Search Results (277)

Thermally conductive epoxy potting compounds require high filler loadings for effective heat dissipation. However, high filler loadings can increase viscosity and brittleness, thereby impairing processability and service reliability. In this study, a mesogen-containing reactive liquid–crystalline epoxy monomer (LCE) was designed, synthesized, and incorporated into a commercial thermally conductive epoxy potting compound to investigate its effects on thermal behavior, rheological and mechanical properties, thermal conductivity, and fracture-surface morphology. The chemical structure and thermotropic liquid–crystalline behavior of LCE were characterized via Fourier-transform infrared spectroscopy, proton nuclear magnetic resonance spectroscopy, differential scanning calorimetry, and polarized optical microscopy. Increasing LCE loading elevated the DSC-derived glass transition temperature (T_g) from 59 °C to 96 °C and markedly increased the room-temperature complex viscosity. Single-point measurements at 25 °C showed a monotonic decrease in thermal conductivity from 0.95 to 0.52 W/(m·K) with increasing LCE content. Mechanical testing revealed that the nominal 10% LCE formulation provided the best balance between load-bearing capacity and ductility among the tested formulations, whereas higher LCE loadings were associated with greater local microstructural variation and reduced mechanical properties. This study clarifies the modulation effect of LCE on the performance balance of highly filled epoxy potting compounds, providing valuable insights for future formulation optimization. Full article

Modulating the physical crosslink architecture of gelatin methacryloyl (GelMA) hydrogels without altering total polymer concentration or introducing exogenous components remains a central challenge in biomaterial design. Here, we present a source blending strategy in which porcine skin gelatin (PG) and salmon skin gelatin (SG), two gelatins with markedly different proline and hydroxyproline contents, are combined at seven compositional ratios (PG weight fractions 0–1.0) and subsequently functionalized to GelMA under standardized conditions (8% v/v methacrylic anhydride, 60 °C, 3 h). Near-complete degrees of substitution (95–98%) were achieved across all formulations, as confirmed by both TNBS and ¹H-NMR analyses. In the parent gelatin mixtures, increasing PG fraction progressively increased viscosity, elastic modulus (G′), gelation temperature (Tgel), and compression modulus at 4 °C, with DSC revealing independent SG (0–15 °C) and PG (20–40 °C) endothermic transitions that suggest partial hindrance of PG triple-helix formation by high SG fractions. These composition-dependent trends were preserved after functionalization to GelMA, albeit with attenuated physical crosslinking due to steric impairment by the methacrylate groups. Photocrosslinked GelMA hydrogels fabricated after pre-incubation at 4 °C exhibited systematically higher compression moduli and lower swelling degrees with increasing PG content, demonstrating that the PG/SG ratio provides an effective means for independently tuning hydrogel mechanics and mesh architecture. In vitro release assays using Rhodamine 6G further demonstrated that pre-incubation at 4 °C prior to photocrosslinking effectively modulates transport kinetics in SG-PG GelMA hydrogels. This strategy delayed characteristic release times and constrained Weibull shape parameters to the anomalous-transport regime (0.75 < β < 1), where diffusion is governed by network chain relaxation. This effect was most pronounced in the 0.4SG:0.6PG formulation, where lower SG content permitted unhindered triple-helix formation, as corroborated by DSC and compression studies. Ultimately, adjusting the pre-incubation temperature and gelatin source combination provides a straightforward, processing-additive-free strategy to achieve programmable release profiles via controlled matrix tortuosity. Full article

