In order to guarantee product safety, efficacy, and regulatory compliance, the pharmaceutical sector needs strong quality control systems. Electronic Quality Management Systems (eQMS) are gradually replacing traditional paper-based Quality Management Systems (QMS) as a result of expanding regulatory requirements and digital technology improvements. An eQMS enhances documentation, traceability, efficiency, and compliance management by integrating quality-related procedures into a single electronic platform. Major advantages of eQMS include enhanced data integrity, improved regulatory compliance, reduction in human error, However, implementation of eQMS is associated with several challenges. Regulations pertaining to electronic systems and data integrity have been established by regulatory bodies such as the USFDA, European Medicines Agency (EMA), ICH, and WHO. Compliance with FDA 21 CFR Part 11 principles is essential for successful implementation of eQMS. The article also highlights future trends such as cloud-based systems, artificial intelligence, predictive analytics, and Industry 4.0 integration that are transforming pharmaceutical quality management. Overall, eQMS represents a significant advancement in pharmaceutical quality assurance and operational excellence.
Atodariya Divyanshi*, Dr. Nidhi Chauhan, Patel Dhruvita, Patel Kiran, Mistry Drashti· Zenodo (CERN European Organi...· 0 citations
Chronic rotator cuff tears are a clinical challenge due to chronic inflammation, progressive tendon degeneration, impairment in regeneration and fatty infiltration. High retear rates, particularly in chronic and large tears, highlight the limitations of mechanical repair alone. These limitations highlight the need for methods that can solve the biomechanical stability and the biological deficits that compromise healing. Orthobiologics utilise allogeneic and autologous substances derived from human cells and tissues to promote pain relief, tissue healing and enhance functioning through anti inflammatory effect. Current strategies include platelet rich plasma injection, bone marrow aspiration injections and mesenchymal stem cell therapy. Biologic augmentation is aimed at improving clinical outcomes and healing rates in rotator cuff repair. Robotics enhances the effectiveness of biologic augmentation by creating a mechanically optimized environment for healing. Robotic and navigation-assisted technologies support improved surgical precision, anchor placement accuracy, footprint restoration, and control of tendon tension. Technologies like three-dimensional imaging and artificial intelligence allow targeted biologic release and customised surgical plans for each individual. Comparing preclinical studies and clinical outcomes remains inconsistent, largely due to variability in biologic preparation, patient related factors such as their chronic condition and tissue quality. In chronic rotator cuff repair, orthobiologics and robotics show an advanced approach in the clinical treatments. Future studies and clinical trials will be critical to confirm and proceed these advanced treatment goals.
*Kirtick Poovendran, Sidra Izhar, Lavanya Chandran, Nalin Aaditya Dharmalingam, Mohammad Ibrahim Hashmi, Athira Pallikadavath Jiothy, Umme Jannath Khanum· Zenodo (CERN European Organi...· 0 citations
Background: Strengthening health security requires effective strategies for identifying and managing public health risks. As one of the most prevalent oral diseases globally, dental caries represents a significant burden on healthcare systems and serves as a key indicator of community health. The early detection of initial lesions is particularly critical to prevent disease progression and reduce treatment costs. However, conventional diagnostic methods, which rely on visual examination and radiography, are often limited by their subjectivity and time-consuming nature reducing their effectiveness for large-scale screening in public healthcare settings. This challenge highlights the need for intelligent risk detection systems. Artificial intelligence (AI) offers a transformative solution with the potential to shift disease diagnostics from a reactive to a proactive model thereby enhancing population-wide health security. Methods: This review highlights how artificial intelligence is transforming the diagnostic process, using dentistry as a powerful example of its broader potential in public health. However, the study also identifies challenges and limitations, such as restricted sample sizes and variability in AI model architectures. Therefore, the findings suggest that further research is needed to validate and scale the application of AI in clinical settings. By enhancing diagnostic accuracy and efficiency, intelligent systems can serve as a vital component in strengthening health security and improving public health outcomes. Results: All included studies demonstrate that artificial intelligence models, particularly deep neural networks, achieve higher accuracy in diagnosing dental diseases from radiographic images. This finding is especially critical for the identification of proximal carious lesions, which are often missed during routine examinations. The results signify a major advancement in early disease detection capabilities, which is a cornerstone of effective health security systems. Conclusions: This review highlights how artificial intelligence is transforming the diagnostic process in dentistry. However, the study also reveals the problems and pitfalls of AI utilization, such as restricted sample sizes and potential differences in the architecture of AI models. Therefore, the findings suggest that more studies are needed to advance the application of AI in clinical settings, as it appears to enhance diagnostic accuracy and effectiveness.
