Search Results (6,890)

Background/Objectives: Curcumin shows broad anticancer activity but limited clinical success as a standalone agent because of poor bioavailability and inconsistent tumor exposure. This review introduces the concept of curcumin as a molecular synergy amplifier and proposes that successful combinations depend on three interdependent determinants: mechanistic complementarity, suppression of adaptive resistance networks, and pharmacokinetic synchronization. Methods: Evidence on combinations with chemotherapeutics, natural bioactives, and nanotechnology-enabled delivery systems was critically evaluated, with emphasis on mechanism, resistance reversal, drug ratio, administration sequence, and tumor exposure. Results: Curcumin enhances therapeutic efficacy by sensitizing cancer cells, suppressing adaptive resistance pathways, targeting cancer stemness, and promoting multiple forms of programmed cell death. Importantly, analysis of current evidence indicates that therapeutic success depends not only on molecular synergy but also on pharmacokinetic synchronization between curcumin and partner agents. Many combinations demonstrating strong in vitro synergy fail to translate in vivo because optimal drug ratios, timing, and tumor exposure cannot be maintained. Nanotechnology-based co-delivery systems partially overcome these limitations through synchronized delivery and controlled release. Conclusions: Curcumin should be viewed as a molecular synergy amplifier whose clinical utility depends on mechanistic complementarity and pharmacokinetic synchronization with co-administered therapies. This framework provides a rationale for the design of next-generation curcumin-based combination therapies and identifies key priorities for clinical translation. Full article

Cartilage oligomeric matrix protein (COMP) influences extracellular matrix remodeling. We investigated its clinical, prognostic, and immunomodulatory significance in colorectal cancer (CRC). COMP was quantified via ELISA in 107 paired CRC and normal tissues. Expression was correlated with clinicopathological features, mutational profiles, microsatellite instability (MSI), tumor-infiltrating lymphocytes (TILs), immune checkpoints, and multiplex cytokine networks. For transcriptomic validation, the FieldEffectCrc dataset was used for Gene Set Enrichment Analysis (GSEA), and The Cancer Genome Atlas (TCGA) CRC cohort for survival analysis. COMP was significantly upregulated in CRC tissues (p < 0.001) and correlated with advanced T, N, and overall pathological stages (all p < 0.05, tau = 0.18, 0.21, and 0.23, respectively). High COMP expression was linked to restricted immune infiltration (reduced stromal TILs, p < 0.05, tau = −0.23), elevated levels in microsatellite stable (MSS) compared to MSI tumors (p < 0.01), and correlated positively with immune exhaustion markers (T-cell immunoglobulin and mucin-domain containing-3 (TIM-3), galectin-9 (GAL9), sialic acid-binding Ig-like lectin 9 (SIGLEC9)). Transcriptomic data linked high COMP to worse disease-specific and progression-free survival, and enrichment in pro-tumorigenic pathways (epithelial-to-mesenchymal transition, angiogenesis, IL-6 signaling). COMP upregulation defines an immunosuppressive microenvironment in CRC, particularly in MSS tumors. It represents an important prognostic biomarker and potential therapeutic target for overcoming immunotherapy resistance. Full article

In this study, five Fe-rich scoria-based glass compositions were prepared using natural scoria, recycled glass cullet, limestone, and magnesite through the melt-quenching technique at a temperature of 1400 °C for 2 h. The effect of Fe₂O₃ content (2.9–14.5 wt%) on the thermal behavior, crystallization, density, and predicted optical properties of glass was investigated. Differential thermal analysis revealed that increasing Fe₂O₃ content leads to a variation in glass transition (Tg = 632–669 °C) and an increase in softening temperatures (Ts = 711–737 °C), accompanied by an expanded thermal stability window (∆T = Tx − Tg) up to 254 °C, indicating enhanced resistance to crystallization and improved thermal stability. The density measurement showed a non-monotonic variation with composition, due to the combined effect of Fe₂O₃ enrichment and network structural modification. The crystallization behavior of the Fe-rich scoria-based glass (H50) was further studied after heat treatment at 900 °C and at 950 °C using XRD and SEM analysis. The heated samples exhibited the formation of crystalline phases including diopside, gehlenite, wollastonite, maghemite, and anorthite. While SEM observation revealed progressive crystal growth and microstructural densification with increasing heat treatment temperature, indicating the transformation from glass to glass–ceramic. In addition, a semi-empirical optical assessment based on literature-derived models suggested increased absorptance from 97.26% to 98.83% and reduced reflectance with increasing Fe₂O₃ content. However, these optical parameters show theoretical estimates and require experimental validation. These findings demonstrate the potential of Fe-rich scoria-based glasses as thermally stable materials for high-temperature and energy-related applications while using natural and industrial waste sources. Full article

