Search Results (3,186)

The long-wave infrared band, which at room temperature covers the infrared radiation of humans and objects, has significant applications across various fields including wireless communication, national defense, military, biomedical, and advanced driver assistance systems. Metalens provides a pathway to lightweight, compact, and integrated solutions for infrared imaging and sensing systems, marking an inevitable trend in future development. This study presents a design for a high numerical aperture of 0.89 in a polarization-insensitive all-chalcogenide metalens operating at 10 µm, utilizing the commercially available chalcogenide glass material As₂Se₃ via a transmission phase approach. Building upon this, we have achieved, for the first time, a high numerical aperture of 0.84 for an all-chalcogenide broadband LWIR achromatic metalens operating in the 9.5–10.5 µm range, with significantly improved focusing performance through the application of particle swarm optimization algorithms. The superior performance of the all-chalcogenide LWIR metalens, combined with the advantages of chalcogenide glass over traditional LWIR materials such as Si or Ge—namely, lower cost, reduced optical loss, and a smaller thermo-optic coefficient—suggests it has significant potential for broader applications. Full article

Background: Paramagnetic manganese (Mn(II)) has emerged as a promising alternative to gadolinium-based contrast agents (GBCAs) due to its favorable magnetic properties. Despite extensive research, no Mn-based agent has yet achieved clinical translation. Because free Mn(II) is toxic, macromolecular complexes incorporating stable macrocyclic DOTA chelators conjugated to polysaccharides may enhance coordination stability and improve the safety profile of Mn(II)-based contrast agents. Methods: Two chemical routes, maleimide- and ester-mediated, were evaluated for covalent coupling of DOTA-based macrocyclic ligands to the backbone of selected poly- and oligosaccharides. Subsequently, DOTA-modified carboxymethyldextran, aminodextran, and chitosan oligosaccharide were labeled with paramagnetic Mn(II) under mild conditions. ATR-FTIR confirmed the successful conjugation of DOTA chelators to the sugar backbone. The conjugates were further characterized by DLS, ICP-MS, and FPLC. In vitro relaxivity was measured at high field strength to evaluate MRI performance. In vivo contrast efficacy was first assessed using in ovo MRI in chicken embryos and subsequently evaluated by biodistribution studies in nude mice. Results: In vitro relaxivity studies demonstrated higher signal enhancement of the poly-/oligosaccharide-DOTA-Mn(II) conjugates compared with MnCl₂ and the clinical agent gadoteridol (ProHance^®). In ovo MRI showed persistent vascular enhancement up to 120 min, while in nude mice, contrast enhancement was observed in the liver, kidneys, and gallbladder 40 min post-injection. Conclusions: Mn(II)-tagged sugar-based imaging probes may offer a promising non-gadolinium alternative to GBCAs, with tunable biodistribution profiles depending on carrier molecular weight. Full article

Background: Magnetic Particle Imaging (MPI) is an emerging biomedical imaging modality that detects superparamagnetic iron oxide nanoparticles (SPIONs), providing high contrast, sensitivity, and quantification capabilities without ionizing radiation, making it particularly suitable for cancer diagnostics. Considerable engineering efforts are underway to translate MPI technology to clinical settings. Most of these MPI scanners feature a cylindrical bore geometry similar to that of other clinical imaging modalities, which limits their potential application primarily to head scanning. Methods: We have developed a single-sided MPI scanner designed to expand the modality’s applicability to other regions of the human body through a unique hardware design developed in our previous work. Imaging experiments were performed on an anatomical breast phantom containing implanted SPION point sources placed at anatomically plausible locations for breast tumors. These point sources served as artificial tumors for evaluating the system’s suitability for breast imaging applications. Results: The scanner successfully detected and clearly resolved the implanted SPION tumors in two orthogonal imaging planes. Tumor positioning was independently validated by ultrasound imaging, confirming MPI’s accurate localization. In addition, sensitivity measurements demonstrated a detection limit of 4.0 $\mathsf{\mu }\mathrm{g}$ of iron, below the estimated 4.8 $\mathsf{\mu }\mathrm{g}$ sensitivity threshold required for breast tumor detection with electronic depth scanning up to 3.5 cm deep. Conclusions: Together, these results demonstrate the capability of a single-sided MPI geometry for breast imaging applications. Imaging an anatomical breast-shaped volume presents significant challenges for MPI due to the size and accessibility constraints of conventional hardware. The results presented highlight the advantages of this approach and support its potential to extend MPI from small-animal imaging to clinically relevant applications. Full article