*Omar I. Alhawshani· Zenodo (CERN European Organi...· 0 citations
Cannabidiol (CBD) and related phytocannabinoids have attracted sustained pharmaceutical interest for their anti-inflammatory, analgesic and antioxidant properties, yet their clinical utility is persistently constrained by negligible aqueous solubility, extensive hepatic first-pass metabolism, and markedly variable oral bioavailability. Transdermal delivery offers a mechanistic route around these limitations by avoiding the gastrointestinal tract and hepatic first pass altogether. Realising this route in practice, however, requires overcoming the stratum corneum barrier, and a broad family of nanocarriers vesicular lipid systems (liposomes, ethosomes, transfersomes), solid lipid nanoparticles and nanostructured lipid carriers, and mesoporous inorganic carriers such as mesoporous silica nanoparticles (MSN) and magnesium aluminometasilicate (MAS) has been developed to that end. This review synthesises the published literature on nanoparticle-based transdermal delivery of cannabinoids and structurally comparable lipophilic actives, covering carrier chemistry and comparative performance, physical and chemical permeation-enhancement strategies, Quality-by-Design (QbD) approaches to nanoparticle and patch optimisation, general principles of transdermal patch evaluation, in-vitro release and permeation kinetics, stability considerations, preclinical and clinical anti-inflammatory evidence, carrier biosafety, the evolving regulatory landscape for cannabinoid-containing topical products, and the emerging role of artificial intelligence in formulation design. Across this evidence base, mesoporous nanoparticle carriers combined with a Quality-by-Design-optimised transdermal patch represent a mechanistically well-supported and increasingly well-evidenced platform for cannabinoid delivery, though unresolved questions in chronic dermal nanotoxicology and an unsettled regulatory environment remain the principal barriers to clinical translation.
Manish Kumar2 Pare Pankaj Rajendra*1· Zenodo (CERN European Organi...· 0 citations
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Anatomy education serves as the cornerstone of medical training across both modern and traditional medicine systems. However, traditional pedagogical approaches—relying heavily on cadaveric dissection and two-dimensional illustrations—face mounting challenges, including cadaver shortages, ethical concerns, and the need to engage technology-oriented Generation Z learners. Artificial Intelligence (AI) has emerged as a transformative force in anatomical education, offering virtual dissection simulations, adaptive learning platforms, intelligent tutoring systems, and generative AI-powered chatbots. In modern medical education, AI tools such as ChatGPT, Anatbuddy, and VR/AR-based platforms have demonstrated significant potential in personalizing learning, generating assessment materials, and enhancing student engagement. Concurrently, in Ayurvedic anatomy education (Sharira Rachana), AI-powered tools like CADAVIZ and AyurSIM are bridging traditional knowledge with contemporary technological methods, enabling three-dimensional visualization of anatomical concepts and addressing resource disparities. This review consolidates existing evidence on AI applications in both streams of anatomy education, evaluates their effectiveness, and identifies key challenges including content accuracy concerns, over-reliance on technology, ethical considerations, and the need for customized knowledge bases. The findings underscore that AI should augment rather than replace traditional teaching methods, with a balanced, ethically guided approach being essential for effective integration. Future directions include developing specialized AI tools for traditional medicine anatomy, integrating real clinical cases, and establishing systematic AI literacy programmes for both educators and students.
