Search Results (2,900)

AuQDs (Au quantum dots) are ultrasmall nanostructures that combine the size-tunable fluorescence and photostability of semiconductor quantum dots with the chemical stability, low toxicity, and versatile surface chemistry of gold nanoparticles. This unique combination endows AuQDs with exceptional biocompatibility and multifunctionality, making them ideal for biomedical applications such as cellular imaging, real-time tracking, targeted drug delivery, diagnostics, therapeutic monitoring, and biosensing. Various synthesis methods—including chemical reduction, hydrothermal, laser ablation, and microwave-assisted techniques—allow for precise control over size and surface properties, optimizing fluorescence and electronic behavior for high-resolution imaging and sensitive detection. Compared to traditional quantum dots, AuQDs offer enhanced safety and biocompatibility, while surpassing larger gold nanoparticles by enabling fluorescence-based imaging. Their surfaces can be functionalized with diverse ligands for targeted delivery and specific biological interactions. In summary, AuQDs are multifunctional nanoprobes that combine superior optical properties, chemical stability, and biocompatibility, making them powerful tools for advanced biomedical diagnostics, therapy, and biosensing. Full article

Integration of machine learning (ML) and artificial intelligence (AI) into point-of-care (POC) sensor systems represents a transformative advancement in healthcare. This integration enables sophisticated data analysis and real-time decision-making in emergency and intensive care settings. AI and ML algorithms can process complex biomedical data, improve diagnostic accuracy, and enable early disease detection for better patient outcomes. Predictive analytics in POC devices supports proactive healthcare by analyzing data to forecast health issues and facilitating early intervention and personalized treatment. This review covers the key areas of ML and AI integration in POC devices, including data analysis, pattern recognition, real-time decision support, predictive analytics, personalization, automation, and workflow optimization. Examples of current POC devices that use ML and AI include AI-powered blood glucose monitors, portable imaging devices, wearable cardiac monitors, AI-enhanced infectious disease detection, and smart wound care sensors are also discussed. The review further explores new directions for POC sensors and ML integration, including mental health monitoring, nutritional monitoring, metabolic health tracking, and decentralized clinical trials (DCTs). We also examined the impact of integrating ML and AI into POC devices on healthcare accessibility, efficiency, and patient outcomes. Full article

Accurate and automated segmentation of white blood cells (WBCs) in whole slide images (WSIs) is a critical step in computational pathology. This study presents a comprehensive evaluation and enhancement of the StarDist algorithm, leveraging its star-convex polygonal modeling to improve segmentation precision in complex WSI datasets. Our pipeline integrates tailored preprocessing, expert annotations from QuPath, and adaptive learning strategies for model training. Comparative analysis with U-Net and Mask R-CNN demonstrates StarDist’s superiority across multiple performance metrics, including Dice coefficient (0.89), precision (0.99), and IoU (0.95). Visual evaluations further highlight its robustness in handling overlapping cells and staining inconsistencies. The study establishes StarDist as a reliable tool for digital pathology, with potential integration into clinical decision-support systems. In addition to Dice and IoU, metrics such as Aggregated Jaccard Index and Boundary F1-Score are gaining popularity for biomedical segmentation. Preprocessing techniques like Macenko stain normalization and adaptive histogram equalization can further improve generalizability. QuPath, an open-source digital pathology platform, was utilized to perform accurate WBC annotations prior to training and evaluation. Full article

Nanotheranostics combines therapeutic and diagnostic functions within multifunctional nanoparticle platforms to enable precision medicine. This review outlines a comprehensive framework for engineering nanotheranostic systems, focusing on core material composition, surface functionalization, and stimuli-responsive drug delivery. Targeting strategies—from ligand-based recognition to biomimetic interfaces—are examined alongside therapeutic modalities such as chemotherapy, photothermal and photodynamic therapies, gene silencing via RNA interference, and radio sensitization. We discuss advanced imaging techniques (fluorescence imaging FI), magnetic resonance imaging (MRI), positron emission tomography (PET), and photoacoustic imaging for real-time tracking and treatment guidance. Key considerations include physicochemical characterization (e.g., article size, surface charge, and morphology), biocompatibility, in-vitro efficacy, and in-vivo biodistribution. We also address challenges such as rapid biological clearance, tumor heterogeneity, and clinical translation, and propose future directions for developing safe, adaptable, and effective nanotheranostic platforms. This review serves as a roadmap for advancing next-generation nano systems in biomedical applications. Full article

