Thromboembolism is reported to be up to 27% in COVID-19 patients due to SARS-CoV-2 infection. Dysregulated systemic inflammation and various patient traits play a vital role in thrombosis progression. To assess odds and associated factors for thrombosis development among Lebanese COVID-19 patients. This was a case-control retrospective study conducted in January-May 2021. Patients infected with COVID-19 and developed thrombosis were classified as cases and patients who were thrombosis-free identified as control. A questionnaire assessed socio-demographics, clinical parameters, and WHO COVID-19 disease severity. Among 267 patients, 26 (9.7%) developed thrombosis and the majority of thrombosis 34.6% was myocardial infarction, and the least (3.8%) was for catheter-related thrombosis. Results showed that the risk of thrombosis development is higher in patients with previous thromboembolic event (OR = 9.160) and previous intake of anti-hypertensive medications at home (OR = 3.116). However, females (OR = 0.330; CI: 0.118-0.925), intake of anticoagulants during hospital admission (OR = 0.126; CI: 0.053-0.300) and non-severe COVID-19 were at lower thrombosis risk (OR = 0.273). Patients who developed thromboembolic events had longer hospital stay (OR = 0.077). Patients with COVID-19 and thromboembolism were at higher risk of mortality as compared to patients with COVID-19 but without thromboembolism. The use of anticoagulants significantly reduced the risk for thromboembolism.

Background: In response to the escalating global concerns surrounding skin cancer, this study aims to address the imperative for precise and efficient diagnostic methodologies. Focusing on the intricate task of eight-class skin cancer classification, the research delves into the limitations of conventional diagnostic approaches, often hindered by subjectivity and resource constraints. The transformative potential of Artificial Intelligence (AI) in revolutionizing diagnostic paradigms is underscored, emphasizing significant improvements in accuracy and accessibility. Methods: Utilizing cutting-edge deep learning models on the ISIC2019 dataset, a comprehensive analysis is conducted, employing a diverse array of pre-trained ImageNet architectures and Vision Transformer models. To counteract the inherent class imbalance in skin cancer datasets, a pioneering “Naturalize” augmentation technique is introduced. This technique leads to the creation of two indispensable datasets—the Naturalized 2.4K ISIC2019 and groundbreaking Naturalized 7.2K ISIC2019 datasets—catalyzing advancements in classification accuracy. The “Naturalize” augmentation technique involves the segmentation of skin cancer images using the Segment Anything Model (SAM) and the systematic addition of segmented cancer images to a background image to generate new composite images. Results: The research showcases the pivotal role of AI in mitigating the risks of misdiagnosis and under-diagnosis in skin cancer. The proficiency of AI in analyzing vast datasets and discerning subtle patterns significantly augments the diagnostic prowess of dermatologists. Quantitative measures such as confusion matrices, classification reports, and visual analyses using Score-CAM across diverse dataset variations are meticulously evaluated. The culmination of these endeavors resulted in an unprecedented achievement—100% average accuracy, precision, recall, and F1-score—within the groundbreaking Naturalized 7.2K ISIC2019 dataset. Conclusion: This groundbreaking exploration highlights the transformative capabilities of AI-driven methodologies in reshaping the landscape of skin cancer diagnosis and patient care. The research represents a pivotal stride towards redefining dermatological diagnosis, showcasing the remarkable impact of AI-powered solutions in surmounting the challenges inherent in skin cancer diagnosis. The attainment of 100% across crucial metrics within the Naturalized 7.2K ISIC2019 dataset serves as a testament to the transformative capabilities of AI-driven approaches in reshaping the trajectory of skin cancer diagnosis and patient care. This pioneering work paves the way for a new era in dermatological diagnostics, heralding the dawn of unprecedented precision and efficacy in the identification and classification of skin cancers.

