Search Results (957)

Modern healthcare remains structurally and conceptually fragmented, with profound clinical and policy implications. At its root lies an ontological fracture: the prevailing biomedical model reduces patients to discrete biological systems (organs, biomarkers, and symptoms) detached from the psychological, social, and ecological contexts in which health and illness are experienced. This is compounded by epistemological fragmentation, where medical knowledge is compartmentalized into increasingly narrow specialties, limiting holistic understanding. These philosophical divisions manifest in downstream operational, informational, financial, and policy dysfunctions duplicative testing, misaligned incentives, disconnected care pathways, and population health failures. To address these multilevel fractures, we propose a unified architecture grounded in three interlocking components. First, the Precision and Personalized Population Health (P3H) framework offers a principle-based realignment toward care that is integrated, personalized, proactive, and population wide. P3H addresses the conceptual shortcomings of fragmented care by focusing on the full human trajectory across time, systems, and determinants. Second, General Purpose Technologies including artificial intelligence, biosensors, mobile diagnostics, and multimodal data systems enable the operationalization of whole-person care at scale, especially in low-resource settings. Third, the AI-WHOLE policy framework (Alignment, Integration, Workflow, Holism, Outcomes, Learning, and Equity) provides governance principles to guide ethical, equitable, and context-specific implementation. We argue that this triadic blueprint is particularly critical for Global South nations, where the lack of legacy infrastructure offers an opportunity for leapfrogging toward integrated, intelligent systems of care. Early models illustrate how policy-aligned, technology-enabled care rooted in whole-person principles can yield improvements in continuity, cost-efficiency, and chronic disease outcomes. This manuscript offers a systems-level strategy to overcome fragmentation and reimagine healthcare delivery, not only by refining clinical tools, but by redefining what it means to care for the human being in full. Full article

Chitooligosaccharides (COS) are short-chain chitosan derivatives with a wide range of biomedical, agricultural, and environmental applications, including antimicrobial therapy, wound healing, and pollutant removal. Reliable quantification of COS is essential but currently relies on high-performance liquid chromatography, mass spectrometry, or capillary electrophoresis, which require costly equipment, complex sample preparation, and are unsuitable for routine or on-site applications. This study reports a rapid, solvent-free, colorimetric assay for COS based on the reaction of 5% aqueous ninhydrin with free amino groups in McIlvaine buffer. The assay was optimized using glucosamine as a model analyte, yielding maximal sensitivity at pH 7.0. The chromophore generated (Ruhemann’s purple) remained stable for over 120 min after reaction, allowing measurements to be taken without strict time constraints. Calibration was linear from 0.4 to 2.2 mM (R² = 0.9926), with low limits of detection (0.006 mM) and quantification (0.018 mM). Increasing absorbance with COS polymerization degree (DP1–DP6) demonstrates specificity for free amino groups, while N-acetyl glucosamine showed a negligible response. Furthermore, the assay was successfully adapted for solid-phase detection on ninhydrin-pretreated filter paper and nitrocellulose, with enhanced sensitivity. This simple, efficient, and low-cost method provides an accessible alternative to instrumental techniques, supporting COS monitoring in laboratory workflows and enabling portable applications in biomedicine, agriculture, and environmental diagnostics. Full article

Light-based diagnostic and therapeutic approaches are increasingly important in modern biomedicine, with organic dyes emerging as versatile optical agents due to their tunable photophysical properties. Precise control over absorption and emission characteristics has enabled their application in fluorescence, photoacoustic, and Raman imaging, as well as in photodynamic and photothermal therapies. However, challenges related to biocompatibility, aqueous stability, and in vivo performance remain critical for clinical translation. Organic dyes that absorb in the near-infrared region are particularly attractive because of their deeper tissue penetration and reduced background interference. This review highlights key structure property relationships of organic dyes and summarizes current design strategies, including chromophore modification, peripheral functionalization for water solubility, and self-assembled nanotheranostic systems. Recent biomedical applications in cancer diagnosis and therapy, bacterial detection, and imaging-guided treatment are discussed, along with future directions for advancing dye-based technologies in healthcare. Full article

