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Validation of salivary proteomic biomarkers for early detection of oral cancer in the Egyptian population
No full textPurpose: The present study evaluated the sensitivity and specificity of important proteomic salivary biomarkers; IL-6, IL-8, and sCD44 in the early detection of oral cancer, and any poss associations with risk factors of oral cancer in an Egyptian population Methods: The present investigation was conducted on 100 individuals; 25 healthy controls, 25 patients having oral potentially malignant disorders (OPMDs) with dysplasia; 25 patients having OPMDs without dysplasia, and 25 oral cancer patients. Demographic data modified gingival index, oral hygiene level, and salivary levels of the biomarkers were assessed. Results: Salivary levels of IL-6, IL-8, and sCD44 progressively increased with increased di severity. Salivary IL-8 and IL-6 levels possess a discriminating potential from normal ti through different degrees of dysplasia to oral cancer, sCD44 levels had a discriminating power between normal and dysplastic tissues with high sensitivity and specificity. positive correlation was found between the three biomarkers and the grade of oral squamous cell carcinoma (OSCC) and with different risk facto Conclusion: This is the first study that evaluated multiple salivary proteomic biomarkers in the Egyptian population, and the results validate the ability of IL-6, IL-8, and sCD44 used as sensitive diagnostic and prognostic biomarkers for screening and early detection of oral cance
Corruption perception, institutional quality and performance of Egyptian listed companies:Evidence from Econometric models
No full textAbstract
Purpose– This study aims to examine the impact of corruption perception and institutional quality on the
performance of publicly listed companies in Egypt.
Design/methodology/approach– Data from 42 publicly traded Egyptian rms during 2013–2022 were
analyzed. Corruption was measured using the Transparency International Corruption Perceptions Index, while
institutional quality was assessed through a composite measure derived from World Bank Governance
Indicators using principal component analysis. The study used a generalized method of moments dynamic
panel model for the analysis.
Findings– The results reveal a negative relationship between corruption and rm performance, both in
market value (Tobin’s Q) and accounting outcomes (ROA). Institutional quality also inversely affects
performance, indicating that corruption and weak governance undermine corporate success in Egypt.
Research limitations/implications– The ndings are speci c to the 2013–2022 period and exclude newly
listed rms due to data limitations. Future studies could expand the sample and timeframe to provide broader
insights.
Practical implications– The study underscores the need for stronger governance and institutional reforms.
Policymakers and regulators must address corruption and improve institutional frameworks to enhance rm
performance and market con dence.
Originality/value– This study challenges the positivist view of corruption, presenting evidence of its
detrimental effects in the Egyptian context. It contributes to the limited research on how corruption perception
and institutional quality affect rm performance in Egypt.
Keywords Corruption perception, Institutional quality, Firm performance, Dynamic panel model,
Egypt, Econometrics
Paper type Research pape
Empowering Sustainable Infrastructure and Sustainable Development Goals Through Industry 5.0 Implementation
No full textThe role of neutrophilia in hyperlactatemia, blood acidosis, impaired oxygen transport, and mortality outcome in critically ill COVID-19 patients
No full textIntroduction: COVID-19 severity and high in-hospital mortality are often associated with severe hypoxemia, hyperlactatemia, and acidosis, yet the key players driving this association remain unclear. It is generally assumed that organ damage causes toxic acidosis, but since neutrophil numbers in severe COVID-19 can exceed 80% of the total circulating leukocytes, we asked if metabolic acidosis mediated by the glycolytic neutrophils is associated with lung damage and impaired oxygen delivery in critically ill patients.
Methods: Based on prospective mortality outcome, critically ill COVID-19 patients were divided into ICU- survivors and ICU-non-survivors. Samples were analyzed to explore if correlations exist between neutrophil counts, lung damage, glycolysis, blood lactate, blood pH, hemoglobin oxygen saturation, and mortality outcome. We also interrogated isolated neutrophils, platelets, and PBMCs for glycolytic activities.
Results: Arterial blood gas analyses showed remarkable hypoxemia in non-survivors with no consistent differences in PCO2 or [HCO3 -]. The hemoglobin oxygen dissociation curve revealed a right-shift, consistent with lower blood-pH and elevated blood lactate in non-survivors. Metabolic analysis of different blood cells revealed increased glycolytic activity only when considering the total number of neutrophils.
