Search Results (2,987)

Breast cancer has emerged as the preeminent global health crisis in oncology, currently standing as the most frequently diagnosed malignancy among women worldwide. Establishing novel predictive biomarkers is paramount to truly personalize treatment approaches, minimize unnecessary toxicity, and significantly improve long-term outcomes for patients with breast cancer. Breast cancer transcriptomic datasets were retrieved from the Gene Expression Omnibus and processed through standardized normalization procedures. Mutation-driven regulation of SYTL4 expression, treatment response to trastuzumab, cancer hallmark enrichment, and survival associations were evaluated using established bioinformatic tools and enrichment analysis based on integrated cancer hallmark gene sets. Additionally, DNA methylation profiles were analyzed. Herein, it is shown that SYTL4 mRNA expression is significantly (p = 2.01 × 10⁻⁴) diminished in breast cancer bearing BRCA1 mutations, suggesting a mechanistic interplay between BRCA1-driven genomic instability and SYTL4-regulated signaling cascades. Kaplan–Meier survival analysis demonstrated that elevated SYTL4 mRNA expression is significantly associated with improved overall survival in HER2-positive breast cancer patients (HR = 0.72; p = 0.034). Consistently, SYTL4 expression was significantly higher in patients who responded to trastuzumab therapy, supporting its potential as a biomarker of therapeutic response. Epigenetic analysis further revealed significant differential DNA methylation of SYTL4 between tumor and unaffected control tissues (p < 2.2 × 10⁻¹⁶), with region-specific hypomethylation in tumor regulatory regions. KEGG pathway and cancer hallmark enrichment analyses indicated that genes with prominent methylation changes are involved in cytokine signaling, growth factor pathways, and extracellular matrix remodeling, with the strongest associations observed for hallmarks related to genome instability, replicative immortality, resisting cell death, and metabolic reprogramming. In summary, we present that the gene SYTL4 is a prospective biomarker for survival and trastuzumab treatment responsiveness. Our observations posit that SYTL4 expression may signify a biological milieu conducive to sustained HER2 reliance and amplified therapeutic vulnerability. 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

Alzheimer’s disease (AD) is increasingly recognized as a biological continuum characterized by early neuropathological and molecular changes that precede the onset of clinical symptoms. Fluid biomarkers have transformed the diagnostic landscape by enabling the in vivo detection of core AD pathologies, particularly amyloid-β deposition and tau-related neurodegeneration. Despite the rapid expansion of candidate biomarkers, however, only a limited number have successfully translated into clinical practice. Discovery-phase approaches, primarily driven by mass spectrometry-based proteomics, enable the unbiased identification of novel biomarker candidates across multiple biological pathways. Research-phase methods, including immunoassays such as enzyme-linked immunosorbent assay (ELISA), electrochemiluminescence immunoassays (ECLIA), microfluidic platforms, and ultrasensitive technologies such as single-molecule array (SIMOA), support analytical and clinical validation in well-characterized cohorts. Clinical implementation has been advanced by fully automated platforms, including Lumipulse and Elecsys, which have obtained regulatory approval for cerebrospinal fluid biomarkers and, more recently, blood-based biomarkers. These developments represent a paradigm shift toward minimally invasive and scalable diagnostic strategies that may reduce dependence on neuroimaging techniques. Nevertheless, major challenges remain, including assay standardization, inter-platform variability, demonstration of clinical utility, and barriers to widespread clinical adoption. This review provides a comprehensive overview of analytical methods used to measure AD fluid biomarkers in cerebrospinal fluid and plasma, structured according to the biomarker development pipeline from discovery to clinical implementation. Overall, the review highlights a fit-for-purpose approach to biomarker development and emphasizes the complementary roles of diverse analytical technologies across the different phases of biomarker translation. Full article