Many biomedical applications rely on the accurate recovery of absorption and scattering properties of human tissue. These characteristics serve as useful diagnostic indicators, holding information regarding the health and physiological status of a human subject. Many experimental methods exist for the determination of these optical properties, though many, such as integrating sphere methods, are not easily used in an in vivo setting. We have constructed and validated a spatially resolved reflectance imaging system that can be used to measure the absolute optical properties of absorbing turbid media in a non-contact, non-invasive fashion. We present detailed calibration procedures that consider our unique incident beam profile and system response with quantitative comparisons between experimentally and computationally obtained reflectance using Monte Carlo methods. Using highly scattering sphere suspensions with added absorption by ink, we show the spatially resolved reflectance imaging system’s ability to recover absorption within 20% of reference collimated transmission measurements and reduced scatter within 6% of those obtained by an extensively tested integrating sphere system, validating our system in preparation for in vivo measurements of the optical properties of human skin. Full article

Field-effect transistor (FET) biosensors using graphene have become one of the most promising biosensing platforms for the early diagnosis of diseases with features such as high sensitivity, label-free detection and application compatibility with point-of-care systems. Herein, we critically discuss recent advances in graphene FET (GFET) biosensor development toward clinically relevant biomarkers associated with representative diseases including cancer, neurodegenerative disease, infectious disease, and inflammatory conditions. Recent progress was reviewed to evaluate GFET architectures, surface functionalization methods, and detection quality. The biomarkers explored were clusterin in Alzheimer’s disease, thrombin in coagulopathy, estrogen receptor α (ER-α) in breast cancer, Carcinoembryonic antigen in lung cancer, microRNAs for malignant tumors, exosomes derived from HepG2 for the hepatocellular carcinoma (HCC) cell line, interleukin-6 (IL-6) for chronic obstructive pulmonary disease (COPD), Polyclonal antibodies and antigens (P24) for HIV and prostate-specific antigen for prostate cancer. The developed devices demonstrate ultralow detection limits at femtomolar to attomolar concentrations with the aid of designed antibodies, aptamers and nanomaterials. Herein, this review presents the sensing mechanisms and biomedical application of various GFET platforms, focusing on their emerging potential as next-generation platforms for rapid, non-invasive and point-of-care diagnostics. Full article

Drug-resistant tuberculosis (DR-TB) represents a major public health threat, particularly in the prisons of Latin America and the Caribbean, where rates are up to 40 times higher than those observed in the general population. These facilities act as community amplifiers due to overcrowding, poor ventilation, diagnostic delays, and treatment discontinuity. This study offers a critical reflection on the magnitude, determinants, and implications of DR-TB in regional penitentiary contexts. A reflective analytical review was conducted in PubMed, Scopus, Web of Science, SciELO, and LILACS, complemented by WHO and PAHO reports, prioritising studies from 2019 to 2024. The findings reveal MDR-TB and pre-extensively drug-resistant (pre-XDR) outbreaks in Peru, Paraguay, and the Dominican Republic, as well as community transmission linked to prisons in Brazil and Colombia. Persistent gaps remain in systematic screening, drug susceptibility testing coverage, and post-release follow-up. Scientific production continues to be uneven and predominantly biomedical, with limited consideration of social and human rights determinants. DR-TB in prisons reflects the structural deficiencies of health and justice systems; its control requires intersectoral policies, genomic surveillance, and strategies that ensure early diagnosis, treatment continuity, and dignified detention conditions. Full article

Climate change poses a growing threat to global health, yet healthcare systems contribute substantially to environmental harm through energy use, waste, and greenhouse gas (GHG) emissions. Among hospital departments, Intensive Care Units (ICUs) are among the most resource- and energy-intensive, generating disproportionately high greenhouse gas (GHG) emissions. The aim of this systematic review is to synthesize the literature on the environmental footprint of ICUs and to develop evidence-based strategies for creating sustainable ‘Green ICUs’ in accordance with the PRISMA 2020 guidelines. Peer-reviewed studies published between 2012 and October 2025 were identified through searches of major biomedical databases. Eligible studies examined the impacts of climate change on human health and infectious diseases, the ecological footprint of medical imaging and personal protective equipment, and sustainability interventions relevant to adult intensive care units. The environmental footprint of ICUs ranges from 88 to 178 kg CO₂-equivalents per patient per day. High electricity consumption, especially from heating, ventilation, and air-conditioning (HVAC) systems, along with single-use medical supplies and diagnostic imaging, drives this impact. Life-cycle assessments consistently demonstrate that reusable textiles, optimized energy systems, and rationalized diagnostic practices significantly reduce emissions and waste. Educational and behavioral interventions were effective in reducing unnecessary consumable use while maintaining patient safety. A “Green ICU” model integrating energy efficiency, sustainable procurement, waste reduction, and staff education can substantially reduce environmental harm without compromising quality of care. Full article

