Search Results (313)

Periodontal disease represents a major global concern characterized by chronic biofilm-driven inflammation, excessive oxidative stress, progressive tissue destruction, and impaired regenerative capacity. Beyond conventional antimicrobial approaches, recent progress has shifted toward host-directed and regenerative therapeutic strategies aimed at restoring both oral function and tissue homeostasis. This review consolidates current developments in nanobiotechnology-based materials that modulate immune responses, scavenge reactive oxygen species, and promote angiogenesis and osteogenesis, thereby facilitating the effective regeneration of dental and periodontal tissues. Emphasis is placed on bioresponsive hydrogels, bioactive scaffolds, and gas-releasing platforms that integrate therapeutic regulation with tissue repair. The discussion further highlights key advances in polymeric and inorganic biomaterials designed to balance antibacterial action with cellular compatibility and regenerative potential. By linking pathophysiological mechanisms with material-guided healing processes, this review provides a comprehensive perspective on emerging nanobiotechnological solutions that bridge patho-therapeutics with regenerative and clinical dentistry. Full article

Sarcoidosis is an immune-mediated granulomatous disease whose etiology has remained unresolved despite more than a century of investigation. Accumulating microbiological and immunopathological evidence now implicates Cutibacterium acnes—a ubiquitous indigenous commensal—as the most consistent antigenic trigger. Its frequent detection within sarcoid granulomas by quantitative PCR, in situ hybridization, and species-specific immunohistochemistry suggests latent intracellular persistence and the potential for endogenous reactivation. To explain how a noncontagious commensal can drive granulomatous inflammation, this review proposes the concept of Endogenous Hypersensitivity Infection (EHI). EHI describes a host-centered process in which reactivation of latent intracellular microbes leads to the breakdown of immune tolerance and provokes Th1-dominant hypersensitivity responses in genetically and immunologically susceptible individuals. This framework bridges the traditional divide between infection and autoimmunity, reframing sarcoidosis as a disorder of disrupted host–commensal homeostasis rather than a classical infectious or autoimmune disease. By integrating microbiological, immunological, and pathological evidence, this review synthesizes the mechanistic basis of EHI and outlines how tolerance failure to C. acnes can account for the paradoxical clinical behavior of sarcoidosis. The EHI paradigm further provides a unifying conceptual lens through which related chronic inflammatory disorders—including Crohn’s disease, chronic rhinosinusitis, and atopic dermatitis—may be reinterpreted. Full article

Gut microbial community assembly involves a critical bioenergetic trade-off, yet the gut microbes with roles in influencing intestinal metabolic homeostasis remain poorly understood in pelagic ecosystems. A central unresolved question is whether microbiome structure is primarily governed by stochastic geographic drift or by deterministic metabolic filters imposed by diet. Here, we test the metabolic release hypothesis, which posits that access to high-quality prey physiologically “releases” the host from obligate dependence on diverse fermentative symbionts. By integrating δ15N analysis with 16S rRNA metabarcoding in the anchoveta from the South Pacific waters (Engraulis ringens), we reveal a profound, diet-induced restructuring of the gut ecosystem. We demonstrate that trophic ascent triggers a deterministic collapse in microbial alpha diversity (rs = −0.683), driven by the near-complete competitive exclusion of fermentative bacteria (rs = −0.874) and the resulting dominance of a specialized proteolytic core. Mechanistically, the bioavailability of zooplankton-derived protein favors efficient endogenous hydrolysis over costly microbial fermentation, rendering functional redundancy obsolete. Crucially, we find that while metabolic function converges, taxonomic identity remains geographically structured (r = 0.532), suggesting that local environments supply the specific taxa to fulfill universal metabolic roles. These findings establish a link between δ15N as a nutritional physiology proxy of anchoveta and its gut for microbial functional state, bridging the gap between nutritional physiology and ecosystem modeling to better inform the management of global forage fish stocks. Full article