Hypertension is a major harbinger of cardiovascular morbidity and mortality. It predisposes to higher rates of myocardial infarction, chronic kidney failure, stroke, and heart failure than most other risk factors. By 2025, the prevalence of hypertension is projected to reach 1.5 billion people. The pathophysiology of this disease is multifaceted, as it involves nitric oxide and endothelin dysregulation, reactive oxygen species, vascular smooth muscle proliferation, and vessel wall calcification, among others. With the advent of new biomolecular techniques, various studies have elucidated a gaping hole in the etiology and mechanisms of hypertension. Indeed, epigenetics, DNA methylation, histone modification, and microRNA-mediated translational silencing appear to play crucial roles in altering the molecular phenotype into a hypertensive profile. Here, we critically review the experimentally determined associations between microRNA (miRNA) molecules and hypertension pharmacotherapy. Particular attention is given to the epigenetic mechanisms underlying the physiological responses to antihypertensive drugs like candesartan, and other relevant drugs like clopidogrel, aspirin, and statins among others. Furthermore, how miRNA affects the pharmaco-epigenetics of hypertension is especially highlighted.

Abstract Background The Arab world is one of the global regions the most directly concerned by, and suffering from climate change’s adverse consequences. As such, there appears to be a strong need for an understanding of how Arab people may emotionally respond to climate change. Providing valid and reliable measures of climate change anxiety (CCA) can help gain a clear overview of the situation in Arab countries, and allow to intervene timely and effectively to mitigate any adverse effects on Arab people’s mental health. To this end, the present study sought to validate the Arabic language version of the Climate Change Anxiety Scale (CCAS) in a sample of native Arabic-speaking adults from the general population of Lebanon. Methods This study adopted a cross-sectional approach and enrolled 763 adults between July and September 2023. Results A confirmatory analysis of the one-factor model showed poor fit indices as follows: CFI = .90, GFI = .83, SRMR = .048 and RMSEA .131 [90% CI .123, .138). The two-factor model showed a satisfactory fit with a high CFI of and a GFI of 0.91 and a SRMR of 0.04 and RMSEA of 0.05 [90% CI 0.04, 0.06]. Both McDonald’s omega and Cronbach alpha values were high for the overall CCAS score (α = .96 and ω = .96) in the whole sample. Configural, metric and scalar invariance across gender was demonstrated. No significant difference was found between males and females in terms of total CCAS scores (24.53 ± 10.59 vs 26.03 ± 11.17, t(761) = -1.82, p = .069). Higher CCA, functional impairment and cognitive impairment scores were significantly associated with higher depression, anxiety and stress. Conclusion The reliability and validity of the CCAS in its Arabic version were proven. The availability of this self-report measure could offer a chance to assess CCA among Adults speaking Arabic, and to spread its future use for screening and research purposes.

The study focused on examining the self-perceived digital competences of Lebanese school teachers and compared their opinions to those of school leaders. The study aimed to investigate whether the digital competence levels of school teachers meet the needs of schools as perceived by school leaders. The European Framework for the Digital Competence of Educators (DigCompEdu) was used as a framework that proposes a comprehensive set of digital competences specifically designed for educators. The findings revealed that while school teachers excel in handling digital resources, they lack competence in promoting learners' digital literacy and their skills of utilizing digital tools for communication, collaboration, and problem-solving. Additionally, school leaders had a different opinion regarding school teachers' competences, indicating a lack of support for the safe and responsible use of digital tools and managing digital resources. The study used a mixed-method approach and a cross-sectional descriptive design with a survey sample size of 170 in-service teachers and an interview sample of 10 school leaders from a variety of Lebanese schools.

This article examines the necessary conditions for the unique existence of solutions to nonlinear implicit ϑ-Caputo fractional differential equations accompanied by fractional order integral boundary conditions. The analysis draws upon Banach’s contraction principle and Krasnoselskii’s fixed point theorem. Furthermore, the circumstances leading to the attainment of Ulam–Hyers–Rassias forms of stability are established. An illustrative example is provided to demonstrate the derived findings.

