Search Results (2,167)

Ocular melanomas, comprising uveal melanoma (UM) and conjunctival melanoma (CoM), represent the most common primary intraocular and ocular surface malignancies in adults. Although rare compared with cutaneous melanoma, they exhibit unique molecular landscapes that provide critical opportunities for biomarker-driven precision medicine. In UM, recurrent mutations in GNAQ and GNA11, together with alterations in BAP1, SF3B1, and EIF1AX, have emerged as key prognostic biomarkers that stratify metastatic risk and guide surveillance strategies. Conversely, in CoM, the mutational spectrum overlaps with cutaneous melanoma, with frequent alterations in BRAF, NRAS, NF1, and KIT, offering actionable targets for personalised treatment. Beyond genomics, epigenetic signatures, microRNAs, and protein-based markers provide further insights into tumour progression, microenvironmental remodelling, and immune evasion. In parallel, liquid biopsy has emerged as a minimally invasive approach for real-time disease monitoring. Analyses of circulating tumour DNA (ctDNA), circulating tumour cells (CTCs), and exosome-derived microRNAs demonstrate increasing potential for early detection of minimal residual disease, prognostic assessment, and evaluation of treatment response. However, the clinical integration of these biomarkers remains limited by tumour heterogeneity, technical variability, and the lack of unified translational frameworks. This review synthesises current knowledge of molecular and liquid biopsy biomarkers in ocular melanoma, highlighting their relevance for diagnosis, prognosis, and treatment personalisation. The integration of established tissue-based molecular markers with novel liquid biopsy technologies will enable a unique framework for biomarker-guided precision oncology and risk-adapted surveillance in uveal and conjunctival melanoma, offering insight into strategies for early detection, therapeutic monitoring, and personalised clinical management. Full article

The past decade has witnessed an unprecedented transformation in breast cancer management, driven by parallel advances in targeted therapies, immunomodulation, drug-delivery technologies, and molecular diagnostic tools. This review summarizes the key achievements of 2015–2025, encompassing all major biological subtypes of breast cancer as well as technological innovations with substantial clinical relevance. In hormone receptor-positive (HR+)/HER2− disease, the integration of CDK4/6 inhibitors, modulators of the PI3K/AKT/mTOR pathway, oral Selective Estrogen Receptor Degraders (SERDs), and real-time monitoring of Estrogen Receptor 1 (ESR1) mutations has enabled clinicians to overcome endocrine resistance and dynamically tailor treatment based on evolving molecular alterations detected in circulating biomarkers. In HER2-positive breast cancer, treatment paradigms have been revolutionized by next-generation antibody–drug conjugates, advanced antibody formats, and technologies facilitating drug penetration across the blood–brain barrier, collectively improving systemic and central nervous system disease control. The most rapid progress has occurred in triple-negative breast cancer (TNBC), where synergistic strategies combining selective cytotoxicity via Antibody-Drug Conjugates (ADCs), DNA damage response inhibitors, immunotherapy, epigenetic modulation, and therapies targeting immunometabolic pathways have markedly expanded therapeutic opportunities for this historically challenging subtype. In parallel, photodynamic therapy has emerged as an investigational and predominantly local phototheranostic approach, incorporating nanocarriers, next-generation photosensitizers, and photoimmunotherapy capable of inducing immunogenic cell death and modulating antitumor immune responses. A defining feature of the past decade has been the surge in patent-driven innovation, encompassing multispecific antibodies, optimized ADC architectures, novel linker–payload designs, and advanced nanotechnological and photoactive delivery systems. By integrating data from clinical trials, molecular analyses, and patent landscapes, this review illustrates how multimechanistic, biomarker-guided therapies supported by advanced drug-delivery technologies are redefining contemporary precision oncology in breast cancer. The emerging therapeutic paradigm underscores the convergence of targeted therapy, immunomodulation, synthetic lethality, and localized immune-activating approaches, charting a path toward further personalization of treatment in the years ahead. Full article

