The 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.

Background: 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.

Cancer 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.

This study evaluates mesalamine-loaded microemulsions designed to enhance drug bioavailability and site-specific delivery in the gastrointestinal (GI) tract, targeting the treatment of Crohn's disease. Mesalamine, an anti-inflammatory drug, is conventionally limited by poor solubility and inconsistent site-specific action. This research examines various microemulsion formulations to improve mesalamine delivery to the colon, assessing parameters such as stability, droplet size, encapsulation efficiency, and pH-controlled release. Optimal formulations demonstrated controlled, targeted release in colonic conditions, high stability, and minimized premature drug release in nontarget GI regions. These findings suggest potential clinical applications for advanced mesalamine therapies in Crohn's disease management.

Background: Evidence-Based Practice (EBP) is essential in modern nursing for improving patient outcomes, enhancing quality of care, and ensuring safety. Despite its proven benefits, adoption remains inconsistent across healthcare settings due to barriers such as lack of training, limited resources, and resistance to change. Understanding these challenges and identifying effective strategies are critical for integrating EBP into routine nursing practice. Methodology: This review synthesizes findings from scientific literature, policy documents, and clinical implementation studies. It evaluates educational interventions, organizational frameworks, and technological tools that support EBP adoption. Key strategies assessed include leadership support, mentorship programs, continuing professional development, and digital platforms facilitating access to clinical guidelines and research evidence. Results: Evidence shows that structured training programs, interdisciplinary collaboration, and leadership engagement significantly increase nurses’ confidence and use of EBP. Digital innovations such as online evidence repositories, AI-driven clinical decision support tools, and mobile health applications further strengthen implementation. However, persistent barriers include time constraints, inadequate staffing, limited funding, and organizational cultures resistant to change. Conclusion: Integrating EBP into nursing requires a multipronged approach that combines leadership commitment, staff empowerment, continuous education, and supportive technologies. Sustainable adoption depends on aligning institutional policies with evidence-based standards and fostering a culture that values inquiry and innovation. Future directions include embedding AI-driven decision tools, strengthening mentorship models, and expanding international collaborations to create a resilient and globally unified evidence-based nursing workforce.

This analysis delves into the promise of mesalamineencapsulated microemulsions in improving bioavailability and therapeutic effectiveness for Crohn's disease. Crohn’s disease, a persistent inflammatory condition of the bowel, frequently necessitates precise medication administration owing to the particular sites of inflammation found in the gastrointestinal system. Mesalamine, a commonly utilised treatment, exhibits restricted efficacy owing to its inadequate absorption in the upper gastrointestinal tract and swift metabolic breakdown. This manuscript explores the obstacles linked to conventional mesalamine formulations and investigates the latest innovations in microemulsion-driven delivery mechanisms aimed at enhancing drug solubility, stability, and precise targeting. Innovative microemulsion methodologies, such as pH-sensitive and enzymeresponsive frameworks, exhibit potential in overcoming the shortcomings of current therapies, creating opportunities for more efficient and patient-centric treatment alternatives.

Background - Studies on behavioral pharmacology are increasingly using zebrafish as model organisms. Numerous anxiety-related behaviors in zebrafish have been documented, yet little is known about how anxiolytic drugs impact these behaviors. Anxiety is currently one of the primary unmet medical needs. Despite the wide variety of anxiolytic drugs available, many patients either do not respond well to current pharmacotherapy or see a lessening of their reactivity with repeated treatment. Search for novel compounds and learn how anxiolytic drugs function. Main body of the abstract - In the first task, we concurrently looked at the adult zebrafish's motility, color, height in the tank, and cohesiveness of the shoal. We examine the effects of buspirone hydrochloride, ethanol, benzodiazepines, and a common anxiolytic drug used in medical facilities for humans. Anxiolysis's symptoms were not brought on by anxiolytic drugs, which work by agonisting GABA receptors. We search for anxiolytic drugs in two genetically distinct populations of zebrafish, and the results show that the light/dark preference test is a sensitive, practical, and cost-effective technique. Two important behavioral characteristics seem to be shoal cohesion and tank height among the various groups of these treatments. Conclusion - The findings show that measuring the effects of human anxiolytic medications may be done simply and sensitively using zebrafish behavior.

Background: 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.

Background: 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.

Medical 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.