Mohammad S. Yousef

Introduction: Traditional physics education has not always aligned with the interests and perceived needs of pre-medical and health science students due to a lack of perceived clinical relevance, leading to disengagement and shallow conceptual understanding. This paper evaluates the pilot implementation of “Physics for Future Doctors,” an innovative one-week interdisciplinary program designed to bridge the gap between physics, medicine, and engineering. Methods: The curriculum anchors abstract mechanics in the context of biomechanics and features three key pedagogical innovations: (1) low-cost, hands-on experiments that render physical principles tangible; (2) a custom interactive digital simulation tool, StaticStudio , designed to enhance spatial reasoning in free-body diagrams; and (3) a unique “multiversity” peer-mentorship model that pairs teaching assistants from engineering and medical schools. Instructional delivery was deliberately scaffolded using Bloom's Taxonomy to guide students from foundational recall to creative application. The program's impact was evaluated through surveys administered to the pilot cohorts. Results: Survey results indicate a significant positive impact on student self-efficacy: the proportion of students rating their understanding at the highest level increased from 41% pre-program to 73% post-program. Furthermore, participants reported high satisfaction with the peer-mentorship model and the integration of digital and physical tools. Discussion: These findings suggest that this context-driven, interdisciplinary framework is a highly effective model for overcoming the “relevance gap.” By combining tangible biomechanical applications with near-peer mentorship, the program successfully enhances student engagement in STEM education.

Background/Objectives: Prostate cancer (PCa) is the second most commonly diagnosed cancer in men globally. Radiation oncologists often find PCa tumor characterization and outcome prediction challenging. Therefore, the potential for artificial intelligence (AI) implementation in radiation oncology has increased in recent years. This systematic review aims to evaluate the efficacy of AI algorithms in characterizing PCa tumors and predicting post-therapy outcomes. Methods: A total of 2055 studies were identified through a comprehensive search across PubMed and Scopus, then exported to Covidence. Inclusion criteria focused on prospective and retrospective cohort studies as well as randomized clinical trials (RCTs) published between 2015 and 2024 that explored the implementation of AI in tumor characterization and outcome prediction of PCa. Two independent reviewers evaluated each paper, and evaluation metrics such as specificity, sensitivity, accuracy, and area under the curve (AUC) were analyzed. The Risk of Bias in Non-randomized Studies of Interventions, Version 2 (ROBINS-I V2) tool was used to assess the risk of bias (ROB). Results: Across the 19 studies analyzed, there was no significant difference in model performance between machine learning (ML) and deep learning (DL) models. AI models using multi-input strategies (e.g., radiomics with clinical markers) generally performed better than single-input models. Of the imaging modalities used for radiomic feature extraction, multiparametric MRI (mpMRI)-trained AI models consistently achieved the highest performance. Conclusions: AI displays considerable potential for integration into clinical workflows for PCa management. However, further studies utilizing larger datasets and external cohorts independent of the sample population are needed to validate clinical utility and improve model transparency for reliable implementation.

Deep brain stimulation (DBS) is a surgical procedure that uses electrical neuromodulation to target specific regions of the brain, showing potential in the treatment of neurodegenerative disorders such as Parkinson’s disease (PD) and Alzheimer’s disease (AD). Despite similarities in disease pathology, DBS is currently only approved for use in PD patients, with limited literature on its effectiveness in AD. While DBS has shown promise in ameliorating brain circuits in PD, further research is needed to determine the optimal parameters for DBS and address any potential side effects. This review emphasizes the need for foundational and clinical research on DBS in different brain regions to treat AD and recommends the development of a classification system for adverse effects. Furthermore, this review suggests the use of either a low-frequency system (LFS) or high-frequency system (HFS) depending on the specific symptoms of the patient for both PD and AD.

Precision medicine—of which precision prescribing is a core component—is becoming a new frontier in today’s healthcare. Both artificial intelligence (AI) and machine learning (ML) have the potential to enhance our understanding of data and therefore our ability to accurately diagnose and treat patients. By leveraging these technologies and processes, we can uncover associations between a person’s genomic makeup and their health, identify biomarkers associated with diseases, fine-tune patient selection for clinical trials, reduce costs, and accelerate drug discovery and vaccine development. Although real-world data pose challenges in terms of collection, representation, and missing or inaccurate data sets, the integration of precision medicine into healthcare is critical. Clearly, precision medicine can benefit from health information innovations that empower decision-making at the patient level. Healthcare fusion is an example of an innovative framework and process [K Zhai et al. ECKM 2022, 20(3), pp. 179–192]. Data science and process improvement are also expected to play a role in resource planning and operational efficiency for optimal patient-centered care. Driving this transformation are advances in ‘omics’ technologies, digital devices, and imaging capabilities, along with an arsenal of powerful analytics tools working across a multitude of institutions and stakeholders. Encompassing this entire ecosystem, medicine will be evidence-based and driven by three key components: (1) Data curation through clinical diagnostics and behavioral apps that capture health and disease states; (2) Individualized solutions driven by advanced data analytics and personalized therapies; and (3) Business models that deliver value and incentivize growth. The aim of this paper is to present a novel conceptual framework to leverage AI and enhance information flow to serve the patient as per components one and two.

Perhaps the main goal of healthcare management is the attainment of effective, efficient, equitable, timely, safe, and patient-centered care. At the core of this lies the need for an integrated pathway for healthcare data storage, analysis, and utilization. The potential exists for a centralized, cloud-based system that links physicians, hospitals, public health agencies, insurance and pharmaceutical companies, and most importantly, patients. Such a system could improve clinical quality management and support the delivery of consistent and effective treatments. Undoubtedly, massive integration of personalized health and large-scale epidemiological and molecular data, coupled with the use of artificial intelligence and machine learning, is already in process. Here, we envision the healthcare fusion framework, which unites all stakeholders in healthcare. This fusion aims to achieve culturally and demographically relevant outcomes in precision medicine and population health, in ways that are convincing to stakeholders and investors. In addition, the proposed framework may prove relevant in informing governmental and private sector responses to sudden public health crises.