This paper presents the synthesis, physicochemical characterisation, and analytical applications of chemiluminescent (CL) labels based on acridinium salts (ALs) for biomedical diagnostics. These compounds emit light as a result of oxidative reactions and represent an established class of reagents widely employed in chemiluminescence immunochemical assays (CLIAs) today. A series of structurally differentiated acridinium labels (AL1–AL5) was synthesised applying mostly original synthetic routes and purified to chromatographic purity (>90%, RP-HPLC). The compounds, including a commercial product treated as a reference, were successfully conjugated to anti-human IgG, yielding stable immunochemical reagents suitable for immunoassays with CL detection. The chemiluminescence properties of the obtained labels and their protein conjugates were investigated in aqueous buffers and in the presence of surfactants. The emission profiles exhibited characteristic flash-type kinetics with emission maxima occurring within 0.15–0.25 s after reaction initiation. The presence of surfactants more or less significantly enhanced the emission intensity, with signal increases of up to approx. 2-fold compared to surfactant-free systems. Analytical calibration demonstrated a linear response of signal derived from native labels over at least one order of magnitude of concentration, with detection limits falling in the range of 10⁻⁹–10⁻¹⁰ M, confirming the high sensitivity of the developed compounds. The experimental results were supported by theoretical studies using density functional theory (DFT), which confirmed the energetic feasibility of the CL reaction pathway and identified structural factors influencing activation barriers. Additional semiempirical calculations (PM7) indicated that the dielectric environment and proximity of ionic species can influence the reaction energetics, providing mechanistic support for the experimentally observed effects of surfactants. The results demonstrate that both molecular structure and microenvironment influence CL efficiency and kinetics of the investigated systems. The developed acridinium labels exhibit analytical performance better or comparable to commercial reagents and are fully compatible with standard immunodiagnostic conjugation protocols, confirming their suitability for use in modern chemiluminescent immunoassays. Full article

Bacterial membrane vesicles (BMVs) are non-replicative, bilayered nanostructures secreted by both Gram-negative and Gram-positive bacteria. Rather than being passive byproducts of cell envelope turnover, BMVs are increasingly recognized as regulated particles that selectively package proteins, lipids, nucleic acids, and other bioactive molecules. Through these cargos, BMVs mediate a wide range of biological processes, including bacterial stress adaption, intercellular communication, virulence delivery, and host immune modulation. In this review, we integrate recent advancements in understanding the molecular mechanisms underlying BMV biogenesis and composition and discuss how their heterogeneity contributes to their functional diversity. Beyond their biological roles, we critically examine the translational potential of BMVs in vaccine development, targeted drug delivery, cancer therapy, diagnostic tools, and biotechnological applications. However, significant challenges related to their safety, efficacy, and large-scale production must be addressed to realize their full clinical potential. We review recent progress and ongoing obstacles in the use of BMVs across various biomedical applications and propose strategies for their clinical translation. Full article

