Search Results (13,352)

In recent years, an increasing amount of research has investigated the impact of chronic inflammation on the development and progression of both neurological and psychiatric disorders, including epilepsy, depression, schizophrenia, and bipolar disorder. Moreover, growing attention is being paid to how inflammatory processes contribute to disease mechanisms, influence symptom severity, and interact with pharmacological treatments in these conditions. Changes in the levels of inflammatory biomarkers, such as cytokines and C-reactive protein, may signal the early stages of neurological disorder development. Furthermore, specific biomarker profiles have been identified for individual diseases, and chronic treatment may affect their blood levels. Over the last two decades, significant progress in the study of inflammatory biomarkers in psychiatric disorders and epilepsy has been achieved, demonstrating an association between biomarkers with symptoms, a potential prognostic role, and possible use in personalising therapy. Furthermore, widely used methods for biomarker evaluation, such as immunoenzymatic assays and flow cytometry, remain essential tools for current research. Despite numerous indications of the importance of inflammation in psychiatry and neurology, the available studies are characterised by considerable heterogeneity in terms of both population selection and methodology. Based on the available data, inflammatory biomarkers represent a promising diagnostic and therapeutic tool for epilepsy and psychiatric disorders. Although existing studies suggest a correlation between inflammation and the symptoms of various disorders, inconsistent results highlight the need for further research to enable wider implementation of these findings in psychiatric and epilepsy practice. Advancing knowledge of inflammatory biomarkers is essential for improving treatment outcomes and promoting the development of targeted interventions. Full article

The increasing demand for high-throughput, real-time, and single-molecule protein analysis in precision medicine has propelled the development of novel sensing technologies. Among these, nanopore-based methods have garnered significant attention for their unique capabilities, including label-free detection, ultra-sensitivity, and the potential for miniaturization and portability. Originally designed for nucleic acid sequencing, nanopore technology is now being adapted for peptide and protein analysis, offering promising applications in biomarker discovery and disease diagnostics. This review examines the latest advances in biological, solid-state, and hybrid nanopores for protein sensing, focusing on their ability to detect amino acid sequences, structural variants, post-translational modifications, and dynamic protein–protein or protein–drug interactions. We critically compare these systems to conventional proteomic techniques, such as mass spectrometry and immunoassays, discussing advantages and persistent technical challenges, including translocation control and signal deconvolution. Particular emphasis is placed on recent advances in protein sequencing using biological and solid-state nanopores and the integration of machine learning and signal-processing algorithms that enhance the resolution and accuracy of protein identification. Nanopore protein sensing represents a disruptive innovation in biosensing, with the potential to revolutionize clinical diagnostics, therapeutic monitoring, and personalized healthcare. Full article

High-performance musical instrument training is a demanding discipline that engages cognitive, neurological, and physical skills. Professional musicians invest substantial time and effort into mastering their repertoire and developing the muscle memory and reflexes required to perform complex works in high-stakes settings. While existing surveys have explored the use of music in therapeutic and general training contexts, there is a notable lack of work focused specifically on the needs of professional musicians and advanced instrumental practice. This topical review explores the potential of EEG-based brain–computer interface (BCI) technologies to integrate real-time feedback of biomechanic and cognitive features in advanced musical practice. Building on a conceptual framework of technology-enhanced musical practice (TEMP), we review empirical studies of broad contexts, addressing the EEG signal decoding of biomechanic and cognitive tasks that closely relates to the specified TEMP features (movement and muscle activity, posture and balance, fine motor movements and dexterity, breathing control, head and facial movement, movement intention, tempo processing, ptich recognition, and cognitive engagement), assessing their feasibility and limitations. Our analysis highlights current gaps and provides a foundation for future development of BCI-supported musical training systems to support high-performance instrumental practice. Full article

