Search Results (4,143)

Lipidomics has emerged as a transformative discipline in biomedical research, providing high-resolution insights into metabolic signaling and disease pathophysiology. The R programming language provides a widely adopted framework for extensible analysis of complex lipidomic datasets due to its robust biostatistical infrastructure. Herein, we present a comprehensive roadmap for lipidomics in R, structured around a standardized analytical lifecycle: from raw data acquisition and preprocessing to structural annotation, statistical modeling and functional interpretation. We critically contextualize and integrate a curated suite of widely adopted R packages (version 4.3.0), including xcms and MSnbase for feature extraction, LipidMS 3.0 for fragmentation-based identification, and lipidr for quality control and normalization. Furthermore, we demonstrate how advanced tools such as mixOmics and clusterProfiler can be integrated to bridge the gap between differential lipid abundance and systems-level biological insights. Particular emphasis is placed on reproducibility, nomenclature standardization and the emerging role of machine learning in biomarker discovery. By synthesizing these resources into a coherent pipeline, this guide provides a structured reference for researchers. Further discussion addresses methodological pitfalls, statistical assumptions and reproducibility constraints that frequently compromise lipidomics studies. Ultimately, this structured approach facilitates systematic tool selection, accelerating the translation of complex lipidomic signatures into reproducible and clinically meaningful discoveries. Full article

Accurate color classification plays a critical role across diverse fields, from textile manufacturing and environmental monitoring to biomedical diagnostics. This study introduces a machine-learning-driven approach to spectral color sensing using SENSIPATCH, a compact, wearable sensor system; while SENSIPATCH integrates multiple sensing modalities, including bioimpedance, electrochemical, thermal, humidity, and vibrational sensors, this work specifically utilizes its spectrometer module, which comprises multi-wavelength LEDs and photodiodes. Targeting the classification of 100 standardized PANTONE colors, the proposed framework is evaluated under controlled lighting conditions to ensure repeatable spectral acquisition. The experimental design includes both firm and loose contact scenarios to emulate variability in wearable placement. A structured data-preprocessing pipeline involving baseline correction, bootstrapping, and Z-score normalization was employed to enhance signal quality and improve model generalization. Five machine learning models were evaluated: Random Forest, SVM, MLP, CNN, and LSTM. The MLP demonstrated the strongest classification performance. Notably, the MLP achieved consistent accuracy across both contact conditions, indicating robustness against sensor placement variations. These results highlight the feasibility of compact LED-based wearable spectroscopy for reliable color classification under controlled measurement conditions, providing a baseline for future extensions to more diverse lighting conditions. 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

The phytochemical variability in Cannabis sativa L. chemovars represents an underexplored factor in environmentally sustainable nanomaterial production. In this study, three distinct chemovars, (i) High-Δ⁹-Tetrahydrocannabinol (THC) (89% THC), (ii) Balanced (60% Cannabidiol (CBD)), and (iii) High-CBD (89% CBD), were comparatively evaluated to determine their suitability for the green synthesis of silver nanoparticles (AgNPs). Ethanolic inflorescence extracts were used to recover bioactive secondary metabolites; among them, the High-CBD extract exhibited the highest total phenolic (3.34 mg gallic acid equivalent/g) and flavonoid (29.49 mg quercetine equivalent/g) contents, together with superior antioxidant capacity (53.16% 2,2-diphenyl-1-picrylhydrazyl free radical (DPPH) inhibition), indicating enhanced redox potential for nanoparticle formation. The terpene profile of High-CBD showed a dominance of myrcene (21.4%), contributing to the stabilization of the system. Using the High-CBD extract, predominantly spherical nanoparticles of 5 ± 0.9 nm were synthesized and confirmed by UV–vis, EDS, and TEM. The biogenic AgNPs demonstrated significant dose-dependent antibacterial activity, with minimum bactericidal concentration (MBC) of 1.0 mg/mL against Staphylococcus aureus and 4.5 mg/mL against Escherichia coli. These findings highlight the critical role of chemovar-dependent phytochemical composition and support a phytochemistry-guided approach for developing silver nanoparticles with potential biomedical applications. Full article

