University Ferhat Abbas of Setif

This study aims to evaluate the gastroprotective effects of Z. hispanica methanol extract (ZME) in an ethanol-induced gastric ulcer model in rats, assess its safety profile, and investigate its chemical composition. Methods: A methanol extract was prepared by macerating 100g of powdered aerial parts of Z. hispanica in 1000mL of 85% methanol for 72h. Total polyphenol content was quantified. Gastric ulcers were induced in Wistar rats using a single oral dose of 70% ethanol. Rats were pre-treated with ZME (50, 250, or 500mg/kg) or ranitidine (50mg/kg) as a reference control. Gastric tissues were examined for ulcerative lesions, and the percentage of protection was calculated. Gastric mucus production was also evaluated to explore the extract's mechanism of action. ZME significantly reduced ethanol-induced gastric mucosal damage in a dose-dependent manner. The highest dose (500mg/kg) provided 97.18% protection. Additionally, ZME significantly enhanced gastric mucus secretion, exceeding that of the positive control. The extract's bioactivity is attributed to its phenol and flavonoid constituents. ZME exhibits strong gastroprotective properties by reinforcing the gastric mucus barrier. These findings validate the traditional use of Z. hispanica for gastrointestinal relief and suggest its potential as a natural therapeutic agent for the management of gastric ulcers.

This work aims to evaluate the antioxidant, anti-inflammatory and immunomodulatory effects of Juniperus phoenicea extract. Polyphenol and flavonoid contents were estimated using colorimetric methods. The antioxidant potential was assessed using 2, 2-diphenyl-l-picrylhy- drazyl (DPPH) radical scavenging, iron chelation, and anti-hemolytic assays. Croton oil-induced ear edema and carrageenan-induced paw edema models were used to evaluate the anti-inflammatory activity. Furthermore, the hemagglutination test was used to assess the immunomodulatory activity. The total polyphenol and flavonoid contents in this extract were 138.15 ± 11.87 µg GAE/mg and 27.5 ± 1.2 µg QE/mg of dry extract, respectively. The methanolic extract exhibited a strong scavenging activity against DPPH radical (IC50 = 0.051 ± 0.002 mg/mL), while in iron chelation it presented low activity (EC50 = 3.61 ± 0.46 mg/mL) compared to the standard EDTA. The results showed that J. phoenicea extract exhibited high protection of erythrocytes in 2, 2′,-azobis (2 amidinopropane) dihydrochloride (AAPH)-induced hemolysis. Treatment with J. phoenicea extract (200 and 400 mg/kg) exhibited significant anti-edematogenic in both inflammatory models induced by croton oil and carrageenan. However, these doses did not show any significant increase/decrease on the antibody titer. This study indicates that J. phoenicea extract possesses antioxidant and anti-inflammatory effects, confirming the use of this plant in folk medicine.

This paper proposes two main contributions to fractional-order modeling and control of biological systems that may exhibit chaotic behavior. First, a fractional-order chaotic model is designed to represent a biological enzyme using bifurcation diagrams and fractional orders tuning inspired by the available integer order model. This new approach improves the biological model by introducing physical properties specific to fractional order systems such as the memory effect, fractal properties, tissue heterogeneity and non-local behavior. Furthermore, this makes the use of a more effective, robust and powerful fractional-order control easier and more natural. The second main contribution is to propose a fractional-order sliding mode surface in order to derive a sliding mode control (SMC) controller that is able to stabilize this fractional-order biological system asymptotically. We successfully performed the stability analysis using the Lyapunov theory. Numerical simulations using MATLAB are given to demonstrate the efficiency of the proposed fractional-order controller with a drastic improvement in convergence time comparatively to the integer-order counterpart.