Microtubules are cylindrical protein polymers that organize the cytoskeleton and play essential roles in intracellular transport, cell division, and possibly cognition. Their highly ordered, quasi-crystalline lattice of tubulin dimers, notably tryptophan residues, endows them with a rich topological and arithmetic structure, making them natural candidates for supporting coherent excitations at optical and terahertz frequencies. The Penrose–Hameroff Orch OR theory proposes that such coherences could couple to gravitationally induced state reduction, forming the quantum substrate of conscious events. Although controversial, recent analyses of dipolar coupling, stochastic resonance, and structured noise in biological media suggest that microtubular assemblies may indeed host transient quantum correlations that persist over biologically relevant timescales. In this work, we build upon two complementary approaches: the parametric resonance model of Nishiyama et al. and our arithmetic–geometric framework, both recently developed in Quantum Reports. We unify these perspectives by describing microtubules as rectangular lattices governed by the imaginary quadratic field $\mathbb{Q}\left(i\right)$, within which nonlinear dipolar oscillations undergo stochastic parametric amplification. Quantization of the resonant modes follows Gaussian norms $N=p^2+q^2$, linking the optical and geometric properties of microtubules to the arithmetic structure of $\mathbb{Q}\left(i\right)$. We further connect these discrete resonances to the derivative of the elliptic L-function, $L\prime (E,1)$, which acts as an arithmetic free energy and defines the scaling between modular invariants and measurable biological ratios. In the appended adelic extension, this framework is shown to merge naturally with the Bost–Connes and Connes–Marcolli systems, where the norm character on the ideles couples to the Hecke character of an elliptic curve to form a unified adelic partition function. The resulting arithmetic–elliptic resonance model provides a coherent bridge between number theory, topological quantum phases, and biological structure, suggesting that consciousness, as envisioned in the Orch OR theory, may emerge from resonant processes organized by deep arithmetic symmetries of space, time, and matter. Full article

Enteric infections remain a major health and economic challenge in swine production, with outcomes determined not only by pathogen virulence but also by the complex interplay between host genetics, immune competence, and the intestinal microbiota. This review synthesises current knowledge on host–pathogen genomic interactions in pigs, with a focus on resilience mechanisms against enteric diseases in swine. For this purpose, 103 articles were used as information sources, retrieved through structured keyword searches in PubMed. The review first addresses host genetic factors, highlighting genomic variants and quantitative trait loci associated with resistance or resilience to viral and bacterial pathogens such as porcine epidemic diarrhoea virus (PEDV) or Escherichia coli. Next, the key factors of the immune system to confer protection are also reviewed, emphasising the role of innate and adaptive responses in controlling each pathogen and disclosing the contribution of regulatory networks that balance pathogen clearance. Finally, the last section of the review is devoted to exploring current knowledge in the involvement of the microbiota in resilience against enteric pathogens, mostly, but not exclusively, enteric bacteria. In this sense, competitive exclusion is a concept which has gained attention in recent years. The review pinpoints and discusses the state of the art about how the microbial community provides colonisation resistance, shapes immune development, and influences pathogen fitness within the intestinal niche. As final perspectives, the review explores future drivers in the genetic immune and microbiota resistance. By bridging host genomic data with functional insights into immunity and microbial ecology, this review underscores the potential of multi-omics approaches to enhance resilience against enteric infections in pigs and advance sustainable swine health management. Full article

