Search Results (9,569)

This study presents the development of an innovative battery-free, RF-powered implantable microdevice designed for intravesical chemotherapy delivery. The system utilizes a custom-designed RF energy harvesting module that enables wireless energy transfer through biological tissue, eliminating the need for internal power sources. Mechanical and electronic components were co-optimized to achieve full functionality within a compact, biocompatible housing suitable for intravesical implantation. The feasibility of the device was validated through simulation studies and ex vivo experiments using biological tissue models. The results demonstrated successful energy transmission, storage, and sequential actuator activation within a biological environment. The proposed system offers a promising platform for minimally invasive, wirelessly controlled drug delivery applications in oncology and other biomedical fields. Full article

Cell detection-tracking tasks are vital for biomedical image analysis with potential applications in clinical diagnosis and treatment. However, it poses challenges such as ambiguous boundaries and complex backgrounds in microscopic video sequences, leading to missed detection, false detection, and loss of tracking. Therefore, we propose an enhanced multiple object tracking algorithm IEGS-YOLO + BoT-SORT, named IEGS-BoT, to address these issues. Firstly, the IEGS-YOLO detector is developed for cell detection tasks. It uses the iEMA module, which effectively combines the global information to enhance the local information. Then, we replace the traditional convolutional network in the neck of the YOLO11n with GSConv to reduce the computational complexity while maintaining accuracy. Finally, the BoT-SORT tracker is selected to enhance the accuracy of bounding box positioning through camera motion compensation and Kalman filter. We conduct experiments on the CTMC dataset, and the results show that in the detection phase, the map50 (mean Average Precision) and map50–95 values are 73.2% and 32.6%, outperforming the YOLO11n detector by 1.1% and 0.6%, respectively. In the tracking phase, using the IEGS-BoT method, the multiple objects tracking accuracy (MOTA), higher order tracking accuracy (HOTA), and identification F1 (IDF1) reach 53.97%, 51.30%, and 67.52%, respectively. Compared with the base BoT-SORT, the proposed method achieves improvements of 1.19%, 0.23%, and 1.29% in MOTA, HOTA, and IDF1, respectively. ID switch (IDSW) decreases from 1170 to 894, which demonstrates significant mitigation of identity confusion. This approach effectively addresses the challenges posed by object loss and identity switching in cell tracking, providing a more reliable solution for medical image analysis. Full article

Ventricular arrhythmias such as ventricular fibrillation (VF) and ventricular tachycardia (VT) are among the leading causes of sudden cardiac death worldwide, making their timely and accurate detection a critical task in modern cardiology. This study presents an advanced framework for the automatic detection of critical cardiac arrhythmias—specifically ventricular fibrillation (VF) and ventricular tachycardia (VT)—by integrating deep learning techniques with neuro-fuzzy systems. Electrocardiogram (ECG) signals from the MIT-BIH and AHA databases were preprocessed through denoising, alignment, and segmentation. Convolutional neural networks (CNNs) were employed for deep feature extraction, and the resulting features were used as input for various fuzzy classifiers, including Fuzzy ARTMAP and the Adaptive Neuro-Fuzzy Inference System (ANFIS). Among these classifiers, ANFIS demonstrated the best overall performance. The combination of CNN-based feature extraction with ANFIS yielded the highest classification accuracy across multiple cardiac rhythm types. The classification performance metrics for each rhythm type were as follows: for Normal Sinus Rhythm, precision was 99.09%, sensitivity 98.70%, specificity 98.89%, and F1-score 98.89%. For VF, precision was 95.49%, sensitivity 96.69%, specificity 99.10%, and F1-score 96.09%. For VT, precision was 94.03%, sensitivity 94.26%, specificity 99.54%, and F1-score 94.14%. Finally, for Other Rhythms, precision was 97.74%, sensitivity 97.74%, specificity 99.40%, and F1-score 97.74%. These results demonstrate the strong generalization capability and precision of the proposed architecture, suggesting its potential applicability in real-time biomedical systems such as Automated External Defibrillators (AEDs), Implantable Cardioverter Defibrillators (ICDs), and advanced cardiac monitoring technologies. Full article

