Search Results (821)

This review presents a critical and comparative analysis of carbon-based electrochemical sensing platforms for the determination of heavy metal ions in water, with emphasis on Pb²⁺, Cd²⁺, and Hg²⁺. The growing discharge of industrial and mining effluents has led to persistent contamination of aquatic environments by toxic metals, creating an urgent need for sensitive, rapid, and field-deployable analytical technologies. Carbon-based nanomaterials, including graphene, carbon nanotubes (CNTs), and MXene, have emerged as key functional components in modern electrochemical sensors due to their high electrical conductivity, large surface area, and tunable surface chemistry. Based on reported studies, typical detection limits for Pb²⁺ and Cd²⁺ using differential pulse voltammetry (DPV) on glassy carbon and thin-film electrodes are in the range of 0.4–1.2 µg/L. For integrated thin-film sensing systems, limits of detection of 0.8–1.2 µg/L are commonly achieved. MXene-based platforms further enhance sensitivity and enable Hg²⁺ detection with linear response ranges typically between 1 and 5 µg/L, accompanied by clear electrochemical or optical signals. Beyond conventional electrochemical detection, this review specifically highlights self-sustaining visual sensors based on MXene integrated with enzyme-driven bioelectrochemical systems, such as glucose oxidase (GOD) and Prussian blue (PB) assembled on ITO substrates. These systems convert chemical energy into measurable colorimetric signals without external power sources, enabling direct visual identification of Hg²⁺ ions. Under optimized conditions (e.g., 5 mg/mL GOD and 5 mM glucose), stable and distinguishable color responses are achieved for rapid on-site monitoring. Overall, this review not only summarizes current performance benchmarks of carbon-based sensors but also identifies key challenges, including long-term stability, selectivity under multi-ion interference, and large-scale device integration, while outlining future directions toward portable multisensor water-quality monitoring systems. Full article

Background/Objectives: Disorganization of the retinal inner layers (DRIL) is an important biomarker of diabetic macular edema (DME) that has a very strong association with visual acuity (VA) in patients. But the unavailability of annotated training data from experts severely limits the adaptability of models pretrained on real-world images owing to significant variations in the domain, posing two primary challenges for the design of efficient computerized DRIL detection methods. Methods: In an attempt to address these challenges, we propose a novel, self-supervision-based learning framework that employs a huge unlabeled optical coherence tomography (OCT) dataset to learn and detect clinically applicable interpretations before fine-tuning with a small proprietary dataset of annotated OCT images. In this research, we introduce a spatial Bootstrap Your Own Latent (BYOL) with a hybrid spatial aware loss function aimed to capture anatomical representations from unlabeled OCT dataset of 108,309 images that cover various retinal abnormalities, and then adapt the learned interpretations for DRIL classification employing 823 annotated OCT images. Results: With an accuracy of 99.39%, the proposed two-stage approach substantially exceeds the direct transfer learning models pretrained on ImageNet. Conclusions: The findings demonstrate the efficacy of domain-specific self-supervised learning for rare retinal pathological detection tasks with limited annotated data. Full article

Mercury’s severe toxicity and persistence demand fast, low-cost, and sustainable detection. In this work, a Juglans regia ethanolic extract is introduced as an efficient biogenic reducing and stabilizing agent for the green synthesis of silver nanoparticles (AgNPs). This plant-mediated route enables environmentally friendly nanoparticle formation with suitable optical properties for sensing applications. To overcome the poor visual selectivity observed in the colloidal AgNPs suspension, the nanoparticles were immobilized onto filter paper to produce a solid-phase colorimetric sensor. The paper-based platform exhibited a highly selective response toward Hg²⁺, showing complete suppression of the yellow coloration exclusively in the presence of Hg²⁺, even when challenged with a 200-fold excess of potentially interfering ions. Quantitative colorimetric analysis revealed a broad linear detection range from 1 × 10⁻⁸ to 1 × 10⁻³ mol dm⁻³ and an excellent limit of detection of 1.065 × 10⁻⁸ mol dm⁻³, with visible color changes consistent with the calculated values. The sensor’s performance was further validated using real tap water samples, with recovery values ranging from 96% to 102%, confirming minimal matrix interference and reliable quantification. Altogether, this study demonstrates that Juglans regia-mediated AgNPs, integrated into a simple paper-based format, provide a fully green, low-cost, and portable platform for sensitive and selective on-site detection of Hg²⁺ in environmental waters. Full article