This study examines the spatiotemporal evolution and driving mechanisms of urban expansion in the Guangxi Zhuang Autonomous Region, China, from 2013 to 2023. Using Suomi-NPP VIIRS nighttime light (NTL) data, we combine Standard Deviational Ellipse (SDE) analysis, centroid migration, kernel density estimation (KDE), landscape metrics, Local Moran’s I (LISA), and system Generalised Method of Moments (system-GMM) estimation. The results show that the centroid of urban development remained within Binyang County while moving overall toward the southeast with recurrent north–south oscillations. The SDE results indicate a stable northeast–southwest orientation, with secondary expansion in other directions. The urban structure is dominated by a strong Nanning core, accompanied by secondary clusters in Liuzhou, Guilin, and other prefecture-level cities. Nanning recorded the largest absolute expansion, followed by secondary centres, including Liuzhou, Guilin, Yulin, Wuzhou, Fangchenggang, Qinzhou, and Beihai, whereas western and northern Guangxi expanded more slowly. The system-GMM results indicate that financial deepening has a marginally significant positive effect on built-up area expansion and fiscal pressure has a marginally significant constraining effect, both at the 10% level; land finance dependency does not emerge as an independent driver in this small panel. We interpret these findings through a Source–Channel–Valve framework, in which financial deepening provides the capital source, land finance represents a hypothesised institutional channel, and fiscal pressure acts as a regulatory constraint. The study provides empirical evidence for sustainable and regionally coordinated urban development in Guangxi and comparable geographically constrained regions. Full article

Port State Control (PSC) inspections play a critical role in enforcing international maritime safety and environmental standards, yet little is known about how compliance behaviour interacts with economic cycles. This study examines the relationship between vessel detentions and freight market conditions using monthly data from the Paris and Tokyo Memoranda of Understanding (MoUs) over the period 2010–2021. A system of simultaneous equations is estimated using the Generalized Method of Moments (GMM) and Three-Stage Least Squares (3SLS) to address the bidirectional relationship between detention activity and freight market conditions, proxied by the Baltic Dry Index (BDI) and, for tanker specifications, the Baltic Dirty Tanker Index (BDTI). The results are consistent with a positive and statistically significant bidirectional relationship: vessel detentions increase during periods of strong freight market conditions, while past detentions are positively associated with freight rates, a pattern consistent with a signalling and sentiment channel. Institutional factors, including flag state quality, classification society affiliation, and ISM-related deficiencies, are also found to significantly influence detention risk, though their direction and magnitude vary across MoUs and vessel segments. These findings are consistent with the presence of opportunistic incentives during economic upswings, challenging the conventional expectation that stronger market conditions promote higher compliance. The study contributes to the literature by linking regulatory compliance with economic cycles and highlighting the importance of adaptive, risk-based enforcement strategies. It is important to note, however, that the aggregate nature of the data does not permit direct identification of firm-level behavioural mechanisms, and the findings should be interpreted as associational evidence consistent with these theoretical mechanisms. Full article

Existing soil and water conservation standards suffer from fragmentation, inconsistent cross-referencing, and limited machine interpretability, hindering efficient regulatory compliance and decision making. To address these challenges, we developed SwacGPT, an intelligent system that integrates domain-specific knowledge graph construction with large language models for enhanced standard interpretation and reasoning. Specifically, we constructed a domain-specific knowledge graph (SwacKG) using a hybrid approach that combines rule-based templates with a pre-trained BERT-based model. This graph systematically organizes conservation standards via multi-dimensional semantic relationships, with 87.8% entity extraction precision and 84.9% relation extraction precision, enabling precise data association across heterogeneous regulatory sources. SwacGPT leverages both the graph-structured knowledge from the SwacKG and original textual content to provide intelligent reasoning capabilities. For rigorous validation, a comprehensive evaluation dataset comprising both objective and subjective questions was designed. Experimental results show that SwacGPT achieves scoring rates of 78.67% on single-choice questions, 81.65% on multiple-choice questions, and 80.5% on subjective short-answer questions, ranking the best among the other five evaluated models. This demonstrates that the synergistic integration of domain-specific KGs with tailored LLMs creates an effective solution for intelligent environmental governance, providing critical decision support for land space optimization and cross-jurisdictional coordination in sustainable land management. Full article

Hemp (Cannabis sativa L.) is a multipurpose crop with significant industrial and therapeutic potential. This article reviews the various uses of hemp in production, building, food, cosmetics and medicine, focusing on its economic, environmental and health benefits. Industrially, hemp has been used for making fabrics, paper, bioplastics, construction materials and biofuels, because of its strong fibres, fast growth and low impact on the environment. Hemp seed oil and protein in the food and beauty industries are gaining more recognition for their nutritional and functional characteristics. Medically, compounds extracted from hemp, especially cannabidiol (CBD) and other non-psychoactive phytochemicals, have been shown to possess significant anti-inflammatory, pain-relieving, neuroprotective, antioxidant and antibacterial properties. This article talks about how better cultivation methods, processing technologies, and extraction techniques can help improve product quality, marketability, regulatory frameworks, safety standards and the quality control measures that are in place to monitor hemp production and utilization, as well as the focus on new policies in developing nations. Even though hemp has a wide range of potentials, the industry still faces difficulties in the form of laws, lack of infrastructure, unequal product standardization, and lack of scientific proof in certain areas of application. This article further identifies research gaps and points out potential areas for innovation, policymaking, and market development to be explored in the future. If backed up by proper regulations and research, hemp has great potential to contribute to the development of environmentally friendly industries, the improvement of public health and the socio-economic upliftment of communities. Full article