Ultrasound imaging is one of the most widespread biomedical imaging techniques thanks to its advantages such as being non-invasive, portable, non-ionizing, and cost-effective. Ultrasound imaging generally provides two-dimensional cross-sectional images, but the quality and interpretative ability vary based on the experience of the examiner, leading to a lack of objectivity and accuracy. To address these issues, there is a growing demand for three-dimensional ultrasound imaging. Among the various types of transducers used to obtain three-dimensional ultrasound images, this paper focuses on the most standardized probe, the linear array transducer, and provides an overview of the system implementations, imaging results, and applications of volumetric ultrasound imaging from the perspective of scanning methods. Through this comprehensive review, future researchers will gain insights into the advantages and disadvantages of various approaches to three-dimensional imaging systems using linear arrays, providing direction and applicability for system configuration and application. Full article

The present study offers a systematic review of the current state of research on lattice structures manufactured by additive technologies for biomedical applications, with the aim of identifying common patterns, such as the use of triply periodic minimal surfaces (TPMS) for bone scaffolds, as well as technological gaps and future research opportunities. Employing the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology, the review process ensures methodological rigor and replicability across the identification, screening, eligibility, and inclusion phases. Additionally, PRISMA was tailored by prioritizing technical databases and engineering-specific inclusion criteria, thereby aligning the methodology with the scope of this field. In recent years, a substantial surge in interdisciplinary research has underscored the promise of architected porous structures in enhancing mechanical compatibility, fostering osseointegration, and facilitating personalized medicine. A growing body of literature has emerged that explores the optimization of geometric features to replicate the behavior of biological tissues, particularly bone. Additive manufacturing (AM) has played a pivotal role in enabling the fabrication of complex geometries that are otherwise unachievable by conventional methods. The applications of lattice structures range from permanent load-bearing implants, commonly manufactured through selective laser melting (SLM), to temporary scaffolds for tissue regeneration, often produced with extrusion-based processes such as fused filament fabrication (FFF) or direct ink writing (DIW). Notwithstanding these advances, challenges persist in areas such as long-term in vivo validation, standardization of mechanical and biological testing, such as ISO standards for fatigue testing, and integration into clinical workflows. Full article

Niosomes are nanocarriers with a bilayer structure, consisting of a polar region and a non-polar region. This unique structure allows them to encapsulate compounds with varying polarities, addressing solubility challenges in the transport and delivery of bioactive molecules. The formation of niosomes involves key structural, geometric, and thermodynamic factors influenced by the choice of surfactants and preparation methods. These factors, including the critical packing factor and the hydrophilic–lipophilic balance (HLB), play a crucial role in determining the properties of the final niosomes. Additionally, the use of Tandford’s equations allows for the calculation of geometric parameters. These factors determine the structural integrity and functional properties of niosomes, making it possible to design functional niosomes with characteristics tailored for specific applications. This ability to design niosomes with desired properties is especially valuable in biomedical fields, where precise control over drug delivery and targeting is essential. This review highlights the importance of niosome formulation and presents examples of niosomes that have been functionalized for specific applications, including anticancer treatments, immunological treatments, and their action in the central nervous system. Full article

Prolonged operation of biomedical devices may compromise electronic component integrity due to cyclic thermal stress, thereby impacting both functionality and safety. Regulatory standards require regular inspections, particularly for surgical applications, highlighting the need for efficient and non-invasive diagnostic tools. This study introduces an integrated system that combines finite element models, infrared thermographic analysis, and artificial intelligence to monitor thermal stress in printed circuit boards (PCBs) within biomedical devices. A dynamic thermal model, implemented in COMSOL Multiphysics^® (version 6.2), identifies regions at high risk of thermal overload. The infrared measurements acquired through a FLIR P660 thermal camera provided experimental validation and a dataset for training a hybrid artificial intelligence system. This model integrates deep learning-based U-Net architecture for thermal anomaly segmentation with machine learning classification of heat diffusion patterns. By combining simulation, the proposed system achieved an F1-score of 0.970 for hotspot segmentation using a U-Net architecture and an F1-score of 0.933 for the classification of heat propagation modes via a Multi-Layer Perceptron. This study contributes to the development of intelligent diagnostic tools for biomedical electronics by integrating physics-based simulation and AI-driven thermographic analysis, supporting automatic classification and localisation of thermal anomalies, real-time fault detection and predictive maintenance strategies. Full article

