Search Results (3,690)

The actomyosin complex—nature’s dynamic engine composed of actin filaments and myosin motors—is emerging as a versatile tool for bio-integrated nanotechnology. This review explores the growing potential of actomyosin-powered systems in biosensing and actuation applications, highlighting their compatibility with physiological conditions, responsiveness to biochemical and physical cues and modular adaptability. We begin with a comparative overview of natural and synthetic nanomachines, positioning actomyosin as a uniquely scalable and biocompatible platform. We then discuss experimental advances in controlling actomyosin activity through ATP, calcium, heat, light and electric fields, as well as their integration into in vitro motility assays, soft robotics and neural interface systems. Emphasis is placed on longstanding efforts to harness actomyosin as a biosensing element—capable of converting chemical or environmental signals into measurable mechanical or electrical outputs that can be used to provide valuable clinical and basic science information such as functional consequences of disease-associated genetic variants in cardiovascular genes. We also highlight engineering challenges such as stability, spatial control and upscaling, and examine speculative future directions, including emotion-responsive nanodevices. By bridging cell biology and bioengineering, actomyosin-based systems offer promising avenues for real-time sensing, diagnostics and therapeutic feedback in next-generation biosensors. Full article

The use of questionnaire survey results as a clinical diagnostic method for schizophrenia lacks a certain degree of objectivity; thus, markers of schizophrenia in different brain signals have been widely investigated. The objective of this investigation was to explore potential markers of schizophrenia by investigating nonequilibrium features in magnetoencephalography (MEG) signals. We propose a new method to quantify the nonequilibrium features of MEG signals: the multiscale permutation time irreversibility (MsPTIRR) index. The results revealed that the MsPTIRR indices of the MEG recordings of patients with schizophrenia were significantly lower than those of the healthy controls (HCs). Moreover, the MsPTIRR indices of the MEG recordings of patients with schizophrenia and HCs differed significantly in the frontal, occipital, and temporal lobe regions. Furthermore, the MsPTIRR indices of the MEG recordings differed significantly between patients with schizophrenia and HCs in the $\theta$, $\alpha$ and $\beta$ bands. Abnormal nonequilibrium features mined in MEG recordings using the MsPTIRR index may be used as potential markers for schizophrenia, assisting in the clinical diagnosis of this disorder. Full article

Immune cells like neutrophils, monocytes/macrophages, and lymphocytes play key roles in the development, progression, and resolution of deep vein thrombosis (DVT) by contributing to inflammation, coagulation, and fibrinolysis. IFN-γ, a cytokine mainly secreted by natural killer (NK) and T cells, is a critical factor in DVT pathogenesis. It links immune responses to coagulation activation by promoting endothelial activation, leukocyte recruitment, cytokine release, and coagulation imbalance. Its strong pro-inflammatory and prothrombotic effects make IFN-γ a promising target for DVT treatment beyond standard anticoagulants. Exploring ways to block IFN-γ signaling or its downstream effects could open doors to novel therapies for DVT, aiding in resolution and preventing post-thrombotic complications. This review delves into DVT pathophysiology, diagnostics, and management, emphasizing the importance of targeting immune cells and IFN-γ to advance treatment options. Full article

Breast cancer is a heterogeneous disease and a leading cause of cancer-related deaths worldwide, underscoring the urgent need for effective biomarkers to guide diagnosis, prognosis, and therapeutic decisions. Bioinformatics methodologies, including genomics, transcriptomics, proteomics, and metabolomics data analysis, are essential for deciphering the complex molecular landscape of breast cancer. Bioinformatics tools facilitate the identification of differentially expressed genes, non-coding RNAs, and proteins, unraveling crucial pathways involved in tumor initiation, progression, and metastasis. By constructing and analyzing protein–protein interaction networks and signaling pathways, bioinformatics approaches can identify potential diagnostic, prognostic, and predictive biomarkers. Herein, we explore the role of bioinformatics in breast cancer research and its potential application in identifying novel therapeutic targets and predicting drug response, ultimately enabling the development of tailored treatment strategies. We also address the challenges and future directions in utilizing bioinformatics for biomarker discovery and validation, emphasizing the need for robust statistical methods, standardized data analysis pipelines, and collaborative efforts to translate bioinformatics insights into improved clinical outcomes for breast cancer patients. Full article

