ObjectiveTo explore the complex network relationships among pain, kinesiophobia, social participation and knee function in patients after total knee arthroplasty (TKA), and to analyze the moderating effects of different socio-structural factors on the rehabilitation network from an ethical equity perspective. MethodsA convenience sampling method was used to select 291 patients who underwent TKA in Qilu Hospital of Shandong University from May to July, 2023. Pain was assessed using Numerical Rating Scale, kinesiophobia with Chinese short version of the Tampa Scale for Kinesiophobia, social participation with Impact on Participation and Autonomy Questionnaire, and knee function with Hospital for Special Surgery Knee Score. A partial correlation network among pain, kinesiophobia, social participation and knee function was constructed using Graphical Least Absolute Shrinkage and Selection Operator. Key variables were identified through node centrality and bridge centrality analysis. Network Comparison Tests (NCT) were used to analyze network differences among subgroups based on different socio-structural characteristics. ResultsIn the network model, the nodes with the highest strength centrality were indoor participation, activity behavior and activity pain. Bridge centrality analysis indicated that activity pain, knee function, indoor participation and activity cognition were key bridge nodes. NCT revealed no significant differences in overall network structure or global strength among subgroups based on residence, education level or payment method (P > 0.05). However, significant differences in edge weights were found for specific edges such as activity cognition-activity behavior and knee function-indoor participation (P < 0.05). ConclusionThere is a network of interactions among pain, kinesiophobia, social participation and knee function in patients after TKA, with nodes such as indoor participation and activity pain playing key roles in the rehabilitation process. Although the overall rehabilitation network is similar across different socio-structural groups, variations exist in specific relational pathways among patients from rural areas, those with lower education levels, and those with out-of-pocket payment. This suggests that clinical rehabilitation interventions should focus on these core nodes and implement targeted support strategies for socio-structurally disadvantaged groups to promote rehabilitation equity.

ObjectiveTo explore the complex network relationships among pain, kinesiophobia, social participation and knee function in patients after total knee arthroplasty (TKA), and to analyze the moderating effects of different socio-structural factors on the rehabilitation network from an ethical equity perspective. MethodsA convenience sampling method was used to select 291 patients who underwent TKA in Qilu Hospital of Shandong University from May to July, 2023. Pain was assessed using Numerical Rating Scale, kinesiophobia with Chinese short version of the Tampa Scale for Kinesiophobia, social participation with Impact on Participation and Autonomy Questionnaire, and knee function with Hospital for Special Surgery Knee Score. A partial correlation network among pain, kinesiophobia, social participation and knee function was constructed using Graphical Least Absolute Shrinkage and Selection Operator. Key variables were identified through node centrality and bridge centrality analysis. Network Comparison Tests (NCT) were used to analyze network differences among subgroups based on different socio-structural characteristics. ResultsIn the network model, the nodes with the highest strength centrality were indoor participation, activity behavior and activity pain. Bridge centrality analysis indicated that activity pain, knee function, indoor participation and activity cognition were key bridge nodes. NCT revealed no significant differences in overall network structure or global strength among subgroups based on residence, education level or payment method (P > 0.05). However, significant differences in edge weights were found for specific edges such as activity cognition-activity behavior and knee function-indoor participation (P < 0.05). ConclusionThere is a network of interactions among pain, kinesiophobia, social participation and knee function in patients after TKA, with nodes such as indoor participation and activity pain playing key roles in the rehabilitation process. Although the overall rehabilitation network is similar across different socio-structural groups, variations exist in specific relational pathways among patients from rural areas, those with lower education levels, and those with out-of-pocket payment. This suggests that clinical rehabilitation interventions should focus on these core nodes and implement targeted support strategies for socio-structurally disadvantaged groups to promote rehabilitation equity.

