Search Results (310)

One of the approaches to increase bioavailability and stability of hydrophobic biologically active compounds is their incorporation into polymer backbone. This work deals with the modification of chitosan (CS) with gossypol (GS), a phenolic compound with confirmed anticancer properties, by the free-radical grafting method. The series of the CS derivatives with increasing content of GS were prepared using pure GS or gossypol acetate (GSA) and compared to the control CS (cCS). The starting CS, cCS, and GS-containing derivatives were characterized using Fourier-transform infrared (FTIR), Raman, and ¹³C ssNMR spectroscopies; elemental and thermogravimetric analysis to evaluate the influence of the radicals and GS on the properties of polymers was performed. The Folin–Ciocalteu (F-C) and 2,2-diphenyl-1-picrylhydrazyl (DPPH) methods were used to evaluate antioxidant properties of GS-modified CSs. Additionally, the polymer solubility and the specific viscosity of the solutions were determined. The content of GS in polymers raised proportionally with increasing amount of GS added to the reaction mixture, thereby enhancing the ability to scavenge free radicals. The type of GS used (GS or GSA) in polymers affected the degree of CS crosslinking (higher for pure GS), polymer solubility (lower for pure GS), the amount of grafted GS (~20% higher for GSA), and antioxidant properties in favor of GSA. Full article

Cyclo[48]carbon (C48) exhibits an aesthetically pleasant structure featuring a cyclic polyyne, and it serves as a prototypical medium-sized ring that moves us towards an understanding of its overall magnetic behavior in a challenging molecular shape through analysis of its induced magnetic field. The isotropic induced magnetic field (NICS) profile shows a strong deshielding region at the ring center and a shielding region near the carbon rim, indicating antiaromatic behavior. Under a perpendicular magnetic field, a pronounced deshielding cone extends from the ring center, whereas a parallel external field induces a localized shielding near the carbon backbone. This results in significant magnetic anisotropy above and below the ring plane, characteristic of its medium-sized cyclic structure. Decomposition of the magnetic shielding highlights that paramagnetic effects predominantly govern the magnetic response and anisotropy of C48, with diamagnetic contributions playing a minor role. These insights suggest that chemical modifications targeting frontier orbitals could effectively tune the magnetic properties of cyclo[48]carbon, providing a foundation for the design of substituted derivatives with tailored diamagnetic anisotropy for advanced material applications. Full article

Advanced deception countermeasures now enable adversaries to inject false targets into synthetic-aperture-radar (SAR) imagery, generating electromagnetic signatures virtually indistinguishable from genuine targets, thus destroying the separability essential for conventional recognition algorithms. To address this problem, we propose a versatile front-end Multi-Feature Extraction and Spatial Transformation Network (MFE-STN), specifically designed for the task of discriminating between true targets and deceptive false targets created by SAR jamming, which can be seamlessly integrated with existing CNN backbones without architecture modification. MFE-STN integrates three complementary operations: (i) wavelet decomposition to extract the overall geometric features and scattering distribution of the target, (ii) a manifold transformation module for non-linear alignment of heterogeneous feature spaces, and (iii) a lightweight deformable spatial transformer that compensates for local geometric distortions introduced by deceptive jamming. By analyzing seven typical parameter-mismatch effects, we construct a simulated dataset containing six representative classes—four known classes and two unseen classes. Experimental results demonstrate that inserting MFE-STN boosts the average F1-score of known targets by 12.19% and significantly improves identification accuracy for unseen targets. This confirms the module’s capability to capture discriminative signatures to distinguish genuine targets from deceptive ones while exhibiting strong cross-domain generalization capabilities. Full article

To overcome the limited mechanical strength and poor stability of conventional gels in high-temperature, high-salinity oilfield environments, a novel nanocellulose-reinforced hydrogel (AM/AA/PCNF) was developed through a multistep chemical modification strategy. Nanocellulose served as a rigid backbone and was successively modified via epoxide ring-opening, methacryloyl esterification, and polydopamine functionalization, forming a three-dimensional network with multiple dynamic crosslinking interactions. The resulting composite hydrogel exhibited outstanding comprehensive properties when the PCNF content was 3 wt%: a tensile strength of 2.6 MPa, fracture energy of 8.95 MJ/m³, and compressive strength of 360 kPa—all markedly superior to those of conventional hydrogel systems. Under simulated downhole conditions (120 °C, 6 MPa, and 5 wt% salinity), the hydrogel demonstrated excellent plugging performance across sand beds of varying particle sizes (60–80 mesh to 20–40 mesh), maintaining cumulative fluid loss within 28.4–42.5 mL. Mechanistic investigations indicate that the enhanced performance stems from the synergistic combination of a rigid nanocellulose scaffold and multiple dynamic interactions, which facilitate a self-adaptive plugging mechanism. The study delivers both theoretical and practical foundations for designing advanced plugging systems. Full article