Percutaneous coronary intervention (PCI) has evolved significantly over the past two decades, yet challenges in achieving optimal stent deployment and long-term outcomes persist, particularly in complex coronary anatomy. Intravascular imaging (IVI) modalities such as intravascular ultrasound (IVUS), optical coherence tomography (OCT), and near-infrared spectroscopy (NIRS) have transformed the precision of PCI by providing detailed cross-sectional visualization of vessel architecture, plaque morphology, and stent apposition. Compared to angiography-guided PCI, imaging-guided PCI enables more accurate lesion assessment, appropriate stent sizing, and detection of suboptimal results including under-expansion, malapposition, and edge dissections, factors strongly linked to restenosis and stent thrombosis. Large-scale randomized trials (e.g., ULTIMATE, ILUMIEN) and meta-analyses have demonstrated that imaging-guided PCI reduces major adverse cardiovascular events (MACE) and improves long-term stent patency, particularly in left main, bifurcation, and calcified lesions. Despite these benefits, adoption remains variable due to cost, procedural complexity, and training gaps. Emerging advances, including artificial intelligence-enhanced imaging, hybrid devices, and fusion of imaging with physiologic assessments, promise to integrate imaging more seamlessly into routine practice. This review summarizes current evidence, practical applications, and future directions of IVI-guided PCI, underscoring its growing role in contemporary interventional cardiology and its potential to personalize and optimize coronary revascularization strategies. Full article

Background: Nanomedicine has demonstrated great potential to improve drug delivery across various diseases. However, accurately monitoring the real-time trafficking of organic nanoparticles (NPs) within biological systems remains a significant challenge. Current detection methods rely heavily on fluorescence, while high-resolution, label-free imaging is often precluded by the limited optical contrast of organic materials, limiting a comprehensive understanding of NP fate. Metallic doping allows simultaneous detection of carriers using multiple imaging and analysis techniques. This study presents a novel approach to prepare gold-doped hybrid NPs compatible with multimodal imaging, thus facilitating multimodal tracking. Methods: Gold-doped NPs were successfully synthesized via nanoprecipitation, yielding stable, monodisperse carriers with optimal size, confirmed by Dynamic Light Scattering and Nanoparticle Tracking Analysis. UV/Vis spectroscopy confirmed effective gold-doping, with doping efficiency of approximately 50%. Transmission Electron Microscopy (TEM) showed gold NP accumulation throughout the polymer core and near the lipid shell. Results: Although gold doping resulted in a slight increase in NP size and zeta potential, no effects on cytocompatibility or cellular uptake by glioblastoma and microglia cells were observed. Furthermore, the optical properties (i.e., the refractive index and the UV spectrum) of the NPs were successfully modified to enable tracking across complementary imaging modalities. Real-time, label-free visualization of NP accumulation in the cytoplasm of U87 cells was achieved via holotomography by exploiting the enhanced refractive index after gold-doping. This observation was confirmed through correlation with fluorescence confocal microscopy, using fluorescently labelled gold-doped NPs. Furthermore, the high electron density of the gold tracer facilitated the precise localization of NPs within intracellular compartments via TEM, bypassing the inherently low contrast of organic NPs. Conclusions: These findings validated the gold-doped NPs as versatile nanoplatforms for multimodal imaging, showcasing their potential for non-invasive, high-resolution tracking and more accurate quantification of intracellular accumulation using diverse analytical techniques. Full article

Background/Objectives: Nanoparticles have emerged as indispensable tools in modern biomedicine, enabling precise diagnostics, targeted therapy, and controlled drug delivery. Despite their rapid progress, the translation of nanoparticle-based systems critically depends on the ability to detect, quantify, and track them across complex biological environments. Over the past two decades, a wide spectrum of detection modalities has been developed, encompassing optical, magnetic, acoustic, nuclear, cytometric, and mass spectrometric principles. Yet, no comprehensive framework has been established to compare these methods in terms of sensitivity, spatial resolution, and clinical applicability. Methods: Here we show a systematic analysis of all broadly applicable nanoparticle detection strategies, outlining their mechanisms, advantages, and drawbacks, and providing illustrative examples of practical applications. Results: This comparison reveals that each modality occupies a distinct niche: optical methods offer high sensitivity but limited penetration depth; magnetic and acoustic modalities enable repeated non-invasive tracking; nuclear imaging ensures quantitative, whole-body visualization; and invasive biochemical or histological assays achieve ultimate detection limits at the cost of tissue integrity. These findings redefine how each technique contributes to nanoparticle biodistribution and mechanistic studies, clarifying which are best suited for translational and clinical use. Conclusions: Placed in a broader context, this review bridges fundamental nanotechnology with biomedical applications, outlining a unified methodological framework that will guide the rational design, validation, and clinical implementation of nanoparticle-based therapeutics and diagnostics. By synthesizing the field into a single comparative framework, it also provides an accessible entry point for newcomers in nanotechnology and related biomedical sciences. Full article

