Search Results (242)

The development of highly sensitive approaches for detecting tumor cells in biological samples remains a critical challenge in laboratory and clinical oncology. In this study, we investigated the structural and magnetic properties of iron oxide nanoparticles incorporated into cellulose microspheres of two size ranges (~100 and ~700 μm) and evaluated their potential for targeted tumor cell isolation. In the smaller microspheres, magnetite-based magnetic nanoparticles (MNPs) were synthesized in situ via co-precipitation, whereas pre-synthesized MNPs were embedded into the larger microspheres. The geometrical characteristics of the resulting magnetic cellulose microspheres (MSCMNs) were assessed by confocal microscopy. Transmission electron microscopy and X-ray diffraction analyses revealed an average magnetic core size of approximately 17 nm. Magnetic properties of the MNPs within MSCMNs were characterized using a highly sensitive nonlinear magnetic response technique, and their dynamic parameters were derived using a formalism based on the stochastic Hilbert–Landau–Lifshitz equation. To evaluate their applicability in cancer diagnostics and treatment monitoring, the MSCMNs were functionalized with a TKD peptide that selectively binds membrane-associated Hsp70 (mHsp70), yielding TKD@MSCMNs. Magnetic separation enabled the isolation of tumor cells from biological fluids. The specificity of TKD-mediated binding was confirmed using Flamma648-labeled Hsp70 and compared with control alloferone-conjugated microspheres (All@MSCMNs). The ability of TKD@MSCMNs to selectively extract mHsp70-positive tumor cells was validated using C6 glioma cells and mHsp70-negative FetMSCs controls. Following co-incubation, the extraction efficiency for C6 cells was 28 ± 14%, significantly higher than that for FetMSC (7 ± 7%, p < 0.05). These findings highlight the potential of TKD-functionalized magnetic cellulose microspheres as a sensitive platform for tumor cell detection and isolation. Full article

Candida biofilms play a critical role in clinical settings, contributing to persistent and device-associated infections and conferring resistance to antifungal agents, particularly in immunocompromised or hospitalized patients. Biofilm formation varies among Candida species, including C. albicans and non-albicans species, such as C. glabrata, C. tropicalis, C. parapsilosis, and C. auris, due to species-specific transcriptional networks that regulate modes of biofilm development, extracellular matrix composition, and metabolic reprogramming. These differences influence biofilm responses to treatment and the severity of infections, which can be further complicated in polymicrobial biofilms that modulate colonization and virulence. Understanding the mechanisms driving biofilm formation and interspecies interactions is essential for developing effective therapies and requires appropriate experimental models. Available models range from simplified in vitro systems to more complex ex vivo and in vivo approaches. Static in vitro models remain widely used due to their simplicity and reproducibility, but they poorly mimic physiological conditions and require careful standardization. Ex vivo tissue models offer a balance between practicality and biological relevance, enabling the study of biofilm physiology, host–microbe interactions and immune responses. In vivo models, primarily in mice, remain the gold standard for testing antifungal therapies, while alternative systems such as Galleria mellonella larvae provide simpler, cost-effective approaches. Advanced in vitro platforms, including organ-on-chip systems, bridge the gap between simplified tests and physiological relevance by simulating fluid dynamics, tissue architecture, and immune complexity. This review aims to examine Candida biofilms across species, highlighting differences in structural diversity and clinical implications, and to provide a guide to the most widely used experimental models supporting studies on Candida biofilm biology for the development of new therapeutic targets or drug testing. Full article

In this study, an innovative bioreactor, named BioAxFlow, particularly suitable for tissue engineering applications, is tested. Unlike traditional bioreactors, it does not rely on mechanical components to agitate the culture medium, but on the unique fluid-dynamics behaviour induced by the geometry of the culture chamber, which ensures continuous movement of the medium, promoting the constant exposure of the cells to nutrients and growth factors. Using the human osteosarcoma cell line SAOS-2, the bioreactor’s ability to enhance cell adhesion and proliferation on polylactic acid (PLA) scaffolds, mimicking bone matrix architecture, is investigated. Cells cultured in the bioreactor showed significant improvement in cell growth and adhesion, compared to static cultures, and a more homogeneous cell distribution upon the scaffold surfaces, which is crucial for the development of functional tissue constructs. The bioreactor also preserves the osteogenic potential of SAOS-2 cells as assessed by the expression of key osteogenic markers. Additionally, it retains the tumorigenic characteristics of SAOS-2 cells, including the expression of pro-angiogenic factors and apoptosis-related genes. These results indicate that the BioAxFlow bioreactor could be an effective platform for tissue engineering and cancer research, offering a promising tool for both regenerative medicine applications and drug testing. Full article

