Search Results (2,085)

Background: Pain is a complex phenomenon characterized by unpleasant experiences with profound heterogeneity influenced by biological, psychological, and social factors. According to the National Health Interview Survey, 50.2 million U.S. adults (20.5%) experience pain on most days, with the annual cost of prescription medication for pain reaching approximately USD 17.8 billion. Theranostic pain nanomedicine therefore emerges as an attractive analgesic strategy with the potential for increased efficacy, reduced side-effects, and treatment personalization. Theranostic nanomedicine combines drug delivery and diagnostic features, allowing for real-time monitoring of analgesic efficacy in vivo using molecular imaging. However, clinical translation of these nanomedicines are challenging due to complex manufacturing methodologies, lack of standardized quality control, and potentially high costs. Quality by Design (QbD) can navigate these challenges and lead to the development of an optimal pain nanomedicine. Our lab previously reported a macrophage-targeted perfluorocarbon nanoemulsion (PFC NE) that demonstrated analgesic efficacy across multiple rodent pain models in both sexes. Here, we report PFC-free, biphasic nanoemulsions formulated with a biocompatible and non-immunogenic plant-based coconut oil loaded with a COX-2 inhibitor and a clinical-grade, indocyanine green (ICG) near-infrared fluorescent (NIRF) dye for parenteral theranostic analgesic nanomedicine. Methods: Critical process parameters and material attributes were identified through the FMECA (Failure, Modes, Effects, and Criticality Analysis) method and optimized using a 3 × 2 full-factorial design of experiments. We investigated the impact of the oil-to-surfactant ratio (w/w) with three different surfactant systems on the colloidal properties of NE. Small-scale (100 mL) batches were manufactured using sonication and microfluidization, and the final formulation was scaled up to 500 mL with microfluidization. The colloidal stability of NE was assessed using dynamic light scattering (DLS) and drug quantification was conducted through reverse-phase HPLC. An in vitro drug release study was conducted using the dialysis bag method, accompanied by HPLC quantification. The formulation was further evaluated for cell viability, cellular uptake, and COX-2 inhibition in the RAW 264.7 macrophage cell line. Results: Nanoemulsion droplet size increased with a higher oil-to-surfactant ratio (w/w) but was no significant impact by the type of surfactant system used. Thermal cycling and serum stability studies confirmed NE colloidal stability upon exposure to high and low temperatures and biological fluids. We also demonstrated the necessity of a solubilizer for long-term fluorescence stability of ICG. The nanoemulsion showed no cellular toxicity and effectively inhibited PGE2 in activated macrophages. Conclusions: To our knowledge, this is the first instance of a celecoxib-loaded theranostic platform developed using a plant-derived hydrocarbon oil, applying the QbD approach that demonstrated COX-2 inhibition. Full article

Research on image sensor (IS)-based visible light positioning systems has attracted widespread attention. However, when the receiver is tilted or under a single LED, the positioning system can only achieve two-dimensional (2D) positioning and requires the assistance of inertial measurement units (IMU). When the LED is not captured or decoding fails, the system’s positioning error increases further. Thus, we propose a novel three-dimensional (3D) visible light positioning system based on image sensors for various environments. Specifically, (1) we use IMU to obtain the receiver’s state and calculate the receiver’s 2D position. Then, we fit the height–size curve to calculate the receiver’s height, avoiding the coordinate iteration error in traditional 3D positioning methods. (2) When no LED or decoding fails, we propose a firefly-assisted unscented particle filter (FA-UPF) algorithm to predict the receiver’s position, achieving high-precision dynamic positioning. The experimental results show that the system positioning error under a single LED is within 10 cm, and the average positioning error through FA-UPF under no light source is 6.45 cm. Full article

