Search Results (35,252)

Although resin-based composite is the most popular direct restoration material in the U.S., composite restorations can fail shortly after placement. The leading cause of failure is recurrent marginal decay. The adhesive that bonds the composite to the tooth is intended to seal the margin, but the degradation of the adhesive seal to dentin leads to gaps that are infiltrated by cariogenic bacteria. The development of strategies to mitigate adhesive degradation is an area of intense interest. Recent studies focus on exploiting hydrogen–bond interactions to enhance polymer network stability. This paper presents the preparation and characterization of model adhesives that capitalize on carbamate-functionalized long-chain silane monomers to enhance polymer stability and mechanical properties in wet environments. The adhesive composition is HEMA/BisGMA, 3-component photoinitiator system, carbamate-functionalized long-chain silane monomers, e.g., commercial SHEtMA (Cb1) and newly synthesized SHEMA (Cb2). Polymerization behavior, water sorption, leachates, and dynamic mechanical properties were investigated. The properties of Cb1 and Cb2 were compared to previously studied middle- (SC4) and short-chain (SC5) silane monomers. Cb1- and Cb2-formulations exhibit greater resilience under wet conditions as compared to middle-chain silane monomers. Dental adhesives containing the carbamate-functionalized long-chain silane monomers exhibit reduced flexibility in water-submersed conditions and enhanced stability as a result of increased hydrogen–bond interactions. The results emphasize the critical role of hydrogen bonding in maintaining structural integrity of dental adhesive formulations under conditions that simulate the wet, oral environment. Full article

Background/Objectives: Mutations in NACHT, LRR and PYD domain-containing protein 2 (NLRP2) and NLRP7 genes, members of the NOD-like receptor (NLR) family of innate immune sensors, result in recurrent miscarriages and reproductive wastage in women. These genes have been identified to be maternal effect genes in humans and mice regulating early embryo development. Previous research in vitro suggests that NLRP2 and NLRP7 regulate DNA methylation and/or immune signaling through inflammasome formation. However, the exact mechanisms underlying NLRP2 and NLRP7 function are not well defined. Methods: To determine the interacting proteins required for NLRP2/NLRP7-mediated regulation of DNA methylation, yeast 2-hybrid screens, coimmunoprecipitation, and FRET studies were performed and verified the ability of novel protein interactions to affect global DNA methylation by 5-methylcytosine-specific ELISA. Results: Various methodologies employed in this research demonstrate a novel protein interaction between human ErbB3-binding protein 1 (EBP1, also known as proliferation-associated protein 2G4 (PA2G4) and NLRP2 or NLRP7. In addition, NLRP2 and NLRP7 regulate EBP1 gene expression. Functionally, global DNA methylation levels appeared to decrease further when NLRP2 and NLRP7 were co-expressed with EBP1, although additional studies may need to confirm the significance of this effect. Conclusions: Since EBP1 is implicated in apoptosis, cell proliferation, DNA methylation, and differentiation, our discovery significantly advances our understanding of how mutations in NLRP2 or NLRP7 may contribute to reproductive wastage in women through EBP1. Full article

Landslides are a major natural hazard capable of causing severe damage to infrastructure, ecosystems, and human life. They result from complex interactions of geological, hydrological, and environmental factors, with soil properties playing a crucial role by influencing the mechanical behavior and moisture dynamics of slope materials that drive initiation and progression. In South Africa, few studies have examined soil influences on landslide susceptibility, and none have been conducted in the Eastern Cape Province. This study investigated the role of soil physical and chemical properties in landslide susceptibility by comparing profiles from landslide scars and stable sites in the Port St. Johns and Lusikisiki region. Samples from topsoil and subsoil horizons were analyzed for soil organic matter (SOM), cation exchange capacity (CEC), saturated hydraulic conductivity (Ksat), exchangeable sodium adsorption ratio (SARexc), and texture. Statistical analyses included the Shapiro–Wilk test to evaluate data normality. For inter-profile comparisons, Welch’s t-test was applied to normally distributed data, while the Mann–Whitney U test was used for non-normal distributions. Intra-profile differences across more than two groups were assessed using the Kruskal–Wallis test for the non-normally distributed data. Results showed that landslide-prone soils had higher SOM, CEC, and Ksat in topsoil, promoting moisture retention and rapid infiltration, which favor pore pressure build-up and slope failure. Non-landslide soils displayed higher sodium-related indices and finer textures, suggesting more uniform water retention and resilience. Vertical variation in landslide soils indicated hydraulic discontinuities, fostering perched saturation zones. Findings highlight landslide initiation as a product of interactions between hydromechanical gradients and chemical dynamics. Full article