Chitosan nanoparticles (Ch NPs) are versatile nanomaterials with expanding agricultural and biomedical applications, highlighting the need for reproducible, low-cost, and scalable synthesis methods to ensure their safe and widespread use in biological systems. This study presents a simple ionic-gelation protocol using a serological pipette–needle dropwise system that minimizes reagent waste and requires no sophisticated equipment. The synthesized Ch NPs were characterized by UV–Vis spectroscopy, ESEM, TEM, EDS, DLS, XRD, and FTIR, confirming nanoscale size, strong positive surface charge, and characteristic chitosan–TPP interactions. To establish a standardized biological safety assessment framework, three representative bioassays were implemented across microbial, plant, and mammalian systems. Antibacterial testing against Xanthomonas oryzae pv. oryzae (Xoo) using a resazurin-based microdilution assay revealed a minimum inhibitory concentration (MIC) of 128 µg/mL, whereas bulk chitosan showed no inhibition up to 512 µg/mL. Phytotoxicity and seed germination assays on rice (Oryza ‘KDML105’) demonstrated no inhibitory effects on germination, with over 90% germination by day 3 and significantly enhanced seedling growth parameters (p < 0.05) at 64–128 µg/mL, indicating non-phytotoxicity. MTT assays confirmed that Ch NPs were non-toxic to both human skin cell lines (HDF and HaCaT) across 2.5–160 µg/mL, showing enhanced cell viability in HDF cells at specific concentrations and stable viability in HaCaT cells, indicating overall biocompatibility. Importantly, all bioassays were conducted under aligned concentration ranges to enable cross-system comparison and reproducibility. This integrated workflow links nanoparticle synthesis with a standardized, multi-system evaluation strategy, supporting the safe application of Ch NPs in biological systems. Full article

Four-dimensional (4D) printing integrates additive manufacturing with stimuli-responsive materials to fabricate biomedical implants and functional healthcare devices that undergo programmed, time-dependent changes in shape or function. Unlike static 3D-printed constructs, 4D-printed systems can respond to clinically relevant stimuli such as temperature, hydration, pH, light (including near-infrared), magnetic fields, or electrical inputs. These triggers drive defined actuation mechanisms, most commonly thermomechanical shape-memory recovery, swelling-induced morphing, and magnetothermal activation. This review synthesizes the principal material platforms used for biomedical 4D printing, including shape-memory polymers and alloys, hydrogels, liquid-crystal elastomers, and responsive composites, and links material choice to device behavior and translational feasibility. Applications are discussed across self-expanding stents, cardiac occluders, tissue-engineered constructs, implantable drug delivery systems, and adaptive wearables. Key translational challenges include sterilization compatibility, manufacturing reproducibility and quality control, safe stimulus delivery, predictable biodegradation and long-term biocompatibility, and regulatory pathway definition. Full article

Triboelectric nanogenerators (TENGs) have emerged as versatile self-powered platforms for wearable and implantable biomedical sensing, offering an alternative to battery-dependent electronic devices. By converting biomechanical energy from physiological motion into electrical signals, TENGs enable simultaneous energy harvesting and active sensing within flexible, lightweight, and biocompatible architectures. This review summarizes recent advances from 2020 to 2025 in triboelectric nanogenerator (TENG)-based cardiovascular monitoring. The discussion focuses on material systems, device configurations, sensing mechanisms, and applications including pulse detection and cuffless blood pressure estimation. Representative studies are compared to highlight emerging trends in wearable and self-powered sensing technologies. However, differences in experimental conditions, anatomical sites, calibration methods, and signal-processing approaches limit direct comparison of reported performance. In addition, challenges such as subject-specific calibration, motion artifacts, and limited clinical validation remain. Overall, this review highlights current progress and outlines key challenges for future development and translation of TENG-based cardiovascular monitoring systems. Full article

