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.

Osteoarthritis (OA), a highly prevalent degenerative joint disease worldwide, is defined by articular cartilage degradation, abnormal bone remodeling, and persistent chronic inflammation. It severely compromises patients’ quality of life, and currently, there is no radical cure. Abnormal mechanical stress is widely regarded as a core driver of OA pathogenesis, and the exploration of mechanical signal perception and transduction mechanisms has become crucial for deciphering OA’s pathophysiological processes. Piezo1, a key mechanosensitive cation channel belonging to the Piezo protein family, has recently gained significant attention due to its pivotal role in mediating cellular responses to mechanical stimuli in joint tissues. This review systematically examines Piezo1’s expression patterns, regulatory mechanisms, and pathological functions in OA, with a particular focus on its dual roles in modulating chondrocyte homeostasis and bone metabolism disorders, while also delving into the underlying molecular signaling pathways and potential therapeutic implications. Piezo1, consisting of approximately 2 500 amino acids and forming a unique trimeric propeller-like structure, is widely expressed in chondrocytes, osteocytes, mesenchymal stem cells, and synovial cells. It exhibits permeability to cations such as Ca²⁺, K⁺, and Na⁺, and directly responds to membrane tension changes induced by mechanical stimuli like fluid shear stress and mechanical overload. In OA patients and animal models, Piezo1 expression is significantly upregulated, especially in cartilage regions subjected to abnormal mechanical stress (e.g., human temporomandibular joint cartilage). This overexpression is closely associated with aggravated cartilage degeneration, increased chondrocyte apoptosis, accelerated cellular senescence, and intensified inflammatory responses. Mechanical overload and pro-inflammatory cytokines (e.g., IL-1β) are key inducers of Piezo1 upregulation: IL-1β activates the PI3K/AKT/mTOR signaling pathway to enhance Piezo1 expression, forming a pathogenic positive feedback loop that inhibits chondrocyte autophagy, promotes apoptosis, and further accelerates joint degeneration. Mechanistically, Piezo1 mediates OA progression through multiple interconnected pathways. When activated by mechanical stress, Piezo1 triggers excessive Ca²⁺ influx, leading to endoplasmic reticulum stress (ERS) and mitochondrial dysfunction, which directly induce chondrocyte apoptosis. This process involves the activation of downstream signaling cascades such as cGAS-STING and YAP-MMP13/ADAMTS5. YAP, a transcriptional regulator, upregulates the expression of matrix metalloproteinase 13 (MMP13) and aggrecanase (ADAMTS5), thereby accelerating cartilage matrix degradation. Additionally, Piezo1-driven Ca²⁺ overload promotes the accumulation of reactive oxygen species (ROS) and upregulates senescence markers (p16 and p21), accelerating chondrocyte senescence via the p38MAPK and NF-κB pathways. Senescent chondrocytes secrete senescence-associated secretory phenotype (SASP) factors (e.g., IL-6, IL-1β), further amplifying joint inflammation. In terms of bone metabolism, Piezo1 maintains joint homeostasis by promoting the differentiation of fibrocartilage stem cells into chondrocytes and balancing bone formation and resorption through regulating the FoxC1/YAP axis and RANKL/OPG ratio. Therapeutically, targeting Piezo1 shows promising potential. Preclinical studies have demonstrated that Piezo1 inhibitors (e.g., GsMTx4) can reduce joint damage and alleviate pain in OA mice. Simultaneously, siRNA-mediated co-silencing of Piezo1 and TRPV4 (another mechanosensitive channel) decreases intracellular Ca²⁺ concentration, inhibits chondrocyte apoptosis, and promotes cartilage repair. Conditional knockout of Piezo1 using Gdf5-Cre transgenic mice alleviates cartilage degeneration in post-traumatic OA models by downregulating MMP13 and ADAMTS5 expression. Despite existing challenges, such as off-target effects of inhibitors, inefficient local drug delivery, and interindividual genetic variability, strategies like developing selective Piezo1 antagonists, optimizing targeted nanocarriers, and combining Piezo1-targeted therapy with physical therapy provide viable avenues for clinical translation. The authors propose that Piezo1 serves as a critical therapeutic target for OA, and future research should focus on deciphering its context-dependent regulatory networks, developing tissue-specific intervention strategies, and validating their efficacy and safety in clinical trials to address the unmet medical needs of OA patients.

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.