This paper examines the integration of three-dimensional (3D) printing and robotic fabrication in contemporary architectural design, with a focus on overcoming the technical limitations that constrain large-scale adoption. While additive manufacturing enables the production of complex geometries and customized structures, its standalone application remains limited by fixed build volumes, planar deposition, lack of tensile reinforcement, open-loop process control, and single-process extrusion. To address these constraints, the paper proposes a functional integration framework that systematically maps robotic fabrication capabilities onto these five critical limitations. Evidence from recent studies demonstrates that such integration has already led to measurable advances, including up to a 90-fold increase in printable volume through mobile robotic systems, robotically fabricated reinforcement systems (e.g., Mesh Mold) achieving post-crack behavior comparable to conventional reinforced concrete, and the implementation of closed-loop sensor-based process control to enhance interlayer bonding. Despite these achievements, interdisciplinary collaboration across architecture, structural engineering, materials science, and robotics remains largely fragmented and is predominantly confined to academic and pilot-scale projects, such as the ETH Zurich DFAB House. Regulatory progress is also limited, with only isolated code-compliant implementations under frameworks such as ICC-ES AC509 and ISO/ASTM 52939. Persistent barriers including high capital costs, loss of information in BIM-to-fabrication workflows, anisotropic material behavior, and the absence of long-term durability standards continue to restrict widespread adoption. These findings suggest that advancing robotic additive manufacturing in architecture requires not only technological innovation but also coordinated cross-disciplinary integration, standardized testing protocols, and harmonized regulatory frameworks. Full article

Mesenchymal stromal cells (MSCs) are multipotent cells characterized by their regenerative capacity and strong immunomodulatory properties. In recent years, MSC-based therapy has attracted significant attention as a potential treatment for a wide range of immune-mediated and degenerative diseases. The therapeutic effects of MSCs are primarily mediated through paracrine signaling and secretion of cytokines that regulate immune responses and promote tissue repair. This review focuses on five key cytokines involved in MSC immunomodulation: interleukin-6 (IL-6), interleukin-10 (IL-10), transforming growth factor-beta (TGF-β), tumor necrosis factor-alpha (TNF-α), and interleukin-1 beta (IL-1β). These cytokines interact within a complex signaling network that allows MSCs to suppress excessive inflammation and restore immune balance. The role of MSC therapy is examined in several clinically relevant conditions, including systemic lupus erythematosus, systemic sclerosis, ischemic stroke, spinal cord injury, diabetes mellitus, and female infertility. Across these diseases, MSCs demonstrate the ability to inhibit pro-inflammatory immune cell activity, promote regulatory immune phenotypes, reduce oxidative stress, and stimulate regeneration through the secretion of growth factors and extracellular vesicles. Despite promising experimental and early clinical findings, several limitations remain, including variability in MSC sources, limited cell survival after transplantation, and the need for optimized dosing strategies. Overall, MSC therapy represents a multifunctional therapeutic approach combining immunomodulation, anti-inflammatory activity, and regenerative support. Further research is required to better understand cytokine interactions, improve standardization of MSC-based treatments, and enhance clinical efficacy across diverse pathological conditions. Full article

