Search Results (2,527)

Glycosaminoglycans (GAGs) are highly negatively charged polysaccharides that play essential roles in numerous physiological and pathological processes through their interactions with proteins. These interactions govern cellular signaling, inflammation, coagulation, and recognition. Surface Plasmon Resonance (SPR) has emerged as a key biophysical technique for label-free, real-time characterization of biomolecular interactions, offering insights into binding kinetics, affinity, and specificity. SPR-based approaches to glycosaminoglycan–protein interaction studies offer powerful tools for elucidating the roles of GAGs in a wide range of physiological and pathological processes. In this review, we systematically discuss experimental strategies, data analysis methods, and representative applications of SPR-based glycosaminoglycan–protein interactions. Special attention is given to the challenges associated with GAG heterogeneity and immobilization, as well as recent technological advances that enhance sensitivity and throughput. To our knowledge, this review represents one of the first systematic and up-to-date summaries specifically focused on recent advances in applying SPR to the study of glycosaminoglycan–protein interactions. Full article

In this study, the nonlinear and non-monotonic behavior of amperometric bienzyme biosensors employing an enzymatic trigger reaction is investigated analytically and computationally using a two-compartment model comprising an enzymatic layer and an outer diffusion layer. The trigger enzymatic reaction is coupled with a cyclic electrochemical–enzymatic conversion (CEC) process. The model is formulated as a system of reaction–diffusion equations incorporating nonlinear Michaelis–Menten kinetics and interlayer partitioning effects. Exact steady-state analytical solutions for substrate and product concentrations, as well as for the output current, are obtained for specific cases of first- and zero-order reaction kinetics. At the transition conditions, biosensor performance is further analyzed numerically using the finite difference method. The CEC biosensor exhibits the highest signal gain when the first enzyme has low activity and the second enzyme has high activity; however, under these conditions, the response time is the longest. When the first enzyme possesses a higher substrate affinity (lower Michaelis constant) than the second, the biosensor demonstrates severalfold higher current and gain compared to the reverse configuration under identical diffusion limitations. Furthermore, increasing external mass transport resistance or interfacial partitioning can enhance the apparent signal gain. Full article

Two structurally tunable (propyl-3-ol)triphenyltin(IV) (Ph₃SnL₁) and (butyl-4-ol)triphenyltin(IV) (Ph₃SnL₂) compounds were investigated at the human serum transferrin (Tf) molecular interface to resolve how ligand architecture and protein metallation modulate organotin(IV) biocompound stability and lobe-selective binding. Steady-state fluorescence spectroscopy revealed efficient quenching of native Tf emission (λ_ex = 280 nm, 296–310 K, pH 7.4) without significant spectral displacement, indicating the predominant formation of non-fluorescent ground-state complexes. Calculated bimolecular quenching constants (K_q ~10¹² M⁻¹ s⁻¹) exceeded the diffusion-controlled aqueous limit, ruling out a collisional dynamic quenching mechanism and confirming static complexation as the principal origin of fluorescence suppression. Double-log binding analysis revealed moderate affinity (K_a ~10²–10³ M⁻¹) and an approximately single dominant binding event per protein (n ≈ 0.65–0.90). Temperature-dependent van’t Hoff evaluation yielded positive ΔH° and ΔS° values, supporting a spontaneous, entropy-favored association process largely governed by hydrophobic and dispersion-type contributions, consistent with lipophilic organotin(IV) scaffold accommodation. Iron (Fe³⁺) loading of Tf markedly enhanced ligand engagement, especially for Ph₃SnL₁, evidencing that metallation-induced lobe closure reshapes pocket accessibility and local polarity relevant for organotin(IV) binding presentation rather than simply strengthening empirical docking scores. Molecular docking localized the most stable Ph₃SnL₂ poses in the sterically confined, rigid C-lobe, while Ph₃SnL₁ preferentially penetrated the more adaptive N-lobe. ONIOM QM/MM refinement of docking poses confirmed strong interfacial stabilization (ΔE_int ≈ –38 to –62 kcal mol⁻¹) and clarified charge–packing interplay without invoking frontier orbital analysis. The results map multiscale structure–interaction relationships defining lobe preference and complex stability at the transferrin interface. Full article