A rapid pace of change presents an unforeseen and perpetual competitive challenge for organizations. Despite the existence of numerous change management models, organizational change leaders often encounter obstacles during the implementation phase that limit change effectiveness. This paper seeks to make a difference in managerial actions, specifically when implementing planned change, by directing them to use specific leadership actions during the change stage. While previous research has acknowledged the potential linkage between leadership and change effectiveness, a comprehensive exploration of this relationship within the realm of strategic change management remains lacking. This study aims to address this gap by comparing transactional and transformational leadership styles through the lens of the leadership-as-practice perspective. Specifically, this paper analyzes the alignment of these leadership styles with the Kotter model for implementing organizational change and investigates which leadership components or behaviors positively impact predetermined stages of change. Using the MLQ and a developed Kotter questionnaire, data was collected from 385 Lebanese employees working in SMEs experiencing change across the five provinces of Lebanon. PLS structural equation modeling was utilized to analyze the results, and the GoF value indicated that the study model is valid to be considered as a PLS global model. Our findings shed light on the dynamic interplay between leadership efficacy and distinct change stages. Rooted in the leadership-as-practice perspective, this study contributes a nuanced understanding of effective leadership’s crucial role in navigating the multifaceted challenges of organizational change, offering insights for practitioners and scholars alike.

Abstract Background Healthcare is a complex system with overarching challenges that arise from the different hierarchically organized structures and the diversity of people interacting and communicating in the same environment. This complexity can be addressed by strengthening healthcare professional leadership and entrepreneurship competencies. This study aims to evaluate the self-perception of healthcare professionals regarding these skills and their association with demographic characteristics and university attributes. Method A cross-sectional survey conducted online from July to December 2021 recruited 245 Lebanese health professionals from different health-related institutions (hospitals, pharmaceutical industry, health professions universities, and others) using snowball sampling. A cluster analysis was performed based on the socio-demographic and work characteristics of the participants to classify their profiles. Bivariate and multivariate analyses were performed after ensuring the adequacy of the models. Significance was set at a P value < 0.05. Results Cluster analysis showed two distinct profiles, reflected by Cluster 1 for older individuals with moderate/high management versus Cluster 2 for younger people with low management profiles. The logistic regression showed that Cluster 1 was significantly associated with higher leadership with administrative, interpersonal, and conceptual skills. The interpersonal skills represented best Cluster 1 (ORa = 7.47), followed by the conceptual skills (ORa = 4.40). Linear regression analysis showed that Cluster 1 was significantly associated with higher decision-making (β = 0.69) and higher tolerance of ambiguity (β = 1.01). No association was found between other subscales, total entrepreneurship scales, and belonging to any cluster (P > 0.05). Conclusion Although most healthcare professionals showed moderate to high perceptions related to their leadership and entrepreneurship, younger ones were aware of the need to develop these skills to meet the challenges of the complex dynamic health system. Educating students and training professionals to acquire these skills would create value in emerging health services while fostering innovation, creativity, and quality improvement in the workplace.

Background and Objectives: Stroke prevention has traditionally concentrated on research to improve knowledge and awareness of the disease in the general population. Since stroke incidents increase with age, there is a need to focus on the elderly, a high-risk group for developing the disease. This study aimed to examine the level of stroke awareness and knowledge, their predictors, and their source of information. Materials and Methods: A prospective cross-sectional study targeted Lebanese senior citizens aged 65 years and above. A total of 513 participants enrolled in the study through a self-administered survey distributed using a snowball sampling technique. Results: Most participants had appropriate baseline knowledge (more than 75% correct answers) of stroke, including risk factors, alarming signs, and preventive measures. Better knowledge of disease risks was significantly associated with having a university degree (ORa = 1.609; p = 0.029). Participants who had previous ischemic attacks showed significantly lower knowledge of the alarming signs (ORa = 0.467; p = 0.036) and prevention measures (ORa = 0.427; p = 0.029). Those suffering from depression had better knowledge of stroke alarming signs (ORa = 2.060.; p = 0.050). Seeking information from pharmacists, physicians, or the internet was not significantly associated with better knowledge of stroke risks, alarming signs, and preventive measures. Conclusions: The present study showed that seniors had fair knowledge of stroke, despite gaps in stroke prevention measures. Healthcare providers could play a leading role in improving public health by educating seniors to enhance awareness about prevention measures, detecting alarming signs, and acting fast to save a life.

In this paper, we present a numerical linear algebra analytical study of some schemes for the Bertozzi–Esedoglu–Gillette–Cahn–Hilliard equation. Both 1D and 2D finite difference discretizations in space are proposed with semi-implicit and implicit discretizations on time. We prove that our proposed numerical solutions converge to continuous solutions.