Search Results (8,288)

Neurodegenerative diseases, including Alzheimer’s disease (AD), Parkinson’s disease (PD), and amyotrophic lateral sclerosis (ALS; Lou Gehrig’s disease), represent a growing global health burden characterized by progressive neuronal loss and functional decline. Despite decades of intensive research, effective disease-modifying therapies remain limited, underscoring the urgent need for innovative therapeutic strategies. This review highlights recent advances in the understanding of disease etiology and emerging treatment approaches, with a particular focus on modalities with translational potential. We discussed novel disease-modifying interventions, including gene and cell therapies, RNA-targeting strategies, and immunotherapies aimed at clearing misfolded proteins such as amyloid-β, tau, and α-synuclein. In parallel, we examined the evolving recognition of neuroinflammation and mitochondrial dysfunction as actionable therapeutic targets, alongside progress in precision medicine and biomarker-guided approaches that enable early diagnosis and individualized treatment. Additionally, we summarized developments in repurposed pharmacological agents, neuroprotective compounds, and lifestyle interventions, emphasizing the importance of integrative, multimodal strategies. Across AD, PD, and ALS, convergent molecular mechanisms, including protein misfolding, oxidative stress, and disrupted proteostasis, present opportunities for cross-disease therapeutic targeting. Finally, we addressed key challenges and future directions, including translating preclinical efficacy into clinical success, optimizing CNS-targeted delivery systems, and navigating ethical considerations surrounding gene editing and stem cell therapies. Full article

As a form of living heritage, Historic Urban Landscapes (HULs) have long been limited by the static perspectives and reductionist tendencies of conventional conservation and research approaches. Although the geological and archaeological concept of “stratification” offers a methodological basis for understanding the diachronic evolution of heritage, its unidimensional temporal lens fails to capture the inherent complexity and systemic nature of historic urban landscapes. To address this gap, this study proposes a “multidimensional stratification” theoretical framework through theoretical critique and paradigm reconstruction. The framework introduces innovations at the ontological, epistemological, and methodological levels, positing that the evolution of historic urban landscapes emerges from the nonlinear interaction and dynamic interweaving of four core dimensions: time, space, society, and value. It further systematizes five intrinsic attributes of such landscapes: authenticity, integrity, continuity, adaptability, and dynamism. Building on this foundation, the paper constructs a systematic analytical pathway—elements–processes–patterns–modes–drivers–characteristics—that enables dynamic analysis from micro-level identification to macro-level generalization, offering a scalable tool for HUL conservation and regeneration. To demonstrate the framework’s applicability, the historic urban area of Shenyang—a nationally designated historical and cultural city—is selected as a case study. Its urban landscape comprises four core districts: the Shengjing City District, the South Manchuria Railway Concession District, the Commercial Port District, and the Tiexi Industrial District, representing historical strata from the Qing dynasty capital, modern colonial planning, commercial opening, to industrial heritage. Using the multidimensional stratification approach, this study elucidates the spatial complexity, temporal nonlinearity, social dynamism, and value pluralism embedded in Shenyang’s historic urban area. Corresponding conservation strategies grounded in holism, dynamism, and differentiation are proposed. The research not only advances the theoretical understanding of HUL but also provides a novel paradigm—integrating holistic, dynamic, and operational perspectives—for the conservation, renewal, and regenerative practice of historic urban landscapes worldwide. Full article

The pharmaceutical development of cannabis-based medicinal products is challenged by significant variability in the quality, composition, and standardization of plant-derived active pharmaceutical ingredients (APIs). In Ukraine, despite recent legislative liberalization, a substantial shortage of standardized raw materials continues to limit the development of innovative dosage forms. This study analyses international practices among API manufacturers to identify technological parameters necessary to overcome domestic market barriers and support the implementation of advanced drug delivery systems. Content analysis was conducted on regulatory documentation, professional literature, and manufacturers’ technical specifications. Candidate evaluation followed predefined inclusion and exclusion criteria. The study assessed compliance with Good Manufacturing Practice (GMP) requirements, extraction and purification technologies, the extent of analytical characterization, and batch-to-batch reproducibility. Purposive sampling enabled a comparative analysis of various technological approaches. Marked heterogeneity was observed in API standardization and analytical control indicators among manufacturers. Possession of a GMP certificate was found necessary but may be insufficient to ensure the pharmaceutical equivalence of materials. Differences in extraction methods and purification levels may affect stability profiles, pharmaceutical development strategies, and risk management related to final product quality. The findings demonstrate that manufacturer selection is a critical decision point in pharmaceutical development, with substantiated supplier choice directly influencing dosage form development and regulatory compliance. Full article

