Search Results (1,624)

Background: Recent studies highlight the role of microsatellite instability (MSI) in tumor progression. This study examines the link between MSI, type of loss of function, and disease progression in low-grade endometrial carcinoma clinically confined to the uterus, focusing on myometrial infiltration. Materials and Methods: This retrospective case-control study analyzed data from 144 women treated for clinical stage I low-grade endometrial carcinoma at two university hospitals. Patients were divided into two groups based on microsatellite status: 118 with microsatellite stability (MSS) and 26 with MSI. Immunohistochemical profiling assessed MMR proteins (MLH1, PMS2, MSH2, MSH6). The primary outcome was the presence of myometrial infiltration, and the secondary outcome was the deepness of infiltration. Data were statistically analyzed using Fisher’s exact, Chi-square, and Wilcoxon tests, with logistic regression applied to evaluate the impact of MSI on these outcomes. Results: Myometrial infiltration was present in 96% of MSS and 98% of MSI cases (p = 0.5). However, deep infiltration (≥50%) was more frequent in patients with MSI (38% vs. 19%, p = 0.042). Stratification by heterodimer loss revealed that loss of MLH1/PMS2 was associated with a higher rate of deep infiltration (47%), while loss of MSH2/MSH6 correlated with lower infiltration risk (14%). In multivariate analysis, MSH2/MSH6 loss remained negatively associated with infiltration (OR 0.88; 95% CI 0.80–0.98; p = 0.020), independent of grade and LVSI. Conclusions: In low-grade endometrial carcinomas clinically confined to the uterus, MSI does not increase the overall prevalence of myometrial infiltration but is associated with deeper invasion, especially in cases with MLH1/PMS2 loss. MSI profiling could aid in risk stratification and therapeutic planning, particularly in candidates for fertility-sparing treatment. Full article

Molecular imaging and precision therapies are transforming ophthalmology, enabling earlier and more accurate diagnosis and targeted treatment of sight-threatening diseases. This review focuses on age-related macular degeneration, diabetic retinopathy, glaucoma, and uveitis, examining high-resolution imaging techniques such as optical coherence tomography (OCT), OCT angiography, MALDI-MSI, and spatial transcriptomics. Artificial intelligence supports these methods by improving image interpretation and enabling personalized analysis. The review also discusses therapeutic advances, including gene therapies (e.g., AAV-mediated RPE65 delivery), stem cell-based regenerative approaches, and biologics targeting inflammatory and neovascular processes. Targeted molecular therapies targeting specific signaling pathways, such as MAPK, are also explored. The combination of single-cell transcriptomics, proteomics, and machine learning facilitates the development of personalized treatment strategies. Although these technologies hold enormous potential, their implementation in routine clinical care requires further validation, regulatory approval, and long-term safety assessment. This review highlights the potential and challenges of integrating molecular imaging and advanced therapies in the future of precision ophthalmic medicine. Full article

This study presents a novel methodology for mapping Fault- and Thrust-based Structural Lineaments (FT-SL) in the rugged and inaccessible Oued-Laou watershed of the Rif Belt, Morocco. Combining optical (Landsat-8 OLI, Sentinel-2 MSI) and radar (Sentinel-1 SAR) remote sensing data, the research employs manual, semi-automatic, and automatic extraction methods enhanced by spatial filtering (Sobel, Laplacian, Kuan). A Knowledge-Based System (KBS) integrated with Multi-Criteria Decision Analysis (MCDA) evaluates the effectiveness of these methods, focusing on lineament statistics, orientation, density distribution, and correlation with existing geological maps. The results highlight Sentinel-1 SAR’s superior performance in detecting subsurface structures, while manual extraction yields the highest accuracy. This study also demonstrates the potential for generalizing this approach to other Alpine orogenic regions, such as the Alps, due to shared geological characteristics. The findings provide a robust framework for structural lineament mapping in mountainous terrains, addressing challenges of accessibility and data scarcity. Full article