Insider threats remain a serious concern for organisations in both public and private sectors. Detecting anomalous behaviour in enterprise environments is critical for preventing insider incidents. While many prior studies demonstrate promising results using deep learning on offline datasets, few address real-time operationalisation or calibrated alert control within a Security Information and Event Management (SIEM) workflow. This paper presents a SIEM-integrated prototype that fuses the Computer Emergency Response Team Insider Threat Test Dataset (CERT) enterprise logs (Logon, Device, HTTP, and Email) with behavioural biometrics from the Balabit mouse dynamics dataset. Per-modality one-dimensional convolutional neural network (1D CNN) branches are trained independently using imbalance-aware strategies, including downsampling, class weighting, and focal loss. A unified 20 × N feature schema ensures train–serve parity and consistent feature validation during live inference. Post-training calibration using Platt and isotonic regression enables analyst-controlled threshold tuning and stable alert budgeting inside the SIEM. The models are deployed in Splunk’s Machine Learning Toolkit (MLTK), where dashboards visualise anomaly timelines, risky users or hosts, and cross-stream overlaps. Evaluation emphasises operational performance, precision–recall balance, calibration stability, and throughput rather than headline accuracy. Results show calibrated, controllable alert volumes: for Device, precision ≈0.70 at recall ≈0.30 (PR-AUC = 0.468, ROC-AUC = 0.949); for Logon, ROC-AUC = 0.936 with an ultra-low false-positive rate at a conservative threshold. Batch CPU inference sustains ≈70.5 k windows/s, confirming real-time feasibility. This study’s main contribution is to demonstrate a calibrated, multi-modal CNN framework that integrates directly within a live SIEM pipeline. It provides a reproducible path from offline anomaly detection research to Security Operations Centre (SOC)-ready deployment, bridging the gap between academic models and operational Cybersecurity practice. Full article

Viral fusion proteins are indispensable mediators of viral entry that orchestrate the fusion of viral and host membranes, making them primary targets for antiviral interventions. Class I fusion proteins, displayed on the surface of enveloped viruses (such as HIV-1, RSV, SARS-CoV-2, Nipah, influenza, and Ebola viruses), share conserved structural features, including the fusion peptide or loop and heptad repeat regions. These elements are essential for the formation of the post-fusion six-helix bundle during membrane fusion. Peptide inhibitors that mimic heptad repeat motifs have consequently emerged as an effective strategy for blocking the fusion process. This review summarizes design strategies for such inhibitors and highlights how sequence and structural insights have enabled their optimization via α-helical stabilization, hydrocarbon stapling, lactam bridges, lipid conjugation, macrocyclization, and multivalency. Using representative examples across major viral systems, this review illustrates how these strategies have led to the development of potent, stable, and even broad-spectrum antiviral peptides. This review provides insights to guide the rational design of next-generation peptide-based fusion inhibitors targeting viral membrane fusion. 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

Robotic systems increasingly operate in complex and distributed environments, where software deployment and orchestration pose major challenges. This paper presents a model-driven approach that automates the containerized deployment of robotic systems in Internet of Robotic Things (IoRT) environments. Our solution integrates Model-Driven Engineering (MDE) with containerization technologies to improve scalability, reproducibility, and maintainability. A dedicated metamodel introduces high-level abstractions for describing deployment architectures, repositories, and container configurations. A web-based tool enables collaborative model editing, while an external deployment automator generates validated Docker and Compose artifacts to support seamless multi-host orchestration. We validated the approach through real-world experiments, which show that the method effectively automates deployment workflows, ensures consistency across development and production environments, and significantly reduces configuration effort. These results demonstrate that model-driven automation can bridge the gap between Software Engineering (SE) and robotics, enabling Software-Defined Robotics (SDR) and supporting scalable IoRT applications. Full article

Dacarbazine (DTIC) is a clinically important anticancer drug whose photosensitivity poses challenges for its stability and interactions with supramolecular hosts. Here, we investigate its complexation with the host 1,10-N,N′-bis-(β-D-ureidocellobiosyl)-4,7,13,16-tetraoxa-1,10-diazacyclooctadecane (TN), a hybrid urea–carbohydrate–diazacrown system, using combined experimental and computational approaches. While TN has been studied as a host molecule, its specific interactions with DTIC and the associated thermodynamic characteristics had not been characterized. Computational results (obtained at the density functional theory level (DFT)) indicate that TN primarily forms non-inclusion complexes, with DTIC engaging in hydrogen bonding with sugar units, urea bridges, and diazacrown ether moieties. Experimental ¹H NMR studies in D₂O confirmed these interaction patterns, showing notable chemical shifts for sugar protons. Conductometric measurements between 293 and 313 K allowed for the determination of formation constants and thermodynamic parameters. The results demonstrate that TN:DTIC complexation is spontaneous, exothermic, and enthalpy-driven, accompanied by decreased system entropy. Comparison with previous studies on cyclodextrin complexes shows that TN forms strong associations with DTIC, owing to its abundant donor–acceptor groups, which facilitate extensive hydrogen-bonding networks. These findings provide new insights into DTIC stabilization and highlight TN’s potential as a multifunctional platform for drug delivery. Full article