ObjectiveTo explore the complex network relationships among pain, kinesiophobia, social participation and knee function in patients after total knee arthroplasty (TKA), and to analyze the moderating effects of different socio-structural factors on the rehabilitation network from an ethical equity perspective. MethodsA convenience sampling method was used to select 291 patients who underwent TKA in Qilu Hospital of Shandong University from May to July, 2023. Pain was assessed using Numerical Rating Scale, kinesiophobia with Chinese short version of the Tampa Scale for Kinesiophobia, social participation with Impact on Participation and Autonomy Questionnaire, and knee function with Hospital for Special Surgery Knee Score. A partial correlation network among pain, kinesiophobia, social participation and knee function was constructed using Graphical Least Absolute Shrinkage and Selection Operator. Key variables were identified through node centrality and bridge centrality analysis. Network Comparison Tests (NCT) were used to analyze network differences among subgroups based on different socio-structural characteristics. ResultsIn the network model, the nodes with the highest strength centrality were indoor participation, activity behavior and activity pain. Bridge centrality analysis indicated that activity pain, knee function, indoor participation and activity cognition were key bridge nodes. NCT revealed no significant differences in overall network structure or global strength among subgroups based on residence, education level or payment method (P > 0.05). However, significant differences in edge weights were found for specific edges such as activity cognition-activity behavior and knee function-indoor participation (P < 0.05). ConclusionThere is a network of interactions among pain, kinesiophobia, social participation and knee function in patients after TKA, with nodes such as indoor participation and activity pain playing key roles in the rehabilitation process. Although the overall rehabilitation network is similar across different socio-structural groups, variations exist in specific relational pathways among patients from rural areas, those with lower education levels, and those with out-of-pocket payment. This suggests that clinical rehabilitation interventions should focus on these core nodes and implement targeted support strategies for socio-structurally disadvantaged groups to promote rehabilitation equity.

The initiation of adaptive immune responses relies on the precise recognition and interpretation of antigenic information. In this process, the specific binding of T cell receptors (TCRs) to peptide-major histocompatibility complex (pMHC) molecules represents one of the key molecular events in the initiation of adaptive immune responses. Accordingly, the structural features of TCR-pMHC complexes provide a fundamental basis for dissecting antigen recognition mechanisms and support rational vaccine design, therapeutic target discovery in TCR-based immunotherapy, and TCR identification and optimization. However, experimental determination of TCR-pMHC structures remains costly, time-consuming, and limited in coverage, making computational approaches essential for rapidly obtaining reliable structural information. Computational methods for predicting the structures of TCR-pMHC complexes have advanced rapidly in recent years, driven by progress in deep learning-based modeling frameworks and the increasing availability of structural and sequence resources. Despite these developments, most existing tools do not adequately distinguish the key structural and biophysical differences between MHC class I (MHC-I) and MHC class II (MHC-II) complexes during model construction. As a consequence, their predictive performance differs substantially between class I and class II complexes. In general, structural predictions for class I complexes outperform those for class II complexes. This discrepancy may be related to several fundamental differences between the two systems, including the architecture of the peptide-binding groove, the distribution of peptide lengths, and the properties of peptide flanking residues (PFRs). Compared with MHC-I molecules, MHC-II molecules usually bind longer antigenic peptides, which typically range from 13 to 25 amino acids in length. PFRs at both termini of these peptides participate in regulating the overall conformation of TCR-pMHC class II complexes and exert a pronounced effect on the geometric and physicochemical characteristics of the TCR-pMHC binding interface. Furthermore, within the TCR recognition interface, the complementarity-determining regions (CDRs) consist of segments that differ markedly in conformational behavior. They commonly include regions that are relatively rigid and structurally stable, together with highly flexible segments exhibiting substantial conformational plasticity. These rigidity-flexibility features constitute an essential structural basis enabling TCRs to recognize diverse peptide-MHC ligands and to accommodate conformational heterogeneity at the interface. However, many current modeling tools, in an effort to enforce global conformational stability or reduce structural noise, tend to over-constrain intrinsically flexible regions. Such oversimplification may lead to inappropriate rigidification of flexible CDR loops, resulting in local structural distortions, compromised interface geometry, or even complete modeling failure for specific complexes. Against this background, the review approaches the field from the perspective of computational differences between MHC-I and MHC-II complexes. We first systematically organize and summarize available resources related to TCRs and pMHCs, including structural datasets, sequence databases, prediction tools, and benchmarking studies. We then focus on five representative tools capable of predicting both class I and class II complexes—AlphaFold2, AlphaFold3, TCRmodel2, tFold-TCR, and TCR-pHLA_ModellerS. After excluding structures present in the training sets of these tools, we constructed a benchmark dataset comprising 25 class I and 10 class II TCR-pMHC complexes in the bound state and conducted a systematic evaluation using this dataset. We first employ widely used general evaluation metrics, including All-Atom Root Mean Square Deviation (All-Atom RMSD), Backbone RMSD, Template Modeling score (TM-score), and DockQ, to assess the global conformational accuracy and interface modeling quality of class I and class II complexes. For class II complexes, we propose for the first time a peptide flanking residue deviation index, including the PFRs-Deviation Index (PFRs-DI), N-PFR-Deviation Index (N-PFR-DI), and C-PFR-Deviation Index (C-PFR-DI), to quantitatively characterize conformational deviations in PFRs. In addition, we propose the CDR conformational consistency index (CCC) designed to qualitatively evaluate the ability of prediction tools to capture TCR CDR conformational flexibility. These metrics collectively assess a tool’s ability to model both overall conformation and critical functional regions, thereby addressing the limitations of existing evaluation criteria that overemphasize global structure while inadequately capturing modeling quality in key functional areas. This establishes a unified analytical framework for MHC-I and MHC-II complexes to guide data resource selection, modeling strategy formulation, and evaluation system development. The framework further advances computational modeling and provides crucial support for multi-scale analysis of TCR-pMHC recognition mechanisms and their biological functions.

