Alzheimer's disease (AD), Parkinson's disease (PD) and multiple sclerosis (MS) are complex neurodegenerative diseases that are significant public health problems worldwide. Drug repurposing involves using drugs that are already known to be safe for some purpose for a new purpose. It's more likely to be a success than developing novel drugs. Therefore, drug repositioning could serve as a method to hasten the drug discovery process while also conserving both expenditure and time. Artificial intelligence (AI), machine learning (ML), and network-based pharmacology has transformed drug repositioning with integrated analysis of multi-omics data, knowledge biomedical knowledge graphs and real-world evidence (RWE). An assessment of the contemporary state of AI-powered drug repositioning and network-based pharmacology tactics applied to neurodegenerative illnesses comprising major approaches, candidate drugs, mechanisms, and translation barriers. The topic of this paper is the integration of multi-omics, systems biology, and sex-specific considerations in drug delivery tactics. Finally, it outlines potential paths towards clinical translation for the field – in AI related precision medicine and experimental testing.
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
Alzheimer's disease (AD), Parkinson's disease (PD) and multiple sclerosis (MS) are complex neurodegenerative diseases that are significant public health problems worldwide. Drug repurposing involves using drugs that are already known to be safe for some purpose for a new purpose. It's more likely to be a success than developing novel drugs. Therefore, drug repositioning could serve as a method to hasten the drug discovery process while also conserving both expenditure and time. Artificial intelligence (AI), machine learning (ML), and network-based pharmacology has transformed drug repositioning with integrated analysis of multi-omics data, knowledge biomedical knowledge graphs and real-world evidence (RWE). An assessment of the contemporary state of AI-powered drug repositioning and network-based pharmacology tactics applied to neurodegenerative illnesses comprising major approaches, candidate drugs, mechanisms, and translation barriers. The topic of this paper is the integration of multi-omics, systems biology, and sex-specific considerations in drug delivery tactics. Finally, it outlines potential paths towards clinical translation for the field – in AI related precision medicine and experimental testing.
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
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Current Good Manufacturing Practice (cGMP) represents a fundamental regulatory and quality framework for ensuring that pharmaceutical products are consistently manufactured and controlled according to predefined standards of quality, safety, efficacy, identity, strength, and purity. With increasing complexity in pharmaceutical manufacturing, globalization of supply chains, adoption of advanced technologies, and growing regulatory expectations, cGMP compliance has evolved from a predominantly inspection-oriented approach to a comprehensive, science- and risk-based pharmaceutical quality system. This review provides an overview of the principles, regulatory framework, and essential components of cGMP applicable to pharmaceutical manufacturing. Major areas discussed include the Pharmaceutical Quality System (PQS), personnel and training, premises and equipment, documentation and data integrity, material management, production and in -process controls, quality control, qualification and validation, deviation management, Corrective and Preventive Action (CAPA), change control, Quality Risk Management (QRM), Product Quality Review (PQR), supplier management, and self-inspection. The review also highlights the roles of major regulatory authorities and guidance frameworks, including the U.S. Food and Drug Administration (FDA), European Medicines Agency (EMA), World Health Organization (WHO), International Council for Harmonisation (ICH), and Central Drugs Standard Control Organization (CDSCO). Particular emphasis is placed on ICH Q9(R1) and ICH Q10 as important frameworks supporting risk-based decision-making, lifecycle management, continual improvement, and effective pharmaceutical quality systems. Emerging trends such as digitalization, continuous manufacturing, Process Analytical Technology (PAT), automation, artificial intelligence, and advanced data analytics are also considered. Effective implementation of cGMP should extend beyond procedural compliance to establish a sustainable quality culture capable of maintaining process control, preventing quality failures, protecting patients, and supporting continuous improvement throughout the pharmaceutical product lifecycle.
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
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
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.
Currently, repeat MRI in pediatric epilepsy is reactive and empiric (mean 537 days) leading to delayed diagnosis of structural lesions, unnecessary sedation and suboptimal surgical referral rates. We developed the ADAPT-Epilepsy protocol, a spatiotemporal predictive model augmented with artificial intelligence, which integrates static clinical risks, dynamic EEG evolution and baseline MRI radiomics into a Structural Evolution Probability Score (SEPS) to optimize repeat MRI timing. The SEPS algorithm was applied retrospectively to a cohort of 260 children with seizures. Inputs included perinatal history, neurological deficits, evolving EEG abnormalities, GTC seizure semiology and radiomic features extracted from initial visually “normal” MRIs. Repeat MRI recommendation was based on a pre-specified high-risk threshold (SEPS ≥0.75). SEPS recommended repeat MRI in 1 9 (7.3%) patients and clinical examination in 28 (1 0.8%) patients. Diagnostic yield increased from 64.3% to 89.5%, mean interval to repeat MRI decreased from 537 to 1 89 days, and surgical referral rate from positive studies increased from 22.2% to 76.5%. SEPS was the only independent predictor of abnormal repeat MRI (OR 8.94, p<0.001). ADAPT-Epilepsy shifts pediatric neuroimaging from reactive to predictive, significantly increasing diagnostic yield, reducing unnecessary scans, and accelerating surgical candidacy. Prospective validation is needed.
