Development Of System Research Articles

Cost-effective sensor-based digital twin for fused deposition modeling 3D printers

ABSTRACT In a highly digitalized world, Digital Twin (DT) technology is becoming vital in manufacturing, especially in additive manufacturing. This research work presents cost-effective digital twin development and implementation for Fused Deposition Modeling (FDM) 3D printers. This developed system enhances real-time process monitoring, anomaly detection, and autonomous control through a logical approach. Sensor data that measures and tracks filament flow, vibrations, and nozzle position are processed in real-time for the detection of anomalies such as layer shifting, under- and over-extrusion, and excessive vibrations. Magnetic encoders enable monitoring the filament flow with 0.73% error, position sensors identify nozzle displacement to monitor layer shifts. The sensor data are stored in the Firebase and visualized in the Unity interface with a 500- 700 ms data lag. In contrast to traditional systems, this developed system works independently without the need for external hosts, providing a very low-cost, modular solution (less than $50) appropriate for small-scale applications. This research also addresses the gap in FDM 3D printers and sensor-based DT by demonstrating practical, real-time interventions for quality assurance in FDM processes.

Haloferax volcanii: a versatile model for studying archaeal biology.

Archaea, once thought limited to extreme environments, are now recognized as ubiquitous and fundamental players in global ecosystems. While morphologically similar to bacteria, they are a distinct domain of life and are evolutionarily closer to eukaryotes. The development of model archaeal systems has facilitated studies that have underscored unique physiological, biochemical, and genetic characteristics of archaea. Haloferax volcanii stands out as a model archaeon due to its ease of culturing, ability to grow on defined media, amenability to genetic and biochemical methods, as well as the support from a highly collaborative community. This haloarchaeon has been instrumental in exploring diverse aspects of archaeal biology, ranging from polyploidy, replication origins, and post-translational modifications to cell surface biogenesis, metabolism, and adaptation to high-salt environments. The extensive use of Hfx. volcanii further catalyzed the development of new technologies and databases, facilitating discovery-driven research that offers significant implications for biotechnology, biomedicine, and core biological questions.

Pre-Formulation Studies of Lipid-Based Formulation Approach for a Poorly Water-Soluble Biopharmaceutics Classification System Class II Model Drug: Bosentan.

This study aimed to perform pre-formulation studies, formulation development, and formulation optimization for self-nanoemulsifying drug delivery systems (SNEDDS), a lipid-based formulation approach to improve the low solubility of bosentan monohydrate (BOS). Pseudo-ternary phase diagrams were created for pre-formulation studies and formulation design for SNEDDS. The SNEDDS was optimized with BBD. The optimized BOS-loaded SNEDDS formulation was characterized by droplet size (DS), polydispersity index (PDI), dispersibility, an efficiency test of self-nanoemulsification, % transmittance, turbidity, robustness, and the effects of pH, viscosity, and thermodynamic and long-term stability studies. The in vitro dissolution studies were performed in distilled water containing 1% sodium lauryl sulfate, which is a Food and Drug Administration-recommended medium, and in biorelevant media. Ex vivo studies were conducted in biorelevant media. The optimum BOS-loaded SNEDDS had a DS of 16.76 nm and PDI of 0.200. The characterization studies satisfied SNEDDS requirements (does not deteriorate when diluted at different pHs; resistant to thermodynamic changes; self-emulsifying within 1 minute; Grade A; and transparent) for both blank and BOS-loaded SNEDDS. In long-term stability studies, it was found to be stable for six months. When in vitro dissolution was compared to the performance of the commercial product (Tracleer®), the BOS-loaded SNEDDS showed 2.88, 7.63, 3.83, and 4.23 increases in the percentages of cumulative dissolution in fasted state simulated intestinal fluid (FaSSIF), fed state simulated intestinal fluid (FeSSIF), FaSSIF-V2, and FeSSIF-V2, respectively. The ex vivo permeation study showed 12.2-, 19.1-, 20.3-, and 13.1-fold increases in drug permeation in FaSSIF, FeSSIF, FaSSIF-V2, and FeSSIF-V2 for the SNEDDS formulation, as compared to the commercial product, respectively. Pre-formulation and formulation studies were carried out successfully, and lipid-based optimum BOS-loaded SNEDDS were obtained. The present study confirms the potential of optimum BOS-loaded SNEDDS, which was found to be stable over the long term, to increase the drug's solubility, in vitro dissolution, and ex vivo permeability. This formulation approach has been promising for further in vivo studies, to improve the oral bioavailability of BOS.