Malignant transformation of benign salivary gland tumors represents a critical biological process that provides valuable insights into head and neck carcinogenesis, with potential implications for oral squamous cell carcinoma (OSCC). Understanding the molecular, epigenetic, and microenvironmental mechanisms underlying this transition is essential for improving early diagnosis, risk stratification, and personalized management strategies. This study presents a comprehensive narrative review of the current literature focusing on benign salivary gland tumors with malignant potential, particularly pleomorphic adenoma and carcinoma ex pleomorphic adenoma, emphasizing molecular alterations, angiogenesis, and tumor microenvironment dynamics. A structured literature search was conducted across major biomedical databases, including PubMed and Scopus, selecting studies that addressed genetic rearrangements, epigenetic modifications, histopathological features, and clinical connections of malignant transformation. The findings highlight recurrent genetic alterations such as PLAG1 and HMGA2 rearrangements, TP53 mutations, and ERBB2 overexpression, along with epigenetic dysregulation through CpG island hypermethylation. Enhanced angiogenesis, marked by increased expression of CD105 and vascular endothelial growth factor, as well as a “cold” immune microenvironment, emerged as key contributors to tumor progression. These mechanisms demonstrate significant overlap with pathways implicated in OSCC development. Benign salivary gland tumors represent a valuable model for studying malignant transformation in head and neck oncology. Interpreting shared molecular and microenvironmental pathways may facilitate the identification of novel biomarkers and support the development of personalized diagnostic and therapeutic approaches for OSCC. Full article

The usage of magnetic nanoparticles (MNPs), particularly iron oxide-based systems such as magnetite (${\mathrm{Fe}}_3$${\mathrm{O}}_4$) and maghemite ($\gamma$-${\mathrm{Fe}}_2$${\mathrm{O}}_3$), has significantly advanced the field of theranostics. These nanoparticles unite therapeutic and diagnostic capabilities due to their favorable magnetic properties and surface engineering potential. However, the path from synthesis to clinical application poses substantial challenges, including optimization of structure–property–function relationships, biocompatibility issues, and effective surface functionalization. Various synthesis methods, such as co-precipitation and thermal decomposition, aim to achieve specific nanoparticle characteristics, although they encounter obstacles related to scalability and reproducibility. Furthermore, characterizing these systems through structural, microstructural and spectroscopic techniques is vital to determine their functional efficacy and ensure their safe biomedical usage. This review comprehensively examines recent advancements and identifies existing challenges in the clinical translation of MNPs, highlighting the need for refined methods and standardized protocols to effectively exploit their theranostic potential. It outlines future directions, emphasizing the importance of green synthesis and robust characterization frameworks to enhance the integration of MNPs in personalized medicine. Full article

Accurate detection of blood cells in microscopic images plays a crucial role in automated hematological analysis and clinical diagnosis. Herein, we proposed an improved YOLOv8n-based model for efficient and precise detection of red blood cells (RBCs), white blood cells (WBCs), and platelets in the BCCD dataset. The baseline YOLOv8n framework was enhanced by integrating GhostConv and C3Ghost modules to reduce model complexity while maintaining high detection performance. A series of ablation experiments were conducted to evaluate the individual and combined effects of these modules on model accuracy and computational efficiency. Experimental results demonstrated that the baseline model achieved an mAP@0.5 of 0.9043 with 3.01 M parameters. After incorporating GhostConv, the model maintained comparable accuracy (mAP@0.5 = 0.9040) with a reduction in parameters to 2.73 M. The C3Ghost integration further decreased parameters to 1.99 M with an mAP@0.5 of 0.8973. The combined model achieved an optimal balance between accuracy (mAP@0.5 = 0.9001) and compactness (1.71 M parameters). Results indicate that the improved YOLOv8n can effectively enhance detection efficiency without sacrificing precision. The proposed lightweight detection framework provides a promising solution for real-time blood cell analysis. Its high accuracy, reduced computational load, and strong generalization ability make it suitable for integration into automated laboratory systems, facilitating rapid and intelligent medical diagnostics in hematology and related biomedical applications. Full article

