ObjectivePost-ischemic acute inflammation and the subsequent persistent dysregulation of the immune microenvironment represent major pathological drivers that aggravate neuronal injury and severely restrict functional recovery following ischemic stroke. Although current reperfusion therapies partially restore blood flow, they fail to effectively modulate the secondary inflammatory cascade and oxidative stress, which remain critical barriers to neurological restoration. To address this challenge, this study aimed to engineer and systematically evaluate a biomimetic nanosystem composed of transforming growth factor-β1 (TGF-β1)-loaded platelet membrane-camouflaged lipid nanoparticles (PLP). This nanosystem was designed to achieve dual lesion-targeted delivery and immune microenvironment remodeling. By verifying its spatiotemporal accumulation, anti-inflammatory activity, and neuroprotective efficacy, we sought to establish an integrated therapeutic strategy that simultaneously enables lesion targeting, immune regulation, and functional recovery after ischemic injury. MethodsThe physicochemical properties of PLP, including hydrodynamic particle size, zeta potential, structural stability, and morphology, were characterized using dynamic light scattering, zeta potential analysis, and transmission electron microscopy. The preservation of platelet membrane-derived adhesion and immunoregulatory proteins was confirmed by SDS-PAGE through comparative analysis of protein band profiles between PLP and native platelet membranes. The in vitro biological activities of PLP were evaluated using two complementary cellular models. LPS-induced M1-polarized RAW264.7 macrophages were employed to assess inflammatory modulation, while oxygen glucose deprivation/reperfusion (OGD/R)-induced BV2 microglial cells and SH-SY5Y neuronal cells were utilized to investigate neuroinflammatory regulation and neuronal protection. For in vivo validation, a transient middle cerebral artery occlusion (tMCAO) mouse model was established to mimic ischemia-reperfusion injury. The spatiotemporal biodistribution and lesion-targeting capability of the PLP were monitored through live fluorescence imaging. Therapeutic efficacy was comprehensively evaluated by triphenyltetrazolium chloride (TTC) staining, glial fibrillary acidic protein (GFAP) immunofluorescence analysis, body weight monitoring, and neurological severity score (NSS) assessment. ResultsPLP nanoparticles displayed a uniform spherical morphology, nanoscale particle size distribution, and stable negative surface charge, indicating favorable colloidal stability and circulation potential. SDS-PAGE results confirmed the effective retention of key platelet membrane proteins associated with endothelial adhesion, immune evasion, and inflammatory regulation, demonstrating the successful biomimetic construction. Optimal therapeutic concentrations were determined in OGD/R-induced BV2 cells, where PLP exhibited excellent cytocompatibility and anti-inflammatory activity.In vitro experiments demonstrated that PLP significantly inhibited the polarization of RAW264.7 macrophages toward the pro-inflammatory M1 phenotype and markedly reduced neuronal apoptosis under ischemia-reperfusion conditions. In vivo fluorescence imaging revealed that PLP rapidly accumulated in the ischemic brain hemisphere and maintained prolonged retention for up to 7 d, suggesting enhanced lesion-specific targeting and sustained drug release. Compared with control group, PLP treatment significantly reduced cerebral infarct volume, attenuated reactive astrogliosis, improved weight recovery, and accelerated neurological functional restoration, as reflected by significantly improved NSS scores. ConclusionThis study establishes a multifunctional biomimetic nanoplatform that integrates platelet membrane-mediated active targeting with the anti-inflammatory, antioxidative, and neuroprotective properties of TGF-β1. The PLP system enables rapid lesion homing and long-term retention while synergistically regulating the post-stroke inflammatory microenvironment by suppressing pro-inflammatory immune activation, reducing neuronal apoptosis, and limiting excessive astrocyte reactivity. Importantly, this study proposes a conceptually therapeutic paradigm that combines targeted delivery with immune microenvironment remodeling to achieve comprehensive neurovascular protection. These findings provide strong experimental evidence supporting the translational potential of biomimetic nanotherapeutics as next-generation precision interventions for ischemic stroke.

