Search Results (3,081)

Therapeutic glycoproteins, a pivotal class of biopharmaceuticals, have transformed modern medicine through their broad applications in oncology, immunotherapy, and infectious disease management. Their structural complexity and biological specificity make them highly effective in targeting disease pathways; however, challenges related to stability, bioavailability, and delivery efficacy limit their full potential. Recent advancements in delivery technologies have sought to address these challenges through innovative approaches such as nanotechnology-based carriers, controlled-release systems, and molecular engineering. These strategies have demonstrated the ability to enhance glycoprotein stability, optimize pharmacokinetics, and achieve targeted delivery with minimal off-target effects. This review provides a comprehensive overview of state-of-the-art lipid-based delivery systems specifically designed to overcome the unique pharmaceutical challenges associated with therapeutic glycoproteins, highlighting their design principles, formulation strategies, mechanisms of encapsulation and release, and therapeutic advantages in improving glycoprotein stability, bioavailability, targeted delivery, and treatment efficacy. In addition to surveying the current landscape, this review delves into the key challenges impeding the widespread adoption of advanced delivery systems, including immunogenicity, manufacturing scalability, and clinical translation. The review concludes with insights into emerging trends in the development of lipid-based delivery systems, positioning glycoprotein therapeutics at the forefront of innovation in biopharmaceuticals. This overview of advancements and challenges aims to provide a roadmap for future progress in the field of glycoprotein delivery and therapeutic applications. Full article

Real-time detection of chemical warfare agents (CWAs) and toxic industrial chemicals underpins military protection, counter-terrorism, and emergency response. Yet field instruments usually fail for a reason unrelated to sensitivity: they cannot identify agents that are not already in their reference libraries, such as novel analogs, mixtures, and degradation products. We argue that this unknown-agent problem is a structural limitation of single-modality sensing, because any one class of information (molecular bonds, ion mobility, elemental composition, or chemical reactivity) is rarely sufficient to resolve an unfamiliar threat. We review the dominant field modalities, including FTIR, Raman/SERS, ion mobility and field-asymmetric ion mobility spectrometry, laser- and spark-induced plasma spectroscopy, metal-oxide sensor arrays, and portable mass spectrometry, and show that their weaknesses are largely complementary. We then set out the principle of orthogonal multimodal sensing, in which complementary information axes are combined by machine learning with anomaly and open-set detection so that unfamiliar agents are recognized as such rather than misidentified. Four hybrid architectures are critically compared, and we examine spark-induced decomposition diagnostics, consumable-free self-decontaminating field systems with edge AI, and the open challenges of standardized datasets, calibration transfer, and validation, before outlining a roadmap toward field-relevant recognition of unidentified chemical threats. Full article

Design of experiments (DOE) offers a powerful, systematic framework for optimizing nanoparticle (NP) formulations by replacing inefficient one-factor-at-a-time (OFAT) methods. By enabling the simultaneous evaluation of multiple variables, DOE uncovers critical factor interactions and identifies true global optima—critical for quality-by-design approaches. Despite its potential for systematic innovation, DOE remains underutilized in nanomedicine due to its perceived complexity; this review provides a practical roadmap to bridge the gap between statistical theory and robust NP optimization. It provides a practical overview of DOE concepts, including factor selection, design choice, graphical interpretation of results (perturbation/contour plots), model validation (regression analysis and ANOVA), and numerical optimization via desirability function (D). Common pitfalls and best-practice strategies are discussed to support reliable model building and decision-making. A practical case study on poly(lactic-co-glycolic acid) (PLGA) NPs illustrates a multistage workflow: utilizing Taguchi screening to isolate key factors, followed by central composite design (CCD), for precise surface mapping. Numerical optimization using Design-Expert^® software maximized EE% (highest importance) within size/zeta ranges, yielding optimal conditions (5 mg drug amount, 4 mL aqueous volume; D = 0.961). Confirmation runs (EE 41.2%, NP size 124 nm, zeta potential −15 mV) validated predictions (EE 47.6%, NP size 133 nm, zeta potential −17.2 mV), confirming model reliability. Ultimately, by bridging conceptual foundations with practical implementation, this review aims to encourage broader adoption of DOE, particularly among emerging formulation scientists, and serves as a roadmap to accelerate scalable NP development, fostering data-driven innovation and improving efficiency in nanomedicine research. Moreover, future integration of artificial intelligence (AI) and artificial neural networks (ANNs) with DOE will drive a predictive, data-driven approach to NP optimization—accelerating robust, scalable, and regulatory-ready nanomedicine development with fewer experiments. Full article