The ovary is among the earliest organs to undergo age-related degeneration, limiting the reproductive potential of elite horses and constraining the growth of the equine industry. Follicular development during estrus is a key determinant of fertility, yet the molecular mechanisms underlying its decline, particularly at the level of specific ovarian cell types, remain poorly understood in equids. Here, we constructed a single-cell transcriptomic atlas to investigate ovarian changes in Kazakh horses. Using single-cell RNA sequencing (scRNA-seq), we profiled 112,861 cells from follicle-containing and follicle-absent ovaries, identifying nine distinct ovarian cell types and their subtypes, each with distinct gene expression signatures. Functional enrichment analyses revealed cell type-specific engagement in biological pathways, including ECM–receptor interaction, PI3K-Akt signaling, and oxytocin signaling. Gene expression patterns indicated tightly regulated processes of ovarian activation and cell differentiation. Notably, stromal cells exhibited high expression of ROBO2, LOC111770199, and TMTC2, while smooth muscle cells (SMCs) were marked by elevated levels of CCL5, KLRD1, and NKG7. Moreover, cell–cell interaction analyses revealed robust signaling interactions among SMCs, endothelial cells, neurons, and proliferating (cycling) cells. Together, these findings provide a comprehensive single-cell transcriptomic map of normal and abnormal ovarian states during estrus in Kazakh horses, offering novel insights into the cellular mechanisms of follicular development and identifying potential diagnostic biomarkers and therapeutic targets for ovarian quiescence in equids. Full article

Induction motors (IMs) are crucial in many industrial applications, offering a cost-effective and reliable source of power transmission and generation. However, their continuous operation imposes considerable stress on electrical and mechanical parts, leading to progressive wear that can cause unexpected system shutdowns. Bearings, which enable shaft motion and reduce friction under varying loads, are the most failure-prone components, with bearing ball defects representing most severe mechanical failures. Early and accurate fault diagnosis is therefore essential to prevent damage and ensure operational continuity. Recent advances in the Internet of Things (IoT) and machine learning (ML) have enabled timely and effective predictive maintenance strategies. Among various diagnostic parameters, vibration analysis has proven particularly effective for detecting bearing faults. This study proposes a hybrid diagnostic framework for induction motor bearings, combining vibration signal analysis with Support Vector Machines (SVMs) and Artificial Neural Networks (ANNs) in an IoT-enabled Industry 4.0 architecture. Statistical and frequency-domain features were extracted, reduced using Principal Component Analysis (PCA), and classified with SVMs and ANNs, achieving over 95% accuracy. The novelty of this work lies in the hybrid integration of interpretable and non-linear ML models within an IoT-based edge–cloud framework. Its main contribution is a scalable and accurate real-time predictive maintenance solution, ensuring high diagnostic reliability and seamless integration in Industry 4.0 environments. Full article

Myelin oligodendrocyte glycoprotein antibody–associated disease (MOGAD) is an inflammatory demyelinating disorder of the central nervous system characterised by heterogeneous clinical and radiological presentations. Accurate interpretation of serum anti–myelin oligodendrocyte glycoprotein (anti-MOG) antibody titres is critical to improve diagnostic precision and prognostic assessment. This single-centre retrospective study evaluated 19 patients diagnosed with MOGAD in 2023, all of whom were seropositive for anti-MOG IgG, as confirmed by live cell-based assays (CBAs) using full-length human MOG and IgG1-specific secondary antibodies. Antibody quantification combined a ratiometric semi-quantitative fluorescence index with classical endpoint dilution titres, enabling classification into low, medium, and high titre groups. Stratification revealed titre-dependent phenotypic heterogeneity: high-titre patients were older at onset and predominantly presented with optic neuritis, often bilateral, and encephalic involvement, whereas low-titre patients more frequently exhibited spinal cord syndromes, cerebellar or brainstem symptoms, and a higher prevalence of cerebrospinal fluid-restricted oligoclonal bands. Semi-quantitative fluorescence ratios correlated consistently with endpoint titres, and exponential decay analysis demonstrated slower signal loss in high-titre sera, confirming assay reliability. No significant association emerged between titre level and monophasic versus relapsing disease course. Anti-MOG antibody titres could serve not only as a diagnostic biomarker but also to capture clinically relevant immunopathological diversity, supporting a titre-stratified approach to diagnosis and early prognostication. Incorporating semi-quantitative metrics alongside clinical and imaging features may refine the diagnostic algorithm and prevent misclassification of atypical presentations. Full article