Background: Myocardial fibrosis, a central pathological process leading to heart failure, lacks specific mechanism-based therapies. Although the anti-inflammatory activity of the natural compound protocatechuic acid is recognized, its direct anti-fibrotic mechanism, particularly concerning the critical role of endothelial–mesenchymal transition (EndMT), remains unexplored. This study aimed to investigate the protective effects and underlying mechanisms of protocatechuic acid. Methods: The study employed both in vivo and in vitro models. For in vivo evaluation, a rat model of myocardial fibrosis was induced by isoproterenol hydrochloride (ISO). For in vitro analysis, human umbilical vein endothelial cells (HUVECs) were stimulated with angiotensin II (Ang II) and subjected to siRNA-mediated histone deacetylase 1 (HDAC1) knockdown, alongside a co-culture model involving HUVECs and the AC16 human cardiomyocyte cells. Additionally, molecular docking and dynamics simulations were performed to evaluate the binding affinity and stability of protocatechuic acid with the target protein, HDAC1. Results: In vivo, protocatechuic acid significantly improved cardiac function, attenuated pathological injury, and reduced collagen deposition in ISO-induced fibrotic rats. It also potently suppressed inflammatory responses and inhibited the EndMT process. These beneficial effects were associated with decreased HDAC1 and increased GATA binding protein 4 (GATA4) expression in perivascular regions, which suggests the modulation of the HDAC1/GATA4 pathway. In vitro, protocatechuic acid suppressed Ang II-induced endothelial inflammation in HUVECs. This effect was replicated by HDAC1 knockdown, thus confirming that the HDAC1/GATA4 pathway mediates its anti-inflammatory action at the cellular level. Furthermore, molecular docking and dynamics simulations indicated that protocatechuic acid stably binds to a key target, HDAC1. Conclusions: Protocatechuic acid alleviates inflammation and EndMT by inhibiting the HDAC1/GATA4 signaling pathway, thereby preserving cardiac function and retarding the progression of myocardial fibrosis. These findings provide a theoretical and experimental foundation for the potential application of protocatechuic acid in treating cardiovascular diseases. Full article

Uncertainty-aware decision-making increasingly relies on multimodal sensing pipelines that must fuse correlated measurements, propagate uncertainty, and trigger reliable control actions. This study develops a unified mathematical framework for multimodal data fusion and Bayesian decision-making under uncertainty. The approach integrates adaptive Covariance Intersection (aCI) for correlation-robust sensor fusion, a Gaussian state–space backbone with Kalman filtering, heteroskedastic Bayesian regression with full posterior sampling via an affine-invariant MCMC sampler, and a Bayesian likelihood-ratio test (LRT) coupled to a risk-sensitive proportional–derivative (PD) control law. Theoretical guarantees are provided by bounding the state covariance under stability conditions, establishing convexity of the aCI weight optimization on the simplex, and deriving a Bayes-risk-optimal decision threshold for the LRT under symmetric Gaussian likelihoods. A proof-of-concept agro-environmental decision-support application is considered, where heterogeneous data streams (IoT soil sensors, meteorological stations, and drone-derived vegetation indices) are fused to generate early-warning alarms for crop stress and to adapt irrigation and fertilization inputs. The proposed pipeline reduces predictive variance and sharpens posterior credible intervals (up to 34% narrower 95% intervals and 44% lower NLL/Brier score under heteroskedastic modeling), while a Bayesian uncertainty-aware controller achieves 14.2% lower water usage and 35.5% fewer false stress alarms compared to a rule-based strategy. The framework is mathematically grounded yet domain-independent, providing a probabilistic pipeline that propagates uncertainty from raw multimodal data to operational control actions, and can be transferred beyond agriculture to robotics, signal processing, and environmental monitoring applications. Full article

