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Precision medicine has emerged as a transformative approach to healthcare, aiming to deliver personalized treatments and therapies tailored to individual patients. However, the realization of precision medicine relies heavily on the availability of comprehensive and diverse medical data. In this context, blockchain-enabled federated learning, coupled with electronic medical records (EMRs), presents a groundbreaking solution to unlock revolutionary insights in precision medicine. This abstract explores the potential of blockchain technology to empower precision medicine by enabling secure and decentralized data sharing and analysis. By leveraging blockchain’s immutability, transparency, and cryptographic protocols, federated learning can be conducted on distributed EMR datasets without compromising patient privacy. The integration of blockchain technology ensures data integrity, traceability, and consent management, thereby addressing critical concerns associated with data privacy and security. Through the federated learning paradigm, healthcare institutions and research organizations can collaboratively train machine learning models on locally stored EMR data, without the need for data centralization. The blockchain acts as a decentralized ledger, securely recording the training process and aggregating model updates while preserving data privacy at its source. This approach allows the discovery of patterns, correlations, and novel insights across a wide range of medical conditions and patient populations. By unlocking revolutionary insights through blockchain-enabled federated learning and EMRs, precision medicine can revolutionize healthcare delivery. This paradigm shift has the potential to improve diagnosis accuracy, optimize treatment plans, identify subpopulations for clinical trials, and expedite the development of novel therapies. Furthermore, the transparent and auditable nature of blockchain technology enhances trust among stakeholders, enabling greater collaboration, data sharing, and collective intelligence in the pursuit of advancing precision medicine. In conclusion, this abstract highlights the transformative potential of blockchain-enabled federated learning in empowering precision medicine. By unlocking revolutionary insights from diverse and distributed EMR datasets, this approach paves the way for a future where healthcare is personalized, efficient, and tailored to the unique needs of each patient.

The emergence of Autonomous and Connected Autonomous Vehicles (CAVs) has transformed the automotive landscape drastically over the past few years by offering enhanced features in the vehicles for drivers’ safety and convenience. These developments have introduced various features in AVs i.e., lane-keeping, cruise control, etc. These features are mainly powered by the Electronic Control Units (ECUs) that communicate using the Controller Area Network (CAN) bus protocol. The components in the AVs communicate with each other by sending and receiving messages via the CAN bus. However, despite increased connectivity, these vehicles have become vulnerable to cyber attacks, as malicious actors can exploit the CAN protocol to manipulate vehicle behavior, which can not only threaten the safety of the passengers but public as well. Hence, several Intrusion Detection Systems (IDS) have been proposed, however, these systems struggle with computational complexity, limited effectiveness against sophisticated attack types, and a lack of interpretability and transparency of detection mechanisms. To address challenges in the existing systems, this paper presents a novel hybrid Deep Learning (DL)-based IDS using DL components such as Convolutional layer and Long Short-Term Memory (LSTM) layers to capture complex patterns in the CAN messages. The proposed IDS uses a residual connection to enhance gradient flow and training stability. The system is evaluated on four common attack types, namely RPM Spoofing, Gear Spoofing, Fuzzy, and Denial of Service (DoS), achieving a detection accuracy of 99.99%. Finally, the outcomes of the proposed IDS are visually interpreted using the Explainable AI (XAI) technique called Local Interpretable Model-agnostic Explanations (LIME) to provide transparency into the model’s decision-making process, thus increasing trust in the system’s deployment in real-world AV environments.

The emergence of Autonomous and Connected Autonomous Vehicles (CAVs) has transformed the automotive landscape drastically over the past few years by offering enhanced features in the vehicles for drivers' safety and convenience. These developments have introduced various features in AVs i.e., lane-keeping, cruise control, etc. These features are mainly powered by the Electronic Control Units (ECUs) that communicate using the Controller Area Network (CAN) bus protocol. The components in the AVs communicate with each other by sending and receiving messages via the CAN bus. However, despite increased connectivity, these vehicles have become vulnerable to cyber attacks, as malicious actors can exploit the CAN protocol to manipulate vehicle behavior, which can not only threaten the safety of the passengers but public as well. Hence, several Intrusion Detection Systems (IDS) have been proposed, however, these systems struggle with computational complexity, limited effectiveness against sophisticated attack types, and a lack of interpretability and transparency of detection mechanisms. To address challenges in the existing systems, this paper presents a novel hybrid Deep Learning (DL)-based IDS using DL components such as Convolutional layer and Long Short-Term Memory (LSTM) layers to capture complex patterns in the CAN messages. The proposed IDS uses a residual connection to enhance gradient flow and training stability. The system is evaluated on four common attack types, namely RPM Spoofing, Gear Spoofing, Fuzzy, and Denial of Service (DoS), achieving a detection accuracy of 99.99%. Finally, the outcomes of the proposed IDS are visually interpreted using the Explainable AI (XAI) technique called Local Interpretable Model-agnostic Explanations (LIME) to provide transparency into the model's decision-making process, thus increasing trust in the system's deployment in real-world AV environments.

