The rise of antibiotic-resistant bacterial infections has become a significant global health threat, necessitating the need for alternative therapeutic strategies. The use of bacteriophages-viruses that particularly infect and lyse bacteria-in phage therapy has resurfaced as a potentially effective substitute for conventional antibiotics. This narrative review aims to explore the mechanisms, applications, challenges, and prospects of phage therapy in combating antibiotic-resistant infections. A thorough analysis of the literature was carried out by exploring online databases, such as Google Scholar, PubMed, Scopus, and Web of Science. The search focused on peer-reviewed articles, clinical trials, and authoritative reports published in the last 10 years. The review synthesized findings from studies on phage mechanisms, therapeutic applications, regulatory challenges, and advances in phage engineering. Phage therapy demonstrates several advantages over antibiotics, including high specificity for target bacteria, the ability to penetrate biofilms, and a lower propensity for resistance development. However, significant challenges remain, such as regulatory and production hurdles, the potential for phage resistance, and interactions with the host immune system. Advances in genetic engineering have enhanced the therapeutic potential of phages, and personalized phage therapy is emerging as a viable approach for tailored treatments. Phage therapy holds significant promise as an alternative to antibiotics, particularly in the fight against antibiotic-resistant bacteria. While challenges persist, ongoing research, technological advancements, and collaborative efforts are crucial for integrating phage therapy into mainstream clinical practice, potentially revolutionizing the treatment of bacterial infections and addressing the global antibiotic resistance crisis.

Vaccine development stands as a cornerstone of public health efforts, pivotal in curbing infectious diseases and reducing global morbidity and mortality. However, traditional vaccine development methods are often time-consuming, costly, and inefficient. The advent of artificial intelligence (AI) has ushered in a new era in vaccine design, offering unprecedented opportunities to expedite the process. This narrative review explores the role of AI in vaccine development, focusing on antigen selection, epitope prediction, adjuvant identification, and optimization strategies. AI algorithms, including machine learning and deep learning, leverage genomic data, protein structures, and immune system interactions to predict antigenic epitopes, assess immunogenicity, and prioritize antigens for experimentation. Furthermore, AI-driven approaches facilitate the rational design of immunogens and the identification of novel adjuvant candidates with optimal safety and efficacy profiles. Challenges such as data heterogeneity, model interpretability, and regulatory considerations must be addressed to realize the full potential of AI in vaccine development. Integrating emerging technologies, such as single-cell omics and synthetic biology, promises to enhance vaccine design precision and scalability. This review underscores the transformative impact of AI on vaccine development and highlights the need for interdisciplinary collaborations and regulatory harmonization to accelerate the delivery of safe and effective vaccines against infectious diseases.

Emergency Departments (EDs) are critical in providing immediate care, often under pressure from overcrowding, resource constraints, and variability in patient prioritization. Traditional triage systems, while structured, rely on subjective assessments, which can lack consistency during peak hours or mass casualty events. AI-driven triage systems present a promising solution, automating patient prioritization by analyzing real-time data, such as vital signs, medical history, and presenting symptoms. This narrative review examines the key components, benefits, limitations, and future directions of AI-driven triage systems in EDs. This narrative review analyzed peer-reviewed articles published between 2015 and 2024, identified through searches in PubMed, Scopus, IEEE Xplore, and Google Scholar. Findings were synthesized to provide a comprehensive overview of their potential and limitations. The review identifies substantial benefits of AI-driven triage, including improved patient prioritization, reduced wait times, and optimized resource allocation. However, challenges such as data quality issues, algorithmic bias, clinician trust, and ethical concerns are significant barriers to widespread adoption. Future directions emphasize the need for algorithm refinement, integration with wearable technology, clinician education, and ethical framework development to address these challenges and ensure equitable implementation. AI-driven triage systems have the potential to transform ED operations by enhancing efficiency, improving patient outcomes, and supporting healthcare professionals in high-pressure environments. As healthcare demands continue to grow, these systems represent a vital innovation for advancing emergency care and addressing longstanding challenges in triage.

Heavy metal contamination in water sources poses a significant threat to environmental and public health, necessitating effective remediation strategies. Nanomaterial-based approaches have emerged as promising solutions for heavy metal removal, offering enhanced selectivity, efficiency, and sustainability compared to traditional methods. This comprehensive review explores novel nanomaterial-based approaches for heavy metal remediation, focusing on factors such as selectivity, regeneration, scalability, and practical considerations. A systematic literature search was conducted using multiple academic databases, including PubMed, Web of Science, and Scopus, to identify relevant articles published between 2013 and 2024. The review identifies several promising nanomaterials, such as graphene oxide, carbon nanotubes, and metal-organic frameworks, which exhibit high surface areas, tunable surface chemistries, and excellent adsorption capacities. Surface functionalization with specific functional groups (e.g., carboxyl, amino, thiol) significantly enhances the selectivity for target heavy metal ions. Advances in regeneration strategies, including chemical desorption, electrochemical regeneration, and photocatalytic regeneration, have improved the reusability and cost-effectiveness of these materials. Scalability remains a critical challenge, but recent developments in synthesis methods, such as green synthesis and continuous-flow synthesis, offer promising solutions for large-scale production. The stability and longevity of nanomaterials have been improved through surface modification and the development of hybrid nanocomposites. Integrating nanomaterials with existing water treatment infrastructure and combining them with other remediation techniques, such as membrane filtration and electrochemical methods, can enhance overall treatment efficiency and feasibility. In conclusion, nanomaterial-based approaches hold immense promise for revolutionizing heavy metal remediation and advancing sustainable water management practices. As future research is geared towards retrofitting existing treatment plants, it is equally critical to mitigate unintended environmental and public health consequences associated with the widespread production and use of nanomaterials, such as their leachability into water systems and environmental persistence.