Dr. Sanjiv Sexena2 Dr. Archana Gautam1*· Zenodo (CERN European Organi...· 0 citations
Tuberculosis (TB) is a chronic infectious disease caused by Mycobacterium tuberculosis and remains one of the leading causes of morbidity and mortality worldwide. Despite the availability of effective chemotherapy, tuberculosis continues to pose a major public health challenge due to delayed diagnosis, poor treatment adherence, HIV co-infection, and the increasing prevalence of multidrug-resistant (MDR-TB) and extensively drug-resistant tuberculosis (XDR-TB). According to the World Health Organization (WHO), approximately 10.8 million people developed tuberculosis globally in 2023, resulting in nearly 1.25 million deaths. Early diagnosis using rapid molecular diagnostic techniques such as GeneXpert MTB/RIF, along with appropriate chemotherapy, has significantly improved treatment outcomes. The standard treatment regimen for drug-susceptible tuberculosis consists of a six-month multidrug therapy, while newer drugs including bedaquiline, delamanid, and pretomanid have revolutionized the management of drug-resistant tuberculosis. Recent advances in host-directed therapy, nanotechnology-based drug delivery systems, artificial intelligence-assisted diagnosis, and novel vaccine candidates have shown promising results in improving disease control and patient outcomes. This review summarizes the epidemiology, etiology, pathophysiology, clinical manifestations, diagnosis, conventional treatment, drug-resistant tuberculosis, recent therapeutic advances, patient counseling, and future perspectives in tuberculosis management. Continuous research, early detection, effective treatment strategies, and global public health initiatives are essential to achieve the WHO End TB Strategy and reduce the worldwide burden of tuberculosis.
Prof. Shital K. Datir1*, Prof. Snehal S. Thakare1, Atharva J. Mandale2, Manish Y. Katare3, Narendra R. Jagtap4, Krushna B. Gole5· Zenodo (CERN European Organi...· 0 citations
Purpose: Medicinal plants are the richest source of structurally diverse biologically active compounds. Herbal leads discovery can also be accelerated using artificial intelligence and multi omics technologies. This review evaluates and summarizes the applications of artificial intelligence (AI), multi-omics, synthetic biology, and biomanufacturing in advancing research for herbal therapeutics. Virtual computational screening, deep learning, and molecular docking extremely accelerate lead identifications, structure-activity predictions, and target identification. Method: We comprehensively examine literature on virtual computational screening, deep learning, molecular docking, multi-omics platforms, genome mining, CRISPR-based synthetic biology, and emerging tools like organ-on-a-chip systems and nanobiotechnology using PubMed, ScienceDirect and Google Scholar. Result: Virtual screening, deep learning, and molecular docking extremely accelerate lead identifications, structure-activity predictions, and target identification. Simultaneously, multi-omics platforms evaluate system-level metabolic networks, regulatory pathways, and host–microbiome interactions. Genome mining and CRISPR-based synthetic biology unlock cryptic metabolic pathways and enable scalable, sustainable production of high-value natural products. Furthermore, tools like organ-on-a-chip systems and nanobiotechnology bridge the gap toward precision herbal medicine and standardization. Conclusion: By integrating these multidisciplinary strategies, herbal drug discovery is evolving from empirical observation toward predictive, sustainable, and mechanism-driven pharmaceutical science. Finally, key challenges like data standardization, explainable AI, experimental validation, and regulatory approval must be addressed for clinical translation.
