Search Results (18,738)

As data analytics increasingly rely on machine learning models for forecasting, classification, and prediction, effective visualisation becomes essential for transforming model outputs into practical insight. Yet the ways these outputs are visualised, and the evidence supporting those designs, remain fragmented across domains. This paper presents a systematic literature review of visualising machine learning model outputs in data analytics, focusing on how predicted outputs are communicated to end-users alongside performance and uncertainty information, and how these visual systems are evaluated in practice. Following PRISMA, we screened 330 articles from ACM Digital Library, IEEE Xplore, and PubMed and included 88 peer-reviewed studies published between Jan 2015 and July 2024. Across the corpus, we identify (1) recurring visual encoding and interaction patterns for interpreting predictions in temporal, spatio-temporal, and event-based settings; (2) common strategies for presenting model validation, calibration, and uncertainty; and (3) a wide range of evaluation approaches, from informal expert feedback to controlled user studies and deployments. The synthesis highlights persistent gaps in rigorous and comparable evaluation, challenges in supporting diverse user goals and expertise levels, and practical constraints that arise in operational contexts. We conclude by distilling practical implications for designing and assessing predictive visualisations, as well as outlining recommendations for future research and practice, with particular attention to improving uncertainty communication, strengthening evaluation rigour and comparability, and adopting evaluation methods that better reflect operational data analytics practice. Full article

Explainable artificial intelligence (X-AI) techniques aim to make the actions and decisions of autonomous systems understandable to humans interacting with these systems. In human–AI teams, explainability supports individual understanding and coordination, shared mental models, and collective decision-making among humans and AI agents. Research has shown that X-AI enhances trust in autonomous systems, improves human–AI team performance, and supports collaboration across domains including aviation, finance, healthcare, hospitality, and sports. However, X-AI technologies face difficult challenges, including a lack of transparency and interpretability due to complex underlying models, also known as the “black-box” nature of AI systems. These technologies also lack any universally accepted evaluation metrics and have limited generalizability across applications. One deficit in the X-AI literature is that most frameworks focus on individual-level outcomes, with limited attention to team-level processes. The current study conducted a systematic literature review adhering to PRISMA guidelines and the SALSA framework. This study introduces the Implementation-Design (I-D) framework that organizes X-AI approaches along two dimensions: implementation, ranging from visual to interactive approaches, and design, ranging from isolated explanations to workflow-integrated systems. This framework captures lower-level engagement, involving individual users, to higher-level understanding that is necessary for teams and collectives. Findings indicate that visual explanation approaches support user engagement, while interactive workflow approaches promote deeper understanding, appropriate reliance, and distributed cognition within human–AI teams. Implications highlight the need for team-oriented explainability grounded in shared mental models, transactive memory systems, and collaborative X-AI artifacts. Practical guidelines are included to support researchers and practitioners in selecting appropriate X-AI techniques based on their context and level of analysis. The I-D framework is offered as a conceptual organizing model to guide research and practice, and empirical validation is identified as a priority for future work. Full article

Background/Objectives: Minimally invasive (percutaneous) pedicle screw fixation (PPSF) was initially introduced for the treatment of degenerative spinal deformities. Since then, its indications have progressively expanded to a broad spectrum of spinal pathologies. This method has gained increasing acceptance in spinal surgery due to lower morbidity when compared with conventional open procedures. This study presents a comprehensive review of the recent literature on hybrid minimally invasive spinal instrumentation techniques, focusing on the combined use of PPSF with open or mini-open approaches and their roles in personalized surgical management. Methods: A literature search was conducted in PubMed and Web of Science to identify studies reporting expanded indications of percutaneous pedicle screw fixation (combined with other approaches), novel surgical techniques, and their clinical outcomes. Results: Thirty-five studies met the inclusion criteria and were categorized according to pathology. Most included studies were retrospective observational investigations corresponding to Oxford CEBM Levels III–IV evidence, with a smaller number of prospective studies and systematic reviews. Conclusions: The findings from this review highlight the expanding role of hybrid methods in the management of complex spinal disorders. These approaches provide adequate stability and enable decompression or deformity correction, while minimizing tissue trauma, blood loss, and perioperative morbidity, thereby facilitating improved recovery and functional outcomes. The included literature predominantly represents moderate levels of evidence, supporting a patient-specific, pathology-driven surgical strategy that optimizes individualized outcomes in spinal surgery. Full article