Objectives: To determine if global epicardial adipose tissue (EAT) radiomics, derived from non-contrast coronary artery calcium (CAC) scans, improves acute coronary syndrome (ACS) prediction beyond traditional risk factors (TRFs) and Agatston score (AS) in individuals without angina. Methods: We retrospectively analyzed 2020 subjects without angina who underwent CAC scans from 2016 to 2019, among whom 76 patients developed acute coronary syndrome (ACS) during a follow-up period until December 2023. One-to-one propensity score matching (PSM) based on TRFs (age, sex, BMI, smoking, diabetes, hypertension, dyslipidemia) and Agatston score ranks (ASR) created 76 ACS and 76 matched non-ACS subjects. A radiomics model was built using 5-fold cross-validation on the matched cohort and tested on the entire unmatched cohort. Statistical tests included AUC comparison. Results: PSM effectively mitigated disparities of TRFs and ASR. The radiomics model achieved AUCs of 0.91 ± 0.01 and 0.89 ± 0.03 for the training and matched test sets, respectively, and 0.89 ± 0.03 on the unmatched cohort. The radiomics score significantly improved ACS prediction over TRF and CAC models (p < 0.001) in both matched and unmatched cohorts. Conclusions: Global EAT radiomic phenotypes from conventional CAC scan improve ACS risk stratification beyond TRFs and AS in individuals without angina. In contrast to PCAT on CCTA, our simple approach appears to be suitable for large-scale applications using preventative CAC scans. Full article

Hydrogels derived from biopolymers have attracted considerable interest in biomedical applications because of their biocompatibility and structural similarity to the extracellular matrix (ECM). Bacterial Cellulose (BC), despite being a promising biopolymer for hydrogel preparation, lacks antimicrobial properties itself. To address this drawback, we prepared Probiotic Bacterial Cellulose (PBC) in our laboratory, which has intrinsic antibacterial properties. No research was found on the preparation of a hydrogel using PBC and κ-carrageenan, which motivated us to develop a PBC/κ-carrageenan-based hydrogel. In the study, a novel biocomposite hydrogel system has been developed by integrating PBC with κ-carrageenan, yielding a multifunctional hydrogel with enhanced antibacterial properties and biocompatibility. The novel hydrogel has been evaluated for its structural, physicochemical, antibacterial, and biocompatible properties. Fourier transform infrared spectroscopy (FTIR) analysis confirmed the formation of intermolecular interactions between PBC and κ-carrageenan. Scanning electron microscopy (SEM) images revealed a porous internal morphology and the presence of probiotic bacteria within the hydrogel networks. Porosity analysis and swelling behaviour indicated an elevated water uptake capacity and structural stability. The composite hydrogel demonstrated promising antibacterial properties against pathogenic bacteria Escherichia coli (Gram-negative) and Staphylococcus aureus (Gram-positive) and exhibited favourable in vitro biocompatibility. The developed PBC/κ-carrageenan hydrogel exhibits a synergistic combination of porosity, swelling capacity, biocompatibility, and antibacterial activity, making it a potential candidate for healthcare applications viz. wound healing and other tissue engineering applications. Full article

Objective: We evaluated whether a deep-learning model could predict the response to neoadjuvant chemotherapy (NAC) in breast cancer using the pre-treatment B-mode ultrasound. Methods: This retrospective study included 245 female patients (253 lesions) treated with NAC between 2017 and 2019. Lesions were categorized as complete response (CR; 103) or non-CR (150) based on postoperative pathology. We trained ResNet18-based models using pre-treatment B-mode ultrasound images (Image) and clinical features. Three training strategies were evaluated: training from scratch (SC); transfer learning (TL); and domain-specific pretraining (USP). Predictive performance was assessed using descriptive statistics. Results: The best-performing model (USP Image) achieved 0.76 accuracy (specificity: 0.80; sensitivity: 0.72), significantly outperforming all other models, including those that used additional clinical features ($p<0.05$). USP improved performance across most model types compared to SC and TL, highlighting the value of domain-specific pretraining. Clinical features added value with SC or TL, but not with USP, suggesting that pretrained models can extract the most relevant information directly from images. Grad-CAM analysis revealed that non-CR predictions focused on the tumor and posterior shadowing—features linked to chemoresistant subtypes. CR predictions focused mainly on more heterogeneous, peritumoral regions. Conclusion: This finding underscores ultrasound’s potential as a low-cost, accessible tool for predictive oncology in personalized, AI-driven treatment planning. Full article