Background/Objectives: Colorectal cancer (CRC) is a leading cause of cancer-related mortality worldwide. Iron metabolism and chronic inflammation are two interrelated processes that significantly influence the initiation and progression of CRC. Iron is essential for cell proliferation, but its excess promotes oxidative stress and DNA damage, while inflammation driven by cytokine-regulated pathways accelerates tumourigenesis. We therefore conducted this narrative review to collate the available evidence on the link between iron homeostasis and inflammatory signalling in CRC and highlight potential diagnostic and therapeutic applications. Methods: This narrative review of preclinical and clinical studies explores the molecular and cellular pathways that connect iron regulation and inflammation to CRC. Key regulatory molecules, such as the transferrin receptor (TFRC), ferroportin (SLC40A1), ferritin (FTH/FTL), hepcidin, and IL-6, were reviewed. Additionally, we summarised the findings of transcriptomic, epigenomic, and proteomic studies. Relevant therapeutic approaches, including iron chelation, ferroptosis induction, and anti-inflammatory strategies, were also discussed. Results: Evidence suggests that CRC cells exhibit altered iron metabolism, marked by the upregulation of transferrin receptor (TFRC), downregulation of ferroportin, and dysregulated expression of ferritin. Inflammatory mediators such as IL-6 activate hepcidin and STAT3 signalling, which reinforce intracellular iron retention and oxidative stress. Increased immune evasion, epithelial proliferation, and genomic instability appear to be linked to the interaction between inflammation and iron metabolism. Other promising biomarkers include ferritin, hepcidin, and composite gene expression signatures; however, their clinical application remains limited. Although several preclinical studies support the use of targeted iron therapies and combination approaches with anti-inflammatory agents or immunotherapy, there is a lack of comprehensive clinical validation confirming their efficacy and safety in humans. Conclusion: Although preclinical studies suggest that iron metabolism and inflammatory signalling form an interconnected axis closely linked to CRC, translating this pathway into reliable clinical biomarkers and effective therapeutic strategies remains a significant challenge. Future biomarker-guided clinical trials are essential to determine the clinical relevance and to establish precision medicine strategies targeting the iron–inflammation crosstalk in CRC. Full article

The integration of artificial intelligence (AI) with various diagnostic nanosensors has opened up new horizons in clinics recently. AI technology offers enhanced sensitivity, accuracy, specificity, and real-time analysis for disease diagnostics. This review focuses on the recent advances in AI-assisted nanosensors for the diagnosis of different diseases in clinical applications. Critical roles of AI in sensor design, optimization, signal processing, and clinical decision support are highlighted. Furthermore, challenges such as limited datasets, regulatory hurdles, and data privacy are discussed, along with future opportunities. This review aims to provide a comprehensive introduction and perspectives on how AI-driven nanosensors are transforming clinical diagnostics and shaping the future of precise medicine. 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

To address the issue of output anomalies that frequently occur in position sensors of permanent magnet synchronous motors within electromechanical actuation systems operating in harsh environments and can lead to degradation in system performance or operational interruptions, this paper proposes an integrated method for fault diagnosis and fault-tolerant control based on the Cubature Kalman Filter (CKF). This approach effectively combines state reconstruction, fault diagnosis, and fault-tolerant control functions. It employs a CKF observer that utilizes innovation and residual sequences to achieve high-precision reconstruction of rotor position and speed, with convergence assured through Lyapunov stability analysis. Furthermore, a diagnostic mechanism that employs dual-parameter thresholds for position residuals and abnormal duration is introduced, facilitating accurate identification of various fault modes, including signal disconnection, stalling, drift, intermittent disconnection, and their coupled complex faults, while autonomously triggering fault-tolerant strategies. Simulation results indicate that the proposed method maintains excellent accuracy in state reconstruction and fault tolerance under disturbances such as parameter perturbations, sudden load changes, and noise interference, significantly enhancing the system’s operational reliability and robustness in challenging conditions. Full article

Current methods for assessing the technical condition of aircraft hydrostatic drives require disassembly of the hydraulic components and their testing on test stands. These methods are expensive and do not provide a quick assessment of their technical condition. The aim of this work is to present the possibility of using an active hydraulic fluid pressure measurement system using a corrective hydraulic accumulator to assess the technical condition of a hydrostatic drive. In the proposed method, the diagnostic information carrier is a change in the corrector’s operation (changes in the course or shape of the output signal), resulting, for example, from wear of the hydraulic pairs of precision hydraulic devices that complete the hydrostatic drive. The output signal from the active pressure measurement system is a control signal that, together with the input interference signal and the output pressure signal, allows for the analysis of changes occurring in the hydrostatic drive. Therefore, based solely on the examination of changes in the corrector’s operation, it is possible to assess various changes occurring in the hydrostatic drive and its hydraulic systems. Studies of pressure step responses in the hydrostatic drive confirmed that hydrostatic drives and their hydraulic systems can be tested using an active hydraulic fluid pressure measurement system with a properly selected corrective hydraulic accumulator. Full article

