Objective: To investigate the relationship between 24 hour activity behaviors and fine motor development in preschool children, and to simulate the effects of reallocating time among different activity behaviors on fine motor development using isotemporal substitution analysis. Methods: From March to July 2022, a stratified cluster random sampling method was used to recruit 447 preschool children aged 3-6 years (254 boys and 193 girls) from Pingxiang City, Jiangxi Province. The 24 hour activity behaviors in preschool children were measured using ActiGraph wGT3-BT accelerometers and subjective sleep reports. Fine motor development was assessed using the Gesell Developmental Schedules. A component linear regression model was employed to analyze the impact of 24 hour activity behaviors on the development of fine motor activities, and the potential effects of adjusting activity behaviors were simulated through an isochoric substitution model. Results: The daily durations of sedentary behavior (SB) was ( 572.92 ±102.96) min (accounting for 39.79% of 24 h), the duration of light physical activity (LPA) was (131.21± 38.11 ) min ( 9.11% ), the duration of moderate to vigorous physical activity (MVPA) was (65.61±22.21) min (4.56%), and sleep duration was (670.65±57.58) min (46.82%). Sleep composition was positively associated with fine motor development ( β =2.74), while MVPA ( β =-0.84) and SB ( β =-1.93) compositions were negatively associated with fine motor development (all P <0.01). Isochoric substitution analysis showed that sleep had positive effects on the development of fine motor skills when replacing other activity behaviors (all P < 0.05), with the substitution effect for MVPA being the most significant and gradually increasing with the duration of substitution (60 min: β =28.66); sleep replacement of SB and LPA also showed positive effects (60 min: β =4.25, 2.00) (all P < 0.05). On the contrary, the substitution of sleep with MVPA showed negative effects (60 min: β =-7.86), and the substitution of LPA and SB with MVPA also showed negative effects (60 min: β =-5.65, -3.40) (all P <0.05). Conclusions The overall composition of 24 hour activity behaviors is associated with fine motor development in preschool children, with sleep playing a crucial role. Ensuring adequate sleep and optimizing the structure of activity behaviors may effectively promote the development of fine motor skills in preschool children.

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.

Plant-derived extracellular vesicles (PDEVs) are nanoscale extracellular vesicles secreted by plant cells, characterized by a lipid bilayer structure. These vesicles carry a variety of bioactive molecules, including proteins, nucleic acids, and lipids, and play essential roles in intercellular communication and physiological regulation in plants. Compared to animal-derived extracellular vesicles, PDEVs offer several advantages, such as a broad range of sources, high biocompatibility, low immunogenicity, and low production costs. Furthermore, PDEVs have demonstrated remarkable potential as natural nanocarriers for drug delivery, due to their ability to efficiently traverse biological barriers, such as the blood-brain barrier, making them promising candidates for drug delivery systems. This review systematically elaborates on the complex composition of PDEVs, which consists of lipids, proteins, and nucleic acids, the typical structural characteristics of their lipid bilayers ranging from 30 to 150 nm, and their versatile loading capabilities as drug carriers, efficiently encapsulating various types of therapeutic agents such as hydrophilic small molecules, hydrophobic drugs, nucleic acids, and proteins. We systematically summarize the recent advancements in strategies for enhancing the loading efficiency of PDEVs, which include methods such as co-incubation, ultrasound-assisted loading, electroporation, freeze-thaw cycles, and microfluidic technology. These techniques are evaluated based on their underlying principles, suitable drug types, and their respective advantages. In addition to loading strategies, we focus on the engineered approaches to achieve targeted delivery using PDEVs, such as genetic engineering modifications, chemical ligand conjugation, membrane fusion technology, and polyethylene glycol (PEG) modification. We discuss the mechanisms of these strategies in enhancing targeting efficiency, prolonging in vivo circulation time, and improving therapeutic efficacy. Further, this review highlights the application of PDEVs in various disease models, including tumor, skin inflammation, metabolic disorders, and neurodegenerative diseases, showcasing their therapeutic potential as multifunctional delivery platforms. The ability of PDEVs to encapsulate diverse therapeutic agents and target specific tissues or cells opens up new avenues for the treatment of complex diseases, offering advantages over conventional drug delivery systems. However, despite the promising applications of PDEVs, several challenges remain in their development and clinical translation. These challenges include variability in source materials, standardization of preparation processes, quality control, scalability of production, and the need for clinical validation. To overcome these obstacles, the integration of advanced technologies such as artificial intelligence-assisted design and multi-omics analysis is proposed as a way to facilitate the precise development of PDEVs. These emerging technologies hold the potential to further enhance the precision and effectiveness of plant-based drug delivery systems, ultimately advancing the field of precision medicine. In conclusion, the use of PDEVs as a platform for drug delivery represents a promising area of research with the potential to revolutionize therapeutic strategies. Their ability to encapsulate and deliver a wide variety of bioactive molecules, along with their inherent advantages in biocompatibility and versatility, makes them a valuable tool in the development of more efficient and targeted therapeutic interventions. Continued research and innovation in this field will pave the way for the clinical implementation of PDEVs in the treatment of various diseases, offering new hope for more effective and sustainable therapeutic options.

