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.

The blue fluorescent carbon dots(TMCDs)and cyan fluorescent carbon dots(TMCDs-H2O)were synthesized fromm-phenylenediamine and tricarballylic acid through air-assisted melting polymerization and one-step hydrothermal method,respectively.Air purging could effectively inhibit the side reactions and reduce the derivative structures in the carbon dots product.The structure and morphology of these two materials were systematically characterized using liquid nuclear magnetic resonance spectroscopy(NMR),mass spectrometry(MS),and transmission electron microscopy.Compared to TMCDs-H2O((3.12±0.63)nm),TMCDs showed a smaller average particle size(approximately(1.85±0.02)nm).The NMR and MS analysis revealed that although the main structure of both types of carbon dots was similar,TMCDs exhibited a simpler structure with higher degree of polymerization.These results suggested that supramolecular interactions might introduce numerous small molecule derivatives into TMCDs-H2O particles,resulting in lower polymerization degree,multiple substructures,and larger particle size characteristics for this material.When employed as chemical sensors for metal ion detection,in the linear range of 1×10-5-5×10-4 mol/L,the detection limits of Fe3+by TMCDs and TMCDs-H2O were 3.3×10-6 mol/L and 3.8×10-6 mol/L,respectively.The experimental results demonstrated that the recoveries of CDs and inductively coupled plasma optical emission spectrometer(ICP-OES)were similarity,whereas TMCDs displayed a considerable relative standard deviation.This study demonstrated that endogenously derived structures in CDs could enhance the performance of metal ion sensing.

A highly efficient oxidase-mimetic silver phosphate/nickel hydroxystannate(Ag3PO4/NiSn(OH)6)composite was synthesized via a precipitation method using nickel hydroxystannate(NiSn(OH)6)as the support.The abundant hydroxyl groups(—OH)on NiSn(OH)6 not only provided nucleation sites for Ag3PO4 nanoparticles but also improved their dispersion and overall material stability.Based on oxidase-like activity of Ag3PO4/NiSn(OH)6 and inhibitory effect of sodium dodecylbenzenesulfonate(SDBS)on this catalytic activity,a novel colorimetric sensing method for SDBS detection was developed.Under optimized experimental conditions,the method exhibited a linear range of 3.69-42.7 μmol/L,with a detection limit of 0.135 μmol/L(S/N=3).The regression equation was ΔA652=0.01125C(μmol/L)+0.1498,with a correlation coefficient(R2)of 0.992.Practical application in dishwashing liquid analysis achieved satisfactory recoveries of 96.9%-106.4%,demonstrating the method's reliability for real sample detection.

Metal-organic frameworks(MOFs)are a class of organic-inorganic hybrid materials formed by the self-assembly of metal ions or metal clusters with organic ligands through coordination,and possess high specific surface area,tunable pore size and diverse structures.In recent years,MOFs and their composites have shown great application potential in the field of drug detection,especially in selective recognition,enhancing detection sensitivity and on-site rapid detection.This paper summarized the structural characteristics,synthesis methods and detection principles of MOFs and their composites,and reviewed the latest research progresses in detection of various drugs such as opioids,amphetamines,cannabinoids,cathinones,cocaine,ketamine,fentanyls and psychotropic drugs.The advantages and challenges of MOFs materials in the pretreatment of complex biological samples,sensor construction and on-site rapid detection were discussed,and the prospects for future development were analyzed,with the aim of providing theoretical support and technical references for promoting the applications of MOFs in anti-drug practice.