Ti alloys are widely used in aerospace and biomedical fields due to their high mechanical properties under severe loading. Interest in additively manufactured Ti6Al4V has increased, but further research is needed to fully characterize their properties. This work compares the effects of surface properties, internal defects, microstructure, hardness, and Hot Isostatic Pressing (HIP) or Vacuum Heat Treatment (VHT) on the fatigue behavior of Ti6Al4V produced by Selective Laser Melting (SLM) and Electron Beam Melting (EBM). Printing parameters and post-processing were optimized to achieve high density and minimal porosity, providing a solid basis for realistic fatigue comparisons. Samples were characterized in terms of microstructure (optical microscopy and SEM), mechanical properties (hardness mapping), surface texture (confocal microscopy), and internal defects (image-based analysis). Uniaxial fatigue limits were determined by a Dixon-Mood staircase method, and failed specimens were analyzed for fracture surfaces and defect areas. Applied load on flaws was evaluated to identify root causes of fatigue failure. Results showed that fatigue of as-printed samples is governed by surface roughness, while machined specimens are controlled by internal defect size. Machining increased the fatigue limit roughly threefold, and HIP further improved it by 10–20% by reducing internal porosity. In conclusion, with properly optimized melting parameters, both EBM and SLM produce similar mechanical performance at comparable roughness, supporting their use for structural components. Full article

Iron-based nanoparticles, particularly iron oxide nanostructures (IONPs), have emerged as versatile and clinically relevant platforms for drug delivery and theranostic applications. Among these, superparamagnetic iron oxide nanoparticles (SPIONs), including magnetite (Fe₃O₄) and maghemite (γ-Fe₂O₃), are the most extensively investigated due to their biocompatibility, magnetic responsiveness, and established safety profiles. Their unique superparamagnetic behavior enables external magnetic-field-guided targeting, magnetic resonance imaging (MRI) contrast enhancement, and magnetically triggered hyperthermia, enabling simultaneous diagnosis and therapy. Surface functionalization with polymers, silica, lipids, peptides, and biomolecules further improves colloidal stability, circulation time, targeting specificity, and controlled drug release. Core–shell architectures and multifunctional hybrid systems have expanded the therapeutic scope of iron nanoparticles, integrating chemotherapy, gene delivery, photothermal therapy, and Fenton reaction–mediated catalytic therapy. Despite promising preclinical outcomes, challenges remain regarding long-term biosafety, oxidative stress induction, biodistribution, large-scale reproducibility, and regulatory translation. This review summarizes the physicochemical properties, synthesis strategies, surface-engineering approaches, drug-loading mechanisms, and biomedical applications of iron-based nanoparticles, highlighting recent advances in multifunctional and peptide-functionalized systems. Critical considerations for clinical translation and future perspectives in precision nanomedicine are also discussed. Full article

Background/Objectives: The Black Caribbean population of Honduras, also referred to locally as Negro Inglés, constitutes one of the country’s nine recognized indigenous and Afro-descendant peoples. Predominantly settled in the Bay Islands and sections of the Caribbean coast, this community traces its ancestry predominantly to West Africa and has remained culturally and linguistically distinct for more than three centuries. Despite its demographic and historical relevance, no population-specific short tandem repeat (STR) database has been established for this group. Methods: Allele frequencies for 23 autosomal STR loci were characterized in 100 unrelated Black Caribbean individuals from the department of Islas de la Bahía. DNA was extracted from blood on FTA cards and amplified with the PowerPlex Fusion 6C System (Promega Corporation). Statistical parameters were computed using Genepop v4.2, Arlequin v3.5 and GDA v1.0. Results: A total of 241 distinct alleles were detected across all 23 loci (mean 10.48 ± 3.85 alleles/locus). Expected heterozygosity ranged from 0.6541 (D13S317) to 0.9350 (SE33), with a mean of 0.8150 ± 0.0664—values consistent with a population of predominantly West African origin. No locus exhibited a significant departure from Hardy–Weinberg equilibrium after Bonferroni correction (α = 0.0022). The combined power of discrimination exceeded 99.9999% and the combined chance of exclusion surpassed 99.9999%. Conclusions: This first genetic characterization of the Honduran Black Caribbean population delivers an essential, population-specific reference dataset for forensic casework, paternity testing, and population genetics research. The data also deepen the understanding of Afro-descendant genetic diversity in Central America and constitute a critical step towards equitable forensic genetic services for all Honduran ethnic communities. Full article