Deep eutectic solvents (DESs) have undergone a remarkable transformation over the past two decades, evolving from a laboratory curiosity into one of the most actively investigated solvent platforms in green chemistry. Yet, despite this rapid expansion, and although the field is well served by numerous topical reviews, it still lacks a corpus-wide, cross-disciplinary synthesis capable of guiding strategic research priorities, identifying critical knowledge gaps, and informing policy and industrial investment decisions. The present work addresses this need through a thorough analysis of global DES research from 2003 to 2025, based on a deduplicated corpus of 17,757 publications retrieved from the Web of Science Core Collection and Scopus following PRISMA-adapted screening guidelines. The analysis maps temporal publication dynamics, geographic and institutional contributions, thematic evolution, journal landscape, component usage patterns, international collaboration networks, market projections, and alignment with the United Nations Sustainable Development Goals. The results document an exponential growth trajectory—from a single publication in 2004 to 3954 in 2025 (CAGR > 30%)—and reveal a clear thematic transition from early electrochemistry-dominated research toward extraction, pharmaceutical, and environmental applications, with machine-learning-assisted design and hydrophobic DES formulations emerging as the most dynamic current frontiers. China leads global output with 6819 publications (38.4%), while the United States and Malaysia achieve the highest citation-per-publication ratios among the leading nations (≈46.7 and ≈38.9, respectively, versus ≈27.6 for China), and Spain pairs a comparatively modest output with a high h-index, indicating that impact is large relative to volume. Type III DESs and NADESs collectively account for approximately 69% of the literature, with choline chloride present in 72% of reported formulations. The global DES market, valued at approximately USD 166 million in 2024, is projected to reach USD 370 million by 2030. Despite this progress, critical translational barriers persist: fewer than 0.3% of publications include techno-economic or life cycle assessment analysis, standardized characterization protocols remain absent, and toxicological datasets are systematically incomplete. This panoramic analysis is intended to serve as an evidence-based reference for researchers prioritizing future directions, for funding agencies assessing the maturity and needs of the field, and for industrial stakeholders evaluating the readiness of DES technologies for scale-up. Full article

Background/Objectives: Hepatocellular carcinoma (HCC) is undergoing a profound epidemiological shift from viral etiologies toward metabolic dysfunction-associated steatohepatitis (MASH). Current targeted therapies often fail to provide effective responses due to the complex, interconnected nature of the tumor microenvironment. This review aims to explain the molecular axis linking chronic metabolic injury to carcinogenesis, focusing specifically on the oncoproteins Astrocyte elevated gene-1/metadherin (AEG-1/MTDH) and staphylococcal nuclease and tudor domain-containing 1 (SND1) as cooperating regulators of this disease network. Methods: This article represents a narrative review of the published literature and does not follow a systematic or exhaustive search protocol. A structured literature search using specific search terms was conducted across PubMed, Scopus, and Web of Science databases, with a last search date of May 2026. Results: Available preclinical data indicate that AEG-1 mediates early preneoplastic injury via dysregulation of hepatic lipid metabolism leading to lipotoxicity and survival of genetically unstable hepatocytes. As the disease progresses, AEG-1 amplifies NF-κB-driven inflammation and, as tumors emerge, recruits SND1 as a cooperating partner to form a gene-silencing complex that suppresses tumor suppressor proteins. Furthermore, AEG-1 and SND1 reprogram surrounding macrophages into an immunosuppressive state and drive tumor resistance to standard anti-angiogenic and chemotherapeutic drugs. Conclusions: The evidence reviewed supports a model in which HCC is driven by an interconnected metabolic-inflammatory-oncogenic cycle. AEG-1 functions as an upstream metabolic and inflammatory driver that cooperates with SND1 in a subset of oncogenic silencing events within this pathogenic network. Therefore, utilizing advanced nanomedicine platforms to simultaneously target these proteins represents a mechanistically rational therapeutic strategy that warrants further preclinical evaluation in advanced HCC. Full article