Rare genetic ocular diseases represent a heterogeneous group of disorders that significantly impair visual function and quality of life. Despite their clinical relevance, many of these conditions remain insufficiently characterized due to complex molecular mechanisms and diagnostic limitations. Recent advances in molecular diagnostics, particularly Next-Generation Sequencing (NGS), have enabled comprehensive and accurate identification of pathogenic variants, offering novel insights into genotype–phenotype correlations and supporting precision medicine approaches. In parallel, the use of alternative biological matrices such as tear fluid has emerged as a promising non-invasive strategy for biomarker discovery and disease monitoring. Tear-based omics, including proteomics and transcriptomics, have identified diagnostic signatures and pathogenic mediators such as non-coding RNAs, microRNAs, and tRNA-derived fragments (tRFs). Among these, tRF-1001 has shown potential both as a biomarker and therapeutic target in ocular neovascular conditions through its modulation of angiogenic pathways. The objective of this review is to show the integration of two rapidly advancing yet frequently isolated fields: next-generation sequencing-based genomics and tear-fluid molecular profiling, positioning them as complementary foundations of precision ophthalmology for rare inherited retinal and optic nerve disorders. Previous reviews have mainly concentrated on either genetic diagnosis or ocular surface biomarkers separately; however, we have introduced a convergent model wherein genomic data furnish diagnostic and prognostic clarity, while tear-omics deliver dynamic, minimally invasive assessments of disease activity, treatment efficacy, and persistent neurovascular stress. By explicitly connecting these two aspects, we have delineated how multi-matrix, multi-omics approaches can expedite early diagnosis, facilitate personalized longitudinal monitoring, and direct focused treatment interventions in rare ocular genetic illnesses. Full article

Diagnosis and prognosis of the amyotrophic lateral sclerosis and frontotemporal dementia (ALS/FTD) spectrum remain largely dependent on clinical assessments due to a lack of established fluid biomarkers. While neurofilaments and the cerebrospinal fluid (CSF) phosphorylated-tau/total-tau ratio (pTau:tTau) have been studied, their limitations, including their lack of clinical implementation and low specificity, necessitate multimodal approaches. This study aimed to characterize the biological features of the ALS/FTD spectrum through integration of clinically available parameters. We conducted a retrospective, single-center, cross-sectional study analyzing routinely collected clinical, neuroimaging, CSF, and serum data from 229 samples, including 45 from patients with ALS, 26 from patients with FTD, 158 from patients with other neurodegenerative diseases, and 29 from cognitively healthy controls. We implemented propensity score-weighted comparisons, an F1 score-based optimal cut-point determination for the pTau:tTau ratio, and a regularized XGBoost-based multimodal feature modeling approach. The biomarker and model performance was evaluated by the area under the precision–recall curve (AUC-PR). Feature importance analysis identified characteristic indicators of the ALS/FTD spectrum. Consistent with the prior literature, the pTau:tTau ratio was significantly reduced in ALS/FTD, but the classification performance was modest (AUC-PR 0.32). A multimodal model integrating clinical, biofluid, and neuroimaging features achieved a notably better performance (AUC-PR 0.75). Feature importance analysis revealed an ALS/FTD signature beyond the pTau:tTau ratio characterized by higher global cognition, younger age, an altered Aβ42/pTau ratio, and immunoglobulin changes (CSF IgG:IgA, serum IgG). Integration of clinical routine data centered on tau, amyloid, and immunological pathophysiology as well as temporal disease dynamics provide a contextualized biological characterization of the ALS/FTD spectrum. This approach offers a foundation for hypothesis generation regarding ALS/FTD pathophysiology and biomarker-supported diagnosis. Full article

There is a growing demand for underwater robots to support offshore tasks such as exploration, environmental monitoring, and critical underwater missions. To enhance the performance of these systems, researchers are increasingly turning to biological inspiration to develop robots that understand and adapt the swimming strategies of aquatic animals. Numerical modeling plays a critical role in evaluating and improving the performance of these complex, multi-body robotic systems. However, developing accurate models for multi-body robots that swim freely in three dimensions remains a significant challenge. This study presents the development and validation of a numerical model of a bio-inspired California sea lion (Zalophus californianus) robot. The model was developed to simulate, analyze, and visualize the robot’s body motions in water. The equations of motion were derived in closed form using the Euler–Poincaré formulation, offering advantages for control and stability analysis. Hydrodynamic coefficients essential for estimating fluid forces were computed using computational fluid dynamics (CFD) and strip theory and further refined using a genetic algorithm to reduce the sim-to-real gap. The model demonstrated strong agreement with experiments, accurately predicting the translation and orientation of the robot. This framework provides a validated foundation for simulation, control, and optimization of bio-inspired multi-body systems. Full article

