Search Results (8,883)

The progressive integration of Whole Slide Imaging (WSI) technology and Artificial Intelligence (AI) architectures is driving a structural transformation in pathology and precision oncology. This structured critical review analyzes and systematizes the impact of this technological transition along two fundamental operational dimensions of the modern biopharmaceutical industry: pre-registration Clinical Research and post-launch strategies governed by Medical Affairs. The first section explores how computational pathology is improving efficiency and reducing risk in drug development. Replacing analog visual assessment—intrinsically subject to inter-observer and intra-observer variability—with quantitative algorithms for cellular classification and segmentation enables optimization of patient recruitment in clinical trials, reducing screening failure rates. This review also examines the emerging role of Spatial Biology in extracting complex topological metrics from the Tumor Microenvironment (TME) and the use of AI for the objective and auditable quantification of critical surrogate endpoints, such as Pathological Complete Response (pCR), while acknowledging that algorithmic precision remains sensitive to pre-analytical variables and dataset biases. In the second section, the study investigates the strategic evolution of Medical Affairs, acting as a vital scientific communication and translational bridge between the complexity of Data Science and clinical hospital practice. Challenges related to AI adoption by clinicians are examined, emphasizing the importance of educational programs based on Explainable AI (XAI) to overcome the cognitive limitations of the black-box paradigm and the complex regulatory validation pathway for Software as a Medical Device (SaMD) under the stringent European IVDR framework—supported by an analysis of historical regulatory benchmarks such as the Paige Prostate case. The paper also explores the potential of AI in the large-scale generation of Real-World Evidence (RWE), applied to the creation of synthetic control arms in pharmacoeconomic settings. In conclusion, the study highlights that the diagnostic algorithm has ceased to be merely a laboratory support tool and has become a strategic asset and an integral adjunct to therapeutic decision-making. Overcoming current challenges related to data privacy through Federated Learning architectures, together with the imminent transition toward Foundation Models, foreshadows a fully data-driven healthcare ecosystem, making continuous skills development (digital upskilling) an essential requirement for professionals in the biopharmaceutical sector. Full article

The obligate mixotroph Mesodinium rubrum significantly impacts coastal ecosystems, yet its population control in oligotrophic waters remains unclear. Integrating field observations from Coast of Sanya (South China Sea) with laboratory nutrient manipulation, we investigated how dissolved inorganic nutrients and prey availability regulate cell cycle progression, using biovolume as a proxy for cycle transitions. Nutrient starvation arrested cells at the small (newly divided) stage. Inorganic replenishment triggered rapid somatic growth and consistent diel biovolume oscillations, expanding in light and shrinking in darkness. However, without cryptophyte prey, cells failed to progress beyond the medium (actively growing) stage and could not accumulate into the large (pre-division) size class, revealing a decoupled regulatory mechanism. Dissolved inorganic nutrients drive cell size expansion (somatic growth), whereas prey-derived organelles serve as a critical prerequisite for division. Field data confirmed that the virtual absence of cryptophytes in Sanya waters restricts M. rubrum to consistently low levels. Our findings demonstrate that population dynamics of this specialist mixotroph transcend traditional nutrient-driven paradigms, underscoring the irreplaceable role of prey in sustaining photosynthetic metabolism and triggering population expansion in oligotrophic systems. Full article

