Search Results (2,349)

Amyloid fibrillization represents an effective strategy for extending and enhancing protein function, particularly for the delivery of hydrophobic active substances. In this study, oat globulin (OG) and its fibrils were complexed with quercetin (Que) to construct the delivery system, and ultrasonic pretreatment was applied during fibril preparation to explore the promoter of complex formation. The results demonstrated that complexation with Que induced a dose-dependent static quenching of the intrinsic fluorescence of the protein/fibrils, with hydrophobic interactions and tryptophan residues being the primary interaction forces and the main fluorescence quenching groups, respectively. In comparison, OG fibrils prepared with ultrasound pretreatment (UOGF) exhibited the strongest encapsulation and loading capacity for Que, ranging from 97.16% at a mass ratio of 200:1 to 42.48% at a ratio of 25:1. Subsequently, complexes were prepared with a ratio of 50:1. Structural analysis revealed that Que primarily interacts with the protein/fibril carriers through hydrogen bonds and hydrophobic interactions, inducing structural changes and ultimately being encapsulated in an amorphous form within the composite material. Additionally, Que promoted the mutual aggregation and cross-linking of protein/fibril units, leading to increased hydrodynamic diameter and zeta-potential. Moreover, UOGF-Que showed the greatest improvement in the thermal stability and the photostability of Que, and enhancing the bioaccessibility. These findings provide valuable insights into using ultrasound as an auxiliary measure for fibril self-assembly to enhance the application potential of fibrils, especially the delivery of hydrophobic functional substances. Full article

Dried banana slices can be nutritious snacks that meet consumers’ needs. However, preserving their color, texture, and antioxidant properties is challenging during convective drying. The new approach aimed to produce high-quality dried banana slices with higher antioxidant activity and lower browning. In this paper, the simultaneous application of ultrasound (at three levels: 0 W, 500 W, and 1000 W) and carboxymethyl cellulose (CMC) coating (the ratio of banana slice mass to the coating solution mass (BS:CS) at three levels: 1:2, 1:3, and 1:4) pretreatments, and their combined effects on various characteristics of the finally obtained dried banana slices were examined. The convective drying of banana slices was carried out at 80 °C and 3 m/s air velocity to achieve a consistent moisture content of roughly 10% (kg water/kg dry matter). As the power of ultrasound was increased from 0 W to 1000 W and with changing the BS:CS ratio from 1:2 to 1:4, the results demonstrated that the effective water diffusion coefficient (D_eff), water absorption capacity (WAC), and antioxidant activity (AA) of the dried banana slices were enhanced; however, their browning index (BI) decreased. Consequently, prior to convective drying, CMC coating using an ultrasonic system can be used as a practical strategy to produce fruit chips with desirable qualitative and nutritional properties. Full article

Oedema is a common clinical finding in critically ill neonates and may reflect systemic illness such as congestive heart failure, hepatic cirrhosis, nephrotic syndrome, sepsis, and acute kidney injury. Oedema is characterised by tissue swelling due to water accumulation in the interstitial space. Currently, the gold standard in clinical practice is visual assessment, which is subjective and limited in accuracy. Alternative methods, such as ultrasound and bioimpedance, have been explored; however, they are unsuitable in neonates and do not provide direct water quantification. Near-infrared spectroscopy (NIRS) is a non-invasive optical method that could measure water content through light interaction between near-infrared light and OH particles within the tissue. This study validated NIRS for oedema assessment using an ex vivo porcine skin model, where controlled oedema was induced by phosphate-buffered saline (PBS) injection. Continuous spectroscopic data were collected via optical fibres positioned perpendicularly and parallel to the tissue. Regression models were developed and evaluated using the spectral data, with partial least squares (PLS) regression outperforming ridge regression (RR) and support vector regression (SVR). Notably, spectra acquired in the parallel configuration yielded superior results (R² = 0.97, RMSE = 0.15). These findings support the potential of NIRS as a reliable, quantitative tool for neonatal oedema assessment. Full article