Aiming at the problems of glare interference, local overexposure and detail loss caused by artificial light sources such as vehicle lamps and street lamps in nighttime road scenes, as well as the challenges of existing glare suppression models with large parameters, high computational complexity and difficulty in deploying on edge devices, this paper proposes a lightweight glare suppression network (LGSNet) based on ghost depthwise separable convolution and Lightweight Parallel Attention. Based on the U-Net architecture, the network introduces ghost depthwise separable convolution blocks (GhostDSC) in the encoder and decoder, which generates ghost features through cheap linear transformations by exploiting feature map redundancy, significantly reducing model parameters and computational costs while maintaining feature representation ability. Meanwhile, a Lightweight Parallel Attention (LPA) module is designed in the decoder stage, which integrates channel attention and pixel attention in parallel, enhancing the network’s attention to glare regions and edge details with extremely low parameter increment to improve detail recovery accuracy. In addition, a joint loss function consisting of background loss, glare loss and reconstruction loss is constructed to collaboratively optimize glare suppression and detail preservation. Experimental results on the public Flare7K++ dataset and the self-built nighttime road glare dataset NRGD show that the proposed method has only 7.45 M parameters, much lower than standard U-Net and Uformer. It achieves competitive results on full-reference metrics such as PSNR, SSIM, LPIPS and no-reference metrics such as NIQE, BRISQUE, PIQE, and can effectively suppress various types of glare interference and restore obscured scene details. It achieves a superior trade-off between model complexity and enhancement performance, significantly reducing the parameter count and computational overhead compared to heavy baselines, thereby offering a highly efficient solution for resource-aware glare suppression tasks. Full article

Protein kinases have key roles in cells as they regulate diverse signal transduction pathways. Mitogen-activated protein kinase (MAPK) signaling route modulates several processes, such as cell proliferation, cell programming, metabolic changes and stress responses. Within the group of proteins participating in this pathway, the MAPK kinase (MEK1) is a dimeric, 393-residue-long, dual-specificity protein kinase that phosphorylates both tyrosine and threonine residues. In this study, we explored the conformational changes occurring during the unfolding of MEK1, by using orthogonal biophysical techniques. Intrinsic fluorescence, extrinsic 8-anilinonapthalene-1-sulfonic acid (ANS) fluorescence, dynamic light scattering (DLS), and far-ultraviolet (UV) circular dichroism (CD) showed that the protein acquired a native-like conformation within a narrow pH range (8.0 to 9.0). Urea and guanidinium hydrochloride (GdmCl) denaturations followed by intrinsic and ANS fluorescence and far-UV CD, at pH 8.1, where the protein acquired a native-like conformation, showed that: (i) the apparent conformational stability of isolated MEK1 was low; and (ii) the unfolding occurred through the presence of intermediates. The presence of several unfolding intermediates was also evidenced through: (i) differential scanning calorimetry (DSC) in the absence of the ligand ATP; and (ii) unfolding simulations with the help of computational techniques based on constraint network analysis (CNA). We propose that the apparent low stability of this protein was related to its flexibility and modulates its ability to interact with diverse molecular partners. Full article

The global transition from petrochemical to sustainable bio-based plastics has been strongly supported by electrospinning (ES), a versatile nanotechnology enabling the fabrication of ultrathin fibers with multifunctional properties. The solution ES process alongside the uniform fibers, a characteristic “beads-on-string” morphology, consisting of alternating cylindrical and spindle-like segments, is frequently observed. Once considered undesirable, these structures are now recognized as functional fibrous architectures with enhanced properties. This work explores the valorization of beaded fibers through combined experimental characterization and modeling, aiming to evaluate the impact of beading on drug diffusion and delivery performance. Poly(3-hydroxybutyrate) (PHB) was selected as the model biopolyester and dipyridamole (DPD) as the model drug. Ultrathin fibers were fabricated using the laboratory electrospinning device, EFV-1 (ICP, Moscow, Russia). The distance between the capillary nozzle and the anodic collector was set to 180 mm, with the capillary tip radius equal to 0.35 mm, and applied voltage between the electrodes was kept constant at 18 kV. Drug release profiles were obtained by simulating DPD diffusion in ellipsoidal (beads) and cylindrical fiber domains. Ultrathin fibers were fabricated by solution electrospinning under environmental conditions (at ambient temperature, 50% relative humidity). Morphology was analyzed via SEM, thermal properties via DSC, and structure via FTIR spectroscopy at different temperatures, including the melting point (~170 °C). Drug release kinetics were monitored using a UV-Vis spectroscopy. The impact of DPD diffusion within the ellipsoidal and cylindrical constituents of polymer filaments was considered to modulate release profiles for the development of innovative pharmaceutical platforms. Diffusion controlled drug release was computationally modeled using a specially designed simulation program, in good agreement with experimental data. The results demonstrate that morphological parameters significantly affect diffusion and release kinetics. The controlled exploitation of bead-on-string architectures may enable the design of electrospun materials with tunable absorption of pollutant filtration, mechanical performance, and flexibility in drug release profiles, for sustainable biopolymers like PHB. Full article

