Search Results (40,027)

Rare genetic disorders of the central nervous system (CNS) remain some of the most complex and challenging diseases to treat for several reasons. Targeting the CNS, especially the brain, presents one of the greatest obstacles in gene therapy using adeno-associated virus (AAV) vectors. Although various AAVs have been identified for their ability to transduce different cells in the CNS, their effectiveness and efficiency are significantly limited by the presence of neutralising antibodies (NAbs) and restricted cargo capacity. Despite these challenges, our understanding of AAV structure and technological advances continue to enable researchers to develop innovative strategies that have resulted in groundbreaking, FDA-approved therapeutic products now available for Leber congenital amaurosis (LCA) (Luxturna^®), spinal muscular atrophy (SMA) (Zolgensma^®), and the two recent gene therapy products for aromatic L-amino acid decarboxylase (AADC) deficiency, Kebilidi^® and Upstaza^®, which currently hold FDA and EMA approval, respectively. This review aims to highlight recent advances in the field of AAV gene therapy for neurological disorders, identify research gaps, and suggest areas for future investigation to enable potential breakthroughs particularly in neurodegenerative, neurodevelopmental, and neuromuscular disorders. We foresee that more tissue- and cell-specific AAV vectors designed using AI-powered platforms will emerge to precisely and efficiently target specific brain regions, transforming how CNS disorders are treated. Full article

Background: Human growth hormone (hGH) deficiency (GHD) is typically treated with daily injections of recombinant human growth hormone (rhGH), which do not fully replicate physiological secretion patterns. This study evaluates a novel approach using synthetic mRNA encoding hGH encapsulated in lipid nanoparticles (LNPs) and designated VTRC-01 to enable endogenous hormone production. Methods: VTRC-01 was administered intramuscularly to hypophysectomized (Hypox) prepubertal Wistar rats, and its efficacy was compared with rhGH. A cohort of healthy rats was included to assess anabolic effects and safety. Results: VTRC-01 stimulated longitudinal growth in both Hypox and healthy rats, achieving effects comparable to rhGH. Treatment induced a significant anabolic response that exceeded the basal growth rate of healthy controls. Conclusions: These findings provide proof-of-concept for hGH mRNA-based therapy as a promising alternative to rhGH. Further improvements in mRNA and LNP technologies are expected to enhance safe hormone production. These promising results underscore the potential of reprogramming via therapeutic mRNA the synthesis of key endocrine regulators (such as hGH) directly within the organism, offering for the first time a powerful pathway for the potential treatment for endocrine therapies targeting growth hormone deficiency. Full article

Cellulose is the most promising abundant renewable polymer material with the highest potential for the future low-carbon biorefineries. However, its utilization in industry is limited by the structural recalcitrance as a result of organization of crystalline domains, fibrillar architecture hierarchy and intramolecular and intermolecular hydrogen bonding which is responsible for access restriction for the catalysts and consequent cleavage of the glycosidic bonds. Therefore, efficient depolymerization of cellulose is of paramount importance as a step in biomass conversion into the low molecular products. In this review, the recent advances in cellulose depolymerization are discussed. The chemical, enzymatic, thermal, thermochemical, mechanochemical, oxidative and hybrid catalytic method is thoroughly discussed. Attention is paid to the mechanism of the depolymerization reaction steps as glycosidic bond activation as hydrolytic, radical mediated, and energy assisted pathways. Selectivity and conversion efficiency based on substrate morphology, solvent system and catalyst design are also discussed. Further, there is a comparison of key performance metrics which are relevant for the industrial process as product yield, carbon efficiency, energy demand, stability of the catalyst, solvent recyclability and impact to the environmental lifecycle. The pros and cons of the various methods are also represented. Processes based on mineral acids enable rapid conversion. However, they suffer from corrosion, waste handling issues and degradation by-products. On the other hand, enzymatic depolymerization processes offer relatively high selectivity but they are limited in terms of feedstock sensitivity and slow reaction kinetics. The downstream valorization mechanisms are also described with the result being that no single available technology is capable of satisfying all industrial requirements. Thus, future progress expects integrated circular processes where advanced catalysis, process intensification and digital optimization strategies take place. Full article