With rapid urbanization, rooftop photovoltaic (PV) systems play an important role in mitigating the energy crisis and reducing emissions, yet achieving scientific and cost-effective deployment at the urban block scale remains challenging. This study proposes a transferable framework that integrates spatial morphology quantification, clustering, and numerical simulation to evaluate PV potential in residential blocks of Jinan, China. Six key morphological indicators were extracted through principal component analysis (PCA), and blocks were classified into five typical types, followed by simulations under different PV material scenarios. The main findings are: (1) Block type differences: Cluster 1 achieved the highest annual generation, 61.76% above average, but with a 75.08% cost increase and a 3.54-year payback. Clusters 4 and 5 showed moderate generation and the shortest payback of 2.91–2.97 years, reflecting better energy–economic balance. (2) PV materials: monocrystalline silicon (m-Si) yielded the highest generation, suitable for maximizing output; polycrystalline silicon (p-Si) produced slightly less but reduced costs by 32.43% and shortened payback by 19.58%, favoring cost-sensitive scenarios. (3) Seasonal variation: PV output peaked in February–March and September–December, requiring priority in grid operation and maintenance. The proposed framework can serve as a useful reference for planners in developing PV deployment strategies, with good transferability and potential for wider application, thereby contributing to urban energy transition and low-carbon sustainable development. Full article

Water hyacinth (Eichhornia crassipes), native to South America, is a highly invasive aquatic plant threatening freshwater ecosystems worldwide. Its rapid proliferation negatively impacts water quality, biodiversity, and navigation. Remote sensing offers an effective means to monitor such aquatic environments by providing extensive spatial and temporal coverage with improved resolution. This systematic review examines remote sensing applications for monitoring water hyacinth and water quality in studies published from 2014 to 2024. Seventy-eight peer-reviewed articles were selected from the Web of Science, Scopus, and Google Scholar following strict criteria. The research spans 25 countries across five continents, focusing mainly on lakes (61.5%), rivers (21%), and wetlands (10.3%). Approximately 49% of studies addressed water quality, 42% focused on water hyacinth, and 9% covered both. The Sentinel-2 Multispectral Instrument (MSI) was the most used sensor (35%), followed by the Landsat 8 Operational Land Imager (OLI) (26%). Multi-sensor fusion, especially Sentinel-2 MSI with Unmanned Aerial Vehicles (UAVs), was frequently applied to enhance monitoring capabilities. Detection accuracies ranged from 74% to 98% using statistical, machine learning, and deep learning techniques. Key challenges include limited ground-truth data and inadequate atmospheric correction. The integration of high-resolution sensors with advanced analytics shows strong promise for effective inland water monitoring. Full article

The harmonized multi-sensor satellite data assists users by providing seamless analysis-ready data with enhanced temporal resolution. The Harmonized Landsat Sentinel (HLS) product has gained popularity due to the seamless integration of Landsat OLI and Sentinel-2 MSI, achieving a temporal resolution of 2.8 to 3.5 days. However, applications that require monitoring intervals of less than three days or cloudy data can limit the usage of HLS data. Gaofen-1 (GF-1) Wide Field of View (WFV) data provides the capacity further to enhance the data availability by harmonization with HLS. In this study, GF-1/WFV data is harmonized with HLS by employing deep learning-based conditional Generative Adversarial Networks (cGANs). The harmonized WFV data with HLS provides an average temporal resolution of 1.5 days (ranging from 1.2 to 1.7 days), whereas the temporal resolution of HLS varies from 2.8 to 3.5 days. This enhanced temporal resolution will benefit applications that require frequent monitoring. Various processes are employed in HLS to achieve seamless products from the Operational Land Imager (OLI) and Multispectral Imager (MSI). This study applies 6S atmospheric correction to obtain GF-1/WFV surface reflectance data, employs MFC cloud masking, resamples the data to 30 m, and performs geographical correction using AROP relative to HLS data, to align preprocessing with HLS workflows. Harmonization is achieved without using BRDF normalization and bandpass adjustment like in the HLS workflows; instead, cGAN learns cross-sensor reflectance mapping by utilizing a U-Net generator and a patchGAN discriminator. The harmonized GF-1/WFV data were compared to the reference HLS data using various quality indices, including SSIM, MBE, and RMSD, across 126 cloud-free validation tiles covering various land covers and seasons. Band-wise scatter plots, histograms, and visual image color quality were compared. All these indices, including the Sobel filter, histograms, and visual comparisons, indicated that the proposed method has effectively reduced the spectral discrepancies between the GF-1/WFV and HLS data. Full article

Maricá, located on the eastern coast of Rio de Janeiro, experiences rapid urban growth driven by infrastructure and economic development. This study presents the first high-resolution projection of Maricá’s urban expansion (2030–2040), integrating oil industry impacts and protected area constraints. Using Sentinel-2 MSI data (10–20 m resolution) classified via Random Forest on Google Earth Engine (90% accuracy) and a Dinamica EGO Cellular Automata model (5 × 5 Moore neighborhood, calibrated on 2015–2020 transitions), results indicate 18.4% urban growth by 2030 (129 km²), expanding to 151 km² (+38.5% total) by 2040, with 72% replacing pastures. This supports sustainable urban management strategies. Full article