ObjectiveThe present study aimed to investigate alterations in white matter microstructure and spontaneous neural activity in male college smokers, and to further explore their associations with nicotine dependence. Given that adolescence and early adulthood represent critical periods for brain maturation, particularly for white matter development, understanding the neural correlates of smoking behavior during this stage is of substantial importance for both neuroscience and public health. MethodsA total of 115 male undergraduate students were initially recruited for this study. After quality control and exclusion procedures, 52 male college smokers and 42 demographically matched healthy non-smokers were included in the final analysis. All participants underwent multimodal magnetic resonance imaging (MRI), including diffusion tensor imaging (DTI) and resting-state functional MRI (rs-fMRI). White matter fiber tracts were reconstructed using the automated fiber quantification (AFQ) method, which enables precise identification and quantification of major fiber bundles. Eighteen major white matter tracts were segmented for each participant. Along the core trajectory of each tract, 100 equidistant nodes were sampled. Fractional anisotropy (FA) was calculated at each node to assess white matter microstructural integrity, while amplitude of low-frequency fluctuation (ALFF) was computed to evaluate spontaneous neural activity within white matter tracts. Between-group differences in FA and ALFF were assessed using two-sample t-tests, with appropriate corrections applied for multiple comparisons. Furthermore, Pearson correlation analyses were conducted to examine the relationships between imaging-derived metrics (FA and ALFF values in regions showing significant group differences) and nicotine dependence severity, as measured by the Fagerström test for nicotine dependence (FTND). ResultsCompared with healthy non-smokers, male college smokers exhibited significantly increased FA values in several white matter tracts, including the left thalamic radiation, right corticospinal tract, forceps major of the corpus callosum, left uncinate fasciculus, and right arcuate fasciculus. These findings suggest altered microstructural organization or increased directional coherence within these pathways. In addition, smokers demonstrated significantly elevated ALFF values in the forceps major, right uncinate fasciculus, and left arcuate fasciculus, indicating enhanced spontaneous neural activity in these white matter regions. Correlation analyses revealed that FA values in the left thalamic radiation and right corticospinal tract were negatively correlated with FTND scores, suggesting that higher levels of nicotine dependence were associated with reduced microstructural integrity or altered fiber organization in these regions. In contrast, ALFF values in the forceps major and right uncinate fasciculus were positively correlated with FTND scores, indicating that greater nicotine dependence was associated with increased spontaneous neural activity in specific white matter pathways. ConclusionThe present study provides evidence that male college smokers exhibit distinct alterations in both white matter microstructure and functional activity. These abnormalities are not uniformly distributed but rather localized to specific fiber tracts implicated in sensorimotor processing, interhemispheric communication, and higher-order cognitive and emotional regulation. Importantly, the observed associations between imaging metrics and nicotine dependence severity suggest that these structural and functional alterations may reflect neurobiological mechanisms underlying addiction. The combination of AFQ-based tract profiling and multimodal MRI offers a sensitive approach for detecting subtle changes along white matter pathways, highlighting its potential utility in identifying neuroimaging biomarkers of nicotine dependence. Overall, these findings indicate that smoking during early adulthood may disrupt ongoing white matter maturation, potentially leading to long-term consequences for brain function. This study provides novel insights into the neural basis of nicotine dependence and underscores the importance of early intervention and prevention strategies targeting young smokers.