Global growth in electric mobility, portable electronics, and renewable energy storage has increased concerns about the environmental and economic impacts of managing end-of-life lithium-ion and lead-acid batteries. Although these batteries support the transition to renewable energy, their disposal presents significant challenges. Recycling has emerged as a key strategy to reduce resource depletion, limit pollution, and recover valuable materials. This study systematically reviewed and quantitatively synthesised the literature published between 2000 and 2025, assessing the environmental impacts of battery recycling programs. The review followed PRISMA guidelines to ensure a transparent and rigorous study selection process. Data from peer-reviewed articles, industry reports, and policy documents were analysed, focusing on indicators such as greenhouse gas emissions, energy use, material recovery efficiency, and economic returns. Statistical methods, including Hedges’ g, heterogeneity testing, and sensitivity analysis within a random-effects model, were applied to account for variability across technologies and battery types. The results show that recycling generally lowers emissions and improves resource recovery compared to virgin material extraction, though performance varies. Lead-acid recycling demonstrates stronger environmental benefits due to mature technologies and established systems, while lithium-ion recycling shows positive but lower gains, limited by higher energy demands and less-developed processes. Overall, recycling is essential for reducing environmental impacts and supporting a circular economy, though lithium-ion systems require further technological and policy advancements. These findings can be used by governments to strengthen regulatory frameworks to support recycling industries and invest in advanced lithium-ion recycling technologies to improve efficiency. Despite the existing limitations, the benefits of recycling outweigh the drawbacks, making it a necessary strategy for sustainable battery waste management. Full article

Cycling infrastructure has emerged as a strategic lever for sustainable urban development in Latin America, yet the factors that determine its effectiveness remain poorly understood and dispersed across fragmented literature. This systematic review, conducted in accordance with PRISMA 2020 guidelines, synthesizes evidence from 16 peer-reviewed studies published between 2020 and January 2026 to identify the key determinants of cycling infrastructure effectiveness in Latin American cities. Five critical dimensions emerged from the analysis: physically segregated infrastructure, gender-responsive design, multimodal connectivity, environmental and public health co-benefits, and adaptation to topographical and climatic conditions. The evidence reveals that segregated bike lanes increase cycling uptake by 48–187%, multimodal integration boosts combined trips by approximately 34%, and gender-sensitive interventions increase female cycling participation by up to 89%. These findings converge on a central insight: infrastructure effectiveness is not achieved through isolated investments but through the coherent, simultaneous implementation of multiple interconnected dimensions. This review contributes an original multidimensional analytical framework to guide evidence-based public policy and investment in sustainable urban mobility across the region. Full article