Niosomes (NIOs), a class of nanovesicular drug delivery system, have garnered significant attention due to their unique architecture, resulting from the self-assembly of non-ionic surfactants (with or without cholesterol) in aqueous media. This bilayered structure enables the encapsulation of both hydrophilic agents in the aqueous core and lipophilic compounds within the lipid bilayer, offering remarkable versatility in therapeutic applications. This article provides an overview of the key principles underlying niosomal formulations, including their composition, preparation methods, formulation conditions and the critical physicochemical parameters influencing vesicle formation and performance. Special emphasis is placed on recent innovations in surface and content modifications that have led to the development of stimuli-responsive niosomal systems, with precise and controlled drug release. These smart carriers are designed to respond to endogenous stimuli (such as pH variations, redox gradients, enzymatic activity, or local temperature changes in pathological sites), as well as to exogenous triggers (including light, ultrasound, magnetic or electric fields, and externally applied hyperthermia), thereby enhancing therapeutic precision. These surface and content modulation strategies effectively transform conventional NIOs into intelligent, stimuli-responsive platforms, reinforcing their innovative role in drug delivery and highlighting their significant potential in the development of smart nanomedicine. Full article

Background and Objectives: Stratifying thyroid nodules according to malignancy risk is a crucial step in early diagnosis and patient care. Recently, deep learning techniques have emerged as powerful tools for medical diagnostics, particularly with convolutional neural networks (CNNs) applied to medical image classification. This study aimed to develop a new hybrid CNN model for classifying thyroid nodules using the TN5000 ultrasound image dataset. Materials and Methods: The TN5000 dataset includes 5000 ultrasound images, with 3572 malignant and 1428 benign nodules. To address the issue of class imbalance, the researchers applied an R-based anomaly data augmentation method and a GAN-based technique (G-RAN) to generate synthetic benign images, resulting in a balanced dataset for training. The model architecture was built on a pre-trained EfficientNet-B3 backbone, further enhanced with squeeze-and-excitation (SE) blocks and residual refinement modules to improve feature extraction. The task was to classify malignant nodules (labeled 1) and benign nodules (labeled 0). Results: The proposed hybrid CNN achieved strong performance, with an accuracy of 89.73%, sensitivity of 90.01%, precision of 88.23%, and an F1-score of 88.85%. The total training time was 42 min. Conclusions: The findings demonstrate that the proposed hybrid CNN model is a promising tool for thyroid nodule classification on ultrasound images. Its high diagnostic accuracy suggests that it could serve as a reliable decision-support system for clinicians, improving consistency in diagnosis and reducing human error. Future work will focus on clinical validation, explainability of the model’s decision-making process, and strategies for integration into routine hospital workflows. Full article

Background and Objective: Accurate classification of standard fetal ultrasound planes is a critical step in prenatal diagnostics, enabling reliable biometric measurements and anomaly detection. Conventional deep learning approaches, particularly convolutional neural networks (CNNs) and transformers, often face challenges such as domain variability, noise artifacts, class imbalance, and poor calibration, which limit their clinical utility. This study proposes a hybrid state–space and vision transformer framework designed to address these limitations by integrating sequential dynamics and global contextual reasoning. Methods: The proposed framework comprises five stages: (i) preprocessing for ultrasound harmonization using intensity normalization, anisotropic diffusion filtering, and affine alignment; (ii) hybrid feature encoding with a state–space model (SSM) for sequential dependency modeling and a vision transformer (ViT) for global self-attention; (iii) multi-task learning (MTL) with anatomical regularization leveraging classification, segmentation, and biometric regression objectives; (iv) gated decision fusion for balancing local sequential and global contextual features; and (v) calibration strategies using temperature scaling and entropy regularization to ensure reliable confidence estimation. The framework was comprehensively evaluated on three publicly available datasets: FETAL_PLANES_DB, HC18, and a large-scale fetal head dataset. Results: The hybrid framework consistently outperformed baseline CNN, SSM-only, and ViT-only models across all tasks. On FETAL_PLANES_DB, it achieved an accuracy of 95.8%, a macro-F1 of 94.9%, and an ECE of 1.5%. On the Fetal Head dataset, the model achieved 94.1% accuracy and a macro-F1 score of 92.8%, along with superior calibration metrics. For HC18, it achieved a Dice score of 95.7%, an IoU of 91.7%, and a mean absolute error of 2.30 mm for head circumference estimation. Cross-dataset evaluations confirmed the model’s robustness and generalization capability. Ablation studies further demonstrated the critical role of SSM, ViT, fusion gating, and anatomical regularization in achieving optimal performance. Conclusions: By combining state–space dynamics and transformer-based global reasoning, the proposed framework delivers accurate, calibrated, and clinically meaningful predictions for fetal ultrasound plane classification and biometric estimation. The results highlight its potential for deployment in real-time prenatal screening and diagnostic systems. Full article