Objective To investigate the clinical safety and efficacy of predictive extracorporeal membrane oxygenation (ECMO) support during liver transplantation in high-risk recipients. Methods A single-center retrospective study was conducted. A total of 35 liver transplant recipients who underwent predictive ECMO assistance during surgery at the Second Affiliated Hospital of Guangxi Medical University from January 2021 to April 2025 were included. Baseline characteristics, ECMO application, ECMO operating parameters and hemodynamic conditions during ECMO operation were analyzed. The cumulative survival rate was calculated using the Kaplan-Meier method and survival curves were plotted. Results Veno-arterial ECMO in 35 recipients was established after induction of general anesthesia. The median ECMO operation time was 8.5 h, and the success rate of weaning from ECMO was 97%. The cardiac index (CI) during the non-liver period and the new liver period was higher than that before cannulation, and CI increased during the non-liver period and before weaning (all P<0.001). The mean invasive arterial pressure and central venous pressure remained stable. No ECMO-related complications occurred in the recipients. The average survival time was (25 ± 3) months. The main causes of death were severe pulmonary infection and multiple organ failure. Conclusions For liver transplant recipients with severe cardiopulmonary dysfunction, hemodynamic disorders, or multiple organ failure risk factors, implementing predictive ECMO support during liver transplantation through active risk prediction is a safe and effective circulatory guarantee strategy.

ObjectiveTo investigate the current status of ethical sensitivity among clinical nursing interns in the context of artificial intelligence （AI） and analyze its influencing factors. MethodsAdopting mixed research methods and convenience sampling， a total of 237 nursing interns from a tertiary A hospital in Xi’an were selected as research subjects. Data were collected using a general information questionnaire and the Nursing Students’ Ethical Sensitivity Scale， and semi-structured interviews were conducted with 8 nursing interns. ResultsThe total score of ethical sensitivity among nursing interns was （33.98±6.32）， which was at a moderate level. Multiple linear regression analysis revealed that academic degree （undergraduate： β=0.147， P<0.05； postgraduate： β=0.139， P<0.05）， affinity for the nursing profession （β=0.118， P<0.05）， whether they had taken nursing ethics courses （β=-0.124， P<0.05）， and familiarity with artificial intelligence （β=-0.159，P<0.05） were influencing factors of ethical sensitivity among nursing interns. Four themes were extracted from semi-structured interviews， namely， the incomplete mastery of nursing ethics-related knowledge among nursing interns， weakened ethical sensitivity in the AI teaching scenario of nursing interns， insufficient professional identity affecting the development of nursing interns’ ethical sensitivity， and technological dependence leading to the lack of humanistic care among nursing interns. ConclusionEthical sensitivity of nursing interns represents a moderate level， primarily influenced by educational background， degree of fondness for the nursing profession， learning experience of nursing ethics courses， and familiarity with AI. Nursing educators and clinical administrators urgently need to establish a sustainable school-hospital collaborative model of nursing ethics education， attach importance to the cultivation of ethical awareness and value guidance for nursing interns， and strengthen the intensity and depth of nursing ethics education among nursing interns， thereby improving their level of ethical sensitivity in the context of AI.

