Search Results (962)

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

Diacylglycerol-O-acyltransferase 1 (DGAT1, EC 2.3.1.20) is a pivotal enzyme in plant triacylglycerol (TAG) biosynthesis. Previous work identified conserved di-arginine (R) motifs (R-R, R-X-R, and R-X-X-R) in its N-terminal cytoplasmic acyl-CoA binding domain. To elucidate their functional significance, we engineered R-rich sequences in the N-termini of Tropaeolum majus and Zea mays DGAT1s. Comparative analysis with their respective non-mutant constructs showed that deleting or substituting R with glycine in the N-terminal region of DGAT1 markedly reduced lipid accumulation in both Camelina sativa seeds and Saccharomyces cerevisiae cells. Immunofluorescence imaging revealed co-localization of non-mutant and R-substituted DGAT1 with lipid droplets (LDs). However, disruption of an N-terminal di-R motif destabilizes DGAT1, alters LD organization, and impairs recombinant oleosin retention on LDs. Further evidence suggests that the di-R motif mediates DGAT1 retrieval from LDs to the endoplasmic reticulum (ER), implicating its role in dynamic LD–ER protein trafficking. These findings establish the conserved di-R motifs as important regulators of DGAT1 function and LD dynamics, offering insights for the engineering of oil content in diverse biological systems. Full article

The transition to efficient production requires innovative water control techniques to maximize irrigation efficiency and minimize waste. Analyzing and optimizing irrigation practices is essential to improve water use and reduce environmental impact. The aim of the research was to identify a discrimination method to analyze the different hydration levels in baby-leaf products. The species being researched was spinach, harvested at the baby leaf stage. Utilizing a large dataset of 261 wavelengths from the hyperspectral imaging system, the feature selection minimum redundancy maximum relevance (FS-MRMR) algorithm was applied, leading to the development of a neural network-based prediction model. Finally, a mathematical classification model K-NN (k-nearest neighbors type) was developed in order to identify a transfer function capable of discriminating the hyperspectral data based on a threshold value of absolute leaf humidity. Five significant wavelengths were identified for estimating the moisture content of baby leaves. The resulting model demonstrated a high generalization capability and excellent correlation between predicted and measured data, further confirmed by the successful training, validation, and testing of a K-NN-based statistical classifier. The construction phase of the statistical classifier involved the use of the experimental dataset and the critical humidity threshold value of 0.83 (83% of leaf humidity) was considered, below which the baby-leaf crop requires the irrigation intervention. High percentages of correct classification were achieved for data within two humidity classes. Specifically, the statistical classifier demonstrated excellent performance, with 81.3% correct classification for samples below the threshold and 99.4% for those above it. The application of advanced spectral analysis and artificial intelligence methods has led to significant progress in leaf moisture analysis and prediction, yielding substantial implications for both agriculture and biological research. Full article

Accurate identification of predator–pest relationships is essential for implementing effective and sustainable biological control in agriculture. However, existing image-based methods struggle to recognize insect co-occurrence under complex field conditions, limiting their ecological applicability. To address this challenge, we propose a hybrid deep learning framework that integrates convolutional neural networks (CNNs) and Transformer architectures for multi-label recognition of predator–pest combinations. The model leverages a novel co-occurrence attention mechanism to capture semantic relationships between insect categories and employs a pairwise label matching loss to enhance ecological pairing accuracy. Evaluated on a field-constructed dataset of 5,037 images across eight categories, the model achieved an F1-score of 86.5%, mAP50 of 85.1%, and demonstrated strong generalization to unseen predator–pest pairs with an average F1-score of 79.6%. These results outperform several strong baselines, including ResNet-50, YOLOv8, and Vision Transformer. This work contributes a robust, interpretable approach for multi-object ecological detection and offers practical potential for deployment in smart farming systems, UAV-based monitoring, and precision pest management. Full article

Background/Objectives: Magnetic resonance imaging (MRI) is a powerful, non-invasive diagnostic tool capable of capturing detailed anatomical and physiological information. MRI contrast agents enhance image contrast but, especially linear gadolinium-based compounds, have been associated with safety concerns. This has prompted interest in alternative contrast agents. Manganese-based contrast agents offer a promising substitute, owing to manganese’s favorable magnetic properties, natural biological role, and strong T1 relaxivity. This review aims to critically assess the structure, mechanisms, applications, and challenges of manganese-based contrast agents in MRI. Methods: This review synthesizes findings from preclinical and clinical studies involving various types of manganese-based contrast agents, including small-molecule chelates, nanoparticles, theranostic platforms, responsive agents, and controlled-release systems. Special attention is given to pharmacokinetics, biodistribution, and safety evaluations. Results: Mn-based agents demonstrate promising imaging capabilities, with some achieving relaxivity values comparable to gadolinium compounds. Targeted uptake mechanisms, such as hepatocyte-specific transport via organic anion-transporting polypeptides, allow for enhanced tissue contrast. However, concerns remain regarding the in vivo release of free Mn²⁺ ions, which could lead to toxicity. Preliminary toxicity assessments report low cytotoxicity, but further comprehensive long-term safety studies should be carried out. Conclusions: Manganese-based contrast agents present a potential alternative to gadolinium-based MRI agents pending further validation. Despite promising imaging performance and biocompatibility, further investigation into stability and safety is essential. Additional research is needed to facilitate the clinical translation of these agents. Full article