Purpose: Marking the one hundredth anniversary of quantum mechanics in 2025, quantum science has become foundational for the development of contemporary technologies, enabling advances in sensing, imaging, computing, and materials engineering. Cancer radiotherapy, although traditionally developed within the scope of classical dosimetric models and phenomenological biological frameworks, is fundamentally initiated by quantum-mechanical radiation-matter interactions. Radiation-induced DNA damage, which ultimately determines therapeutic effectiveness, originates from primary quantum-mechanical processes involving particle transport, electronic excitation and ionisation, followed by successive physicochemical and chemical stages including water radiolysis and radical formation. As scientific disciplines undergo a rapid “quantum transition,” radiation cancer treatment is increasingly positioned to benefit from deeper integration of quantum principles and emerging quantum technologies. Methods: This review examines how quantum mechanics governs the primary radiation-matter interactions that initiate the physical, physicochemical, chemical, and ultimately biological stages of radiation action at the (sub)cellular level, with particular emphasis on track structure, water radiolysis, DNA damage induction, and multiscale biological response. Contemporary approaches to DNA damage modeling are discussed, including track-structure Monte Carlo methods, nanodosimetric frameworks, and multi-scale simulation approaches that connect microscopic interaction events with biological outcomes. Key quantum concepts relevant to radiation therapy are outlined, together with emerging quantum technologies such as nanoscale quantum sensing, quantum lasers, quantum dots, and quantum computing, which are evaluated for their potential roles in dosimetry, imaging, treatment planning, and radiation transport simulations. In this context, artificial intelligence (AI) is considered a complementary tool to accelerate computation and integrate quantum-informed data across multiple scales. Results: The review highlights that quantum-informed modeling enables a more consistent description of radiation-induced processes across spatial and temporal scales, linking microscopic interaction mechanisms to DNA damage formation and macroscopic biological outcomes. Recent advances in track-structure and radiobiological modeling provide new opportunities for improving predictions of radiation effects and treatment response. Emerging quantum technologies show potential to enhance measurement sensitivity, improve simulation efficiency, and enable more precise control of radiation delivery. Furthermore, AI-assisted approaches facilitate the extraction of predictive patterns from complex datasets, supporting faster and more accurate estimation of biological endpoints such as DNA damage and cell survival. Conclusions: The quantum aspects of advanced treatment modalities, including proton and heavy-ion therapy, ultrafast radiation delivery, and the FLASH effect, as well as future concepts such as laser-plasma-driven and coherence-informed radiotherapy systems, indicate a promising direction for next-generation cancer treatment. By critically assessing both opportunities and limitations, this work provides a coherent framework for integrating DNA damage modeling, quantum principles, quantum-inspired techniques, emerging quantum technologies, and advanced computational tools to guide future developments in radiation oncology. Full article

The global energy transition and rapid electrification are driving increased demand for copper. However, conventional pyrometallurgical and hydrometallurgical extraction routes are increasingly challenged by declining ore grades and stricter environmental regulations. Bioleaching involves the microbial catalysis of sulfide mineral dissolution and provides a sustainable method for copper recovery from low-grade ores, tailings and secondary resources. This review provides a critical and integrated analysis of copper sulfide bioleaching, covering microbial diversity, molecular mechanisms, mineralogical controls, operational parameters, and industrial applications. This review also examines the functional roles of prominent acidophiles, including the functional roles of prominent acidophiles, including Acidithiobacillus spp., Leptospirillum spp. and thermophilic archaea, in the oxidation of iron and sulfur, mitigation of passivation, and metal solubilization. The molecular underpinnings of these processes are explored by investigating iron and sulfur oxidation gene networks (the rus operon and sox cluster), copper resistance systems (CopA, CusCBA) and biofilm formation pathways. The mineralogical controls on the behavior of chalcopyrite (refractory/passivating), chalcocite (highly reactive) and bornite (intermediate) are critically assessed. The synergistic effects of key operational parameters (temperature, pH, redox potential, aeration and particle size) on leaching kinetics and microbial community dynamics are investigated. The scalability, efficiency and environmental footprint of industrial applications such as heap, dump, stirred-tank and in situ bioleaching are discussed. Despite more than four decades of commercial development, several challenges remain, such as slow chalcopyrite dissolution, passivation, metal toxicity, and scale-up limitations. Emerging solutions such as synthetic microbial consortia, multi-omics technologies, artificial intelligence-assisted optimization, and digital twins are identified as transformative approaches for next-generation biomining. In this review, microbiology, mineralogy, electrochemistry, and process engineering are integrated to demonstrate that biotechnological leaching is among the most promising technologies for the sustainable production of copper and to identify future directions for its industrial application. Full article