Breast milk (BM) is a unique biological fluid that represents the optimal nutritional source for infants, uniquely adapted through millions of years of evolution. BM is not only a nutritional fluid but a dynamic biological system, evolved to provide optimal growth, immune protection, and neurodevelopmental support. Its unique composition—including macronutrients, micronutrients, bioactive molecules, and stem cells—makes it essential in early life. Breastfeeding further promotes psychological well-being, secure attachment, and maternal–infant bonding. Yet, in recent decades, concern has grown over environmental contaminants in BM, including endocrine-disrupting chemicals (EDCs) and micro/nanoplastics. These pollutants have the potential to disrupt endocrine signaling, neurodevelopment, metabolic programming, and immune development, thereby undermining the natural advantages of breastfeeding. Therefore, a better understanding of the unique features of BM, while investigating the effects of these contaminants, is important for safeguarding maternal and infant health. This perspective article highlights the current knowledge on BM and indicates the need for further research. It also emphasizes the need for appropriate public health measures aimed at reducing exposure to pollutants and lowering associated risks, as well as preventive strategies to protect breast milk and breastfeeding in such a changing environment, as it is uniquely designed to promote the health of children. Full article

Cardiometabolic diseases, including cardiovascular disorders and type 2 diabetes mellitus, are the leading cause of morbidity and mortality worldwide, placing a significant burden on healthcare systems. Although advances in imaging and risk stratification have improved disease management, conventional diagnostic and prognostic tools often lack the requisite sensitivity and specificity for early and precise risk stratification. This limitation stems from their poor ability to capture the full molecular complexity of these conditions, underscoring an urgent need for innovative biomarkers to bridge these gaps. MicroRNAs, small non-coding RNAs that regulate gene expression post-transcriptionally, have emerged as promising candidates. Their characteristics offer several advantages over traditional methods, including exceptional stability in biological fluids, strong tissue and disease specificity, and the ability to reflect dynamic pathological changes. These unique features enable miRNAs to detect subtle molecular alterations that may precede clinical symptoms, thereby overcoming key limitations of current diagnostic approaches. Altered circulating miRNA profiles have been linked to pathological processes such as endothelial dysfunction, inflammation, oxidative stress, and maladaptive cardiac remodeling. This review provides a comprehensive overview of the current evidence supporting the diagnostic and prognostic role of circulating miRNAs in cardiometabolic disease. We highlight their potential as early detection biomarkers, tools for patient stratification, and indicators of therapeutic response. Furthermore, we discuss key limitations to clinical translation, including methodological variability, challenges in sample handling, differences in normalization strategies, and platform-dependent quantification inconsistencies. Overcoming these obstacles and achieving robust large-scale clinical validation will be essential to fully harness the potential of miRNAs as next-generation molecular signatures in precision medicine. Full article

To simultaneously improve mass transfer and minimize pressure drop in proton exchange membrane fuel cells (PEMFCs), this study proposes a novel bionic flow field inspired by the streamlined abdominal structure of the peregrine falcon. A three-dimensional channel geometry is developed from this biological prototype and integrated into a single-channel PEMFC model for numerical simulation. A series of computational fluid dynamics (CFD) analyses compare the new design against conventional straight, trapezoidal, and sinusoidal flow fields. The results demonstrate that the falcon-inspired configuration enhances oxygen delivery, optimizes water management, and achieves a more uniform current density distribution. Remarkably, the design delivers a 9.45% increase in peak power density while significantly reducing pressure drop compared to the straight channel. These findings confirm that biologically optimized aerodynamic structures can provide tangible benefits in PEMFC flow field design by boosting electrochemical performance and lowering parasitic losses. Beyond fuel cells, this bio-inspired approach offers a transferable methodology for advanced energy conversion systems where efficient fluid transport is essential. Full article