The concentration of electric fields at the end region of stator bars in large generators can readily induce corona discharge. Under long-term operation, corona discharge may cause drift in the surface conductivity and nonlinear coefficient of anti-corona materials, thereby weakening their capability to homogenize the tangential electric field. In severe cases, this can lead to charring failure of the anti-corona material. To improve the electrical-parameter stability and surface morphological resistance to corona aging of silicon carbide (SiC)-based anti-corona materials under long-term corona exposure, epoxy-resin-based anti-corona materials were investigated in this study. Scanning electron microscopy (SEM) and Fourier-transform infrared spectroscopy (FTIR) were first employed to analyze the effects of corona aging on the microstructure and chemical structure of the anti-corona layer, thereby revealing its failure mechanism. Subsequently, the evolution of surface conductivity, nonlinear coefficient, and surface morphology of bisphenol A epoxy resin (EP)- and hydrogenated bisphenol A epoxy resin (H-EP)-based anti-corona materials during 120 h of corona aging was comparatively investigated. On this basis, different mass fractions of organically modified montmorillonite (OMMT) were introduced into the H-EP-based anti-corona material for synergistic modification. The OMMT used in this study had a particle size of approximately 5 μm and an interlayer spacing of 2.6 nm, and its lamellar morphology and dispersion state in the epoxy matrix were characterized by cross-sectional SEM. Meanwhile, the trap-regulation mechanism of the OMMT-modified anti-corona materials was analyzed using isothermal surface potential decay (ISPD). The results show that erosion of the epoxy resin matrix by corona discharge is the primary cause of internal conductive-pathway disruption and anti-corona layer failure. Compared with the EP-based material, the H-EP-based material exhibited better conductivity and nonlinear stability during aging, although a certain degree of drift still occurred. The incorporation of an appropriate amount of OMMT further improved the corona resistance of the material. Among the investigated samples, the material containing 1 wt% OMMT showed the best performance, with its conductivity stabilized within the range of 10^−13–10⁻¹¹ S, the lowest variation rate of 104.76%, a relatively stable nonlinear coefficient, and slight surface damage. The ISPD results indicate that the interfaces introduced by OMMT increase the deep-trap density and suppress carrier migration, thereby stabilizing the conductive network. Overall, the synergistic effect of the H-EP matrix and 1 wt% OMMT can effectively enhance the corona resistance of SiC-based anti-corona materials. Full article

Cannabis sativa L. is an economically significant multi-use crop valued for fiber, seed, and phytochemical production. Compared with other crops, advancement in Cannabis sativa has been slow due to regulatory constraints and genetic resource limitations. Recent advances in technology have transformed the research landscape, supporting a deeper understanding of the genetic architecture underlying key agronomic traits. This review summarizes current progress in Cannabis sativa genetics and genomics, mainly focusing on structural genome organization, including chromosome-level assemblies and emerging pangenomic resources that capture species-wide diversity. We explore the molecular basis of key agronomic traits, including sex determination, cannabinoid biosynthesis, fiber quality, seed composition, disease resistance, and abiotic stress tolerance, highlighting their complex regulatory networks. Functional genomics tools including virus-induced gene silencing, transient expression systems, and CRISPR/Cas9 genome editing are reviewed as approaches enabling direct gene functional validation. We further review integration of these resources with molecular breeding strategies, including marker-assisted and genomic selection, to accelerate elite genotype development. Finally, we address persistent challenges such as genomic complexity, reference bias, and phenotyping limitations while outlining future research directions. Together, these advances position C. sativa as a compelling system for both fundamental plant biology and applied crop improvement. Full article