The transition towards smart megacities has deeply integrated Artificial Intelligence (AI) with power–transport networks. While AI empowers complex operations like multi-network coordinated dispatch and emergency rescue, current algorithm-centric perspectives largely ignore its massive physical energy costs. Accordingly, this Perspective examines the dual role of AI, considering it not only as an intelligent decision-support tool but also as a potential source of additional stress on physical infrastructure. First, through a structured synthesis of the representative literature, we deconstruct the functional dependencies between algorithms and physical infrastructures, identifying how AI reshapes the operational paradigms of power, ground transport, and aerial networks under routine and emergency scenarios. We then introduce the concept of the “Computation–Energy Paradox.” Integrating conceptual analysis with a quantitative case study of a typical community, we illustrate a plausible failure mechanism: during extreme disasters, intensified AI invocation for emergency management generates surging computational loads, which paradoxically exacerbate power shortages and reduce the operating margin of already weakened systems. In addition, we analyze core engineering bottlenecks, including spatiotemporal computation–energy mismatches and physical constraints in extreme edge environments. To address these challenges, we outline a prospective roadmap encompassing lightweight emergency AI and computation–power-coordinated offloading mechanisms. Finally, the sustainable development of such systems suggests a paradigm shift: AI must evolve from a purely virtual algorithm into a physical component of an integrated compute–power–transport system. Full article

Morocco’s National Green Hydrogen Roadmap targets large-scale hydrogen exports, yet the offshore wind and tidal resources of the Atlantic Sahara coast remain underexplored, and single-resource electrolysis plants suffer from low, variable electrolyser utilisation. This study presents a reproducible, model-based techno-economic assessment of a 560 MW hybrid offshore wind–tidal hub at Dakhla that produces hydrogen by proton exchange membrane (PEM) electrolysis and exports it as liquid hydrogen (LH₂) to Jorf Lasfar. The assessment is entirely theoretical: it couples reanalysis-based resource characterisation, harmonic tidal modelling, hourly dispatch, and discounted levelised cost of hydrogen (LCOH) analysis, and does not include experimental or in situ measurements. The hybrid plant reaches a 45.5% capacity factor and produces 36,781 t of hydrogen per year at 60% electrolyser utilisation. The 2025 base-case production LCOH is 7.53 USD/kg (10.04 USD/kg delivered), falling to 4.45 USD/kg under a 2030 learning scenario that approaches the national 2–4 USD/kg target band. Because the wind and tidal resources are almost uncorrelated, hybridisation firms the supply and reduces electrolyser cycling rather than adding bulk energy; capacity factor and electrolyser-specific energy consumption are the dominant cost drivers. This work provides the first integrated wind–tidal hydrogen assessment for the Moroccan Atlantic coast and a transparent platform for future optimisation. Full article

The transition to a new industrial paradigm beyond Industry 5.0 demands hyperconnectivity and massive processing, creating a paradox where high computational demand can threaten corporate sustainability goals (ESG). This study aims to structure and prioritize a Multilayer Framework of multicloud technologies to balance disruptive advances, lean optimization, and decarbonization. A mixed, sequential, and exploratory-normative approach was used. Initially, the literature was triangulated with expert panels and suppliers to map 21 technological functionalities, structuring them into four layers of a bidirectional value flow. Then, a hybrid multi-criteria modeling (AHP-TOPSIS) was applied to rank these technologies against five market constraints. Calibration with AHP revealed that Cyber Resilience, approximately 38%, and Process Optimization, approximately 27%, lead executive priorities, surpassing environmental impact or cost efficiency. As a result, the TOPSIS ranking highlighted Human–Machine Symbiosis (BCI/neuroergonomic readiness), Zero Trust architecture, Federated Learning, and GenAI KPI Analytics as the leading functionalities in their respective layers, with Hyper-BPM emerging as a closely associated optimization engine at the governance layer. Finally, the proposed roadmap was assessed using an anonymized industrial Proof of Concept (PoC) in a brownfield advanced manufacturing facility, providing evidence of its operational feasibility for integrating lean optimization with legacy systems and ESG-oriented monitoring. It is concluded that industrial sustainability does not rely solely on green technologies, but on decentralized orchestration along the Edge-Cloud continuum. Environmental gains are therefore more likely to emerge when cyber governance and lean-oriented operational management jointly support decentralized multicloud orchestration. Full article