RNA modifications constitute a crucial class of post-transcriptional chemical alterations that profoundly influence RNA stability and translational efficiency, thereby shaping cellular protein expression profiles. These diverse chemical marks are ubiquitously involved in key biological processes, including cell proliferation, differentiation, apoptosis, and metastatic potential, and they exert precise regulatory control over these functions. A major advance in the field is the recognition that RNA modifications do not act in isolation. Instead, they participate in complex, dynamic interactions—through synergistic enhancement, antagonism, competitive binding, and functional crosstalk—forming what is now termed the “RNA modification interactome” or “RNA modification interaction network.” The formation and functional operation of this interactome rely on a multilayered regulatory framework orchestrated by RNA-modifying enzymes—commonly referred to as “writers,” “erasers,” and “readers.” These enzymes exhibit hierarchical organization within signaling cascades, often functioning in upstream-downstream sequences and converging at critical regulatory nodes. Their integration is further mediated through shared regulatory elements or the assembly into multi-enzyme complexes. This intricate enzymatic network directly governs and shapes the interdependent relationships among various RNA modifications. This review systematically elucidates the molecular mechanisms underlying both direct and indirect interactions between RNA modifications. Building upon this foundation, we introduce novel quantitative assessment frameworks and predictive disease models designed to leverage these interaction patterns. Importantly, studies across multiple disease contexts have identified core downstream signaling axes driven by specific constellations of interacting RNA modifications. These findings not only deepen our understanding of how RNA modification crosstalk contributes to disease initiation and progression, but also highlight its translational potential. This potential is exemplified by the discovery of diagnostic biomarkers based on interaction signatures and the development of therapeutic strategies targeting pathogenic modification networks. Together, these insights provide a conceptual framework for understanding the dynamic and multidimensional regulatory roles of RNA modifications in cellular systems. In conclusion, the emerging concept of RNA modification crosstalk reveals the extraordinary complexity of post-transcriptional regulation and opens new research avenues. It offers critical insights into the central question of how RNA-modifying enzymes achieve substrate specificity—determining which nucleotides within specific RNA transcripts are selectively modified during defined developmental or pathological stages. Decoding these specificity determinants, shaped in large part by the modification interactome, is essential for fully understanding the biological and pathological significance of the epitranscriptome.

ObjectiveTobacco-related diseases remain one of the leading preventable public health challenges worldwide and are among the primary causes of premature death. In recent years, accumulating evidence has supported the classification of nicotine addiction as a chronic brain disease, profoundly affecting both brain structure and function. Despite the urgency, effective diagnostic methods for smoking addiction remain lacking, posing significant challenges for early intervention and treatment. To address this issue and gain deeper insights into the neural mechanisms underlying nicotine dependence, this study proposes a novel graph neural network framework, termed TI-GNN. This model leverages functional magnetic resonance imaging (fMRI) data to identify complex and subtle abnormalities in brain connectivity patterns associated with smoking addiction. MethodsThe study utilizes fMRI data to construct functional connectivity matrices that represent interaction patterns among brain regions. These matrices are interpreted as graphs, where brain regions are nodes and the strength of functional connectivity between them serves as edges. The proposed TI-GNN model integrates a Transformer module to effectively capture global interactions across the entire brain network, enabling a comprehensive understanding of high-level connectivity patterns. Additionally, a spatial attention mechanism is employed to selectively focus on informative inter-regional connections while filtering out irrelevant or noisy features. This design enhances the model’s ability to learn meaningful neural representations crucial for classification tasks. A key innovation of TI-GNN lies in its built-in causal interpretation module, which aims to infer directional and potentially causal relationships among brain regions. This not only improves predictive performance but also enhances model interpretability—an essential attribute for clinical applications. The identification of causal links provides valuable insights into the neuropathological basis of addiction and contributes to the development of biologically plausible and trustworthy diagnostic tools. ResultsExperimental results demonstrate that the TI-GNN model achieves superior classification performance on the smoking addiction dataset, outperforming several state-of-the-art baseline models. Specifically, TI-GNN attains an accuracy of 0.91, an F1-score of 0.91, and a Matthews correlation coefficient (MCC) of 0.83, indicating strong robustness and reliability. Beyond performance metrics, TI-GNN identifies critical abnormal connectivity patterns in several brain regions implicated in addiction. Notably, it highlights dysregulations in the amygdala and the anterior cingulate cortex, consistent with prior clinical and neuroimaging findings. These regions are well known for their roles in emotional regulation, reward processing, and impulse control—functions that are frequently disrupted in nicotine dependence. ConclusionThe TI-GNN framework offers a powerful and interpretable tool for the objective diagnosis of smoking addiction. By integrating advanced graph learning techniques with causal inference capabilities, the model not only achieves high diagnostic accuracy but also elucidates the neurobiological underpinnings of addiction. The identification of specific abnormal brain networks and their causal interactions deepens our understanding of addiction pathophysiology and lays the groundwork for developing targeted intervention strategies and personalized treatment approaches in the future.