Background/Objectives: Magnetite nanoparticles represent promising candidates for a broad spectrum of biomedical applications, ranging from in vitro diagnostic assays to in vivo imaging, hyperthermia, and targeted drug and gene delivery, with some nanoagents already approved for clinical use. A critical determinant of their functionality is the nanoparticle coating, which facilitates beneficial interactions within biological systems. In the context of tumor-targeted therapeutic delivery, key design parameters—particularly surface coatings—can be optimized to enhance treatment efficacy by modulating blood circulation kinetics, biodistribution, and other critical properties. However, current preclinical screening methods primarily rely on cell culture models to identify potential nanocarriers, yet these systems often poorly correlate with actual in vivo performance. This discrepancy highlights the necessity of incorporating more biologically relevant testing platforms, such as high-throughput in vivo assays. Methods: In this work, we employed an original magnetic particle quantification (MPQ) technology to systematically evaluate the blood circulation kinetics and biodistribution patterns for magnetite nanoparticles with 17 different coatings across multiple organs and tissues, including the liver, spleen, lungs, kidneys, heart, tumor, brain, peripheral blood, muscle, and bone. This methodology offers high sensitivity, user-friendly operation, and provides quantitative measurements across a broad dynamic range of nanoparticle concentrations. These advantages enabled high-throughput acquisition of precise blood circulation and biodistribution data. In addition, histological analysis was conducted to evaluate nanoparticle penetration depth within tumor tissue. Results: Here we conducted a comprehensive study of the effect of 17 different polymer-, lectin-, and small molecule-based coatings on the behavior of magnetite nanoparticles in vivo. For each type of obtained nanoparticles, we implemented passive targeting as well as magnetic targeting, the latter using an external magnetic field localized in the tumor area. Conclusions: The collected dataset provides critical insights into how surface modifications influence nanoparticle performance in complex biological systems, offering valuable guidance for optimizing therapeutic nanocarrier design. Full article

Background: Atrial fibrillation (AF) is a prevalent arrhythmia closely associated with cardiometabolic disorders and systemic inflammation. Epicardial adipose tissue (EAT), located in direct contact with the atrial myocardium, has emerged as a biologically active tissue involved in atrial remodeling through inflammatory, fibrotic, and electrophysiological mechanisms. The objective of this review is to summarize current translational and clinical evidence on the role of EAT in AF pathophysiology and to discuss its implications for diagnostic assessment, interventional management, and cardiometabolic therapeutic strategies. Methods: A narrative, structured review of experimental, translational, and clinical studies was conducted using major biomedical databases. The literature was evaluated with a focus on mechanisms linking EAT to atrial remodeling, noninvasive imaging techniques for EAT characterization, echocardiographic and electroanatomical markers of atrial disease, outcomes of catheter ablation strategies, and pharmacological interventions targeting metabolic and inflammatory pathways. Results: The available evidence indicates that increased EAT volume and altered inflammatory activity are associated with atrial fibrosis, conduction abnormalities, and impaired atrial function, contributing to AF initiation and persistence. Multimodality imaging, including cardiac computed tomography and cardiac magnetic resonance, enables quantitative and qualitative assessment of EAT and supports clinical phenotyping. Clinical studies report an association between higher EAT burden and increased AF recurrence after pulmonary vein isolation, particularly in patients with persistent AF. Emerging cardiometabolic therapies, such as glucagon-like peptide-1 receptor agonists and dual GIP/GLP-1 agonists, have been shown to reduce EAT volume and inflammatory markers, although direct evidence linking these interventions to improved AF outcomes remains limited. Conclusions: EAT represents a relevant pathophysiological interface between metabolic disease and AF with potential clinical implications. Incorporating EAT assessment into routine evaluation may enhance risk stratification and support personalized AF management. Further prospective studies are required to define its role as a therapeutic target in clinical practice. Full article

Developmental Language Disorder (DLD) is a common neurodevelopmental condition that affects language acquisition in children. However, standardized diagnostic tools for Tunisian Arabic, a widely spoken yet underrepresented dialect, is still lacking. This study presents a multimodal biomedical informatics framework that integrates clinical assessments, speech recordings, and artificial intelligence (AI) for early DLD detection. Three linguistic tasks (the CLT Task, the Arabic Verb Evaluation Task, and the Nonword Repetition Task) were adapted for Tunisian Arabic, and spontaneous speech samples were collected from children with typical development and those with DLD. Statistical analyses revealed significant deficits in verb production, past-tense morphology, and phonological memory in the DLD group. For automated screening, we developed two systems: a Random Forest classifier based on structured clinical and linguistic features and a multimodal deep learning model using Wav2Vec2 acoustic embeddings. The best model achieved an F1 score of 0.85, demonstrating the feasibility of AI-assisted DLD screening. This work introduces the first standardized dataset and computational baseline for DLD in Tunisian Arabic, providing clinically relevant tools for early identification and supporting research on underrepresented Arabic dialects. This work also highlights future implications, including potential applications in early screening, the integration of acoustic markers, and the development of culturally adapted assessment tools for underrepresented languages. Full article