We study stochastic production planning in capacity-constrained manufacturing systems, where feasible operating states are restricted to a convex safe-operating region. The objective is to minimize the total cost that combines a quadratic production effort with an inventory holding cost, while automatically halting production when the state leaves the safe region. We derive the associated Hamilton–Jacobi–Bellman (HJB) equation, establish the existence and uniqueness of the value function under broad conditions, and prove a concavity property of the transformed value function that yields a robust gradient-based optimal feedback policy. From an operations perspective, the stopping mechanism encodes hard capacity and safety limits, ensuring bounded risk and finite expected costs. We complement the analysis with numerical methods based on finite differences and illustrate how the resulting policies inform real-time decisions through two application-inspired examples: a single-product case calibrated with typical process-industry parameters and a two-dimensional example motivated by semiconductor fabrication, where interacting production variables must satisfy joint safety constraints. The results bridge rigorous stochastic control with practical production planning and provide actionable guidance for operating under uncertainty and capacity limits. Full article

Background/Objectives: This study evaluated the antifungal, enzyme inhibitory, and anticancer properties of the ethyl acetate (EtOAc) leaves extract of Rubus ulmifolius Schott using in vitro assays and in silico analysis. Methods: Antifungal activity was assessed against five fungal strains by measuring inhibition zones. Enzyme inhibition assays were conducted for acetylcholinesterase (AChE), butyrylcholinesterase (BChE), and urease. Antiproliferative effects were tested against HT-29 colorectal, SK-OV-3 ovarian, and A549 lung cancer cells using the MTT assay. Network pharmacology and molecular docking analyses were performed on major compounds previously identified by GC–MS (gallic acid, caffeic acid, catechin, and fructofuranose) to uncover the potential mechanisms of the plant in colorectal and ovarian cancers. Results: The extract displayed notable antifungal activity, particularly against Penicillium sp., Aspergillus fumigatus, and Candida albicans, with inhibition zones of 22.5 ± 0.7 to 26.8 ± 1.3 mm. Enzyme assays revealed moderate inhibition of AChE (IC₅₀ = 92.94 ± 1.97 µg/mL), weaker activity against BChE (IC₅₀ = 274.93 ± 2.32 µg/mL), and modest inhibition of urease (IC₅₀ = 262.60 ± 1.41 µg/mL). The extract exhibited strong antiproliferative effects against HT-29 and SK-OV-3 cells (IC₅₀ = 2.41 ± 0.13 and 4.63 ± 0.26 µg/mL, respectively), whereas activity against A549 lung cancer cells was limited. Network pharmacology predicted 52 and 44 overlapping target genes between the major compounds and colorectal and ovarian cancers, respectively. Protein–protein interaction networks identified hub genes for each cancer type, with key shared targets including EGFR, ESR1, PTGS2, and STAT3. Molecular docking confirmed favorable binding between these targets and the compounds, particularly catechin, which showed interactions comparable to those of reference inhibitors. Conclusions: These findings suggest that R. ulmifolius may possess multi-target antifungal, neuroprotective, and anticancer potential, warranting further in vitro pharmacological and preclinical validation. Full article