Invasive candidiasis, predominantly driven by multidrug-resistant Candida species and intractable biofilms, represents an escalating global health crisis with mortality rates rivaling major infectious diseases. The clinical efficacy of conventional antifungal agents—azoles, polyenes, and echinocandins—is severely compromised by poor tissue penetration, dose-limiting systemic toxicity, and the rapid evolution of complex resistance mechanisms. Here, we review the two-decade structural evolution of nanotechnological interventions designed to overcome these pharmacological and biological barriers. We systematically analyze advanced nanosystems, including lipid-based formulations, natural polymers, and biogenic metallic nanostructures, highlighting their capacity to penetrate the dense extracellular polymeric substance (EPS), combat potential fungal ‘nano-resistance’, and significantly reduce metabolically dormant persister cell populations. The literature search was performed using the electronic databases PubMed, Scopus, Web of Science, and Google Scholar. Publications indexed between 2015 and 2025 were primarily considered, while seminal studies published before 2015 were included when necessary to provide historical context and foundational knowledge. We place specific emphasis on next-generation biomimetic and exosome-inspired nanocarriers, which significantly reduce systemic host toxicity while maximizing targeted antifungal efficacy. In this context, the synergistic integration of smart nanocarriers to actively disassemble fungal resistance networks, such as the target of rapamycin (TOR) signaling pathway and sphingolipid biosynthesis. Finally, we outline a strategic roadmap to bridge the translational “Valley of Death”. By prioritizing manufacturing standardization, comprehensive long-term biosecurity profiling, and rationally designed biomimetic platforms, we propose an alternative way to outpace the evolutionary adaptations of fungal pathogenesis and translate these innovations into the clinic. Full article

Background/Objectives: Medical devices are essential to modern healthcare, but their adoption is often driven by regulatory conformity, clinical enthusiasm, procurement pressures and vendor-led innovation rather than systematic evaluation of comparative value. CE marking and related regulatory mechanisms are necessary for market access; however, they do not determine whether a device improves patient-related outcomes compared with existing alternatives, whether its benefits justify its total costs, or whether it can be implemented safely in routine care. This narrative review examines why medical devices require a dedicated life-cycle health technology assessment (HTA) approach and proposes an operational framework linking assessment to adoption, evidence generation, reassessment and disinvestment. Methods: A structured targeted search covered peer-reviewed literature and policy or institutional documents addressing HTA, medical devices, regulation, economic evaluation, real-world evidence, hospital-based HTA, procurement digital and AI-enabled devices, patient involvement and post-market reassessment. Results: Medical devices differ from pharmaceuticals through user dependence, learning curves, procedure dependence, short product life cycles, incremental modification, heterogeneous comparators, limited randomized evidence and hidden life-cycle costs. These features create clinical, economic, organizational and implementation uncertainty after market entry. The proposed model specifies six linked phases: horizon scanning and early dialogue, pre-adoption appraisal, an explicit adoption decision, controlled implementation, real-world monitoring and scheduled or trigger-based reassessment leading to continuation, scale-up, restriction, or disinvestment. Practical constraints include fragmented data infrastructure, the cost of maintaining registries and residual confounding in real-world evidence. Conclusions: Medical device HTA should move beyond one-time pre-adoption assessment toward a decision-linked life-cycle model that integrates comparative value, patient and public involvement, procurement, implementation governance, real-world evidence, version monitoring, reassessment and disinvestment. This approach can support responsible innovation, patient safety, transparent procurement and sustainable health-system value. Full article

Male hypogonadism is associated with metabolic and cardiovascular comorbidities, and emerging evidence implicates testosterone deficiency in immune dysregulation that may elevate cancer risk. To review current evidence on the relationship between male hypogonadism, immune function, and cancer risk, focusing on mechanisms linking testosterone deficiency to immune suppression and oncologic outcomes. PubMed/MEDLINE, Google Scholar, and SciSpace were systematically searched (through April 2026) using predefined search strings. After removal of duplicates (n = 1535 records screened), 156 full-text articles were assessed for eligibility; 20 studies met predefined inclusion criteria (comprising 4 experimental studies, 4 prospective/RCT studies, 7 observational studies, and 5 reviews used as secondary literature) and were included in a narrative synthesis. Testosterone deficiency was consistently associated with elevated IL-6, TNF-α, IL-1β, and CRP, impaired neutrophil maturation, and reduced NK-cell cytotoxicity. Androgen deprivation augmented thymic output and anti-tumor T cell responses in prostate cancer models, yet promoted chronic inflammation in other contexts. Epidemiologically, low testosterone correlated with increased colorectal cancer risk and poorer survival in advanced malignancies; the prostate cancer relationship followed a paradoxical saturation model. The immunological consequences of hypogonadism are context-dependent. Testosterone deficiency drives pro-inflammatory signaling that may promote carcinogenesis, while androgen-mediated immunosuppression can paradoxically impair anti-tumor surveillance. No simple linear relationship exists between hypogonadism and cancer risk via immune suppression. Prospective studies are needed to guide clinical decisions on testosterone replacement therapy in hypogonadal men. Full article