Objective To develop a predictive model for postoperative mortality risk in patients with acute aortic dissection(AAD)using the Extreme Gradient Boosting(XGBoost)algorithm combined with Shapley Additive Explanation(SHAP),and to establish a prediction website to serve as a diagnostic and therapeutic support platform for clinicians and patients.Methods A retrospective cohort study design was adopted.Data from 782 AAD patients who underwent surgical treatment at the Fourth Hospital of Hebei Medical University from January 2013 to December 2023 were collected,including basic information and initial serum biomarker test results.Patients were randomly divided into training and test sets at a 7:3 ratio.An external validation set consisting of 313 AAD patients admitted to the Second Hospital of Hebei Medical University from January 2020 to December 2023 was also established for further model validation.Variables were screened using LASSO regression,and an XGBoost machine learning model was constructed and interpreted using SHAP.The predictive performance of the model was evaluated using receiver operating characteristic(ROC)curve analysis.Using the Shiny package,the XGBoost model was deployed to shinyapps.io to create a prediction website for postoperative mortality risk in AAD patients.One patient was selected by simple random sampling from the test set and the external validation set respectively for the prediction example on the Shiny webpage.Results The XGBoost model demonstrated high predictive performance for postoperative mortality in AAD patients,with area under the ROC curve(AUC)values of 0.928(95%CI 0.901-0.956)in the training set,0.919(95%CI 0.891-0.949)in the test set,and 0.941(95%CI 0.915-0.967)in the external validation set.SHAP values indicated the following order of variable importance in the model(from highest to lowest):"lactate dehydrogenase""blood chlorine""multiple organ injury""carbon dioxide combining power""prothrombin time""α-hydroxybutyric acid""creatine kinase isoenzyme""Stanford classification""combined use of bedside blood purification""gender""acute kidney injury""gastrointestinal bleeding""brain injury"and"shock".A risk prediction website for adverse postoperative outcomes in AAD patients was developed using XGBoost-SHAP method(https://dun-dunxiaolu.shinyapps.io/document/)and validated with examples.One randomly selected patient from each of the test and external validation sets was applied:the predicted mortality risk value for patient 1(who died postoperatively)was 0.9539,and that for patient 2(who survived postoperatively)was 0.0206.Conclusions The XGBoost-SHAP model demonstrates high accuracy in predicting postoperative mortality risk for AAD patients.The online prediction tool established based on this model enhances the identification efficiency of high-risk postoperative mortality patients.

Objective To explore the characteristics of traditional Chinese medicine(TCM)syndrome,syndrome elements and their combination,and the distribution of TCM syndrome types in advanced non-small cell lung cancer(NSCLC)patients suffering from hypothyroidism after treatment with immune checkpoint inhibitors(ICIs).Methods The analysis was conducted on 168 patients with NSCLC at stage ⅢB-ⅣB confirmed by pathological findings,whose epidermal growth factor receptor/anaplastic lymphoma kinase(EGFR/ALK)was negative,and then suffering from hypothyroidism after treatment with ICIs from January 2020 to June 2023,who admitted to Zhongshan Hospital of Chinese Medicine Affiliated to Guangzhou University of Chinese Medicine.The patients'information collected by four diagnostic methods of TCM was analyzed,and then cluster analysis was used to explore the characteristics of TCM syndrome and the distribution of TCM syndrome types of immunotherapy-induced hypothyroidism in advanced NSCLC.Moreover,the distribution of TCM syndrome types in the patients with different genders,age groups,and hypothyroidism grades was analyzed.Results(1)The TCM syndrome of hypothyroidism appearing in 168 patients with advanced NSCLC after immunotherapy was characterized by deficiency type,which manifested as follows:cough,fatigue and weakness,amnesia,lusterless complexion,spontaneous sweating,dry skin,white sputum,unwilling to talk,dizziness,nocturnal polyuria,blurred vision,emaciation,poor skin elasticity,poor appetite or even anorexia,somnolence,long-term poor appetite,edema,insomnia,low voice,dull pain,light white color of fingernails,spitting,frequently intolerance of cold,thirst,bright pale complexion,preference of warmth and aversion to cold,thirst with preference of hot drink,dyspnea,frequent constipation,dry stools,puffiness of face and eyelid,dreaminess,abdominal fullness,lumbar pain,and weakness in defecation.The tongue manifestation and pulse condition were characterized by white and thin coating,pale-red tongue,tongue with tooth-marks,pale and enlarged tongue,pale tongue,deep pulse,slippery pulse,feeble pulse,weak cubital pulse,and thready pulse.