalized diabetic foot patients in the Third People's Hospital of Yunnan Province,and to further provide theoretical guidance for anti-infection treatment of these patients.Methods A retrospective analysis was conducted on the demographic data,severity of foot ulcers,and related laboratory test results of 536 patients with diabetic foot who were detected to have bacterial infection in the Third People's Hospital of Yunnan Province from January 2019 to January 2023.Results Among the 536 diabetic foot patients,pathogenic bacteria were cultured from 268 cases(50.0%)of Gram-positive bacterial infections,214 cases(39.9%)of gram-negative bacterial infections,2 cases(0.4%)of fungal infections,and 52 cases(9.7%)of mixed bacterial infections.The main pathogens among gram-positive bacteria were Staphylococcus aureus,Staphylococcus epidermidis and Enterococcus faecalis.for Gram-negative bacteria,the main pathogens were Escherichia coli,Enterobacter cloacae and Klebsiella pneumoniae.There were 31 cases of multi-drug resistant bacteria,and the multi-drug resistance rate was(5.78%).Among Gram-positive bacteria,all multidrug-resistant strains were Staphylococcus aureus,while among Gram-negative bacteria,the multi-drug resistant strains included Acinetobacter baumannii(1 case),Klebsiella pneumoniae(2 cases),Proteus common(2 cases),Pseudomonas aeruginosa(5 cases),Proteus mirabilis(1 case)and Enterobacter cloacae(1 case).The 536 patients were divided into Wagner grade 1 and 2 groups(78 cases),Wagner grade 3 group(274 cases),and Wagner grade 4 and 5 groups(184 cases).There were 73 cases of single bacterial infections and 5 cases of mixed bacterial infections in Wagner grade 1 and 2 group,including 51 cases(65.4%)of gram-positive bacteria,21 cases(26.9%)of gram-negative bacteria and 1 case(1.3%)of fungi.There were 248 cases of single bacterial infections and 26 cases of mixed bacterial infections in Wagner3 group,with 144 cases(52.6%)of gram-positive bacteria,103 cases(37.6%)of gram-negative bacteria,and 1 case(0.4%)with fungi.In the Wagner grade 4 and 5 groups,there were 163 cases of single bacterial infections and 21 cases of mixed bacterial infection,with 73 strains(39.7%)of gram-positive bacteria,90 strains(48.9%)of gram-negative bacteria and 0 strain(0%)of fungi.The predominant infectious pathogens in Wagner grades 1,2 and 3 were gram-positive bacteria,while those in Wagner grades 4 and 5 patients were mainly gram-negative bacteria.There were statistically significant differences in white blood cell counts,neutrophil percentage,bacterial classification,length of hospital stay,erythrocyte sedimentation rate and albumin levels among diabetic foot patients with different Wagner grades(P<0.01).With the increase of Wagner grade,patients had higher white blood cell counts and hypersensitive C-reactive protein levels,longer hospital stays,and lower albumin levels;however,there were no statistically significant differences in age,sex,duration of diabetes,smoking history,alcohol consumption history and history of hypertension(P>0.05).Conclusion The bacterial infection situation in patients with diabetic foot ulcers is related to different Wagner grades.The higher the Wagner grades,the greater the likelihood of infection with gram-negative bacteria.Antibiotics can be reasonably selected according to the Wagner grades of patients upon admission,actively controlling infection,while also enhancing,shortening hospital stays,and reducing amputation rates,thereby improving the prognosis of diabetic foot patients.

Objective To analyze the bacterial distribution characteristics,drug resistance characteristics and related risk factors of multidrug-resistant organisms(MDRO)in patients with diabetic foot infection(DFI)in some areas of Yunnan Province to provide empirical reference for clinical treatment.Methods Clinical data of 300 DFI patients admitted to the Department of Endocrinology of the Third People's Hospital of Yunnan Province from January 2019 to December 2023 were collected.Based on the results of drug sensitivity tests and matching of basic data,patients were divided into the MDRO group(n=60)and the non-MDRO group(n=240).A retrospective analysis was conducted on the distribution of pathogenic bacteria,drug resistance characteristics of MDRO and risk factors for MDRO infection in DFI patients.Results In 60 patients with MDRO infections,62 strains of MDRO were cultured,with 58 strains from single MDRO infections and 4 strains from mixed MDRO infections.Of the 60 patients,2 were cultured for 2 types of MDRO.Among the strains,there were 45 gram-positive bacteria(72.58％)which were all Staphylococcus aureus,17 strains of gram-negative bacteria(27.42％)mainly including Pseudomonas aeruginosa,Enterobacter cloacae and Klebsiella pneumoniae.Among common MDRO,Staphylococcus aureus showed complete resistance to penicillin G and oxacillin(100％),with high resistance to erythromycin and clindamycin(＞80％),but no resistance to tigacycline vancomycin was observed.The resistance of Klebsiella pneumoniae and Enterobacter cloacae to cephalosporin antibiotics was obvious,and the resistance rate to imipenem and amikacin was low.Pseudomonas aeruginosa was 100％resistant to ticacillin/clavulanate potassium,imipenem,tigacycline and cotrimoxazole,but showed no resistance to cefepime,ciprofloxacin,gentamicin and amikacin.There were statistically significant differences between the two groups in regional distribution,duration of diabetic foot,lower extremity arterial disease,venous plasma glucose levels and glycosylated hemoglobin(P＜0.05).Binary Logistic regression analysis showed that region and duration of diabetic foot disease were independent risk factors for MDRO infection in DFI patients(P＜0.05).Conclusion In some areas of Yunnan Province,the distribution of MDRO in DFI patients is mainly gram-positive bacteria,with varying antibiotic sensitivities among different pathogens.Multiple factors lead to MDRO infections in DFI patients,which assists clinical practitioners in early identification of high-risk DFI patients with MDRO infections and provide empirical reference for clinical treatment.