Connected and Automated Electric Vehicles (CAEVs) are rapidly evolving into complex Cyber-Physical-Social Systems (CPSS), generating structural tensions between technological innovation and public safety. Current research in public governance exhibits significant fragmentation. Scholars frequently isolate data privacy compliance from algorithmic safety auditing, treating them as distinct silos. To bridge this gap, this study applies the PRISMA framework to systematically synthesize 135 core peer-reviewed articles, exposing the endogenous limitations of unidimensional regulatory paradigms. Our analysis yields three central insights. First, traditional “notice-and-consent” models fail under the ubiquitous data collection demands of modern V2X environments. Macro-level policies must translate into foundational Privacy-Enhancing Technologies (PETs) through “Law-as-Code” mechanisms. Second, the opacity of end-to-end algorithmic decision-making deconstructs traditional tort liability systems. This necessitates ex-ante quantitative auditing mechanisms—such as Explainable Artificial Intelligence (XAI) and enhanced Threat Analysis and Risk Assessment (TARA 2.0)—to mitigate adversarial attacks and physical-level safety hazards. Third, overcoming cross-national regulatory fragmentation requires constructing a “dual-track synergistic” governance architecture. This framework institutionalizes the coupling of data lifecycle quality workflows with the algorithmic Safety of the Intended Functionality (SOTIF). Ultimately, this review advocates for adaptive regulatory sandboxes and advances the harmonization and mutual recognition of global standards (e.g., ISO/SAE 21434, UN R155/156). Addressing current methodological and empirical data constraints, future academic inquiry must pivot. Researchers should target the value alignment challenges of Large Language Models (LLMs) in autonomous driving and implement multi-stakeholder participatory policy pilots designed to reconcile diverse social values. Full article

Surface water quality is critical to ecosystem health and sustainable development, yet conventional monitoring falls short of spatiotemporally continuous assessment. Remote sensing coupled with machine learning has become a powerful paradigm for large-scale quantitative retrieval of water quality parameters (WQPs). This review examines the progress and prospects of machine-learning-driven multi-source remote sensing for surface WQP retrieval. A systematic literature review following PRISMA 2020 guidelines, covering 437 Web of Science Core Collection publications (2000–2025), reveals exponential growth, with China and the United States contributing 70.3% of total output. A critical synthesis covers four dimensions: (1) characteristics and fusion strategies of satellite, airborne, and ground-based remote sensing data; (2) modeling features of traditional machine learning (SVR, RF, GBDT), deep learning (CNN, RNN, Transformer), and hybrid approaches; and (3) retrieval advances for optically active versus non-optically active parameters—the former approaches operational readiness while the latter remains constrained by weak indirect spectral correlations; and (4) uncertainty sources and mitigation strategies across the data–model–parameter chain. Five key challenges are identified: limited model generalizability, insufficient physical interpretability, optical heterogeneity and parameter coupling, scarce in situ data, and multi-source fusion bottlenecks. Five future directions are proposed—transfer learning, physically informed explainable machine learning, non-optically active parameter retrieval, benchmark dataset development, and intelligent multi-source fusion—offering a roadmap toward operational surface water quality monitoring. Full article