In recent years, the classification of underwater images has become one of the most remarkable areas of research in computer vision due to its useful applications in marine sciences, aquatic robotics, and sea exploration. Underwater imaging is pivotal for the evaluation of marine eco-systems, analysis of biological habitats, and monitoring underwater infrastructure. Extracting useful information from underwater images is highly challenging due to factors such as light distortion, scattering, poor contrast, and complex foreground patterns. These difficulties make traditional image processing and machine learning techniques struggle to analyze images accurately. As a result, these challenges and complexities make the classification difficult or poor to perform. Recently, deep learning techniques, especially convolutional neural network (CNN), have emerged as influential tools for underwater image classification, contributing noteworthy improvements in accuracy and performance in the presence of all these challenges. In this paper, we have proposed a modified ResNet-18 model for the binary classification of underwater images into raw and enhanced images. In the proposed modified ResNet-18 model, we have added new layers such as Linear, rectified linear unit (ReLU) and dropout layers, arranged in a block that was repeated three times to enhance feature extraction and improve learning. This enables our model to learn the complex patterns present in the image in more detail, which helps the model to perform the classification very well. Due to these newly added layers, our proposed model addresses various complexities such as noise, distortion, varying illumination conditions, and complex patterns by learning vigorous features from underwater image datasets. To handle the issue of class imbalance present in the dataset, we applied a data augmentation technique. The proposed model achieved outstanding performance, with 96% accuracy, 99% precision, 92% sensitivity, 99% specificity, 95% F1-score, and a 96% Area under the Receiver Operating Characteristic Curve (AUC-ROC) score. These results demonstrate the strength and reliability of our proposed model in handling the challenges posed by the underwater imagery and making it a favorable solution for advancing underwater image classification tasks. Full article

Ultra-high dose rate radiotherapy known as Flash radiotherapy (FLASH-RT) offers tremendous opportunities to improve the therapeutic ratio of radiotherapy by sparing the normal tissue while maintaining similar tumoricidal efficacy. However, the underlying biophysical basis of the FLASH effect remains under active investigation with several proposed mechanisms involving oxygen depletion, altered free-radical chemistry, and differential biological responses. This article provides an overview of available experimental and computational tools that can be utilized to probe the tumor and normal tissue microenvironment. We analyze in vitro, ex vivo, and in vivo systems used to study FLASH responses. We describe various computational and imaging technologies that can potentially aid in understanding the biophysics of FLASH-RT and lead to safer clinical translational. Full article

Magnetotactic bacteria (MTB), a unique group of Gram-negative prokaryotes, have the remarkable ability to biomineralize magnetic nanoparticles (MNPs) intracellularly, making them promising candidates for various biomedical applications such as biosensors, drug delivery, imaging contrast agents, and cancer-targeted therapies. To fully exploit the potential of MTB, a precise understanding of the structural, surface, and functional properties of these biologically produced nanoparticles is required. Given these concerns, this review provides a focused synthesis of the most widely used microscopic and spectroscopic methods applied in the characterization of MTB and their associated MNPs, covering the latest research from January 2022 to May 2025. Specifically, various optical microscopy techniques (e.g., transmission electron microscopy (TEM), scanning electron microscopy (SEM), and atomic force microscopy (AFM)) and spectroscopic approaches (e.g., localized surface plasmon resonance (LSPR), surface-enhanced Raman scattering (SERS), and X-ray photoelectron spectroscopy (XPS)) relevant to ultrasensitive MTB biosensor development are herein discussed and compared in term of their advantages and disadvantages. Overall, the novelty of this work lies in its clarity and structure, aiming to consolidate and simplify access to the most current and effective characterization techniques. Furthermore, several gaps in the characterization methods of MTB were identified, and new directions of methods that can be integrated into the study, analysis, and characterization of these bacteria are suggested in exhaustive manner. Finally, to the authors’ knowledge, this is the first comprehensive overview of characterization techniques that could serve as a practical resource for both younger and more experienced researchers seeking to optimize the use of MTB in the development of advanced biosensing systems and other biomedical tools. Full article