Aerosol-assisted plasma amplification laser-induced breakdown spectroscopy(LIBS)was employed for phosphorus detection in water.To address the multivariate coupling effects in LIBS and nebulization sampling system,an orthogonal experiment was employed to systematically optimize the key experimental parameters.Using the orthogonal experimental design,the parametric effects of laser energy(output voltage),signal acquisition delay,liquid velocity,and gas velocity on the signal to background ratio(SBR)of P I 213.618 nm were evaluated,and the optimal conditions were achieved,including laser energy of 86 mJ,delay time of 3 μs,gas velocity of 1.05 mL/min,and liquid velocity of 60 μL/min,which were in agreement with the control-variable optimization results.Moreover,the SBR response trends at P I 213.618 nm with all experimental parameters was strong in consistency with control-variable optimization results,which demonstrated the validity of the orthogonal array experimental design.This study established an efficient and accurate parameter optimization methodology for complex LIBS systems,significantly advancing the application of LIBS in environmental monitoring.

Objective To observe the effects of LINC01915 on the proliferation,migration and invasion of human colorectal cancer cells,and to explore its possible regulatory mechanism.Methods The colorectal cancer cell lines of SW620,SW480,LOVO,HCT116 and the normal colorectal cell line of NCM460 were selected,and the expression of LINC01915 was detected by RT-qPCR.HCT116 cell line with the highest expression of LINC01915 was taken and divided into the upregulation group,the downregulation group,the upregulation control group,the downregulation control group and the blank group.The transfection efficiency of each group was detected by fluorescence microscope;RT-qPCR was used to detect the expression of LINC01915 in each group;methyl thiazolyl tetrazolium(MTT)assay was used to detect the proliferative activity in each group;scratch wound healing assay and Transwell assay were used to detect the migration and invasion activities in each group;RT-qPCR was used to detect the expression of miR-92a-3p and mRNA expression of large tumor suppressor homolog 2(LATS2),transcription factor 21(TCF21)and Kruppel like factor 4(KLF4);and Western blot was used to detect the expression of LATS2,TCF21,and KLF4 proteins.Dual-fluorescein reporter assay was used to verify the targeting relationship between LINC01915 and miR-92a-3p.Results The expression of LINC01915 in various human colorectal cancer cell lines were lower than that in the NCM460 cell(P<0.05),and the highest LINC01915 expression in human colorectal cancer cell lines was observed in HCT116(P<0.05).The transfection efficiency of cells in each transfection group was high.Compared with the blank group and the upregulation control group,the expression of LINC01915,and mRNA and protein expression of LATS2,TCF21,and KLF4 in the upregulation group increased(P<0.05),and the A value,scratch healing rate,number of invasive cells and miR-92a-3p expression decreased(P<0.05).Compared with the blank group and the downregulation control group,the expression of LINC01915,and mRNA and protein expression of LATS2,TCF21,and KLF4 in the downregulation group decreased(P<0.05),and the A value,scratch healing rate,number of invasive cells and miR-92a-3p expression increased(P<0.05).LINC01915 had binding sites with miR-92a-3p,and compared with the miR-NC group,the miR-92a-3p mimics group showed a decrease in the luciferase activity of WT-LINC01915(P<0.05).Conclusion The expression of LINC01915 in the human colorectal cancer cell lines decreases,and upregulation of LINC01915 expression can inhibit cell proliferation,migration,and invasion,which may be related to the up-regulation of the expression of LATS2,TCF21 and KLF4 by inhibiting miR-92a-3p.