Ansh Mishra1, Aarti Yadav2, Govind Gupta2, Priya3, Km. Deeksha4*· Zenodo (CERN European Organi...· 0 citations
Rapid urbanization has significantly increased pressure on transportation systems, healthcare, energy distribution, environmental monitoring, waste management, public safety, and other municipal services. Traditional city management approaches are increasingly unable to process the enormous volume of heterogeneous data generated by modern urban environments. The integration of the Internet of Things (IoT) with cloud computing provides an effective solution for developing scalable smart city platforms capable of supporting real-time monitoring, intelligent decision-making, and efficient resource management. This paper presents a scalable cloud-enabled smart city architecture that integrates IoT sensing devices, edge gateways, cloud infrastructure, big data analytics, and artificial intelligence to support multiple smart city services within a unified platform. The proposed architecture employs a layered framework consisting of perception, communication, edge computing, cloud services, data analytics, application, and security layers to improve scalability, interoperability, reliability, and service availability. The study critically reviews recent advances in IoT cloud integration, identifies major challenges including security, privacy, latency, interoperability, and energy efficiency, and proposes practical strategies for addressing these limitations through containerization, micro services,edge cloud collaboration, and AI-driven resource orchestration. The proposed framework demonstrates how cloud computing can dynamically allocate computational resources to accommodate growing IoT deployments while maintaining quality of service. The paper concludes that scalable IoT-cloud platforms represent the foundation for next-generation smart cities and recommends future integration with digital twins, federated learning, block chain, and 6G communication technologies for improved sustainability and resilience.
1*Mustapha Malami Idina, 2Abubakar Jibo Magayaki, 3Mubarak Jibril Yeldu· Zenodo (CERN European Organi...· 0 citations
Driven by artificial intelligence, or AI, today's medicinal science has the potential of rapidly transforming a doctor's knowledge of patient reactions to a variety of medications. In clinical practice, one has seen that sometimes a given medication is effective and sometimes ineffective in a patient. This difference could be caused by genetics, lifestyle, disease conditions, environmental exposure or none of the above. With the advent of AI, drug response prediction and optimization are now better supported with many various approaches. Random Forest (RF) and Support Vector Machine (SVM) are some of the common machine learning algorithms employed to predict the activity of a particular drug, identify the biomarkers and classify patients as responders and non-responders. These techniques aid doctors with the informed decisions, particularly regarding chemotherapy. Gradient Boosting models also predict clinical risks and bad drug reactions with lot of precision, such as XGBoost, and LightGBM. The use of complex biological patterns by deep learning models is enhancing the field of pharmacology research further. The use of Artificial neural Networks (ANNs) to explore drug interactions with the target and to simulate Pharmacokinetics – the mechanisms of body uptake, distribution and disposition of drugs. CNNs also are extremely effective when performing image analysis tasks, such as detecting tumors or modeling and assessing the effectiveness of cancer medications with medical images. The Recurrent Neural Networks (RNNs) are designed to see data changing over time and so help doctors stay abreast of patients' progress and predict their long-term outcomes. More sophisticated AI techniques are making a large impact too. Graphical models are used to describe complex molecular and drug–drug network data and graphical networks are used in Graph Neural Networks (GNNs) to understand the relationships between different molecules and drugs. This is really important to identify safe and effective combined therapies. Drug Safety Monitoring and Evidence-Based Decision Making can be enhanced through the use of Natural Language Processing (NLP), as this technology can extract valuable information from clinical notes, research articles, and electronic health records. Using Reinforcement Learning (RL), flexible treatment plans can be developed by optimising the dose of drug administration in real-time. Pro-bayarian networks: Bayes models when a doctor doesn’t know what to do. Combination of clinical with multi-omics data (including genomic, transcriptomic, and proteomic data) is one of the most powerful of AI. Such a combination helps understand disease mechanisms and patient variability better, resulting in improved patient stratification, personalized dosages and therapeutic outcomes.