The rapid expansion of clean energy markets, coupled with the growing importance of non-ferrous metals and technological innovation, has created a highly interconnected financial and economic system. Understanding the dynamics of these interdependencies is essential for assessing market resilience, investment diversification, and the sustainability of the global energy transition. This paper investigates the dynamic dependence and connectedness between clean energy, non-ferrous metals, and technological innovation indices, with particular attention to the impact of the COVID-19 pandemic as a global stress event. Using daily data from December 2004 to July 2020, we employ a comprehensive empirical framework that combines copula-based dependence modeling with a dynamic connectedness approach. This methodology allows us to capture nonlinear relationships, tail dependencies, and volatility spillovers across markets. The results reveal that the dependence structure between clean energy and the other sectors is symmetric and time-varying, with stronger linkages observed between clean energy and technological innovation than with non-ferrous metals. The connectedness analysis indicates a moderate level of total spillovers, with clean energy acting as the main transmitter of shocks and technological innovation as the primary receiver. Focusing on the COVID-19 period, we find a significant increase in both dependence and connectedness, suggesting that these markets become more severely integrated during periods of extreme uncertainty. These findings support the presence of contagion effects and highlight the reduced effectiveness of diversification strategies during crisis episodes. The results offer forward-looking implications for investors and policymakers regarding risk transmission, portfolio management, and the resilience of markets supporting the global transition toward sustainable energy. Full article

Food packaging constitutes a pivotal enabler within the contemporary food industry, requiring continuous innovation to address evolving challenges. Traditional packaging systems typically provide passive protection, which is inadequate for addressing dynamic microbial shifts and spoilage-induced microenvironmental instabilities. In contrast, corrective responsive food packaging (CRFP) takes a distinct approach through the integration of sensing capabilities and targeted active intervention. Upon detection of specific stimuli, CRFP systems precisely deliver bioactive agents to mitigate food deterioration. This review systematically summarizes recent advances in CRFP technology, offering a comprehensive overview of its core response mechanisms, functional materials, advanced carrier systems, and future research priorities. Special emphasis is given to (i) stimuli-responsive systems, including single-stimulus (pH, enzyme, humidity, temperature, and light) and multi-stimulus-responsive systems, detailing their triggering mechanisms and practical applications; and (ii) functional materials and carriers, exploring their synergistic effects for optimized bioactive release. This review aims to provide a structured framework for the design and implementation of CRFP, facilitating its translation from laboratory to industrial practice and contributing to the development of sustainable and efficient food preservation strategies. Full article

Cataract remains the preeminent cause of reversible blindness globally, with cataract extraction and intraocular lens (IOL) implantation serving as the definitive surgical intervention. Nevertheless, its long-term efficacy is undermined by formidable postoperative complications, specifically posterior capsule opacification (PCO) and endophthalmitis, which necessitate effective prophylactic strategies. IOL modification has emerged as a pivotal paradigm to effectively mitigate these complications. Current approaches encompass surface modification, drug delivery IOLs, and photo-responsive IOLs. Driven by the rapid interdisciplinary convergence of materials science, ophthalmology and pharmacology, the field has also evolved to have combined modification strategies and multifunctional systems. This review provides a comprehensive overview of the recent progress in IOL modification for postoperative complication prophylaxis. By categorizing recent advancements into three major types—surface modification, drug delivery systems, and photo-responsive IOLs—we critically evaluate their mechanisms, advantages, and limitations. Furthermore, we offer strategic insights to accelerate the development of IOL modification and bridge the gap between innovation and clinical translation. Full article

Machine learning (ML) has become a key enabling technology for optimizing renewable energy systems and supporting global sustainability objectives. This paper presents a comprehensive review of recent advances in ML-based optimization techniques applied to clean and renewable energy systems, with particular emphasis on wind energy, hybrid energy systems, energy storage, and intelligent energy management. A systematic literature review covering peer-reviewed publications from 2021 to 2025 was conducted, resulting in the analysis of 138 high-quality journal and conference studies. The reviewed studies were categorized according to evolutionary algorithm-based hybrid models, classical neural networks, and deep learning architectures, including Convolutional Neural Network (CNN), LSTMs, GRUs, and attention-based models. The analysis demonstrates that hybrid ML–metaheuristic frameworks significantly enhance forecasting accuracy, system reliability, fault diagnosis, and multi-objective optimization compared to traditional methods. These intelligent approaches directly contribute to Sustainable Development Goals SDG-7 (Affordable and Clean Energy), SDG-9 (Industry, Innovation, and Infrastructure), and SDG-13 (Climate Action). Key challenges and future research directions are discussed, highlighting the need for scalable, explainable, and real-time ML solutions to enable resilient, low-carbon, and sustainable energy systems. Full article

