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.

Objectives: . The prognostic value of the pretreatment albumin-to-alkaline phosphatase ratio (AAPR) in head and neck cancer (HNC) remains uncertain. This meta-analysis aimed to evaluate the predictive role of AAPR for survival outcomes in patients with HNC. Methods: . A comprehensive search of the Cochrane Library, PubMed, and Embase databases was conducted to identify relevant studies published up to July 30, 2024. We included studies on AAPR and survival outcomes in HNC patients. Results: . Eight studies comprising 1,737 HNC patients were analyzed using random-effects models. Lower AAPR values were significantly correlated with worse overall survival (hazard ratio [HR], 2.08), progression-free survival (HR, 2.00), and disease-free survival (HR, 2.18). Sensitivity analyses confirmed the robustness of these results, with no significant publication bias detected. Conclusion . Our findings suggest that pretreatment AAPR could serve as a valuable and cost-effective prognostic indicator in HNC, potentially aiding clinicians in risk stratification and treatment decision-making. However, additional validation studies are warranted to confirm its clinical applicability.

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.

OBJECTIVE To construct and evaluate a perioperative pharmaceutical care model for orthopedic joint replacement based on clinical pharmacy specialty collaboration and medication therapy management （MTM）， so as to provide a reference for improving the level of clinical rational drug use and reducing medical costs.METHODS Patients undergoing orthopedic joint replacement were taken as the research subjects. A specialized collaboration group was established， consisting of the head of the clinical pharmacy group， ward-based clinical pharmacists， and multi-specialty clinical pharmacists. The practice path， service contents，and workflow of MTM for the perioperative period of orthopedic joint replacement were clarified，and a perioperative pharmaceutical care model for orthopedic joint replacement was developed. Data of patients in the orthopedic diagnosis-related groups （DRG）-IC29 group of the department of orthopedics in Daping Hospital，Army Medical University were collected from July 2022 to June 2023 （pre-intervention， 581 cases） and from July 2023 to June 2024 （post-intervention， 843 cases）. The work-implementation indicators in the post-intervention period were counted， and the efficiency indicators and pharmaceutical management indicators were compared between the pre- and post-intervention periods. Meanwhile，200 patients were randomly selected from the pre-intervention and post-intervention groups， respectively， to evaluate the rate of rational medical orders.RESULTS From July 2023 to June 2024， the collaboration group implemented MTM for 843 patients undergoing orthopedic joint replacement via the established perioperative pharmaceutical care model. A total of 1 163 drug‑related problems （DRPs） were identified， and 79 patients with high medication‑related risks were screened out. The most common types of DRPs included （possible） adverse drug events/reactions （48.93%）， unnecessary drug therapy （21.58%）， and ineffective drug therapy （16.25%）. Among the 1 163 DRPs， 895 （76.96%） were completely resolved. Compared with the pre‑intervention period， the post‑intervention period showed reductions in average hospitalization cost per case， average drug cost per case， average length of hospital stay， proportion of drug cost， utilization rate and use intensity of antibacterial agents， and per‑capita drug cost of six main drug categories. The rate of rational medical orders increased from 78.25% to 98.11%.CONCLUSIONS The perioperative pharmaceutical care model for orthopedic joint replacement based on clinical pharmacy specialty collaboration and MTM can help pharmacists identify and resolve DRPs during the perioperative period of orthopedic joint replacement，improve the level of clinical rational drug use，and reduce treatment costs.

This comprehensive review systematically explores the multifaceted applications, inherent challenges, and promising future directions of artificial intelligence (AI) within the medical domain. It meticulously examines AI's specific contributions to basic medical research, disease prevention, intelligent diagnosis, treatment, rehabilitation, nursing, and health management. Furthermore, the review delves into AI's innovative practices and pivotal roles in clinical trials, hospital administration, medical education, as well as the realms of medical ethics and policy formulation. Notably, the review identifies several key challenges confronting AI in healthcare, encompassing issues such as inadequate algorithm transparency, data privacy concerns, absent regulatory standards, and incomplete risk assessment frameworks. Looking ahead, the future trajectory of AI in healthcare encompasses enhancing algorithm interpretability, propelling generative AI applications, establishing robust data-sharing mechanisms, refining regulatory policies and standards, nurturing interdisciplinary talent, fostering collaboration among industry, academia, and medical institutions, and advancing inclusive, personalized precision medicine. Emphasizing the synergy between AI and emerging technologies like 5G, big data, and cloud computing, this review anticipates a new era of intelligent collaboration and inclusive sharing in healthcare. Through a multidimensional analysis, it presents a holistic overview of AI's medical applications and development prospects, catering to researchers, practitioners, and policymakers in the healthcare sector. Ultimately, this review aims to catalyze the deep integration and innovative deployment of AI technology in healthcare, thereby driving the sustainable advancement of smart healthcare.