This study investigates the thermodielectric properties of polyurethane-based microcomposites filled with hybrid microfiller systems based on combinations of wurtzite boron nitride (wBN), aluminium nitride (AlN), and aluminium hydroxide (Al(OH)${}_3$). The dielectric properties (${\epsilon }_r$, $tan\delta$) in the mHz–MHz frequency range and the effective thermal conductivity ($k_{\mathrm{eff}}$) were experimentally characterised for filler loadings up to 40 wt.%. The hybrid systems (wBN+AlN, wBN+Al(OH)${}_3$, and AlN+Al(OH)${}_3$) yielded thermal conductivities in the range of 0.40–0.50 W·m${}^{-1}$·K${}^{-1}$ at 40 wt.% total loading ($0.19\le \varphi \le 0.23$), showing modest synergistic enhancement and remaining within the quasi-linear regime of the Nan model. These results demonstrate that the overall thermal transport in the composites depends far more on the formation of particle percolation networks than on the intrinsic thermal conductivity of the individual fillers, even when accounting for Kapitza interfacial resistance, as confirmed by simulations. Importantly, even at high filler loadings, the electrical insulation properties remain suitable for highly energy-dense applications in electric aircraft. In particular, $tan\delta$ values are comparable to or better than those of unfilled polyurethane, while dielectric strength results lie within the industrially relevant range. Full article

Accurate fault location is essential for improving the reliability and service restoration capability of distribution networks. With the increasing penetration of distributed generation, power electronic devices, and flexible loads, fault transient signals become increasingly nonlinear and nonstationary, posing challenges to conventional impedance-based, traveling-wave-based, and feature-engineering-based methods. To improve transient fault feature representation, this paper proposes a wavelet-embedded residual attention convolutional neural network (CNN) for distribution network fault location. The task is formulated as a multi-class classification problem, in which each predefined line section is treated as a candidate fault location class. The proposed method embeds discrete wavelet decomposition into the convolutional feature extraction process, enabling low-frequency trend components and high-frequency transient components to be jointly represented and fused by subsequent trainable network modules. Residual connections improve deep feature propagation, and an attention mechanism enhances fault-sensitive representations. Simulation studies on the IEEE 33-bus distribution system show that the proposed method outperforms multi-layer perceptron (MLP), support vector machine (SVM), standard CNN, ResNet, and Attention-CNN, achieving 98.27% accuracy and a 98.33% F1-score. The class-wise results and robustness tests under different transition resistances, noise levels, and fault types further verify the effectiveness and adaptability of the proposed method. Full article

In engineering applications such as filling and slope protection, natural river sand suffers from high compressibility and poor erosion resistance. Microbially induced calcium carbonate precipitation (MICP) can mitigate these issues by sand solidification, but the optimal volumetric ratio of bacterial solution to cementation solution ($r_v$) for natural river sand remains unclear. This study used natural river sand (0.063–1.6 mm), Bacillus subtilis, and a cementation solution (2 M urea + 2 M ${\mathrm{C}}_{\mathrm{a}}{\mathrm{C}\mathrm{l}}_2$, 1:1). Eight $r_v$ values from 2:5 to 3:1 were tested. Compressibility was evaluated via one-dimensional consolidation tests, and erosion resistance via a slope model. Results show a non-linear “U-shaped” relationship between $r_v$ and compression index ($C_c$). The optimal $r_v$ = 3:2 yields the lowest $C_c$ (0.044). Higher or lower ratios increase $C_c$ to ≥0.064. Microscopy reveals that at $r_v$ = 3:2, a dense, continuous ${\mathrm{C}}_{\mathrm{a}}{\mathrm{C}\mathrm{O}}_3$ network fills pores, whereas excess bacteria cause sparse cementation and too few cause local agglomeration. The optimal ratio reduces erosion modulus by 55.0–57.5% compared to untreated slopes. This work provides a quantitative, eco-friendly optimization strategy for MICP-treated natural river sand, balancing mechanical performance with ecological adaptability (pH within vegetation tolerance). Full article