Theranostics is a novel approach that integrates diagnostic and therapeutic efficacy on a single platform, holding great promise for precision medicine by enabling real-time monitoring of disease progression and therapeutic response. Despite significant advances, the successful development and delivery of theranostic systems are critically limited by multiple biological barriers present at systemic, tissue, cellular, anatomical, and immunological levels. These barriers restrict bioavailability, target accessibility, and therapeutic efficacy, while often increasing off-target accumulation and adverse effects. This review provides a comprehensive overview of the major biological barriers encountered in theranostic development, including physiological barriers such as plasma protein binding, renal clearance, and hepatic metabolism; anatomical barriers like endothelial linings, the blood–brain barrier (BBB), and the tumor microenvironment; cellular barriers involving membrane permeability, intracellular trafficking, and endo-lysosomal entrapment; and immunological barriers such as immune recognition, inflammatory responses, and complement activation. Special emphasis is placed on the BBB, highlighting its structural complexity, transport mechanisms, and strategies such as molecular Trojan-horse technology, receptor-mediated and adsorptive-mediated transcytosis, and nanocarrier-based approaches to enhance central nervous system delivery. The review further discusses targeted delivery challenges, including receptor heterogeneity and multidrug resistance, and critically evaluates current strategies to overcome these barriers through surface functionalization, stimuli-responsive systems, biomimetic carriers, and controlled-release mechanisms. Finally, recent advances, clinical challenges, and future perspectives—including personalized theranostics, artificial intelligence—assisted design, and next-generation barrier-penetrating systems—are explored. Overall, this review aims to provide a structured understanding of biological barriers in theranostics and highlight innovative approaches to improve their translational potential. Full article

Wind turbines are increasingly deployed at larger scales and in harsher operating environments, leading to greater structural complexity, stronger load variability, and higher maintenance demands across both drivetrain and structural components. Reported field data indicate that gearboxes and bearings account for approximately 30–40% of total turbine downtime, while blade-related failures contribute roughly 20–25% of reported failure events, primarily through fatigue, delamination, leading-edge erosion, and lightning-induced defects. In parallel, large-scale and offshore turbines show increasing susceptibility to tower fatigue cracking, corrosion-assisted degradation, and flange joint bolt-preload loss under cyclic and environmental loading. This review provides a comprehensive applied-engineering synthesis of failure mechanisms, reliability challenges, and monitoring strategies for major wind turbine components, including gearboxes, bearings, blades, towers, and flange joints. A wide range of condition monitoring, structural health monitoring (SHM), and prognostics and health management (PHM) approaches is critically examined, including vibration analysis, acoustic emission, ultrasonic inspection, infrared thermography, impedance-based sensing, electromagnetic methods, machine vision, SCADA-based diagnostics, and artificial-intelligence-assisted fault classification. The review compares these techniques in terms of detectable damage types, spatial coverage, sensitivity, deployment practicality, and limitations under real operating conditions. In addition, statistical reliability methods and data-driven models are discussed to interpret failure trends and uncertainty. Recent AI-based studies have reported fault classification accuracies exceeding 90% under controlled or semi-controlled conditions; however, their field reliability remains constrained by data imbalance, domain shift, limited labeled failure datasets, model interpretability, and insufficient validation under realistic turbine operating environments. The main contribution of this review is an integrated applied synthesis that connects drivetrain and structural failure mechanisms with measurable monitoring indicators, diagnostic technologies, AI-enabled PHM limitations, and predictive-maintenance decision needs. The paper provides practical guidance for monitoring design, early fault detection, predictive maintenance, and long-term reliability improvement in next-generation wind turbine systems. Full article