Background: The full-scale Russian invasion of Ukraine in 2022 triggered the largest displacement crisis in Europe in recent decades. Displacement may affect both clinical outcomes and mental health among people living with HIV (PLHIV). Evidence comparing displaced PLHIV with host-country patients within the same healthcare system remains limited. This study aimed to compare epidemiological characteristics, clinical staging, and mental health outcomes between war-displaced Ukrainian PLHIV and Slovak PLHIV receiving care in the same clinical setting, with particular attention to sex-specific differences. Methods: This cross-sectional study included 137 PLHIV receiving care at the HIV/AIDS Centre, University Hospital Bratislava, Slovakia (69 from Ukraine and 68 from Slovakia). Anxiety and depressive symptoms were assessed using the Generalized Anxiety Disorder-7 (GAD-7) and Patient Health Questionnaire-9 (PHQ-9) scales. Scores were categorized into three severity groups (0–4, 5–9, ≥10). Results: Age distribution was comparable between cohorts (p = 0.2438). Transmission patterns differed substantially: heterosexual transmission predominated among Ukrainian participants, whereas men who have sex with men (MSM) transmission predominated among Slovak men (p < 0.001). Ukrainian patients were more frequently classified in CDC stage C, while Slovak patients more often presented in stage A. The combined antiretroviral therapy coverage was 100% in both cohorts and viral suppression rates were high (HIV RNA < 200 copies/mL: 91.3% in Ukraine vs. 94.1% in Slovakia). Overall anxiety and depressive symptom severity did not differ significantly between cohorts (GAD-7 p = 0.4145; PHQ-9 p = 0.7661). However, within the Ukrainian cohort, women demonstrated higher depressive symptom severity compared with men (p = 0.0478). Conclusions: War-displaced Ukrainian PLHIV achieved comparable biomedical outcomes to host-country patients within a structured healthcare system. However, depressive vulnerability emerged at the intersection of gender and displacement. These findings highlight the importance of integrating gender-sensitive mental health screening and psychosocial support into routine HIV care for displaced populations. 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

Nipah virus (NiV) is a highly pathogenic zoonotic paramyxovirus responsible for sporadic outbreaks of severe disease with high case fatality rates in South and Southeast Asia. Human infection occurs through spillover from natural reservoirs, primarily fruit bats, or via human-to-human transmission, and is characterized by a broad clinical spectrum ranging from asymptomatic infection to acute respiratory disease and fatal encephalitis. Following entry via ephrin-B2 and ephrin-B3 receptors, NiV exhibits marked endothelial and neuronal tropism, leading to systemic vasculitis, disruption of the blood–brain barrier, and direct infection of the central nervous system. Disease progression is driven by a complex interplay between viral replication strategies and host immune responses. NiV effectively counteracts innate immunity through multiple viral proteins that inhibit interferon signaling, while simultaneously inducing dysregulated inflammatory responses that contribute to tissue damage and multi-organ failure. Neurological involvement represents the most severe manifestation, often resulting in acute or relapsing encephalitis with long-term sequelae among survivors. Despite the severity of the disease, no licensed antiviral therapies or human vaccines are currently available. Therapeutic development has focused on neutralizing monoclonal antibodies targeting viral glycoproteins and small-molecule antivirals that inhibit viral RNA synthesis, both of which show promising results in preclinical models, but remain limited by timing and translational challenges. In parallel, several vaccine platforms—including viral vectors, mRNA-based constructs, and recombinant protein subunits—have advanced to early-phase clinical trials, demonstrating encouraging immunogenicity. Beyond biomedical interventions, effective outbreak containment relies on integrated public health strategies. The “Kerala model” highlights the importance of rapid case identification, isolation, contact tracing, and community engagement within a One Health framework to mitigate transmission and reduce mortality. This review synthesizes the current knowledge on NiV pathogenesis, immune evasion, clinical manifestations, and emerging therapeutic and vaccine strategies, while highlighting critical gaps and future directions for improving the preparedness and response to this high-consequence emerging pathogen. Full article

Microfluidic electrokinetic flows play a central role in applications such as lab-on-a-chip diagnostics, microelectronics cooling, and biomedical sample manipulation. These systems involve intricate heat transfer processes, including Joule heating from ionic currents, temperature-driven flow instabilities, and coupled thermal–fluid interactions, that crucially affect device performance, reliability, and scalability. Current challenges include non-equilibrium charge dynamics, incomplete thermophysical property data for complex fluids, and thermal crosstalk in integrated platforms. This review summarizes the literature published over the past 20 years, with occasional reference to earlier work, covering the fundamental mechanisms of heat generation and dissipation in electrokinetic microflows; analytical, numerical, and experimental approaches for characterizing thermal effects; and discussion on the limitations and application-driven opportunities. It also highlights open questions and future research directions and offers a comprehensive view of design principles and guidelines for developing robust, thermally optimized electrokinetic microfluidic technologies. Full article