Mycogenic nanomaterials, nanoparticles (NPs) biosynthesized through fungal enzymatic and metabolic activity, have emerged as a compelling alternative to chemically synthesized nanomaterials, offering fundamental biocompatibility, green production conditions, and biologically functional surface coatings. Fungi, acting as natural “nanofactories,” harness reductases, oxidoreductases, secreted proteins, and secondary metabolites to reduce metal ions into stable NPs under ambient conditions, simultaneously capping the particles with biomolecules that enhance colloidal stability, biocompatibility, and secondary biological activity. Unlike previous reviews that have addressed either biosynthesis mechanisms or applications in isolation, this review uniquely adopts a structured “Promise vs. Barrier” framework across six interconnected thematic pillars, offering the first comprehensive critical synthesis that simultaneously maps mechanistic frontiers, biodiversity gaps, and translational barriers within mycogenic nanotechnology. The present review critically examines both the extraordinary promise and the persistent barriers facing mycogenic nanotechnology across biosynthetic mechanisms, fungal biodiversity, nanomaterial portfolio expansion, biomedical applications, environmental and agricultural utility, and industrial scalability. We highlight how emerging multiomics approaches, integrating transcriptomics, proteomics, and metabolomics, are beginning to decode the molecular blueprints of fungal NP synthesis, while acknowledging that mechanistic knowledge gaps, limited genetic toolkits for non-model fungi, and the absence of standardized protocols continue to impede progress. The fungal kingdom represents a vast, underexplored reservoir of nanofactory potential, with fewer than 1% of known species evaluated to date; strategic bioprospecting using genome mining and machine learning is beginning to unlock this diversity. Mycogenic NPs demonstrate broad-spectrum antimicrobial activity against multidrug-resistant pathogens, selective anticancer activity, biosensing capacity, and applications in wound healing, sustainable agriculture, environmental remediation, and smart food packaging. However, critical deficits persist in clinical validation, long-term toxicity data, manufacturing reproducibility, and regulatory clarity. The review concludes with a tiered roadmap, spanning immediate mechanistic priorities through to long-term synthetic biology and AI-integrated commercialization, and calls for coordinated international action on standardization, reference material development, and harmonized regulatory frameworks to bridge the gap between laboratory promise and real-world application. Full article

Deep learning models are widely used in autonomous vehicle systems for perception, localization, and decision-making. However, their lack of transparency poses significant challenges in safety-critical environments. This systematic review presents a unified mathematical framework for explainable deep learning which integrates multimodal inputs, graph-theoretic road geometry, uncertainty modeling, and intrinsically interpretable representations. Road-structured priors that include lane topology and spatial constraints are incorporated into learning and optimization processes for ensuring model predictions and explanations to remain physically and semantically grounded. The review synthesizes methods across saliency-based, concept-based, causal, and intrinsic explainability, and extends them to vision-language models. This enables language-grounded, human-interpretable reasoning in autonomous vehicle systems. While vision-language models offer a new paradigm for semantic explainability, their limitations such as hallucinations, misgrounding, and reduced reliability under distribution shifts are also critically examined. Along with the role of road priors in improving alignment and robustness, another key contribution of this work is its quantitative evaluation metrics for road-aware explainability. These evaluation metrics link the explanations to spatial consistency, uncertainty alignment, and graph-structured reasoning. The overall framework connects latent representations, predictions, and explanations within a single formulation, enabling systematic comparison and analysis across models. Based on a PRISMA-guided review of 164 studies, this research identifies gaps in real-world reliability, temporal reasoning, and standardized evaluation, and outlines future directions including human-in-the-loop systems, regulatory readiness, and language-based auditing. Overall, this study advances a mathematically grounded and road-aware perspective on explainable vehicle AI which significantly bridges the gap between high-performance models and transparent, trustworthy autonomous systems. Full article

Background/Objectives: The evaluation of cognition is central to many neurological conditions, including traumatic brain injury, Alzheimer’s disease, Lewy body disease, frontotemporal degeneration, and vascular disorders. In clinical practice, particularly in aging populations, cognitive complaints often arise in the context of mixed neurological processes, requiring careful integration of cognitive and non-cognitive findings. Despite this, there remains limited clarity regarding the respective roles of neurologists and clinical neuropsychologists and the distinction between cognitive and neuropsychological assessments, terms that are often used interchangeably despite important differences in methodology and scope. This lack of a shared framework has practical consequences. Cognitive test results, when interpreted in isolation for diagnosis, may be misconstrued as comprehensive measures of brain function, particularly when non-cognitive neurological features such as motor, cerebellar, or vestibular abnormalities should have been considered (but were not). Methods: In this narrative review, we synthesize clinical guidelines, consensus statements, regulatory sources, and representative empirical literature to articulate a competence-based framework in which cognitive assessment is a medically integrated process incorporating history, functional evaluation, neurological examination, and the targeted use of standardized neuropsychological instruments. Results: Neurologists are trained to establish medical diagnoses and integrate cognitive findings into the context of neurological disease, while neuropsychologists contribute detailed psychometric characterization, culturally and demographically informed interpretation, cognitive phenotyping, functional characterization, and validity assessment in complex clinical and medicolegal contexts. Although neuropsychologists are qualified to diagnose neurocognitive disorders using standardized diagnostic criteria, attribution to specific neurological etiologies requires a comprehensive medical evaluation that extends beyond cognitive testing alone. Conclusions: We outline a tiered approach to evaluation that aligns assessment methods with clinical questions and supports accurate diagnosis, interdisciplinary collaboration, and patient-centered care. Full article