For a family of 24-atom triazine macrocycles, a single intramolecular hydrogen bond (IMHB) network leads to a conserved, hinge-like motif in solution. Modifications to the backbone of these macrocycles preserve this motif. Modifications to peripheral sites lead to conformational isomers due to hindered bond rotation while conserving the hinge motif. Here, a competitive IMHB network is introduced by the addition of a hydrogen bond donor on the periphery. Cyclization remains quantitative, but multiple conformers result. Three conformers are derived from the hinge motif. Three others are attributed to a new motif that utilizes the new IMHB network. Crystallographic analysis confirms this hypothesis and establishes that this new motif differs significantly from the original with respect to overall shape and disposition of groups. Variable temperature ¹H NMR spectroscopy is used to partially assign the spectra because conformers adopting the hinge motif undergo dynamic motion on the NMR timescale, while the new motif appears static. QTAIM analysis corroborates the hydrogen bond designations in the new conformer and categorizes these interactions as moderate and strong. Full article

Biosynthesis of ribose-5-phosphate (R5P) underlies all biosynthetic processes associated with biomass growth. Actively dividing cells continuously require building blocks for genome replication, synthesis of ribosomes and other derivatives containing R5P as a carbohydrate backbone. The main source of R5P in the cell is the pentose phosphate pathway (PPP), which is an anabolic sensor designed to coordinate the level of pentose phosphates and reduced NADPH required for anabolic processes. This review is devoted to a comparative analysis of R5P biosynthesis pathways among different domains of microorganisms, the features of PPP regulation in bacterial cells depending on physiological conditions, as well as genetic modifications of PPP and their effect on cell viability. We emphasize that ribose metabolism is a factor in the consolidation of cellular homeostasis under conditions of intensive biomass growth and the discrepancy between the processes of ribose synthesis and consumption is marked by spontaneous cell death. Full article

This article develops an interdisciplinary framework that applies topological and graph-theoretical methods to public procurement markets and digital platform economies. Conceptualizing legal–economic interactions as dynamic networks of nodes and edges, we show how structural properties—centrality, clustering, connectivity, and boundary formation—shape contestability, resilience, and compliance. Using EU-relevant contexts (public procurement directives and the Digital Markets Act), we formalize network representations for buyers, suppliers, platforms, and regulators; define operational indicators; and illustrate an empirical, value-weighted buyer → supplier network to reveal a sparse but highly modular architecture with a high-value backbone. We then map these structural signatures to concrete legal levers (lotting and framework design, modification scrutiny, interoperability and data-access duties) and propose dashboard-style diagnostics for proactive oversight. The findings demonstrate how topological modelling complements doctrinal analysis by making hidden architectures visible and by linking measurable structure to regulatory outcomes. We conclude with implications for evidence-informed regulatory design and a research agenda integrating graph analytics, comparative evaluation across jurisdictions, and machine-learning-assisted anomaly detection. Full article

Obesity has become increasingly prevalent, impacting up to 41 percent of women in the United States between 2021 and 2023, leading to a rise in short- and long-term adverse health events. With regard to reproductive health, obesity is associated with menstrual irregularities, poorer reproductive and obstetric outcomes, and an increased risk of endometrial cancer. Obesity can lead to hyperandrogenism and anovulation, which is consistent with polycystic ovarian syndrome (PCOS). The prevalence of obesity is higher in women with PCOS compared to the general population. Although PCOS increases the risk of obesity, not all women with PCOS are obese, and not all women with obesity develop PCOS. However, individuals with both PCOS and obesity often present with a more extreme phenotype, with increased risk of chronic anovulation, glucose intolerance, dyslipidemia, metabolic syndrome, vitamin D deficiency, and decreased fertility. Therefore, weight loss is the backbone of patient management in women with obesity and PCOS, and is associated with improvement in cardiovascular risk, as well as improvement in menstrual cycles, ovulation, and pregnancy rate. Lifestyle modifications are often the first-line intervention, with data supporting low glycemic index diets, including ketogenic and DASH diets, along with vitamin D supplementation to improve hormonal imbalances, insulin sensitivity, and menstrual cycles in those who do not have normal vitamin D levels. Furthermore, with the recent widespread adoption of newer FDA-approved medications for weight loss, including GLP-1 (glucagon-like peptide) receptor agonists, new data are emerging regarding the impact of PCOS and longer-term cardiovascular risk. The treatment of PCOS requires a personalized approach, with consideration of a patient’s reproductive goals, tolerance of risk, and acceptance of behavioral and financial commitments, as well as consideration of other medical comorbidities. This narrative review explores different weight loss treatment options, comparing lifestyle modifications (including diet, physical activity, mindfulness, stress management, and cognitive behavioral training), weight loss medications, and bariatric surgery and their respective impact on PCOS to assist clinicians in guiding their patients towards an effective, individualized intervention. Full article

