Medicinal plants are increasingly cultivated in agroecosystems irrigated with treated or untreated wastewater, biosolids and contaminated surface water. Pharmaceutical residues are recognised contaminants of emerging concern, but their implications for botanical-drug quality, therapeutic consistency and safety remain insufficiently characterised. This Perspective argues that pharmaceutical wastewater irrigation is a plausible yet underexamined driver of metabolite reprogramming in medicinal plants. Chronic exposure to antibiotics, non-steroidal anti-inflammatory drugs, antiepileptics, antidepressants, hormones and transformation products may alter secondary metabolism through oxidative stress, xenobiotic detoxification, rhizosphere microbiome disturbance and modified nutrient signalling. These processes may change phenolic, flavonoid, alkaloid, terpenoid, glycoside and volatile metabolites that underpin pharmacognostic quality and ethnopharmacological reliability. Medicinal plants may also accumulate parent pharmaceuticals, transformation products and, under some conditions, microbial signatures associated with antibiotic resistance. Building on Carter et al.’s source-pathway-receptor framework and Helmecke et al.’s regulatory risk synthesis, we shift attention from residue burden to how exposure history alters the medicinal metabolome. Evidence from antibiotic-induced metabolite changes in Pinellia ternata supports this proposition, while indicating compound- and context-specific effects. We advance a balanced position: metabolite reprogramming is biologically credible, but food-crop studies often report de minimis residue risks and inconsistent rhizosphere-resistome effects. Future work should integrate wastewater profiling, matched controls, targeted and untargeted metabolomics, transformation-product discovery, microbiome analysis, digestion and bioaccessibility testing, bioactivity assays and probabilistic mixture-risk assessment.
E. O. Diovu, C. Nnadi, Ugwu Okechukwu Paul-Chima· Frontiers in Pharmacology· 0 citations
The integration of Artificial Intelligence (AI) and Machine Learning (ML) into renewable energy systems (RES) is increasingly recognized as a practical pathway for improving operational efficiency, reliability, and sustainability. Renewable sources, such as solar, wind, and hydropower, as well as hybrid configurations, are inherently intermittent and operationally complex, creating persistent challenges for grid stability, asset reliability, and energy optimization. Conventional maintenance strategies, whether reactive or preventive, often lead to unplanned downtime, inefficient inspections, and increased lifecycle costs. In response, AI/ML-enabled predictive maintenance (PdM) leverages high-frequency sensor data, SCADA streams, and historical performance records to detect anomalies, diagnose faults, and estimate remaining useful life (RUL), enabling proactive maintenance interventions. Beyond maintenance, AI/ML supports operational optimization through energy generation forecasting, load prediction, grid integration, storage scheduling, and adaptive control, thereby strengthening system resilience and lowering operational costs. Unlike prior reviews that treat PdM and RES optimization as separate topics, this work provides a unified, decision-oriented synthesis that explicitly links (i) maintenance outcomes (fault detection/diagnosis/RUL) and (ii) operational outcomes (forecasting, dispatch, storage scheduling, and grid control) through shared data pipelines and coupled decision trade-offs. The review further provides a structured mapping that connects AI/ML methods to (a) RES asset types (solar, wind, hydropower, hybrid, and emerging marine systems), (b) decision objectives (fault/RUL, forecasting, dispatch and control), and (c) deployment settings (IoT/SCADA, edge-cloud, and digital-twin-enabled monitoring). Emerging technologies, including digital twins, edge AI, federated learning, and explainable AI (XAI), are discussed as enabling mechanisms for real-time monitoring, privacy-preserving learning, adaptive decision-making, and transparency in critical infrastructure. Cybersecurity risks including vulnerabilities arising from expanded IoT/edge/cloud connectivity and adversarial threats to AI-driven control are highlighted as a critical adoption barrier alongside data quality, interoperability with legacy systems, scalability, and model interpretability. By consolidating fragmented evidence across maintenance and optimization and highlighting deployment trade-offs, this review provides an implementation-oriented reference for AI/ML-enabled operation of modern renewable energy systems.
Ugwu Chinyere Nneoma, O. Chukwudi, U. Nnenna et al.· Frontiers in Energy Research· 0 citations
Background Preventable adverse drug events (ADEs) remain a major source of hospital morbidity, mortality, and healthcare costs worldwide. Clinical decision support systems (CDSS) integrated into electronic health records (EHRs) were developed to reduce unsafe prescribing, yet evidence of their real-world effectiveness remains mixed. The emergence of artificial intelligence (AI) and machine learning (ML) offers new opportunities to enhance medication safety but also introduces risks such as algorithmic bias, technology-induced error, and reduced clinician vigilance. Objectives This narrative review critically examines: (1) evidence for the effectiveness of CDSS in reducing preventable ADEs; (2) human factors influencing interactions between clinicians and AI-enabled safety tools; and (3) conceptual, methodological, and governance challenges affecting the safe implementation of digital health technologies. Methods A structured narrative review was conducted using the SANRA framework and reported in accordance with PRISMA-ScR guidance where applicable. Searches of PubMed/MEDLINE, CINAHL, Embase, Scopus, and IEEE Xplore covered literature published between January 2015 and March 2024, supplemented by seminal earlier studies. Following eligibility screening, 75 studies were included in a thematic synthesis and quality appraisal using established risk-of-bias tools. Results Five themes emerged: the transition from passive to adaptive decision support; AI's dual role as both a safety enhancer and a source of new risks; persistent alert fatigue; the often-overlooked contribution of nursing vigilance; and gaps in equity, governance, and technology-induced error research. From these findings, we propose the Clinical Safety Intelligence Loop (CSIL), a conceptual framework that positions AI within a sociotechnical system while embedding equity, governance, and continuous feedback as core components. Conclusion Achieving medication safety improvements requires moving beyond technology-focused solutions toward systems-level approaches integrating AI, clinician cognition, organizational culture, and governance. The CSIL offers a useful framework for guiding this transformation, although further empirical validation is needed.
M. Alruwaili, U. Paul-Chima, Ugwu Chinyere Nneoma· Frontiers in Digital Health· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
