International Journal of Health Sciences and Engineering
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Transforming Ocular Toxicity Assessment Through AI: A White Paper on the ICE Test Analysis Tool
No full textOcular toxicity assessment is a critical component of safety evaluation for pharmaceuticals, chemicals, and consumer products. The Isolated Chicken Eye (ICE) test is a widely accepted alternative method for identifying severe eye irritants, offering ethical and scientific advantages over traditional in vivo testing. Recent advances in artificial intelligence (AI) have opened new opportunities to enhance the accuracy, consistency, and efficiency of ICE test analysis. This white paper explores the integration of AI-driven analytical frameworks into ocular toxicity assessment, focusing on automated data interpretation, image-based scoring, and predictive modeling. By reducing subjectivity and improving reproducibility, AI-supported ICE test analysis has the potential to strengthen decision-making, accelerate safety evaluations, and support regulatory acceptance. The convergence of AI and alternative toxicity testing represents a transformative step toward more reliable, ethical, and data-driven ocular safety assessment
Transforming Drug Discovery and Wellness Through AI-Powered Scientific Prompt Generation: A White Paper on Swalife Biotech\u27s Discovery Suite
No full textThe convergence of artificial intelligence (AI) and life sciences is redefining the landscape of drug discovery and preventive healthcare. AI-powered scientific prompt generation has emerged as a novel approach to accelerating research workflows, enhancing hypothesis development, and supporting data-driven decision-making. This white paper presents Swalife Biotech’s Discovery Suite, an integrated AI-driven platform designed to transform drug discovery and wellness innovation through intelligent prompt engineering tailored to scientific and biomedical applications. By leveraging domain-specific knowledge, machine learning models, and structured scientific reasoning, the Discovery Suite enables efficient target identification, lead optimization, and wellness solution development. The platform supports multidisciplinary research by bridging computational intelligence with biological insight, reducing development timelines, and improving translational relevance. This approach highlights the growing role of AI-assisted scientific creativity in advancing therapeutic discovery and personalized wellness strategies
Immunological Memory in Viral Infections: Lessons from COVID-19
No full textImmunological memory is basically the immune system’s way of not starting from scratch every time it sees a virus. Once the body has gone through that first encounter, the next one is usually quicker and more effective-though how well this works can depend a lot on the virus itself. B cells and T cells are the central players here, but over the past few years scientists have noticed that even some innate immune cells can be “trained” to respond a little better the second time. The strength and the durability of this memory, however, are uneven. Some viruses, like measles, give protection that pretty much lasts a lifetime. Others, such as the seasonal coronaviruses, don’t leave much of a lasting impression at all, which is why people can keep catching them. COVID-19 came along and forced researchers to study these differences in real time. Both infection and vaccination against SARS-CoV-2 create a layered form of immune memory-antibodies at first, but also memory B cells and T cells that stick around. The antibody levels fade within a few months, but the cellular memory seems to last longer and has been especially important in preventing serious disease. Vaccination, especially with the mRNA platforms, has turned out to be very effective in building this long-term memory. And when people have both infection and vaccination- so-called hybrid immunity- the protection is broader and more durable than either alone. That said, the story isn’t finished. People respond differently depending on age and health, the virus itself keeps mutating, and the current vaccines don’t really boost mucosal immunity in the respiratory tract. Understanding all of this in the context of COVID-19 doesn’t just help with today’s vaccine strategies- it also gives clues for how we might handle whatever virus shows up next
Recent Advances in Nanotechnology for Drug Delivery
No full textBackground: For many years, the mainstay of therapeutic intervention has been traditional drug delivery methods, such as tablets, capsules, injections, and topical formulations. Nevertheless, these methods have significant limitations that ultimately restrict clinical results and patient safety, including inadequate bioavailability, systemic toxicity, lack of regulated release, and poor selectivity. The development of nanotechnology has made it possible to precisely and logically build nanoscale carriers, opening up revolutionary avenues for medication delivery. These nanocarriers—ranging from liposomes and polymeric nanoparticles to dendrimers, inorganic platforms, and biomimetic systems—offer unprecedented control over pharmacokinetics, target-site accumulation, and multifaceted therapy. Methodology: This analytical review collates evidence from recent scientific literature—including PubMed, clinical trials, regulatory agency reports, and mainstream research platforms. A systematic approach is used to summarize the evolution of nanocarrier designs, mechanism of action (passive/active targeting, stimuli-responsive release, controlled/sustained delivery), and the diverse applications in cancer therapy, infectious disease management, gene delivery (siRNA, CRISPR), barrier-crossing strategies (e.g., blood–brain barrier), and personalized medicine. The review also critically evaluates recent innovations—such as smart, multifunctional and biodegradable nanocarriers, nanorobots, hybrid theranostic platforms, green synthesis, and clinically translated FDA-approved products—while outlining future opportunities including integration with artificial intelligence, patient-specific