(2)The disease-location syndrome elements usually involved in the lung,spleen,and kidney,and the disease-nature syndrome elements usually involved in qi deficiency,yang deficiency,blood deficiency,and water retention.(3)The cluster analysis yielded three syndrome types,and they were lung and spleen qi deficiency syndrome,kidney yang deficiency syndrome,and qi deficiency and water retention syndrome in decreasing sequence of occurrence frequency.(4)Statistically significant difference of the distribution of TCM syndrome types was presented in the patients with various age groups(P<0.01).Lung and spleen qi deficiency syndrome was the main syndrome type in the patients aged 60-69 years old,kidney yang deficiency syndrome was frequently seen in the patients being or over 70 years old,and qi deficiency and water retention syndrome was frequently seen in the patients less than 50 years old.No statistically significant difference of the distribution of TCM syndrome types was presented in the patients with various genders and in the patients with various grades of hypothyroidism(P>0.05).Conclusion The immunotherapy-induced hypothyroidism in patients with advanced NSCLC is usually differentiated as the TCM syndrome types of lung-qi and spleen-qi deficiency,kidney yang deficiency,and qi deficiency and water retention.Deficiency of healthy qi contributes to the fundamental pathogenesis of the development and progression of the disease.Clinicians should pay attention to the changes of symptoms in time and monitor the thyroid function indicators of the patients,thus to avoid serious immunotherapy-related adverse events(irAEs).

ObjectiveThis study leverages structural data from antigen-antibody complexes of the influenza A virus neuraminidase (NA) protein to investigate the spatial recognition relationship between the antigenic epitopes and antibody paratopes. MethodsStructural data on NA protein antigen-antibody complexes were comprehensively collected from the SAbDab database, and processed to obtain the amino acid sequences and spatial distribution information on antigenic epitopes and corresponding antibody paratopes. Statistical analysis was conducted on the antibody sequences, frequency of use of genes, amino acid preferences, and the lengths of complementarity determining regions (CDR). Epitope hotspots for antibody binding were analyzed, and the spatial structural similarity of antibody paratopes was calculated and subjected to clustering, which allowed for a comprehensively exploration of the spatial recognition relationship between antigenic epitopes and antibodies. The specificity of antibodies targeting different antigenic epitope clusters was further validated through bio-layer interferometry (BLI) experiments. ResultsThe collected data revealed that the antigen-antibody complex structure data of influenza A virus NA protein in SAbDab database were mainly from H3N2, H7N9 and H1N1 subtypes. The hotspot regions of antigen epitopes were primarily located around the catalytic active site. The antibodies used for structural analysis were primarily derived from human and murine sources. Among murine antibodies, the most frequently used V-J gene combination was IGHV1-12*01/IGHJ2*01, while for human antibodies, the most common combination was IGHV1-69*01/IGHJ6*01. There were significant differences in the lengths and usage preferences of heavy chain CDR amino acids between antibodies that bind within the catalytic active site and those that bind to regions outside the catalytic active site. The results revealed that structurally similar antibodies could recognize the same epitopes, indicating a specific spatial recognition between antibody and antigen epitopes. Structural overlap in the binding regions was observed for antibodies with similar paratope structures, and the competitive binding of these antibodies to the epitope was confirmed through BLI experiments. ConclusionThe antigen epitopes of NA protein mainly ditributed around the catalytic active site and its surrounding loops. Spatial complementarity and electrostatic interactions play crucial roles in the recognition and binding of antibodies to antigenic epitopes in the catalytic region. There existed a spatial recognition relationship between antigens and antibodies that was independent of the uniqueness of antibody sequences, which means that antibodies with different sequences could potentially form similar local spatial structures and recognize the same epitopes.