Objective: Alongside evaluating the geometric and dosimetric performance of an AI auto-segmentation algorithm on conventional (primary) organs at risk (OARs), the aim of this work was to highlight its added advantages in terms of the dosimetric evaluation of secondary OARs, which are commonly under-reported despite their influence on overall toxicity. Methods: The study included 50 head and neck cancer patients, with volumetric modulated arc radiotherapy (VMAT) plans created based on both manual contouring and auto-contouring using a commercially available auto-segmentation tool. Quantitative assessment of auto-contouring was undertaken on 10 primary OARs using geometric performance metrics (the Dice Similarity Coefficient (DSC), Hausdorff distance (HD), sensitivity, and precision) as well as dosimetric differences between manual vs. auto-segmentation. Once the algorithm was validated on primary OARs, a dosimetric assessment of 29 secondary OARs delineated by the tool was conducted. Results: Manual contouring required 21.20 ± 2.25 min, while 9.25 ± 1.42 min were needed for auto-segmentation with adjustment. Dosimetric differences were found for the brainstem and the right submandibular gland. The mandible presented the maximum HD value of 24.09 ± 18.83 mm. Sensitivity, precision and DSC ranged from 0.77 to 0.93. For the 29 secondary OARs, 1500 dosimetric values were collected. Of these, 8.5% exceeded the dose constraints. The most impacted OAR was the constrictor muscle, exceeding the constraint in 56% of cases, while the glottis came in second. The dosimetric results also raised concerns regarding organs without defined dose constraints. Conclusions: When validated for primary OARs, auto-segmentation offers a more comprehensive dosimetric evaluation of secondary OARs to further reduce the dose to sensitive structures. Full article

Cerebrovascular diseases, including stroke and intracranial aneurysm, affect millions worldwide and remain a leading cause of mortality and disability. Time-of-Flight Magnetic Resonance Angiography (TOF-MRA) enables non-invasive visualization of intracranial arteries. However, the complex cerebrovascular anatomy, characterized by variable diameters, tortuous trajectories, and intricate branching, renders manual segmentation time-consuming, subjective, and prone to inter-observer variability. While deep learning models achieve strong segmentation performance, existing 3D approaches typically require millions of parameters, limiting deployment in resource-constrained clinical settings. To address this challenge, this paper proposes a Lightweight Intracranial Vascular Segmentation Network (LIVAS-Net), a parameter-efficient 3D encoder–decoder architecture using 3D Ghost convolution modules. It incorporates a novel Vessel Continuity Refinement Branch (VCRB), which aims to correct discontinuities in logit space through per-voxel learnable gating. Two model variants are introduced, LIVAS-Net (129K parameters, 18.3 GFLOPs) and LIVAS-L Net (2.97M parameters, 87.8 GFLOPs), achieving 7.9× and 1.6× fewer FLOPs than the standard 3D U-Net (144.5 GFLOPs), respectively. Evaluation on the multi-center COSTA benchmark shows a DSC of 0.8943 (HD95: 1.97 mm) and 0.9235 (HD95: 0.77 mm) on the ADAM test set, outperforming 3D U-Net (DSC: 0.8762). Cross-center evaluation on three external COSTA datasets yields overall DSCs of 0.7834 and 0.7967 versus 0.6998 for 3D UNet. Further evaluation on the CereVessMRA dataset (N = 271) reveals that LIVAS-Net achieves the highest DSC (0.669), demonstrating promising experimental results warranting future clinical validation in resource-constrained settings. Full article