Globally, grasslands, savannas, and wetlands are degrading rapidly and increasingly being replaced by woody vegetation. Woody Plant Encroachment (WPE) disrupts natural landscapes and has significant consequences for biodiversity, ecosystem functioning, and key ecosystem services. This review synthesizes findings from 159 peer-reviewed studies identified through a PRISMA-guided systematic literature review to evaluate the drivers of WPE, its ecological impacts, and the remote sensing (RS) approaches used to monitor it. The drivers of WPE are multifaceted, involving interactions among climate variability, topographic and edaphic conditions, hydrological change, land use transitions, and altered fire and grazing regimes, while its impacts are similarly diverse, influencing land cover structure, water and nutrient cycles, carbon and nitrogen dynamics, and broader implications for ecosystem resilience. Over the past two decades, RS has become central to WPE monitoring, with studies employing classification techniques, spectral mixture analysis, object-based image analysis, change detection, thresholding, landscape pattern and fragmentation metrics, and increasingly, machine learning and deep learning methods. Looking forward, emerging advances such as multi-sensor fusion (optical– synthetic aperture radar (SAR), Light Detection and Ranging (LiDAR)–hyperspectral), cloud-based platforms including Google Earth Engine, Microsoft Planetary Computer, and Digital Earth, and geospatial foundation models offer new opportunities for scalable, automated, and long-term monitoring. Despite these innovations, challenges remain in detecting early-stage encroachment, subcanopy woody growth, and species-specific patterns across heterogeneous landscapes. Key knowledge gaps highlighted in this review include the need for long-term monitoring frameworks, improved socio-ecological integration, species- and ecosystem-specific RS approaches, better utilization of SAR, and broader adoption of analysis-ready data and open-source platforms. Addressing these gaps will enable more effective, context-specific strategies to monitor, manage, and mitigate WPE in rapidly changing environments. Full article

Background/Objectives: In the modern healthcare landscape, breast cancer remains one of the most prevalent malignancies and a leading cause of mortality among women worldwide. Early and accurate prediction of breast cancer plays a pivotal role in effective diagnosis, treatment planning, and improving survival outcomes. However, due to the complexity and heterogeneity of medical data, achieving high predictive accuracy remains a significant challenge. This study proposes an intelligent hybrid system that integrates traditional machine learning (ML), deep learning (DL), and ensemble learning approaches for enhanced breast cancer prediction using the Wisconsin Breast Cancer Dataset. Methods: The proposed system employs a multistage framework comprising three main phases: (1) data preprocessing and balancing, which involves normalization using the min–max technique and application of the Synthetic Minority Over-sampling Technique (SMOTE) to mitigate class imbalance; (2) model development, where multiple ML algorithms, DL architectures, and a novel ensemble model are applied to the preprocessed data; and (3) model evaluation and validation, performed under three distinct training–testing scenarios to ensure robustness and generalizability. Model performance was assessed using six statistical evaluation metrics—accuracy, precision, recall, F1-score, specificity, and AUC—alongside graphical analyses and rigorous statistical tests to evaluate predictive consistency. Results: The findings demonstrate that the proposed ensemble model significantly outperforms individual machine learning and deep learning models in terms of predictive accuracy, stability, and reliability. A comparative analysis also reveals that the ensemble system surpasses several state-of-the-art methods reported in the literature. Conclusions: The proposed intelligent hybrid system offers a promising, multidisciplinary approach for improving diagnostic decision support in breast cancer prediction. By integrating advanced data preprocessing, machine learning, and deep learning paradigms within a unified ensemble framework, this study contributes to the broader goals of precision oncology and AI-driven healthcare, aligning with global efforts to enhance early cancer detection and personalized medical care. Full article

Camellia oleifera, a woody oil crop unique to China, plays a crucial role in alleviating the global pressure of edible oil supply and maintaining ecological security. However, it remains challenging to accurately forecast Camellia oleifera yield in complex landscapes using only remote sensing data. The aim of this study is to develop an interpretable deep learning model, namely Shapley Additive Explanations–guided Attention–long short-term memory (SALSTM), for estimating Camellia oleifera yield by integrating an improved spectral bloom index and environmental parameters. The study area is located in Hengyang City in Hunan Province. Sentinel-2 imagery, meteorological observation from 2019 to 2023, and topographic data were collected. First, an improved spectral bloom index (ISBI) was constructed as a proxy for flowering density, while average temperature, precipitation, accumulated temperature, and wind speed were selected to represent environmental regulation variables. Second, a SALSTM model was designed to capture temporal dynamics from multi-source inputs, in which the LSTM module extracts time-dependent information and an attention mechanism assigns time-step-wise weights. Feature-level importance derived from SHAP analysis was incorporated as a guiding prior to inform attention distribution across variable dimensions, thereby enhancing model transparency. Third, model performance was evaluated using root mean square error (RMSE) and coefficient of determination (R²). The result show that the constructed SALSTM model achieved strong predictive performance in predicting Camellia oleifera yield in Hengyang City (RMSE = 0.5738 t/ha, R² = 0.7943). Feature importance analysis results reveal that ISBI weight > 0.26, followed by average temperature and precipitation from flowering to fruit stages, these features are closely associated with C. oleifera yield. Spatially, high-yield zones were mainly concentrated in the central–southern hilly regions throughout 2019–2023, In contrast, low-yield zones were predominantly distributed in the northern and western mountainous areas. Temporally, yield hotspots exhibited a gradual increasing while low-yield zones showed mild fluctuations. This framework provides an effective and transferable approach for remote sensing-based yield estimation of flowering and fruit-bearing crops in complex landscapes. Full article