An incision point acts as the pivot point when a minimally invasive surgery procedure is applied. The assistive robot arms employed for such operation must have the capability to perform a remote center of motion (RCM) at this pivot point. Other than designing RCM mechanisms, a common practice is to use a readily available spatial serial robot arm and control it to impose this RCM constraint. When this assistive robot is required to be backdriven by the surgeon, the relation between the interaction forces/moments and the motion with RCM constraint becomes challenging. This paper carefully formulates a hybrid position/force control scheme for this relationship when any readily available robot arm that is coupled with a force/torque sensor is used for an RCM task. The verification of the formulation is carried out on a readily available robot arm by implementing the additional constraints that are derived from a surgical robot application. Full article

A core challenge in multimodal emotion recognition lies in the precise capture of the inherent multimodal interactive nature of human emotions. Addressing the limitation of existing methods, which often process visual signals (facial expressions) and physiological signals (EEG, ECG,EOG, and GSR) in isolation and thus fail to exploit their complementary strengths effectively, this paper presents a new multimodal emotion recognition framework called the Gated Biological Visual Network (GBV-Net). This framework enhances emotion recognition accuracy through deep synergistic fusion of facial expressions and physiological signals. GBV-Net integrates three core modules: (1) a facial feature extractor based on a modified ConvNeXt V2 architecture incorporating lightweight Transformers, specifically designed to capture subtle spatio-temporal dynamics in facial expressions; (2) a hybrid physiological feature extractor combining 1D convolutions, Temporal Convolutional Networks (TCNs), and convolutional self-attention mechanisms, adept at modeling local patterns and long-range temporal dependencies in physiological signals; and (3) an enhanced gated attention fusion module capable of adaptively learning inter-modal weights to achieve dynamic, synergistic integration at the feature level. A thorough investigation of the publicly accessible DEAP and MAHNOB-HCI datasets reveals that GBV-Net surpasses contemporary methods. Specifically, on the DEAP dataset, the model attained classification accuracies of 95.10% for Valence and 95.65% for Arousal, with F1-scores of 95.52% and 96.35%, respectively. On MAHNOB-HCI, the accuracies achieved were 97.28% for Valence and 97.73% for Arousal, with F1-scores of 97.50% and 97.74%, respectively. These experimental findings substantiate that GBV-Net effectively captures deep-level interactive information between multimodal signals, thereby improving emotion recognition accuracy. Full article

Recent advances in exoskeleton-assisted rehabilitation have highlighted the significance of lower limb movement intention recognition through deep learning. However, discrete motion phase classification and continuous real-time joint kinematics estimation are typically handled as independent tasks, leading to temporal misalignment or delayed assistance during dynamic movements. To address this issue, this study presents iTransformer-DTL, a dual-task learning framework with an improved Transformer designed to identify end-to-end locomotion modes and predict joint trajectories during sit-to-stand transitions. Employing a learnable query mechanism and a non-autoregressive decoding approach, the proposed iTransformer-DTL can produce the complete output sequence at once, without relying on any previously generated elements. The proposed framework has been tested with a dataset of lower limb movements involving seven healthy individuals and seven stroke patients. The experimental results indicate that the proposed framework achieves satisfactory performance in dual tasks. An average angle prediction Mean Absolute Error (MAE) of 3.84° and a classification accuracy of 99.42% were obtained in the healthy group, while 4.62° MAE and 99.01% accuracy were achieved in the stroke group. These results suggest that iTransformer-DTL could support adaptable rehabilitation exoskeleton controllers, enhancing human–robot interactions. Full article

The work presents a finite element analysis of the mechanical interaction of adjacent tissues in degenerative conditions of the intervertebral disc. To address this, we developed a three-dimensional finite element model that included the L1–L2 vertebrae, the intervertebral disc, and the hyaline endplate. Nonlinear elasticity theory was employed for the numerical computations, allowing for the consideration of hyperelastic properties of soft tissues. The research findings revealed significant trends associated with the increase in stiffness of the intervertebral disc: in the model with severe degeneration of annulus fibrosus and nucleus pulposus, the yield strength on the cortical bone is reached at a displacement of 3.2 mm, whereas with moderate stiffness of annulus fibrosus and nucleus pulposus, the bone's strength reserve is significantly higher, and the maximum stresses under such loading conditions reach 50 MPa. In cases with a healthy intervertebral disc, the established stress values differed by almost 50 percent, the maximum value being 41 MPa. Full article