The integration of collaborative robots into industrial environments requires rigorous safety validation under the Power and Force Limiting (PFL) regime. This review article systematically maps the technological and normative development of certified Pressure and Force Measurement Devices (PFMDs) and experimental biofidelic instruments for Physical Human–Robot Interaction (pHRI) between the years 2011 and 2026. A quantitative screening of 68 studies revealed a publication peak in impact metrology in 2021. This peak occurred with a five-year latency after the release of the ISO/TS 15066 technical specification. Although global interest in collaborative robotics steadily grows, the publication trend indicates a gradual shift in scientific focus from reactive testing toward proactive prevention. A methodological deconstruction of four Research Questions (RQs) identifies persistent limitations in safety evaluation. The findings demonstrate that the internal structure of conventional sensors induces nonlinear shock filtering and parasitic oscillations (RQ1). Furthermore, the rigid fixation of test stands generates unrealistic pressure spikes. This physical limitation forces a transition to flexible and pendulum-based configurations (RQ2). Commercial flat films physically fail due to sensor saturation and introduced stiffness. Such failures accelerate the development of conformable electronic skins (e-skins) and multimodal test manikins (RQ3). To ensure interlaboratory reproducibility within the current ISO 10218-2:2025 standard, the text defines imperative metrological parameters. These parameters strictly include frequency response, calibration protocols, and volumetric mapping of inertial masses (RQ4). Furthermore, the analysed publications were systematically stratified into distinct technological categories, strictly reflecting their primary engineering domains, ranging from empirical metrological evaluation and sensor hardware design to advanced numerical modeling. Finally, the vision for future research anticipates a definitive shift toward proactive anti-collision technologies, encompassing Artificial Intelligence (AI), machine vision, and Augmented Reality/Virtual Reality/Mixed reality (AR/VR/MR). Future methodologies must also consider demographic anisotropies and the cognitive fatigue of the human operator. Full article

Electroencephalography (EEG) is a core technique for brain activity monitoring. However, conventional EEG systems suffer from complicated setup and poor portability, which drives the development of ear EEG technology. Ear EEG is divided into in-ear and around-ear types, both with unique application strengths. This review mainly discusses in-ear EEG, as it features a compact structure and fits well with daily wearable use cases. Current research on in-ear EEG is limited to feasibility verification and small-sample experiments. Researchers have not yet combined personalized design with signal processing algorithms systematically, and multi-center clinical trials are still absent. These issues have become the major bottleneck hindering its clinical transformation. This paper reviews the latest advances in ear-EEG systems, focusing on structural innovation and material development to summarize key achievements in hardware design. It also summarizes its typical applications in brain-computer interfaces (BCI), covering steady-state responses, event-related potentials and motor imagery. Meanwhile, it analyzes the application of in-ear EEG in brain state monitoring, including sleep tracking, epilepsy detection, drowsiness evaluation and emotion recognition. Finally, future directions for in-ear EEG are outlined, including personalized design and intelligent signal processing. This review provides a technical framework for beginners and identifies key directions for future research. Full article

Industrial water reuse and resource recovery are essential for advancing circular economy principles in water-intensive industries. Despite technological maturity, large-scale implementation continues to lag due to high costs, effluent variability, integration challenges, and weak economic returns. Going beyond technology, this paper critically examines what truly works at scale, why most systems fail, and how to build resilient multi-stream recovery solutions. Drawing on major European demonstration projects (INCOVER, RESURGENCE, MEloDIZER) and recent literature, the paper demonstrates that multi-stream systems significantly outperform single-resource approaches. Success depends less on individual technologies and more on modular design, digital integration, sector-specific adaptation, and supportive governance. The study introduces the Industrial Circular Performance Framework (ICPF) and provides clear, actionable pathways to move from promising pilots to bankable, resilient circular industrial water systems. Full article

Digital policy has become an important instrument for promoting sustainable agricultural modernization. However, its effectiveness depends on the strategic responses of the government, agricultural operators, and farmers. This study develops a theoretical framework to examine how digital policy affects sustainable agricultural modernization through multi-agent interaction. Specifically, it constructs a three-party evolutionary game model and a Stackelberg game model to analyze strategy evolution under different implementation costs, subsidies, and penalties, as well as the government’s first-mover role in subsidy design. The results show that digital policy does not promote sustainable agricultural modernization through a simple linear pathway. Instead, it operates by reshaping the incentive structures of agricultural operators and farmers. Lower government implementation costs increase the likelihood of active policy implementation, while subsidies for agricultural operators and farmers strengthen their willingness to adopt digital tools, engage in standardized production, and participate in digital agricultural activities. However, the marginal effect of subsidies weakens as participation and digitalization increase, indicating that unlimited subsidy expansion may reduce policy efficiency and increase fiscal pressure. This study contributes to the literature by linking digital policy design, multi-agent strategic interaction, and sustainable agricultural modernization within a unified theoretical framework. It highlights that effective digital agricultural policy requires incentive compatibility, fiscal sustainability, inclusive participation, and adaptive governance, rather than reliance solely on digital technology investment or subsidy expansion. Full article