Background: The accurate and timely diagnosis of neurodegenerative disorders such as Parkinson’s disease, Alzheimer’s disease, and major depressive disorder critically depends on real-time monitoring and precise interpretation of authentic neurotransmitter (NT) signal dynamics in complex biological fluids (CBFs), including cerebrospinal fluid. These NT dynamics are governed by both the type and concentration of NTs present in the CBFs. However, current biosensors face significant limitations in sensitivity and selectivity, thereby hindering reliable estimation (detection and quantification) of NTs. Though nanomaterials and bioenzymes have been utilized to modify sensor interfaces for enhanced performance, issues like signal convolution, electrode fouling, and inter-NT crosstalk persist. Objectives: This review aims to evaluate and synthesize current research on the use of artificial intelligence (AI), particularly machine learning (ML), pattern recognition (PR), and deep learning (DL), to improve the automated detection and quantification of neurotransmitters from complex biological fluids. Design: A systematic review of 33 peer-reviewed studies was conducted, focusing on the integration of AI methods in neurotransmitter estimation. The review includes an analysis of commonly studied NTs, the methodologies for their detection, data acquisition techniques, and the AI algorithms applied for signal processing and interpretation. Results: The studies reviewed demonstrate that AI-based approaches have shown considerable potential in overcoming traditional biosensor limitations by effectively deconvoluting complex, multiplexed NT signals. These techniques allow for more accurate NT estimation in real-time monitoring scenarios. The review categorizes AI methodologies by their application and performance in NT signal analysis. Conclusions: AI-enhanced NT monitoring represents a promising direction for advancing diagnostic and therapeutic capabilities in neurodegenerative diseases. Despite current challenges, such as sensor stability and NT interaction complexity, AI integration, particularly in applications like closed-loop deep brain stimulation (CLDBS), offers significant potential for more effective and personalized treatments. Full article

Animal-borne tags are widely used for tracking and monitoring the movements, behaviour, and ecology of marine animals. Tagging can, however, adversely affect the hydrodynamic force balance and welfare of tagged animals, and consequently, the reliability and accuracy of data, such as by increasing drag, altering swimming characteristics, and reducing the survival rate of tagged animals. Therefore, it is important to understand and quantify the impact of tagging on marine animal hydrodynamics and to optimize the choice of tag and attachment position. In this study, computational fluid dynamics (CFD) modelling is used to simulate the flow around tagged and untagged mako sharks (Lamnidae) across their swim speed range for two dominant tag shapes, tagging sites, and body sizes. The results indicate that fin mounted tags can have a significant impact on shark hydrodynamics and energetic balance, increasing drag between 17.6% and 31.2% for a mako shark (2.95 m fork length) across the range of flow velocities tested (0.5 to 9.1 m/s). In comparison, the optimal tagging site for archival tags attached to the dorsal musculature leads to a minimal increase in drag for the larger sharks (>1.5 m), which becomes considerable for small sharks (1 m fork length; 5.1% to 7.6% increase) and leads to an average energetic cost equivalent to 7% of the daily energetic requirement of an untagged animal. Other aspects of the force balance are considered, which reveal a range of varied and complex effects. Recommendations for animal size thresholds (>1.5 m FL) and refinements of tagging practice are suggested. Full article

As the demand for marine resource development escalates, underwater robots have gained prominence as a technological alternative to human participation in deep-sea exploration, resource assessments, and other intricate tasks, underscoring their academic and engineering importance. Traditional underwater robots, however, typically exhibit limited resilience to environmental disturbances and are readily obstructed or interfered with by aquatic vegetation, sediments, and other physical impediments. This paper examines the biological locomotion mechanisms of black ghostfish, which utilize undulatory fins and flapping wings, and presents a coupled undulatory-flapping propulsion strategy to facilitate rapid movement and precise posture adjustment in underwater robots. A multimodal undulatory-flapping bio-inspired underwater robotic platform is proposed, with a systematic explanation of its structure and motion principles. Additionally, kinematic and dynamic models for coordinated propulsion with multiple actuators are developed, and the robot’s performance under various driving modes is evaluated using computational fluid dynamics simulations. The simulation outcomes confirm the viability of the developed dynamic model. A prototype was constructed, and a PID-based control algorithm was developed to assess the robot’s performance in linear movement, turning, and other behaviors in both undulatory fin and flapping modes. Experimental findings indicate that the robot, functioning in undulatory fin propulsion mode at a frequency of 2.5 Hz, attains a velocity of 0.35 m/s, while maintaining attitude angle fluctuation errors within ±5°. In the flapping propulsion mode, precise posture modifications can be executed. These results validate the feasibility of the proposed multimodal bio-inspired underwater robot design and provide a new approach for the development of high-performance, autonomous bio-inspired underwater robots. Full article