Estuarine turbidity fronts are narrow transition zones where suspended particulate matter concentrations change sharply. Their detection from remote sensing imagery remains challenging because conventional methods rely on empirical thresholds, are sensitive to mixed pixels, and often lack transferability and physical interpretability. Here, we evaluate the potential of foundation-model representations by integrating AlphaEarth 64-dimensional embeddings with Sentinel-2 spectral and multi-scale spatial features. A 229-dimensional feature set is constructed and fed into a two-step framework combining random forest classification with an empirically calibrated optical mask based on low red-band reflectance. The fused feature set achieves an overall accuracy of 91.2%, an F1 score of 87.5%, and a Kappa coefficient of 0.807, outperforming both spectral–spatial features alone and AlphaEarth embeddings alone. To elucidate the contribution mechanism of AlphaEarth embeddings, we conduct two complementary SHAP analyses: one evaluating each dimension’s direct contribution to front classification, and the other assessing its capacity to predict Sentinel-2 band reflectance. Only nine dimensions overlap between the respective top 20 lists, revealing a clear functional division within the embedding space—some dimensions primarily encode spectral reflectance information, while others encode spatial context, edge patterns, or topological structures that are not directly accessible from local spectral features. This division represents the added value of AlphaEarth beyond conventional optical data. The empirically calibrated optical mask reduces candidate frontal area by 59.09% in turbid estuaries and restores linear front morphology. However, leave-one-estuary validation yields F1 scores ranging from 0.33 to 0.84, substantially below the within-estuary score of 0.93, demonstrating limited cross-region transferability and challenging the assumption of domain invariance in foundation-model embeddings. These findings highlight both the value of fusing foundation-model representations with local spectral–spatial features and the critical need for domain-adaptation strategies to improve generalization across contrasting estuarine hydrodynamic regimes. Full article

The emergence of biological membranes was a critical step in the origin of cellular life because compartmentalization enabled molecular concentration, selective interactions, and increasingly complex chemical evolution. While fatty acids are widely considered the primary constituents of primitive membranes, the origin of the hydrophilic molecular scaffolds required for more stable amphiphilic systems remains unresolved. In this review, we propose a new conceptual framework in which low-molecular-weight polyols—including ethylene glycol, glycerol, tetritols, and related sugar alcohols—served as key molecular intermediates linking abiotic carbohydrate chemistry with the emergence of proto-lipids and protomembranes during a pre-phosphate stage of Earth history. Experimental and theoretical studies indicate that abiotic carbon chemistry can generate abundant polyols capable of esterification, etherification, hydrogen bonding, and reversible complexation with borate species. We hypothesize that borate-mediated stabilization of sugars and polyols promoted molecular selection, while sulfur-rich geochemical environments supplied chemically diverse amphiphiles and redox-active reaction networks. Building upon these observations, we propose a pH-dependent evolutionary model in which acidic sulfur-rich environments favored sulfo-protolipids, near-neutral environments promoted mixed polyol–fatty acid membranes, and alkaline boron-rich systems facilitated borate-associated amphiphiles and dynamic supramolecular membrane organization. We further suggest that borate-cross-linked polyol hydrogels acted as transitional soft-matter systems connecting molecular synthesis, membrane self-assembly, compartmentalization, and the emergence of proto-informational assemblies. Modern glycolipids, sulfolipids, archaeal ether lipids, and calditol-containing tetraether membranes are discussed as structural analogues, rather than direct evolutionary descendants, supporting the chemical versatility of polyol-based membrane architectures. Although the proposed evolutionary framework remains hypothetical, it integrates current knowledge from prebiotic organic chemistry, membrane biophysics, boron coordination chemistry, sulfur geochemistry, and systems chemistry into a unified and experimentally testable model for the evolution of proto-lipids, protomembranes, and early protocellular organization. Full article