The energy transition is rapidly increasing the penetration of inverter-based resources (IBRs), thereby reducing the share of conventional directly grid-connected synchronous generation in modern power systems. In scenarios with very high shares of IBRs, potentially reaching 100% inverter-based operation, key features that have traditionally guaranteed power system stability and security, such as inertia, short circuit strength, fault response, and damping of oscillations, are significantly changing. This review paper examines the main challenges of operating and planning power systems with a high penetration of inverter-based resources. These challenges are grouped into three main areas: (i) technical issues, including frequency and voltage stability, system strength, fault behavior, control interactions and oscillations; (ii) regulatory issues, such as the evolution of grid codes, ride-through requirements, grid-forming specifications and testing, compliance assessment, and model validation; and (iii) market issues, focusing on how non energy services, like synthetic inertia, damping, voltage support, and stability services, are defined, measured, and procured. The paper discusses the compromises between system performance, implementation costs, and overall system robustness, relying on lessons learned from existing specifications and international standards. Finally, it outlines key research needs and provides recommendations for developing coherent technical requirements and market mechanisms to support the reliable operation of inverter-dominated power systems. Full article

This study evaluates the adoption and efficacy of the 5th Dimension Building Information Modelling (5D-BIM) as a cost dimension for mega rail projects, extending the discussion beyond just technological implementation to consider broader policy and practical implications. The purpose of this article is to understand the governance context of 5D-BIM implementation for rail and transport projects and evaluate the effectiveness of the 5D-BIM framework as currently applied by conducting semi-structured interviews with key stakeholders. Drawing on semi-structured interviews with 22 stakeholders across government, industry, and technology providers, the research examines current 5D-BIM practices. While the primary focus of the research is 5D BIM implementations within the state of Victoria, Australia, which is currently experiencing a surge in rail projects, interviews were also conducted with additional stakeholders from international rail projects for context. The findings reveal fragmented adoption, varying levels of organisational maturity, and significant policy and implementation gaps, particularly in the role of government as the primary client of transport infrastructure. The results of the interviews emphasise the centrality of government and regulatory context in driving the adoption and implementation of 5D-BIM as the primary client of transportation infrastructure and identify actionable recommendations for policymakers and practitioners towards a more integrated approach to 5D-BIM in mega rail projects. While 5D-BIM demonstrates clear benefits in enhancing cost estimation, coordination, and decision-making, its effectiveness is constrained by the absence of clear standards, limited BIM literacy, and inconsistent regulatory guidance. This study provides one of the first empirical validations of the 5D-BIM governance framework, demonstrating that its success is driven less by technological capability and more by policy alignment, standardisation, and institutional leadership. Full article

This study investigates the determinants of economic growth in the Next-11 economies over the period 1996–2024, with particular emphasis on the roles of structural, institutional, and human capital factors. Using a comprehensive panel dataset for eleven emerging economies, the analysis employs three robust estimation techniques—Driscoll–Kraay Standard Errors (DKSEs), Feasible Generalized Least Squares (FGLSs), and Panel-Corrected Standard Errors (PCSEs)- to address common econometric issues such as heteroskedasticity, serial correlation, and cross-sectional dependence. The empirical results reveal that industrial output, energy consumption, human capital, institutional quality, and foreign direct investment significantly contribute to economic growth. Among these factors, industrial output and energy consumption exhibit particularly strong and consistent positive effects across all estimation methods, highlighting the importance of structural transformation and energy availability in supporting economic expansion. In contrast, trade openness shows a negative and statistically significant relationship with economic growth in most model specifications, suggesting that structural constraints, import dependence, and limited domestic productive capacity may restrict the growth benefits of external integration in these economies. The study also explores the conditional effects of foreign direct investment through interaction terms with human capital and institutional quality. The findings indicate that the growth-enhancing impact of foreign investment depends significantly on domestic absorptive capacity, particularly the availability of skilled labor and effective governance structures. These results emphasize the importance of complementary policies aimed at strengthening education systems, improving institutional quality, and enhancing regulatory effectiveness. From a policy perspective, the findings suggest that the Next-11 economies should prioritize industrial development, energy infrastructure expansion, human capital investment, and institutional reforms to maximize the benefits of globalization and foreign investment. Overall, the study contributes to the literature by providing robust empirical evidence on the interconnected roles of structural, institutional, and human capital factors in shaping economic growth in emerging economies. Full article