ObjectiveTo evaluate the efficacy and safety of Janus kinase （JAK） inhibitors combined with Chinese herbal medicine （CHM） in treating rheumatoid arthritis （RA）. MethodsClinical data from 169 RA patients were retrospectively collected. Among them， 71 cases received JAK inhibitors as the control group， while 98 cases received JAK inhibitors plus CHM as the observation group， both treated for 24 weeks. The rheumatoid factor （RF）， cyclic citic peptide antibody （anti-CCP）， erythrocyte sedimentation rate （ESR）， C-reactive protein （CRP）， and white blood cell count （WBC） were recorded before and after treatment. Databases including CNKI， Wanfang， VIP， PubMed and Web of Science were searched from inception till August 31st， 2025 for randomized controlled trials （RCTs） on the combined use of JAK inhibitors and CHM for RA. The methodological quality of the included studies was evaluated using the risk of bias assessment tool. Meta-analyses were performed for RF， anti-CCP， ESR， CRP， 28-joint disease activity score （DAS28）， overall clinical effective rate， and incidence of adverse events. Sensitivity analysis were also performed. ResultsThe retrospective study demonstrated that after treatment， ESR， CRP， and anti-CCP levels decreased in the observation group， while ESR and CRP levels decreased in the control group （P＜0.05）. Moreover， ESR and RF levels in the observation group were lower than those in the control group （P＜0.05）. A total of 9 RCTs involving 770 patients were included in the meta-analysis. The results indicated that the JAK inhibitors plus CHM group was superior to the JAK inhibitors group in reducing RF （MD=-8.97， 95%CI -15.01 to -2.94， P=0.004）， CRP （MD=-3.34， 95%CI -3.82 to -2.86， P＜0.001）， ESR （MD=-5.33， 95%CI -7.98 to -2.69， P＜0.001）， and DAS28 score （MD=-0.54， 95%CI -0.74 to -0.34， P＜0.001）， as well as in improving the overall clinical effective rate （OR=4.53， 95%CI 2.55 to 8.03， P＜0.001）. No statistically significant differences were observed between groups in anti-CCP levels （SMD=-2.08， 95%CI -4.41 to 0.24， P=0.080） or incidence of adverse events （OR=0.93， 95%CI 0.55 to 1.57， P=0.790）. ConclusionThe combination of JAK inhibitors and CHM demonstrates remarkable efficacy in treating RA， contributing to improved disease activity and reduced inflammatory markers with a favorable safety profile.

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.

Objectives: Sarcopenia, an age-related skeletal muscle disorder, is assessed by handgrip strength, gait speed, and muscle mass, yet population-specific reference data and standardized scores for Asian adults remain limited. We aimed to establish sex- and age-specific normative reference distributions, smoothed centile curves, and modelbased standardized T scores for these indicators in Asian adults. Methods: We retrospectively analyzed adults aged ≥ 40 years attending a tertiary-center Adult Preventive Health Program in 2023–2024. To assess construct validity, we compared sarcopenia indicators across age groups using analysis of variance (ANOVA). Sex- and age-specific reference distributions were estimated using Generalized Additive Models for Location, Scale, and Shape (GAMLSS), from which smooth model-based age-specific centiles (5th–95th) were derived. Individual values were standardized to model-based Z scores and transformed to T scores on an approximate 100-point scale. Asian Working Group for Sarcopenia (AWGS) cut-offs were used in receiver operating characteristic (ROC) analyses to determine corresponding T-score thresholds. Results: Among 8095 participants, model-based mean handgrip strength, gait speed, and skeletal muscle mass were 20.03±5 kg, 1.12±0.37 m/s, and 6.93±1.43 kg/m² in women and 30.69±7.46 kg, 1.12±0.37 m/s, and 8.79±1.95 kg/m² in men. GAMLSS-derived centile curves demonstrated age-related declines in strength and gait speed and milder changes in muscle mass. AWGS cut-offs corresponded to T scores in the mid-40s for strength and gait and around 40–41 for muscle mass. Conclusions We established sex- and age-specific normative values and GAMLSS-based reference distributions, and derived model-based Z and T scores that provide standardized sarcopenia metrics in Asian adults, align with AWGS thresholds, and support detection of muscle decline and risk stratification.

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.

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.