This study examines the Facebook pages of all four telecom operators in Bangladesh to understand their communication strategies on Facebook and their customers’ engagement patterns. More specifically, this article explores the companies’ communication strategies deployed in terms of five aspects: i) visual vs. textual content; ii) cultural elements; iii) purposes of posts; iv) intimacy with customers; and v) use of different languages. This research also explores customers’ engagement patterns relating to the number of reactions (e.g., likes), comments, and shares. Analyzing their Facebook posts from 2013–2017, this quantitative descriptive study finds that the corporations used Facebook not only to disseminate information but also to build communities around their brands by embedding visual content and cultural elements heavily in status messages. This article also discusses the real-world implications of these findings.

Background: The COVID-19 lockdown reduced anthropogenic activities worldwide and therefore provided a unique opportunity to investigate the factors that impact air pollution. Here, we investigated the effect of the COVID-19 lockdown on particulate matter (PM _2.5 ) levels in Education City, Doha, Qatar. Methods: Ambient air samples were collected in real time from two stations within Education City during 2019 and 2020. PM _2.5 data collected from four different seasons during the lockdown were compared with their corresponding pre-lockdown levels. Results: No significant changes in PM _2.5 levels were observed during the spring and fall seasons. A 10% decline in the PM _2.5 level was observed post-lockdown in the summer season, whereas a 33% increase in the PM _2.5 level was observed post-lockdown during the winter season. Conclusion: The decline in PM _2.5 levels in Education City during the 2020 COVID-19 lockdown was transient and modest. No significant decline in PM _2.5 levels was detected for most of the year. Therefore, anthropogenic activities (vehicular and industrial) could have had season-dependent effects on ambient PM _2.5 levels within Education City.

Background: Elevated PM _2.5 levels pose serious health hazards and are implicated in numerous acute and chronic conditions. Delineating the contributions of meteorological factors to PM _2.5 levels is a daunting task, especially in confined or semiconfined urban spaces. This study aims to (1) characterize the influence of wind speed and direction on outdoor PM _2.5 levels within a semiconfined urban environment, and (2) develop a simple and readily accessible data mining method for this purpose. The ultimate goal is to evaluate the extent to which PM _2.5 correlations demonstrated in open spaces hold in semiconfined outdoor settings with irregular terrain. Methods: In this study, data mining techniques were applied to retrieve patterns pertaining to the effects of meteorological factors on PM _2.5 levels. As a proof of concept, a feasible framework was developed to elucidate the associations between wind speed and direction and PM _2.5 levels during May 2020 in Education City, Doha, Qatar. Results and Discussion: The results showed a modest negative correlation between wind speed and PM _2.5 levels, at low to moderate, but not high, PM _2.5 readings. Meanwhile, no correlation was detected between wind direction and PM _2.5 levels. Conclusions: Limited by the geographical location, microenvironment, and duration of this study, it can be said with moderate statistical confidence that low PM _2.5 readings are associated with high wind speeds. As a result, increasing wind speed may be beneficial at low to moderate PM _2.5 levels. However, delineating a single contributing factor to high PM _2.5 readings may prove infeasible. Moreover, an association with wind direction was not immediately obvious, possibly due to microenvironmental limitations. These findings underscore the applicability of data mining and the importance of microenvironmental factors in air quality research and mitigation.

Virtual reality (VR) describes a family of technologies which immerse users in sensorily-stimulating virtual environments. Such technologies have increasingly found applications in the treatment of neurological and mental health disorders. Depression, anxiety, and other mood abnormalities are of concern in the growing older population—especially those who reside in long-term care facilities (LTCFs). The transition from the familiar home environment to the foreign LTCF introduces a number of stressors that can precipitate depression. However, recent studies reveal that VR therapy (VRT) can promote positive emotionality and improve cognitive abilities in older people, both at home and in LTCFs. VR thus holds potential in allowing older individuals to gradually adapt to their new environments—thereby mitigating the detrimental effects of place attachment and social exclusion. Nevertheless, while the current psychological literature is promising, the implementation of VR in LTCFs faces many challenges. LTCF residents must gain trust in VR technologies, care providers require training to maximize the positive effects of VRT, and decision makers must evaluate both the opportunities and obstacles in adopting VR. In this review article, we concisely discuss the implications of depression related to place attachment in LTCFs, and explore the potential therapeutic applications of VR.

Virtual reality (VR) describes a family of technologies which immerse users in sensorily-stimulating virtual environments. Such technologies have increasingly found applications in the treatment of neurological and mental health disorders. Depression, anxiety, and other mood abnormalities are of concern in the growing elderly population – especially those who reside in long-term care facilities (LTCFs). The transition from the familiar home environment to the foreign LTCF introduces a number of stressors that can precipitate depression. However, recent studies reveal that VR therapy (VRT) can promote positive emotionality and improve cognitive abilities in the elderly, both at home and in LTCFs. VR thus holds potential in allowing elderly individuals to gradually adapt to their new environments – thereby mitigating the detrimental effects of place attachment and social exclusion. Nevertheless, while the current psychological literature is promising, the implementation of VR in LTCFs faces many challenges. LTCF residents must gain trust in VR technologies, care providers require training to maximize the positive effects of VRT, and decision makers must evaluate both the opportunities and obstacles in adopting VR. Here, we concisely review the implications of depression related to place attachment in LTCFs, and explore the potential therapeutic applications of VR.