Semantic communication has emerged as a promising paradigm for next-generation wireless networks, offering substantial efficiency gains by prioritizing the transmission of task-relevant meaning over bit-level accuracy. However, while its benefits in bandwidth reduction and intelligent data representation are well established, its potential to provide intrinsic reconstruction resistance without relying on conventional cryptographic mechanisms remains largely unexplored. This paper investigates whether semantic communication system architectures themselves can contribute to intrinsic reconstruction resistance for image transmission. We propose an autoencoder-based semantic communication framework in which images are encoded into latent representations and transmitted over a wireless channel, with decoding performed using architecture-specific neural networks. Unlike traditional secure communication approaches that depend on encryption, the proposed method leverages architectural uniqueness and representation-level abstraction to limit unauthorized reconstruction. To systematically analyze this, we evaluate eight adversarial scenarios encompassing variations in encoder–decoder architecture and initialization, including both matched (worst-case) and maximum mismatched (best-case) conditions. The system is modeled using a standard Alice–Bob–Mallory framework, where an adversary attempts to reconstruct intercepted semantic representations without full architectural knowledge. Performance is evaluated using peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) for reconstruction quality, alongside semantic accuracy measured via a convolutional neural network (CNN)-based classifier and embedding cosine similarity to assess information leakage. Experimental results demonstrate that architectural mismatches substantially degrade both visual reconstruction and semantic interpretability for unauthorized receivers, while matched configurations enable substantial recovery. It is important to emphasise that the proposed approach does not provide cryptographic confidentiality; rather, it offers architecture-dependent resistance to unauthorised semantic reconstruction under restricted adversarial assumptions. Overall, the results show that semantic communication systems can exhibit intrinsic reconstruction resistance through architecture-dependent latent-space organisation, reducing reliance on additional cryptographic overhead under restricted adversarial assumptions, while also highlighting limitations when adversaries possess full architectural and initialisation knowledge. Full article

To address the low computational efficiency of conventional forward modeling that hinders real-time inversion of borehole resistivity logging data in deviated/horizontal wells through anisotropic formations, this paper presents an adaptive inversion method based on a deep neural network forward surrogate model. A resistivity response database covering deviation angle, mud invasion, and anisotropy is constructed using three-dimensional finite-element forward modeling. The deep neural network architecture is systematically optimized by varying the number of hidden layers and neurons per layer, comparing five activation functions, evaluating four training algorithms, and testing five batch size ratios. The resulting deep learning-based forward model achieves a speedup of over two orders of magnitude compared with 3D finite-element modeling while maintaining high accuracy (maximum relative error < 1%). By integrating this fast forward model with an adaptively modified Levenberg–Marquardt algorithm, rapid inversion in anisotropic formations is realized. Numerical simulations and field data processing demonstrate that the proposed method accurately extracts uninvaded resistivity, invasion depth, and anisotropy coefficient, with an efficiency gain of approximately 98% over traditional approaches. Reconstructed logs show excellent agreement with measured data, providing robust support for real-time evaluation of deviated and horizontal wells. Full article