The creation of innovative systems that are able to combine diagnosis and therapy is a crucial opportunity in nuclear medicine. Here, we propose a methodological and multiscale approach for the development of a theranostic platform based on AuNRs functionalized with radiopharmaceuticals. AuNRs offer a versatile and effective system due to their unique physicochemical properties and the possibility of surface functionalization with targeting molecules. Within this framework, key challenges include the functionalization of AuNRs to target the cell nucleus, the loading of AuNRs with radiopharmaceuticals, and the investigation of Auger electron emission from AuNRs under gamma irradiation. Multiscale modelling is employed to describe the behaviour of the system within the cellular environment and to predict potential radiobiological enhancement effects, including synergistic interactions between functionalized AuNRs and radiopharmaceutical agents such as ^99mTc-sestaMIBI. The experimental activity includes gamma irradiation studies, along with the structural and physical characterization of nanomaterials and in vitro biological investigations on T98G cells, to evaluate cytotoxicity and metabolic alterations, with the aim of assessing the potential synergistic effects of the combined system. Full article

In the context of mass customization and mixed-model production systems, Master Production Scheduling (MPS), which determines production start dates, plays a critical role. However, in such environments, MPS faces a dual challenge: ensuring due-date adherence under multiple capacity constraints while also reducing operational instability caused by uneven day-to-day consumption of critical components, referred to as Replenishment and Industrial Characteristics (RICs). This paper proposes a new mathematical model for MPS with a Smoothing Mechanism for RICs (MPS-SM). This bi-objective formulation extends a baseline due-date-driven model with an explicit production smoothing/leveling (also known as Heijunka) term, minimizing deviations of RIC usage from weekly ideal levels. By embedding smoothing directly into MPS, the approach provides a pre-leveling effect that can reduce (or ideally eliminate) downstream complexity, specifically related to schedule modifications required in a separate smoothing stage. To reflect changing scheduling priorities, smoothing is weighted through an innovative context-aware non-linear weekly function that assigns lower importance near execution and greater importance farther into the horizon. The models are evaluated in a rolling-horizon simulation-optimization framework using data from a real-world truck manufacturer. Several experiments over 300 discrete-event simulated days show that MPS-SM consistently reduces RIC variability while inducing a controlled increase in lateness penalties. Full article

The convergence of artificial intelligence and robotic surgery is redefining the management of genitourinary cancers by enhancing diagnostic accuracy, surgical precision, and training efficiency. This narrative review explores recent advancements in artificial intelligence applications across the cancer care continuum, with a focus on prostate, kidney, and bladder malignancies. Artificial intelligence tools, particularly those based on machine learning and deep learning, have demonstrated strong performance in analyzing imaging data, segmenting tumors, predicting pathological features, and supporting clinical decision-making. Intraoperatively, artificial intelligence enables skill assessment, personalized feedback, and real-time navigation by processing data from surgical videos and robotic system sensors. Augmented reality and intraoperative modeling further enhance visualization and margin control during complex procedures. The review also discusses emerging technologies such as single-port robotic platforms, which offer advantages in confined anatomical spaces and support less invasive approaches. Additionally, the growing field of telesurgery is addressed, highlighting its feasibility for complex urologic operations across vast distances. While many of these innovations are still in early stages of clinical validation, their integration into practice has the potential to improve oncologic and functional outcomes, expand access to expert care, and foster the development of next-generation surgical strategies in urologic oncology. Full article

Drag-reducing coating technology is a core approach to enhancing the performance of ice and snow sports equipment. By regulating the interfacial characteristics between the equipment surface and the ice or snow medium, it significantly reduces frictional resistance during motion, thereby optimizing athletes’ speed performance and control precision. This paper aims to review the current research status and challenges in this technological field. The review first elaborates on the fundamental principles of applying drag-reducing coatings to key equipment such as skis, sleds, and ice skates, covering current mainstream coating material systems, key preparation processes, and comprehensive performance evaluation methods. Furthermore, integrating multidisciplinary advances in surface engineering, fluid dynamics, and materials science, this review specifically examines how these disciplines can be harnessed to address the unique tribological challenges of snow/ice interfaces. It focuses on cutting-edge research directions such as micro-nano-structured coatings driven by biomimetic design concepts and smart coatings with environmental responsiveness. By synthesizing existing research achievements and potential technological bottlenecks, this paper aims to provide a systematic, theoretical basis and innovative ideas for the future development of a new generation of high-performance, intelligent ice and snow sports equipment. Full article