Background: Swallowing is a complex biomechanical process, and its impairment (dysphagia) poses major health risks for older adults. Current diagnostic methods such as videofluoroscopic swallowing (VFSS) and fiberoptic endoscopic evaluation of swallowing (FEES) are effective but invasive, resource-intensive, and unsuitable for continuous monitoring. This study proposes a novel end-to-end RGB–D framework for automated swallowing event localization in continuous video streams. Methods: The framework enhances the AdaTAD backbone through three key innovations: (i) finding the optimal strategy to integrate depth information to capture subtle neck movements, (ii) examining the best adapter design for efficient temporal feature adaptation, and (iii) introducing a Kolmogorov–Arnold Network (KAN) decoder that leverages Chebyshev polynomials for non-linear temporal modeling. Evaluation on a proprietary swallowing dataset comprising 641 clips and 3153 annotated events demonstrated the effectiveness of the proposed framework. We analysed and compared the modification strategy across designs of adapters, decoders, input channel combinations, regression methods, and patch embedding techniques. Results: The optimized configuration (VideoMAE + GRNConvNeXtAdapter + KAN + RGD + boundary regression + sinusoidal embedding) achieved an average mAP of 83.25%, significantly surpassing the baseline I3D + RGB + MLP model (61.55%). Ablation studies further confirmed that each architectural component contributed incrementally to the overall improvement. Conclusions: These results establish the feasibility of accurate, non-invasive, and automated swallowing event localization using depth-augmented video. The proposed framework paves the way for practical dysphagia screening and long-term monitoring in clinical and home-care environments. Full article

Pharmacological chaperones of heterogeneous nuclear ribonucleoproteins (hnRNPs) show promise as potential neuroprotective drug candidates. They are expected to prevent the accumulation of neurotoxic hnRNP biocondensates and aggregates, which are hallmarks of severe degenerative diseases. Here, we present the first rational design of oligonucleotide chaperones of hnRNP A1. This design was inspired by previous studies on the specificity of the RNA recognition motif (RRM) and the RGG motif of hnRNP A1 for endogenous nucleic acids. To obtain robust and specific chaperones, we combined an RRM-binding sequence with an RGG-binding G-quadruplex oligonucleotide that inhibits hnRNP A1 aggregation and introduced various modifications into the sugar-phosphate backbone of the oligonucleotide. Modifications that locked the RRM-binding sequence in a conformational state characteristic of RNA improved chaperone affinity and activity. The former was assessed using microscale thermophoresis assays, while the latter was evaluated using fluorimetry and microscopy. The leading chaperone bound to hnRNP A1 at micromolar concentrations and inhibited the assembly of its condensates and amyloid-like aggregates (fibrils) by over 90%. Full article

Oral administration remains the most patient-friendly drug delivery route, yet its efficacy is limited by physiological barriers including gastric degradation and inefficient cellular uptake. pH-responsive nanogels have shown promise for gastrointestinal drug delivery, though their effectiveness is often constrained by poor membrane interaction. Inspired by natural membrane-anchoring mechanisms, a series of comb-like anionic polymers were designed via grafting alkylamines of different chain lengths (C₁₀, C₁₄, C₁₈) at varying densities (10–30%) onto a biodegradable poly(L-lysine isophthalamide) (PLP) backbone. These pH-responsive comb-like polymers self-assembled into nanogels for loading the hydrophobic chemotherapeutic agent camptothecin. The alkyl length and grafting density significantly influenced pH-responsive behavior, membrane disruption, and drug release profiles. The optimal formulation—the nanogel prepared with PLP grafted 30% C₁₄—achieved a high drug-loading capacity, ideal particle size and stability, and offered superior protection in acidic conditions (only 7 ± 5% release at pH 1.2 over 24 h), while enabling rapid intestinal release (78 ± 2% at pH 7.4 within 24 h). The nanogels significantly enhanced cellular uptake, cytoplasmic delivery, and cytotoxicity against colorectal carcinoma cells. This study demonstrates the key role of hydrophobic modification in designing effective oral nanocarriers, providing a promising platform for the treatment of intestinal diseases. Full article