Cryptomeria japonica var. sinensis Miquel (Chinese cedar) is an ecologically and economically vital evergreen conifer endemic to China. Renowned for its rapid growth and superior timber quality, the species has evolved from a traditional forestry resource into a multifaceted model species for phytochemistry and biotechnology. This review synthesizes recent advancements in its biology, chemical profiling, industrial utilization, and molecular breeding. Taxonomically and morphologically distinct from the Japanese variety, Chinese cedar exhibits robust physiological plasticity and complex transcriptomic responses to abiotic stresses. Phytochemically, the tree is a prolific reservoir of volatile essential oils, complex terpenoids, and flavonoids. These bioactive secondary metabolites demonstrate potent antimicrobial, antioxidant, and neuroprotective properties, driving broad applications in modern pharmacology and sustainable agrochemicals. Industrially, the timber is increasingly utilized in the fabrication of advanced engineered structural composites and circular bioenergy production. Concurrently, breakthrough biotechnological platforms including a chromosome-level reference genome assembly, multi-omics, somatic embryogenesis, and CRISPR/Cas9-mediated genome editing have advanced molecular breeding, accelerating the targeted development of climate-resilient and non-pollen-producing (hypoallergenic) elite cultivars. However, as global climate change and historical habitat fragmentation severely threaten ancient wild populations, this review advocates for integrated in situ and ex situ conservation strategies. By identifying critical research gaps in translational pharmacology, precision gene editing, and comparative genomics, this review provides a comprehensive foundation for understanding the biology, phytochemistry, biotechnology, and utilization of this invaluable botanical resource, offering a strategic roadmap for its sustainable management and commercial valorization. Full article

Background: The rapid integration of Artificial Intelligence (AI), specifically Clinical Decision Support Systems (CDSS), into healthcare offers substantial efficiency but introduces critical ethical and legal challenges, particularly the perpetuation of systemic bias against older adults (“Digital Ageism”). While technological advances may improve care, they can violate fundamental bioethical principles when models are trained on unrepresentative data. Aim: This article argues that traditional clinical risk-management models are structurally insufficient to address opaque algorithmic bias and presents a conceptual, multidimensional governance framework designed to prevent the codification of human ageism into AI infrastructure. Methods: Drawing on systemic failures observed during the COVID-19 pandemic, the normative model integrates legal and governance standards aligned with the EU AI Act, Explainable AI (XAI) tools, and a three-phase implementation protocol. Results: To illustrate potential application without overburdening medical staff, the article introduces a theoretical Targeted Escalation Protocol and an Autonomous High-Load Safety Mode. The latter applies deterministic hardcoded constraints to contain age-dominant outputs during acute surges while preserving attending-clinician authority. The framework is explored through an Intensive Care Unit (ICU) thought experiment. Conclusions: The framework provides a structured roadmap for policymakers, ethicists, and healthcare administrators to move from reactive defensive medicine toward proactive ethical safety, safeguarding the dignity of the aging population while aiming to mitigate institutional legal exposure. Full article

Digital transformation in higher education requires integrated designs connecting strategy, governance, infrastructure, interoperability, applications, cybersecurity, accessibility, analytics, and continuous improvement. This study analyzes the Integrated University Digital Transformation Model implemented at Universidad Técnica de Machala, Ecuador, between 2023 and April 2026. A qualitative embedded case study with a design-science orientation examined institutional documents, technical records, governance evidence, cloud migration reports, interoperability mechanisms, system descriptions, analytics outputs, and cloud security assessment records through thematic analysis, process tracing, and artifact evaluation. Findings showed alignment between the Strategic Information Technology Plan and institutional planning; formal IT governance and information security structures; an API- and microservices-based architecture; a staged migration of 52 institutional servers in four operational lots, with documented post-migration monitoring indicating 99.85% average server uptime and no critical incidents attributable to the migration during the monitored period; an automated and traceable admission allocation process within a broader portfolio of integrated academic–administrative systems; and dashboards supporting evidence-informed decisions. MMGSI-Cloud complemented the architecture by assessing governance capabilities, identifying improvement priorities, and linking findings to an institutional roadmap. The case indicates that university digital transformation is strengthened when technological implementation is integrated with formal governance, systematic assessment, evidence-based planning, and institutional accountability. Full article