Persistent luminescence nanoparticles (PLNPs) represent a unique class of optical materials. They possess the ability to absorb and store energy from external excitation sources and emit light persistently once excitation terminates. Because of this distinctive property, PLNPs have attracted considerable attention in various areas. Especially in recent years, PLNPs have revealed marked benefits and extensive application potential in fields such as biological detection, imaging, targeted delivery, as well as integrated diagnosis and treatment. Not only do they potently attenuate autofluorescence interference arising from biological tissues, but they also demonstrate superior signal-to-noise ratio and sensitivity in in vivo imaging scenarios. Therefore, regarding the current research, this paper firstly introduces the classification, synthesis methods, and luminescence mechanism of the materials. Subsequently, the research progress of PLNPs in biological detection and imaging and medical treatment in recent years is reviewed. The challenges faced by materials in biomedical applications and the outlook of future development trends are further discussed, which delivers an innovative thought pattern for developing and designing new PLNPs to cater to more practical requirements. Full article

Cellulose nanofibril (CNF)-based hydrogels, owing to their sustainability, biocompatibility, and versatile mechanical properties, are promising for biomedical applications. This review analyzes the recent advances and biomedical applications of CNF hydrogels. CNF hydrogels can be prepared via physical and chemical crosslinking. Physical crosslinking involves surface charge density control, pH manipulation, and flow-based processing to generate stable networks, whereas chemical crosslinking employs agents such as epichlorohydrin and citric acid to form permanent covalent bonds. These approaches enable precise control over hydrogel properties, including mechanical strength, porosity, and stimuli responsiveness. CNF hydrogels are particularly promising in drug delivery systems and tissue engineering. CNFs as drug delivery vehicles offer enhanced bioavailability and drug loading capacity owing to their open pore structure and large surface area. Recent developments in stimuli-responsive and injectable CNF hydrogels have enabled controlled drug release and improved targeting capabilities. Moreover, CNF hydrogels serve as effective scaffolds for cell growth and tissue regeneration, with applications in cartilage engineering and wound healing. Integrating CNF hydrogels with 3D bioprinting technology has generated complex tissue structures. However, several challenges remain, including the need for the standardization of toxicology assessments, optimization of large-scale production processes, and development of sophisticated control mechanisms for drug delivery. Future research should advance manufacturing technologies, improve long-term stability, and develop standardized testing protocols for regulatory compliance. Full article

The search for novel natural resources, such as extracts from algae and plant for use as reductants and capping agents for the synthesis of nanoparticles, may be appealing to medicine and nanotechnology. This study aimed to use Malva parviflora fruit extract as a novel source for the green synthesis of silver nanoparticles (AgNPs) and to evaluate their characterization. The results of biosynthesized AgNP characterization using multiple techniques, such as UV–Vis spectroscopy, scanning electron microscopy (SEM), FTIR analysis, and zeta potential (ZP), demonstrated that M. parviflora AgNPs exhibit a peak at 477 nm; possess needle-like and nanorod morphology with diameters ranging from 156.08 to 258.41 nm; contain –OH, C=O, C-C stretching from phenyl groups, and carbohydrates, pyranoid ring, and amide functional groups; and have a zeta potential of −21.2 mV. Moreover, the antibacterial activity of the M. parviflora AgNPs was assessed against two multidrug-resistant strains, including Staphylococcus aureus MRSA and Escherichia coli ESBL, with inhibition zones of 20.33 ± 0.88 mm and 13.33 ± 0.33 mm, respectively. The minimum bactericidal concentration (MBC) was 1.56 µg/mL for both. SEM revealed structural damage to the treated bacterial cells, and RAPD-PCR confirmed these genetic alterations. Additionally, M. parviflora AgNPs showed antioxidant activity (IC₅₀ = 0.68 mg/mL), 69% protein denaturation inhibition, and cytotoxic effects on MCF-7 breast cancer cells at concentrations above 100 µg/mL. These findings suggest that M. parviflora-based AgNPs are safe and effective for antimicrobial and biomedical applications, such as coatings for implanted medical devices, to prevent biofilm formation and facilitate drug delivery. Full article