In the contemporary era, nanotechnology has become a central pillar in numerous domains, particularly in cosmetics, nanoelectronics, nanomedicine, and nanobiotechnology. Defined by its focus on materials with dimensions ranging from 0.1 to 100 nm, nanotechnology offers unique physicochemical properties—such as enhanced reactivity, conductivity, and permeability—attributable to the nanoscale. These properties facilitate greater interaction with biological systems, notably improving cellular uptake and functional efficacy. The increasing demand for eco-friendly and biocompatible nanomaterials has driven interest in green synthesis routes, particularly those utilising plant extracts. These methods stand out due to their low toxicity and environmental impact, positioning it as a safer alternative to conventional chemical or microbial methods. Plant-extract-mediated nanoparticles demonstrate promising applications in diagnostics, drug delivery, regenerative medicine, and neurotherapeutics. Their role in precision medicine, including gene and drug delivery and the imaging of neurological disorders, underscores green nanotechnology’s transformative potential. This review highlights recent advances in the synthesis, functionality, and biomedical applications of plant-based nanoparticles, emphasizing their relevance in in vitro models and prospective clinical settings. Full article

Hyperspectral sensing allows for the capture of spatially resolved spectral data, a capability critical for applications spanning from remote sensing to biomedical diagnostics. Nevertheless, the widespread adoption of this technology is hindered by the bulk and complexity of traditional systems based on diffractive optics. To overcome these hurdles, substantial research efforts have been dedicated to system miniaturization via component scaling and computational imaging. This review outlines the technological progression of compact hyperspectral imaging, ranging from miniaturized dispersive elements and tunable filters to computational snapshot designs using optical multiplexing. Although these approaches decrease system volume, they generally treat the sensor as a passive intensity recorder requiring external encoding. Therefore, we focus here on the rising paradigm of sensor-level integration made possible by nanomanufacturing. We examine optics-free architectures where spectral discrimination is embedded directly into the pixel, distinguishing between pixel-level nanophotonic filtering and intrinsic material-based selectivity. We specifically highlight emerging platforms such as compositionally engineered and cavity-enhanced perovskites, as well as electrically tunable organic or two-dimensional (2D) material heterostructures. To conclude, this review discusses persistent challenges regarding fabrication uniformity and stability, providing an outlook on the future of scalable and fully integrated hyperspectral vision systems. Full article

Accurate delineation of tumor margins is critical for complete resection and minimizing recurrence, yet existing imaging modalities such as MRI, CT, and fluorescence imaging suffer from limitations including high cost, limited accessibility, and intraoperative constraints. In this study, we propose a low-cost, non-invasive approach for subsurface imaging based on near-infrared (NIR) illumination combined with deep learning. A controlled experimental setup was developed in which structured patterns displayed on an electronic paper screen were concealed beneath a tissue-mimicking chicken phantom and imaged using a NIR-sensitive camera under halogen illumination. A convolutional autoencoder based on a U-Net architecture was trained on approximately 10,000 paired samples to reconstruct hidden structures from highly scattered surface images. The proposed method achieved strong reconstruction performance, with the best model reaching a peak signal-to-noise ratio (PSNR) of 20.14 dB, structural similarity index (SSIM) of 0.92, and feature similarity index (FSIM) of 0.94, significantly outperforming conventional Wiener filtering. Qualitative results demonstrated accurate recovery of subsurface shapes with minor smoothing artifacts. While generalization to out-of-distribution samples remains limited, the findings highlight the potential of combining NIR imaging and deep learning for safe, rapid, and cost-effective subsurface visualization. This work establishes a foundation for future development toward clinically relevant tumor margin detection. Full article