Oligometastasis represents a clinically relevant state of limited metastatic disease that could be amenable to selected local therapies in carefully chosen patients. Although initial trials such as SABR-COMET demonstrated a survival benefit with aggressive local treatment, breast cancer was underrepresented. Subsequent breast cancer-specific trials, including NRG-BR002, failed to show a clear survival benefit, highlighting uncertainties and the need for further refinement in patient selection and integration with systemic approaches. The definitions of oligometastasis continue to evolve, incorporating radiological, clinical, and biological features. Advances in imaging and molecular profiling suggest that oligometastatic breast cancer might represent a distinct biological subtype, with potential biomarkers including PIK3CA mutations and YAP/TAZ expression. Organ-specific strategies using stereotactic radiotherapy, surgery, and proton therapy have shown favorable local control in certain settings, though their impact on the overall survival remains under investigation. Emerging techniques, including circulating tumor DNA (ctDNA) analysis, are being explored to improve patient selection and disease monitoring. Ongoing trials may provide further insight into the role of local therapy, particularly in hormone receptor-positive or HER2-positive subtypes. Local and systemic strategies need to be carefully coordinated to optimize the outcomes. This review summarizes the current definitions of and evidence and therapeutic considerations for oligometastatic breast cancer and outlines potential future directions. Full article

Hydrogen sulfide (H₂S), once regarded solely as a highly toxic gas, is now recognized as a crucial signaling molecule in plants, bacteria, and mammals. In humans, H₂S signaling plays a role in numerous physiological and pathological processes, including vasodilation, neuromodulation, and cytoprotection. To exploit its biological functions and therapeutic potential, a wide range of H₂S-releasing compounds, known as H₂S donors, have been developed. These donors are designed to release H₂S under physiological conditions in a controlled manner. Among them, self-reporting H₂S donors are seen as a particularly innovative class, combining therapeutic delivery with real-time fluorescence-based detection. This dual functionality enables spatiotemporal monitoring of H₂S release in biological environments, eliminating the need for additional sensors or probes that could disrupt cellular homeostasis. This review summarizes recent advancements in self-reporting H₂S donor systems, organizing them based on their activation triggers, such as specific bioanalytes, enzymes, or external stimuli like light. The discussion covers their design strategies, performance in biological applications, and therapeutic potential. Key challenges are also highlighted, including the need for precise control of H₂S release kinetics, accurate signal quantification, and improved biocompatibility. With continued refinement, self-reporting H₂S donors offer great promise for creating multifunctional platforms that seamlessly integrate diagnostic imaging with therapeutic H₂S delivery. Full article

Background: Potent androgen receptor pathway inhibitors induce small cell neuroendocrine prostate cancer (SCNC), a highly aggressive subtype of metastatic androgen deprivation-resistant prostate cancer (ARPC) with limited treatment options and poor survival rates. Patients with metastases in the liver have a poor prognosis relative to those with bone metastases alone. The mechanisms that underlie the different behavior of ARPC in bone vs. liver may involve factors intrinsic to the tumor cell, tumor microenvironment, and/or systemic factors, and identifying these factors is critical to improved diagnosis and treatment of SCNC. Metabolic reprogramming is a fundamental strategy of tumor cells to colonize and proliferate in microenvironments distinct from the primary site. Understanding the metabolic plasticity of cancer cells may reveal novel approaches to imaging and treating metastases more effectively. Methods: Using magnetic resonance (MR) imaging and spectroscopy, we interrogated the physiological and metabolic characteristics of SCNC patient-derived xenografts (PDXs) propagated in the bone and liver, and used correlative biochemical, immunohistochemical, and transcriptomic measures to understand the biological underpinnings of the observed imaging metrics. Results: We found that the influence of the microenvironment on physiologic measures using MRI was variable among PDXs. However, the MR measure of glycolytic capacity in the liver using hyperpolarized ¹³C pyruvic acid recapitulated the enzyme activity (lactate dehydrogenase), cofactor (nicotinamide adenine dinucleotide), and stable isotope measures of fractional enrichment of lactate. While in the bone, the congruence of the glycolytic components was lost and potentially weighted by the interaction of cancer cells with osteoclasts/osteoblasts. Conclusion: While there was little impact of microenvironmental factors on metabolism, the physiological measures (cellularity and perfusion) are highly variable and necessitate the use of combined hyperpolarized ¹³C MRI and multiparametric (anatomic, diffusion-, and perfusion- weighted) ¹H MRI to better characterize pre-treatment tumor characteristics, which will be crucial to evaluate treatment response. Full article

Compared to widely used near-infrared OCT (NIR-OCT) systems, visible light OCT (vis-OCT) is an emerging imaging modality that leverages visible light to achieve high-resolution, high-contrast imaging and enables detailed spectroscopic analysis of biological tissues. In this review, we provide an overview of the state-of-the-art technology development and biomedical applications of vis-OCT. We also discuss limitations and future perspectives for advancing vis-OCT. Full article