AIM To prepare the borneol-menthol eutectic mixture-loaded nanoemulsion gel of tetramethylpyrazine.METHODS The equilibrium solubilities of tetramethylpyrazine in different oil phases,emulsifiers and co-emulsifiers were determined,after which compatibility experiment was performed,and pseudo-ternary phase diagram was drawn.With Km value,oil phase proportion and water phase consumption as influencing factors,particle size,PDI and saturated drug loading as evaluation indices,the formulation was optimized by central composite design-response surface method.The drug-loaded nanoemulsion was dispersed into carbomer 940 gel matrix to prepare nanoemulsion gel,then the physicochemical properties,in vitro drug release and transdermal absorption properties were investigated.RESULTS The optimal formulation was determined to be 3.31∶6.16∶1.56∶88.97 for eutectic mixture-EL-40-1,2-propylene glycol-water ratio,the particle size,PDI and saturated drug loading were 37.85 nm,23.04 and 5.82 mg/g,respectively.The obtained white,semi-solid nanoemulsion gel demonstrated the average pH value and viscosity of 6.68±0.07 and(289.69±1.06)mPa·s,respectively,whose in vitro drug release accorded with Higuchi equation,and the accumulative permeability per unit area was(2 048.23±55.6)μg/cm2 within 24 h,which were 3.72 and 1.21 times higher than those of hydrogel and aqueous solution,respectively.CONCLUSION The borneol-menthol eutectic mixture-loaded nanoemulsion gel of tetramethylpyrazine meets preparation requirements,thus can achieve the effective transdermal delivery of raw medicine.

Aim To study the mechanism of salidro-side combined with rosavin in rats with ischemic stroke.Methods The MCAO rats was established by using thread-embolic method.The rats were divided into the sham group,MCAO group,salidroside com-bined with rosavin group,and positive control group;the drug was given continuously for seven days.Western blot was used to detect apoptosis indicators.Proteomics was used to analyse differential proteins(DEPs).STEP receptor inhibitor was injected into the lateral ventricles,the rats were administered for seven days,then the apoptosis indicators were detected.Re-sults Salidroside combined with rosavin could reduce neurological function scores in MCAO rats and inhibit cell apoptosis.Quantitative proteomics identified 496 DEPs in brain tissue and discovered core proteins STEP,p38,and CRTC1.Salidroside combined with rosavin could promote the STEP and CRTC1 while in-hibiting p38 protein.After treatment with STEP inhibi-tor,those effects were reversed.Conclusion Salidro-side combined with rosavin can inhibit cell apoptosis in MCAO rats,which is closely related to the regulation of the STEP/p38/CRTC1 signaling pathway.

Aim To investigate the role and underlying mechanisms of transient receptor potential cation chan-nel 6(TRPC6)in epileptogenesis using a kainic acid(KA)-induced mouse model.Methods C57BL/6 and TRPC6-KO(KO)mice were divided into two groups and implanted with cannulas for microinjection of KA(0.03 g·L-1,5 μL)into the lateral ventricle to establish the acute epilepsy model group,with saline injection serving as the control group.The Racine score was used to record the uninterrupted seizure grade of mice within two hours after KA administration.Immunohistochemistry was used to detect neuronal loss and tissue damage in the hippocampus brain region of mice.Immunofluorescence staining,Western blot and qPCR were used to detect the expressions of TRPC6,NLRP3,ASC,Caspase-1,p62,Atg7,Atg5,Beclin-1,LC3b-Ⅱ/LC3b-Ⅰ.in the hippocampus.Results KA induced significant neuronal loss and tissue damage in the hippocampal CA3 brain region of epilep-sy mice,while the expression levels of TRPC6,NL-RP3,ASC and Caspase-1 and other proteins in the hippocampus brain area of epilepsy mice increased,and the protein expression of autophagy-related proteins Atg7,Atg5,Beclin-1,LC3b-Ⅱ/LC3b-Ⅰ increased,while the expression of p62 protein decreased.TRPC6 knockout exacerbated KA-induced epileptogenesis,neuronal injury,inflammatory response and autophagy activation.Conclusion TRPC6 is involved in KA-in-duced epileptigenesis,and the mechanism may be re-lated to the activation of NLRP3 inflammasome-autoph-agy signaling caused by TRPC6 deletion.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.