The peach landrace ‘Liuyefeitao’ exhibits the unique reproductive trait of self-compatibility combined with cross-incompatibility, contrasting with typical Prunus species in this way. In preliminary studies involving controlled pollination assays, we showed complete pollen tube arrest in cross-pollinated styles, whereas self-pollination enabled full tube elongation. S-genotyping identified a homozygous S₂S₂ genotype with intact S₂-RNase but a truncated PpSFB2 due to a frameshift mutation. Transcriptome profiling of the styles revealed 7937 differentially expressed genes (DEGs) between self- and cross-pollination treatments, with significant enrichment in plant MAPK signaling, plant–pathogen interactions, and plant hormone signaling transduction pathways (|Fold Change| ≥ 2, FDR < 0.01). Notably, PpSLFL3 (a pollen F-box gene) showed down-regulation in cross-pollinated styles, as validated by means of qRT-PCR. Protein interaction assays revealed direct binding between PpSLFL3 and S₂-RNase via Y2H and BiFC analysis, suggesting its role in mediating SCF complex-dependent degradation. We propose that insufficient PpSLFL3 expression during cross-pollination disrupts SCF ubiquitin ligase complex-mediated degradation of non-self S₂-RNase, leading to the toxic degradation of RNA in pollen tubes by S₂-RNase. This mechanism is mechanistically similar to unilateral reproductive barriers in Solanaceae but represents a novel regulatory module in Rosaceae. Our findings provide critical insights into the evolution of cross-incompatibility systems and molecular breeding strategies for Prunus species. Full article

Cyclophilins (Cyps), a family of peptidyl-prolyl isomerases, play essential roles in the life cycle of coronaviruses by interacting with viral proteins and modulating host immune responses. In this systematic review, we examined cell culture, animal model, and clinical studies assessing the anti-viral efficacy of cyclosporine A (CsA, PubChem CID: 5284373) and its non-immunosuppressive derivatives against coronaviruses. CsA demonstrated robust anti-viral activity in vitro across a broad range of coronaviruses, including but not limited to HCoV-229E, SARS-CoV, MERS-CoV, and SARS-CoV-2, with potent EC₅₀ values in the low micromolar range. Non-immunosuppressive analogs such as Alisporivir and NIM811 exhibited similar inhibitory effects. In vivo, CsA treatment significantly reduced viral load, ameliorated lung pathology, and improved survival in coronavirus-infected animals. Clinical studies further indicated that CsA administration was associated with improved outcomes in COVID-19 patients, including reduced mortality and shorter hospital stays. Mechanistic studies revealed that CsA disrupts the formation of viral replication complexes, interferes with critical Cyp–viral protein interactions, and modulates innate immune signaling. These findings collectively demonstrate the therapeutic potential of cyclophilin inhibitors as broad-spectrum anti-virals against current and emerging coronaviruses. Full article

Steroid hormones play critical roles in the development and progression of NSCLC through both genomic and non-genomic pathways. This review summarizes the expression profiles and molecular functions of estrogen, progesterone, androgen, and glucocorticoid receptors in NSCLC. Estrogen and progesterone receptors exhibit gender-specific prognostic significance, while glucocorticoid receptors influence tumor growth and immune responses. Emerging evidence supports the use of anti-estrogen therapies and glucocorticoids as adjuncts to existing treatment strategies, including immunotherapy. The crosstalk between hormone signaling and oncogenic pathways such as EGFR or immune checkpoints offers opportunities for novel combination therapies. However, challenges remain in biomarker development, drug resistance, and managing the dual effects of glucocorticoids. A deeper understanding of hormone–tumor–immune interactions is essential to optimize hormone-targeted interventions in NSCLC. Full article