Craters are among the most prominent and significant geomorphological features on the lunar surface. The complex and variable environment of the lunar surface, which is characterized by diverse textures, lighting conditions, and terrain variations, poses significant challenges to existing crater detection methods. To address these challenges, this study introduces DBYOLO, an innovative deep learning framework designed for lunar crater detection, leveraging a dual-backbone feature fusion network, with two key innovations. The first innovation is a lightweight dual-backbone network that processes Lunar Reconnaissance Orbiter Camera (LROC) CCD images and Digital Terrain Model (DTM) data separately, extracting texture and edge features from CCD images and terrain depth features from DTM data. The second innovation is a feature fusion module with attention mechanisms that is used to dynamically integrate multi-source data, enabling the efficient extraction of complementary information from both CCD images and DTM data, enhancing crater detection performance in complex lunar surface environments. Experimental results demonstrate that DBYOLO, with only 3.6 million parameters, achieves a precision of 77.2%, recall of 70.3%, mAP50 of 79.4%, and mAP50-95 of 50.4%, representing improvements of 3.1%, 1.8%, 3.1%, and 2.6%, respectively, over the baseline model before modifications. This showcases an overall performance enhancement, providing a new solution for lunar crater detection and offering significant support for future lunar exploration efforts. Full article

Artificial intelligence (AI) is an important tool for improving agricultural tasks. In particular, object detection methods based on convolutional neural networks (CNNs) enable the detection and classification of objects directly in the field. Combined with unmanned aerial vehicles (UAVs, drones), these methods allow efficient crop monitoring. The primary challenge is to develop models that are both accurate and feasible under real-world conditions. This study addresses this challenge by evaluating marigold flower detection using three groups of CNN detectors: canonical models, including YOLOv2, Faster R-CNN, and SSD with their original backbones; modified versions of these detectors using DarkNet-53; and modern architectures, including YOLOv11, YOLOv12, and the RT-DETR. The dataset consisted of 392 images from marigold fields, which were manually labeled and augmented to a total of 940 images. The results showed that YOLOv2 with DarkNet-53 achieved the best performance, with 98.8% mean average precision (mAP) and 97.9% F1-score (F1). SSD and Faster R-CNN also improved, reaching 63.1% and 52.8%, respectively. Modern models obtained strong results: YOLOv11 and YOLOv12 reached 96–97%, and RT-DETR 93.5%. The modification of YOLOv2 allowed this classical detector to compete directly with, and even surpass, recent models. Precision–recall (PR) curves, F1-scores, and complexity analysis confirmed the trade-offs between accuracy and efficiency. These findings demonstrate that while modern detectors are efficient baselines, classical models with updated backbones can still deliver state-of-the-art results for UAV-based crop monitoring. Full article

The astounding performance of transformers in natural language processing (NLP) has motivated researchers to explore their applications in computer vision tasks. A detection transformer (DETR) introduces transformers to object detection tasks by reframing detection as a set prediction problem. Consequently, it eliminates the need for proposal generation and post-processing steps. Despite competitive performance, DETR initially suffered from slow convergence and poor detection of small objects. However, numerous improvements are proposed to address these issues, leading to substantial improvements, enabling DETR to achieve state-of-the-art performance. To the best of our knowledge, this paper is the first to provide a comprehensive review of 25 recent DETR advancements. We dive into both the foundational modules of DETR and its recent enhancements, such as modifications to the backbone structure, query design strategies, and refinements to attention mechanisms. Moreover, we conduct a comparative analysis across various detection transformers, evaluating their performance and network architectures. We aim for this study to encourage further research in addressing the existing challenges and exploring the application of transformers in the object detection domain. Full article

The design and the synthesis of functional films with enhanced functionality represent a significant step forward in sustainable material development due to their potential applications. In this study, a novel chitosan derivative (CS-MeIm) was synthetized by chemically modifying chitosan (CS) structure with 1-methyl-1H-imidazole (MeIm), a heterocyclic compound known for its biological properties. This functionalization not only enhances the intrinsic capabilities of CS but also provides a strategic platform for advanced material engineering. The modified compound, CS-MeIm, was incorporated at 10 wt% into films based on CS matrix, which was also reinforced with 1 or 5 wt% of chitin nanowhiskers (ChNw), to improve their functionality for its potential applications. The fabrication process was optimized to ensure the homogeneity and the structural integrity of the films, which were extensively evaluated to study their thermal stability, mechanical integrity, and bioactivity. The incorporation of the imidazole ring into the CS backbone provided a marked enhancement in antioxidant capacity from 3 to 15 μmol Trolox/gram of film; and excellent antimicrobial activity against common microbes, particularly against E. coli with an efficacy of 99.999%. The findings reveal that this chemical modification not only raises the intrinsic properties of CS but also introduces a versatile platform for creating biodegradable films with high functionality. Full article