Aggregation-induced emission (AIE) luminogens are materials that exhibit enhanced light emission in the aggregated state, primarily due to the restriction of intramolecular motions, which reduces energy loss through non-radiative pathways. Tetraphenylethylene (TPE) and its derivatives are prominent examples of AIE-active materials, owing to their ease of synthesis, tuneable photophysical properties, and strong aggregation tendencies. This review provides an overview of the fundamental AIE mechanisms in TPE-based systems, with a focus on the role of restricted intramolecular rotation (RIR) and π-twisting in governing their emission behaviour. It explores the influence of molecular structure, electronic configuration, and intermolecular interactions on fluorescence properties. Furthermore, recent advances in practical applications of TPE-based AIE luminogens are highlighted across a spectrum of biological imaging domains, including cellular imaging, tissue and in vivo imaging, and organelle-targeted imaging. Additionally, their integration into multifunctional and theranostic platforms, along with the development of stimuli-responsive and self-assembled systems, underscores their versatility and expanding potential in biomedical research and diagnostics. This review aims to offer valuable insights into the design principles and functional potential of TPE-based AIE luminogens, guiding the development of next-generation materials for advanced bioimaging technologies. Full article

Diagnosing gastrointestinal disorders (GIDs) remains a significant challenge, particularly when relying on wireless capsule endoscopy (WCE), which lacks advanced imaging enhancements like Narrow Band Imaging (NBI). To address this, we propose a novel framework, the Spectrum-Aided Vision Enhancer (SAVE), especially designed to transform standard white light (WLI) endoscopic images into spectrally enriched representations that emulate both hyperspectral imaging (HSI) and NBI formats. By leveraging color calibration through the Macbeth Color Checker, gamma correction, CIE 1931 XYZ transformation, and principal component analysis (PCA), SAVE reconstructs detailed spectral information from conventional RGB inputs. Performance was evaluated using the Kvasir-v2 dataset, which includes 6490 annotated images spanning eight GI-related categories. Deep learning models like Inception-Net V3, MobileNetV2, MobileNetV3, and AlexNet were trained on both original WLI- and SAVE-enhanced images. Among these, MobileNetV2 achieved an F1-score of 96% for polyp classification using SAVE, and AlexNet saw a notable increase in average accuracy to 84% when applied to enhanced images. Image quality assessment showed high structural similarity (SSIM scores of 93.99% for Olympus endoscopy and 90.68% for WCE), confirming the fidelity of the spectral transformations. Overall, the SAVE framework offers a practical, software-based enhancement strategy that significantly improves diagnostic accuracy in GI imaging, with strong implications for low-cost, non-invasive diagnostics using capsule endoscopy systems. Full article

Ischemic stroke is one of the leading causes of morbidity and mortality worldwide, with carotid atherosclerosis being its key etiological factor. MicroRNA-21 (miR-21) regulates intracellular signal pathways responsible for vascular changes and ischemic brain injury, and is recognized as a potential diagnostic and prognostic biomarker. It modifies the activity of macrophages (MΦ) and vascular smooth muscle cells, causing inflammation and affecting the stability of atherosclerotic plaques. A deficiency of miR-21 in macrophages stimulates the inflammatory response and plaque growth. It promotes both the synthesis of extracellular matrix, stabilizing the plaque, and the degradation of the fibrin cap, which leads to plaque instability. The effect of miR-21 on endothelial cells differs: it stimulates both NO· synthesis and inflammation. During ischemic stroke, miR-21 demonstrates neuroprotective effects by modulating post-ischemic inflammation and protecting the integrity of the blood–brain barrier. Therapy targeting miR-21 shows potential in experimental models, but it requires cell-specific delivery and precise timing. Further research efforts should focus on the effects of miR-21 on different cell types, as well as the development of new technologies for diagnostic and therapeutic applications. Full article

Structured illumination microscopy (SIM) with π/2 phase-shift modulation traditionally relies on frequency-domain computation, which greatly limits processing efficiency. In addition, the illumination regime inherent in structured illumination techniques often results in poor visual quality of reconstructed images. To address these dual challenges, this study introduces DM-SIM-LLIE (Differential Low-Light Image Enhancement SIM), a novel framework that integrates two synergistic innovations. First, the study pioneers a spatial-domain computational paradigm for π/2 phase-shift SIM reconstruction. Through system differentiation, mathematical derivation, and algorithm simplification, an optimized spatial-domain model is established. Second, an adaptive local overexposure correction strategy is developed, combined with a zero-shot learning deep learning algorithm, RUAS, to enhance the image quality of structured light reconstructed images. Experimental validation using specimens such as fluorescent microspheres and bovine pulmonary artery endothelial cells demonstrates the advantages of this approach: compared with traditional frequency-domain methods, the reconstruction speed is accelerated by five times while maintaining equivalent lateral resolution and excellent axial resolution. The image quality of the low-light enhancement algorithm after local overexposure correction is superior to existing methods. These advances significantly increase the application potential of SIM technology in time-sensitive biomedical imaging scenarios that require high spatiotemporal resolution. Full article