Accurate registration of optical and synthetic aperture radar (SAR) images is a fundamental prerequisite for multi-source remote sensing data fusion and analysis. However, due to the substantial differences in imaging mechanisms, optical–SAR image pairs often exhibit significant radiometric discrepancies and spatially varying geometric inconsistencies, which severely limit the robustness of traditional feature or region-based registration methods in cross-modal scenarios. To address these challenges, this paper proposes an end-to-end Optical–SAR Registration Network (OSR-Net) based on multi-constraint joint optimization. The proposed framework explicitly decouples cross-modal feature alignment and geometric correction, enabling robust registration under large appearance variation. Specifically, a multi-modal feature extraction module constructs a shared high-level representation, while a multi-scale channel attention mechanism adaptively enhances cross-modal feature consistency. A multi-scale affine transformation prediction module provides a coarse-to-fine geometric initialization, which stabilizes parameter estimation under complex imaging conditions. Furthermore, an improved spatial transformer network is introduced to perform structure-preserving geometric refinement, mitigating spatial distortion induced by modality discrepancies. In addition, a multi-constraint loss formulation is designed to jointly enforce geometric accuracy, structural consistency, and physical plausibility. By employing a dynamic weighting strategy, the optimization process progressively shifts from global alignment to local structural refinement, effectively preventing degenerate solutions and improving robustness. Extensive experiments on public optical–SAR datasets demonstrate that the proposed method achieves accurate and stable registration across diverse scenes, providing a reliable geometric foundation for subsequent multi-source remote sensing data fusion. Full article

Understanding particle transport in rough-walled fractures is essential for predicting flow behavior, clogging, and permeability evolution in natural and engineered subsurface systems. This study develops a fully coupled CFD–DEM framework to investigate how self-affine fractal roughness, represented by the Joint Roughness Coefficient (JRC), governs fluid–particle interactions across multiple scales. Nine fracture geometries with controlled roughness were generated using a fractal-based surface model, enabling systematic isolation of roughness effects. The results show that increasing JRC introduces a hierarchy of geometric perturbations that reorganize the flow field, amplify shear and velocity-gradient fluctuations, and enhance particle–wall interactions. Particle migration exhibits a nonlinear response to roughness due to the competing influences of disturbance amplification and the formation of preferential high-velocity pathways. Furthermore, roughness-controlled scaling relations are identified for mean particle velocity, residence time, and energy dissipation, revealing JRC as a fundamental parameter linking geometric complexity to transport efficiency. Based on these findings, a unified mechanistic framework is established that conceptualizes fractal roughness as a multiscale geometric forcing mechanism governing hydrodynamic heterogeneity, particle dynamics, and dissipative processes. This framework provides new physical insight into transport behavior in rough fractures and offers a scientific basis for improved prediction of clogging, proppant placement, and transmissivity evolution in subsurface engineering applications. Full article

Glucagon-like peptide-1 (GLP-1) is an incretin hormone and therapeutic agent for Type II diabetes mellitus. However, recombinant production in E. coli yields insufficient quantities, increasing manufacturing costs and limiting patient access. Improving yield and productivity is crucial to make GLP-1 treatments more affordable. An optimized bioprocess was developed to enhance the yield of recombinant GLP-1 (rGLP-1) analogues. Expression constructs encoding monomeric and concatemeric GLP-1 fused to GST were designed. Batch fermentations of these clones at varying pre-induction specific growth rates guided the fed-batch strategy for yield enhancement. The specific yield of monomer construct exhibited higher yields than the concatemer. Process optimization achieved a specific yield (Yp/x) of 116.7 mg/g, a dry cell weight of 88.9 g/L, and a volumetric yield of 10.3 g/L. The specific productivity of soluble rGLP-1 reached 0.4 g/L/h. Purification via affinity chromatography and enterokinase cleavage yielded authentic GLP-1 peptide confirmed by Western blot and mass spectrometry. The developed high-yield fermentation process significantly enhances rGLP-1 productivity in E. coli, potentially reducing upstream production costs by 20–30% and enabling wider accessibility to affordable GLP-1 therapies. Full article