Health-professions students are increasingly using artificial intelligence (AI) tools, especially large language model (LLM)-based chatbots like ChatGPT, for academic tasks as well as informal drug information retrieval, symptom checking, and self-medication decision-making.[2,9,3,7] Due to their dual roles as future gatekeepers of safe pharmaceutical use, trainees in sciences connected to medicine, and consumers of health information, pharmacy students hold a special place in this conversation.[2,3,20,33,56] In order to describe what is known about pharmacy (and allied health) students' knowledge of AI, their attitudes toward its use in clinical and self-care contexts, and their actual practices—including the use of AI for drug information, dosing guidance, and self-diagnosis-adjacent tasks—this review synthesizes nine cross-sectional Knowledge, Attitude, and Practice (KAP) studies conducted between 2022 and 2026 across Zambia.[3] Saudi Arabia.[2,6] India.[1,4,8] Syria.[9] Malaysia.[5] and other settings. AI awareness is almost universal (82–100%) in all contexts.[2,3,5,6,8,9] but conceptual understanding of AI subtypes, medical uses, and limitations is generally lower (30–60%).[2,3,5,6,8,9] Concerns concerning data privacy, false information, over-reliance, and professional displacement temper the generally positive attitudes.[1,2,6,7,8] Practice is still limited: formal curricular training is uncommon (7–46%).[2,3,6,7,9] and the majority of self-reported AI use focuses on academic activities (summarizing, presentations, exam preparation) rather than verified clinical decision-support.[3,4,5,7] There is a dearth of direct data on AI-assisted self-diagnosis and self-medication, particularly among pharmacy students. In order to outline the emerging risk landscape and suggest curricular and regulatory responses, this review draws conclusions from related findings, such as the use of ChatGPT for "getting drug information".[2] perceived AI usefulness in "medication management".[1,2] and expressed willingness to trust AI-generated dosing recommendations.[2]
*1Dr. Jegathis Kumar M., 2Karthikeyan S., 3Magesh Kumar· Zenodo (CERN European Organi...· 0 citations
In order to guarantee product safety, efficacy, and regulatory compliance, the pharmaceutical sector needs strong quality control systems. Electronic Quality Management Systems (eQMS) are gradually replacing traditional paper-based Quality Management Systems (QMS) as a result of expanding regulatory requirements and digital technology improvements. An eQMS enhances documentation, traceability, efficiency, and compliance management by integrating quality-related procedures into a single electronic platform. Major advantages of eQMS include enhanced data integrity, improved regulatory compliance, reduction in human error, However, implementation of eQMS is associated with several challenges. Regulations pertaining to electronic systems and data integrity have been established by regulatory bodies such as the USFDA, European Medicines Agency (EMA), ICH, and WHO. Compliance with FDA 21 CFR Part 11 principles is essential for successful implementation of eQMS. The article also highlights future trends such as cloud-based systems, artificial intelligence, predictive analytics, and Industry 4.0 integration that are transforming pharmaceutical quality management. Overall, eQMS represents a significant advancement in pharmaceutical quality assurance and operational excellence.
Atodariya Divyanshi*, Dr. Nidhi Chauhan, Patel Dhruvita, Patel Kiran, Mistry Drashti· Zenodo (CERN European Organi...· 0 citations
Post-surgical bone infection is one of the most challenging complications in orthopaedic surgery due to the hypo vascularization of the affected tissue, bacterial biofilm formation, and inadequate antibiotic penetration to the infection site.[1,2] Currently available antibiotic-loaded systems, including polymethyl methacrylate (PMMA) bone cement, have a number of disadvantages, such as the need for surgical removal and low adaptability to heat-sensitive antibiotics.[3,4] Three-dimensional (3D) printing is a promising technology that allows the fabrication of patient-specific biodegradable scaffolds.[5–7] Such scaffolds can be loaded with antibiotics and possess both osteogenic and antibiotic elution properties.[5–7] The present review highlights recent advances in the development of antibiotic-loaded 3D-printed scaffolds for the treatment of post-surgical infections[8,9], including the description of various scaffolding technologies, materials suitable for 3D printing, methods for antibiotic incorporation, and the mechanism of their antibacterial and osteogenic activity.[5,6] Additionally, the preclinical and clinical evaluation of antibiotic-loaded 3D-printed scaffolds, as well as the regulatory, safety, and translational aspects of their application in orthopaedics and future perspectives, including the use of stimuli-responsive scaffolds and artificial intelligence, are discussed.[10–12] Overall, antibiotic-loaded 3D-printed scaffolds represent a new generation of scaffolds with good prospects for future clinical applications in orthopaedics.[12,13]
Devi Nivedita Sanaboyina*1, A. Lakshmi Naga Venkata Srilikitha2, O. Venu Chandrika3, S. Venkata Ramanjaneyulu4, J. Joy5· Zenodo (CERN European Organi...· 0 citations
