Chlorinated volatile organic compounds (CVOCs) and 1,4-dioxane kinetics and equilibrium adsorption studies on selective macrocyclic adsorbents.

Chlorinated volatile organic compounds (CVOCs) are often found in combination with 1,4-dioxane which has been used as a solvent stabilizer. It would be desirable to separate these compounds since biodegradation of 1,4-dioxane follows an aerobic pathway while anaerobic conditions are needed for biodegrading CVOCs. Conventional adsorbents such as activated carbon (AC) and carbonaceous resins have high adsorption capacities for 1,4-dioxane and CVOCs but lack selectivity, limiting their use for separation (Liu et al., 2019). In the current work, two macrocyclic adsorbents, ß-CD-TFN and Res-TFN, were examined for selective adsorption of chlorinated ethenes in the presence of 1,4-dioxane. Both adsorbents exhibited rapid adsorption of the CVOCs and minimal adsorption of 1,4-dioxane. Res-TFN had a higher adsorption capacity for CVOCs than ß-CD-TFN (measured linear partition coefficient, Kd 2140 -9750 L⋅kg-1 versus 192-918 L⋅kg-1 for 1,1, DCE, cis-1,2-DCE and TCE, respectively) and was highly selective for CVOCs(TCE Kd ~117 Kd for 1,4-dioxane). By comparison, TCE and 1,4-dioxane adsorption on AC was approximately equal at 100 µg⋅L-1 and approximately 1/3 of the adsorption of TCE on the Res-TFN. The greater adsorption and selectivity of Res-TFN suggest that it can be used as a selective adsorbent to separate CVOCs from 1,4-dioxane to allow separate biodegradation.

Novel Gas Sensing Approach: ReS2/Ti3C2Tx Heterostructures for NH3 Detection in Humid Environments.

Continuous monitoring of ammonia (NH3) in humid environments poses a notable challenge for gas sensing applications because of its effect on sensor sensitivity. The present work investigates the detection of NH3 in a natural humid environment utilizing ReS2/Ti3C2Tx heterostructures as a sensing platform. ReS2 nanosheets were vertically grown on the surface of Ti3C2Tx sheets through a hydrothermal synthetic approach, resulting in the formation of ReS2/Ti3C2Tx heterostructures. The structural, morphological, and optical properties of ReS2/Ti3C2Tx were investigated using various state-of-the-art techniques, including scanning electron microscopy, transmission electron microscopy, X-ray diffraction, X-ray photoelectron spectroscopy, zeta potential, Brunauer-Emmett-Teller technique, and Raman spectroscopy. The heterostructures exhibited 1.3- and 8-fold increases in specific surface area compared with ReS2 and Ti3C2Tx, respectively, potentially enhancing the active gas adsorption sites. The electrical investigations of the ReS2/Ti3C2Tx-based sensor demonstrated enhanced selectivity and superior sensing response ranging from 7.8 to 12.4% toward 10 ppm of NH3 within a relative humidity range of 15-85% at room temperature. These findings highlight the synergistic effect of ReS2 and Ti3C2Tx, offering valuable insights for NH3 sensing in environments with high humidity, and are explained in the gas sensing mechanism.

Integration of renewable energy sources using multiport converters for ultra-fast charging stations for electric vehicles: An overview.

The rise of electric vehicles (EVs) necessitates an efficient charging infrastructure capable of delivering a refueling experience akin to conventional vehicles. Innovations in Extreme Fast Charging (EFC) offer promising solutions in this regard. By harnessing renewable energy sources and employing sophisticated multiport converters, EFC systems can meet the evolving demands of EV refueling. A single-stage topology simplifies the converter design, focusing on efficient DC-AC conversion, vital for feeding solar power into the grid or charging stations. It provides power factor correction, harmonics filtering, and mitigates power quality issues, ensuring stable and efficient operations. Converters with Maximum Power Point Tracking (MPPT) capability facilitate the efficient integration of solar PV systems in charging stations, ensuring maximum solar energy utilization for EV charging. The ability to operate in different modes allows seamless integration with energy storage systems, storing excess solar energy for use during night-time or peak demand periods, enhancing overall efficiency and reliability. Advanced converters support bidirectional energy flow, enabling EV batteries to discharge back to the grid, aiding grid stability and energy management. However, robust control algorithms are needed to handle dynamic conditions like partial shading more effectively. Our review focuses on integrating renewable energy sources with multiport converters, providing insights into a novel EV charging station framework optimized for EFC topology. We highlight the advantages of multiport non-isolated converters over traditional line frequency transformers, particularly in medium voltage scenarios, offering enhanced efficiency and versatility for EFC applications.

An Ultrafast Multibit Memory Based on the ReS2/h-BN/Graphene Heterostructure.