The recently proposed concept of “precursory fingerprint” is a logical consequence of the commonsense statement that seismic structures are unique and that their expected preshock behaviors, including precursory phenomena, are also unique. Our new prediction-related research strategy is conceptually based on the principles of (1) the uniqueness of seismogenic structures, (2) interconnected and interacting geospheres, and (3) non-equivalence of Earth’s surface spots in terms of precursory signal receptivity. The precursory fingerprint of a given seismic structure is a unique assemblage of precursory signals of various natures (seismic, physical, chemical, and biological), detectable in principle by using a system of proper monitoring equipment that consists of a matrix of n sensors placed on the ground at “sensitive” spots identified beforehand and on orbiting satellites. In principle, it is composed of a combination of signals that are emitted by the “responsive sensors”, in addition to the “non-responsive sensors”, coming from the sensor matrix, monitoring as many virtual precursory processes as possible by continuously measuring their relevant parameters. Each measured parameter has a pre-established (by experts) threshold value and an uncertainty interval, discriminating between background and anomalous values that are visualized similarly to traffic light signals (green, yellow, and red). The precursory fingerprint can thus be viewed as a particular configuration of “precursory signals” consisting of anomalous parameter values that are unique and characteristic to the targeted seismogenic structure. Presumably, it is a complex entity that consists of pattern, space, and time components. The “pattern component” is a particular arrangement of the responsive sensors on the master board of the monitoring system yielding anomalous parameter value signals, that can be re-arranged, after a series of experiments, in a spontaneously understandable new pattern. The “space component” is a map position configuration of the signal-detecting sensors, whereas the “time component” is a characteristic time sequence of the anomalous signals including the order, occurrence time before the event, transition time between yellow and red signals, etc. Artificial intelligence using pattern-recognition algorithms can be used to follow, evaluate, and validate the precursory signal assemblage and, finally, to judge, together with an expert board of human operators, its “precursory fingerprint” relevance. Signal interpretation limitations and uncertainties related to dependencies on sensor sensibility, focal depth, and magnitude can be established by completing all three phases (i.e., experimental, validation, and implementation) of the precursory fingerprint-based earthquake prediction research strategy. Full article

Background: Anaplastic lymphoma kinase (ALK) is a key receptor tyrosine kinase involved in regulating signaling pathways critical for cell proliferation, differentiation, and survival. Mutations or rearrangements of the ALK gene lead to aberrant kinase activation, driving tumorigenesis in various cancers. Although ALK inhibitors have shown clinical benefits, drug resistance remains a significant barrier to long-term efficacy. Developing novel ALK inhibitors capable of overcoming resistance is therefore essential. Methods: A structure-based pharmacophore model was constructed using the 3D structures of five approved ALK inhibitors. Systematic virtual screening of the Topscience drug-like database was performed incorporating PAINS filtering, ADMET prediction, and molecular docking to identify promising candidates. In vitro antiproliferative assays, molecular docking, molecular dynamics simulations, and MM/GBSA binding free energy calculations were used to evaluate biological activity and elucidate binding mechanisms. Results: Two candidates, F1739-0081 and F2571-0016, were identified. F1739-0081 exhibited moderate antiproliferative activity against the A549 cell line, suggesting potential for further optimization. Computational analyses revealed its probable binding modes and interactions with ALK, supporting the observed activity. Conclusions: This study successfully identified novel ALK inhibitor candidates with promising biological activity. The integrated computational and experimental approach provides valuable insights for the rational design of optimized ALK inhibitors to address drug resistance in cancer therapy. Full article

Longevity and healthy aging result from the complex interaction of genetic, epigenetic, microbial, behavioral, and environmental factors. The central nervous system—particularly the cerebral cortex—and the autonomic nervous system (ANS) play key roles in integrating external and internal signals, shaping energy metabolism, immune tone, and emotional regulation. This narrative review examines how the brain–ANS axis interacts with epigenetic regulation, telomere dynamics, the gut microbiome, and the exposome to influence biological aging and resilience. Relevant literature published between 2010 and 2025 was selected through comprehensive database searches (PubMed, Scopus, Google Scholar), with a focus on studies addressing the multisystemic determinants of aging. Emphasis is placed on lifestyle-related exposures, such as diet, physical activity, psychosocial support, and environmental quality, that modulate systemic physiology through neurovisceral pathways. Drawing on empirical findings from classical Blue Zones and recent observational research in the Cilento region of southern Italy, this review highlights how context-specific factors—such as clean air, mineral-rich water, Mediterranean dietary patterns, and strong social cohesion—may foster bioelectric, metabolic, and neuroimmune homeostasis. By integrating data from neuroscience, systems biology, and environmental epidemiology, the review proposes a comprehensive model for understanding healthy longevity and supports the development of personalized, context-sensitive strategies in geroscience and preventive medicine. Full article