Solid-State Proton Donors in Hydroxyl-Rich Ni3Ge2O5(OH)4 for Solar-Driven Hydrogen Evolution.

The hydrogen evolution reaction (HER) through water splitting is one of the most promising solutions to global energy and environmental challenges. In this study, using hydroxyl-rich Ni3Ge2O5(OH)4 as a photocatalyst, a novel, kinetically self-activated solar-driven gas-solid system for H2 production was demonstrated, where solid-state lattice hydroxyls (Ni-OH) serve as proton donors due to their low reaction barrier. The sustainable regeneration of Ni-OH is achieved through gaseous H2O molecule dissociation at the resultant oxygen vacancies (OV), which plays a critical role in enhancing proton activity. Under 5 h of testing, the system achieved a cocatalyst-free H2 yield of 311.8 μmol g-1 at the gas-solid interface, approximately 35 times higher than that of the triphasic system, and even 3.4 and 2.1 times higher than H2O splitting systems using triethanolamine or methanol as sacrificial agents, respectively. This work provides valuable insights into the design of advanced photocatalysts and the development of sustainable H2 production systems.

Data-driven prediction of insulation properties in dielectric composites by machine learning

Abstract Materials exhibiting superior thermal and electrical insulation characteristics are highly sought after for the development of electrical power systems and electronic equipment. Nevertheless, the composition and architecture complicatedly influence the insulation performance due to the vast array of design variables. Conventional design methods approaches typically involve extensive trial-and-error experiments, necessitating substantial investment and effort to achieve optimal results. The simulation of electrical trees using the finite element method (FEM) serves as a crucial metric for assessing insulation characteristics. A prolonged evolution period of the electrical tree is indicative of superior insulation performance. This approach provides accurate simulations, albeit at a significant computational cost. In this study, we introduce a machine learning (ML) model that enables the precise and efficient prediction of the development time of electrical trees in dielectric composites. The linear and nonlinear ML models are thoroughly trained, including both ElasticNet and a multi-layer perceptron, leveraging a dataset constructed from FEM simulations. The ElasticNet model demonstrates minimal prediction errors, offering a viable substitute for the FEM model and significantly improving the accuracy of predictions. In addition, an analysis is conducted to determine the influence of each input feature on the predictive outcomes, providing critical insights that facilitate and improve the design process of dielectric composites. To substantiate its effectiveness, the ElasticNet model has been employed to forecast existing experimental outcomes, thereby confirming its practicality and dependability.

Development of Robotic System for Exploration in Extreme Cold Regions (Tested in Antarctica)

Development of Robotic System for Exploration in Extreme Cold Regions (Tested in Antarctica)

Invitro studies of human erythropoiesis using a 3D silk-based bone marrow model that generates erythroblastic islands.

The pursuit of exvivo erythrocyte generation has led to the development of various culture systems that simulate the bone marrow microenvironment. However, these models often fail to fully replicate the hematopoietic niche's complex dynamics. In our research, we use a comprehensive strategy that emphasizes physiological red blood cell (RBC) differentiation using a minimal cytokine regimen. A key innovation in our approach is the integration of a 3-dimensional (3D) silk-based scaffold engineered to mimic both the physical and chemical properties of human bone marrow. This scaffold facilitates critical macrophage-RBC interactions and incorporates fibronectin functionalization to support the formation of erythroblastic island (EBI)-like niches. We observed diverse stages of erythroblast maturation within these niches, driven by the activation of autophagy, which promotes organelle clearance and membrane remodeling. This process leads to reduced surface integrin expression and significantly enhances RBC enucleation. Using a specialized bioreactor chamber, millions of RBCs can be detached from the EBIs and collected in transfusion bags via dynamic perfusion. Inhibition of autophagy through pharmacological agents or α4 integrin blockade disrupted EBI formation, preventing cells from completing their final morphological transformations, having them trapped in the erythroblast stage. Our findings underscore the importance of the bone marrow niche in maintaining the structural integrity of EBIs and highlight the critical role of autophagy in facilitating organelle clearance during RBC maturation. RNA sequencing analysis further confirmed that these processes are uniquely supported by the 3D silk scaffold, which is essential for enhancing RBC production exvivo.