With the advancement of electronics and communication technologies, there is growing interest in high-performance microwave-absorbing materials. The material composition and structural design are critical factors influencing the electromagnetic wave (EMW) absorption capabilities. X-type barium ferrite (Ba₂Co₂Fe₂₈O₄₆) exhibits advantages in enhancing high-frequency magnetic loss and interface polarization through its unique hexagonal crystal structure and morphological design, while also optimizing impedance matching to a certain extent. However, the effective absorption bandwidth (EAB) of single-phase barium ferrite is often restricted. Therefore, doping with other elements is necessary to broaden the EAB. In this study, La³⁺-substituted X-type hexagonal ferrites Ba₂Co₂Fe_28−xLa_xO₄₆ (x = 0.00, 0.05, 0.10, 0.15, and 0.20) were successfully synthesized via a high-temperature solid-state reaction method, and the effects of different La³⁺ doping concentrations on the electromagnetic parameters and wave-absorbing performance of Ba₂Co₂Fe₂₈O₄₆ were investigated. After doping, the materials demonstrated excellent electromagnetic absorption performance: when x = 0.15, RL_min = −48.36 dB; when x = 0.10, EAB = 9.03 GHz (RL ≤ −5 dB). Full article

A novel modified generalized Riccati equation mapping neural network-based approach is the basic theme of this study by exploring the nonlinear dynamical characteristics of the the strain wave model’s soliton solutions, which govern wave propagation in micro structured solids. Strain waves are particularly intriguing, since they preserve their form and speed throughout transmission. The nonlinear dynamical behaviors of strain waves may be modeled by partial differential equations in micro structured materials. In the realm of micro structured solids, there exists a class of phenomena that are referred to as micro strain waves. These waves arise in solids possessing intricate internal architectures, including periodic lattices, precisely engineered metamaterials Understanding these waves is key to designing more complex materials and new acoustic technologies. The activation function and the weight function of the neural network are assigned to each input layer, hidden layer and output layer and the neural network itself is a multi-layer computational network. Using the structure of the neural network, every neuron in the first hidden layer is given solutions to the Riccati equation, and the new highly expressive trial functions are generated in a systematic way. In this way, a large variety of exact soliton solutions are obtained, such as bright, dark, kink, and combined solitons as well as periodic and hyperbolic wave profiles. The influence of the essential physical and mathematical parameters is explored systematically using three-dimensional, two-dimensional and contour visualizations, which illustrate how parameter variations lead to changes in the amplitude, shape and stability of the wave structures. The solutions presented reveal the dynamic properties of micro strain solitons which leads to new avenues of investigation in the study of related nonlinear phenomena in micro structured solids. In a broader context, our results highlight the great potential of analytical techniques using neural networks as a powerful and versatile toolset to study complex nonlinear wave models within the applied sciences from acoustics to photonics to smart materials engineering. Full article

The plastic pieces of synthetic polymers, which were previously regarded as primary pollutants of the environment, are increasingly being discovered as internal pollutants of the human body. This review provides a comprehensive overview of the available evidence on human exposure, tissue distribution, and associated biological effects of micro- and nanoplastics. Ingesting contaminated food and water is the major exposure pathway, with inhalation and dermal contact being secondary routes. Various organ systems have been identified as containing polymer particles through the use of advanced analytical methods, including blood, liver, lungs, placenta, breast milk, and brain tissue. Experimental animal studies suggest associations with tissue injury, metabolic illness, and neurotoxicity. Polyethylene, polypropylene, polystyrene, and polyethylene terephthalate are the most frequently found polymers in human samples. New clinical findings indicate potential health implications, though current human evidence remains largely associative rather than causal: a cardiovascular study observed more than a two-fold rise in mortality among patients with polymer-containing arterial plaques, and recent evidence demonstrates over-accumulation of polymers in brain tissue, raising questions about neuroinflammatory processes. Detection technologies have advanced substantially, with deep learning-based polymer classification achieving 95–99% accuracy and ultrasensitive electrochemical and surface plasmon resonance biosensors reaching detection limits approaching 10⁻¹¹ M. Despite these advances, critical issues remain, including lack of standardized analytical procedures, absence of chronic exposure models for humans, and insufficient longitudinal epidemiological data. To address these gaps, physiologically relevant experimental systems including organoids and organ-on-chip platforms will be required, in addition to well-designed prospective cohort studies. Full article