Owing to its inherent advantages—such as being non-destructive, rapid, highly molecule-specific, and minimally interfered with by moisture—Raman spectroscopy has been widely adopted in the fields of skin barrier function assessment, monitoring the transdermal penetration of active cosmetic ingredients, and the identification and quality control of cosmetic products. Despite these strengths, the practical application of this technique faces a significant bottleneck: the strong fluorescence background generated by endogenous skin components and exogenous cosmetic additives. Endogenous skin substances, such as structural proteins (e.g., collagen and elastin), metabolic coenzymes (e.g., nicotinamide adenine dinucleotide), and pigments (e.g., melanin), together with exogenous cosmetic constituents like organic colorants, chemical sunscreens, and fragrances, often possess strong absorption and emission characteristics. When excited by lasers, these components produce a fluorescence background that can be 10⁶ to 10⁸ times stronger than the Raman scattering signals, effectively masking the inherently weak vibrational fingerprint information. In recent years, driven by the rapid development of optoelectronic hardware and artificial intelligence algorithms, fluorescence suppression strategies have evolved from isolated, single-method approaches into comprehensive, multi-level synergistic systems. These systems are categorized into three distinct tiers: sample preparation, signal acquisition, and data processing. At the sample preparation level, techniques such as photobleaching and surface-enhanced Raman spectroscopy (SERS) are employed to eliminate or bypass the generation of fluorescence at the source. At the signal acquisition level, instrumental improvements—including the use of long-wavelength near-infrared excitation (typically 785 nm or 1 064 nm), confocal spatial filtering, and shifted excitation Raman difference spectroscopy (SERDS)— are utilized to physically isolate Raman signals from the fluorescence background. Furthermore, at the data processing level, numerical baseline correction methods such as polynomial fitting, penalized least squares (e.g., airPLS, arPLS), wavelet transform, and derivative algorithms are increasingly integrated into the analytical pipeline to extract Raman spectral features from mixed signals without increasing hardware costs or acquisition time. This review provides a systematic categorization and critical evaluation of these fluorescence suppression methods, detailing their underlying principles, technical advantages, and inherent limitations in diverse experimental setups. By focusing on critical application scenarios—including skin barrier assessment, percutaneous absorption monitoring, the routine quality control of cosmetics, and the emerging field of portable on-site detection—this paper explores the current state of technique selection and optimization. Finally, the article discusses future development trends, emphasizing the necessity of constructing adaptive, tiered suppression strategies, developing intelligent and automated data processing algorithms, and promoting the integration of portable, multi-modal diagnostic devices. The objective of this review is to provide a comprehensive technical reference to facilitate the transition of Raman spectroscopy from a specialized laboratory tool into a routine, robust analytical platform for advancements in skin science and cosmetic research.

Gastric cancer remains a highly prevalent malignancy worldwide, with surgical resection currently constituting the cornerstone of treatment aimed at improving long-term patient survival. Owing to their notable advantages, including reduced surgical trauma and accelerated postoperative recovery, minimally invasive procedures are progressively supplanting conventional open surgery and have become the mainstream approach in gastric cancer management. Concurrently, the rapid advancement of artificial intelligence (AI) technologies has enabled real-time intraoperative monitoring of surgical scenes, thereby furnishing novel technical support for adjunctive decision-making, surgical navigation, and skill assessment during gastrectomy. This article provides a systematic review of the current status of AI applications in minimally invasive gastric cancer surgery, with a particular focus on research progress pertaining to instrument recognition, surgical phase identification, delineation of normal anatomical structures, detection of metastatic foci, and early warning of intraoperative adverse events. Furthermore, we discuss the potential value of AI in enhancing surgical efficiency, ensuring patient safety, and optimizing surgical education. On this basis, we further analyze the principal challenges and inherent risks confronting current AI systems, with the aim of informing future technological innovation and facilitating clinical translation.

Objective:To analyze the clinical characteristics of children with head and neck rhabdomyosarcoma (RMS) and to summarize the mid-long term efficacy of Beijing Children′s Hospital Rhabdomyosarcoma 2006 (BCH-RMS-2006) regimen and China Children′s Cancer Group Rhabdomyosarcoma 2016 (CCCG-RMS-2016) regimen.Methods:A retrospective cohort study. Clinical data of 137 children with newly diagnosed head and neck RMS at Beijing Children′s Hospital, Capital Medical University from March 2013 to December 2021 were collected. Clinical characteristic of patients at disease onset and the therapeutic effects of patients treated with the BCH-RMS-2006 and CCCG-RMS-2016 regimens were compared. The treatments and outcomes of patients with recurrence were also summarized. Survival analysis was performed by Kaplan-Meier method, and Log-Rank test was used for comparison of survival rates between groups.Results:Among 137 patients, there were 80 males (58.4%) and 57 females (41.6%), the age of disease onset was 59 (34, 97) months. The primary site in the orbital, non-orbital non-parameningeal, and parameningeal area were 10 (7.3%), 47 (34.3%), and 80 (58.4%), respectively. Of all patients, 32 cases (23.4%) were treated with the BCH-RMS-2006 regimen and 105 (76.6%) cases were treated with the CCCG-RMS-2016 regimen. The follow-up time for the whole patients was 46 (20, 72) months, and the 5-year progression free survival (PFS) and overall survival (OS) rates for the whole children were (60.4±4.4)% and (69.3±4.0)%, respectively. The 5-year OS rate was higher in the CCCG-RMS-2016 group than in BCH-RMS-2006 group ((73.0±4.5)% vs. (56.6±4.4)%, χ2=4.57, P=0.029). For the parameningeal group, the 5-year OS rate was higher in the CCCG-RMS-2016 group (61 cases) than in BCH-RMS-2006 group (19 cases) ((57.3±7.6)% vs. (32.7±11.8)%, χ2=4.64, P=0.031). For the group with meningeal invasion risk factors, the 5-year OS rate was higher in the CCCG-RMS-2016 group (54 cases) than in BCH-RMS-2006 group (15 cases) ((57.7±7.7)% vs. (30.0±12.3)%, χ2=4.76, P=0.029). Among the 10 cases of orbital RMS, there was no recurrence. In the non-orbital non-parameningeal RMS group (47 cases), there were 13 (27.6%) recurrences, after re-treatment, 7 cases survived. In the parameningeal RMS group (80 cases), there were 40 (50.0%) recurrences, with only 7 cases surviving after re-treatment. Conclusions:The overall prognosis for patients with orbital and non-orbital non-parameningeal RMS is good. However, children with parameningeal RMS have a high recurrence rate, and the effectiveness of re-treatment after recurrence is poor. Compared with the BCH-RMS-2006 regimen, the CCCG-RMS-2016 regimen can improve the treatment efficacy of RMS in the meningeal region.