Theranostic materials, which combine therapeutic and diagnostic capabilities, represent a promising advancement in cancer treatment by improving both the precision and personalization of therapies. Recently, metal peroxides (MePOs) have attracted significant interest from researchers for their potential use in both cancer diagnosis and therapy. This review provides an overview of recent developments in the application of MePOs for innovative cancer treatment strategies. The unique properties of MePOs, such as oxygen generation, are highlighted for their potential to improve therapeutic outcomes, especially in hypoxic tumor microenvironments. Initially, methods for MePO synthesis are briefly discussed, including hydrolyzation–precipitation, reversed-phase microemulsion, and sonochemical techniques, emphasizing the role of surfactants in regulating the particle size and enhancing bioactivity. Next, we discuss the main therapeutic approaches where MePOs have shown promise. These applications include chemotherapy, photodynamic therapy (PDT), immunotherapy, and radiation therapy. Overall, we focus on integrating MePOs into theranostic platforms to enhance cancer treatment and enable diagnostic imaging for improved clinical outcomes. Finally, we discuss potential future research directions that could lead to clinical translation and the development of advanced medicines. Full article

Background/Objectives: The heterogeneity of cancer disease and the frequent ineffectiveness and resistance observed with currently available treatments highlight the importance of developing new antitumor therapies. The properties of gold nanoparticles, such as their photon-energy heating, are attractive for oncology therapy; this can be effective and localized. The combination of chemotherapy and hyperthermia is promising. Our aim was to evaluate the combination therapy of photon hyperthermia with 5-fluorouracil (5-FU) both in vitro and in vivo. Methods: This study evaluated the antitumor efficacy of a combined chemo-photothermal therapy using 5-fluorouracil (5-FU) and branched gold nanoshells (BGNSs) in a colorectal cancer model. BGNSs were synthesized via a seed-mediated method and characterized by electron microscopy and UV–vis spectroscopy, revealing an average diameter of 126.3 nm and a plasmon resonance peak at 800 nm, suitable for near-infrared (NIR) photothermal applications. In vitro assays using SW620-GFP colon cancer cells demonstrated a ≥90% reduction in cell viability after 24 h of combined treatment with 5-FU and BGNS under NIR irradiation. In vivo, xenograft-bearing nude mice received weekly intratumoral administrations of the combined therapy for four weeks. The group treated with 5-FU + BGNS + NIR exhibited a final tumor volume of 0.4 mm³ on day 28, compared to 1010 mm³ in the control group, corresponding to a tumor growth inhibition (TGI) of 100.74% (p < 0.001), which indicates not only complete inhibition of tumor growth but also regression below the initial tumor volume. Thermographic imaging confirmed that localized hyperthermia reached 45 ± 0.5 °C at the tumor site. Results: These findings suggest that the combination of 5-FU and BGNS-mediated hyperthermia may offer a promising strategy for enhancing therapeutic outcomes in patients with colorectal cancer while potentially minimizing systemic toxicity. Conclusions: This study highlights the potential of integrating nanotechnology with conventional chemotherapy for more effective and targeted cancer treatment. Full article

Benzoxazine resins are gaining attention for their impressive thermal stability, low water uptake, and strong mechanical properties. In this work, two new bio-based benzoxazine monomers were developed using renewable arbutin: one combined with 3-(2-aminoethylamino) propyltrimethoxysilane (AB), and the other with furfurylamine (AF). Both were synthesized using a simple Mannich-type reaction and verified through FT-IR and ¹H-NMR spectroscopy. By blending these monomers in different ratios, copolymers with adjustable thermal, dielectric, and surface characteristics were produced. Thermal analysis showed that the materials had broad processing windows and cured effectively, while thermogravimetric testing confirmed excellent heat resistance—especially in AF-rich blends, which left behind more char. The structural changes obtained during curing process were monitored using FT-IR, and XPS verified the presence of key elements like carbon, oxygen, nitrogen, and silicon. SEM imaging revealed that AB-based materials had smoother surfaces, while AF-based ones were rougher; the copolymers fell in between. Dielectric testing showed that increasing AF content raised both permittivity and loss, and contact angle measurements confirmed that surfaces ranged from water-repellent (AB) to water-attracting (AF). Overall, these biopolymers (AB/AF copolymers) synthesized from arbutin combine environmental sustainability with customizability, making them strong candidates for use in electronics, protective coatings, and flame-resistant composite materials. Full article