Objectives: Curcumin possesses well-documented anticancer activity; however, its clinical translation is hindered by poor aqueous solubility and limited bioavailability. The present study aimed to engineer pH-dependent bovine serum albumin (BSA)–based nanocarriers for curcumin delivery and to evaluate their physicochemical characteristics, controlled release behavior under gastrointestinal pH conditions, and in vitro anticancer efficacy against the human colon cancer cell line Colo-205. Methods: Curcumin-loaded bovine serum albumin nanoparticles (Cu-BSA-NPs) were fabricated using a desolvation technique followed by chemical crosslinking. Particle size, zeta potential, and polydispersity index (PDI) were assessed by dynamic light scattering. Morphology was examined using scanning electron microscopy (SEM), while structural and thermal properties were evaluated by Fourier-transform infrared spectroscopy (FTIR) and differential scanning calorimetry (DSC). Drug loading capacity and entrapment efficiency were quantified spectrophotometrically. In vitro drug release was investigated using a gastrointestinal pH-transition model (pH 1.2, 6.8, and 7.4). Cytotoxic activity was assessed using the sulforhodamine B (SRB) assay on Colo-205 cells. Results: The engineered Cu-BSA-NPs exhibited particle sizes ranging from 96.7 ± 10.5 to 126.4 ± 35.8 nm, with PDI values between 0.289 and 0.581 and zeta potentials from −18.2 ± 1.01 to −34 ± 1.0 mV, indicating nanoscale dimensions and moderate colloidal stability. SEM analysis revealed spherical nanoparticles with smooth surfaces and uniform morphology. Entrapment efficiency ranged from 6.59 ± 1.11% to 52.98 ± 0.65%, while drug loading efficiency varied between 1.308 ± 0.206% and 16.744 ± 0.266%. In vitro release studies demonstrated minimal drug release under acidic (pH 1.2) and near-neutral (pH 6.8) conditions, followed by significantly enhanced release at pH 7.4, confirming pH-dependent behavior of the albumin matrix. Cytotoxicity studies showed significant antiproliferative activity against Colo-205 human colon cancer cells. Conclusions: The findings demonstrate successful engineering of albumin-based nanocarriers capable of modulating curcumin release under physiologically relevant pH conditions and enhancing in vitro anticancer activity. Although limited to in vitro evaluation, this study highlights the potential of protein-based nanoplatforms as adaptable delivery systems for colon cancer therapy. Further in vivo investigations are warranted to validate their translational and therapeutic potential. Full article

Maxillofacial defects impair facial aesthetics and oral function, arising from trauma, tumor resection, or congenital anomalies; however, reconstruction using Computer-Aided Design (CAD) and autologous grafts remains complex and time-intensive, and is associated with donor-site morbidity. Although deep learning (DL) has advanced automated reconstruction, existing models often address isolated tasks, lack integrated multi-scale feature learning, and rely on small datasets. This study proposes the Maxillofacial Implant-generation Network (MaxI-Net), a fast, resource-efficient three-dimensional DL framework for end-to-end maxillofacial defect reconstruction and patient-specific implant generation, with a completion step of cavity filling within the assembly. The model employs a 3D encoder–bottleneck-decoder architecture integrating hybrid dilated convolutions, residual connections, squeeze-and-excitation (SE) blocks, and 3D Convolutional Block Attention Modules (CBAM) with multi-scale feature fusion. It was trained on 921 Cone Beam-Computed Tomography (CBCT) scans, augmented to 11,973 maxillary defect pairs, using Dice loss and Adam optimisation with Automatic Mixed Precision, and benchmarked against UNet, UNETR, SegResNet, and SwinUNETR. MaxI-Net achieved the following: superior Dice Similarity Coefficient (DSC) = 0.778; 95th percentile Hausdorff Distance (HD95) = 3.453 mm; DSC Standard Deviation (SD) = 0.094; 95% confidence interval (CI) for mean DSC: 0.775–0.782). It was statistically validated against all competing architectures via pairwise Wilcoxon signed-rank tests, with significant DSC improvements confirmed across all comparators (p < 0.001) and rank-biserial effect sizes ranging from r = 0.250 against the closest competitor SegResNet* with high efficiency (0.06 s/volume; 9.6 min/epoch). Internal cavity filling of the generated implants was performed as a brief manual post-processing step in Autodesk Fusion 360 prior to biomechanical validation. Biomechanical validation using a finite element analysis (FEA) of polyether–ether–ketone (PEEK) implants (~26.53 g) showed 41% stress reduction under physiological loads (100–400 N), predicting a ~9.2-year lifespan. Full article