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

AIM To study the chemical constituents from the sticks and leaves of Croton cascarilloides Raeusch.and their biological activities.METHODS The 95％ethanol extract from the sticks and leaves of C.cascarilloides was isolated and purified by MCI,silica gel,Sephadex LH-20 and semi-preparative HPLC,then the structures of obtained compounds were identified by physicochemical properties and spectral data.LPS-induced NO RAW264.7 cell model induced by LPS was used to evaluate its anti-inflammatory activity in vitro.GES-1 injury model induced by taurocholic acid was used to screen the gastric mucosal protection activity.RESULTS Fourteen compounds were isolated and identified as bullatantriol(1),(-)-boscialin(2),(+)-dehydrovomifoliol(3),3-(hydroxylacetyl)-indole(4),pinoresinol(5),3,7-dimethyl-octa-1,7-diene-3,6-ol(6),(+)-syringaresinol(7),curcasinlignan B(8),cleomiscosin C(9),cleomiscosinD(10),2,6-dimethyl-octa-1,7-dien-3,6-diol(11),vanillin(12),vanillic acid(13),methyl vanillate(14).Compound 4 had certain anti-inflammatory activity,with IC50 values of 73.62 μmol/L.The protective rates of 25 μmol/L compounds 1-4,6,9-12 and 14 on gastric mucosal epithelial cells were 30.07％,34.18％,23.91％,30.92％,17.51％,19.69％,31.76％,22.46％,30.56％and 14.49％,respectively.CONCLUSION Compounds 1-14 are isolated from this plant for the first time.Compound 4 shows anti-inflammatory activity,1-4,6,9-12 and 14 show different degrees of gastric mucosal epithelial cell protective activity.

AIM To study the chemical constituents from Pyrolae Herba and their activity against pathogenic bacteria associated with sports injuries.METHODS Silica gel and Sephadex LH-20 were used for isolation and purification,then the structures of obtained compounds were identified by physicochemical properties and spectral data.The antibacterial activity was detected by filter paper method.RESULTS Twenty five compounds were isolated and identified as manuleoside H(1),5-hydroxy-3,7-dimethoxy-4,-methyl flavone(2),5-hydroxy-3,7,4'-methoxy-flavone(3),5-hydroxy-7,3',4'-trimethoxy-flavone(4),5,7-dihydroxy-3-methoxy-flavone(5),7.5,4'-trihydroxyflavone(6),ethyl vanillate(7),clovandiol(8),hydroxydihydrobovolide(9),zanthopyranone(10),eugenin(11),berchemol(12),3,7-dimethoxy-5,3',4'-trihydroxyflavone(13),2,5-dimethyl-7-hydroxychromone(14),rutalinium(15),formononetin(16),8-hydroxy-4',7-dimethoxy-isoflavones(17),isoline(18),dearabinosyl pneumonanthoside(19),3,5-dihydroxycinnamic acid(20),2,4,6-trihydroxyacetophenone(21),p-isopropyl benzoic acid(22),geraldone(23),ethylsyringin(24),4-hydroxyphenylethyl-2'-hydroxypropionate(25).Compounds 8,10,12,16 and 24 showed bacteriostatic zone on pathogenic bacteria susceptible to sports injuries.CONCLUSION All the compounds are isolated from Pyrolae Herba for the first time.Compounds 8,10,12,16 and 24 have inhibitory activity against pathogenic bacteria susceptible to sports injuries.