Epipolythiodioxopiperazines (ETPs), characterized by a diketopiperazine (DKP) core bridged by disulfide or polysulfide bonds, exhibit exceptional structural diversity and functional adaptability. This review comprehensively explores their multifaceted properties, covering chemical structural characteristics, therapeutic application potential, and ecological functional value. Structural diversity arises from variations in the core DKP scaffold, sulfur bridge connectivity patterns, and additional modifications. Biosynthesis involves initial DKP assembly, enzyme-catalyzed sulfur incorporation and oxidation to form the signature sulfur bridge of ETPs, diversification by tailoring enzymes, and distinct regulatory mechanisms. ETPs possess diverse biological activities, including cytotoxicity, antitumor activity, antimicrobial properties, and immunomodulatory functions. From an ecological standpoint, ETPs mediate fungal–host interactions and influence competition and symbiosis within fungal communities. Furthermore, this review also addresses the current challenges and outlines future research directions. In summary, as a class of significant compounds spanning the fields of chemistry, biology, medicine, and ecology, ETPs deserve focused attention for their research value and application prospects. Full article

Tuberculosis (TB), caused by Mycobacterium tuberculosis, continues to be a leading cause of death from a single infectious agent worldwide. Conventional antibiotic therapies face significant limitations, including multidrug resistance, poor treatment adherence, limited penetration into granulomas, and systemic toxicity. Recent advances in nanomedicine have paved the way for nanotheranostic approaches that integrate therapeutic, diagnostic, and preventive functions into a single platform. Nanotheranostic systems enable targeted drug delivery to infected macrophages and granulomatous lesions, real-time imaging for disease monitoring, and controlled, stimuli-responsive release of antitubercular agents. These platforms can be engineered to modulate host immune responses through host-directed therapies (HDTs), including the induction of autophagy, regulation of apoptosis, and macrophage polarization toward the bactericidal M1 phenotype. Additionally, nanocarriers can co-deliver antibiotics, immunomodulators, or photosensitizers to enhance intracellular bacterial clearance while minimizing off-target toxicity. The review also discusses the potential of nanotechnology to improve TB prevention by enhancing vaccine efficacy, stability, and targeted delivery of immunogens such as BCG and novel subunit vaccines. Key nanoplatforms, including polymeric, lipid-based, metallic, and hybrid nanoparticles, are highlighted, along with design principles for optimizing biocompatibility, multifunctionality, and clinical translatability. Collectively, nanotheranostic strategies represent a transformative approach to TB management, bridging diagnosis, therapy, and prevention in a single, adaptable platform to address the unmet needs of this global health challenge. Full article

Silicone implants are widely used in medical applications, particularly for breast augmentation and reconstruction. However, ongoing concerns regarding their long-term safety and biocompatibility necessitate comprehensive characterization. This review critically evaluates the chemical, physical, and biological testing approaches currently used to assess silicone implants, and specifically silicone breast implants, biocompatibility, and highlights the limitations of existing ISO 10993-based protocols, which often apply a one-size-fits-all model. We propose an application-specific framework to improve the relevance and precision of biocompatibility assessments. Chemical analyses, including Fourier transform infrared (FTIR) spectroscopy, Raman spectroscopy, and nuclear magnetic resonance (NMR) spectroscopy, provide essential information on polymer structure, integrity, and composition, thereby supporting quality control and market surveillance. Physical characterization methods, such as scanning electron microscopy (SEM), atomic force microscopy (AFM), and contact angle measurements, assess the surface morphology, hydrophobicity, and potential defects that may influence the host response. Mechanical testing, which evaluates properties such as tensile strength and fatigue resistance, simulates in vivo stress conditions to predict the long-term durability. Biological evaluations guided by ISO 10993 use in vitro and in vivo models to assess cytotoxicity, adhesion, inflammation, and tissue integration. However, these are often not tailored to the implant type, surface features, or duration of exposure. Emerging tools, such as organ-on-a-chip platforms and machine learning models, offer new possibilities for predictive and context-specific evaluation. We advocate a standardized, modular strategy that integrates chemical, physical, and biological testing with clinical data to bridge preclinical assessments and real-world outcomes, with a specific focus on silicone breast implants. The aim of this approach is to improve patient safety, regulatory clarity, and device innovation across the global landscape of silicone implant development. Full article