The integration of nanotechnology into drug repurposing strategies is redefining the development landscape for diagnostic, therapeutic, and theranostic agents. In radiopharmacy, nanoplatforms are increasingly being explored to enhance or extend the use of existing radiopharmaceuticals, complementing earlier applications in other biomedical fields. Many of these nanoplatforms evolve into multifunctional systems by incorporating additional imaging modalities (e.g., MRI, fluorescence) or non-radioactive therapies (e.g., photodynamic therapy, chemotherapy). These hybrid constructs often emerge from the reformulation, repositioning, or revival of previously approved or abandoned compounds, generating entities with novel pharmacological, pharmacokinetic, and biodistribution profiles. However, their translational potential faces significant regulatory hurdles. Existing frameworks—typically designed for single-modality drugs or devices—struggle to accommodate the combined complexity of nanoengineering, radioactive components, and integrated functionalities. This review examines how these systems challenge current norms in classification, safety assessment, preclinical modeling, and regulatory coordination. It also addresses emerging concerns around digital adjuncts such as AI-assisted dosimetry and software-based therapy planning. Finally, the article outlines international initiatives aimed at closing regulatory gaps and provides future directions for building harmonized, risk-adapted frameworks that support innovation while ensuring safety and efficacy. Full article

Magnetically separable TiO₂-based composite photocatalysts have gained significant interest in the past two decades; however, the optimization of their synthesis and the stabilization of the magnetic iron oxide within the composite is still an open challenge. The present study investigates the photocatalytic behavior and recyclability of TiO₂-Fe₃O₄ composites, with emphasis on a possible correlation between pollutant degradation efficiency, recyclability, iron oxide stability, and the phase composition of the chosen TiO₂ base. Magnetite nanoparticles were synthesized under varied temperature and alkaline conditions to identify optimal parameters for achieving the desirable magnetic properties. The magnetic nanoparticles were integrated into composite systems with either commercial TiO₂ (Evonik Aeroxide P25 with anatase–rutile mixed phase) or a hydrothermally synthesized anatase TiO₂. The P25-based composite removed 99% paracetamol from aqueous solutions under UV-A irradiation and demonstrated successful recyclability, maintaining 96% paracetamol degradation efficiency after four uses. In contrast, the anatase TiO₂-based magnetic composite exhibited a lower performance (70%) and a significantly hindered recyclability (45% after four cycles). The difference in performance was attributed to variations in the phase composition of the employed TiO₂ in the composites and, consequently, in their charge separation mechanisms. Full article

The integration of deep learning (DL) and nanozyme-based biosensing has emerged as a transformative strategy for next-generation medical diagnostics. This review explores how DL architectures enhance nanozyme design, functional optimization, and predictive modeling by elucidating catalytic mechanisms such as dual-atom active sites and substrate-surface interactions. Key applications include disease biomarker detection, medical imaging enhancement, and point-of-care diagnostics aligned with the ASSURED criteria. In clinical contexts, advances such as wearable biosensors and smart diagnostic platforms leverage DL for real-time signal processing, pattern recognition, and adaptive decision-making. Despite significant progress, challenges remain—particularly the need for standardized biomedical datasets, improved model robustness across diverse populations, and the clinical translation of artificial intelligence (AI)-enhanced nanozyme systems. Future directions include integration with the Internet of Medical Things, personalized medicine frameworks, and sustainable sensor development. The convergence of nanozymes and DL offers unprecedented opportunities to advance intelligent biosensing and reshape precision diagnostics in healthcare. Full article

Laser-induced forward transfer (LIFT) bioprinting enables precise deposition of biological materials for advanced biomedical applications. This study presents a parametric analysis of the donor–receiver distances (1.0, 1.5, 2.0, 2.5, and 3.0 mm) in LIFT bioprinting, investigated through high-speed video and image analysis of 4 × 4 spot arrays. Droplet velocity was quantified and jet trajectory characterized, revealing that increased distances reduced spatial resolution, with significant shape deterioration observed beyond 2.0 mm. Thus, a maximum 2.0 mm donor–receiver gap was determined as optimal for acceptable printing resolution. As an application, a microfluidic device was fabricated using LCD 3D printing with a biocompatible resin and glass-bottomed configuration. The chamber height was matched to the validated 2.0 mm distance, ensuring compatibility with LIFT printing. Computational fluid dynamics simulations were conducted to model fluid flow conditions within the device. Subsequently, LLC cells were successfully printed inside the microfluidic chamber, cultured under continuous flow for 24 h, and demonstrated normal proliferation. This work highlights LIFT bioprinting’s viability and precision for integrating cells within microfluidic platforms, presenting promising potential for organ-on-chip applications and future biomedical advancements. Full article