Background/Objectives: Natural products derived from plants, animals, and microorganisms have long been used as nutritional supplements and are increasingly recognized for their potential role in preventing and managing human diseases. This narrative review aims to summarize current evidence on the therapeutic relevance of natural products as dietary supplements across major disease categories and to highlight their mechanisms of action, clinical efficacy, and safety considerations. Methods: A narrative literature review was conducted using peer-reviewed articles, systematic reviews, and clinical studies focusing on natural products used as nutritional supplements in disease management. Relevant data were analyzed thematically, with emphasis on bioactive compounds, mechanisms of action, and evidence from preclinical and clinical research. Results: Natural products, particularly plant-derived polyphenols, flavonoids, terpenoids, omega-3 fatty acids, and probiotic-derived metabolites, exhibit diverse biological activities, including antioxidant, anti-inflammatory, immunomodulatory, and antimicrobial effects. Evidence suggests potential benefits in cardiovascular diseases, metabolic disorders such as diabetes and obesity, neurodegenerative conditions, certain cancers, gastrointestinal disorders, and infectious diseases. However, clinical efficacy varies depending on compound type, dosage, and formulation. Key limitations include low bioavailability, variability in composition, and insufficient large-scale clinical trials. Safety concerns such as herb–drug interactions and lack of standardization remain significant challenges. Conclusions: Natural products as nutritional supplements represent a promising adjunct strategy in the prevention and management of various human diseases. While preclinical and early clinical evidence is encouraging, stronger clinical validation, improved standardization, and clearer regulatory frameworks are required to fully integrate these agents into evidence-based medical practice. Full article

Background: Ischemic stroke remains a leading cause of mortality and long-term disability worldwide, with complex and heterogeneous pathophysiological processes. Single-cell and single-nucleus RNA sequencing (sc/snRNA-seq) has been increasingly applied to investigate cellular heterogeneity at high resolutions. Methods: We systematically searched PubMed, Web of Science, and Embase to identify studies that applied sc/snRNA-seq in ischemic stroke research. Based on the retrieved literature, we summarized the bioinformatic analytical methods and application strategies reported in these studies, focusing on how sc/snRNA-seq has been utilized across different research contexts. Results: The application of sc/snRNA-seq in ischemic stroke has expanded rapidly across species and sample types. A wide range of downstream bioinformatic analyses have been employed, including clustering, differential expression analysis, trajectory inference, gene regulatory network analysis, and cell–cell communication analysis. These approaches have been applied to investigate diverse biological processes in ischemic stroke. In addition, these analytical strategies have been extended to multiple biological contexts, including extracerebral tissues, stroke-related modifiers, and their associated complications. Furthermore, integrative analytical approaches that combine multiple datasets, bulk transcriptomics, and other omics data have been increasingly utilized. Advances in temporal and spatial resolutions have enabled analyses across different stages and anatomical regions. Conclusions: This review systematically summarizes the analytical methods and application strategies of sc/snRNA-seq in ischemic stroke. These approaches provide a structured perspective for understanding the application of single-cell technologies in this field. Future studies may benefit from standardized designs and coordinated analytical strategies to facilitate more systematic investigations. Full article

Disturbance and vegetation-recovery monitoring is gaining growing attention in remote sensing-based studies, especially in studies focusing on protected areas. Despite this growth, the methodological diversity and the research themes addressed across these studies have not yet been systematically examined. This systematic review examines peer-reviewed studies published after 2015 to identify and document how disturbances and post-disturbance recovery are monitored using satellite Earth Observation data. We systematically reviewed 105 studies, following the PRISMA and PSALSAR guidelines, and classified the publications by disturbance type, ecosystem type, geographic region, satellite and auxiliary datasets, analytical methods, and validation approaches employed. The results reveal that the research is concentrated in a small group of countries and predominantly focused on forest ecosystems. Disturbance detection dominates the research literature, while recovery modeling and long-term predictive analysis remain underexplored. Validation practices are highly inconsistent, with limited use of standardized approaches. Overall, this review highlights substantial methodological progress over the past decade and identifies research gaps in disturbance and recovery assessment, multi-sensor integration, and alignment with emerging biodiversity and restoration policy frameworks. Full article