In modern power systems such as new energy generation and smart grids, inverters serve as core equipment for electrical energy conversion and transmission. Their operational reliability directly impacts system power supply quality and safety stability. Currently, research on inverter fault diagnosis technology primarily focuses on linear load conditions, with diagnostic method design and validation based on linear load characteristics. However, with the rapid advancement of power electronics technology, power electronic loads such as variable frequency drives, charging stations, and distributed power sources are increasingly prevalent in power systems. These loads exhibit nonlinear and time-varying characteristics under complex operating conditions, leading to a growing variety of inverter faults with significantly diversified and complex fault signatures. Traditional diagnostic methods fail to adapt to the unique characteristics of power electronic loads, making it difficult to accurately identify various faults. Consequently, they no longer meet the diagnostic demands of practical engineering scenarios. In addition, current diagnostic methods for open-circuit power transistors, intermittent faults, and sensor faults often employ different approaches, which consume significant controller resources and are prone to mutual interference, leading to false triggers. This paper takes a three-phase four-wire inverter as the research subject. Targeting the challenge of fault diagnosis under power electronic load conditions, it proposes a comprehensive diagnostic method capable of simultaneously diagnosing power switch open circuits, intermittent faults, and current sensor faults. First, the characteristics of various faults are analyzed. Subsequently, fault diagnosis variables are constructed using the actual arm voltage of the inverter and the ideal arm voltage. Logical rules for each type of fault are established, and diagnosis is performed through fuzzy logic inference. Finally, experiments validated the effectiveness of this fault diagnosis scheme, with open-circuit faults detected in less than 2 ms, intermittent faults in less than 0.5 ms, and sensor faults in less than 3 ms. Full article

Bone defects from trauma, tumour resection, infection, and degenerative disease remain a major clinical burden, and autografts face limitations of supply and donor-site morbidity. Three-dimensional (3D) printing offers a route to patient-specific, architecturally defined bone scaffolds, while biopolymers from natural sources provide biodegradability, biocompatibility, and extracellular matrix-mimicking cues consistent with sustainable, green biomaterials science. This review synthesises recent progress in 3D printing of biopolymer-based scaffolds for bone tissue engineering. We first examine the principal feedstocks—alginate, gelatin and gelatin methacryloyl, collagen, chitosan, silk fibroin, cellulose, and microbial polyesters—and their preparation, crosslinking chemistry, and printability. We then compare extrusion, light-based, and indirect printing technologies and the process–property relationships governing resolution, mechanical competence, and cell viability. Composite and functionalisation strategies, including biopolymer–bioceramic hybrids and controlled delivery of growth factors and antimicrobial agents, are analysed as routes to osteoinduction, vascularisation, and infection control. Finally, we evaluate translational performance in preclinical models and outline central challenges of vascularisation, mechanical–degradation matching, scalability, and regulatory standardisation. Biopolymer 3D printing is positioned as a ve rsatile, sustainable platform whose clinical maturation depends on integrated material, structural, and biological design. Full article