Background/Objectives: Malignant melanoma is characterized by constitutive PI3K/Akt/mTOR and MAPK activation, driving aggressive behavior and therapeutic resistance. Alpha-lipoic acid (αLA), a naturally occurring dithiol compound with an established clinical safety profile, has shown anticancer potential; however, its integrated molecular mechanisms in melanoma remain poorly defined. This study aimed to comprehensively evaluate the cytotoxic and mechanistic effects of αLA in B16F10 murine melanoma cells. Methods: Antiproliferative effects were assessed by MTT assay at four concentrations (250, 500, 750, 1000 µM) over 48 h. Protein levels of apoptotic markers (Bax, Bcl-2, Caspase-3, AIF), kinase signaling components (p-Akt, p-mTOR, p-ERK, p-JNK), ER stress markers (GRP78, GADD153/CHOP), and cell cycle regulator Wee1 were quantified by ELISA at a specifically selected sub-lethal concentration of 750 µM (inducing ~38% growth inhibition). Results: αLA dose-dependently inhibited B16F10 proliferation. At 750 µM, it triggered robust intrinsic apoptotic signaling, evidenced by a nearly 10-fold shift in the Bax/Bcl-2 ratio and greater than 9-fold Caspase-3 activation. Elevated AIF suggested profound mitochondrial stress and the potential priming of concurrent caspase-independent cell death mechanisms. αLA suppressed survival signaling by reducing p-Akt (44%), p-mTOR, p-ERK, and p-JNK. Treatment triggered lethal ER stress via GRP78 and GADD153/CHOP upregulation and upregulated Wee1, suggesting the induction of stress-responsive checkpoint signaling. The simultaneous CHOP upregulation and p-Akt suppression highlight a concurrent dysregulation of stress and survival pathways, suggesting a potential pro-apoptotic interplay. Conclusions: αLA exerts potent multi-target anticancer effects by inducing a broad spectrum of associated molecular changes, including the suppression of PI3K/Akt/mTOR and MAPK networks, induction of ER stress, engagement of cell cycle checkpoints, and activation of the mitochondrial Bax/Bcl-2/Caspase-3 axis. Importantly, these correlative findings do not establish proven pathway dependencies. Nevertheless, this concurrent dysregulation positions αLA as a potential disruptor of inter-pathway resilience underlying drug resistance. Full article

In the field of optical metrology, wavelength-tunable interferometry is widely used to obtain the phase information of measured objects. Due to the modulo 2π operation, the extracted phase is inherently wrapped into the range of −π to π, which necessitates phase unwrapping to restore the actual phase profile. However, traditional phase-shifting methods suffer from low accuracy caused by phase shift miscalibration, coupling signals, atmospheric turbulence, and measurement noise. To address these issues, this paper proposes a deep learning-based phase-unwrapping method using a deep convolutional neural network, which formulates the unwrapping task as a multiclass classification problem. The proposed method employs an encoder–decoder residual network (ResNet) architecture that treats phase unwrapping as a pixel-wise semantic segmentation task, enabling end-to-end continuous phase reconstruction. It also adopts a 2N − 1 algorithm-based dataset generation strategy that inherently suppresses phase-shift miscalibration and harmonic coupling errors without relying on Zernike polynomial representations. Furthermore, a large-scale data augmentation pipeline (16-fold expansion to 20,992 training samples) endows the network with a strong generalization capability and noise immunity. The quantitative experimental results demonstrate that the proposed method achieves 100% phase-unwrapping accuracy under noise-free conditions and 99.03% accuracy under severe noise (standard deviation = 1.5), substantially outperforming the quality-guided method (QG, 69.87%) and the transport-of-intensity equation method (TIE, 77.53%) under identical conditions. On real interferometric data acquired using a wavelength-tuning interferometer, the proposed method successfully unwraps the phase even under heavy noise where conventional methods fail completely. These results confirm that the proposed method has favorable noise resistance and potential applicability in high-precision optical metrology.. Full article

Cetuximab and panitumumab are anti-EGFR monoclonal antibodies widely used for the treatment of colorectal cancers. However, due to various mechanisms of resistance to these targeted therapies, the patients’ responses vary. These resistances remain a major obstacle in treatment and overcoming them has become a key emphasis of current therapeutic strategies. Intrinsic and acquired resistance often lead to reactivation of downstream signaling pathways, mainly the RAS-RAF-MEK-ERK (MAPK pathway) and PI3K-AKT axes. Prior existing mutations in KRAS, NRAS, and BRAF result in primary resistance by constantly activating the signals, irrespective of EGFR inhibition. That said, acquired resistance manifests under therapeutic burden through the process of clonal evolution via KRAS and BRAF alterations, restoring MAPK pathway activity despite EGFR inhibition. In addition to those mutations, tumor cells exploit mechanisms independent of EGFR, such as the pathway bypass, which includes amplification of ERBB family receptors like HER2 (ERBB2) and activation of MET signaling. To overcome these resistances, novel strategies have emerged, which target multiple nodes within the oncogenic networks. Such methods include vertical pathway inhibition, multi-kinase inhibition, liquid-biopsy-guided therapy, and anti-EGFR rechallenge. Reactivation driven by secondary mutation can be prevented by targeting multiple nodes within the MAPK cascade simultaneously, which is referred to as the vertical pathway inhibition. Overall, this review underscores that overcoming therapeutic resistance requires a multidimensional approach that integrates molecular profiling, rational combination therapies, and adaptive treatment. Finally, these advances underscore the shift toward precision oncology, where therapy is tailored to tumor evolution, leading to improved response and patient outcome. Full article