Integrating artificial intelligence (AI) with wearable sensor technologies can revolutionize the monitoring and management of various chronic diseases and acute conditions. AI-integrated wearables are categorized by their underlying sensing techniques, such as electrochemical, colorimetric, chemical, optical, and pressure/stain. AI algorithms enhance the efficacy of wearable sensors by offering personalized, continuous supervision and predictive analysis, assisting in time recognition, and optimizing therapeutic modalities. This manuscript explores the recent advances and developments in AI-powered wearable sensing technologies and their use in the management of chronic diseases, including COVID-19, Diabetes, and Cancer. AI-based wearables for heart rate and heart rate variability, oxygen saturation, respiratory rate, and temperature sensors are reviewed for their potential in managing COVID-19. For Diabetes management, AI-based wearables, including continuous glucose monitoring sensors, AI-driven insulin pumps, and closed-loop systems, are reviewed. The role of AI-based wearables in biomarker tracking and analysis, thermal imaging, and ultrasound device-based sensing for cancer management is reviewed. Ultimately, this report also highlights the current challenges and future directions for developing and deploying AI-integrated wearable sensors with accuracy, scalability, and integration into clinical practice for these critical health conditions. Full article

Iron-based catalysts for peroxymonosulfate (PMS) and peroxydisulfate (PDS) activation represent a cornerstone of advanced oxidation processes (AOPs) in environmental remediation, prized for their cost-effectiveness, environmental compatibility, and high catalytic potential. These catalysts, including zero-valent iron, iron oxides, and iron-organic frameworks, activate PMS/PDS through heterogeneous and homogeneous pathways to generate reactive species such as sulfate radicals (SO₄•⁻) and hydroxyl radicals (•OH). However, their large-scale implementation is constrained by inefficient iron cycling, characterized by sluggish Fe³⁺/Fe²⁺ conversion and significant iron precipitation, leading to catalyst passivation and oxidant wastage. This comprehensive review systematically dissects innovative strategies to augment iron cycling efficiency, encompassing advanced material design through elemental doping, heterostructure construction, and defect engineering; system optimization via reductant incorporation, bimetallic synergy, and pH modulation; and external field assistance using light, electricity, or ultrasound. We present a mechanistic deep-dive into these approaches, emphasizing facilitated electron transfer, suppression of iron precipitation, and precise regulation of radical versus non-radical pathways. The performance in degrading persistent organic pollutants—including antibiotics, per- and polyfluoroalkyl substances (PFASs), and pesticides—in complex environmental matrices is critically evaluated. We further discuss practical challenges related to scalability, long-term stability, and secondary environmental risks. Finally, forward-looking directions are proposed, focusing on rational catalyst design, integration of sustainable processes, and scalable implementation, thereby providing a foundational framework for developing next-generation iron-persulfate catalytic systems. Full article

Intravascular imaging has revolutionized the assessment and management of coronary artery disease, providing unparalleled insights into plaque morphology, lesion severity, and percutaneous coronary intervention (PCI) optimization. This comprehensive review explores the current landscape of intravascular imaging, detailing the principles and clinical utility of intravascular ultrasound (IVUS) and optical coherence tomography (OCT). We discuss the role of these technologies in various clinical scenarios, ranging from stable coronary artery disease to acute coronary syndromes, emphasizing their ability to refine diagnostic accuracy and therapeutic decision-making. A key focus is placed on their application in identifying vulnerable plaques, a critical step in preventing adverse cardiovascular events. Furthermore, we highlight the role of intravascular imaging in guiding PCI, improving stent deployment, and reducing procedural complications. Finally, we explore emerging imaging modalities and technological advancements poised to further enhance coronary assessment, including hybrid imaging techniques. In addition to established modalities, this review examines emerging imaging technologies and the growing integration of artificial intelligence (AI) and hybrid imaging systems, which hold promise for automated plaque characterization, improved reproducibility, and enhanced decision support during PCI. By summarizing the latest evidence and future directions, this review aims to provide a comprehensive reference for clinicians and researchers seeking to optimize the use of intravascular imaging in contemporary cardiovascular practice. Full article