Fungal infections represent an escalating global health challenge due to their increasing incidence, the emergence of multidrug-resistant pathogens, and the limited development of new antifungal agents. Therapeutic efficacy is compromised by mutations in drug targets, overexpression of efflux pumps, alterations in the ergosterol biosynthetic pathway, biofilm-associated tolerance, and extensive genomic plasticity. The growing prevalence of antifungal resistance and the limited availability of effective therapeutic options highlight the urgent need to strengthen epidemiological surveillance and accelerate research into innovative therapeutic strategies. In this review, we discuss the potential of lipid-based drug delivery systems (LDDSs) as a versatile strategy to optimize antifungal administration and overcome resistance mechanisms. Liposomes (LPs), solid lipid nanoparticles (SLNs), nanostructured lipid carriers (NLCs), and lipid nanoparticles (LNPs) offer high biocompatibility, efficient encapsulation of hydrophobic compounds, structural stability, and controlled drug release. Their nanoscale properties facilitate penetration into biofilms, promote intracellular uptake, and reduce the impact of efflux-mediated drug extrusion, thereby improving cellular penetration and circumventing resistance pathways. In addition, LDDSs increase bioavailability, reduce toxicity, and promote drug accumulation within poorly accessible tissue compartments. Overall, LDDSs represent a promising approach to expand the therapeutic arsenal against both superficial and invasive fungal infections, particularly those caused by multidrug-resistant pathogens. Full article

Background/Objectives: Recombinant adeno-associated virus (AAV) stands at the forefront of gene therapy development, requiring stable formulations to support the expanding therapeutic applications. The growing diversity of serotypes and engineered capsids often creates complex challenges for formulation development, thus demanding innovative formulation development strategies beyond traditional manual approaches to characterize a large formulation design space quickly to discover stable formulations. Methods: Here, we address this critical need through a high-throughput automation platform that dramatically enhances formulation development efficiency and capability through rapid formulation preparation and high-throughput AAV analytics. This system prepares 96 distinct formulations in 40 min and completes AAV compounding in 20 min per plate, with precise control of pH, buffer components, and AAV titers. Results: In a proof-of-concept formulation development study using AAV1, we screened 128 formulations across multiple buffer systems, pH ranges, and excipient combinations. This comprehensive approach successfully identified optimal stable high-titer AAV1 formulations (1.2 × 10¹⁴ vector genome (vg)/mL) that maintained stability under frozen, refrigerated, and room temperature storage conditions. Conclusions: Our study demonstrated that this automation platform combined with high-throughput AAV analytics significantly accelerates formulation development, conserves AAV material, and enables systematic exploration of broader formulation design space. It allows us to achieve identification of robust and stable AAV formulations within a timeframe unmatched by traditional formulation development approaches. Full article

Technology enterprises are leveraging artificial intelligence (AI) to foster disruptive innovation, aiming to seize first-mover advantages in technological catch-up and strategic transformation. Most existing studies adopt static research methods such as empirical analysis to explore corporate disruptive innovation from the dimensions of technology, market, organization and value creation. However, few scholars dynamically investigate the impacts of multi-stakeholder interactions on the disruptive innovation of AI enterprises from the perspective of innovation ecosystem by employing evolutionary game theory. Against this backdrop, this paper adopts the evolutionary game approach to explore how the bounded rational strategic interactions among AI enterprises, incumbent enterprises and governments in the innovation ecosystem affect the evolutionary dynamics of AI enterprises’ disruptive innovation behaviors. It also examines under what conditions of benefits, costs, risks and policies the system can evolve toward a stable strategic equilibrium. The findings reveal that the sustainable advancement of disruptive innovation by AI enterprises is not merely driven by the unilateral willingness of individual firms. Instead, it is jointly shaped by the innovation investment of AI enterprises, cooperative responses of incumbent enterprises, and regulatory and supportive policies of governments, as well as comprehensively influenced by base benefits, R&D investment pressure, technology spillover effects and niche competition risks. This research provides theoretical references for improving the innovation governance and policy support system of the AI industry. Future research can further analyze the influence of strategic interactions among more heterogeneous stakeholders on the evolutionary process of disruptive innovation of AI enterprises. Full article

This review provides a comprehensive analysis of the multiple factors influencing sunscreen efficacy, integrating studies published between 2016 and 2026. Beyond the type and concentration of UV filters, sunscreen performance is strongly affected by formulation design, photostability, environmental exposure, and user application practices. Formulation strategies involving emulsion systems, excipients, solubilization methods, and encapsulation technologies directly influence sun protection factor (SPF), cosmetic acceptability, and safety. Recent advances, including nanoparticle-based carriers, hybrid organic–inorganic systems, and antioxidant-enriched formulations, have shown potential to improve photostability, broaden UV protection, and reduce systemic absorption and environmental impact. However, inadequate application and insufficient reapplication remain major limitations to real-world photoprotection. In addition, differences in skin type, age, and lifestyle reinforce the need for more personalized sunscreen approaches. Growing concerns regarding the environmental effects of UV filters also highlight the importance of sustainable formulations and stricter regulatory policies. Overall, optimizing sunscreen efficacy requires not only technological innovation but also improved public education, transparent labeling, and user adherence. Future research should focus on multifunctional, eco-friendly, and user-centered sunscreens capable of providing effective and sustainable photoprotection. Full article