Background: Artificial intelligence (AI) is transforming medical practice, enhancing diagnostic accuracy, personalisation, and clinical efficiency. However, this transition raises complex ethical challenges related to transparency, accountability, fairness, and human oversight. This study examines how the literature conceptualises and distributes ethical responsibility in AI-assisted healthcare. Methods: This semi-systematic, theory-informed thematic review was conducted in accordance with the PRISMA 2020 guidelines. Publications from 2020 to 2025 were retrieved from PubMed, ScienceDirect, IEEE Xplore databases, and MDPI journals. A semi-quantitative keyword-based scoring model was applied to titles and abstracts to determine their relevance. High-relevance studies (n = 187) were analysed using an eight-category ethical framework: transparency and explainability, regulatory challenges, accountability, justice and equity, patient autonomy, beneficence–non-maleficence, data privacy, and the impact on the medical profession. Results: The analysis revealed a fragmented ethical landscape in which technological innovation frequently outperforms regulatory harmonisation and shared accountability structures. Transparency and explainability were the dominant concerns (34.8%). Significant gaps in organisational responsibility, equitable data practices, patient autonomy, and professional redefinition were reported. A multilevel ethical responsibility model was developed, integrating micro (clinical), meso (institutional), and macro (regulatory) dimensions, articulated through both ex ante and ex post perspectives. Conclusions: AI requires governance frameworks that integrate ethical principles, regulatory alignment, and epistemic justice in medicine. This review proposes a multidimensional model that bridges normative ethics and operational governance. Future research should explore empirical, longitudinal, and interdisciplinary approaches to assess the real impact of AI on clinical practice, equity, and trust. Full article

As one of the fastest growing economies in the world, it is critical for India to build climate-resilient infrastructure. Its rapidly increasing population growth and the consequent migration to urban areas, coupled with climate risks, have made infrastructure development a key priority area in India. Thus, ensuring that existing infrastructure and future constructions integrate strategies to address climate-related risks is vital. Despite the growing recognition of the importance of legal and policy landscapes on climate-resilient infrastructure, there is a very limited number of studies on this topic, especially in India. Thus, this study sought to bridge this gap by evaluating the influence of the Indian legal and policy framework on climate-resilient infrastructure. To this end, an analytical, comparative, and evaluative approach was adopted, employing benchmarks from international legal provisions and best practices from Japan’s legal and policy systems. In addition to doctrinal legal analysis using primary and secondary sources, case studies on the Mumbai Coastal Road Project and the Chandigarh–Manali Highway were performed to assess how the extant laws operate in the field. The findings indicate that while India’s legal system is gradually incorporating climate-risk considerations into its infrastructure sector, the effects of these considerations remain constrained due to institutional co-ordination challenges and limited enforceable obligations. Therefore, streamlining climate-resilient infrastructure governance requires more robust climate change legislation and improved implementation mechanisms. The findings of this study provide useful insights for legislators and policymakers in strengthening climate resilience integration within India’s infrastructure governance framework. Full article

Wee1-like protein kinase 2 (WEE2) is an oocyte-specific kinase that regulates meiotic arrest and fertilization. Its largely restricted expression in female germ cells and absence in somatic tissues make it a highly selective target for reproductive health interventions. Despite its central role in human fertility, no clinically approved WEE2 modulator is available. In this study, we employed an integrated in silico approach that combines structure-based virtual screening, molecular dynamics (MD) simulations, and MM-PBSA free-energy calculations to identify repurposed drug candidates with potential WEE2 inhibitory activity. Screening of ~3800 DrugBank compounds against the WEE2 catalytic domain yielded ten high-affinity hits, from which Midostaurin and Nilotinib emerged as the most mechanistically relevant based on kinase-targeting properties and pharmacological profiles. Docking analyses revealed strong binding affinities (−11.5 and −11.3 kcal/mol) and interaction fingerprints highly similar to the reference inhibitor MK1775, including key contacts with hinge-region residues Val220, Tyr291, and Cys292. All-atom MD simulations for 300 ns demonstrated that both compounds induce stable protein–ligand complexes with minimal conformational drift, decreased residual flexibility, preserved compactness, and stable intramolecular hydrogen-bond networks. Principal component and free-energy landscape analyses further indicate restricted conformational sampling of WEE2 upon ligand binding, supporting ligand-induced stabilization of the catalytic domain. MM-PBSA calculations confirmed favorable binding free energies for Midostaurin (−18.78 ± 2.23 kJ/mol) and Nilotinib (−17.47 ± 2.95 kJ/mol), exceeding that of MK1775. To increase the translational prioritization of candidate hits, we place our structure-based pipeline in the context of modern machine learning (ML) and deep learning (DL)-enabled virtual screening workflows. ML/DL rescoring and graph-based molecular property predictors can rapidly re-rank docking hits and estimate absorption, distribution, metabolism, excretion, and toxicity (ADMET) liabilities before in vitro evaluation. Full article