Reviews of artificial intelligence (AI) for mobile robots usually cover one competence—perception, SLAM, path planning, control, or reinforcement learning—and rarely show how these combine into a working system. We take the opposite view and treat autonomy as one pipeline: sensing, perception, localization and mapping, prediction, planning, visual servoing and control, high-level decision-making, and continual learning. We survey how AI has reshaped each stage for industrial and service robots across Industry 4.0, 5.0, and the emerging Industry 6.0. Using a structured, PRISMA-informed protocol with explicit search strings, inclusion criteria, and cross-embodiment transfer rules, we screen the literature, analyze a corpus drawn mainly from the last five years, and position it against prior surveys with a coverage matrix that exposes their single-block focus. Four findings stand out. Perception and localization approach engineering maturity through multimodal fusion and foundation vision models. Planning and control stay effective but computationally demanding. Decision-making, now driven by large language and vision–language–action models, is powerful yet unverifiable and fails under safety constraints. Lifelong learning is almost absent from deployed systems. The decisive weaknesses sit at the interfaces: at the perception–planning, planning–control, and control–decision handoffs the sim-to-real gap, limited on-robot compute, and scarce industrial data compound. We compare AI families by technology readiness, catalog datasets and benchmarks, examine the safety-certification barrier, and consolidate cross-cutting gaps. We close with a staged roadmap toward Industry 6.0 and a next-generation architecture coupling a foundation perception backbone, a world model and digital twin, a continual-learning memory, and a reasoning core wrapped by a safety monitor. The aim is to move from cataloging algorithms to engineering integrated autonomy. Full article

Many genotoxic impurities (GTIs) remain “invisible” to conventional LC-MS due to poor ionization or chemical instability under electrospray ionization, yet their sub-ppm acceptable intake limits under ICH M7(R2) demand exceptional analytical sensitivity. Derivatization—the chemical introduction of ionizable moieties, stable tags, or MS/MS information carriers—offers a powerful strategy to overcome this limitation. This review provides a critical systematic overview of derivatization techniques for LC-MS analysis of GTIs over the past decade (2015–2025, based on a literature search across PubMed, Web of Science, and Scopus). We construct a functional-group-based strategic framework covering alkyl halides, nitroaromatics, sulfonyl chlorides, hydroxylamine, alcohols, aldehydes, carboxylic acids, and amines, while placing specific emphasis on typical impurities within these classes such as methyl iodide, methyl chloride, nitrobenzene, and benzenesulfonyl chloride, and discuss the evolution of reagents from simple “reaction tags” to “MS/MS information carriers” that provide characteristic neutral losses or product ions for enhanced selectivity. Quantitative analysis reveals that derivatization typically enhances ESI response by 2–3 orders of magnitude, consistently achieving LODs below 1 ppm—the ICH M7(R2) threshold. Key analytical trade-offs are critically evaluated, including the balance between derivatization efficiency and reaction time, by-product management, and the fundamental kinetic and chromatographic constraints that render post-column derivatization impractical for most GTIs. We conclude with perspectives on high-throughput automation, smart multifunctional reagents, online integration, and green chemistry, aiming to provide a practical roadmap for developing robust, sensitive, and regulatory-compliant LC-MS methods for GTI control. Full article