Nitric oxide (NO) is a gaseous biocompatible radical molecule with demonstrated biomedical and antimicrobial benefits. Developing adaptable, long-lasting delivery systems for NO has become an essential goal for both combating resistant bacterial growth and providing sustained medical benefits. Silsesquioxane (SQ)-based organogels were chosen and synthesized as robust, tunable NO-release platforms. These highly stable SQ gel frameworks, composed of silicon–oxygen backbones with variable R groups, exhibited high porosity and surface area and offered chemical versatility, enabling control over NO loading and release. 3-Mercaptopropyl groups were utilized as sulfur-based NO-releasing substituents (-RSNOs), with additional R groups capable of altering accessibility to RSNO sites through hydrophobicity and steric hindrance. The NO release profile, rate, and duration of the functionalized gels were also tailored by adjusting the number of RSNO sites in the elastomeric system, thereby enabling a customizable release profile. This combination of NO-releasing silsesquioxanes with silicone elastomers yields composite materials that are integratable into biomedical applications, offering NO release up to 40 days within modeled physiological conditions in PBS buffer. Full article

CRISPR technologies are transforming cardiovascular therapy development by creating an increasingly seamless pipeline from potential target discovery to clinical translation. What began as a genome-editing tool has evolved into a versatile platform that enables researchers to precisely interrogate and modulate cardiac biology with tools such as base- and prime-editors, and CRISPR inhibition and activation. In this review, we follow the use of CRISPR across the stages of biomedical research through to bench-to-bedside application. This review begins by addressing how genome-wide and focused CRISPR screens discover developmental regulators, disease drivers, and drug-response pathways, making the first steps in identifying therapeutic targets. We then explore how CRISPR engineering creates progressively more relevant disease model systems to validate mechanisms of disease and test interventions, helping bridge the translational gaps between the lab and the clinic. Finally, we consider how CRISPR technologies are beginning to enter cardiovascular clinical trials, while highlighting the key challenges that still limit this translation. By linking the latest advances of modern CRISPR platforms to the stages of therapeutic development, this review highlights how CRISPR technology is reshaping the pipeline from molecular insight to clinical innovation in cardiac disease. Full article

Kolmogorov–Arnold Networks (KANs) have recently gained increasing attention as an alternative to conventional neural architectures, mainly because they replace fixed activation functions with learnable univariate mappings defined along network edges. This design not only increases modeling flexibility but also makes it easier to interpret how inputs are transformed within the network while maintaining parameter efficiency. KANs are particularly well suited for sensor-driven systems where transparency, robustness, and computational constraints are critical. This study provides a survey of KAN-based approaches for processing sensor data. A literature review conducted from 2024 to 2026 examined the deployment of KAN models in industrial and mechanical sensing, medical and biomedical sensing, and remote sensing and environmental monitoring, utilizing a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)-based methodology. We first revisit the theoretical foundations of KANs and their main architectural variants, including spline-based, polynomial-based, monotonic, and hybrid formulations, to structure the discussion. From a practical standpoint, we then examine how KAN modules are integrated into modern deep learning pipelines, such as convolutional, recurrent, transformer-based, graph-based, and physics-informed architectures. KAN-based models demonstrate comparable predictive performance as conventional machine learning models, while having fewer parameters and more interpretable representations. Several limitations persist, including computational overhead, sensitivity to noisy signals, and resource-constrained device deployment challenges. Real-world sensor systems encounter significant challenges in adopting KAN-based models, including scalability in large-scale sensor networks, integration with hardware architectures, automated model development, resilience to out-of-distribution conditions, and the need for standardized evaluation metrics. Collectively, these observations provide a clearer understanding of the current and potential limitations of KAN-based models, offering practical guidance on the development of interpretable and efficient learning systems for future sensor equipment applications. Full article