The concept of agnostic biomarkers—molecular modifications that guide therapy irrespective of tumor origin—has gained increasing relevance in oncology, including colorectal cancer (CRC). This review aims to critically evaluate the role of such biomarkers in CRC, highlighting their clinical significance as therapeutic targets and indicators of prognosis. Through a PubMed search using the terms “agnostic treatment AND colorectal cancer,” eight key studies were identified and qualitatively analyzed. We focus on several biomarkers commonly regarded as agnostic across tumor types, including BRAF V600E mutation, receptor tyrosine kinase (RTK) and PI3K fusions, the CpG island methylator phenotype (CIMP), high tumor mutational burden (TMB), and microsatellite instability (MSI). These markers are inspected for their prevalence in CRC, underlying pathophysiological mechanisms of cancer promotion, and predictive or prognostic implications. Moreover, we integrate findings from broader oncologic studies to contextualize the evolving role of agnostic biomarkers beyond organ-specific paradigms. Emerging evidence suggests that leveraging these molecular signatures may inform the use of targeted and immunotherapeutic agents as first-line options in select CRC populations. Collectively, agnostic biomarkers represent an auspicious avenue for personalizing CRC treatment, particularly in advanced-stage disease where traditional treatment options remain limited. Full article

The rapid and accurate assessment of regional soil salinity is crucial for effective salinization management. This study proposes an enhanced remote sensing inversion method by integrating both driving and response environmental variables to address lag effects and incomplete factor consideration in existing models. The Yellow River Delta, a coastal saline–alkaline region, was selected as the study area, where soil salinity-sensitive spectral parameters were derived from Sentinel-2 MSI imagery. Six environmental variables, including precipitation, distance from the sea, and soil moisture, were analyzed. Four scenarios were constructed: (1) using only spectral parameters; (2) spectral parameters with driving variables; (3) spectral parameters with response variables; and (4) combining both types. Four modeling methods were employed to assess inversion accuracy. The results show that incorporating either driving or response variables improved accuracy, with validation R² increasing by up to 0.149 and RMSE decreasing by up to 0.097 when both were used. The suitable model, integrating soil moisture, distance from the sea, and chlorophyll content, achieved a calibration R² of 0.813 and validation R² of 0.722. These findings demonstrate that combining both driving and response variables enhances model performance and provides valuable insights for soil salinization management. Full article

Algal blooms pose a serious threat not only to the lake ecosystem of Lake Bosten but also by negatively impacting its rapidly developing fisheries and tourism industries. This study focuses on Lake Bosten as the research area and utilizes multi-source remote sensing imagery from Landsat TM/ETM+/OLI and Sentinel-2 MSI. The Adjusted Floating Algae Index (AFAI) was employed to extract algal blooms in Lake Bosten from 2004 to 2023, analyze their spatiotemporal evolution characteristics and driving factors, and construct a Long Short Term Memory (LSTM) network model to predict the spatial distribution of algal-bloom frequency. The stability of the model was assessed through temporal segmentation of historical data combined with temporal cross-validation. The results indicate that (1) during the study period, algal blooms in Lake Bosten were predominantly of low-risk level, with low-risk bloom coverage accounting for over 8% in both 2004 and 2005. The intensity of algal blooms in summer and autumn was significantly higher than in spring. The coverage of medium- and high-risk blooms reached 2.74% in the summer of 2004 and 3.03% in the autumn of 2005, while remaining below 1% in spring. (2) High-frequency algal bloom areas were mainly located in the western and northwestern parts of the lake, and the central region experienced significantly more frequent blooms during 2004–2013 compared to 2014–2023, particularly in spring and summer. (3) The LSTM model achieved an R² of 0.86, indicating relatively stable performance. The prediction results suggest a continued low frequency of algal blooms in the future, reflecting certain achievements in sustainable water-resource management. (4) The interactions among meteorological factors exhibited significant influence on bloom formation, with the q values of temperature and precipitation interactions both exceeding 0.5, making them the most prominent meteorological driving factors. Monitoring of sewage discharge and analysis of agricultural and industrial expansion revealed that human activities have a more direct impact on the water quality of Lake Bosten. In addition, changes in lake area and water environment were mainly influenced by anthropogenic factors, ultimately making human activities the primary driving force behind the spatiotemporal variations of algal blooms. This study improved the timeliness of algal-bloom monitoring through the integration of multi-source remote sensing and successfully predicted the future spatial distribution of bloom frequency, providing a scientific basis and decision-making support for the sustainable management of water resources in Lake Bosten. Full article