Objective To evaluate the forensic application value of used dental floss as a source of bio-logical evidence for individual identification by analyzing the effects of dental floss sample collection methods,DNA extraction methods,preservation conditions,and sampling sites on the success rate of STR typing.Methods Dental floss samples were collected using three techniques:direct cutting,cotton swab wiping,and flocked swab wiping,respectively.DNA was extracted respectively by the Chelex,spin column-based and magnetic bead-based methods.DNA quantification and STR typing were per-formed using the Qubit kit and FGI HumDNA Typing kit(Platinum),respectively.Storage environ-ments(temperature and humidity,ultraviolet radiation)and sampling locations(the floss part,the handle part)on DNA quantity and STR typing were evaluated.Results Through conducting a statisti-cal analysis of three key indicators of average DNA mass concentration,STR locus detection rate,and typing accuracy rate,the direct cutting method demonstrated the highest efficacy,followed by cotton swab wiping mothed,and the flocked swab wiping method had the lowest efficacy.Direct cutting yielded an average DNA mass concentration greater than(4.94±1.87)ng/μL,with STR locus detection and accuracy rates of 100%.Bead-based DNA extraction method produced superior DNA concentration and quality compared to spin column-based and Chelex methods,regardless of whether the sampling technique used.Preservation conditions had a significant impact on the DNA analysis of samples.Par-ticularly,the STR typing accuracy of samples preserved at 55℃/50%RH for 35 days dropped to(81.82±12.31)%,and that of samples exposed to ultraviolet radiation for 12 h dropped to(55.46±34.31)%.DNA concentration from the handle part of dental floss was extremely low,with an STR typing accuracy of only(30.91±27.35)%.Conclusion Using cotton swabs to wipe or directly cutting the thread of dental floss samples,and combining this approach with the magnetic bead method for DNA extraction,can best guarantee the concentration and quality of DNA.In addition,samples should be stored in low-temperature,low-humidity environment,protected from light and ultraviolet radiation.

Carbon nanomaterials have unique physicochemical properties and great application potential, and great potential for medical applications, often in the form of biosensor systems. The development of novel carbon nanomaterials such as carbon nanotubes and graphene, has led to the emergence of new nanobiosensors capable of performing functions such as minimally invasive surgery, disease monitoring, tumor detection and drug delivery. In this review, the application progress of biosensors based on carbon nanomaterials was mainly introduced from the aspects of minimally invasive surgery, disease monitoring, tumor detection and drug delivery. It is of great significance to develop electronic devices or portable detection devices based on carbon nanomaterials for use in the biomedical field.

Background: The immune system plays a critical role in the development and progression of osteoporosis and fractures. However, the causal relationships between antibody immune responses, immune cells, and these bone conditions remain unclear. This study aimed to explore these relationships using Mendelian randomization (MR) analysis. Methods: We collected complete blood count data from patients with fractures and healthy individuals and analyzed their differences. Then, we conducted a 2-sample, 2-step MR analysis to investigate the causal effects of antibody immune responses on osteoporosis and fractures, using inverse-variance weighted (IVW) as the primary method. We also explored whether immune cells mediate the pathway between antibodies and osteoporosis or fractures. Finally, we analyzed the functions and expression levels of key genes involved. Results: Overall, the fracture group exhibited increased white blood cell count, absolute neutrophil count, absolute monocyte count, platelet count, and their respective proportions, while absolute lymphocyte count, absolute eosinophil count, absolute basophil count, red blood cell count, and their proportions were decreased. We identified 44 causal relationships between antibodies and osteoporosis or fractures, with 7 supported by multiple MR methods, and 5 showing odds ratios significantly deviating from 1 in the IVW analysis. Epstein-Barr virus-related antibodies had a notable impact on osteoporosis and fractures. The human leukocyte antigen (HLA) gene family, particularly HLA-DPB1, emerged as a significant risk factor. However, immune cells were not found to mediate these effects. Conclusions This study elucidated the causal relationships between antibody immune responses, immune cells, and osteoporosis or fractures. The HLA gene family plays a crucial role in the interaction between antibodies and these bone conditions, with HLA-DPB1 identified as a key risk gene. Immune cells do not serve as mediators in this process. These findings provide valuable insights for future research.