Accurate multi-class segmentation of the prostate in T2-weighted magnetic resonance imaging (MRI) into the peripheral zone (PZ), central gland (CG) and tumour is essential for targeted biopsy guidance and treatment planning. We present DSBANet, an encoder–decoder architecture that combines a pretrained ResNet-50 encoder, Atrous Spatial Pyramid Pooling, Multi-Scale Attention Fusion on skip connections, a Feature Fusion Module, deep supervision and boundary refinement. We evaluate eight architectures across three input dimensionalities (2D, 2.5D, 3D), yielding 24 models trained under identical conditions on the Prostate158 dataset. DSBANet achieves the best anatomy segmentation with PZ DSC of 0.8176 and CG DSC of 0.7888 among 2D models. To address the severe class imbalance of the tumour class, we further train DSBANet 2D with a class-weighted cross-entropy term and tumour-positive slice oversampling, raising per-case tumour DSC from 0.003 to 0.170 (a sixty-fold absolute improvement). A systematic eight-variant ablation study, evaluated under matched-pairs effect-size analysis, identifies the SE-Residual blocks and skip-connection attention as the largest contributors to tumour segmentation, while every architectural component contributes a directionally consistent gain. Full article

Background: Cardiac magnetic resonance (MRI) images often exhibit low contrast, anatomical variability, and indistinct boundaries, particularly in the myocardium (MYO) and right ventricle (RV). These challenges can reduce the reliability of both manual and automated segmentation, highlighting the need for more robust and boundary-aware approaches. Methods: In this study, an Aspect-Aware Boundary-Resilient UNet3D (ABR-UNet3D) architecture is proposed for cardiac MRI segmentation. The model incorporates an Aspect-Aware Complementary Attention (AAC) module that combines multi-planar contextual information with a complementary gating mechanism to enhance boundary representation. The method was evaluated on the ACDC dataset under consistent training conditions. In addition to Dice Similarity Coefficient (DSC) and Intersection over Union (IoU), boundary-based metrics, including the 95th percentile Hausdorff Distance (HD95), Average Surface Distance (ASD), and Surface Dice, were employed. Furthermore, a five-fold cross-validation protocol and detailed ablation studies were conducted to assess robustness and analyze the contribution of individual AAC components. Results: The proposed method achieved a mean DSC of 0.9603 in single-run experiments on the ACDC dataset and showed consistent performance in anatomically challenging regions, particularly for RV and MYO segmentation. In addition, five-fold cross-validation experiments resulted in an average DSC of 0.952 ± 0.009 and IoU of 0.908 ± 0.012, indicating stable performance across different data splits within the evaluated dataset. Boundary-based metrics also showed improved surface agreement and lower boundary errors compared with the evaluated baseline models. Ablation studies further indicated that the combined use of multi-planar contextual information and complementary gating contributes more effectively to segmentation performance than the individual components used separately. Conclusions: The results suggest that the proposed ABR-UNet3D architecture provides a stable and competitive segmentation framework for cardiac MRI images within the scope of the ACDC dataset. By jointly modeling contextual information and boundary refinement, the method improves segmentation reliability in challenging regions while maintaining competitive and consistent performance with respect to existing approaches. Full article

In this study, single-helical maize amylose (SHMAM) was successfully prepared via the sodium chloride-based eutectic solvent method. Incorporation of SHMAM into wheat flour for steamed buns significantly enhanced its hardness, with a 5% addition level yielding the maximum effect (hardness increased from 2318.7 ± 157.4 g to 3224.7 ± 98.1 g). Comprehensive structural characterization including FT-IR, XRD, DSC and ¹³C solid-state NMR revealed that during steaming hydrogen bonds formed between the C6 hydroxyl groups of SHMAM and sulfhydryl groups of Cys, α-amino groups of Lys, phenolic hydroxyl groups of Tyr, and ε-amino groups of Arg in glutenin. These interactions induced the conversion of β-sheets into α-helices and β-turns. As a result, a denser, more mechanically robust glutenin–starch network was formed, accompanied by a decreased water-holding capacity of glutenin and restricted interfacial water mobility between starch and glutenin phases. Collectively, these synergistic interactions enhanced dough compactness, stabilized the microstructural integrity of the dough matrix, and improved the hardness of the final steamed bun. This work establishes a novel, green, and scalable strategy for precisely modulating steamed bun texture, with broad implications for quality optimization in traditional wheat-based foods and potential benefits for dietary health. Full article