Physiological and transcriptomic analysis of Spartina alterniflora in response to imazapyr acid stress

As a key aspect of managing of invasive alien species in China, the prevention and control of Spartina alterniflora have become an important part of the work in coastal provinces, and imazapyr acid has been gradually applied in the control work due to its advantages of high efficiency and low toxicity. In this study, we applied 6.0 L/acre of 25% imazapyr acid aqueous stress treatment, and determined and analyzed the physiological activities and transcriptome profiles of S. alterniflora under sustained stress. Chlorophyll fluorescence was used as a technical tool to analyze the mechanism of photosynthesis and the photosynthetic physiological status of S. alterniflora. We analyzed the root system structure of S. alterniflora using a root system imaging system, and characterized the transcriptome of S. alterniflora by high-throughput sequencing technology. Specifically, after imazapyr acid exposure, the fluorescence imaging area of leaves were all decreased, and the fluorescence indexes such as Fv/Fm, Y(II) and PIabs were significantly decreased, while Y(NO) was significantly increased, and Y(NPQ) showed an increase followed by a decrease. Meanwhile, total root length, root surface area and biomass of S. alterniflora were suppressed after imazapyr acid exposure. In transcriptomic analysis, imazapyr acid inhibited the expression of genes involved in phenylpropanoid biosynthesis, nucleotide sugar-related metabolism, valine, leucine and isoleucine biosynthesis, and DNA replication in S. alterniflora. These results indicate that the effects of imazapyr acid stress on the leaves of S. alterniflora are heterogeneous, with the leaves initiating photoprotective mechanisms to ensure the normal functioning of the photosystem in the early stage of stress, and the PSII reaction centers being damaged in the late stage of stress, ultimately destroying the photosynthetic system. Meanwhile, imazapyr acid stress alters basic physiological processes such as metabolism and growth and development of S. alterniflora, thus affecting the growth and development of the plant root system, and ultimately leading to the death of S. alterniflora.

The Effect of Early Warning Systems for Sepsis on Mortality: A Systematic Review and Meta-analysis.

The Surviving Sepsis Campaign strongly recommends that all hospitals screen for sepsis as part of performance improvement. The effect of screening for sepsis on mortality, time to antibiotics, and length of stay is uncertain. A systematic literature search was conducted using Cochrane Library, Google Scholar, Ovid Embase, Ovid Medline, Scopus, and Web of Science Core Collection from earliest entry to June 1, 2024. We included all randomized controlled studies of any type of alert system to screen adult patients for sepsis. Risk of bias was assessed using the Cochrane Risk of Bias Tool. Outcomes were pooled using random effects meta-analysis. Strength of evidence was rated using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) system. In total, we found 7 studies of 3409 patients with sepsis. The pooled odds ratio for mortality for patients randomized to early warning systems was 0.84 (95% CI, 0.60, 1.18). The average time to antibiotics was reported in 4 studies and found to be 0.08h faster in the screening group (95% CI, - 0.44, 0.28). Length of stay was reported in 4 studies and found to be 0.27days less in the screening group (95%, - 1.21, 0.66). All differences were non-significant. Overall strength of evidence was low due to risk of bias and imprecision. Based on the current body of randomized controlled studies, there is insufficient evidence to recommend screening for sepsis. Guidelines should reconsider current recommendations for screening for sepsis. CRD42024563222.

Exercise improves stair climbing performance after stroke: A systematic review of randomized trials with meta-analysis.

Exercise improves stair climbing performance after stroke: A systematic review of randomized trials with meta-analysis.

Whole genome and transcriptome analyses identify genetic markers associated with growth traits in Qinchuan black pig