Oral health represents a complex interplay between local microbial ecology, host immune responses, and systemic physiology. Far from being an isolated entity, the oral cavity is the entry point of the gastrointestinal and respiratory tracts and harbors up to one trillion microorganisms. While commensal species maintain ecological balance, pathogenic bacteria such as Porphyromonas gingivalis drive inflammatory conditions like gingivitis and periodontitis. Studies suggest that as chronic inflammation persists and is manifested through sustained breakdown of periodontal tissues, systemic dissemination of oral pathogens contributes to bacteremia, endothelial dysfunction, and neuroinflammation. As a result, increasing evidence has been found linking these oral pathogens and inflammatory mediators to systemic conditions including Alzheimer’s disease, cardiovascular disease, and arthritis. This narrative review synthesizes current evidence linking oral health to systemic disease while addressing practical strategies to strengthen preventive care. Evidence-based interventions are presented as accessible tools for reducing both oral and systemic inflammatory burden. Importantly, this article emphasizes the public health imperative of bridging mechanistic insights with actionable oral hygiene practices. By promoting evidence-based strategies such as scaling and root planing, dietary sugar reduction, and judicious use of antimicrobial agents, individuals may reduce their risk of chronic inflammatory and degenerative diseases. Future interdisciplinary research is needed to clarify causal mechanisms and optimize preventive frameworks integrating oral-systemic health. Full article

The human immunodeficiency virus (HIV) remains a major global health challenge. A promising therapeutic strategy involves identifying human proteins capable of physically blocking viral entry by interacting with key components of the HIV attachment system. To address this challenge systematically, we developed a computational pipeline for prioritizing protein–protein interaction and applied it to identify host proteins interacting with the viral glycoprotein gp120 and cellular receptors (CD4, CCR5, CXCR4, CCR2). Our approach combined large-scale interaction modeling using AlphaFold 3 with a comprehensive comparative analysis framework. We screened a panel of 55 candidate human proteins selected through integrated bioinformatics analysis. The pipeline incorporated model confidence assessment, quantitative contact analysis, and normalization against reference interactions to generate a robust ranking of candidates. Key findings reveal several important patterns. Chemokine CCL27 uniquely demonstrated high binding potential to both CCR5 co-receptor and viral gp120, suggesting its potential for dual-blockade capability. Analysis of natural ligand interactions with chemokine receptors showed marked disparity: CC-chemokine family members exhibited significantly greater binding capacity for CCR5 and CCR2 receptors compared to CXC-family ligand interactions with CXCR4. This binding imbalance may potentially drive selective viral pressure and influence tropism evolution during disease progression. We also identified potential interactions between HIV entry components and neuropeptides including PNOC and NPY, as well as various membrane receptors beyond classical coreceptors. Furthermore, cluster analysis revealed clear separation between receptor-type and ligand-type interactors, supporting the biological plausibility of our predictions. While acknowledging limitations related to model refinement, this study provides a systematically ranked set of candidate targets for HIV therapeutic development. Beyond identifying specific HIV interaction candidates, this study establishes a generalizable computational pipeline for the prioritization of protein–protein interaction in pathogen-host systems, effectively bridging large-scale modeling. Full article