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Educational technology has changed the teaching and learning process in many ways. As teachers needed new strategies to adapt to emerging technology tools and to interact with students, immediate feedback and digital instructional resources had become the norm. Teaching and learning expectations are growing because of technological improvements. Many digital libraries and online resources are now easily accessible to teachers and students. Therefore, a qualified teacher must be aware of the new teaching requirements and the adjustments related to educational technology trends. Additionally, it is important to adopt a Technological Pedagogical Content Knowledge (TPCK) framework and to clarify how teachers and students can collaborate to achieve learning outcomes through updated educational practices. This collaborative education can occur because technology makes it easier for students and teachers to interact together, despite the physical distance that could separate them. This paper emphasizes the optimum use of educational technologies and the potential of new digital tools that have already been exploited in an unprecedented way. It aims to provide a comprehensive review of current technologies with pedagogic recommendations, to highlight the importance of educational technologies in teaching, and to reconsider strategies for acquiring digital skills in the Artificial Intelligence era. The discussion revolves around identifying and depicting various advanced educational technologies that can be integrated with engaging pedagogical approaches. Furthermore, it explains how each technology can enhance the learning experience, emphasizing the teacher's role in the technology-based educational process.

Mariam Heikal,1 Halim Saad,2 Pia Maria Ghanime,3 Tarek Bou Dargham,4 Maya Bizri,5 Firas Kobeissy,6 Wassim El Hajj,1 Farid Talih2 1Department of Computer Science, American University of Beirut, Beirut, Lebanon; 2Department of Psychiatry, Faculty of Medicine, American University of Beirut, Beirut, Lebanon; 3Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA; 4Department of Neurosurgery, Duke University Medical Center, Durham, NC, USA; 5Department of Psychiatry and Psychology, Cleveland Clinic, Cleveland, OH, USA; 6Department of Neurobiology, Morehouse School of Medicine, Atlanta, GA, USACorrespondence: Mariam Heikal, Department of Computer Science, American University of Beirut, Beirut, Lebanon, Email mas177@mail.aub.eduObjective: Delirium is a common and acute neuropsychiatric syndrome that requires timely intervention to prevent its associated morbidity and mortality. Yet, its diagnosis and symptoms are often overlooked due to its variable clinical presentation and fluctuating nature. Thus, in this study, we address the barriers to delirium diagnosis by utilizing a machine learning-based predictive algorithm for incident delirium that relies on archived electronic health records (EHRs) data.Methods: We used the Medical Information Mart for Intensive Care (MIMIC) database to create a detailed dataset for identifying delirium in intensive care unit (ICU) patients. Our approach involved training machine learning models on this dataset to pinpoint critical clinical features for delirium detection. These features were then refined and applied to non-ICU patients using EHRs from the American University of Beirut Medical Center (AUBMC).Results: Our study assessed machine learning models like Extreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), Classification and Regression Trees (CART), Random Forest (RF), Neural Oblivious Decision Ensembles (NODE), and Logistic Regression (LR), highlighting superior delirium detection in diverse clinical settings. The CatBoost model excelled in ICU environments with an F1 Score of 89.2%, while XGBoost performed best in general hospital settings with a 75.4% F1 Score. Interpretations using Tabular Local Interpretable Model-agnostic Explanations (LIME) revealed critical indicators such as prothrombin time and hematocrit levels, enhancing model transparency and clinical applicability. These clinical insights help differentiate the delirium predictors between ICU patients, who are often sensitive to various factors.Conclusion: The proposed predictive algorithm improves delirium detection rates and streamlines efficiency in hospital electronic systems, thereby enabling prompt interventions to prevent delirium progression and associated complications. The clinical indicators for delirium that we identified in general hospital settings and ICU can greatly help healthcare professionals identify potential causes of delirium and reduce misdiagnosis.Keywords: Delirium, ICU delirium, Hospital-acquired delirium, electronic health records, machine learning, clinical indicators

Abstract This retrospective study analyzed prognostic factors for neurological improvement and ambulation in 194 adult patients (≥ 15 years) with traumatic cervical spinal cord injuries treated at the neurological SCI unit (SCIU) at the Karolinska University Hospital Stockholm, Sweden, between 2010 and 2020. The primary outcome was American spinal injury association impairment scale (AIS) improvement, with secondary focus on ambulation restoration. Results showed 41% experienced AIS improvement, with 51% regaining ambulation over a median follow-up of 3.7 years. Significant AIS improvement (p

Persistent security challenges in Industry 4.0 due to the limited resources of IoT devices necessitate innovative solutions. Addressing this, this study introduces the ASCON algorithm for lightweight authenticated encryption with associated data, enhancing confidentiality, integrity, and authenticity within IoT limitations. By integrating Digital Twins, the framework emphasizes the need for robust security in Industry 4.0, with ASCON ensuring secure data transmission and bolstering system resilience against cyber threats. Practical validation using the MQTT protocol confirms ASCON’s efficacy over AES-GCM, highlighting its potential for enhanced security in Industry 4.0. Future research should focus on optimizing ASCON for microprocessors and developing secure remote access tailored to resource-constrained devices, ensuring adaptability in the digital era.