The flower thrips, Frankliniella intonsa, is a major pest threatening citrus production. However, chemical control remains the primary management measure, which poses significant risks on ecosystems. Hence, it is urgent to prioritize more eco-friendly measures to efficiently control thrips. The ladybird, Cheilomenes sexmaculata, is a predominant natural enemy in the local citrus agroecosystem and could play a key role in suppressing thrips in agricultural landscapes. Although some ladybirds are known to be attracted to herbivore-induced plant volatiles (HIPVs), little is known about the specific attractive compounds and the effect of F. intonsa-infested lemon plants on the predatory response of C. sexmaculata. Here, we studied the chemical interaction between F. intonsa, C. sexmaculata, and lemon plants. In dual-choice behavioral assays, C. sexmaculata adults significantly preferred volatiles from F. intonsa-infested plants over those from healthy plants. Volatile collection and analysis identified six monoterpenes, five of which (α-pinene, β-pinene, sabinene, myrcene, and eucalyptol) individually attracted C. sexmaculata at specific concentrations. Moreover, a blend of these five compounds, formulated at their optimal attractive concentrations, elicited a stronger attraction in C. sexmaculata than individual compounds, indicating a synergistic interaction. This attractive blend can thus be used to develop a kairomone-based lure to enhance biological control and to complement existing integrated pest management approaches against thrips in lemon agroecosystems. Full article

Cocoa production in West Africa supplies most of the global demand but is increasingly constrained by yield stagnation, soil degradation, disease pressure, and climate variability. This review examines how integrating regenerative agriculture (RA) with unmanned aerial systems (UAS) and artificial intelligence (AI) can support more precise and resilient cocoa management across heterogeneous smallholder landscapes. A PRISMA-guided systematic review of peer-reviewed literature published between 2000 and 2024 was conducted, yielding 49 core studies analyzed alongside supporting evidence. The synthesis evaluates regenerative agronomic outcomes, UAV-derived multispectral, thermal, and structural diagnostics, and AI-based analytical approaches for stress detection, yield estimation, and management zoning. Results indicate that regenerative practices consistently improve soil health and yield stability, while UAS data enhance spatial targeting of rehabilitation, shade management, and stress interventions. AI models further improve predictive capacity and decision relevance when aligned with data availability and institutional context, although performance varies across systems. Reported yield stabilization or improvement typically ranges from 12–30% under integrated approaches, with concurrent reductions in fertilizer and water inputs where spatial targeting is applied. The review concludes that effective scaling of RA–UAS–AI systems depends less on technical sophistication than on governance arrangements, extension integration, and cooperative service models, positioning these tools as enabling components rather than standalone solutions for sustainable cocoa intensification. Full article

Accurate estimation of above-ground biomass (AGB) is vital for carbon accounting, biodiversity conservation, and sustainable forest management, especially in tropical regions under strong anthropogenic pressure. This study estimated and mapped AGB in the Atacora Mountain Chain, Togo, using a multi-source remote sensing approach within Google Earth Engine (GEE). Field data from 421 plots of the 2021 National Forest Inventory were combined with Sentinel-1 Synthetic Aperture Radar, Sentinel-2 multispectral imagery, bioclimatic variables from WorldClim, and topographic data. A Random Forest regression model evaluated the predictive capacity of different variable combinations. The best model, integrating SAR, optical, and climatic variables (S1S2allBio), achieved R² = 0.90, MAE = 13.42 Mg/ha, and RMSE = 22.54 Mg/ha, outperforming models without climate data. Dense forests stored the highest biomass (124.2 Mg/ha), while tree/shrub savannas had the lowest (25.38 Mg/ha). Spatially, ~60% of the area had biomass ≤ 50 Mg/ha. Precipitation correlated positively with AGB (r = 0.55), whereas temperature showed negative correlations. This work demonstrates the effectiveness of integrating multi-sensor satellite data with climatic predictors for accurate biomass mapping in complex tropical landscapes. The approach supports national forest monitoring, REDD+ programs, and ecosystem restoration, contributing to SDGs 13, 15, and 12 and offering a scalable method for other tropical regions. Full article