During orthodontic treatment, clinical monitoring of patients is a crucial factor in determining treatment success. It aids in timely problem detection and resolution, ensuring adherence to the intended treatment plan. In recent years, digital technology has increasingly permeated orthodontic clinical diagnosis and treatment, facilitating clinical decision-making, treatment planning, and follow-up monitoring. This review summarizes recent advancements in digital technology for monitoring orthodontic tooth movement, related complications, and appliance-wearing compliance. It aims to provide insights for researchers and clinicians to enhance the application of digital technology in orthodontics, improve treatment outcomes, and optimize patient experience. The digitization of diagnostic data and the visualization of dental models make chair-side follow-up monitoring more convenient, accurate, and efficient. At the same time, the emergence of remote monitoring technology allows orthodontists to promptly identify oral health issues in patients and take corresponding measures. Furthermore, the multimodal data fusion method offers valuable insights into the monitoring of the root-alveolar relationship. Artificial intelligence technology has made initial strides in automating the identification of orthodontic tooth movement, associated complications, and patient compliance evaluation. Sensors are effective tools for monitoring patient adherence and providing data-driven support for clinical decision-making. The application of digital technology in orthodontic monitoring holds great promise. However, challenges like technical bottlenecks, ethical considerations, and patient acceptance remain.

ObjectiveThe controllability changes of structural brain network were explored based on the control and brain network theory in young smokers, this may reveal that the controllability indicators can serve as a powerful factor to predict the sleep status in young smokers. MethodsFifty young smokers and 51 healthy controls from Inner Mongolia University of Science and Technology were enrolled. Diffusion tensor imaging (DTI) was used to construct structural brain network based on fractional anisotropy (FA) weight matrix. According to the control and brain network theory, the average controllability and the modal controllability were calculated. Two-sample t-test was used to compare the differences between the groups and Pearson correlation analysis to examine the correlation between significant average controllability and modal controllability with Fagerström Test of Nicotine Dependence (FTND) in young smokers. The nodes with the controllability score in the top 10% were selected as the super-controllers. Finally, we used BP neural network to predict the Pittsburgh Sleep Quality Index (PSQI) in young smokers. ResultsThe average controllability of dorsolateral superior frontal gyrus, supplementary motor area, lenticular nucleus putamen, and lenticular nucleus pallidum, and the modal controllability of orbital inferior frontal gyrus, supplementary motor area, gyrus rectus, and posterior cingulate gyrus in the young smokers’ group, were all significantly different from those of the healthy controls group (P<0.05). The average controllability of the right supplementary motor area (SMA.R) in the young smokers group was positively correlated with FTND (r=0.393 0, P=0.004 8), while modal controllability was negatively correlated with FTND (r=-0.330 1, P=0.019 2). ConclusionThe controllability of structural brain network in young smokers is abnormal. which may serve as an indicator to predict sleep condition. It may provide the imaging evidence for evaluating the cognitive function impairment in young smokers.

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).

Pancreatic cancer is characterized by significant drug resistance,and despite continuous advancements in treatment regimens,the 5-year survival rate of patients remains low.The nuclear factor-κB(NF-κB)signaling pathway,frequently mutated in tumors,has been identified as a critical factor in triggering drug resistance.Multiple studies have demonstrated that strategies targeting NF-κB signaling transduction exhibit promising outcomes in pancreatic cancer treatment.Therefore,exploring the relationship between the NF-κB signaling pathway and drug resistance in pancreatic cancer has become a research hotspot in pancreatic cancer treatment.This review summarizes recent advances in the relationship between NF-κB signaling pathway and tumor drug resistance,as well as its role in pancreatic cancer treatment.Specifically,the mechanisms by which the NF-κB signaling pathway mediates drug resistance in pancreatic cancer are elaborated from two perspectives:chemotherapy and immunotherapy,aiming to provide insights for pancreatic cancer treatment and future research.

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.