Abstract Rhabdomyosarcoma 2-associated transcript (RMST) long non-coding RNA has previously been shown to cause Kallmann syndrome (KS), a rare genetic disorder characterized by congenital hypogonadotropic hypogonadism (CHH) and olfactory dysfunction. In the present study, we generated large deletions of approximately 41.55 kb in the RMST gene in human pluripotent stem cells using CRISPR/Cas9 gene editing. To evaluate the impact of RMST deletion, these cells were differentiated into hypothalamic neurons that include 10–15% neurons that express gonadotrophin-releasing hormone (GnRH). We found that deletion in RMST did not impair the neurogenesis of GnRH neurons, however, the hypothalamic neurons were electro-physiologically hyperactive and had increased calcium influx activity compared to control. Transcriptomic and epigenetic analyses showed that RMST deletion caused altered expression of key genes involved in neuronal development, ion channels, synaptic signaling and cell adhesion. The in vitro generation of these RMST-deleted GnRH neurons provides an excellent cell-based model to dissect the molecular mechanism of RMST function in Kallmann syndrome and its role in hypothalamic neuronal development.

Ectopic thyroid tissue is a rare congenital anomaly, with the presence of three simultaneous ectopic foci being exceedingly rare. We describe a case of a totally asymptomatic 26-year-old male discovered to have triple ectopic thyroid following incidental elevated thyroid-stimulating hormone (TSH) levels. Subsequent ultrasonography of the neck showed an absent thyroid gland in its conventional location. A Technetium-99m pertechnetate (Tc-99m) thyroid scan showed three distinct foci of radiotracer uptake in the upper cervical, lingual, and sublingual regions, corresponding to triple ectopic thyroid. An extensive review of the literature was conducted to provide a broader understanding and deeper insights into this uncommon condition. This case underscores the pivotal role of Technetium-99m thyroid scanning in diagnosing triple ectopic thyroid, particularly in instances where the orthotopic thyroid gland is absent. A comprehensive understanding of this rare entity is indispensable for radiologists and clinicians, enabling accurate diagnosis and informed decision-making regarding the appropriate therapeutic strategies.

Este editorial está siendo co-publicado por todas las revistas de la International Collaboration of Pharmacy Journal Editors

IntroductionThe COVID-19 pandemic has exacerbated stress and anxiety among young people, particularly university students, impacting their mental well-being and daily life. Given the rise in social isolation and economic uncertainty, the adoption of mindfulness practices such as sophrology, meditation, and yoga becomes essential for improving their mental health. This study aims to evaluate the impact of these practices on stress levels and their effect on engagement in physical activities among youths.MethodThis study employed a mixed methods design to assess the impact of different wellness and mindfulness practices on stress levels and physical activity (PA) engagement among university students. Quantitative data were gathered electronically from 218 students at the University of Rouen and the university of Paris-Cité through validated questionnaires, including custom tools on sports and mindfulness practices and the Perceived Stress Scale (PSS). The sample consisted mainly of young adults aged 18 to 35, with a majority of 170 female and 48 male participants. Qualitative insights were gathered through semi-structured interviews with three directors of the university's sports and physical activities department (SUAPS), as well as four wellness program instructors. Data analysis involved statistical techniques using Statistical Analysis System (SAS) software for quantitative data, while qualitative data were analyzed thematically using Sphinx software, a tool for textual analysis. Ethical approval for the study was obtained, and confidentiality of all participants was maintained throughout the research.Results and conclusionThe COVID-19 pandemic had significant effects on French university students’ PA, mental health, and stress levels. Lockdowns led to a decline in sports practices for many, while others adapted by increasing their engagement in fitness and wellness practices. The majority of participants (64.68%) reported moderate levels of stress. A significant correlation was found between motivations for mental health support and stress management, and higher levels of stress (p = 0.0000 and p = 0.0024, respectively). Regular participation in wellness activities was associated with lower stress (p = 0.0193). The findings reinforce the idea that incorporating wellness practices into educational environments can strengthen students’ mental resilience and overall well-being, equipping them with essential tools to effectively cope with future stressors.

This paper aims to examine the role of organizational culture, mainly ethics, in the field of artificial intelligence. In particular, it investigates the contribution of ethics in implementing artificial models developed for financial decision making in organizations. The research methodology is based on developing an artificial model using a ready dataset from a developed country, Poland, which includes financial ratios of bankrupt and non-bankrupt companies, and then testing this model on a self-developed dataset of bankrupt companies in a developing country, Lebanon. However, the research suffers one limitation related to gathering accounting and financial data necessary to build new datasets in Lebanon which in turn should open up future lines of research. Nevertheless, the ultimate goal was to conclude if the same model would be reliable in both cultures due to its dependence on objective measures and criteria (i.e. financial ratios) or if specific factors (e.g. organizational culture/ethics) might act as barriers facing the optimal utilization of AI algorithms and models in Lebanese organizations. The empirical findings revealed that the absence of ethical organizational conduct, high level of corruption, and poor official governance are major examples of the obstacles confronting the rewarding application of AI in Lebanon.