Objective: To analyze the current status and influencing factors for latent tuberculosis infection (LTBI) among the elderly in Nanchuan District, Chongqing Municipality, so as to provide a reference for developing targeted prevention and control measures and reducing the risk of tuberculosis incidence. Methods: From June to December 2024, a multi-stage random cluster sampling method combined with probability proportional to population size sampling was used to select residents aged ≥65 years from four townships (streets) in Nanchuan District as study participants. Demographic information, disease history, and history of close contact with tuberculosis were collected through a questionnaire survey. Tuberculin skin test (TST) was used to detect Mycobacterium tuberculosis infection. LTBI was defined as a mean induration diameter of ≥10 mm on the TST, with active pulmonary tuberculosis excluded. Factors affecting LTBI among the elderly were identified using a multivariable logistic regression model. Results: A total of 3 699 participants were surveyed, including 1 707 males (46.15%) and 1 992 females (53.85%). The median age was 71.00 (interquartile range, 8.00) years. There were 285 participants (7.70%) with pulmonary disease, 18 (0.49%) with immune system disease, 47 (1.27%) with a history of tuberculosis, and 17 (0.46%) with a history of close contact with tuberculosis. LTBI was detected in 628 participants, with a detection rate of 16.98%. Multivariable logistic regression analysis showed that elderly participants with senior high school education or above (OR=2.292, 95%CI: 1.508-3.484), those with immune system disease (OR=3.226, 95%CI: 1.240-8.393), and those with a history of tuberculosis (OR=4.797, 95%CI: 2.436-9.447) were significantly associated with a higher risk of LTBI. Conclusions The detection rate of LTBI among the elderly in Nanchuan District was relatively low. Educational level, immune system disease, and history of tuberculosis were influencing factors for LTBI risk in this population. It is necessary to strengthen health education, screening, and preventive interventions.

Objective To explore the prognosis-related genes of lung adenocarcinoma(LUAD)and establish a prognostic prediction model for LUAD.Methods The differentially expressed genes of LUAD tissues and adjacent normal tissues in the GSE43458,GSE7670,and GSE140797 datasets were screened.The protein-protein interaction(PPI)network analysis,gene ontology(GO)function,Kyoto Encyclopedia of Genes and Genomes(KEGG)signaling pathway enrichment analyses,least absolute shrinkage and selection operator for prognosis coefficient screening,disease-specific survival analysis,Cox regression analysis,and gene correlation analysis were performed.Independent prognostic genes of LUAD were screened from the differential genes,and a prognostic prediction model of LUAD was established.The expression of independent prognostic genes was analyzed,and the predictive model was evaluated by receiver operating characteristic(ROC)curve.The GSE68465 data set was used as an external validation set to verify the predictive model.Results There were 197 up-regulated differential genes and 77 down-regulated differential genes in LUAD and adjacent normal tissues common to the three datasets.Through stepwise screening,two genes,IL7R and SLC2A1,were identified as independent prognostic factors for LUAD.IL7R was an independent protective factor,while the SLC2A1 was an independent risk factor.A prediction model for curve was built with IL7R and SLC2A1.The prediction model for LUAD constructed with IL7R and SLC2A1 is as follows:Risk score=(-0.694)×expression level of IL7R+0.763×expression level of SLC2A1.Conclusion This study screened out IL7R as an independent prognostic protective factor for LUDA,and SLC2A1 as an independent prognostic risk factor for LUAD.The LUDA prediction model constructed based on these two genes has good predictive ability and generalization ability,which can provide references for the research and clinical individualized treatment of LUAD.

Metal-organic frameworks(MOFs)are porous materials composed of metal ions or metal clusters and organic ligands by coordination,which have the advantages of large specific surface area,good thermal stability and adjustable pore size,and have a promising application in gas chromatographic separation.In recent years,MOFs materials have been used as stationary phases for gas chromatography mainly including ZIF,MIL,UiO-66,HKUST-1,IRMOFs,etc.Based on the molecular sieve effect,van der Waals forces,hydrogen bonding and π-π interactions,the pore size,pore microenvironment,unsaturated metal site and special functional group of the MOFs stationary phase materials can be specifically designed and regulated.MOFs materials as stationary phases have unique separation performance for n-alkanes and their isomers,aromatic compounds and their isomers,alcohols/ketones/aldehydes and their isomers,and chiral compounds.The combination of organic polymers and novel nanomaterials with MOFs materials can improve the separation performance and stability of MOFs.Therefore,MOFs materials are expected to be the promising stationary phase that can be applied to gas separation in complex environments.In this article,the research advances of various stationary phases based on MOFs for gas chromatography in recent years were reviewed.The separation performance and separation mechanism of MOFs stationary phases for mixed gas samples were discussed,and the development trends in the future were prospected.