The exponential growth of data in the big data era has made it imperative to improve the data storage density and calculation speed. Therefore, the development of a multibit memory with an ultrafast operational speed is of great significance. In this work, a floating-gate (FG) memory based on the ReS2/h-BN/graphene van der Waals heterostructure is reported. The device exhibits ultrafast and multilevel nonvolatile memory characteristics, notably featuring an exceptionally large memory window of 113.36 V, a substantial erasing/programming current ratio of 107, an ultrafast operational speed of 30 ns, outstanding endurance exceeding 1000 cycles, and retention performance exceeding 1100 s. Furthermore, the device exhibits both electrically and optically tunable multilevel nonvolatile memory behavior. By controlling the voltage and light pulse parameters, the device achieves an electrical memory state of 130 levels (>7 bits) and an optical memory state of 45 levels (>5 bits).

Research on sports image classification method based on SE-RES-CNN model.

As computer image processing and digital technologies advance, creating an efficient method for classifying sports images is crucial for the rapid retrieval and management of large image datasets. Traditional manual methods for classifying sports images are impractical for large-scale data and often inaccurate when distinguishing similar images. This paper introduces an SE module that adaptively adjusts the weights of input feature mapping channels, and a Res module that excels in deep feature extraction, preventing gradient vanishing, multi-scale processing, and enhancing generalization in image recognition. Through extensive experimentation on network structure adjustments, the SE-RES-CNN neural network model is applied to sports image classification. The model is trained on a sports image classification dataset from Kaggle, alongside VGG-16 and ResNet50 models. Training results show that the proposed SE-RES-CNN model improves classification accuracy by approximately 5% compared to VGG-16 and ResNet50 models. Testing revealed that the SE-RES-CNN model classifies 100 out of 500 sports images in 6 s, achieving an accuracy rate of up to 98% and a single prediction time of 0.012 s. This validates the model's accuracy and effectiveness, significantly enhancing sports image retrieval and classification efficiency. This validates the model's accuracy and effectiveness, significantly enhancing sports image retrieval and classification efficiency.

Evaluation of 3-O-ß-D-galactosylated resveratrol-loaded polydopamine nanoparticles for hepatocellular carcinoma treatment.

In our previous studies, 3-O-ß-D-galactosylated resveratrol (Gal-Res) was synthesized by structural modification and then 3-O-ß-D-galactosylated resveratrol polydopamine nanoparticles (Gal-Res NPs) were successfully prepared to improve the bioavailability and liver distribution of Res. However, the pharmacodynamic efficacy and specific mechanism of Gal-Res NPs on hepatocellular carcinoma remain unclear. Herein, liver cancer model mice were successfully constructed by xenograft tumor modeling. Gal-Res NPs (34.2 mg/kg) significantly inhibited tumor growth of the liver cancer model mice with no significant effect on their body weight and no obvious toxic effect on major organs. Additionally, in vitro cellular uptake assay showed that Gal-Res NPs (37.5 µmol/L) increased the uptake of Gal-Res by Hepatocellular carcinoma (HepG2) cells, and significantly inhibited the cell migration and invasion. The experimental results of Hoechst 33342/propyl iodide (PI) double staining and flow cytometry both revealed that Gal-Res NPs could remarkably promote cell apoptosis. Moreover, the Western blot results revealed that Gal-Res NPs significantly regulated the Bcl-2/Bax and AKT/GSK3ß/ß-catenin signaling pathways. Taken together, the in vitro/in vivo results demonstrated that Gal-Res NPs significantly improved the antitumor efficiency of Gal-Res, which is a potential antitumor drug delivery system.

Towards Automated Model Selection for Wind Speed and Solar Irradiance Forecasting.

Given the recent increase in demand for electricity, it is necessary for renewable energy sources (RESs) to be widely integrated into power networks, with the two most commonly adopted alternatives being solar and wind power. Nonetheless, there is a significant amount of variation in wind speed and solar irradiance, on both a seasonal and a daily basis, an issue that, in turn, causes a large degree of variation in the amount of solar and wind energy produced. Therefore, RES technology integration into electricity networks is challenging. Accurate forecasting of solar irradiance and wind speed is crucial for the efficient operation of renewable energy power plants, guaranteeing the electricity supply at the most competitive price and preserving the dependability and security of electrical networks. In this research, a variety of different models were evaluated to predict medium-term (24 h ahead) wind speed and solar irradiance based on real-time measurement data relevant to the island of Crete, Greece. Illustrating several preprocessing steps and exploring a collection of "classical" and deep learning algorithms, this analysis highlights their conceptual design and rationale as time series predictors. Concluding the analysis, it discusses the importance of the "features" (intended as "time steps"), showing how it is possible to pinpoint the specific time of the day that most influences the forecast. Aside from producing the most accurate model for the case under examination, the necessity of performing extensive model searches in similar studies is highlighted by the current work.

Incorporating ReS2 Nanosheet into ZnIn2S4 Nanoflower as Synergistic Z-Scheme Photocatalyst for Highly Effective and Stable Visible-Light-Driven Photocatalytic Hydrogen Evolution and Degradation.