Background: Liver steatosis assessment by 2D ultrasound is widely used but remains subjective. We previously developed a deep learning (DL) algorithm for objective steatosis quantification. This study aimed to (1) establish histology-based cutoffs, (2) evaluate their transferability across different imaging views, and (3) validate performance on a new scanner not included in training. Methods: We retrospectively analyzed 588 ultrasound studies from 457 histology-proven cases and prospectively collected paired scans using a new scanner (Philips Affiniti 70). Images from right intercostal, left hepatic lobe, and subcostal views were processed with the DL algorithm, and mean values from 3–5 images per view were correlated with histology. Results: Across three views, the DL algorithm achieved AUROCs of 0.891–0.936 across steatosis grades, consistently outperforming FibroScan’s controlled attenuation parameter (0.840–0.905), especially in moderate-to-severe steatosis (p < 0.001). Cutoffs established from right intercostal images (N = 565) were applied to images from left hepatic lobe (N = 464) and subcostal views (N = 341), yielding accuracies of 0.792–0.850. On Affiniti 70 images, AUROCs remained high (0.838–0.896), supporting scanner generalizability. Conclusions: The DL algorithm provides accurate, view-independent steatosis grading across different ultrasound scanners and outperforms CAP, supporting its real-world use for objective, reproducible quantification. Full article

Modifying proteins through grafting with polyphenols has received much attention recently due to its immense application potential. This stems from the formation of protein-polyphenol complexes, altering the structural and functional properties of the constituent molecules. In food systems, the interaction between proteins and polyphenols, including covalent and non-covalent binding, represents a green, simple, and effective strategy to transform difficult-to-process protein sources into high-value functional ingredients. In addition, the complexes formed can increase stability, biological activity, and bioavailability of polyphenols, thereby expanding their applications. Gaining insight into protein-polyphenol complexes is essential for developing novel complexes, formulations, and other applications utilizing protein and natural polyphenols. Thus, this review outlines the binding affinities and interaction mechanisms, explains factors affecting complex formation, revisits structural modulation of protein, modern processing technologies, and systematically discusses the synergistic bioactivities of the resulting complexes. We also discuss strategies to address the applications of protein–polyphenol complexes for developing functional food products with prolonged shelf life. These applications can be expanded to other industrial areas, such as pharmaceuticals and material engineering, contributing towards better nutritional quality, beneficial healthy aspects, and sustainability. Full article

