Beirut Arab University

Bacterial contamination is a persistent issue in food preservation, medical hygiene, and water purification, necessitating the development of eco-friendly and effective antibacterial solutions. In this context, silver nanoparticles (AgNPs) were biosynthesized using an aqueous extract of Marrubium vulgare L. leaf powder - a plant scarcely explored for nanoparticle synthesis. The process was optimized to enhance surface plasmon resonance (SPR) using a Box-Behnken design based on response surface methodology (RSM), taking into account silver nitrate concentration, extract-to-silver ratio, temperature, and reaction time. The statistical model showed impressive prediction capacity (R-2 > 98 %), which was experimentally validated, with high reproducibility. The biosynthesized AgNPs exhibited a well-defined SPR peak around 440 nm, appearing as spherical, uniformly dispersed particles with an average size of 35.35 +/- 12.56 nm. XRD analysis confirmed their crystalline structure, while a zeta potential of -48.5 mV indicated excellent colloidal stability - a finding reinforced through assessments over time, demonstrating that the nanoparticles remain highly stable and easily reproducible. The involvement of plant-derived biomolecules was thoroughly examined, revealing that significant proportions of polyphenols (82.07 %), flavonoids (88 %), and tannins (45.49 %) contributed to nanoparticle reduction and stabilization. The antibacterial activity of the synthesized nanoparticles was tested against both Gram-positive and Gram-negative bacteria, showing effective inhibition with zones ranging from 7.23 +/- 0.59 mm to 10.60 +/- 0.81 mm. Their consistent performance across different bacterial types highlights their versatility and strong potential as a multifunctional antibacterial solution. Overall, these findings position M. vulgare-based AgNPs as a highly stable, reproducible, and eco-friendly alternative for alleviating bacterial contamination in various applications.

Anastomotic leak after one-anastomosis gastric bypass (OAGB) remains a serious postoperative complication with significant morbidity and mortality. Despite multiple available strategies, no standardized approach exists for managing gastrojejunostomy leaks. This study presents a simple, minimally invasive technique designed to control leakage and promote tract closure. This single-centre case series included five consecutive patients who developed gastrojejunostomy leaks following OAGB between 2020 and 2025. All patients were managed using a novel minimally invasive technique involving placement of a Foley catheter through the leak site to achieve internal sealing, combined with controlled external drainage. Clinical data, postoperative course, and outcomes were recorded. Five patients (mean age 44.4 years; mean BMI 55.4 kg/m²) were treated using this technique. All leaks were identified at the gastrojejunostomy, with onset ranging from 2 to 7 days postoperatively. Oral fluid intake was initiated on the second day following clinical improvement. Median postoperative hospital stay was 4 days. The Foley catheter was maintained for a mean duration of 23.8 days before removal. All patients were followed up, with a mean follow-up duration of 4 months. This minimally invasive salvage technique appears feasible and safe for managing gastrojejunostomy leaks after OAGB. It offers a straightforward alternative that supports controlled external drainage and facilitates leak closure. Larger studies are needed to validate its reproducibility and broader applicability. There is no universally accepted standardized approach for the management of gastrojejunostomy leaks following OAGB. Minimally invasive strategies that combine leak control, external drainage, and preservation of nutrition are increasingly favored. The minimally invasive salvage technique demonstrated promising preliminary outcomes, achieving drainage, leak closure, and early recovery in our series.

Early and accurate anomaly detection in Intensive Care Unit (ICU) monitoring is vital for improving patient outcomes and reducing mortality. Traditional machine learning techniques often face challenges in handling the noisy, high-dimensional, and limited datasets characteristic of ICU physiological signals. In this study, we present an early investigation of a quantum-enhanced framework employing a classically simulated Quantum Support Vector Machine (QSVM) to improve the detection of critical anomalies in real-world ICU time-series data. Utilizing the publicly available PhysioNet 2012 Challenge dataset, we extract comprehensive statistical and temporal features from vital signs including heart rate, blood pressure, respiratory rate, and oxygen saturation. Our QSVM model leverages a quantum kernel mapped via Qiskit’s ZZFeatureMap and is rigorously benchmarked against classical machine learning classifiers such as Support Vector Machines with radial basis function kernels, Random Forest, and XGBoost. Experimental results demonstrate that the QSVM shows competitive and robust classification performance, particularly excelling in scenarios with limited and noisy data, with an AUC of 0.92, particularly excelling in scenarios with limited and noisy data. Furthermore, the quantum kernel’s ability to implicitly project data into a high-dimensional Hilbert space provides enhanced separability of complex physiological patterns. This work constitutes one of the first simulation-based applications of quantum machine learning to critical care anomaly detection, underscoring the promise of hybrid quantum-classical approaches for advancing real-time clinical decision support systems.

Precise segmentation of brain tumors from multimodal MRI scans is essential for accurate neuro-oncological diagnosis and treatment planning. To address this challenge, we propose a label-free optimization-driven segmentation framework based on the alpha-expansion graph cut algorithm, offering improved computational efficiency and interpretability compared to deep learning alternatives. The method relies on structured optimization and handcrafted features, including local intensity patches, entropy-based texture descriptors, and statistical moments, to compute voxel-wise unary potentials via gradient-boosted decision trees (XGBoost). These are integrated with spatially adaptive pairwise terms within a graph model optimized through alpha-expansion. Evaluation on 146 BraTS validation volumes demonstrates reliable whole-tumor overlap, with a mean Dice score of 0.855 +/- 0.184 and a 95% Hausdorff distance of 18.66 mm. Bootstrap analysis confirms the statistical stability of these results. The low computational overhead and modular design make the method particularly suitable for transparent and resource-constrained clinical deployment scenarios.

This work is devoted to the analysis of well-posedness and stabilization properties of both the classical and truncated Timoshenko beam models resting on a two-parameter elastic foundation. We rigorously investigate the effects of the elastic subgrade on the dynamic behavior of the systems and consider the influence of viscous damping mechanisms acting on the angular rotation. For each model, we establish the existence, uniqueness, and continuous dependence of solutions. Moreover, we analyze the long-time behavior of the solutions, proving exponential or polynomial energy decay depending on the parameters of the wave speeds. The results highlight the differences in stability between the classical and truncated models and contribute to the mathematical theory of damped elastic structures interacting with elastic media.