We present a miniaturized, inexpensive, and user-friendly microfluidic platform to support biological applications. The system integrates a mini-incubator providing controlled environmental conditions and housing a microfluidic device for long-term cell culture experiments. The incubator is designed to be compatible with standard inverted optical microscopes and Raman spectrometers, allowing for the non-invasive imaging and spectroscopic analysis of cell cultures in vitro. The microfluidic device, which reproduces a dynamic environment, was optimized to sustain a passive, gravity-driven flow of medium, eliminating the need for an external pumping system and reducing mechanical stress on the cells. The platform was tested using Raman analysis and adherent tumoral cells to assess proliferation prior and subsequent to hydrogen peroxide treatment for oxidative stress induction. The results demonstrated a successful adhesion of cells onto the substrate and their proliferation. Furthermore, the platform is suitable for carrying out optical monitoring of cultures and Raman analysis. In fact, it was possible to discriminate spectra deriving from control and hydrogen peroxide-treated cells in terms of DNA backbone and cellular membrane modification effects provoked by reactive oxygen species (ROS) activity. The 800–1100 cm⁻¹ band highlights the destructive effects of ROS on the DNA backbone’s structure, as its rupture modifies its vibration; moreover, unpaired nucleotides are increased in treated sample, as shown in the 1154–1185 cm⁻¹ band. Protein synthesis deterioration, led by DNA structure damage, is highlighted in the 1257–1341 cm⁻¹, 1440–1450 cm⁻¹, and 1640–1670 cm⁻¹ bands. Furthermore, membrane damage is emphasized in changes in the 1270, 1301, and 1738 cm⁻¹ frequencies, as phospholipid synthesis is accelerated in an attempt to compensate for the membrane damage brought about by the ROS attack. This study highlights the potential use of this platform as an alternative to conventional culturing and analysis procedures, considering that cell culturing, optical imaging, and Raman spectroscopy can be performed simultaneously on living cells with minimal cellular stress and without the need for labeling or fixation. Full article

Medical phantoms are essential to imaging calibration, clinician training, and the validation of therapeutic procedures. However, most ultrasound phantoms prioritize acoustic realism while neglecting the viscoelastic and anisotropic properties of fibrous soft tissues. This gap limits their effectiveness in modeling realistic biomechanical behavior, especially in wave-based diagnostics and therapeutic ultrasound. Current materials like gelatine and agarose fall short in reproducing the complex interplay between the solid and fluid components found in biological tissues. To address this, we developed a soft, anisotropic composite whose dynamic mechanical properties resemble fibrous biological tissues such as skin and skeletal muscle. This material enables wave propagation and vibration studies in controllably anisotropic media, which are rare and highly valuable. We demonstrate the tunability of damping and stiffness aligned with fiber orientation, providing a versatile platform for modeling soft-tissue dynamics and validating biomechanical simulations. The phantoms achieved Young’s moduli of 7.16–11.04 MPa for skin and 0.494–1.743 MPa for muscles, shear wave speeds of 1.51–5.93 m/s, longitudinal wave speeds of 1086–1127 m/s, and sound absorption coefficients of 0.13–0.76 dB/cm/MHz, with storage, loss, and complex moduli reaching 1.035–6.652 kPa, 0.1831–0.8546 kPa, and 2.138–10.82 kPa. These values reveal anisotropic response patterns analogous to native tissues. This novel natural fibrous composite system affords sustainable, low-cost ultrasound phantoms that support both mechanical fidelity and acoustic realism. Our approach offers a route to next-gen tissue-mimicking phantoms for elastography, wave propagation studies, and dynamic calibration across diverse clinical and research applications. Full article

Termites pose significant threats to the structural integrity of embankments due to their nesting and tunneling behavior, which leads to internal voids, water leakage, or even dam failure. This review systematically classifies and evaluates current termite detection techniques in the context of embankment maintenance, focusing on physical sensing technologies and biological characteristic-based methods. Physical sensing methods enable non-invasive localization of subsurface anomalies, including ground-penetrating radar, acoustic detection, and electrical resistivity imaging. Biological characteristic-based methods, such as electronic noses, sniffer dogs, visual inspection, intelligent monitoring, and UAV-based image analysis, are capable of detecting volatile compounds and surface activity signs associated with termites. The review summarizes key principles, application scenarios, advantages, and limitations of each technique. It also highlights integrated multi-sensor frameworks and artificial intelligence algorithms as emerging solutions to enhance detection accuracy, adaptability, and automation. The findings suggest that future termite detection in embankments will rely on interdisciplinary integration and intelligent monitoring systems to support early warning, rapid response, and long-term structural resilience. This work provides a scientific foundation and practical reference for advancing termite management and embankment safety strategies. Full article