• Optimized concrete mix design using recycled coarse aggregates through DNN and IGWO. • Multi-objective optimization framework reveals trade-offs between density and ultrasonic pulse velocity. • Innovative use of machine learning for modeling water content, density, and UPV in recycled aggregate concrete. • Hybrid approach of Grey Wolf Optimizer and Deep Neural Network achieves superior predictive accuracy. • Validated models offer a sustainable framework for concrete mix design in eco-friendly construction. This study presents a thorough methodology for forecasting and enhancing the essential performance characteristics of concrete with Recycled Coarse Aggregates (RCA), including water content (Wt), density, and Ultrasonic Pulse Velocity (UPV). The attributes are represented as functions of cement content (300–400 kg/m³), RCA percentage (0–100 %), and slump (5–12 ± 1 cm). An experimental database was created using a three-factor, three-level design, followed by exploratory analysis using correlation matrices and three-dimensional response surface plots to identify significant input-output associations. Various modeling techniques, such as Response Surface Methodology (RSM), Decision Tree (DT), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and a Deep Neural Network (DNN) enhanced by the Improved Grey Wolf Optimizer (IGWO), were assessed. The accuracy of the model was meticulously evaluated by 4-fold cross-validation, Taylor diagrams, and radar plots, demonstrating that the DNN-IGWO hybrid surpassed all other models, achieving the minimal prediction errors and the maximal correlation coefficient (R 2 ) values for all target responses. The validated models were later employed as surrogates inside a multi-objective optimization framework employing the Multi-Objective Grey Wolf Optimizer (MOGWO). The optimization produced well-distributed Pareto fronts, emphasizing the intrinsic trade-off between density and UPV. The resultant "knee" solutions identify specific concrete compositions that optimize UPV (3.6–3.7) while ensuring a high density (≥2400 kg/m³) and moderate water content. This study provides a scientifically robust, data-driven paradigm for the sustainable design of concrete mixtures, facilitating the appropriate incorporation of recycled aggregates while enhancing material qualities.

Background/Objectives: There is high demand for Anshenbunao syrup (ABS) in Chinese medicine owing to its steady therapeutic efficacy for insomnia and neurasthenia. However, it contains a substantial proportion of Polygoni Multiflori Radix Praeparata (PMRP), which is associated with reported cases of drug-induced liver injury (DILI). Here, we aim to establish an integrated approach combining PK screening with a dual-model toxicity verification system to systematically identify liver injury components (from high to low concentrations and from direct to idiosyncratic hepatotoxicity) to accurately uncover diverse potential hepatotoxicity markers. Methods: A sensitive UPLC-MS/MS method was used to accurately quantify the components in plasma at the ng/mL level and conduct a pharmacokinetic analysis. Rat models were used to evaluate exposure levels of the eight active constituents and three major metabolites after a single oral gavage dose of 10 mL/kg ABS and identify the quality markers. The early-stage and high-throughput assessment of direct and idiosyncratic hepatotoxicity was conducted in vitro utilizing HepG2 cells. After the administration of the quality markers (0.01-80 μM), CCK-8 was used to detect cell viability on both normal and susceptible cells, and the latter was induced by lipopolysaccharide. Results: As a result, seven quality markers were screened based on their contents and exposure levels in rat plasma by UPLC-MS/MS, including emodin (EM), liquiritin (LI), 2,3,5,4'-Tetrahydroxystilbene-2-O-β-D-glucoside (TSG), icariin, emodin-8-O-β-D-glucoside, baohuoside I (BA), and 18β-glycyrrhetinic acid (GTA). Moreover, the half maximal inhibitory concentration values of both normal cells and the lipopolysaccharide-induced immune stress liver injury cells were fitted within the concentration range of 0.01-80 μM, based on which, EM, BA, and GTA were identified as the principal hepatotoxic constituents in ABS at elevated concentrations. This study is the first to demonstrate that TSG, EM, LI, and GTA exhibit synergistic cytotoxicity in LPS-sensitized hepatocytes at clinically relevant concentrations, whereas EM was also a direct hepatotoxic component. Given that TSG is one of the major ingredients in ABS, the underappreciated idiosyncratic hepatotoxicity could elevate the risk of adverse clinical outcomes. Conclusions: In conclusion, this study effectively identifies hepatotoxic constituents in ABS and evaluates their hazards under immune stress and toxicity profiles in clinical concentrations, which also provides a robust foundation for the awareness of PMRP-induced DILI due to ABS.

Abstract Radio Frequency (RF) jamming is a prevalent threat in Mobile Ad-hoc NETworks (MANETs), yet effective detection on resource-constrained nodes remains challenging. This paper presents a distributed three-tier edge-fog-cloud framework leveraging lightweight machine learning for real-time detection across heterogeneous devices. A priority-based adaptation algorithm dynamically allocates computational tasks based on node capabilities. The framework achieves detection accuracies of 85.6% (edge/ lightweight Random Forest), 88.8% (fog/compressed Random Forest), and 97.8% (cloud/LightGBM). The dynamic adaptation mechanism enables efficient load distribution, reducing energy consumption by 66.9% compared to static cloud deployment with only an 8.2% accuracy cost. Cross-dataset validation on two independent physical-layer datasets – covering weak jamming and deceptive attacks under vehicular mobility (10-60 km/h) – demonstrates exceptional robustness with <1% mean accuracy variance, validating the framework’s effectiveness in dynamic, resource-constrained MANET environments.