Withering is a critical but poorly understood step in yellow tea processing. This study integrated metabolomics (ultra-performance liquid chromatography–tandem mass spectrometry, UPLC-MS/MS), transcriptomics (RNA sequencing, RNA-seq), and sensory evaluation to investigate dynamic changes in key metabolites and gene expression during withering (0–10 h) of Niangniang tea (NNT). Transcriptomic analysis was performed on leaves sampled directly during withering, while metabolomic profiling and sensory evaluation were conducted on finished teas processed from the corresponding withered leaves. A total of 1064 metabolites were identified. Sensory evaluation showed that 8 h of withering yielded the highest sweet-mellow taste scores. Theanine, L-aspartic acid, and L-arginine peaked at 8 h, while galloylated catechins decreased. Key regulatory genes (CsTA, CsSCPL, CsPDX2.1) were identified, with expression patterns correlating with sweet-related amino acid accumulation and catechin conversion. Integrative analysis delineated a molecular roadmap in which downregulation of CsPDX2.1 after 6 h was associated with reduced theanine hydrolysis, and upregulation of CsTA correlated with galloylated catechin conversion, collectively contributing to sweet-mellow taste. These findings provide a scientific basis for optimizing withering duration in large-leaf yellow tea production. Full article

The convergence of Artificial Intelligence (AI) and Building Information Modeling (BIM) is reshaping how the Architecture, Engineering, and Construction (AEC) sector manages projects across their lifecycle, yet the systemic uptake of this convergence remains poorly understood in developing-economy contexts such as Morocco. This study examines the state, opportunities, and barriers of BIM–AI integration in Morocco’s AEC sector through a PRISMA-guided scoping review combined with a bibliometric analysis. An initial global search of Scopus and Web of Science identified 1842 records; after successive screening for relevance to BIM–AI integration and to the Moroccan context, six core studies were retained for detailed synthesis. The bibliometric analysis, covering the broader filtered corpus, shows a publication trend with three distinct growth phases between 2015 and 2025 and reveals that Moroccan research output on digital construction is disproportionately concentrated on energy-efficiency applications rather than construction management or structural engineering. The synthesis of the six core studies further indicates that BIM–AI integration in Morocco remains at an early, largely 3D-focused stage, with significant conceptual and empirical gaps. Building on these findings, this study proposes a three-tiered strategic roadmap, targeting policy, industry, and academia, to accelerate digital transformation and innovation in Morocco’s construction sector. Full article

Testicular torsion is a time-critical urological emergency in which surgical detorsion, the only accepted treatment, simultaneously rescues and injures the gonad. Restoration of blood flow triggers a burst of reactive oxygen and nitrogen species, neutrophil recruitment, inflammasome activation, and regulated germ cell death, so that a substantial proportion of anatomically salvaged testes still undergo atrophy and functional loss. A large experimental literature, running to several hundred reports, has shown that antioxidants of almost every chemical class attenuate this injury in rodent models, yet no antioxidant has entered routine clinical use as an adjunct to detorsion in humans, and we are not aware of any adequately powered randomized trial in this setting. This narrative review integrates the redox pathophysiology of testicular ischemia–reperfusion injury with an appraisal of the agents tested against it, and then asks why the translational gap has proved so durable. We examine model heterogeneity, the dominance of pretreatment designs that cannot be reproduced in an emergency department, the reliance on short-term surrogate biochemistry rather than fertility endpoints, and the sobering precedents of neuroprotection and cardioprotection. We close with a roadmap for first-in-human evaluation, covering candidate prioritization, route and timing of administration, biomarker selection, and a feasible trial architecture. Full article

The exponential growth of the Internet of Things (IoT) has generated massive data streams traditionally processed by centralized cloud architectures, which increasingly face latency, bandwidth, and privacy limitations. Shifting artificial intelligence to resource-constrained edge nodes, known as TinyML, offers a robust decentralized alternative, though it introduces severe memory, compute, and energy bottlenecks. To map this transition, a systematic literature review was conducted following PRISMA guidelines, analyzing peer-reviewed studies published between 2021 and 2026 across major databases. The analysis identifies primary architectural paradigms and evaluates the efficacy of state-of-the-art model compression techniques, such as quantization, pruning, and knowledge distillation. Furthermore, the findings reveal that hardware–software co-design and custom neural accelerators are crucial for overcoming operational bottlenecks, while also highlighting persistent security and privacy challenges in on-device learning. Ultimately, while deploying complex models on microcontrollers is increasingly viable, achieving optimal performance demands holistic optimization strategies. This review synthesizes current research gaps and provides a strategic roadmap to guide future interdisciplinary efforts toward resilient, energy-efficient, and secure next-generation intelligent edge systems. Full article