Myocardial fibrosis(MF)is the leading cause of car-diac insufficiency.Its complex pathogenesis and lack of effective treatment are key issues to be addressed in the cardiovascular field.Mitochondrial quality control system(MQC)is an impor-tant mechanism for eukaryotic cells to maintain the stability of mitochondrial form,quantity and quality.MQC disorders,which are characterized by low level of mitochondrial biogenesis,exces-sive mitochondrial oxidative stress,mitochondrial autophagy de-fect and mitochondrial dynamics disorder,play a crucial role in mediating the pathophysiological process of MF.Consequently,this article reviews the role of MQC in MF pathogenesis and the latest research,in order to better understand the molecular mech-anism of MF and provide reference for the development of more natural drugs in the future.

Objective:To validate the accuracy of three-dimensional anatomical models reconstructed from cone beam CT (CBCT) using micro-CT as the gold standard, and to evaluate the feasibility of performing anatomical analyses on such models.Methods:A total of 13 isolated deciduous teeth with intact roots were collected, including 5 anterior teeth and 8 molars, with a total of 34 root canals. The teeth were extracted from children aged 3-9 years who visited Peking University Hospital of Stomatology from January 2019 to April 2022 due to trauma or periapical disease, and were then scanned by micro-CT (with a voxel size of 0.018 mm) and CBCT (with a voxel size of 0.125 mm), respectively. Using a threshold-based semi-automated region segmentation method, anatomical models of these isolated teeth were reconstructed from the two CTs. Subsequently, the two CT reconstructed models were registered based on the iterative closest point algorithm, followed by deviation analysis. The key anatomical parameters were measured on the micro-CT and CBCT models, respectively, and the differences were calculated.Results:The CBCT reconstruction models were relatively accurate in the hard tissue morphology, and 97.1% (33/34) of the root canals were identified accurately. When it comes to the deviation analysis, the average distance between the matched points on the CBCT reconstruction models and the micro-CT models was (0.01±0.03) mm for the hard tissue, and (0.00±0.03) mm for the pulp chambers and canals, which did not affect clinical observation. The hard tissue and root canal length of CBCT models were both smaller than those of micro-CT models ( P<0.05), with a 95% limits of agreement of (-0.70, 0.14) mm for the hard tissue, and a 95% limits of agreement of (-1.93, 1.00) mm for the pulp chambers and canals. The impact of these differences on clinical operations was all within the acceptable range. Conclusions:Using micro-CT as a validate standard, CBCT with a voxel size of 0.125 mm was proved to be an effective tool for the reconstruction of deciduous teeth. Therefore, the reconstructed models were appropriate for studying deciduous teeth anatomy.

Objective To investigate the distribution and antimicrobial resistance profiles of clinically isolated Haemophilus influenzae and Moraxella catarrhalis in hospitals across China from 2015 to 2021,and provide evidence for rational use of antimicrobial agents.Methods Data of H.influenzae and M.catarrhalis strains isolated from 2015 to 2021 in CHINET program were collected for analysis,and antimicrobial susceptibility testing was performed by disc diffusion method or automated systems according to the uniform protocol of CHINET.The results were interpreted according to the CLSI breakpoints in 2022.Beta-lactamases was detected by using nitrocefin disk.Results From 2015 to 2021,a total of 43 642 strains of Haemophilus species were isolated,accounting for 2.91％of the total clinical isolates and 4.07％of Gram-negative bacteria in CHINET program.Among the 40 437 strains of H.influenzae,66.89％were isolated from children and 33.11％were isolated from adults.More than 90％of the H.influenzae strains were isolated from respiratory tract specimens.The prevalence of β-lactamase was 53.79％in H.influenzae strains.The H.influenzae strains isolated from children showed higher resistance rate than the strains isolated from adults.Overall,779 strains of H.influenzae did not produce β-lactamase but were resistant to ampicillin(BLNAR).Beta-lactamase-producing strains showed significantly higher resistance rates to these antimicrobial agents than the β-lactamase-nonproducing strains.Of the 16 191 M.catarrhalis strains,80.06％were isolated from children and 19.94％isolated from adults.M.catarrhalis strains were mostly susceptible to both amoxicillin-clavulanic acid and cefuroxime,evidenced by resistance rate lower than 2.0％.Conclusions The emergence of antibiotic-resistant H.influenzae due to β-lactamase production poses a challenge for clinical anti-infective treatment.Therefore,it is very important to implement antibiotic resistance surveillance for H.influenzae and guide rational antibiotic use.All local clinical microbiology laboratories should actively improve antibiotic susceptibility testing and strengthen antibiotic resistance surveillance for H.influenzae.