The worsening jobs–housing imbalance in high-density urban areas has become increasingly prominent. However, existing studies predominantly focus on the city and regional scales, with limited attention to small-scale spatial units, failing to provide empirical evidence for refined regulation of transport and land use. As a typical compact development mode at the small scale, Transit-Oriented Development (TOD) offers an ideal spatial unit for examining micro-level jobs–housing relationships. However, its influence on jobs–housing balance remains unclear. This study takes 72 built subway station areas in Shenzhen’s highest-density built-up area as the research object. Employing quadratic polynomial regression and XGBoost-SHAP methods, it reveals the effects of transportation supply and land use on the employment–residential ratio, as well as threshold effects and indicator synergies. The main findings are as follows: (1) Transportation supply (Node), land use (Place), and TOD degree (TODness) all exhibit U-shaped effects on the employment–residential ratio, with inflection points at Node = 0.330, Place = 0.412, and TODness = 0.776, identifying the critical transition from balance to employment polarization. (2) Number of subway directions is the most critical indicator, together with betweenness centrality, floor area ratio, population size, and land use entropy, each exhibiting notable threshold effects. (3) A significant interaction effect exists between betweenness centrality and land use entropy, suggesting that traffic network connectivity and land use evenness can jointly regulate the employment–residential ratio in both directions. This study provides a quantitative basis for phased regulation of jobs–housing relationships in high-density TOD station areas, with findings transferable to other high-density cities. Full article

Rock glaciers are critical indicators of periglacial environments and the spatial distribution of mountain permafrost. Given their complex deformation patterns and temporal variability, which may indicate progressive destabilization, a quantitative evaluation of their kinematic activity is critical from both climatological and geohazard perspectives. This study applies Persistent Scatterer Interferometric Synthetic Aperture Radar (PSInSAR) to Sentinel-1 radar imagery on both ascending and descending orbits, in order to detect and map moving areas (MA) within the Pirin Mountains (Bulgaria). The primary objective of this study is to update the existing rock glacier inventory (RoGI) by integrating high-resolution Line-of-Sight (LOS) velocity data in accordance with the latest international standards established by the Rock Glacier Inventories and Kinematics (RGIK) standing committee. A secondary objective is to investigate the spatial relationships between the identified moving areas (MAs) and other surrounding geomorphological features (e.g., talus slopes), hence providing a wider context for slope dynamics and landform evolution. The results identified MAs with PSInSAR-derived Line-of-Sight (LOS) velocities reaching up to 10 cm yr⁻¹, which were subsequently classified according to RGIK kinematic categories. A substantial proportion of the detected moving areas occur outside mapped rock glacier boundaries and may reflect a range of geomorphological processes, including permafrost-related creep, talus creep, or other forms of slope deformation. The LOS velocity data were used to assess the activity status of 74 rock glacier units within the regional inventory, classifying 8 as transitional (velocity exceeding 1 cm yr⁻¹) and 66 as relict. Furthermore, we analyse the spatial distribution of these moving areas in relation to primary topographic variables, such as elevation, aspect, and slope. The results highlight the influence of topographic control factors and rock glacier dynamics and provide new insights into the distribution of active periglacial landforms and terrain potentially affected by permafrost in the Balkan Peninsula under changing climatic conditions. Full article

The kallikrein–kinin system (KKS) and the kallikrein-related peptidase (KLK) family are interconnected proteolytic networks that regulate inflammatory signaling, vascular permeability, extracellular matrix remodeling, and tissue barrier dynamics. Beyond their classical vasoactive and inflammatory functions, accumulating evidence indicates that kinin peptides, including bradykinin, Lys-bradykinin, and their des-Arg⁹ metabolites, together with selected KLKs, modulate cell–cell and cell–extracellular matrix adhesion. Through B1 and B2 kinin receptor activation, the KKS influences endothelial adhesion molecule expression, leukocyte integrin activation, neutrophil trafficking, focal adhesion kinase/Src signaling, cytoskeletal remodeling, and matrix metalloproteinase activity. In parallel, KLKs directly reshape the adhesive microenvironment by cleaving junctional proteins, including E-cadherin and desmosomal components, and extracellular matrix substrates such as fibronectin, laminin, vitronectin, fibrinogen, and collagens. These coordinated actions affect epithelial and endothelial barrier integrity, leukocyte transmigration, angiogenesis, fibrosis, epithelial–mesenchymal transition, tumor cell migration, invasion, and metastatic dissemination. This review critically summarizes current evidence linking KKS and KLK activity to adhesion-dependent processes in inflammation and cancer, emphasizing how proteolytic signaling may either preserve tissue homeostasis or promote pathological barrier disruption depending on cellular context, receptor expression, protease activity, and microenvironmental cues. Understanding these mechanisms may refine the identification of adhesion-related biomarkers and support the development of targeted therapeutic strategies for inflammatory disorders, fibrotic remodeling, and cancer progression. Full article