Objective:To analyze the relationship between 24-hour urinary calcium (24 h UCa) level and the risk of end-stage kidney disease (ESKD), cardiovascular disease (CVD), and all-cause mortality.Methods:In the Chinese Cohort Study of Chronic Kidney Disease, we examined 3 375 patients aged 18-74 years with CKD stages 1-4. Kaplan-Meier survival and Cox proportional hazard regression models were used to test a time-to-event association between levels of 24 h UCa and incidence of ESKD, CVD, and all-cause mortality.Results:During a follow-up of 4.17 (3.37, 5.20) years, 179, 145, 104 and 38 ESKD events occurred in <0.60, 0.60-, 1.20-, ≥2.32 mmol 24 h UCa groups. Higher levels of 24 h UCa (1.20-,≥2.32 mmol) were independently associated with a lower incidence of ESKD events in patients with CKD, with HR (95% CI) of 0.71 (0.54-0.93) and 0.43 (0.29-0.64), respectively. No significant associations with CVD and all-cause mortality endpoints were detected. Conclusion:Among patients with CKD, levels of 24 h UCa displayed an association with the risk of ESKD among patients with CKD stages 1-4.

The integration of multi-source data and the establishment of early warning indicator systems constitute pivotal elements for advancing surveillance and early warning capacities in respiratory infectious diseases. Given the multifaceted transmission mechanisms and complex contributing factors inherent in respiratory infectious diseases, surveillance datasets and associated early warning indicators demonstrate notable heterogeneity and sophisticated interrelationships. Furthermore, as surveillance and early warning requirements significantly vary across diverse epidemiological scenarios, accurate assessment of the value and applicability of distinct data types and indicators is imperative. This paper systematically reviews and synthesizes recent advancements in surveillance data and early warning indicators for respiratory infectious diseases, drawing on both domestic and international research. Particular attention is dedicated to analyzing the applicability and efficacy of various data types and indicators within multiple practical contexts, aiming to provide robust theoretical frameworks and methodological guidance to facilitate the development of resilient and efficient surveillance and early warning systems for respiratory infectious diseases.

Aim To explore the mechanism of the syn-ergistic effect of the ferroptosis inducer erastin com-bined with shikonin in promoting ferroptosis in human hypertrophic scar fibroblasts(HSFBs).Methods Hypertrophic scar tissues provided by the General Hos-pital of Ningxia Medical University were collected,and HSFBs were extracted.HSFBs were identified by HE staining and immunofluorescence.The inhibitory rates of Era and SHK on HSFBs at different concentrations were detected by CCK-8 assay,and the IC50 value was calculated.CompuSyn software was used to calculate the co-use index(CI).Control group,Erastin(Era)group,shikonin(SHK)group and Era+SHK group were set up,and the number and morphological chan-ges of cells were observed after 24 hours of interven-tion.The ability of cell migration and invasion was de-tected by scratch test and Transwell test.The changes of malondialdehyde(MDA),total iron ion and reactive oxygen species(ROS)were detected by corresponding biochemical kits.The expressions of collagen I,α-SMA and GOT1,SLC7A11,GPX4 and FTH1 were detected by Western blot.Results The IC50 value of Era and SHK of primary HSFBs was 2.22 μmol·L-1 and 3.94μmol·L-1 respectively,which was used as the single drug concentration for subsequent experiments.The CompuSyn software was employed to calculate the CI value when the two drugs were used in combination,and the concentrations corresponding to CI=0.39597(Era:1.2 μmol·L-1+SHK:1.5 μmol·L-1)were selected as subsequent combination concentrations(Because when CI was equal to 0.395 97,the concen-tration of each drug was lower than the concentration of single drug,and the inhibition rate of combined drug was greater than 50％).Compared with the monother-apy group,the number of HSFBs in the SHK+Era group was significantly reduced,cell membrane showed breakage and vesiculation,cell wrinkling became smal-ler,and cytoplasm was concentrated.The migration and invasion ability of HSFBs in the SHK+Era group were obviously weakened(P＜0.05),and the expres-sion of fibrosis-related proteins collagen Ⅰ and α-SMA was reduced(P＜0.05);the contents of MDA,total i-ron ions,and ROS in HSFBs of the SHK+Era group increased(P＜0.05),and the protein expression lev-els of SLC7A11,GOT1,GPX4,and FTH1 further de-creased(P＜0.05).Conclusions Erastin in combi-nation with shikonin can synergistically inhibit the pro-liferation,migration and fibrosis levels of HSFBs.The mechanism may be that erastin enhances the inhibition of shikotin on GOT1,increases the levels of cellular i-ron ions,ROS,and lipid peroxides,thereby promoting ferroptosis in HSFBs.