Lentiviral vectors(LVs)are key gene delivery tools for integrating target genes into the host genome,but they may also pose risks of insertional mutagenesis.The vector copy number(VCN)in cells is critical for determining the safety of gene modification.However,the reliability and accuracy of its quantification process are influenced by multiple factors.Developing cell reference materials with specific vector copy numbers represents a viable approach to enhance the reliability and consistency of measurement results,enabling quality control of the quantification process and traceability of outcomes.However,the preparation of such reference materials faces challenges in cell sample design,preparation protocols,and advanced quantification techniques.In this study,T lymphocyte cell line Jurkat-based reference materials with LV gene copy numbers of 1 and 2 copy/cell were developed.A high-precision duplex digital polymerase chain reaction(dPCR)method was established to quantify the LV gene and endogenous genes simultaneously.Additionally,the results of dPCR were cross-validated through next-generation sequencing and flow cytometric analysis.Ultimately,confocal microscopy characterization results showed that the developed cell reference materials had intact morphology.The quantification result of VCN-1 was(1.07±0.11)copy/cell,and that of VCN-2 was(2.09±0.21)copy/cell.These cell reference materials demonstrated compliance with stability and homogeneity requirements,and could be applied for quality control throughout the VCN measurement workflow and metrological traceability,improving the accuracy,comparability,and validity of copy number measurements.

Objective To explore a deep learning-based automatic bone age estimation model for elbow joint X-ray images of Chinese Han adolescents and children and evaluate its performance.Methods A total of 943(517 males and 426 females)elbow joint frontal view X-ray images of Chinese Han ado-lescents and children aged 6.00 to<16.00 years were collected from East,South,Central and North-west China.Three experimental schemes were adopted for bone age estimation.Scheme 1:Directly in-put preprocessed images into the regression model;Scheme 2:Train a segmentation network using"key elbow joint bone annotations"as labels,then input segmented images into the regression model;Scheme 3:Train a segmentation network using"full elbow joint bone annotations"as labels,then in-put segmented images into the regression model.For segmentation,the optimal model was selected from U-Net,UNet++and TransUNet.For regression,VGG16,VGG19,InceptionV2,InceptionV3,ResNet34,ResNet50,ResNet101 and DenseNet121 models were selected for bone age estimation.The dataset was randomly split into 80%(754 samples)for training and validation for model fitting and hyperparameter tuning,and 20%(189 samples)as an internal test set to test the performance of the trained model.An additional 104 elbow joint X-ray images from the same demographic and age group were col-lected and used as an external test set.Model performance was evaluated by comparing the mean ab-solute error(MAE),root mean square error(RMSE),accuracies within±0.7 years(P±0.7 years)and±1.0 years(P±1.0 years)between the estimated age and the actual age,and by drawing radar charts,scat-ter plots,and heatmaps.Results When segmented with Scheme 3,the UNet++model achieved good segmentation performance with a segmentation loss of 0.000 4 and an accuracy of 93.8%at a learning rate of 0.000 1.In the internal test set,the DenseNet121 model with Scheme 3 yielded the best results with MAE,P±0.7 years and P±1.0 years being 0.83 years,70.03%,and 84.30%,respectively.In the external test set,the DenseNet121 model with Scheme 3 also performed best,with an average MAE of 0.89 years and an average RMSE of 1.00 years.Conclusion When performing automatic bone age estima-tion using elbow joint X-ray images in Chinese Han adolescents and children,it is recommended to use the UNet++model for segmentation.The DenseNet121 model with Scheme 3 achieves optimal per-formance.Using segmentation networks,especially that trained with annotation areas encompassing the full elbow joint including the distal humerus,proximal radius,and proximal ulna,can improve the ac-curacy of bone age estimation based on elbow joint X-ray images.