Photoacoustic imaging (PAI) is an emerging hybrid biomedical imaging modality that combines the high molecular contrast of optical excitation with the deep tissue penetration of ultrasound detection. This review presents recent advances in PAI-based techniques for the detection and characterization of gynecological diseases in women, with particular focus on endometriosis and uterine-related disorders. We summarize the application of PAI across preclinical and translational studies, highlighting progress in photoacoustic microscopy, spectroscopic photoacoustic imaging, and endoscopic and probe-based implementations for non-invasive, high-resolution tissue evaluation. The role of functional and contrast-enhanced PAI approaches is discussed, emphasizing their ability to enhance diagnostic sensitivity, enable longitudinal monitoring, and provide detailed information on vascular, biochemical, and structural tissue characteristics. Furthermore, the expanding applications of PAI in assessing uterine, cervical, and ovarian pathologies, including tumor detection and tissue remodeling, are reviewed. Finally, current challenges, limitations, and future directions toward clinical translation are addressed. Collectively, this review underscores the potential of photoacoustic imaging as a powerful, non-invasive platform for early diagnosis, disease monitoring, and improved management of women’s health conditions. Full article

Background/Objectives: Magnetic nanoparticles have emerged as powerful tools for biomedical imaging, targeted drug delivery, and hyperthermia therapy. Magnetic particle imaging (MPI) is among the most promising technologies built around its properties: a radiation-free, quantitative tomographic modality that detects superparamagnetic iron oxide nanoparticles (SPIONs) directly against a biologically silent background. This review synthesizes MPI’s physical principles, nanoparticle design strategies, and preclinical applications within the broader landscape of magnetic material engineering for biomedical use. Methods: A systematic review was conducted covering MPI signal generation and image reconstruction, nanoparticle core synthesis and surface coating approaches, and preclinical applications, spanning cell tracking, oncological imaging, vascular perfusion, neuroimaging, and MPI-guided theranostics. Studies were selected to provide quantitative benchmarks and direct comparisons with competing modalities where available. Results: MPI delivers signal-to-background ratios above 1000:1, iron-mass linearity at R² ≥ 0.99, regardless of tissue depth, and acquisition rates up to 46 volumes per second. Tracer architecture—encompassing single-core particles, multicore nanoflowers, and stimuli-responsive cluster designs—is the primary determinant of sensitivity, environmental robustness, and theranostic capability. Preclinical results include detection of cell populations in the low thousands, earlier ischaemia identification than diffusion-weighted MRI, real-time drug release quantification, and spatially confined tumour hyperthermia. Three translational bottlenecks are identified: the absence of a clinically approved tracer with optimal relaxation dynamics, hardware performance losses when scaling to human-bore systems, and overestimation of passive tumour accumulation in murine models. Conclusions: MPI illustrates how progress in magnetic material design directly expands clinical imaging and theranostic possibilities. Successful translation will require indication-driven, interdisciplinary development that integrates materials science, scanner engineering, and regulatory strategy in parallel. Full article

Three-dimensional seed phenotyping requires imaging systems capable of achieving micron-level resolution across a centimeter-level field of view (FOV), a goal constrained by the resolution–FOV trade-off in conventional light field architectures. This paper presents a hardware–software co-optimized framework that integrates a reconfigurable optical system with computational imaging pipelines to address this limitation. At the hardware level, we develop a tunable-focus lens module that enables flexible adjustment of the effective focal length, combined with a custom-designed microlens array (MLA). A mathematical model is established to analyze the interdependencies among FOV, lateral resolution, depth of field (DOF), and system configuration, guiding the design of individual optical components. On the computational side, we propose a hybrid aberration correction strategy: first, a co-calibration of lens and MLA aberrations based on line-feature detection; second, a conditional generative adversarial network (cGAN) with attention-guided residual learning to enhance sub-aperture images, achieving a PSNR of 34.63 dB and an SSIM of 0.9570 on seed datasets. Experimentally, the system achieves a resolution of 6.2 lp/mm at MTF50 over a 2–3 cm FOV, representing a 307% improvement over the initial configuration (1.52 lp/mm). The reconstruction pipeline combines epipolar plane image (EPI) analysis with multi-view consistency constraints to generate dense 3D point clouds at a density of approximately 1.5 × 10⁴ points/cm² while preserving spectral and textural features. Validation on bitter melon and rice seeds demonstrates accurate 3D reconstruction and accurate extraction of morphological parameters across a large area. By integrating optical and computational design, this work establishes a reconfigurable imaging framework that overcomes the resolution–FOV limitations of conventional light field systems. The proposed architecture is also applicable to robotic vision and biomedical imaging. Full article