Intraoperative ultrasound (IOUS) has developed from a rudimentary adjunct into a versatile modality that now plays a crucial role in neurosurgery. Offering real-time, radiation-free and repeatable imaging at the surgical site, it provides distinct advantages over intraoperative magnetic resonance (MRI) and computed tomography (CT) in terms of accessibility, workflow integration and cost. The clinical spectrum of IOUS is broad: in cranial surgery it enhances the extent of resection of gliomas and metastases, supports dissection in meningiomas and enables localization of MRI-negative pituitary adenomas; in spinal surgery, it guides resection of intradural and intramedullary tumors, assists in myelotomy planning and confirms decompression in degenerative conditions such as cervical myelopathy and ossification of the posterior longitudinal ligament. IOUS also offers unique insights into cerebrospinal fluid disorders, including arachnoid webs, cysts, syringomyelia and Chiari malformation, where it visualizes cord compression and CSF flow restoration. In trauma and oncological emergencies, it provides immediate confirmation of decompression, directly influencing surgical decisions. Recent innovations, including contrast-enhanced ultrasound, elastography, three-dimensional navigated systems and experimental integration with artificial intelligence and robotics, are extending its functional scope. Despite heterogeneity of evidence and operator dependence, IOUS is steadily transitioning from an adjunctive tool to a cornerstone of multimodal intraoperative imaging, bridging precision, accessibility and innovation in contemporary neurosurgical practice. Full article

Photoacoustic tomography is an innovative non-ionizing imaging technique that combines optical contrast with ultrasound resolution for 3D object characterization. While promising, its broader adoption is limited by challenges such as shallow penetration depth and strong optical scattering. To address these issues, this study introduces a dual illumination and detection photoacoustic tomography method, specifically designed for symmetrical objects like hollow metallic cylinders. The illumination system plays a critical role in determining the quality of photoacoustic signals and, thus, the final image. This approach enhances spatial resolution and contrast by using complementary light delivery and signal detection. In industrial settings, where accurate and efficient non-destructive testing is essential, traditional techniques often lack the precision required. The dual illumination and detection strategy offers significant improvements in effective resolution, contrast, defect detection, and artifact reduction, surpassing the limitations of unidirectional approaches. This technique not only strengthens the characterization of metal structures but also contributes to a deeper understanding of their physical behavior. Applications extend across various fields, including aerospace and biomedical engineering. This paper explores the underlying principles and potential of this advanced imaging modality, highlighting its value in modern diagnostic and inspection technologies. Full article

Background/Objectives: Precise breast ultrasound (BUS) segmentation supports reliable measurement, quantitative analysis, and downstream classification yet remains difficult for small or low-contrast lesions with fuzzy margins and speckle noise. Text prompts can add clinical context, but directly applying weakly localized text–image cues (e.g., CAM/CLIP-derived signals) tends to produce coarse, blob-like responses that smear boundaries unless additional mechanisms recover fine edges. Methods: We propose XBusNet, a novel dual-prompt, dual-branch multimodal model that combines image features with clinically grounded text. A global pathway based on a CLIP Vision Transformer encodes whole-image semantics conditioned on lesion size and location, while a local U-Net pathway emphasizes precise boundaries and is modulated by prompts that describe shape, margin, and Breast Imaging Reporting and Data System (BI-RADS) terms. Prompts are assembled automatically from structured metadata, requiring no manual clicks. We evaluate the model on the Breast Lesions USG (BLU) dataset using five-fold cross-validation. The primary metrics are Dice and Intersection over Union (IoU); we also conduct size-stratified analyses and ablations to assess the roles of the global and local paths and the text-driven modulation. Results: XBusNet achieves state-of-the-art performance on BLU, with a mean Dice of 0.8766 and IoU of 0.8150, outperforming six strong baselines. Small lesions show the largest gains, with fewer missed regions and fewer spurious activations. Ablation studies show complementary contributions of global context, local boundary modeling, and prompt-based modulation. Conclusions: A dual-prompt, dual-branch multimodal design that merges global semantics with local precision yields accurate BUS segmentation masks and improves robustness for small, low-contrast lesions. Full article