Milk adulteration through centrifugation, which artificially reduces the somatic cell count (SCC), represents a significant challenge to food authenticity and public health. This fraudulent practice alters the native molecular architecture of milk, masking inflammatory conditions such as subclinical mastitis and distorting product quality. Conventional analytical and microscopic techniques remain insufficiently sensitive to detect the subtle physicochemical changes associated with centrifugation, highlighting the need for molecular-level, data-driven diagnostics. The dataset included 128 paired raw milk samples and approximately 25,000 bright-field micrographs acquired across multiple microscopes, of which 95% were confirmed to be of high quality. In this study, advanced machine learning (ML) and deep learning (DL) approaches were applied to identify centrifugation-induced alterations in raw milk microstructure. Bright-field micrographs (pixel size 0.27 µm) of paired unprocessed and centrifuged samples were obtained under standardized optical conditions and analyzed using convolutional neural networks (ResNet-18/50, Inception-v3, Xception, NasNet-Mobile) and hybrid attention architectures (MaxViT, CoAtNet). Model performance was evaluated using the harmonic average of recalls across five micrographs per sample (HAR₅). Human microscopy experts (n = 4) achieved only 18% classification accuracy—below the random baseline (25%)—confirming that centrifugation-induced modifications are not visually discernible. In contrast, DL architectures reached up to 97% accuracy (HAR₅, Xception), successfully identifying subtle molecular cues. Class activation and sensitivity analyses indicated that models focused not on milk fat globule (MFG) boundaries but on high-frequency nanoscale variations related to the reorganization of casein micelles and solid non-fat fractions. The findings strongly suggest that centrifugation adulteration constitutes a molecular reorganization event rather than a morphological alteration. The integration of optical microscopy with AI-driven molecular analytics establishes deep learning as a precise and objective tool for detecting fraudulent milk processing and improving food integrity diagnostics. Full article

Alzheimer’s disease (AD) is a worldwide problem due to the lack of effective therapy and accurate methods for timely diagnosis. The complexity of AD’s pathophysiology complicates the development of effective therapeutic agents, as most drugs act on only one therapeutic target, bypassing others. The design and development of multifunctional agents capable of altering metal ion-induced abnormalities, oxidative stress, and toxic beta amyloid (Aβ) aggregates is of interest. Herein, we report the first boron dipyrromethene (BODIPY) based bifunctional copper chelator with clioquinol, BDP-CLQ, capable of both optical detection of Aβ fibrils and copper chelation, with multiple anti-AD properties. Foremost, BDP-CLQ demonstrated a 3-fold and 5-fold fluorescence increase at 650 nm and 565 nm in the presence of Aβ and effective copper chelation (pK_d = 16.6 ± 0.3). In addition, BDP-CLQ demonstrated a potent inhibition of Aβ aggregation, reduction in Aβ-induced stiffness of neuronal cells, and antioxidant activity. BDP-CLQ is the first BODIPY-based fluorescent probe with multiple anti-AD activities, as well as the first clioquinol-based probe capable of Aβ optical visualization. This study demonstrates the prospects of the development of clioquinol-based theranostic probes since this allows combining several promising anti-AD actions in a single molecule and developing multi-targeted drugs. Full article

Feature-based indirect SLAM is more robust than direct SLAM; however, feature extraction and descriptor computation are time-consuming. In this paper, we propose GL-VSLAM, a general lightweight visual SLAM approach designed for RGB-D and stereo cameras. GL-VSLAM utilizes sparse optical flow matching based on uniform motion model prediction to establish keypoint correspondences between consecutive frames, rather than relying on descriptor-based feature matching, thereby achieving high real-time performance. To enhance positioning accuracy, we adopt a coarse-to-fine strategy for pose estimation in two stages. In the first stage, the initial camera pose is estimated using RANSAC PnP based on robust keypoint correspondences from sparse optical flow. In the second stage, the camera pose is further refined by minimizing the reprojection error. Keypoints and descriptors are extracted from keyframes for backend optimization and loop closure detection. We evaluate our system on the TUM and KITTI datasets, as well as in a real-world environment, and compare it with several state-of-the-art methods. Experimental results demonstrate that our method achieves comparable positioning accuracy, while its efficiency is up to twice that of ORB-SLAM2. Full article