Jinan Ibrahim Khaleel Karam Moayed Abo*· Zenodo (CERN European Organi...· 0 citations
Currently, repeat MRI in pediatric epilepsy is reactive and empiric (mean 537 days) leading to delayed diagnosis of structural lesions, unnecessary sedation and suboptimal surgical referral rates. We developed the ADAPT-Epilepsy protocol, a spatiotemporal predictive model augmented with artificial intelligence, which integrates static clinical risks, dynamic EEG evolution and baseline MRI radiomics into a Structural Evolution Probability Score (SEPS) to optimize repeat MRI timing. The SEPS algorithm was applied retrospectively to a cohort of 260 children with seizures. Inputs included perinatal history, neurological deficits, evolving EEG abnormalities, GTC seizure semiology and radiomic features extracted from initial visually “normal” MRIs. Repeat MRI recommendation was based on a pre-specified high-risk threshold (SEPS ≥0.75). SEPS recommended repeat MRI in 1 9 (7.3%) patients and clinical examination in 28 (1 0.8%) patients. Diagnostic yield increased from 64.3% to 89.5%, mean interval to repeat MRI decreased from 537 to 1 89 days, and surgical referral rate from positive studies increased from 22.2% to 76.5%. SEPS was the only independent predictor of abnormal repeat MRI (OR 8.94, p<0.001). ADAPT-Epilepsy shifts pediatric neuroimaging from reactive to predictive, significantly increasing diagnostic yield, reducing unnecessary scans, and accelerating surgical candidacy. Prospective validation is needed.
Jinan Ibrahim Khaleel Karam Moayed Abo*· Zenodo (CERN European Organi...· 0 citations
Background: Sepsis is a life-threatening clinical syndrome caused by a dysregulated host response to infection resulting in organ dysfunction. Bloodstream infections are important contributors to sepsis and may rapidly progress to severe systemic inflammation, organ dysfunction, and septic shock. Despite advances in antimicrobial therapy and critical care, delayed diagnosis, antimicrobial resistance, altered pharmacokinetics, and biological heterogeneity continue to complicate the management of sepsis. Objective: This review summarizes current evidence on the epidemiology, etiology, pathophysiology, clinical manifestations, diagnostic approaches, pharmacotherapeutic management, antimicrobial resistance, antimicrobial stewardship, and emerging therapeutic strategies in sepsis and bloodstream infections. Methods: A narrative literature review was conducted using PubMed/MEDLINE, Google Scholar, Scopus, ScienceDirect, and the Cochrane Library. Literature published primarily between 2015 and 2025 was considered, with selected landmark studies and guidelines published before 2015 included where relevant. Peer-reviewed studies, clinical guidelines, randomized controlled trials, cohort studies, systematic reviews, meta-analyses, and relevant narrative reviews addressing sepsis and its pharmacotherapeutic management were considered. Results: Contemporary management of sepsis involves early recognition, appropriate empirical antimicrobial therapy, microbiological investigation, source control, fluid resuscitation, vasopressor support when required, and individualized antimicrobial dosing. Antimicrobial resistance has increasingly complicated empirical treatment and highlights the importance of antimicrobial stewardship, pharmacokinetic/pharmacodynamic optimization, therapeutic drug monitoring, and timely de-escalation. Emerging approaches include biomarker-guided management, immune phenotyping, endothelial and glycocalyx protection, microbiome-directed strategies, and artificial intelligence-assisted prediction. Conclusion: Effective management of sepsis requires an integrated and individualized approach combining rapid diagnosis, appropriate antimicrobial therapy, hemodynamic stabilization, source control, antimicrobial stewardship, and multidisciplinary critical care. Future research should focus on precision medicine approaches that account for the biological heterogeneity of sepsis and enable individualized therapeutic decision-making.
D. Rasagna*· World Journal of Pharmacy an...· 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