Cardiorenal syndrome (CRS) reflects the intricate and bidirectional interplay between cardiac and renal dysfunction, commonly resulting in diagnostic uncertainty, therapeutic dilemmas and poor outcomes. While traditional biomarkers like serum creatinine (Cr) and natriuretic peptides remain widely used, their limitations in specificity, timing and contextual interpretation often hinder optimal management. This narrative review synthesizes the current evidence on established and emerging biomarkers in CRS, with emphasis on their clinical relevance, integration into real-world practice, and potential to inform precision therapy. Markers of glomerular filtration rate beyond creatinine—such as cystatin C—offer more accurate assessment in frail or sarcopenic patients, while tubular injury markers such as NGAL, KIM-1, and urinary L-FABP (uL-FABP) provide early signals of structural renal damage. The FDA-approved NephroCheck^® test—based on TIMP-2 and IGFBP7— enables risk stratification for imminent AKI up to 24 h before functional decline. Congestion-related markers such as CA125 and bio-adrenomedullin outperform natriuretic peptides in certain CRS phenotypes, particularly in right-sided heart failure or renally impaired patients. Fibrosis and inflammation markers (galectin-3, sST2, GDF-15) add prognostic insights, especially when combined with NT-proBNP or troponin. Rather than presenting biomarkers in isolation, this review proposes a framework that links them to specific clinical contexts—such as suspected decongestion-related renal worsening or persistent congestion despite therapy—to support actionable interpretation. A tailored, scenario-based, multi-marker strategy may enhance diagnostic precision and treatment safety in CRS. Future research should prioritize prospective biomarker-guided trials and standardized pathways for clinical integration. Full article

Traumatic brain injury (TBI) remains a major global health problem, contributing significantly to morbidity and mortality worldwide. Despite advances in understanding its complex pathophysiology, current therapeutic strategies are insufficient in addressing the long-term cognitive, emotional, and neurological impairments. While the primary mechanical injury is immediate and unavoidable, the secondary phase involves a cascade of biological processes leading to neuroinflammation, blood–brain barrier (BBB) disruption, and systemic immune activation. The heterogeneity of patient responses underscores the urgent need for reliable biomarkers and targeted interventions. Emerging evidence highlights the gut–brain axis as a critical modulator of the secondary phase, with microbiota-derived extracellular vesicles (MEVs) representing a promising avenue for both diagnosis and therapy. MEVs can cross the intestinal barrier and BBB, carrying biomolecules that influence neuronal survival, synaptic plasticity, and inflammatory signaling. These properties make MEVs promising biomarkers for early detection, severity classification, and prognosis in TBI, while also offering therapeutic potential through modulation of neuroinflammation and promotion of neural repair. MEV-based strategies could enable tailored interventions based on the individual’s microbiome profile, immune status, and injury characteristics. The integration of multi-omics with artificial intelligence is expected to fully unlock the diagnostic and therapeutic potential of MEVs. These approaches can identify molecular subtypes, predict outcomes, and facilitate real-time clinical decision-making. By bridging microbiology, neuroscience, and precision medicine, MEVs hold transformative potential to advance TBI diagnosis, monitoring, and treatment. This review also identifies key research gaps and proposes future directions for MEVs in precision diagnostics and gut microbiota-based therapeutics in neurotrauma care. Full article

The growth of greenhouse gas emissions, driven by the use of internal combustion engines (ICE), highlights the urgent need for sustainable solutions, particularly in the shipping sector. Non-invasive predictive maintenance using acoustic signal analysis has emerged as a promising strategy for fault diagnosis in ICEs. In this context, the present study proposes a hybrid Deep Learning (DL) model and provides a novel publicly available dataset containing real operational sound samples of ICEs, labeled across 12 distinct fault subclasses. The methodology encompassed dataset construction, signal preprocessing using log-mel spectrograms, and the evaluation of several Machine Learning (ML) and DL models. Among the evaluated architectures, the proposed hybrid model, BiGRUT (Bidirectional GRU + Transformer), achieved the best performance, with an accuracy of 97.3%. This architecture leverages the multi-attention capability of Transformers and the sequential memory strength of GRUs, enhancing robustness in complex fault scenarios such as combined and mechanical anomalies. The results demonstrate the superiority of DL models over traditional ML approaches in acoustic-based ICE fault detection. Furthermore, the dataset and hybrid model introduced in this study contribute toward the development of scalable real-time diagnostic systems for sustainable and intelligent maintenance in transportation systems. Full article