Visual inspection of civil infrastructure for structural health assessment, as performed by structural engineers, is expensive and time-consuming. Therefore, automating this process is highly attractive, which has received significant attention in recent years. With the increasing capabilities of computers, deep neural networks have become a standard tool and can be used for structural health inspections. A key challenge, however, is the availability of reliable datasets. In this work, the U-net and DeepLab v3+ convolutional neural networks are trained on a synthetic Tokaido dataset. This dataset comprises images representative of data acquired by unmanned aerial vehicle (UAV) imagery and corresponding ground truth data. The data includes semantic segmentation masks for both categorizing structural elements (slabs, beams, and columns) and assessing structural damage (concrete spalling or exposed rebars). Data augmentation, including both image quality degradation (e.g., brightness modification, added noise) and image transformations (e.g., image flipping), is applied to the synthetic dataset. The selected neural network architectures achieve excellent performance, reaching values of 97% for accuracy and 87% for Mean Intersection over Union (mIoU) on the validation data. It also demonstrates promising results in the semantic segmentation of real-world structures captured in photographs, despite being trained solely on synthetic data. Additionally, based on the obtained results of semantic segmentation, it can be concluded that DeepLabV3+ outperforms U-net in structural component identification. However, this is not the case in the damage identification task. Full article

With the widespread use of sensors in applications such as autonomous driving and intelligent security, stable and efficient target tracking from diverse sensor data has become increasingly important. Self-supervised visual tracking has attracted increasing attention due to its potential to eliminate reliance on costly manual annotations; however, existing methods often train on incomplete object representations, resulting in inaccurate localization during inference. In addition, current methods typically struggle when applied to deep networks. To address these limitations, we propose a novel self-supervised tracking framework based on image synthesis and domain adversarial learning. We first construct a large-scale database of real-world target objects, then synthesize training video pairs by randomly inserting these targets into background frames while applying geometric and appearance transformations to simulate realistic variations. To reduce domain shift introduced by synthetic content, we incorporate a domain classification branch after feature extraction and adopt domain adversarial training to encourage feature alignment between real and synthetic domains. Experimental results on five standard tracking benchmarks demonstrate that our method significantly enhances tracking accuracy compared to existing self-supervised approaches without introducing any additional labeling cost. The proposed framework not only ensures complete target coverage during training but also shows strong scalability to deeper network architectures, offering a practical and effective solution for real-world tracking applications. Full article

Nanoparticles (NPs) synthesised through biogenic routes have emerged as a sustainable and innovative platform for biomedical applications such as antibacterial, anticancer, antiviral, anti-inflammatory, drug delivery, wound healing, and imaging diagnostics. Among these, silver nanoparticles (AgNPs) have attracted significant attention due to their unique physicochemical properties and therapeutic potential. This review examines the biogenic synthesis of AgNPs, focusing on microbial, plant-based, and biomolecule-assisted approaches. It highlights how reaction conditions, such as pH, temperature, and media composition, influence nanoparticle size, shape, and functionality. Particular emphasis is placed on microbial synthesis for its eco-friendly and scalable nature. The mechanisms of AgNP formation and their structural impact on biomedical performance are discussed. Key applications are examined including antimicrobial therapies, cancer treatment, drug delivery, and theranostics. Finally, the review addresses current challenges, such as reproducibility, scalability, morphological control, and biosafety, and outlines future directions for engineering AgNPs with tailored properties, paving the way for sustainable and effective next-generation biomedical solutions. Full article