In recent years, oleosome-associated proteins (OPs) have gained increasing attention in the food and nutrition sectors due to their balanced amino acid composition and excellent functional properties. However, their low extraction yield, high hydrophobicity, and poor solubility hinder broader application in food systems. This review provides a concise overview of OPs’ structural features, current extraction strategies, and the impact of modification techniques on their structural and functional attributes. Special emphasis is placed on hybrid extraction methods that integrate physical treatments (e.g., ultrasound, heating, colloid milling) with traditional chemical approaches to enhance yield while preserving protein functionality. Furthermore, the review discusses how physical and chemical modifications effectively regulate OPs’ solubility, emulsifying capacity, aggregation behavior, and self-assembly characteristics. The regulatory mechanisms of different processing conditions on protein conformation and intermolecular interactions are summarized to guide functional optimization. Finally, the current technical challenges are outlined and future research directions are proposed, including the industrial scaling of hybrid extraction, precise control of structural modification, and application of OPs in emulsified and gel-based delivery systems. This work offers theoretical insight and practical guidance for the high-value utilization of OPs in food and related industries. Full article

Background and Objectives: Ultrasound (US)-guided peripheral regional anesthesia (pRA) is gaining increasing importance in emergency medicine as an effective, low-ridsk alternative to general anesthesia (GA), procedural sedation (PS), or opioid therapy. By enabling rapid, direct pain management in the emergency department (ED), pRA can help preserve scarce surgical and anesthetic resources and, in some cases, avoid inpatient admissions. The aim of this study was to analyze the indications, techniques, and clinical impact of pRA in the orthopedic-focused ED of an affiliated hospital. Materials and Methods: All pRA and PS procedures performed over a six-year period were retrospectively reviewed among 35,443 orthopedic-trauma emergency patients. pRA was carried out under US guidance with standardized monitoring. Diagnoses, block techniques, effectiveness, and complications were analyzed descriptively. Results: A total of 1292 patients (3.7%) underwent either pRA (n = 1117; 3.2%) or PS (n = 175; 0.5%). pRA was performed in 22% of cases for interventions such as reductions or extensive wound management. In 78%, pRA was applied for analgesia, for example, in the diagnostic work-up and treatment of non-immediately operable fractures, lumbago, or arthralgia. The most common pRA techniques were brachial plexus blocks (54%) and femoral nerve blocks (25%). Fascial plane blocks (6.1%) and paravertebral blocks (1.5%) were rarely used. PS was performed in 175 of 1292 patients (13%), although pRA would have been feasible in 159 of these cases. No complications of pRA were observed, and GA could routinely be avoided. Conclusions: US-guided pRA proved to be an effective and safe alternative to PS, GA, or systemic analgesia for selected indications, allowing immediate treatment without the need for operative capacities. To ensure safe application, these techniques should be an integral part of the training curriculum for ED personnel. Full article

Introduction: This study evaluates the real-world application of anifrolumab in managing articular involvement in systemic lupus erythematosus (SLE), providing insights into its efficacy and safety in routine clinical practice. Additionally, a systematic review examines anifrolumab’s role specifically in joint manifestations of SLE, consolidating existing real-world data on its therapeutic impact in articular disease. Methods: This monocentric case series presents data from four patients with SLE-related arthritis treated with anifrolumab. Clinical outcomes, including joint symptoms, clinimetric indices (DAS28, SLEDAI-2K, and SLICC), and treatment tolerability, were assessed. Ultrasound evaluation did not represent an outcome since it was not performed regularly. A systematic review was conducted to explore anifrolumab’s real-world application in articular disease manifestations, offering a comparative perspective. Results: All patients achieved complete remission of arthritis and lupus disease activity within four months, with no serious adverse reactions and without treatment discontinuation. Additionally, two patients completely discontinued corticosteroid (GC) therapy within two months, while the remaining two significantly reduced their GC doses. Only three promising relevant articles emerged from the systematic review, underlining the need for further studies to better support the role of anifrolumab in the treatment of arthritis in SLE. Conclusions: These findings highlight anifrolumab’s practical utility in real-world settings, particularly for articular involvement, while the systematic review contextualizes its impact within SLE management. The results underscore anifrolumab’s potential as a valuable treatment option for joint manifestations of SLE, addressing an unmet clinical need in routine practice. This evidence may assist clinicians in selecting the most suitable therapeutic approach based on predominant clinical features, thus enhancing personalized treatment strategies in SLE. Full article