The initiation of adaptive immune responses relies on the precise recognition and interpretation of antigenic information. In this process, the specific binding of T cell receptors (TCRs) to peptide-major histocompatibility complex (pMHC) molecules represents one of the key molecular events in the initiation of adaptive immune responses. Accordingly, the structural features of TCR-pMHC complexes provide a fundamental basis for dissecting antigen recognition mechanisms and support rational vaccine design, therapeutic target discovery in TCR-based immunotherapy, and TCR identification and optimization. However, experimental determination of TCR-pMHC structures remains costly, time-consuming, and limited in coverage, making computational approaches essential for rapidly obtaining reliable structural information. Computational methods for predicting the structures of TCR-pMHC complexes have advanced rapidly in recent years, driven by progress in deep learning-based modeling frameworks and the increasing availability of structural and sequence resources. Despite these developments, most existing tools do not adequately distinguish the key structural and biophysical differences between MHC class I (MHC-I) and MHC class II (MHC-II) complexes during model construction. As a consequence, their predictive performance differs substantially between class I and class II complexes. In general, structural predictions for class I complexes outperform those for class II complexes. This discrepancy may be related to several fundamental differences between the two systems, including the architecture of the peptide-binding groove, the distribution of peptide lengths, and the properties of peptide flanking residues (PFRs). Compared with MHC-I molecules, MHC-II molecules usually bind longer antigenic peptides, which typically range from 13 to 25 amino acids in length. PFRs at both termini of these peptides participate in regulating the overall conformation of TCR-pMHC class II complexes and exert a pronounced effect on the geometric and physicochemical characteristics of the TCR-pMHC binding interface. Furthermore, within the TCR recognition interface, the complementarity-determining regions (CDRs) consist of segments that differ markedly in conformational behavior. They commonly include regions that are relatively rigid and structurally stable, together with highly flexible segments exhibiting substantial conformational plasticity. These rigidity-flexibility features constitute an essential structural basis enabling TCRs to recognize diverse peptide-MHC ligands and to accommodate conformational heterogeneity at the interface. However, many current modeling tools, in an effort to enforce global conformational stability or reduce structural noise, tend to over-constrain intrinsically flexible regions. Such oversimplification may lead to inappropriate rigidification of flexible CDR loops, resulting in local structural distortions, compromised interface geometry, or even complete modeling failure for specific complexes. Against this background, the review approaches the field from the perspective of computational differences between MHC-I and MHC-II complexes. We first systematically organize and summarize available resources related to TCRs and pMHCs, including structural datasets, sequence databases, prediction tools, and benchmarking studies. We then focus on five representative tools capable of predicting both class I and class II complexes—AlphaFold2, AlphaFold3, TCRmodel2, tFold-TCR, and TCR-pHLA_ModellerS. After excluding structures present in the training sets of these tools, we constructed a benchmark dataset comprising 25 class I and 10 class II TCR-pMHC complexes in the bound state and conducted a systematic evaluation using this dataset. We first employ widely used general evaluation metrics, including All-Atom Root Mean Square Deviation (All-Atom RMSD), Backbone RMSD, Template Modeling score (TM-score), and DockQ, to assess the global conformational accuracy and interface modeling quality of class I and class II complexes. For class II complexes, we propose for the first time a peptide flanking residue deviation index, including the PFRs-Deviation Index (PFRs-DI), N-PFR-Deviation Index (N-PFR-DI), and C-PFR-Deviation Index (C-PFR-DI), to quantitatively characterize conformational deviations in PFRs. In addition, we propose the CDR conformational consistency index (CCC) designed to qualitatively evaluate the ability of prediction tools to capture TCR CDR conformational flexibility. These metrics collectively assess a tool’s ability to model both overall conformation and critical functional regions, thereby addressing the limitations of existing evaluation criteria that overemphasize global structure while inadequately capturing modeling quality in key functional areas. This establishes a unified analytical framework for MHC-I and MHC-II complexes to guide data resource selection, modeling strategy formulation, and evaluation system development. The framework further advances computational modeling and provides crucial support for multi-scale analysis of TCR-pMHC recognition mechanisms and their biological functions.