Introduction: Brain tumor, marked by abnormal and rapid cell growth, poses severe health risks and requires accurate diagnosis for effective treatment. Classifying brain tumors using deep learning techniques applied to Magnetic Resonance Imaging (MRI) images has attracted the attention of many researchers, and specifically, reducing the bias of models and enhancing robustness is still a very pertinent active topic of attention. Methods: For capturing diverse information from different feature sets, we propose a Features Concatenation-based Brain Tumor Classification (FCBTC) Framework using Hybrid Deep Learning Models. For this, we have chosen three pretrained models—ResNet50; VGG16; and DensetNet121—as the baseline models. Our proposed hybrid models are built by the fusion of feature vectors. Results: The testing phase results show that, for the FCBTC Model-3, values for Precision, Recall, F1-score, and Accuracy are 98.33%, 98.26%, 98.27%, and 98.40%, respectively. This reinforces our idea that feature diversity does improve the classifier’s performance. Conclusions: Comparative performance evaluation of our work shows that, the proposed hybrid FCBTC Models have performed better than other proposed baseline models. Full article

The sparse iterative covariance-based estimation (SPICE) method has recently gained significant attraction in the field of scanning radar super-resolution imaging because of its angular resolution enhancement capability. However, it is unable to preserve the target profile, and the estimator is constrained by high computational complexity and memory consumption. In this paper, a grid-updating split SPICE-TV algorithm is presented. The method allows for the efficient updating of reconstruction results with both contour and resolution, and a recursive grid-updating implementation framework of the split SPICE-TV has the capability to reduce the computational complexity. First, the scanning radar angular super-resolution problem is transformed into a constrained optimization problem by simultaneously employing sparse covariance fitting criteria and TV regularization constraints. Then, the split Bregman method is employed to derive an efficient closed-form solution to the problem. Ultimately, the matrix inversion problem is transformed into an online iterative equation to reduce the computational complexity and memory consumption. The superiority of the proposed method is verified by simulation and experimental data. Full article

This update is aimed at various specialists who deal with fractures, such as emergency physicians, general practitioners, orthopedists, and sports medicine physicians. The Global Burden of Disease 2019 Fracture Collaborators estimated the worldwide incidence to be at 178 million, i.e., 2.2 fractures per 1000 people per year. Traditionally, X-rays are the first choice for suspected fractures. However, many fractures can also be detected or excluded with ultrasound. This option is especially attractive when available at the “point of care,”, i.e., at the patient’s bedside in the ambulatory or emergency setting. Point-of-care ultrasound provides clinicians with a simple, cost-effective imaging tool without radiation and complex infrastructure. The evidence suggests that ultrasound has high diagnostic sensitivity and can reliably rule out many fractures with a high degree of certainty. When applied correctly, it could potentially save millions of radiographs and, in some cases, even compete with the accuracy of X-rays and CT scans. These findings suggest a potential paradigm shift. This update discusses the advantages of ultrasound, its examination technique, sonoanatomy of fractures, and relevant indication groups, including its application for analgesia through nerve, fascia, and fascial plane blocks. Ultrasound’s diagnostic value supports its integration into routine fracture assessment, particularly in emergency and ambulatory care settings Full article

Thiophene derivatives are particularly attractive for application in drug development for their versatile pharmacological properties. We synthesized a series of four compounds with thiophene carboxamide as a scaffold. The structures were established based on HR-MS and 1D- and 2D-NMR. The purity of the compounds was established to be greater than 92% by thin-layer chromatography and NMR. The cytotoxic effects of the newly synthesized compounds were evaluated against the normal HaCaT cell line and A375, HT-29, and MCF-7 cancer cell lines. The cytotoxic assessment revealed that two compounds exhibit a significant cytotoxic effect on all cancer cell lines. To investigate their potential underlying mechanisms of action, several tests were performed: immunofluorescence imaging, caspase-3/7 assay, mitochondrial membrane potential (JC-1) assay, and 2′,7′–dichlorofluorescein diacetate (DCFDA) assay. MB-D2 proved to be the most cytotoxic and effective in terms of caspase 3/7 activation, mitochondrial depolarization and decrease in ROS production; these effects did not occur in normal HaCaT cells, revealing that MB-D2 has a high selectivity against A375 cancer cells. Full article