Calcium (Ca²⁺) functions as a second messenger in plants, coordinating development and stress responses through cytosolic Ca²⁺ dynamics. The P-type Ca²⁺-ATPases of the ECA (P-IIA) and ACA (P-IIB) subfamilies are central to Ca²⁺ homeostasis and signal termination by extruding Ca²⁺ from the cytosol. In this study, genome-wide identification was performed to identify the P-type Ca²⁺-ATPases according to the maize B73 v5 reference genome, followed by phylogenetic, structural, chromosomal, syntenic, network, and expression analyses. Nineteen genes were identified, comprising 4 ECAs and 15 ACAs. All 19 members retained the DKTGT phosphorylation site, while the CaATP_NAI (N- terminal autoinhibitory) extension distinguished all 15 ACAs from the 4 ECAs. Collinearity analysis revealed 11 maize–rice syntenic pairs, implicating segmental duplication. ECAs were preferentially expressed in reproductive tissues, whereas ACAs were broadly expressed across vegetative organs. RNA-seq-based profiling detected distinct stress-responsive expression patterns of Ca²⁺-ATPase genes. Under abiotic stress, ZmACA12-1 was consistently upregulated under drought, ZmACA6-2 dominated the heat response, and ZmACA4-2 showed the broadest cross-stress repression. Under biotic stress, ACA members again dominated, with ZmACA1-1 being the most broadly pathogen-responsive member, ZmECA1-3 the principal ECA-class responder, and ZmACA9 exhibiting consistent pathogen-associated repression. Additionally, ZmACA12-1 and ZmACA4-4 showed genotype-dependent regulation between resistant and susceptible lines. Collectively, these candidates represent priority targets for functional validation of the calcium efflux mechanisms that underlie maize adaptation to both abiotic and biotic stresses. Full article

Fast and accurate resistance prediction is critical in early-stage ship design. While Michell’s thin-ship theory provides rapid evaluations, its linear assumptions limit accuracy, particularly as hull forms deviate from ideal slenderness. This paper introduces a physics-preserving neural calibration method that improves Michell’s theory without replacing the underlying solver. We train a two-dimensional convolutional encoder–decoder, conditioned on Froude numbers via global FiLM modulation, to predict a bounded correction to the geometric effective-slope field. Because the solver remains unchanged, the learned correction acts as an interpretable spatial perturbation rather than a black-box resistance map. Evaluated under a strict leave-one-family-out (LOFO) protocol on a fleet of five slender hull families (DTMB, NPL-4A, Wide-Light Canoe, Wigley, and Delft 372), the neural calibration achieves a mean absolute percentage error (MAPE) of 0.0741. This represents a 24% improvement over a reproduced 2020 baseline and a 7.9% improvement over the uncorrected Michell solver. The 2020 baseline is the rigid boundary-layer and phase-deflection correction of an earlier study by the present group, re-evaluated here on the present hulls at their measured attitudes. Ablation studies show that much of this aggregate gain is captured by a bounded global slope offset, indicating that a spatially uniform displacement correction accounts for most of the improvement on slender hulls, while the spatially varying field mainly adds per-family headroom. Finally, we map the physical boundaries of this approach. Dedicated recovery campaigns on fuller forms (KCS and Series 60) show that the model regresses compared to baselines. This confirms that while the correction successfully refines the linear source distribution for slender hulls, it cannot synthesize missing physics, such as stagnation pressure, separated flow, or wave interference, for fuller or unrelated geometries. Full article