Multi-modal knowledge graph completion (MMKGC) aims to complete knowledge graphs by integrating structural information with multi-modal (e.g., visual, textual, and numerical) features and leveraging cross-modal reasoning within a unified semantic space to infer and supplement missing factual knowledge. Current MMKGC methods have advanced in terms of integrating multi-modal information but have overlooked the imbalance in modality importance for target entities. Treating all modalities equally dilutes critical semantics and amplifies irrelevant information, which in turn limits the semantic understanding and predictive performance of the model. To address these limitations, we proposed a modality information aggregation graph attention network with adversarial training for multi-modal knowledge graph completion (MIAGAT-AT). MIAGAT-AT focuses on hierarchically modeling complex cross-modal interactions. By combining the multi-head attention mechanism with modality-specific projection methods, it precisely captures global semantic dependencies and dynamically adjusts the weight of modality embeddings according to the importance of each modality, thereby optimizing cross-modal information fusion capabilities. Moreover, through the use of random noise and multi-layer residual blocks, the adversarial training generates high-quality multi-modal feature representations, thereby effectively enhancing information from imbalanced modalities. Experimental results demonstrate that our approach significantly outperforms 18 existing baselines and establishes a strong performance baseline across three distinct datasets. Full article

Frozen shoulder (FS), traditionally regarded as an idiopathic musculoskeletal disorder characterized by pain, stiffness, and capsular fibrosis, is increasingly recognized as the clinical manifestation of systemic endocrine, metabolic, vascular, and immunological dysfunctions. This narrative review reframes FS within a broader neuro–endocrine–immunometabolic model, emphasizing the central role of estrogen deficiency, resistance, and receptor-level disruption, together with their interactions with thyroid dysfunction, endothelial health, and lifestyle-related low-grade inflammation (LGI). Evidence from epidemiological, clinical, and mechanistic studies shows that estrogen signaling failure weakens anti-inflammatory, antifibrotic, and antioxidant defenses, predisposing peri- and postmenopausal women to more severe FS phenotypes. Thyroid dysfunction, particularly hypothyroidism, further contributes to fibrosis and pain sensitization. Endothelial dysfunction—driven by poor diet, advanced glycation end-products (AGEs), and oxidative stress—impairs vascular integrity and promotes local microvascular inflammation. In parallel, lifestyle factors such as sedentarism, circadian misalignment, psychosocial stress, and environmental exposures sustain systemic LGI and hormonal resistance. Together, these interconnected mechanisms suggest that FS is not merely a localized joint pathology but a systemic disorder requiring integrative clinical strategies that combine orthopedic management with endocrine evaluation, metabolic monitoring, dietary interventions, circadian health, and stress regulation. In addition, this review outlines specific clinical implications, highlighting how an integrative, personalized approach that targets hormonal, metabolic, vascular, and lifestyle dimensions may improve pain, function, and long-term prognosis in FS. This paradigm shift underscores the need for future research to focus on stratified patient profiling and interventional trials targeting hormonal, vascular, and lifestyle axes to improve outcomes, particularly in women who remain disproportionately affected by FS. Full article

Traditional emotion decoding methods typically rely on short sequences with limited context and coarse-grained emotion categories. To address these limitations, we proposed the Semantic and Emotion Decoding Generative Pre-trained Transformer (SED-GPT), a non-invasive method for long-sequence fine-grained semantics and emotions decoding on extended narrative stimuli. Using a publicly available fMRI dataset from 8 participants, this exploratory study investigates the feasibility of reconstructing complex semantic and emotional states from brain activity. SED-GPT achieves a BERTScore-F1 of 0.650 on semantic decoding and attains a cosine similarity (CS) of 0.504 and a Jensen–Shannon similarity (JSS) of 0.469 for emotion decoding (p < 0.05). Functional connectivity analyses reveal persistent coupling between the language network and the emotion network, which provides neural evidence for the language–emotion interaction mechanism in Chinese. These findings should be interpreted as pilot-level feasibility evidence. Full article