Background: β-site amyloid precursor protein cleaving enzyme 1 (BACE1) inhibitors demonstrated amyloid-lowering efficacy but failed in phase II/III clinical trials due to adverse effects and limited disease-modifying outcomes. This study employed an integrated network pharmacology and molecular docking approach to quantitatively elucidate the multitarget mechanisms of 4 (phase II/III) discontinued BACE1 inhibitors (Verubecestat, Lanabecestat, Elenbecestat, and Umibecestat) and the preclinical compound AM-6494 in Alzheimer’s disease (AD). Methods: Drug-associated targets were intersected with AD-related genes to construct a protein–protein interaction (PPI) network, followed by topological analysis to identify hub proteins. Gene Ontology (GO) and KEGG pathway enrichment analyses were performed using statistically significant thresholds (p < 0.05, FDR-adjusted). Molecular docking was conducted using AutoDock Vina to quantify binding affinities and interaction modes between the selected compounds and the identified hub proteins. Results: Network analysis identified 10 hub proteins (CASP3, STAT3, BCL2, AKT1, MTOR, BCL2L1, HSP90AA1, HSP90AB1, TNF, and MDM2). GO enrichment highlighted key biological processes, including the negative regulation of autophagy, regulation of apoptotic signalling, protein folding, and inflammatory responses. KEGG pathway analysis revealed significant enrichment in the PI3K–AKT–MTOR signalling, apoptosis, and TNF signalling pathways. Molecular docking demonstrated strong multitarget binding, with binding affinities ranging from approximately −6.6 to −11.4 kcal/mol across the hub proteins. Umibecestat exhibited the strongest binding toward AKT1 (−11.4 kcal/mol), HSP90AB1 (−9.5 kcal/mol), STAT3 (−8.9 kcal/mol), HSP90AA1 (−8.5 kcal/mol), and MTOR (−8.3 kcal/mol), while Lanabecestat showed high affinity for AKT1 (−10.6 kcal/mol), HSP90AA1 (−9.9 kcal/mol), BCL2L1 (−9.2 kcal/mol), and CASP3 (−8.5 kcal/mol), respectively. These interactions were stabilized by conserved hydrogen bonding, hydrophobic contacts, and π–alkyl interactions within key regulatory domains of the target proteins, supporting their multitarget engagement beyond BACE1 inhibition. Conclusions: This study demonstrates that clinically failed BACE1 inhibitors engage multiple non-structural regulatory proteins that are central to AD pathogenesis, particularly those governing autophagy, apoptosis, proteostasis, and neuroinflammation. The identified ligand–hub protein complexes provide a mechanistic rationale for repurposing and optimization strategies targeting network-level dysregulation in Alzheimer’s disease, warranting further in silico refinement and experimental validation. Full article

Airborne iodine-131 plays a pivotal role in both nuclear medicine and nuclear safety due to its radiotoxicity, volatility, and affinity for the thyroid gland. Although the total exhaled activity after medical I-131 therapy is minimal, over 95% of this activity appears in volatile organic forms, which evade standard filtration and reflect metabolic pathways of iodine turnover. Our experimental work in patients and mice confirms the metabolic origin of these species, modulated by thyroidal function. In nuclear reactor environments, both under routine operation and during accidents, organic iodides such as [¹³¹I]CH₃I have also been identified as major airborne components, often termed “penetrating iodine” due to their low adsorption to conventional filters. This review compares the molecular speciation, environmental persistence, and dosimetric impact of airborne I-131 across clinical, technical, and accidental release scenarios. While routine reactor emissions yield negligible doses (<0.1 µSv/year), severe nuclear incidents like Chernobyl and Fukushima have resulted in significant thyroid exposures. Doses from these events ranged from tens of millisieverts to several Sieverts, particularly in children. We argue that a deeper understanding of chemical forms is essential for effective risk assessment, filtration technology, and emergency preparedness. Iodine-131 exemplifies the dual nature of radioactive substances: in nuclear medicine its radiotoxicity is therapeutically harnessed, whereas in industrial or reactor contexts it represents an unwanted hazard. The same physicochemical properties that enable therapeutic efficacy also determine, in the event of uncontrolled release, the range, persistence, and the potential for unwanted radiotoxic exposure in the general population. In nuclear medicine, exhaled activity after radioiodine therapy is minute but largely organically bound, reflecting enzymatic and metabolic methylation processes. During normal reactor operation, airborne iodine levels are negligible and dominated by inorganic vapors efficiently captured by filtration systems. In contrast, major accidents released large fractions of volatile iodine, primarily as elemental [¹³¹I]I₂ and organically bound iodine species like [¹³¹I]CH₃I. The chemical nature of these compounds defined their atmospheric lifetime, transport distance, and deposition pattern, thereby governing the thyroid dose to exposed populations. Chemical speciation is the key determinant across all scenarios. Exhaled iodine in medicine is predominantly organic; routine reactor releases are negligible; severe accidents predominantly release elemental and organic iodine that drive environmental transport and exposure. Integrating these domains shows how chemical speciation governs volatility, mobility, and bioavailability. The novelty of this review lies not in introducing new iodine chemistry, but in the systematic comparative synthesis of airborne radioiodine speciation across medical therapy, routine nuclear operation, and severe accident scenarios, identifying chemical form as the unifying determinant of volatility, environmental transport, and dose. Full article