Small and medium Unmanned Aerial Vehicles (UAVs) are commonly equipped with diverse sensors for situational awareness, including cameras, Frequency-Modulated Continuous-Wave (FMCW) radars, Light Detection and Ranging (LiDAR) systems, and ultrasonic sensors. However, optical systems are constrained by adverse weather and darkness, while the limited detection range of compact FMCW radars-typically a few hundred meters-is often insufficient for higher-speed UAVs, particularly those operating Beyond Visual Line of Sight (BVLOS). This paper presents a Collision Avoidance System (CAS) based on a lightweight pulse radar, targeting medium UAV platforms (10–300 kg MTOM) where installing large, nose-mounted radars is impractical. The system is designed for obstacle detection at ranges of 1–3 km, directly addressing the standoff distance limitations of conventional sensors. Beyond its primary sensing function, the pulse architecture offers several operational advantages. Its lower time-averaged power also results in a reduced electromagnetic footprint, mitigating interference and supporting emission-control objectives. Furthermore, pulse radar offers greater robustness against interference in dense electromagnetic environments and lower power consumption, both of which directly enhance UAV operational endurance. Field tests demonstrated a one-to-one correspondence between visually identified objects and radar detections across 1–3 km, with ${\mathrm{P}}_{\mathrm{FA}}$ = 1.5%, confirming adequate standoff for tens of seconds of maneuvering time, with range resolution of 3.75 m and average system power below 80 W. Full article

Fluorescent nanoprobes operating in the NIR-II window have gained considerable attention for biomedical imaging because of their deep-tissue penetration, reduced scattering, and high spatial resolution. Their tunable optical behavior, flexible surface chemistry, and capacity for multifunctional design enable sensitive detection and targeted visualization of biological structures in vivo. This review highlights recent advances in the design and optical engineering of four widely studied NIR-II nanoprobe families: quantum dots, carbon dots, upconversion nanoparticles, and dye-doped silica nanoparticles. These materials were selected because they offer well-defined architectures, controllable emission properties, and substantial mechanistic insight supporting discussions of imaging performance and translational potential. Particular focus is placed on emerging strategies for activatable, targeted, and ratiometric probe construction. Recent efforts addressing biosafety, large-scale synthesis, optical stability, and early preclinical validation are also summarized to clarify the current progress and remaining challenges that influence clinical readiness. By outlining these developments, this review provides an updated and focused perspective on how engineered NIR-II nanoprobes are advancing toward practical use in biomedical imaging and precision diagnostics. Full article

Neurofibromatosis type 1 (NF1) is an inherited, autosomal dominant syndrome that affects about 1 in every 3000 people worldwide. Early tumor detection is crucial for surveillance and intervention, especially given the potential for serious complications, including visual impairment, skeletal deformities, and malignancy. Therefore, it is essential for pediatricians and other healthcare professionals who provide care to these patients to be aware of all signs, treatments, and management strategies to deliver the best possible care. This study aims to develop a consensus for the diagnosis, treatment, and management of benign and malignant tumors associated with pediatric patients with NF1. Delphi methodology was used to achieve consensus among experts on the diagnostic accuracy, therapeutic efficacy, safety, and surveillance of pediatric patients with NF1. The consensus made 24 recommendations: gliomas in the optic pathway—6 statements, non-optical gliomas—2 statements, plexiform neurofibromas—5 statements, malignant peripheral nerve sheath tumors (MPNST)—6 statements, melanoma—1 statement, juvenile myelomonocytic leukemia (JMML)—1 statement, pheochromocytoma and paraganglioma—2 statements, and gastrointestinal stromal tumors (GIST)—1 statement. This consensus represents the first Brazilian recommendations on malignant and benign tumors in pediatric patients with NF1, providing a framework to standardize and optimize the clinical application for this disease. Full article