Fungal diseases continue to limit global crop production and drive major economic losses. Conventional diagnostic and control approaches depend on time-consuming culture-based methods and broad-spectrum chemicals, which offer limited precision. Advances in molecular identification have changed this landscape. PCR, qPCR, LAMP, sequencing and portable platforms enable rapid and species-level detection directly from plant tissue. These tools feed into RNA-based control strategies, where knowledge of pathogen genomes and sRNA exchange enables targeted suppression of essential fungal genes. Host-induced and spray-induced gene silencing provide selective control without the long-term environmental costs associated with chemical use. CRISPR/Cas9 based tools now refine both diagnostics and resistance development, and bioinformatics improves target gene selection. Rising integration of artificial intelligence indicates a future in which disease detection, prediction and management connect in near real time. The major challenge lies in limited field validation and the narrow range of fungal species with complete molecular datasets, yet coordinated multi-site trials and expansion of annotated genomic resources can enable wider implementation. The combined use of molecular diagnostics and RNA-based strategies marks a shift from disease reaction to disease prevention and moves crop protection towards a precise, sustainable and responsive management system. This review synthesizes the information related to current molecular identification tools and RNA-based management strategies, and evaluates how their integration supports precise and sustainable approaches for fungal disease control under diverse environmental settings. Full article

Cloud-based Earth observation platforms, such as data cubes, enable reproducible analyses of long-term satellite time series for climate and urban studies. In parallel, Essential Climate Variables (ECVs) provide a standardised framework for monitoring climate dynamics, with urban land cover and temperature being particularly relevant in historic urban contexts. This study analyses long-term trends and statistical associations between satellite-based ECVs and urbanisation indicators within the Historic Urban Landscape (HUL) of Sfax (Tunisia) from 1985 to 2021. Using the Digital Earth Africa (DEA) data cube, we derived six urban spectral indices (USIs), land surface temperature, air temperature at 2 m, wind characteristics, and precipitation from Landsat and ERA5 reanalysis data. An automated and reproducible Python-based workflow was implemented to assess USI behaviour, evaluate their performance against the Global Human Settlement Layer (GHSL), and explore spatio-temporal co-variations between urbanisation and climate variables. Results reveal a consistent increase in air and surface temperatures alongside a decreasing precipitation trend over the study period. The USIs demonstrate comparable accuracy levels (≈88–90%) in delineating urban areas, with indices based on SWIR and NIR bands (NDBI, BUI, NBI) showing the strongest statistical associations with temperature variables. Correlation and multivariate regression analyses indicate that temporal variations in USIs are more strongly associated with air temperature than with land surface temperature; however, these relationships reflect statistical co-variation rather than causality. By integrating satellite-based ECVs within a data cube framework, this study provides an operational methodology for long-term monitoring of urban-climate interactions in historic Mediterranean cities, supporting both climate adaptation strategies and the objectives of the UNESCO HUL approach. Full article

Spinal cord injury (SCI) remains one of the most formidable challenges in regenerative medicine, often resulting in permanent loss of motor, sensory, and autonomic function. Cell-based therapies offer a promising path toward repair by providing donor neurons and glia capable of integrating into host circuits, modulating the injury environment, and restoring function. Early studies employing fetal neural tissue and neural progenitor cells (NPCs) have demonstrated proof-of-principle for survival, differentiation, and synaptic integration. More recently, pluripotent stem cell (PSC)-derived donor populations and engineered constructs have expanded the therapeutic repertoire, enabling precise specification of interneuron subtypes, astrocytes, and oligodendrocytes tailored to the injured spinal cord. Advances in genetic engineering, including CRISPR-based editing, trophic factor overexpression, and immune-evasive modifications, are giving rise to next-generation donor cells with enhanced survival and controllable integration. At the same time, biomaterials, pharmacological agents, activity-based therapies, and neuromodulation strategies are being combined with transplantation to overcome barriers and promote long-term recovery. In this review, we summarize progress in designing and engineering donor cells and tissues for SCI repair, highlight how combination strategies are reshaping the therapeutic landscape, and outline opportunities for next-generation approaches. Together, these advances point toward a future in which tailored, multimodal cell-based therapies achieve consistent and durable restoration of spinal cord function. Full article