profiling, and regenerative medicine. Results: Nanotechnology-based drug delivery systems have successfully demonstrated improved bioavailability, reduced systemic toxicity, targeted and responsive drug release, and the ability to cross biological barriers. Major clinical milestones comprise FDA approval of nanomedicines (e.g., Doxil®, Abraxane®), the use of lipid nanoparticles in mRNA COVID-19 vaccines, and promising results in gene and immunotherapies. Smart nanocarriers now allow on-demand, sustained, and sitespecific drug release. The rapid integration of AI and machine learning into nanomedicine is enabling optimized, personalized treatments, with green nanotechnology advancing environmental safety and sustainability. Furthermore, nanomaterials are contributing to regenerative medicine and tissue engineering, facilitating precision tissue repair and stem cell modulation. Conclusion: Nanotechnology is revolutionizing the landscape of drug delivery by addressing the limitations of traditional systems and advancing medicine towards precision, adaptability, and sustainability. The ongoing progress in smart, multifunctional, and patient-specific nanomedicines, supported by clinical translation and regulatory approvals, underscores the vast therapeutic potential of this field
Impact of Electronic Health Records and Automation on Pharmaceutical Management Efficiency: A Narrative Review
No full textThe integration of Electronic Health Records (EHRs) and automation in pharmaceutical management has significantly improved medication safety, inventory control, and workflow efficiency. EHRs facilitate realtime access to patient data, enabling healthcare providers to make informed decisions while reducing prescription errors and ensuring adherence to treatment protocols. Automation technologies, including computerized physician order entry (CPOE), robotic dispensing systems, and artificial intelligence (AI)-driven inventory management, have optimized pharmaceutical supply chains, minimized wastage, and enhanced medication dispensing accuracy. However, challenges such as interoperability issues, cybersecurity threats, high implementation costs, and resistance to technological adoption hinder the full potential of these advancements. Addressing these challenges requires the development of standardized data-sharing protocols, regulatory frameworks for AIdriven decision-making, and enhanced cybersecurity measures. Future advancements in AI, blockchain technology, and predictive analytics hold promise for further improving pharmaceutical management. This review explores the impact of EHRs and automation on pharmaceutical efficiency, highlighting both the benefits and limitations of these technologies while discussing strategies for their effective implementation in modern healthcare systems
Technology Integration in Nursing Science
No full textBackground: Nursing science, traditionally grounded in compassionate and holistic care, is undergoing a transformative shift through technology integration. Advances in electronic health records (EHRs), artificial intelligence (AI), robotics, telehealth, and simulation-based education have redefined clinical practice, education, and research. This evolution addresses critical gaps in traditional nursing practice, such as fragmented communication, manual documentation errors, and delayed clinical decision-making. Methodology: This review critically examined literature, historical developments, and current applications of technology in nursing, synthesizing evidence across clinical, educational, and research domains. The analysis focused on technological tools, implementation challenges, and their impact on patient care, professional development, and system efficiency. Results: Findings indicate that technology enhances patient safety, improves workflow efficiency, supports predictive analytics, and strengthens nursing education through simulation and immersive learning. AI and wearable devices enable personalized and proactive care, while telehealth expands access in underserved populations. However, barriers such as high costs, digital literacy gaps, workflow disruptions, resistance to change, and cybersecurity concerns persist. Ethical implications—especially regarding AI and patient data—remain central to responsible integration. Conclusion: Technology integration has become indispensable in nursing science, enabling precision, efficiency, and global connectivity while preserving nursing’s foundational values of compassion and advocacy. Overcoming financial, infrastructural, and ethical challenges will require sustained leadership, inclusive training, and robust policy frameworks. Future directions point toward precision nursing, AI-driven care, robotics, and smart hospital ecosystems, ensuring equitable access and advancing nursing science worldwide
Community Engagement Strategies in Enhancing General Social Services
No full textBackground: Community engagement is increasingly recognized as a vital approach in strengthening general social services by ensuring inclusivity, equity, and sustainability. Methodology: This review synthesizes theoretical models, historical evolution, and practical frameworks of community engagement, examining strategies such as public dialogue, participatory planning, co-creation, educational outreach, digital engagement, and participatory research. Both needsbased and strengths-based approaches were considered to highlight their roles in service design and delivery. Results: Evidence indicates that effective engagement improves trust, social capital, cultural relevance, and accountability in service provision. Engagement levels ranging from community-oriented to community-owned models demonstrate varying impacts, with deeper community involvement fostering empowerment, resilience, and sustainable change. Success factors include tailoring approaches to context, building trust, empowering marginalized groups, and fostering collaborative partnerships. Conclusion: Community engagement is a transformative process in general social services, shifting the paradigm from top-down delivery to inclusive, communitydriven models. By prioritizing local voices and co-ownership, engagement strategies enhance service responsiveness, promote social justice, and create sustainable pathways for improved social outcomes