Conclusion: This indicates the role of neutrophilia in hyperlactatemia and lung damage, subsequently contributing to mortality outcomes in severe SARS-CoV-2 infection
Synergizing GIS and genetic algorithms to enhance road management and fund allocation with a comprehensive case study approach
No full textThis study identifies a critical knowledge gap, revealing how the deterioration of roads, compounded by extensive usage and additional factors, poses significant risks to the road networks’ functionality. Without a robust fund allocation and prioritization strategy, the extent of this risk may be overlooked, adversely affecting the performance of essential infrastructure elements. Our research introduces an integrated decision-making model for existing road infrastructures to address this gap. This innovative approach combines a Geographic Information System (GIS)-based road management model with a fund allocation prioritization strategy, enhanced by an optimization engine via a genetic algorithm. The primary aim is to precisely determine Maintenance and Repair (M&R) interventions tailored to the condition states, thereby improving the Pavement Condition Index (PCI) of the road segments. The research is structured around three key objectives: (1) develop a detailed GIS-based road management database incorporating inspection data and attributes of road infrastructure for proactive M&R decision-making; (2) efficiently allocate funds to maintain service delivery on deteriorated roads; and (3) pinpoint the optimal type and timing of M&R interventions to boost the condition and performance of the road segments. Anticipated results will provide asset managers with a comprehensive decision support system for executing effective M&R practices
Metabolome classification of Tongkat Ali (Eurycoma longifolia jack) and its commercial products via UHPLC-QTOF-MS-MS and its protective effect against 5-flurouracil-Induced testicular toxicity in male rats
No full textAI-Driven Robotic System for Predictive Maintenance: Urban Road Defect Detection in Smart Cities
No full textThis paper presents the development and evaluation of an integrated AI & robotic solution for predictive maintenance in smart cities, focusing on urban road defect detection. The proposed system integrates a mobile robot equipped with a high-resolution camera and a GPS module to capture video footage and geolocation data of road surfaces. The collected data is processed using cloud-based AI models, with YOLOv9 and Roboflow 3.0 Object Detection (Fast) identified as the most effective for analyzing footage to detect cracks and potholes. Experimental results validate the system\u27s ability to accurately identify defects and generate timely reports for maintenance teams. While the approach proved effective in reducing maintenance costs, improving road safety, and extending infrastructure lifespan, limitations were identified, including GPS inaccuracies within a few meters and the challenges posed by an insufficient dataset. These findings emphasize the potential of robotic systems for enhancing urban infrastructure management while highlighting areas for future improvement
Pre-Programmable Directional Stiffness in Continuum Robotic Arms: Effects of Truss Configurations
No full textThis paper investigates the implementation of trussed designs in modular tendon-driven continuum arms (CAs) to address stiffness and stability limitations while maintaining adaptability. Various truss configurations— Single-Level, Combined-Level, and Alternated trusses—were analyzed using Finite Element Analysis (FEA) to evaluate their impact on flexural and axial stiffness under realistic loading conditions. Results demonstrate that truss placement significantly influences performance, with bottom-level trusses improving flexural stiffness by up to 33%, middle-level trusses enhancing axial stiffness by 43%. Further, Combined-Level configurations provide superior overall stiffness, with MiddleBottom trusses achieving a 66.5% improvement in flexural stiffness and a 70.9% increase in axial stiffness, while fully trussed configurations maximize rigidity with an 87.8% increase in flexural stiffness and 98.5% in axial stiffness. Alternated configurations achieve anisotropic stiffness for directional control. Additionally, the proposed trussed wing approach enhances stiffness programmability with reduced complexity, eliminating the need for full segment disassembly or tendon re-routing. The proposed trussed CAs enable tailored stiffness and directional control in robotics, medical devices, and aerospace systems
EMG-Based Intraoperative Neuromonitoring Using Advanced Machine Learning Approaches
No full textIntraoperative neuromonitoring (IONM) plays a critical role in minimizing nerve damage during surgeries by providing real-time feedback on neural integrity. This study evaluated models associated with deep learning and machine learning models for electromyography classification of signal during intraoperative neuromonitoring (IONM). The CNNLSTM model achieved the highest accuracy (85.2%), outperforming traditional models like KNN (53%), RF (62%), and CNN (76%). This demonstrates the degree to which the CNN-LSTM model can gain insight into temporal and spatial dependencies throughout the EMG signals, which makes it optimal for real-time classification in IONM applications. This implies that deep learning techniques can improve surgical procedures\u27 safety and efficacy
Effects of die type on mechanical performance and failure behaviors of self-piercing riveting joints in AA5052
No full textThis study investigates the effects of self-piercing riveting (SPR) die type on the joint quality and failure modes of AA5052 SPR joints. Three die types: flat, pip, and ball-shaped were designed and processed for the SPR process, and 30 riveted specimens were prepared. A 2D axisymmetric finite element (FE) simulation model was established using LS-Dyna to evaluate the impact of each die type. The accuracy of the FE model was validated by comparing simulated joint cross-sections with experimental results. Subsequently, the quality and forming mechanisms of the SPR joints fabricated using each die type were analyzed. Mechanical properties were assessed through peak load and energy absorption during single-lap shear testing. Moreover, the macroscopic failure modes were observed via scanning electron microscopy (SEM) to determine failure modes. Results showed that specimens produced with the flat-type die exhibit the highest interlock value and mechanical properties. However, their forming quality is lower than those of the pip-type die due to the higher rivet head height. The pip die enhances rivet flaring, yielding mechanical properties superior to the flat-type die specimens. In contrast, specimens formed with the ball-shaped type die exhibit the lowest forming quality and mechanical performance. All specimens shared a rivet pull-out failure mode, though the degree of rivet hole deformation varied with riveting quality; the higher the quality, the greater the rivet hole deformation