Background/Objectives: Advances in understanding immune checkpoint pathways and tumor immune biology have enabled the development of immune checkpoint inhibitors (ICIs), particularly targeting the PD-1/PD-L1 axis, which has transformed cancer immunotherapy. While they have shown remarkable success in various cancer types, including melanoma, non-small cell lung cancer, and gastrointestinal malignancies, variability in patient response, immune-related adverse events (irAEs), and resistance mechanisms remain significant. This review aims to evaluate clinical pharmacology, mechanisms of action, resistance pathways, and pharmacogenomic influences shaping interindividual responses to ICIs. Methods: This comprehensive review synthesizes current literature on FDA-approved ICIs, exploring their clinical use, underlying biological mechanisms, and emerging pharmacogenomic data. It also assesses key biomarkers such as tumor mutational burden (TMB), microsatellite instability (MSI), HLA diversity, and epigenetic factors influencing ICI efficacy and safety. Results: We outline key mechanisms contributing to ICI resistance, including T cell dysfunction, altered antigen presentation, and immunosuppressive tumor microenvironment components. Furthermore, we highlight promising pharmacogenomic findings, including single-nucleotide polymorphisms (SNPs) in PD-1/PD-L1 and immune-regulatory genes, offering predictive and prognostic utility. Variability in PD-L1 expression and the role of epigenetic modifications are also addressed as challenges in treatment optimization. Conclusions: Interindividual variability in ICI response underscores the need for biomarker-driven strategies. By integrating pharmacogenomic insights with clinical pharmacology, future approaches may support more personalized and effective use of ICIs. Combination therapies and novel modalities hold promise for overcoming resistance, enhancing therapeutic efficacy, and enabling precision oncology. Full article

Accurate characterization of surface solar irradiance at fine spatial, temporal, and spectral resolution is central to applications such as solar energy and environmental monitoring. On the one hand, modeling radiative transfer to achieve such accuracy requires detailed characterization of a wide range of factors, including the vertical profiles of gaseous and particulate absorbers and scatterers, wavelength-resolved surface reflectivity, and the three-dimensional morphology of clouds. On the other hand, satellite-based remote sensing products typically provide top-of-the-atmosphere irradiance at coarse spatial resolutions, where individual pixels can span several kilometers, failing to capture fine-scale intra-pixel variability. In this study, we introduce a machine learning framework that integrates large-scale remote sensing satellite data with hyperlocal, second-by-second ground-based measurements from an ensemble of low-cost spectral sensors to estimate the wavelength-resolved surface solar irradiance spectra at the hyperlocal level. The satellite data are obtained from the Harmonized Sentinel-2 MSI (MultiSpectral Instrument), Level-2A Surface Reflectance (SR) product, which offers high-resolution surface reflectance data. By leveraging machine learning, we model the relationship between satellite-derived surface reflectance and ground-based spectral measurements to predict high-resolution, wavelength-resolved irradiance, using target data obtained from an NIST-calibrated reference instrument. By utilizing a low-cost sensor ensemble that is easily deployable at scale, combined with downscaled satellite data, this approach enables accurate modeling of intra-pixel variability in surface-level solar irradiance with high temporal resolution. It also enhances the utility of the Harmonized Sentinel-2 MSI data for operational remote sensing. Our results demonstrate that the model is able to estimate surface solar irradiance with an R² ≈ 0.99 across all 421 spectral bins from 360 nm to 780 nm at 1 nm resolution, offering strong potential for applications in solar energy forecasting, urban climate research, and environmental monitoring. Full article