Undular bores are classical shallow-water phenomena in which a sharp transition between two flow states is replaced, in a dispersive theory, by an oscillatory wave train. In non-dispersive shallow-water theory, the bore is associated with an apparent loss of mechanical energy. In dispersive models, this energy can be interpreted as being redistributed into the oscillatory tail. The aim of this short article is to formulate a possible extension of this interpretation when surface tension is included. The capillary contribution modifies the long-wave dispersion coefficient through a Bond-number-dependent term and adds an additional surface energy to the total energy functional. We derive the basic capillary-gravity KdV scaling, identify the modified energy density, and discuss how surface tension may affect the amplitude, wavelength, and energy flux of the trailing oscillations. The proposed direction is relevant for small-scale laboratory bores, tidal-bore fronts, and shallow tidal currents in which a rapid transition generates short dispersive oscillations. Special attention is paid to the critical value $Bo=1/3$, where the classical KdV dispersion vanishes, and a fifth-order correction is required. Full article

The rapid evolution of Urban Air Mobility (UAM) necessitates high-performance morphing structures capable of seamless transitions between flight and ground modes. This research presents a rigorous structural optimization framework for a wing deployment mechanism, addressing the critical challenge of minimizing stress concentrations in cantilevered revolute joints. To overcome the computational prohibitive cost of traditional multibody dynamics, a Generalized Spectral Surrogate Model (GSSM) is introduced. This novel approach maps the mechanism’s geometric parameters to its kinetic response using polynomial-modulated Fourier series, reducing the evaluation time of ${10}^5$ design configurations from 4.2 h to merely 0.8 s while maintaining a determination coefficient $R^2>0.995$. Comparative analysis demonstrates that the GSSM outperforms Artificial Neural Networks and Kriging models in capturing periodic kinematic boundaries without spurious local minima. Through a weighted topological analysis, the study identifies a global optimum ($L_2=0.5$ m, ${\theta }_2=64.2^{\circ }$) that effectively shunts 70.3% of the aerodynamic load to the robust vehicle chassis. The proposed solution deviates from the theoretical unconstrained minimum by only 0.24%, providing a validated mathematical basis for the rapid synthesis of reliable aerospace mechanisms. Full article

The modern agricultural sector faces a multidimensional crisis, mainly consisting of an aging workforce, labor shortages, exhausting working conditions, and high rates of work-related accidents. In response, the authors carried out a systematic literature review (SLR), to explore the latest research on smart agricultural technologies, their effects on occupational safety, ergonomics, and worker health, and pinpoint obstacles to sustainable adoption. A thorough search was performed solely in the Scopus database, covering peer-reviewed publications from 2020 to 2026, strictly following the PRISMA 2020 guidelines. Based solely on Scopus, this study provides a focused synthesis, with the results suggesting that hazards such as chemical exposure and musculoskeletal strain are significantly reduced with the use of innovations such as unmanned vehicles, exoskeletons, and collaborative robots. These technologies also show great promise in cutting down resource waste, helping farmers practice sustainable agriculture. However, a recurring gap between research and real-life deployment exists, as adoption is hindered by cost considerations, reliability issues, and ergonomic problems. To achieve a sustainable technological transition in agriculture, it is necessary to simultaneously bridge three critical gaps: technological (ensuring robust field performance), ergonomic (design and testing processes based on real end-users and their needs), and socio-economic (addressing adoption barriers). Full article