BackgroundGrowth traits are economically important traits in pig breeding. However, the genetic mechanism of growth traits is still unclear. Qinchuan Black (QCB) pigs are crossbred and produced by hybridizing Guanzhong Black (GZB) pigs and Large White (LW) pigs, its characteristics include fast growth and excellent meat quality. In this study, whole genome and transcriptome analyses revealed the candidate genes associated with growth traits in QCB pigs based on imputed low-coverage whole-genome resequencing data.ResultsIn total, we used 197 low-depth whole-genome resequencing data with an average depth of 3.5X, and then the data were imputed to resequencing data using SWIM reference panel, the imputation accuracy parameters, allele frequency r2 and concordance rate were 0.86 and 95.83%, respectively. We used two methods to investigate the candidate genes affecting the growth traits of QCB pigs, a total of 371 PSGs were identified, which related to muscle tissue development, tissue development and system development. A total of 30,489,782 SNPs were retained. A GWAS of ten growth traits by using fixed and random model circulating probability unification (FarmCPU) model, was performed in QCB pigs. We discovered seven genome wide significant SNPs and eight genome wide suggestive significant SNPs associated with body weight at 2 months (2-BW), body length at 2 months (2-BL), body height at 2 months (2-BH) and body height at 4 months (4-BH), and eighteen potential candidate genes were discovered. Transcriptomic data revealed that 18 differentially expression genes related to muscle and growth and development. Additionally, whole genome and transcriptome analyses found six genes (TENM3, CTNND2, RIMS1, PCDH7, ADGRL3 and CTNNA3) may affect the growth traits in Qinchuan Black pigs.ConclusionOur study shows that more candidate genes associated with pig growth traits can be identified by whole genome and transcriptome analyses. We found that six genes may be new key candidate genes affecting pig growth traits. In conclusion, this study elucidated the molecular genetic mechanisms of growth traits and identified new molecular breeding targets, offering a robust scientific basis for advancing breeding strategies and genetic investigations within this breed.

Development of portable soil organic matter system based image and spectral fusion

Abstract Soil organic matter (SOM) content is an important indicator of agricultural soil fertility. A portable detection device was designed by combining near-infrared (NIR) spectroscopy with soil image information technology to rapidly and accurately determine the SOM content. The system extracts the RGB color histogram from pre-processing soil images, such as image cropping and overexposure removal, to improve the validity of image data. Subsequently, the color histogram information is fused with near-infrared spectral data. Meanwhile, a self-attention generative adversarial network (SA-GAN) is proposed to expand SOM fusion data, addressing the challenge of limited soil sample availability for deep learning. 120 soil samples and their corresponding NIR data, image data, and true values of organic matter were collected from the North China Plain, China. Three models, namely, Support Vector Machine (SVM), Partial Least Squares Regression (PLSR), and Convolutional Neural Network (CNN) were used for SOM content prediction. The experimental results show that after data fusion and expansion, the R² values of SVM, PLSR, and CNN models improved from 0.59, 0.55, and 0.60 to 0.73, 0.76, and 0.88, respectively. Concurrently, the RMSEs decreased from 7.84, 8.11, and 5.65 to 3.60, 3.21, and 2.08, indicating higher predictive accuracy across all models. In addition, the portable device integrated with the prediction model was validated in the field, achieving R² of 0.80. It is proven that the system can effectively detect the SOM content in real-time, which provides important technical support and a reference basis for guiding smart agricultural production.

Privacy - Preserving Technique in cybersecurity: Balancing Data Protection and User Rights

Increasing technological complexity of cyber threats creates a major challenge between securing data privacy and maintaining potent cybersecurity practices. The paper examines privacy-protecting security methods in cybersecurity by detailing organizational approaches to defend private information throughout the cyber threat detection and mitigation process. Organizations need to establish the appropriate levels of data security because implementations that limit privacy too much threaten their security capabilities but weak protection measures create vulnerabilities to data breaches. The research implements Cybersecurity: Suspicious Web Threat Interactions data to examine actual cyber threats which comprise phishing attacks and malware and unauthorized access attempts. The effectiveness of data protection approaches including encryption and differential privacy together with homomorphic encryption and federated learning and anonymization solutions gets tested for their ability to secure confidential information throughout cybersecurity operations. The research investigates threat detection accuracy together with computational efficiency and GDPR and CCPA compliance effects when using these techniques. Results demonstrate that security frameworks gain significant improvements from privacy-preserving systems because these systems decrease breach threats and meet all regulatory compliance requirements. The main limiting factors for these privacy-preserving methods consist of excessive computational requirements as well as adversarial threat vectors and the detection versus protection trade-offs that need improvement. This paper presents strategic guidance about privacy-aware cybersecurity models which optimize security capabilities together with data protected information. This research investigates cybersecurity and privacy-preserving methods to assist the development of ethical systems meeting regulatory standards which protect users from advancing cyber threats through privacy-protected mechanisms.