Background: The management of diabetic foot disease and knee osteoarthritis (OA) with smart orthotics holds significant importance during the early stages of these conditions, given their potential consequences, including functional impairment, chronic pain, and economic burden. Real-time monitoring of plantar foot pressure enables early detection of abnormal force distribution and gait biomechanics, allowing for the redirection of forces away from affected ulcers or arthritic joints. This is the first systematic review to synthesise clinical evidence for smart orthotics technology with real-time plantar pressure sensor biofeedback across both diabetic foot ulcer prevention and knee osteoarthritis management simultaneously. A search of the PROSPERO register confirmed no existing registration covers this specific combination. Objectives: To examine the clinical evidence for the use of standard and smart orthotics in the prevention and management of diabetic foot ulcers (DFUs) and knee OA, and to evaluate their impact on plantar pressure redistribution, ulcer recurrence, pain, biomechanics, and economic burden. Eligibility criteria: Studies published in English involving human adult participants (≥18 years) with a clinical diagnosis of diabetes mellitus (at risk of DFU or with peripheral neuropathy) or knee OA, where the intervention involved any orthotic device or smart/intelligent insole with clinical outcomes reported, were included. Studies on healthy individuals only, those not reporting participant age, and non-weight-bearing protocols not differentiated from weight-bearing were excluded. Information sources: Five databases were searched: CINAHL (EBSCO Information Services, Ipswich, MA, USA), PubMed Advanced (National Library of Medicine, Bethesda, MD, USA), Wiley Online Library (John Wiley & Sons, Hoboken, NJ, USA), Cochrane Library (Cochrane Collaboration, London, UK), and Google Scholar (Google LLC, Mountain View, CA, USA). Searches were completed in May 2026. Methods: We conducted a comprehensive literature review. This review was structured and reported with reference to the PRISMA 2020 statement (Preferred Reporting Items for Systematic Reviews and Meta-Analysis; University of Ottawa, Ottawa, ON, Canada) to guide transparency of reporting. It does not constitute a full Cochrane-style systematic review; risk of bias assessment was applied to key included studies and GRADE (Grading of Recommendations Assessment, Development and Evaluation; McMaster University, Hamilton, ON, Canada) certainty ratings were applied informally and narratively rather than as formal per-outcome evidence profiles. Five databases were searched yielding 92,637 records. After removal of 398 duplicates by Rayyan, 92,239 records remained. A subsequent automated keyword-based relevance filter applied within Rayyan (Rayyan AI, Doha, Qatar), prior to human screening, excluded 84,572 records that did not contain any terms related to orthotics, diabetic foot, or knee osteoarthritis, yielding 7667 records for human title/abstract screening. A narrative synthesis approach was adopted owing to the heterogeneity of study designs and outcome measures across included studies, which precluded meta-analysis. This review was not prospectively registered. A complete list of all 78 included studies, including those not individually discussed in the results and discussion. Results: The available clinical studies report promising findings for orthotics and smart orthotics in pain reduction, ulcer prevention, and potential reduction in economic burden, though conclusions are limited by small sample sizes, heterogeneity, and predominantly open-label designs. Recent research found that orthotics can be used to alter the gait pattern that influences knee OA by reducing excessive force on the affected joint. A randomised controlled trial demonstrated an 80% relative risk reduction in DFU recurrence (RR = 0.20; 95% CI: 0.06–0.79; p = 0.022), with absolute event rates of 6.3% in the intervention group versus 30.8% in controls (ARR = 24.5%); a second trial reported a 71% reduction in ulcer incidence over 18 months; and a third randomised controlled trial demonstrated statistically significant plantar pressure reduction (p < 0.01) in patients with diabetic neuropathy. Conclusions: The available evidence suggests that orthotics may be associated with improved pressure redistribution, reduced ulcer incidence, and benefit in the management of knee OA. Although the number of studies directly comparing smart orthotics with standard orthotics remains limited, the limited comparative studies suggested that smart orthotics showed promising results in reducing ulcer incidence, providing the patient with real-time feedback to offload via their electronic devices. These findings, while preliminary, highlight the potential of smart orthotic technology as an adjunct to standard orthotic care in reducing the overall burden of diabetic foot disease and knee osteoarthritis. Limitations: The primary methodological limitation of this review is the open-label design of all included smart orthotic trials, which precludes participant blinding and introduces performance bias. However, this limitation is structural and inherent to the wearable technology field—analogous to surgical trials—and is substantially mitigated by the use of objective primary outcome measures (plantar pressure and ulcer recurrence) across the three included RCTs, the consistency of effect direction across independent RCTs conducted in different countries, and a narrative sensitivity analysis confirming robustness of findings (Risk of Bias Across Studies Section). Formal per-outcome GRADE evidence profiles were not produced; overall certainty of evidence was assessed narratively with reference to GRADE domains and is judged to be low to moderate for smart orthotics in DFU prevention and low for knee OA management, consistent with the Level 2–3 evidence base and open-label study designs. Future adequately powered, multi-site RCTs with standardised outcome reporting, minimum 24-month follow-up, and integrated health economic modelling are the highest priority to extend these preliminary findings. Registration: This review was not prospectively registered. Full article