Metastasis-associated protein 1 (MTA1), an oncogenic transcriptional regulator, is overexpressed in canine urothelial carcinoma (UC) and is associated with aggressive clinicopathological features. However, its functional role and molecular mechanisms in canine UC remain poorly understood. Here, we investigated the contribution of MTA1 to epithelial-to-mesenchymal transition (EMT) and its interaction with BRAF signaling. MTA1 silencing in two canine UC cell lines significantly inhibited cell proliferation, cell survival, migration, and xenograft tumor growth. Mechanistically, MTA1 knockdown reduced the expression of MTA2, MTA3, and COX2, while producing unexpected changes in key EMT regulators, including Snail, Slug, and Cyclin D1, suggesting the activation of compensatory signaling pathways. MTA1 silencing also decreased mutant BRAF expression in AxA cells while increasing wild-type BRAF expression in SH cells, indicating context-dependent regulation of BRAF signaling. In AxA cells, reduced AKT phosphorylation following MTA1 knockdown further supports functional crosstalk between the BRAF and MTA1/AKT signaling pathways. Collectively, these findings identify MTA1 as a critical regulator of canine UC progression and reveal complex signaling interactions that support its potential as a therapeutic target for canine UC. Full article

The building sector is one of the largest contributors to global energy demand and carbon emissions, making the transition to net-zero buildings (NZBs) a critical component of climate change mitigation strategies. However, the persistent building energy performance gap (BEPG), defined as the discrepancy between predicted and actual energy consumption, continues to hinder the achievement of net-zero operational performance. Accordingly, this review examines the role of artificial intelligence (AI) in enabling NZBs through the integration of energy-efficient building systems, renewable energy technologies, and intelligent operational control. A comprehensive review of the literature published between 2018 and 2025 was conducted, focusing on three complementary domains: heating, ventilation, and air conditioning (HVAC) system efficiency as the demand-side pillar, renewable energy integration as the supply-side pillar, and AI as the enabling layer connecting both domains. Synthesis of the reviewed literature reveals that demand-side HVAC technologies achieve energy savings ranging from 20% to 67%, while supply-side renewable energy integration increases photovoltaic (PV) self-consumption by 11–13%. Furthermore, AI-driven optimization, particularly through reinforcement learning (22.3% ± 8.4% energy savings) and digital twins (up to 70% renewable energy utilization), substantially enhances building performance within integrated energy management frameworks. The reviewed studies further demonstrate that AI techniques, including machine learning, deep learning, reinforcement learning, and digital twins, enable accurate energy forecasting (R² > 0.90), intelligent operational control, and effective coordination of integrated PV–battery energy storage system–electric vehicle systems, improving building energy flexibility and reducing grid fluctuations by up to 12.78%. Despite these advances, challenges related to data quality, interoperability, model explainability, cybersecurity, and limited large-scale real-world validation remain significant barriers to widespread adoption. Overall, the evidence indicates that AI serves as a key enabler for reducing the BEPG and improving the reliability, resilience, and operational efficiency of NZBs, thereby supporting the transition toward intelligent, low-carbon built environments. Full article

This study investigates how institutional quality conditions the relationship between energy transition and environmental sustainability in nine CIS economies over the period 1996–2024, drawing on annual panel data sourced from the World Development Indicators. In the empirical framework, carbon dioxide emissions are specified as the dependent variable, while industrial output, foreign direct investment (FDI), renewable energy consumption, economic growth, trade openness, overall energy use, and an institutional quality index are included as key determinants of environmental pressure. Methodologically, the paper employs second-generation panel econometric techniques, commencing with cross-sectional dependence diagnostics and panel unit root tests, and proceeding to long-run estimation through FMOLS and CCR. The robustness of these estimates is reinforced using Driscoll-Kraay standard errors, while the System-GMM estimator is applied to address heteroskedasticity, serial correlation, cross-sectional dependence, and endogeneity concerns. The results indicate that industrial activity, energy consumption, and FDI significantly increase CO₂ emissions, whereas greater reliance on renewable energy and stronger institutional quality help to alleviate environmental degradation. Under more rigorous specifications, trade openness and economic growth are found to reduce emissions, pointing to emerging decoupling patterns within CIS countries. Importantly, the interaction between renewable energy and institutional quality reveals a pronounced complementary effect, suggesting that stronger governance frameworks amplify the environmental benefits of energy transition. Taken together, the findings underscore that environmental sustainability across CIS economies is jointly determined by structural, economic, and institutional factors, with institutional quality serving as a critical lever for advancing progress toward SDG 13. Full article