Exploring the Interactive Effects of γ‐Tocopherol, Ellagic Acid, and β‐Sitosterol in Iron Walnut Oil

ABSTRACTIron walnut oil is recognized for its high content of polyunsaturated fatty acids and bioactive compounds, which face significant oxidative stability challenges during storage and processing. This study investigates the antioxidant interactions among γ‐tocopherol, ellagic acid, and β‐sitosterol in iron walnut oil during the thermal oxidation, aiming to enhance its oxidative stability. The antioxidant efficacy of γ‐tocopherol, ellagic acid, and β‐sitosterol, both individually and in combination, was evaluated. γ‐Tocopherol demonstrated the strongest antioxidant activity, and its combination with ellagic acid exhibited a synergistic effect (SE), markedly enhancing the oxidative stability of iron walnut oil by reducing hydrogen peroxide formation and preserving unsaturated fatty acids, whereas β‐sitosterol exhibited limited efficacy, suggesting antagonistic interactions. The effects on fatty acid profile and polar components were analyzed via nuclear magnetic resonance (NMR) and total polar content analysis. The findings suggest that optimizing natural antioxidant combinations can effectively extend the shelf life of iron walnut oil and improve its application potential in the food industry, aligning with the demand for clean‐label and sustainable products.Practical Application: The study provides valuable insights for food industry professionals working with polyunsaturated fatty acid (PUFA)‐rich oils. By demonstrating that γ‐tocopherol and ellagic acid significantly enhance the oxidative stability of iron walnut oil, the research offers practical guidance for formulating walnut oil‐based products with extended shelf life. These findings can directly inform the development of natural antioxidant systems in food preservation, aligning with consumer demands for clean‐label and sustainable products. Researchers can use this information to optimize antioxidant combinations in various food formulations, potentially reducing waste and improving the nutritional profile of food products. The study also highlights the importance of careful antioxidant selection to avoid antagonistic interactions, which is crucial for the industrial application of health‐focused products.

AI-Supported Decision Making in Multi-Agent Production Systems Using the Example of the Oil and Gas Industry

This study focuses on the development of a decision support system for complex production systems. As a promising approach to resource allocation challenges, the application of AI tools, particularly the multi-agent approach, is proposed. It is hypothesized that a decision support system based on multi-agent systems (MASs), grounded in an invariant ontology and utilizing game-theoretic tools, will enhance the effectiveness of managerial decisions by accounting for the inherent multi-agent nature of complex production systems, manifested in diverse objective functions and intricate interaction structures. The work identifies key features of intelligent agent interactions in multi-agent systems. Drawing on interaction types relevant to complex production systems and the selected MAS implementation architecture, an ontological model of multi-agent interactions and an ontological model for managing complex production systems are designed. The proposed models are tested using data from an oil and gas enterprise, and a formalization of utility functions for its agents is provided using game-theoretic tools. The resulting ontological model of multi-agent interactions in complex production systems is practical and easy to implement. It enables the design of MASs for diverse complex production systems, as demonstrated through a resource management case study in the oil and gas industry. Future study will refine the ontological model via industry-specific validation and expand the mathematical and computational tools for model implementation.

The Effect of Substituent Groups on D-A Organic Molecule System for Organic Solar Cells (OSCs) Technology: “A Computational Approach”

Abstract In this work, the density functional theory (DFT) method of Becke’s three-parameter (B3LYP) was used for entirely investigations of D-A organic molecules as a donor (D) designed for organic solar cell (OSC) uses. We designed three D-A molecules with three different substituent groups (H, OCH3, and F) in the donor (D) unit, linked to a strong acceptor moiety. We investigated the open-circuit voltages (VOC), frontier molecular orbitals (FMOs), and photophysical characteristics using DFT and TD-DFT methodologies. A low binding energy (Eb = 2.2061 eV), the smallest bandgap (2.7484 eV) as well as excitation energy (2.5423 eV), and the highest maximum absorption wavelength (𝜆max) values of 467.07 nm and 487.69 nm in both the gas phase and acetonitrile solvent, respectively, are just a few of the promising photovoltaic characteristics that have demonstrated that the designed molecule M2 is the best nominee intended for solar cell applications compared to the others. According to this theoretical model, D-A unit alterations of substituent groups in the position of donor unit are a workable substitute for achieving the required optoelectronic characteristics. Consequently, the M1, M2, and M3 molecules that have been produced are remarkable and highly recommended to experimenters for the development of future solar cell systems with great efficiency.