Convolutional Neural Networks (CNNs) excel at extracting local features, but due to their restricted receptive fields, they often struggle to capture large-scale global context. Transformers leverage self-attention mechanisms to facilitate global interactions, yet the computational cost of standard self-attention scales quadratically with image resolution. To overcome these limitations, we propose MFDA-UNet, which adopts a hybrid architecture of convolution and linear attention for synergistic feature processing. To fully leverage their respective strengths, we design the Mamba-inspired Frequency-Decoupled Attention (MFDA) block. Through frequency decoupling, this block utilizes convolutions to process high-frequency local information, while employing linear attention to model the long-range dependencies of low-frequency global information. To enhance the feature representation capability of linear attention, we construct the Mamba-Enhanced Linear Attention (MELA) block. Inspired by MILA, this block injects Positional Encoding to substitute the forget gate functionality of Mamba and integrates the Mamba block structure into the linear attention mechanism. This design effectively strengthens representational power, accomplishing long-range dependency modeling with highly efficient linear complexity. Furthermore, we introduce the Gated Cross-Scale Attention (GCSA) module to optimize traditional skip connections. It aggregates features via cross-scale linear attention and incorporates Mamba’s high-performance gating mechanism for adaptive feature filtering, achieving precise feature fusion and selection. We conducted extensive experiments on four multi-modal benchmarks: ISIC 2017, ISIC 2018, Synapse, and ACDC. MFDA-UNet achieved improvements in the DSC by 0.44%, 0.15%, 0.53%, and 0.84% across the respective datasets compared to the second-best models. By capturing local and global multi-scale semantics with relatively low computational overhead, MFDA-UNet provides an efficient and robust solution for medical image segmentation. Full article

This study investigates the hydrothermal modification of commercial titanium dioxide (TiO₂) in the presence of a natural licorice root extract (Glycyrrhiza glabra L.), serving as a stabilizing and growth-modulating agent. The experimental framework combines hydrothermal treatment in a Teflon-lined autoclave with subsequent thermal calcination to elucidate the structural, morphological, and chemical evolution of the material. The plant-based extract significantly influences particle assembly during synthesis, fostering the formation of an initial organic–inorganic hybrid system that results in enhanced morphological homogeneity compared to pristine TiO₂. Thermal analyses (TGA and DSC) demonstrated the progressive decomposition of the organic components with increasing temperature, yielding a thermally stable, predominantly inorganic material at 600 °C. Scanning Electron Microscopy (SEM) observations confirmed a more uniform particle distribution in the modified samples. X-ray diffraction (XRD) patterns corroborated that the primary crystalline phase of TiO₂ remains intact across all conditions, with structural variations limited to peak definition and long-range organization. Furthermore, FTIR spectroscopy supported the preservation of characteristic TiO₂ vibrational features while indicating a gradual depletion of weakly bound surface species following thermal treatment. In conclusion, these findings demonstrate that natural extracts can effectively function as growth-modulating agents, steering material organization without altering its intrinsic chemical properties. This approach aligns with the principles of Green Chemistry and the circular economy, highlighting the potential of renewable plant-based resources as functional additives for the sustainable processing of inorganic materials. Rather than seeking to outperform commercial benchmarks, this work establishes a viable and low-environmental-impact strategy for morphological and structural modulation. Full article

This work presents a correlation-based framework for comparative assessment of single-phase forced convection of binary hydrofluoroether/ethyl acetate (HFE/EA) mixtures in rectangular minichannels. Density, kinematic viscosity, and thermal conductivity were measured at 293.1, 313.1, and 328.1 K for selected compositions of HFE-7100/EA, HFE-7300/EA, and HFE-73DE/EA. Because several DSC-derived mixture-specific heat values were not sufficiently reliable for direct use, the mixture-specific heat capacity was estimated from literature-supported pure-component values using an ideal-mixture, mass-fraction-weighted approximation and used only for evaluation of the Prandtl number. Heat transfer was then assessed using the Sieder–Tate correlation for laminar thermally developing flow in two representative minichannel geometries. The highest predicted values for the HFE-7100/EA and HFE-7300/EA families were obtained for the most EA-rich retained compositions, whereas in the retained HFE-73DE/EA subset, the 50/50 mixture performed best because of its higher thermal conductivity. Validation against an experimental dataset for pure HFE-7100 in the short module showed systematic overprediction, with a mean relative difference of −13.44% and a MAPE of 15.6%. The calculated values should, therefore, be used for relative comparison rather than treated as unbiased absolute predictions. Full article