Objective This study reports on the obligatory parasitism of water mites Arrenurus sp. on Anopheles messeae at the Manzhouli Port, Inner Mongolia, China. Methods Duing July 2024, a survey on the mosquito diversity was conducted at the Manzhouli Port. Captured mosquitoes and their ectoparasites were identified to species level. Results A total of 1840 adult mosquitoes were collected, representing species from three genera: Culex(Cx. modestus, Cx. pipiens pallens), Aedes(Ae. dorsalis, Ae. flavidorsalis, Ae. flavescens), and Anopheles (An. messeae). Among all the mosqutioes specimens,3 out of 150 captured An. messeae were found to carry ectoparasitic mites, with number of 2,4,27 mites separately. Morphological and molecular identification reached the same result as water mites(Hydrachnidiae, Hydracrina). COI gene sequence showed 94% similarity with the closest species Arrenurus truncatellus. Conlusions Literature review suggests water mites are host-specific parasitism of mosquito species and herein with the first record of Arrenurus sp. parasiting on An. Messeae in the most high-latitude region globally.

This study investigates the genetic diversity and evolutionary relationships of different Artemisia argyi germplasm resources to provide a basis for germplasm identification, variety selection, and resource protection. A total of 192 germplasm resources of A. argyi were studied, and EST-based simple sequence repeat(EST-SSR) primers were designed based on transcriptomic data of A. argyi. Polymerase chain reaction(PCR) amplification was performed on these resources, followed by fluorescence capillary electrophoresis to detect genetic diversity and construct DNA fingerprints. From 197 pairs of primers designed, 28 pairs with polymorphic and clear bands were selected. A total of 278 alleles were detected, with an average of 9.900 0 alleles per primer pair and an average effective number of alleles of 1.407 2. The Shannon's diversity index(I) for the A. argyi germplasm resources ranged from 0.148 1 to 0.418 0, with an average of 0.255 7. The polymorphism information content(PIC) ranged from 0.454 5 to 0.878 0, with an average of 0.766 9, showing high polymorphism. Cluster analysis divided the A. argyi germplasm resources into three major groups: Group Ⅰ contained 136 germplasm samples, Group Ⅱ contained 45, and Group Ⅲ contained 11. Principal component analysis also divided the resources into three groups, which was generally consistent with the clustering results. Mantel test results showed that the genetic variation in A. argyi populations was to some extent influenced by geographic distance, but the effect was minimal. Structure analysis showed that 190 germplasm materials had Q≥ 0.6, indicating that these germplasm materials had a relatively homogeneous genetic origin. Furthermore, 8 core primer pairs were selected from the 28 designed primers, which could distinguish various germplasm types. Using these 8 core primers, DNA fingerprints for the 192 A. argyi germplasm resources were successfully constructed. EST-SSR molecular markers can be used to study the genetic diversity and phylogenetic relationships of A. argyi, providing theoretical support for the identification and molecular-assisted breeding of A. argyi germplasm resources.
Artemisia/classification* ; Microsatellite Repeats ; Genetic Variation ; Expressed Sequence Tags ; DNA Fingerprinting ; Phylogeny ; Polymorphism, Genetic ; DNA, Plant/genetics* ; Genetic Markers