Objective To observe the clinical efficacy of Biyuan Tongqiao Granules combined with moxibustion in the treatment of allergic rhinitis(AR)of lung qi deficiency and cold type,and to explore its possible mechanism of action.Methods A total of 138 patients diagnosed with AR in the otolaryngology outpatient and inpatient departments of Linyi People's Hospital from November 2020 to November 2022 were selected as the study subjects.The patients were randomly divided into an observation group and a control group using a random number table,with 69 cases in each group.The control group was treated with Mometasone Furoate Nasal Spray,while the observation group received Biyuan Tongqiao Granules combined with moxibustion in addition to the control group's treatment.Both groups were treated for 4 weeks.After the ending of treatment,the clinical efficacy of the two groups was evaluated.Changes in traditional Chinese medicine(TCM)syndrome scores,serum levels of interleukin-6(IL-6),interleukin-8(IL-8),and C-reactive protein(CRP)were observed.The proportions of Treg cells and Th17 cells were compared before and after treatment,and the Treg/Th17 ratio,as well as levels of transforming growth factor β1(TGF-β1)and interleukin-17(IL-17)were calculated.The safety and incidence of adverse reactions in the two groups were evaluated.Six months after the end of treatment,the recurrence rate of patients was followed up and compared between the two groups.Results(1)After treatment,the TCM syndrome scores of both groups were significantly improved(P＜0.05),and the observation group showed significantly better improvement in TCM syndrome scores compared to the control group,with a statistically significant difference(P＜0.05).(2)After treatment,the serum levels of IL-6,CRP,and IL-8 in both groups were significantly improved(P＜0.05),and the observation group showed significantly better improvement in serum IL-6,CRP,and IL-8 levels compared to the control group,with a statistically significant difference(P＜0.05).(3)After treatment,the levels of Treg,Th17,Treg/Th17,TGF-β1,and IL-17 in both groups were significantly improved(P＜0.05),and the observation group showed significantly better improvement in these levels compared to the control group,with a statistically significant difference(P＜0.05).(4)The total effective rate in the observation group was 97.10%(67/69),while it was 76.81%(53/69)in the control group.The efficacy of the observation group was superior to that of the control group,with a statistically significant difference(P＜0.05).(5)At the 6-month follow-up,the recurrence rate in the observation group was 5.80%(4/69),while it was 24.64%(17/69)in the control group.The recurrence rate in the observation group was lower than that in the control group,with a statistically significant difference(P＜0.05).(6)There was no statistically significant difference in the incidence of adverse reactions between the two groups(P＞0.05).Conclusion Biyuan Tongqiao Granules combined with moxibustion in the treatment of AR of lung qi deficiency and cold type can significantly improve patients'clinical symptoms,help regulate the Treg/Th17 immune imbalance,improve inflammatory factor levels,and has a low recurrence rate,good safety,and significant efficacy.

ObjectiveBased on ultra-performance liquid chromatography-quadrupole-electrostatic field orbitrap high resolution mass spectrometry（UPLC-Q-Orbitrap-MS）， the chemical constituents of Qili Qiangxin capsules was identified， and their distribution in vivo was analyzed. MethodsUPLC-Q-Orbitrap-MS was used to detect the sample solution of Qili Qiangxin capsules， as well as the serum， brain， heart， lung， spleen， liver and kidney tissues of mice after oral administration. Using the Thermo Xcalibur 2.2 software， the compound information database was constructed， and the molecular formulas of compounds corresponding to the quasi-molecular ions were fitted. Based on the information of retention time， accurate relative molecular mass and fragments， the compounds and their distribution in vivo were analyzed by comparing with the data of reference substances and literature. ResultsA total of 233 compounds， including 70 terpenoids， 60 flavonoids， 23 organic acids， 17 alkaloids， 20 steroids， 7 coumarins and 36 others， were identified or predicted from Qili Qiangxin capsules， 73 of which were identified matching with standard substances. Tissue distribution results showed that 71， 17， 38， 33， 32， 58 and 43 migrating components were detected in blood， brain， heart， lung， spleen， liver and kidney， respectively. Thirty-seven components were absorbed into the blood and heart， including quinic acid， benzoylaconitine benzoylmesaconine and so on. Fourteen components were absorbed into the blood and six tissues， including calycosin， methylnissolin， formononetin， alisol B， alisol A and so on. ConclusionThis study comprehensively analyzes the chemical components of Qili Qiangxin capsules and their distribution in vivo. Among them， astragaloside Ⅳ， salvianolic acid B， ginsenoside Rb₁， ginsenoside Rb₃， ginsenoside Rd， ginsenoside Rg₃， calycosin-7-glucoside， and sinapine may be the important components for the treatment of heart failure， which can provide useful reference for its quality control and research on pharmacodynamic material basis.