Laser Speckle Imaging (LSI) is a non-invasive optical technique used to assess biological activity by detecting dynamic variations in speckle patterns. These patterns exhibit statistical symmetry in static regions, while biological activity induces symmetry breaking that can be captured through the Graphic Absolute Value of Differences (GAVD), producing the activity map $I_{GAVD}$. This work evaluates the effect of four contrast enhancement algorithms: Histogram Equalization (HE), Contrast Limited Adaptive Histogram Equalization (CLAHE), Multiscale Morphological Contrast Enhancement (MMCE), and Multiscale Top-Hat Transform with an Open-Close Close-Open (OCCO) filter, applied to intermediate LSI images, with the final activity map used for quantitative evaluation. Each method represents a distinct enhancement paradigm: HE and CLAHE are histogram-based techniques for global and local contrast adjustment, whereas MMCE and OCCO-MTH are morphological approaches that emphasize structural preservation and local detail enhancement. The dataset consisted of images of Phaseolus vulgaris (SP) and Lactuca sativa (SL) seeds. Evaluation was conducted through expert visual inspection and quantitative analysis using contrast, entropy, spatial frequency (SF), structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), and contrast improvement ratio (CIR). All metrics were computed on $I_{GAVD}$ activity maps, which reflect bioactivity through the disruption of statistical symmetry. Non-parametric statistical tests (Friedman, aligned Friedman, and Quade) revealed that CLAHE and MMCE significantly improved image quality compared to the original images ($p<0.05$). Among the evaluated algorithms, CLAHE increased global contrast by approximately 25% and entropy by 6% relative to the original speckle frames, enhancing the visibility of bioactive regions. MMCE achieved the highest bioactivity contrast ratio (CIR = 0.64), while OCCO-MTH provided the best structural fidelity (SSIM = 0.91) and noise suppression (PSNR = 30.7 dB). These results demonstrate that suitable contrast enhancement can substantially improve the interpretability of LSI activity maps without altering acquisition hardware. This finding is particularly relevant for experimental applications aiming to maximize information quality without modifying acquisition hardware. Full article

Object detection in high-resolution satellite imagery is a critical technology for various applications, yet it faces persistent challenges due to extreme variations in object scale, orientation, and density. The development of numerous public datasets has been pivotal for advancing the field. To continue this progress and expand the diversity of sensor data available for research, we introduce the KOMPSAT Fine-Grained Object Detection (KFGOD) dataset, a new large-scale benchmark for fine-grained object detection. KFGOD is uniquely constructed using 70 cm and 55 cm resolution optical imagery from the KOMPSAT-3 and 3A satellites, sources not covered by existing major datasets. It provides approximately 880,000 object instances across 33 fine-grained classes, encompassing a wide range of ships, aircraft, vehicles, and infrastructure. The dataset ensures high quality and sensor consistency, covering diverse geographical regions worldwide to promote model generalization. For precise localization, all objects are annotated with both oriented (OBB) and horizontal (HBB) bounding boxes. Comprehensive experiments with state-of-the-art detection models provide benchmark results and highlight the challenging nature of the dataset, particularly in distinguishing between visually similar fine-grained classes. The KFGOD dataset is publicly available and aims to foster further research in fine-grained object detection and analysis of high-resolution satellite imagery. Full article

Background/Objectives: The purpose of this study was to evaluate the implementation of ultra-widefield swept-source optical coherence tomography (SS-OCT) in characterizing peripheral retinal degenerations and rhegmatogenous lesions, and to assess its potential implications for clinical management. These lesions are often challenging to visualize with conventional techniques, highlighting the need for advanced imaging modalities to improve detection and characterization. Methods: We conducted a retrospective observational study involving patients diagnosed with peripheral retinal degenerations and/or rhegmatogenous lesions referred to our center. All participants underwent comprehensive ophthalmological evaluation, including slit-lamp biomicroscopy, dilated fundus examination, and peripheral SS-OCT imaging. Key parameters assessed included the presence of vitreoretinal attachment, vitreous traction, full-thickness retinal defects, and subretinal fluid associated with the peripheral lesions under investigation. Results: A total of 107 eyes from 95 patients were included. The mean spherical equivalent was −2.18 ± 2.5 diopters, and mean BCVA was 0.03 ± 0.11. Peripheral SS-OCT imaging successfully captured and characterized 130 retinal lesions, including retinal tears (n = 34), lattice degeneration (n = 25), retinal holes (n = 21), peripheral retinoschisis (n = 17), and schisis/detachment (n = 7). Less commonly observed lesions were snail track degeneration (n = 4), white without pressure (n = 4) microcystic degeneration (n = 2), dialysis (n = 2), condensed vitreous (n = 2), and paving stone degeneration (n = 1). SS-OCT provided high-resolution visualization of the peripheral retina and vitreoretinal interface, revealing findings such as vitreous traction, everted edges in retinal holes, and associated subretinal fluid, some of which were not clinically detectable and, in several cases, directly influenced management decisions. Conclusions: Ultra-widefield SS-OCT significantly enhanced the visualization of peripheral retinal degenerations and rhegmatogenous lesions, providing clinically meaningful details that may influence diagnosis and clinical decision-making. Full article