Object detection in drone-captured imagery has attracted significant attention due to its wide range of real-world applications, including surveillance, disaster response, and environmental monitoring. Although the majority of existing methods are developed under closed-set assumptions, and some recent studies have begun to explore open-vocabulary or open-world detection, their application to UAV imagery remains limited and underexplored. In this paper, we address this limitation by exploring the relationship between images and textual semantics to extend object detection in UAV imagery to an open-vocabulary setting. We propose a novel and efficient detector named Unmanned Aerial Vehicle Open-Vocabulary Detector (UAV-OVD), specifically designed for drone-captured scenes. To facilitate open-vocabulary object detection, we propose improvements from three complementary perspectives. First, at the training level, we design a region–text contrastive loss to replace conventional classification loss, allowing the model to align visual regions with textual descriptions beyond fixed category sets. Structurally, building on this, we introduce a multi-level text-guided fusion decoder that integrates visual features across multiple spatial scales under language guidance, thereby improving overall detection performance and enhancing the representation and perception of small objects. Finally, from the data perspective, we enrich the original dataset with synonym-augmented category labels, enabling more flexible and semantically expressive supervision. Experiments conducted on two widely used benchmark datasets demonstrate that our approach achieves significant improvements in both mean mAP and Recall. For instance, for Zero-Shot Detection on xView, UAV-OVD achieves 9.9 mAP and 67.3 Recall, 1.1 and 25.6 higher than that of YOLO-World. In terms of speed, UAV-OVD achieves 53.8 FPS, nearly twice as fast as YOLO-World and five times faster than DetrReg, demonstrating its strong potential for real-time open-vocabulary detection in UAV imagery. Full article

For the first time, a mixture of PbTe and Pb- and Te-oxides coated with carbon, under electron beam irradiation (EBI), was transformed into quantum dots, nanocrystals, nanoparticles and grains of PbTe with a sintered appearance. A small portion of non-stoichiometric phases was also obtained. By selecting conditions that favor the instantaneous transformation, the Gibbs free energy barrier is lowered for obtaining different PbTe structures. The driving force associated with the high-energy milling requires 4 h of processing time to reach a complete transformation, while a high-energy source kinetically affects precursor surfaces to cause an abrupt global chemical transformation instantly. Importantly, the size of the PbTe structures increases as they approach the irradiation point, implying a growth process that is affected by the local temperature reached during the EBI. Imaging after the EBI process revealed morphological variations in PbTe, which can be attractive for use in thermoelectric materials. The results of this study provide the first insights into electron-beam-induced reactions using a multiphase mixture of precursors. Therefore, it is believed that this proposal can also be applied to obtain other binary semiconductor structures, even ternary ones. Full article

Carbon nanodots have recently attracted attention as fluorescence imaging probes and magnetic resonance imaging (MRI) contrast agents in diagnostic and therapeutic applications due to their unique optical properties. In this work we report the synthesis of biocompatible Mn (II)-doped carbon nanodots and their performance as fluorescence and MRI contrast agents in in vitro assays. The thermal decomposition of a Diphenylhydantoin–Mn(II) complex assured the incorporation of manganese (II) ions in the carbon dots. The obtained materials display a favorable spin density for MRI applications. The synthesized Mn(II)-CNDs also displayed remarkable photoluminescence, with a bright blue emission and good response in in vitro fluorescence imaging. Cytotoxicity investigations revealed good cell viability on malignant melanoma cell lines in a large concentration range. A cytotoxic effect was observed for MG-63 osteosarcoma and breast adenocarcinoma cell lines. The in vitro MRI assays demonstrated the potentialities of the Mn(II)-CNDs as T2 contrast agents at low dosages, with relaxivity values higher than those of commercial ones. Due to the simplicity of their synthetic pathway and their low cytotoxicity, the prepared Mn(II)-CNDs are potential alternatives to currently used contrast agents based on gadolinium complexes. Full article