Background/Objectives: Transforming growth factor-beta (TGF-β) is a multifunctional cytokine involved in immune regulation, extracellular matrix turnover, and tissue repair. Its role in periodontitis remains controversial due to conflicting human studies. This systematic review addressed the PICO-based question: in adults with periodontitis (population), how does the expression and regulation of TGF-β isoforms (intervention/exposure) compare with healthy or post-treatment states (comparator) regarding clinical outcomes (outcomes)? Methods: A systematic search of PubMed and Scopus was conducted on 1 July 2025 for human studies published in English between 2010 and 2025. Eligible studies investigated TGF-β expression, function, or genetic regulation in periodontal tissues or biological fluids. Screening and quality appraisal were performed according to PRISMA guidelines, using design-specific risk-of-bias tools. The review protocol was prospectively registered in PROSPERO (CRD420251138456). Results: Fifteen studies met inclusion criteria. TGF-β1 was the most frequently analyzed isoform and was consistently elevated in diseased gingival tissue and gingival crevicular fluid, correlating with probing depth and attachment loss. Several studies reported post-treatment reductions in TGF-β, supporting its value as a dynamic biomarker. Additional findings linked TGF-β signaling to immune modulation, fibrosis, bone turnover, and systemic comorbidities. Evidence for TGF-β2 and TGF-β3 was limited but suggested isoform-specific roles in epithelial–mesenchymal signaling and scar-free repair. Conclusions: Current evidence supports TGF-β, particularly TGF-β1, as a central mediator of periodontal inflammation and repair, with promise as both a biomarker and therapeutic target. Standardized, isoform-specific, and longitudinal studies are needed to clarify its diagnostic and translational utility. Full article

Farmland shelterbelts are vital ecological infrastructure for sustaining agriculture in arid regions, where high winds, soil erosion, and water scarcity severely constrain productivity. While their protective functions—reducing wind speed, controlling erosion, moderating microclimates, and enhancing yields—are well documented, previous studies have largely examined individual structural elements in isolation, leaving their interactive effects and trade-offs poorly understood. This review synthesizes current research on the structural optimization of shelterbelts, emphasizing the critical relationship between their physical and biological attributes and their protective functions. Key structural parameters—such as optical porosity, height, width, orientation, and species composition—are examined for their individual and interactive impacts on shelterbelt performance. Empirical and modeling studies indicate that moderate porosity maximizes wind reduction efficiency and extends the leeward protection zone, while multi-row, multi-species configurations effectively suppress soil erosion and improve microclimate conditions. Sheltered areas experience reduced evapotranspiration, increased humidity, and moderated temperatures, collectively enhancing crop water use efficiency and yielding significant improvements in crop production. Advanced methodologies, including field monitoring, wind tunnel testing, computational fluid dynamics, and remote sensing, are employed to quantify benefits and refine designs. A multi-objective optimization framework is essential to balance competing goals: maximizing wind reduction, minimizing water consumption, enhancing biodiversity, and ensuring economic viability. Future challenges involve adapting designs to climate change, integrating water-efficient and native species, leveraging artificial intelligence for predictive modeling, and addressing socio-economic barriers to implementation. Building on this evidence, we propose a multi-objective optimization framework to balance competing goals: maximizing wind protection, minimizing water use, enhancing biodiversity, and ensuring economic viability. We identify key research gaps including unresolved porosity thresholds, the climate resilience of alternative species compositions, and the limited application of optimization algorithms and outline future priorities such as region-specific design guidelines, AI-driven predictive models, and policy incentives. This review offers a novel, trade-off–aware synthesis to guide next-generation shelterbelt design in arid agriculture. Full article

The development of bioactive glasses (BGs) and ceramics, such as β-Tricalcium phosphate (β-TCP), Hydroxyapatite (HAp), and apatite-wollastonite (A-W), has revolutionized regenerative medicine (RM), offering innovative solutions for bone and tissue repair, due to the ability of these materials to bond with living bone tissue. Despite significant advancements, evaluating the bioactivity and biological responsiveness of these biomaterials remains a critical challenge. This review provides a comprehensive synthesis of the available methodologies, critically analyzing their advantages, disadvantages, and the possible gap between in vitro and in vivo assessments, including false positives and false negatives. Classical immersion tests techniques for bioactivity evaluation in simulated physiological solutions, such as simulated body fluid (SBF), Tris-buffer (TRIS), or phosphate-buffered saline (PBS) solutions, are discussed, along with the more innovative Simulated Wound Fluid (SWF). Additionally, traditional standardized methods, such as MTT, BrdU, EdU, and XTT, as well as emerging methods like qPCR and immunocytochemistry, used to study cellular behavior, proliferation, adhesion, and differentiation, are compared. Staining assays, including crystal violet, neutral red, and alizarin red, have also been investigated for their effectiveness in evaluating cellular adhesion and quantification. Notably, while all techniques have shown promise in studies involving BGs and ceramics, a multi-parametric approach remains the most reliable strategy for assessing bioactivity and biological responsiveness, highlighting the need for comprehensive studies to validate the results. Finally, the choice between static and dynamic approaches represents a further critical issue, as it significantly affects assay outcomes. Full article