Iron(III) (Fe(III)) complexes have recently emerged as safer alternatives to magnetic resonance imaging (MRI) contrast agents (CAs), reigniting interest in biomedical research. Although gadolinium Gd(III)-based contrast agents (CAs) have been widely used in MRI over the past four decades, their use in the current clinical routine is severely constrained due to concerns about high toxicity and environmental impact. Research is now focusing on synthesizing safer contrast agents with alternative paramagnetic ions like Fe(III) or Mn(II). MRI CAs with integrated potent therapeutic moieties may offer synergistic advantages over traditional contrast agents in clinical use. The study explored the use of salinomycin-ferrihydrite composites as possible effective ensembles of imaging and therapeutic units in the same molecule, evaluating their anticancer activity and influence on the signal in MRI. The composites were characterized using Mössbauer spectroscopy and ICP-MS for iron content determination. The in vitro relaxivity measurements in a high-field MR scanner demonstrated the potency of the composites as T₂ enhancers. The antitumor activity of one selected Sal-ferrihydrite composite was tested in three human cancer cell lines: A549 (non-small cell lung cancer); SW480 (colon cancer); and CH1/PA1 (ovarian teratocarcinoma) by the MTT cell viability assay. The new Sal-ferrihydrite composite showed a pronounced cytotoxicity in all three human cancers in line with enhanced signal in MRI, which makes it a promising candidate for future biomedical applications. The superior cytotoxic effect, together with the strong signal enhancement, makes these compounds promising candidates for further detailed investigations as future theranostic agents. Full article

Background/Objectives: Artificial Intelligence (AI) is improving dentistry through increased accuracy in diagnostics, planning, and workflow automation. AI tools, including machine learning (ML) and deep learning (DL), are being adopted in oral medicine to improve patient care, efficiency, and lessen clinicians’ workloads. AI in dentistry, despite its use, faces an issue of acceptance, with its obstacles including ethical, legal, and technological ones. In this article, a review of current AI use in oral medicine, new technology development, and integration barriers is discussed. Methods: A narrative review of peer-reviewed articles in databases such as PubMed, Scopus, Web of Science, and Google Scholar was conducted. Peer-reviewed articles over the last decade, such as AI application in diagnostic imaging, predictive analysis, real-time documentation, and workflows automation, were examined. Besides, improvements in AI models and critical impediments such as ethical concerns and integration barriers were addressed in the review. Results: AI has exhibited strong performance in radiographic diagnostics, with high accuracy in reading cone-beam computed tomography (CBCT) scan, intraoral photographs, and radiographs. AI-facilitated predictive analysis has enhanced personalized care planning and disease avoidance, and AI-facilitated automation of workflows has maximized administrative workflows and patient record management. U-Net-based segmentation models exhibit sensitivities and specificities of approximately 93.0% and 88.0%, respectively, in identifying periapical lesions on 2D CBCT slices. TensorFlow-based workflow modules, integrated into vendor platforms such as Planmeca Romexis, can reduce the processing time of patient records by a minimum of 30 percent in standard practice. The privacy-preserving federated learning architecture has attained cross-site model consistency exceeding 90% accuracy, enabling collaborative training among diverse dentistry clinics. Explainable AI (XAI) and federated learning have enhanced AI transparency and security with technological advancement, but barriers include concerns regarding data privacy, AI bias, gaps in AI regulating, and training clinicians. Conclusions: AI is revolutionizing dentistry with enhanced diagnostic accuracy, predictive planning, and efficient administration automation. With technology developing AI software even smarter, ethics and legislation have to follow in order to allow responsible AI integration. To make AI in dental care work at its best, future research will have to prioritize AI interpretability, developing uniform protocols, and collaboration between specialties in order to allow AI’s full potential in dentistry. Full article

Introduction: Patients with generalized epilepsy are rarely referred for advanced diagnostics like magnetoencephalography (MEG). This is due to the assumption that generalized seizures cannot be localized noninvasively. Methods: We present simultaneous MEG (306 channels) and EEG (64 channels) data from seven patients with drug-resistant generalized epilepsy. Three patients experienced typical generalized seizures during their MEG clinical evaluation. In total, 38 epileptiform events (three seizures, 35 interictal discharges) were analyzed using two software platforms and three localization methods: equivalent current dipole (ECD), sLORETA (via SWARM), and dynamic statistical parametric mapping (dSPM). Individual head models were created from each patient’s MRI. Results: MEG successfully localized seizure onset zones, showing distinct hypersynchronous discharges on all sensors as well as alternately during interictal discharges. Localization was consistent across methods and generalized events within subjects, revealing cortical sources in all cases, with rapid propagation (27–60 ms) across networks. Conclusions: This study demonstrates that MEG can meaningfully localize both seizures and interictal discharges in generalized epilepsy. This supports a broader use for MEG beyond focal epilepsy. Incorporating MEG in drug-resistant cases including generalized epilepsies may improve diagnosis and guide treatments including non-surgical options. Full article