Bispecific peptides represent an emerging therapeutic platform in immunotherapy, offering simultaneous engagement of two distinct molecular targets to enhance specificity, functional synergy, and immune modulation. Their compact structure and modular design enable precise interaction with protein–protein interfaces and shallow binding sites that are otherwise difficult to target. This review summarizes current design strategies of bispecific peptides, including fused, linked, and self-assembled architectures, and elucidates their mechanisms in bridging tumor cells with immune effector cells and blocking immune checkpoint pathways. Recent developments highlight their potential applications not only in oncology but also in autoimmune and infectious diseases. Key translational challenges, including proteolytic stability, immunogenicity, delivery barriers, and manufacturing scalability, are discussed, along with emerging peptide engineering and computational design strategies to address these limitations. Bispecific peptides offer a versatile and adaptable platform poised to advance precision immunotherapy and expand therapeutic options across immune-mediated diseases. Full article

Background: During maxillary sinus floor augmentation, the elevated sinus mucosa may come into close contact with the pristine mucosa. The presence of xenograft granules can lead to unintended mechanical and biological interactions between the two layers, and the resulting tissue damage remains poorly understood. The aim of this study was to perform a focused histological evaluation of graft-mediated interactions between the elevated and pristine sinus mucosae. Methods: Histological slides from five previously published rabbit sinus augmentation studies using grafts with different resorption rates were retrospectively analyzed. The following main patterns of tissue alteration were identified: (1) Proximity stage, characterized by epithelial thickening, goblet cell hyperactivity, and ciliary shortening; (2) Fusion stage, with epithelial interpenetration and loss of distinct mucosal boundaries; (3) Synechiae stage, featuring connective tissue bridges linking the two mucosae; and (4) Pristine mucosa lesions, caused by direct contact between residual graft particles and the pristine sinus mucosa. Results: A total of 192 sinuses were evaluated. Sinuses augmented with slowly resorbable grafts showed proximity stage in 22.3% of cases, fusion in 7.7%, direct lesions in 9.6%, and only one instance of synechia. In contrast, the faster resorbable xenograft presented only 11.1% of proximity stage, without further alterations. Conclusions: In this rabbit model, xenografts were associated with histological alterations of the sinus mucosa, while synechiae formation was rare. These preclinical findings should not be directly extrapolated to humans but may provide a basis for future investigations. Full article

Background: Cinnarizine (CIN) is a poorly soluble drug used in the treatment of vestibular disorders. Its solubility can be improved by complexation with cyclodextrins (CDs). This study focused on the preparation of 1:1 CIN/CD complexes with β-cyclodextrin (βCD) and its derivatives hydroxypropyl-β-cyclodextrin (HPβCD) and sulfobutylether-β-cyclodextrin (SBEβCD) by mechanical activation. Methods: Complexes were obtained under optimized grinding conditions using a high-energy vibrational mill with ZrO₂ grinding media. Solid products were characterized by DSC, TGA, XRPD, and FTIR spectroscopy. Dissolution studies were performed in phosphate buffer (pH 4.5). The effect of βCD and HPβCD on CIN stability was assessed under hydrolytic (acidic, neutral, and basic) and oxidative conditions. A stability-indicating UHPLC-DAD-HRMS method was developed and validated, enabling CIN quantification in the presence of degradation products, whose structures were proposed based on HRMS/MS data. Potential toxicity, bioaccumulation, and mutagenicity of degradation products were predicted using QSAR modeling. Accelerated stability studies (40 °C, 75% RH) were conducted to evaluate long-term stability. Results: Solid-state analyses confirmed CIN/CD interactions in the ground products. The highest dissolution efficiency was observed for CIN/HPβCD complexes, while CD complexation did not alter CIN permeability in biomimetic membrane assays. CIN and its complexes demonstrated satisfactory chemical stability, with no degradation products detected under accelerated conditions. Conclusions: Solid-state complexes of CIN with CDs enhanced dissolution without compromising stability, supporting their potential as promising candidates for novel pharmaceutical formulations. Full article