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
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.
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
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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
Background: Non-muscle invasive bladder cancer (NMIBC) constitutes the majority of bladder cancer cases and is primarily treated with intravesical Bacillus Calmette-Guérin (BCG) immunotherapy. However, up to 40% of patients do not respond to BCG, highlighting the need for reliable predictive tools to guide personalized treatment strategies. Artificial intelligence (AI) has emerged as a promising approach to address this challenge by integrating complex clinical and biological data. Methods: A comprehensive literature search was conducted across multiple databases up to November 2025. Studies evaluating AI techniques—including deep learning, machine learning, radiomics, and multimodal models—for predicting BCG response were included. Data on model performance, input modalities, and clinical applicability were analyzed using narrative synthesis. Results: AI-based models demonstrated superior predictive performance compared to traditional clinical risk stratification tools. Deep learning models showed high accuracy, particularly with histopathological and genomic data, though interpretability remains limited. Classical machine learning approaches offered improved transparency with comparable performance. Radiomics enabled non-invasive prediction of tumor characteristics and immune microenvironment features. Multimodal models integrating imaging, pathology, molecular, and clinical data consistently achieved the highest predictive accuracy. Certain AI models also demonstrated potential in guiding treatment selection and identifying patients unlikely to benefit from BCG therapy. Conclusion: AI-based predictive models hold significant potential for improving treatment decision-making and enabling personalized therapy in NMIBC. However, challenges such as limited prospective validation, data heterogeneity, and lack of standardization must be addressed before routine clinical implementation.
1Evangelin Gabriel Rajesh Kumar, 2Monisha Krishnakumar, 3*Lashika L. K.· Zenodo (CERN European Organi...· 0 citations
In the face of the growth and planned growth of industrial developments in artificial intelligence, embodied AI, immersive media/virtual reality, and the fintech industry and cryptocurrencies – and the energy requirements for each of these and all of them together – this paper examines the question of whether the plans for growth and future development in these areas is probable, possible, problematic, or impossible. Additionally, the examination is situated atop both state-based and criminal threats to use the Internet and mobile Internet to wreak havoc and instability.
Thom Gencarelli· Roger Williams University -...· 0 citations
By-products generated throughout the entire pear (Pyrus spp.) industrial chain, including peels, branches, leaves, and unripe fruits, are abundant sources of high-value bioactive components. Current research indicates that bioactive compounds such as phenolics, triterpenoids, and arbutin exhibit significant "spatiotemporal heterogeneity", characterized by considerably higher concentrations in peels, branches, leaves, and unripe fruits compared to mature pulp. This distribution pattern underscores the superiority of these by-products as raw materials for extraction. Regarding extraction techniques, while traditional organic solvent extraction is well-established, its application is constrained by issues of solvent residues and environmental toxicity. A comparative analysis reveals that green, efficient extraction technologies offer a promising solution to overcome the limitations of traditional methods regarding mass transfer efficiency and solvent residues. These technologies represent a critical pathway for the valorization of pear processing by-products, provided that extraction strategies are flexibly selected based on the physical characteristics of the raw materials and the stability of the target components. Bioactive components from pears possess immense development potential in the fields of skin-whitening cosmetics and gut health-promoting foods. However, future research should prioritize multi-scale industrial validation, artificial intelligence-driven multi-objective process optimization, and the in-depth elucidation of synergistic interactions among components, aiming to advance the comprehensive, high-value utilization of pear resources.
Yue ZHAO, Wei ZHOU, Shaohua ZHU et al.· DOAJ (DOAJ: Directory of Ope...· 0 citations
The rapid integration of artificial intelligence (AI) into advertising has introduced deepfake technology as a powerful yet controversial persuasive tool. Given limited empirical research on Nigerian audiences’ responses to deepfake advertising, this study investigated perceptions of AI-generated deepfake advertisements featuring Pastor Enoch Adeboye and journalist Seun Okinbaloye. A descriptive survey design was adopted, with a structured questionnaire administered to 192 respondents in Lagos State. Descriptive statistics and regression analysis examined perceived authenticity, trust, credibility, ethical concerns, and advertising effectiveness. The study was anchored on Source Credibility Theory, the Elaboration Likelihood Model, and Uses and Gratifications Theory. Findings indicated that audience awareness (R = 0.873, p < 0.05), content-driven perception (R = 0.610, p < 0.05), credibility (R = 0.667, p < 0.05), and ethical concerns (R = 0.734, p < 0.05) significantly influenced engagement with and acceptance of deepfake advertising. Although high-quality deepfakes may enhance attention and recall, respondents raised concerns about consent, manipulation, and misinformation. Religious figures attracted greater moral scrutiny than media professionals. The study concludes that deepfake advertising has strategic potential but may undermine brand credibility and public trust when ethical transparency is lacking.
Ade Inasa-Thomas, Olatayo Agogo, Adekunle Christiana Adetola· Zenodo (CERN European Organi...· 0 citations
The rapid integration of artificial intelligence (AI) into advertising has introduced deepfake technology as a powerful yet controversial persuasive tool. Given limited empirical research on Nigerian audiences’ responses to deepfake advertising, this study investigated perceptions of AI-generated deepfake advertisements featuring Pastor Enoch Adeboye and journalist Seun Okinbaloye. A descriptive survey design was adopted, with a structured questionnaire administered to 192 respondents in Lagos State. Descriptive statistics and regression analysis examined perceived authenticity, trust, credibility, ethical concerns, and advertising effectiveness. The study was anchored on Source Credibility Theory, the Elaboration Likelihood Model, and Uses and Gratifications Theory. Findings indicated that audience awareness (R = 0.873, p < 0.05), content-driven perception (R = 0.610, p < 0.05), credibility (R = 0.667, p < 0.05), and ethical concerns (R = 0.734, p < 0.05) significantly influenced engagement with and acceptance of deepfake advertising. Although high-quality deepfakes may enhance attention and recall, respondents raised concerns about consent, manipulation, and misinformation. Religious figures attracted greater moral scrutiny than media professionals. The study concludes that deepfake advertising has strategic potential but may undermine brand credibility and public trust when ethical transparency is lacking.