Thiosemicarbazones and thiocarbohydrazones are key sulfur-containing organic compounds known for their diverse biological, pharmaceutical, and industrial applications. Beyond their well-established therapeutic potential, their strong chelating ability allows them to form stable complexes with transition metals, enabling uses in catalysis, corrosion inhibition, and dyeing processes. Their structural characteristics and dynamic conformations critically influence both biological activity and industrial performance, making nuclear magnetic resonance (NMR) spectroscopy an indispensable tool for their analysis. This review provides a comprehensive overview of the conformational and functional properties of bioactive thiosemicarbazones and thiocarbohydrazones, with a focus on how experimental NMR techniques are used to investigate their conformational behavior. In addition to experimental findings, available computational data are discussed, offering complementary insights into their structural dynamics. The integration of experimental and theoretical approaches offers a robust framework for predicting the behavior and interactions of these compounds, thereby informing the rational design of novel derivatives with improved functionality. By highlighting key structural features and application contexts, this work addresses a critical gap in the current understanding of these promising agents across both biomedical and industrial domains. Full article

Cyclodextrins, since their discovery in the late 19th century, have gained tremendous interest in biomedical research, beginning with their recognition as safe pharmaceutical excipients, and continuing with exploiting their potential for enhancing the therapeutic response of active pharmaceutical ingredients, and also to be used as drugs for specific medical purposes. This review presents an integrative perspective on amphiphilic cyclodextrins, the manuscript being divided into two parts, one devoted to the properties of amphiphilic cyclodextrins, while the second one is dedicated to their biomedical applications, with an emphasis on cancer therapy. Full article

Quasi-zero-stiffness (QZS) structures can provide a near constant force output in a certain range of displacement without force sensors and controllers. Therefore, they can be used in overload protection, vibration isolation, and biomedical application. In this paper, we propose a novel QZS structure based on cosine-curve compliant beams, which have a large QZS stoke and compact layout. The proposed QZS structure is composed of two half-period cosine-curve compliant beams with negative stiffness and two one-period cosine-curve compliant beams with positive stiffness. Then, we conducted the modeling of the force-displacement relationship of the compliant beams and analyzed the influence of the parameters on the mechanical performance. Based on the influence analysis, we propose the optimization processes to achieve QZS and obtain a QZS structure with the required force-displacement behavior. Finally, the mechanical performance of the QZS structure is verified through compression experiments on the prototype. Full article

The material selection process can be viewed as a multi-criteria decision-making (MCDM) problem with multiple objectives, which are often conflicting and of different importance. The selection of the most suitable biomaterial is considered as a very complex, important, and responsible task that is influenced by many factors. In this paper, a procedure for biomaterial selection based on MCDM is proposed by using a developed decision support system (DSS) named MCDM Solver. Within the framework of the developed DSS, the complete procedure for selecting the criteria weights was developed. Also, in addition to the adapted standard MCDM methods such as TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) and VIKOR (VIšekriterijumsko KOmpromisno Rangiranje), an extended WASPAS (Weighted Aggregated Sum Product Assessment) method was developed, enabling its application for considering target-based criteria in solving biomaterial selection problems. The proposed MCDM Solver enables a structured decision-making process helping decision-makers rank biomaterials with respect to multiple conflicting criteria and make rational and justifiable decisions. For the validation of the developed DSS, two case studies, i.e., the selection of a plate for internal bone fixation and a hip prosthesis, were presented. Finally, lists of potential biomaterials (alternatives) in the considered case studies were ranked based on the selected criteria, where the best-ranked one presents the most suitable choice for the specific biomedical application. Full article

This study investigates the phenolic composition and antioxidant potential of root extracts from three Opuntia ficus-indica varieties (green, red, and orange) using ultra-high-performance liquid chromatography coupled with diode array detection and electrospray ionization–tandem mass spectrometry (UHPLC-DAD-ESI-MS/MS). Phenolic compounds were extracted with a hydromethanolic solvent and quantified by spectrophotometric assays, while antioxidant activity was assessed through DPPH, ABTS, iron III reduction, hydroxyl radical, and nitric oxide scavenging methods. A total of 26 compounds were identified, including piscidic acid, epicatechin-3-O-gallate, and isovitexin, with several phenolics newly reported for O. ficus-indica roots. The green and red varieties showed the highest phenolic contents (up to 147.82 mg/g extract) and strong antioxidant capacity, particularly in ABTS (IC₅₀ = 29.38 μg/mL) and hydroxyl radical inhibition (>90%). Relative Antioxidant Capacity Index (RACI) analysis confirmed a consistent correlation between phenolic/flavonoid content and antioxidant efficacy. These findings highlight the analytical relevance of UHPLC-DAD-ESI-MS/MS for profiling underutilized plant matrices and support the potential use of O. ficus-indica root extracts as natural sources of bioactive compounds for pharmaceutical and biomedical applications. Full article