Spectrogram-based representations have grown to dominate the feature space for deep learning audio analysis systems, and are often adopted for speech analysis also. Initially, the primary motivation behind spectrogram-based representations was their ability to present sound as a two-dimensional signal in the time–frequency plane, which not only provides an interpretable physical basis for analysing sound, but also unlocks the use of a range of machine learning techniques such as convolutional neural networks, which had been developed for image processing. A spectrogram is a matrix characterised by the resolution and span of its dimensions, as well as by the representation and scaling of each element. Many possibilities for these three characteristics have been explored by researchers across numerous application areas, with different settings showing affinity for various tasks. This paper reviews the use of spectrogram-based representations and surveys the state-of-the-art to question how front-end feature representation choice allies with back-end classifier architecture for different tasks. Full article

Mesoporous carbons based on silica hard templates were used to investigate physical processes in confined pores. Nitrogen adsorption, scanning electron microscopy, and scattered X-ray analyses revealed two classes of materials: carbons with moderate and highly developed mesoporosity. The pore structure was strongly dependent on pore expanders which proved essential for generating open, accessible architectures. All carbons exhibited a basic, graphitic surface (pH_PZC = 8.4–10.9), enriched in electron-donating oxygen functionalities. Differential scanning calorimetry studies of confined water showed that melting point depression follows the Gibbs–Thomson relationship, confirming the strong dependence of phase transitions on pore size and water–surface interactions. Adsorption experiments using methylene blue demonstrated that capacity is governed by surface area, pore volume, and pore size distribution. For carbon with the largest average pore size, adsorption of various dyes revealed that uptake decreases with increasing molecular size, whereas affinity depends strongly on electrostatic interactions. Kinetic studies indicated that carbons with larger mesopores exhibit the fastest adsorption, and that large, complex dye molecules undergo significant diffusion limitations. Overall, the results show that the interplay between pore structure, adsorbate size, and surface chemistry influences both the equilibrium uptake and adsorption kinetics in mesoporous carbon materials. Full article

Zanthoxylum piperitum is a food and culinary plant commonly used in East Asia. In traditional medicine, its fruits, seeds, and bark have been utilized to treat digestive disorders, pain, and stomachache. Prior research has demonstrated its health benefits, particularly its significant antioxidant properties. However, limited research has investigated the specific metabolites responsible for these pharmacological effects. In this study, the antioxidant activities (EC₅₀: 9.1–1084.5 μg/mL) and metabolite profiles of different organs (fruits, pericarps, and seeds) of Z. piperitum collected from different regions were comparatively analyzed. Chemical structures of 91 metabolites from different organs were identified using UHPLC-Orbitrap-MS/MS based on untargeted metabolomics. The LC-DPPH method was employed to screen antioxidants from the extracts of the most active organ (the pericarps). The potential effects of the active compounds on oxidation-related diseases were evaluated by integrating compound–target interaction network analysis. Protein–protein interaction (PPI) networks revealed EGFR, STAT3, AKT1, TNF, BCL2, CASP3, ESR1, PPARA, CYP19A1, and CDK2 as central hub genes. The significance of compound and target interactions was further supported by molecular docking studies, which demonstrated favorable binding affinities, with most proteins exhibiting docked scores below −4.27 kcal/mol. The extracts of Z. piperitum fruits and pericarps also exhibited antioxidative activity against ROS production in LPS-stimulated RAW264.7 cells. Our findings demonstrate the application of an optimized extraction process and underscore the medicinal value of this food-plant by characterizing its bioactive constituents. The results indicate that Z. piperitum may serve not only as a health-promoting food but also has the potential for prevention or treatment of oxidative-stress-related diseases. Future research should focus on in vivo studies by exploring the therapeutic mechanisms of actions of the active extracts. Full article