Glioblastoma multiforme (GBM) is the most aggressive and lethal primary brain tumor in adults, characterized by high intratumoral heterogeneity, therapy resistance, and poor prognosis. Nuclear factor-κB (NF-κB) signaling plays a pivotal role in GBM pathogenesis by promoting proliferation, invasion, inflammation, immune evasion, and treatment resistance. This review provides a comprehensive overview of canonical and non-canonical NF-κB signaling pathways and their molecular mechanisms in GBM, with a focus on their regulation in glioma stem-like cells (GSCs), interactions with key oncogenic factors (including STAT3, FOSL1, and TRPM7), and roles in maintaining tumor stemness, metabolic adaptation, and angiogenesis. We further discuss the reciprocal regulatory dynamics between NF-κB and non-coding RNAs (ncRNAs), particularly microRNAs, highlighting novel ncRNA-mediated epigenetic switches that shape GBM cell plasticity and subtype specification. Additionally, we examine the influence of NF-κB in modulating the tumor microenvironment (TME), where it orchestrates pro-tumorigenic cytokine production, immune cell reprogramming, and stromal remodeling. Finally, we review current NF-κB-targeting therapeutic strategies in GBM, including clinical trial data on small-molecule inhibitors and combinatorial approaches. Understanding the multifaceted roles of NF-κB in GBM offers new insights into targeted therapies aimed at disrupting tumor-promoting circuits within both cancer cells and the TME. Full article

This study investigates the effect of walnut green husk extract (WE) on gut microbiota, metabolites, and immune-antioxidant changes in fattening pigs through gut microbiota-metabolite interactions. A total of 60 healthy fattening pigs (Duroc × Landrace × Yorkshire) with an initial body weight of 65.2 ± 3.1 kg were randomly assigned to two groups (n = 30 per group): the control group (NC), which was fed a basal diet, and the WE group, which was fed the basal diet supplemented with 0.1% walnut green husk extract (WE). Dietary supplementation with 0.1% WE significantly increased the relative abundances of beneficial bacteria (e.g., Firmicutes, Lactobacillus) and reduced pathogenic bacteria (e.g., Proteobacteria, Shigella). Untargeted metabolomics identified 170 differentially accumulated metabolites, among which propionic acid—a key short-chain fatty acid with immunomodulatory effects—was significantly upregulated by 1.09-fold (p = 0.03) and showed a positive correlation with beneficial microbial abundances. These metabolites were enriched in glycerophospholipid and α-linolenic acid metabolism pathways, where eicosadienoic acid inhibited the nuclear factor kappa-B (NF-κB) pathway for anti-inflammatory effects, and methyl cinnamate synergistically regulated mitogen-activated protein kinase (MAPK) signaling with Lactobacillus. Serum analyses showed that WE significantly enhanced IgA, IgM, and IgG levels by 3.97-fold, 4.67-fold, and 4.43-fold (p < 0.01), reduced malondialdehyde (MDA) concentration by 82.8% (p < 0.01), and trended to improve antioxidant capacity via glutamine. Mechanistically, WE promoted short-chain fatty acid production by beneficial bacteria, forming a “microbiota–metabolite–immunity” cascade to enhance lipid metabolism and alleviate intestinal inflammation. These findings highlight that WE provides multi-omics evidence for its application as a functional feed additive. Full article