O MONITORAMENTO DOS SINAIS VITAIS PARA A TOMADA DE DECISÕES TERAPÊUTICAS

Introduction: Monitoring vital signs is a fundamental component of clinical practice, playing a crucial role in the early detection of changes in a patient’s health status and in guiding therapeutic decision-making. Vital signs such as body temperature, heart rate, blood pressure, and respiratory rate provide essential information about the functioning of vital systems and may indicate the presence of acute or chronic conditions. Objective: To understand the importance of vital sign monitoring in therapeutic decision-making. Methodology: This research is based on a literature review using the databases Google Scholar and Scientific Electronic Library Online (SciELO). A total of 24 scientific articles were analyzed, following inclusion criteria: full publications, in Portuguese, indexed in the mentioned databases, and published within the last nine years. The Health Sciences Descriptors (DeCS) used were: vital signs, evaluation of therapeutic intervention outcomes, and clinical deterioration. Results and Discussion: Systematic monitoring of vital signs is directly linked to patient safety. This practice plays a key role in preventing adverse health events and contributes to the efficiency of healthcare services by optimizing resource use and minimizing the costs associated with avoidable complications. Final Considerations: Vital sign monitoring is an essential practice in hospital and intensive care settings, where patients are more vulnerable. Technological advancements have significantly improved the accuracy and effectiveness of this monitoring through the development of electronic devices and remote monitoring systems. Thus, the importance of monitoring vital signs lies in its ability to provide continuous and accurate data, which are fundamental for maintaining homeostasis and promoting patient safety.

Intramolecular Switching Oxygen Reduction Reaction Driven by Dual‐crystalline Bimetallic MOF induces Self‐Potential‐Resolved Electrochemiluminescence biosensor

Framework materials, particularly metal‐organic frameworks (MOFs), have emerged as versatile platforms for multifunctional applications due to their tunable structures and properties. In this study, a series of bimetallic ZnRuMOFs with dual‐crystalline phase structure was designed and synthesized. This unique dual‐crystalline bimetallic MOF configuration enables intramolecular autocatalysis and selective oxygen reduction reaction (ORR). By utilizing endogenous dissolved O2 as a green co‐reactant, an electrochemiluminescence (ECL)‐coupled ORR system with potential‐resolving capability was developed. Experimental results, supported by density functional theory (DFT) calculations, elucidated the ability of MOFs with different phase structures to catalyze specific reactive oxygen species (ROS) generation and the mechanism of ECL luminescence mediated by intramolecular autocatalysis using ZnRuMOF as the luminophor. The selectively generated ROS function as intramolecular coreactants, reducing radical transfer losses and significantly enhancing ECL stability. Leveraging these findings, a novel potentiometrically resolved ECL biosensor for the cardiovascular disease biomarker microRNA 24 was constructed. The biosensor integrates ROS‐mediated intramolecular modulation with a platinum single‐atom catalysts (Pt‐N‐SAC). This study demonstrates that precise modulation of MOF structures reveals distinct catalytic properties associated with different MOF configurations, offering a new strategy for the development of simple yet efficient monoluminescent ECL systems.

The development and validation of a risk stratification system for assessing axillary status after neoadjuvant therapy in node-positive breast cancer: a multicenter, prospective, observational study.

The development and validation of a risk stratification system for assessing axillary status after neoadjuvant therapy in node-positive breast cancer: a multicenter, prospective, observational study.

Preparation and evaluation of oral enteric sustained-release liquid formulations of aspirin.

To develop an enteric sustained-release nanoparticle formulations of aspirin for applicability to a broader population. Aspirin-loaded nanoparticles were prepared using poly(lactic-co-glycolic acid) (PLGA) and the enteric polymer Eudragit L100-55 through an optimized oil-in-water emulsification solvent evaporation method. The nanoparticles were subjected to comprehensive physicochemical characterization, including particle size, zeta potential, and morphological analysis. Additionally, their stability, in vitro drug release profile, cytotoxicity, intestinal absorption, and in vivo pharmacokinetics were systematically evaluated. The nanoparticles exhibited well-defined spherical morphology, uniform particle size, and favorable surface charge, demonstrating excellent biocompatibility. In the in vitro drug release study, AS-PLGA@NPs exhibited pronounced enterolysis effect. The morphology of the nanoparticles was confirmed by scanning electron microscopy (SEM) at different release stages. In vivo intestinal absorption and pharmacokinetic studies in rats demonstrated that AS-PLGA-EL@NPs enhanced drug absorption, prolonged drug release, and showed higher bioavailability compared to conventional enteric-coated tablets. The development and preparation of an oral enteric sustained-release nanoparticle delivery system for aspirin has the potential to serve a broader population, with promising applications in various therapeutic contexts.