The Addiction Severity Index (ASI) is a globally recognized tool for assessing substance use disorder (SUD) severity. Despite its widespread use, no validated Arabic version exists. This study aimed to validate the Arabic ASI-5 in Egypt and Saudi Arabia and evaluate its psychometric properties. : A cross-sectional study recruited 400 participants (200 per country) from inpatient/outpatient SUD treatment centers. The ASI-5 underwent forward-backward translation, pilot testing, and cultural adaptation. Internal consistency (Cronbach's α), test-retest reliability (Spearman's ρ), and inter-observer reliability were assessed. Convergent and discriminant validity were evaluated using adjusted Spearman's correlations. The Arabic ASI-5 demonstrated acceptable internal consistency (α = 0.61-0.82), with medical (α = 0.78) and psychiatric (α = 0.82) domains showing the highest reliability. Lower consistency in drug (α = 0.62) and legal (α = 0.61) domains reflected cultural and methodological factors. Test-retest ( = 0.55-0.98) and inter-observer reliability ( = 0.78-0.99) were strong. Convergent validity was robust for medical and psychiatric domains (r = 0.70-0.85). The Arabic ASI-5 is a reliable tool for assessing SUD severity in Arabic-speaking populations. Further refinement of drug and legal domains is recommended to enhance cultural relevance.

Background: The Addiction Severity Index (ASI) is a globally recognized tool for assessing substance use disorder (SUD) severity. Despite its widespread use, no validated Arabic version exists. This study aimed to validate the Arabic ASI-5 in Egypt and Saudi Arabia and evaluate its psychometric properties. Methods: : A cross-sectional study recruited 400 participants (200 per country) from inpatient/outpatient SUD treatment centers. The ASI-5 underwent forward-backward translation, pilot testing, and cultural adaptation. Internal consistency (Cronbach's [alpha]), test-retest reliability (Spearman's [rho]), and inter-observer reliability were assessed. Convergent and discriminant validity were evaluated using adjusted Spearman's correlations. Results: The Arabic ASI-5 demonstrated acceptable internal consistency ([alpha] = 0.61-0.82), with medical ([alpha] = 0.78) and psychiatric ([alpha] = 0.82) domains showing the highest reliability. Lower consistency in drug ([alpha] = 0.62) and legal ([alpha] = 0.61) domains reflected cultural and methodological factors. Test-retest ([rho] = 0.55-0.98) and inter-observer reliability ([rho] = 0.78-0.99) were strong. Convergent validity was robust for medical and psychiatric domains (r = 0.70-0.85). Conclusion: The Arabic ASI-5 is a reliable tool for assessing SUD severity in Arabic-speaking populations. Further refinement of drug and legal domains is recommended to enhance cultural relevance. Keywords: addiction severity index, substance use disorder, cross-cultural validation, psychometrics

Introduction: Prenatal and infant care is at the top of worldwide healthcare agendas, proving a focal aspect of the Well-Being Index. They refer to the medical needs of women before, during and after child birth as well as the medical needs of infants and children. This paper posits that delivery and early childhood health issues involve a combination of biological, environmental, and socio-economic factors that necessitates an integrated model for advancing the health of women and children and subsequently acing up the survival rates of the later.Aim of work: To explore the multifaceted contributions of OB-GYNs, pediatricians, nurses, and midwives to maternal and child healthcare.Methods: We conducted a comprehensive search in the MEDLINE database's electronic literature using the following search terms: Enhancing, Patient Outcomes, Maternal, Child Healthcare, Role, Practitioners, OB-GYNs, Pediatricians, Nurses, and Midwives. The search was restricted to publications from 2016 to 2024 in order to locate relevant content. We performed a search on Google Scholar to locate and examine academic papers that pertain to my subject matter. The selection of articles was impacted by certain criteria for inclusion.Results: The publications analyzed in this study encompassed from 2016 to 2024. The study was structured into various sections with specific headings in the discussion section.Conclusion: Improving outcomes of patients in maternal and child health call for the use of multiple professionals such as OB-GYNs, pediatricians nurses, midwives among others with their unique perspectives. As such, creating partnerships and using the biological, psychosocial and environmental model, these practitioners can transform the health of mothers and children for the better. With an understanding that health care has issues of disparities, chronic illnesses and mental health among other issues, the future of advancing towards enhanced solutions to the pressing issues will indeed demand parallel qualities of enhanced fruitful interprofessional relations and patient-centered equities. Ideally, there are evidently emerging trends in approaches to the vaccine delivery system such as digital technologies, community-based model, and education to the providers. For this reason, therefore, societies should be encouraged to invest early in maternal and child health to ensure that the future is created to cater for the future generations.