In this study, the effects of speed and red-light cameras (SRLCs) and traffic signal countdown timers (TSCTs) on the operation of pre-timed signalized intersections were studied through startup lost times (SLTs) and saturation time headways (STHs). The study used the beanplots package version 1.3.1 in R statistical software to graph and find the first STH that occurred in a queue. Then, one-way analysis of variance was used twice to explore the effects of the separate and joint use of SRLCs and TSCTs on the operation of pre-timed signalized intersections. The results show that SRLC use does not have a significant direct impact on the operation of pre-timed signalized intersections, but SRLC interacts negatively with TSCT use. In addition, TSCT use was shown to improve the operation of pre-timed signalized intersections by decreasing the SLT and STH. For SLT, the effect size of TSCT use depends on the presence or absence of SRLC use, and its reduction ranges from 0.5 to 1.25 s per queue. As for STH, the effect size of TSCT use does not depend on the presence or absence of SRLC use, and its reduction ranges from 0.08 to 0.12 s per vehicle, corresponding to 0.8–1.2 s per queue, given that there are 10 vehicles in the queue. Full article

Complex engineering systems—such as ultra-long horizontal wells in energy exploitation and distributed rail transit infrastructure—operate under harsh physical and environmental conditions, where accurate physical modeling and real-time parameter estimation are essential for ensuring safety, efficiency, and reliability. Traditional empirical and black-box data-driven approaches often fail to account for the underlying physical mechanisms, thereby limiting interpretability and generalizability. To address this, we propose a unified framework that integrates physics-informed scenario-based modeling with data-driven parameter inversion. In the first stage, critical system parameters—such as friction coefficients in drill string movement or contact forces in rail–wheel interactions—are explicitly formulated based on mechanical theory, leveraging symmetries and boundary conditions to improve model structure and reduce computational complexity. In the second stage, model parameters are identified or updated through inverse modeling using historical or real-time field data, enhancing predictive performance and engineering insight. The proposed methodology is demonstrated through two representative cases. The first involves friction estimation during tripping operations in the SU77-XX-32H5 ultra-long horizontal well of the Sulige Gas Field, where a mechanical load model is constructed and field-calibrated. The second applies the framework to rail transit systems, where wheel–rail friction is estimated from dynamic response signals to support condition monitoring and wear prediction. The results from both scenarios confirm that incorporating physical symmetry and data-driven inversion significantly enhances the accuracy, robustness, and interpretability of engineering analyses across domains. Full article

Abscisic acid (ABA) is a central phytohormone that orchestrates plant responses to abiotic stresses, such as drought, salinity, and extreme temperatures, while also influencing growth and development. The regulatory networks underpinning ABA-mediated stress tolerance have been the focus of intensive research, revealing sophisticated mechanisms of biosynthesis, signal transduction, and gene regulation. Recent advances in genetic, genomic, and biochemical approaches have illuminated the complexity of ABA’s interactions with other hormonal and environmental signaling pathways, providing a multidimensional understanding of plant adaptation. This review critically synthesizes current knowledge on ABA’s regulatory frameworks, identifies key gaps in our understanding, and discusses the potential integration of omics and emerging technologies to uncover new insights. By offering a comprehensive synthesis of recent findings, this paper aims to stimulate further research into the interplay of ABA with other signaling pathways, highlighting its translational potential for crop improvement under changing environmental conditions. Full article

Understanding asymmetric interactions among heterogeneous traffic participants is essential for managing congestion and enhancing safety at urban signalized intersections. This study proposes a cellular automaton modeling framework that captures the spatial and behavioral asymmetries among vehicles, bicycles, and pedestrians, with a particular focus on right-of-way hierarchies and conflict anticipation. Beyond simulation, the framework integrates a behavior pattern mining module that applies unsupervised trajectory clustering to identify recurrent interaction patterns emerging from mixed traffic flows. Simulation experiments are conducted under varying demand levels to investigate the propagation of congestion and the structural distribution of conflicts. The results reveal distinct asymmetric behavior patterns, such as right-turn vehicle blockage, non-lane-based bicycle overtaking, and pedestrian-induced disruptions. These patterns provide interpretable insights into the spatiotemporal dynamics of intersection performance and offer a data-driven foundation for optimizing signal control and multimodal traffic flow separation. The proposed framework demonstrates the value of combining microscopic modeling with data mining techniques to uncover latent structures in complex urban traffic systems. Full article