Food,drug and environment related cases are becoming more and more frequent,and the demand for on-site rapid detection is also increasing.Nanozymes are nanomaterials with enzyme-like catalytic activity,which have the advantages of high catalytic efficiency,good stability,economy,adjustability,multifunctionality and large-scale preparation.The colorimetric sensing technology based on nanozymes combined with smart phones has wide range of applications in the field of food,drugs and environment detection,and is expected to become an important means for relevant departments to combat crime.This paper summarized the progresses of nanozymes in the field of environmental,food and drug crime(EFDC)detection,focusing on the detection mechanism of different types of nanozymes and the current status of research on the detection of EFDC,and prospected the future development of nanozymes.The possible future prospects of machine learning(ML)in the field of nanozymes colorimetric sensing technology and the challenges in detection of EFDC were also discussed.

Insulin is an important protein hormone that participates in multiple metabolic pathways.Biosynthetic insulin has been widely used in the treatment of type 1 and type 2 diabetes.Currently,the number of reported cases of insulin overdose both at home and abroad is gradually increasing,and insulin homicide is no longer a means of"committing murder without leaving a trace".At present,there are no systematic protocols for the identification of insulin overdose in the field of forensic medi-cine in China.This article introduces the causes,toxicological characteristics,forensic examination,labo-ratory testing methods and indicator reference of insulin overdose.Based on the identification practice and research results and referring to relevant studies on insulin overdose at home and abroad,this pa-per aims to provide recommendations and references for the formulation of forensic identification guide-lines for insulin overdose cases.

Objective To explore the application value of fractional anisotropy(FA)analysis of RGB component transformation in different directions of fibers in substantia nigra in Parkinson's disease(PD).Methods There were 35 cases of PD and 37 cases of normal control group.After being performed by brain diffusion tensor imaging(DTI)scanning,the sequence was imported into 3DSlicense 5.6.0,and the diffusion module was used to implement pseudo color mapping based on FA,locate and segment substantia nigra,and use the substantia nigra mask as the tracking starting point.After forming tracing,fibers were imported into DTIANALYSIS 1.51,converting the RGB components into FA values for analysis,and visualized the analysis result.At the same time,fiber length,fiber density,and segmented FA point cloud percentage were compared.Results Compared with the normal group,the length of substantia nigra fibers in the PD group was shorter[(95.14±19.85)mm vs(115.99±21.39)mm,P＜0.01],and there was a statistical difference between the two groups.There was no statistical difference in fiber density[(0.07±0.05)/mm3 vs(0.10±0.12)/mm3,P＞0.05]between control group and PD group.The percentage of FA segment point clouds in the PD group was lower than that in the normal group at 0.9-1,but the principal component characteristics of the point cloud ratios in each FA segment were not significant.Conclusion Based on the transformation of RGB components into FA analysis,the length,density,and FA values of substantia nigra nerve fibers in PD patients can be quantified and visualized,providing a basis for the study of PD neural pathways.