Targeted Drug Delivery Systems in Oncology: A Review of Recent Patents and future directions
No full textCancer remains a global health challenge, necessitating innovative treatment strategies to improve outcomes while minimizing side effects. Targeted Drug Delivery Systems (TDDS) have revolutionized oncology by addressing limitations of traditional chemotherapy, such as systemic toxicity, lack of specificity, and drug resistance. Utilizing nanotechnology, biomarker-based targeting, and immunotherapy, TDDS enables precise drug delivery to tumors, enhancing efficacy while protecting healthy tissues. Nanotechnology has facilitated the development of liposomes, dendrimers, micelles, and solid lipid nanoparticles, leveraging the Enhanced Permeability and Retention (EPR) effect for tumor accumulation. Examples like Doxil, a PEGylated liposomal doxorubicin, have improved ovarian cancer treatment by reducing cardiotoxicity. Biomarker-based approaches, such as antibody-drug conjugates (ADCs), further enhance specificity. Trastuzumab emtansine (Kadcyla), targeting HER2-positive breast cancer, has demonstrated improved survival rates. TDDS also integrates with immunotherapy to boost immune checkpoint inhibitors, enhance antigen delivery, and optimize cytokine therapy. Lipid nanoparticles and dendrimers are being engineered to improve immune responses while minimizing adverse effects. However, challenges such as tumor heterogeneity, drug resistance, high production costs, and regulatory barriers limit widespread adoption. Ongoing research focuses on overcoming these barriers through personalized medicine, AI-driven designs, and sustainable platforms. TDDS represents a paradigm shift in oncology, combining precision and safety to improve patient outcomes. By integrating emerging technologies and addressing current limitations, TDDS holds the potential to transform cancer treatment, offering hope for better survival and quality of life
Generative Healing: AI-Driven Reconstruction of Damaged Medical Images for Low-Infrastructure Healthcare
No full textMedical imaging plays a pivotal role in clinical diagnostics, yet in many resource-limited hospitals and rural healthcare centers, the acquisition and preservation of high-quality CT and MRI scans are often compromised due to hardware degradation, motion artifacts, transmission noise, and incomplete data capture. These issues severely impact diagnostic accuracy and limit timely medical intervention. In response, this paper presents a robust Generative AI-based reconstruction framework that virtually restores degraded or partially corrupted medical images without requiring additional scans or expensive infrastructure upgrades. The proposed system integrates a Variational Autoencoder (VAE) to model global anatomical priors, a Generative Adversarial Network (GAN) for generating visually realistic textures, and an attention mechanism that adaptively prioritizes damaged regions during reconstruction. Trained on annotated CT and MRI datasets from public repositories such as BraTS and TCIA, the model optimizes a hybrid loss function combining pixelwise, adversarial, and perceptual components to balance accuracy and realism. Extensive quantitative evaluations demonstrate the superiority of the proposed method over traditional models. It achieves a Peak Signal-toNoise Ratio (PSNR) of 31.2 dB, Structural Similarity Index (SSIM) of 0.91, Fréchet Inception Distance (FID) of 32.6, and an average Radiologist Grading Score (RGS) of 4.6 out of 5. Furthermore, the model is successfully deployed on a Raspberry Pi 4B, achieving 2.1 frames per second (FPS) inference, validating its real-time applicability in low-power settings. This framework offers a scalable, cost-effective solution to bridge the diagnostic imaging gap in under-resourced healthcare environments
AI-Driven Personalized Cognitive Behavioral Therapy: Future Potential and Risks
No full textBackground: Cognitive Behavioral Therapy (CBT) is a gold-standard intervention for depression, anxiety, and related disorders. With the growth of digital mental healthcare, Artificial Intelligence (AI) has emerged as a transformative force, enabling personalization, real-time monitoring, and scalability. AI-driven CBT tools, including chatbots, natural language processing, and machine learning models, are now capable of tailoring interventions dynamically, reducing dropout rates, and expanding access to underserved populations. Methodology: This review consolidates findings from randomized controlled trials, metaanalyses, implementation studies, and economic evaluations of AIenabled CBT platforms. It examines technological modalities such as conversational AI, predictive machine learning, and adaptive generative AI tools. Emphasis is placed on clinical efficacy, engagement metrics, safety considerations, ethical challenges, and scalability across diverse populations. Results: Evidence demonstrates that AI-driven CBT achieves moderate-to-high symptom reduction in depression and anxiety, with improved adherence compared to static digital interventions. Platforms like Woebot and Wysa show reduced dropout rates and higher therapeutic alliance through personalized interactions. Machine learning enhances risk stratification and symptom prediction, while economic models reveal cost-effectiveness in public health systems. Nonetheless, key risks persist, including data privacy concerns, algorithmic bias, reduced human connection, and clinical safety issues requiring rigorous oversight. Conclusion: AI-driven personalized CBT has strong potential to revolutionize mental healthcare by improving access, scalability, and treatment personalization while lowering costs. However, ethical safeguards, cultural sensitivity, and hybrid clinician-AI models are essential to balance automation with human empathy. Future development should focus on explainable AI, equity-driven design, and robust clinical validation to ensure safe and effective adoption