Ade Inasa-Thomas, Olatayo Agogo, Adekunle Christiana Adetola· Zenodo (CERN European Organi...· 0 citations
SignificanceAgainst the backdrop of the carbon peaking and carbon neutrality goals, power systems are integrating a high share of renewable energy. However, renewable power generation is uncertain, variable, and intermittent. This makes it difficult to maintain supply-demand balance in power systems. Therefore, power systems urgently need flexible resources that can provide effective balancing support. Supply-side flexibility still relies heavily on thermal power units. These units are increasingly constrained by carbon reduction requirements and technical limits. It is therefore necessary to further unlock the flexibility potential of demand-side resources. Industrial users are characterized by substantial electricity consumption, substantial carbon-reduction potential, considerable adjustable capacity, and mature automation and control systems. At the technical level, industrial users can improve their process routes. At the operational level, they can reschedule production batches and adjust equipment power. These measures create flexibility for power-system balancing and low-carbon operation. Therefore, industrial users have become a major focus for developing demand-side flexibility for carbon reduction. Industrial users are evolving from conventional loads into integrated resources that can function as generation, load, and energy storage. This transition gives industrial users three main resource attributes. Distributed energy, self-owned power plants, and waste-heat generation provide on-site power. Adjustable production loads can coordinate production with system dispatch. Electrical, thermal, hydrogen, and intermediate-product storage enable energy transfer across time. Therefore, this paper provides a review of the process reconfiguration of industrial users in supporting the low-carbon transition of power systems.ProgressFirst, from the perspective of the transition of industrial users from consumers to prosumers, their basic connotation and main resource classifications are summarized. Second, representative industrial scenarios are examined, including iron and steel, electrolytic aluminum, and cement. For each scenario, the process characteristics and flexibility mechanisms are analyzed. For the steel industry, special attention is given to the flexibility differences among three process routes: the blast furnace-basic oxygen furnace long-process route, the scrap-based short-process route, and the hydrogen-based direct reduced iron (H‒DRI) short-process route. This research also examines the multi-level regulation capability of electrolytic aluminum. This capability mainly comes from the thermal inertia of aluminum reduction cells. It also discusses the flexibility of the cement industry in terms of start-stop scheduling and smooth power adjustment. The industries differ in their dominant flexibility mechanisms. Long-process steelmaking mainly relies on self-generation fueled by by-product gases. Scrap-based electric-arc-furnace (EAF) production can shift loads through batch scheduling. In H‒DRI processes, electrolyzers, hydrogen storage, and intermediate-product storage can be coordinated to provide flexibility. Electrolytic aluminum provides fast frequency response and different levels of load adjustment under cell thermal constraints. Cement plants mainly adjust crushing, raw-material preparation, and grinding. Clinker kilns generally remain in continuous operation. For flexibility potential assessment based on the process reconfiguration of industrial users, this paper proposes a three-dimensional modeling framework for industrial users. The framework covers physical characteristics, economic incentives, and carbon benefits. The physical dimension focuses on coupling constraints among material flows and energy flows. The economic dimension considers the willingness of users to provide flexibility. The dimension of carbon benefit captures how carbon reduction benefits affect feasible regulation boundaries. The assessment should distinguish theoretical potential from actually available potential. Physical modeling identifies the feasible regulation region under equipment, production, material-balance, and energy-coupling constraints. Economic modeling accounts for energy costs, production adjustment losses, operational risks, and management costs. Carbon benefit modeling needs to further incorporate marginal carbon emissions, green electricity consumption, carbon market compliance, and product carbon footprint accounting. For flexibility control strategies enabled by the process reconfiguration of industrial users, coordination should be designed across short-, medium-, and long-term time scales. At short timescales, electrolytic aluminum is a representative resource for rapid frequency response. At medium timescales, batch processes such as EAF steelmaking can enable intraday load shifting. At long timescales, electrolysis combined with hydrogen storage can support cross-seasonal balancing. These decisions must also account for multiple uncertainties. These include renewable energy output, market prices, product demand, equipment states, and material supply. The interactions among multiple market mechanisms, including electricity and carbon markets, should also be considered.Conclusions and ProspectsBy participating in power system flexibility regulation, industrial users can promote renewable energy integration, reduce the carbon footprint of industrial products, and support the coordinated low-carbon transition of both the industrial and power sectors. Future research should develop a unified model that captures material flows, energy flows, and industrial production constraints. Furthermore, it is essential to promote the deep integration of artificial intelligence with industrial production and power system control. Meanwhile, credible accounting frameworks for industrial carbon emissions and product carbon footprints must be established, alongside the improvement of multi-market benefit allocation mechanisms. Ultimately, these efforts will enable the large-scale, normalized, and market-driven participation of industrial users in power system flexibility regulation.
Gengrui Chen, Hui Hongxun, GAO Hongjun et al.· DOAJ (DOAJ: Directory of Ope...· 0 citations
To review the relevant researches on the application of clinical decision support system based on artificial intelligence in emergency department from four aspects: overview,application status, challenges and future development trends,aiming to enhance the intelligent management of medical decision⁃making of emergency department, and to provide reference for the development of smart medicine.
Danlu Chen, CHEN Ru, SHAN Yawei et al.· DOAJ (DOAJ: Directory of Ope...· 0 citations