Research and innovation in metabolomics tools to measure metabolite accumulation within plants have led to important discoveries with respect to the improvement of plant stress tolerance, development, and crop yield. Traditional metabolomics analyses have commonly utilized gas chromatography–mass spectrometry and liquid chromatography–mass spectrometry, but these methods are often performed without regard for the spatial locations of metabolites within tissues. Methods for mass spectral imaging (MSI) have recently been developed to detect and spatially resolve metabolite accumulation and are rapidly being adopted on a wider scale. Since 2010, the number of publications incorporating mass spectral imaging has grown from approximately 80 articles to over 378 on a yearly basis, constituting an increase of at least 350% during this time frame. Spatially resolved metabolite accumulation data provides unique insights into the function and regulation of plant biochemical pathways. Mass spectral imaging is commonly paired with desorption ionization technologies, including matrix-assisted laser desorption ionization (MALDI) and desorption electrospray ionization (DESI), to generate accurate, spatially resolved metabolomics data from prepared tissue segments. Here, we describe the most recent advancements in sample preparation methods, mass spectral imaging technologies, and data processing tools that have been developed to address the limits of MSI technology. Additionally, we summarize recent applications of MSI technologies in plant metabolomics and discuss potential avenues for future research advancements within the plant biology community through the use of these technologies. Full article

Surface functionalization is a key enabler that imparts solid materials with excellent chemoselectivity. With this aim, halloysite and sepiolite clay particles were functionalized with carboxyethylsilanetriol sodium salt (CES) and 3-aminopropyltriethoxysilane (APTES), affording carboxy-terminated and amino-terminated clay, respectively. In the case of halloysite, the grafting occurs at Al-OH groups of the lumen surface (tube inner surface) and Al-OH and Si-OH groups at the edges and external surface defects of the nanotubes. For sepiolite, silanol groups located on the edges of the structural channels were at the origin of a chemical reaction between this fibrous clay and the terminal alkoxysilane. The resulting modified clays were examined for removal of Congo red (CR) and malachite green (MG) as anionic and cationic dyes, respectively. Clay bearing only carboxylic groups display more affinity towards cationic dye (MG), recording 926 mg·g⁻¹ and 387 mg·g⁻¹ for HNT-CES and SEP-CES, respectively, while amino-functionalized clays show very high adsorption for anionic dye (CR), reaching 1232 and 1228 mg·g⁻¹ for HNT-APTES and SEP-APTES, respectively. Simultaneous grafting of the two silyl coupling reagents was also attempted through one-pot and sequential grafting method, with the latter being more appropriate to access amphoteric clay featuring both carboxylic and amino groups. The behavior of the bifunctional adsorbents was investigated with respect to pristine and monofunctional clay. The obtained results provide insights to fulfill the requirement for handling complex water effluent containing both anionic and cationic pollutants, towards more sustainable development. Full article

Small lakes (<10 km²) provide a range of ecosystem services but are often overlooked in both monitoring efforts and limnological studies. Remote sensing has been increasingly used to complement in situ monitoring or to provide water colour data for unmonitored inland water bodies. However, due to spatial, radiometric, and spectral constraints, it has been heavily focused on large lakes. Sentinel-2 MSI is the first sensor with the capability to consistently retrieve a wide range of essential water quality variables, such as chlorophyll-a concentration (chl-a) and water transparency, in small water bodies, and to provide long time series. Here, we provide and validate a framework for retrieving two variables, chl-a and turbidity, over lakes with diverse optical characteristics using Sentinel-2 imagery. It is based on GRS for atmospheric and sun glint correction, WaterDetect for water detection, and inversion models that were automatically selected based on two different sets of optical water types (OWTs)—one for each variable; for chl-a, we produced a blended product for improved spatial representation. To validate the approach, we compared the products with more than 600 in situ data from 108 lakes located in the Adour–Garonne river basins, ranging from 3 to ∼5000 ha, as well as remote sensing reflectance (Rrs) data collected during 10 field campaigns during the summer and spring seasons. Rrs retrieval (n = 65) was robust for bands 2 to 5, with MAPE varying from 15 to 32% and achieving correlation from 0.74 up to 0.92. For bands 6 to 8A, the Rrs retrieval was much less accurate, being influenced by adjacency effects. Glint removal significantly enhanced Rrs accuracy, with RMSE improving from 0.0067 to 0.0021 sr⁻¹ for band 4, for example. Water quality retrieval showed consistent results, with an MAPE of 56%, an RMSE of 11.4 mg m⁻³, and an r of 0.76 for chl-a, and an MAPE of 47%, an RMSE of 9.7 NTU, and an r of 0.87 for turbidity, and no significant effect of lake area or lake depth on retrieval errors. The temporal and spatial representations of the selected parameters were also shown to be consistent, demonstrating that the framework is robust and can be applied over lakes as small as 3 ha. The validated methods can be applied to retrieve time series of chl-a and turbidity starting from 2016 and with a frequency of up to 5 days, largely expanding the database collected by water agencies. This dataset will be extremely useful for studying the dynamics of these small freshwater ecosystems. Full article