Inspired by natural photosynthesis, the visible-light-driven Z-scheme system is very effective and promising for boosting photocatalytic hydrogen production and pollutant degradation. Here, a synergistic Z-scheme photocatalyst is constructed by coupling ReS2 nanosheet and ZnIn2S4 nanoflower and the experimental evidence for this direct Z-scheme heterostructure is provided by X-ray photoelectron spectroscopy, ultraviolet photoelectron spectroscopy, and electron paramagnetic resonance. Consequently, such a unique nanostructure makes this Z-scheme heterostructure exhibit 23.7 times higher photocatalytic hydrogen production than that of ZnIn2S4 nanoflower. Moreover, the ZnIn2S4/ReS2 photocatalyst is also very stable for photocatalytic hydrogen evolution, almost without activity decay even storing for two weeks. Besides, this Z-scheme heterostructure also exhibits superior photocatalytic degradation rates of methylene blue (1.7 × 10-2 min-1) and mitoxantrone (4.2 × 10-3 min-1) than that of ZnIn2S4 photocatalyst. The ultraviolet-visible absorption spectra, transient photocurrent spectra, open-circuit potential measurement, and electrochemical impedance spectroscopy reveal that the superior photocatalytic performance of ZnIn2S4/ReS2 heterostructure is mostly attributed to its broad and strong visible-light absorption, effective separation of charge carrier, and improved redox ability. This work provides a promising nanostructure design of a visible-light-driven Z-scheme heterostructure to simultaneously promote photocatalytic reduction and oxidation activity.

Intelligent breast cancer diagnosis with two-stage using mammogram images.

Breast cancer (BC) significantly contributes to cancer-related mortality in women, underscoring the criticality of early detection for optimal patient outcomes. Mammography is a key tool for identifying and diagnosing breast abnormalities; however, accurately distinguishing malignant mass lesions remains challenging. To address this issue, we propose a novel deep learning approach for BC screening utilizing mammography images. Our proposed model comprises three distinct stages: data collection from established benchmark sources, image segmentation employing an Atrous Convolution-based Attentive and Adaptive Trans-Res-UNet (ACA-ATRUNet) architecture, and BC identification via an Atrous Convolution-based Attentive and Adaptive Multi-scale DenseNet (ACA-AMDN) model. The hyperparameters within the ACA-ATRUNet and ACA-AMDN models are optimized using the Modified Mussel Length-based Eurasian Oystercatcher Optimization (MML-EOO) algorithm. The performance is evaluated using a variety of metrics, and a comparative analysis against conventional methods is presented. Our experimental results reveal that the proposed BC detection framework attains superior precision rates in early disease detection, demonstrating its potential to enhance mammography-based screening methodologies.

Charge self-regulation over in-plane two-dimensional/two-dimensional hetero-cocatalyst for robust photocatalytic hydrogen generation.

The rationally designing and constructing atomic-level heterointerface of two-dimensional (2D) chalcogenides is highly desirable to overcome the sluggish H2O-activation process toward efficient solar-driven hydrogen evolution. Herein, a novel in-plane 2D/2D molybdenum disulfide-rhenium disulfide (ReS2-MoS2) heterostructure is well-designed to induce the charge self-regulation of active site by forming electron-enriched Re(4-δ)+ and electron-deficient S(2-δ)- sites, thus collectively facilitating the activation of adsorbed H2O molecules and its subsequent H2 evolution. Furthermore, the obtained in-plane heterogenous ReS2-MoS2 nanosheet can powerfully transfer photoexcited electrons to inhibit photocarrier recombination as observed by advanced Kelvin probe measurement (KPFM), in-situ X-ray photoelectron spectroscopy (XPS) and femtosecond transient absorption spectroscopy (fs-TAS). As expected, the obtained ReS2-MoS2/TiO2 photocatalyst achieves an outperformed H2-generation rate of 6878.3 µmol h-1 g-1 with visualizing H2 bubbles in alkaline/neutral conditions. This work about in-plane 2D/2D heterostructure with strong free-electron interaction provides a promising strategy for designing novel and efficient catalysts for various applications.

Resveratrol suppresses hepatic fatty acid synthesis and increases fatty acid ß-oxidation via the microRNA-33/SIRT6 signaling pathway.

Hyperlipidemia is a strong risk factor for numerous diseases. Resveratrol (Res) is a non-flavonoid polyphenol organic compound with multiple biological functions. However, the specific molecular mechanism and its role in hepatic lipid metabolism remain unclear. Therefore, the aim of the present study was to elucidate the mechanism underlying how Res improves hepatic lipid metabolism by decreasing microRNA-33 (miR-33) levels. First, blood miR-33 expression in participants with hyperlipidemia was detected by reverse transcription-quantitative PCR, and the results revealed significant upregulation of miR-33 expression in hyperlipidemia. Additionally, after transfection of HepG2 cells with miR-33 mimics or inhibitor, western blot analysis indicated downregulation and upregulation, respectively, of the mRNA and protein expression levels of sirtuin 6 (SIRT6). Luciferase reporter analysis provided further evidence for binding of miR-33 with the SIRT6 3'-untranslated region. Furthermore, the levels of peroxisome proliferator-activated receptor-γ (PPARγ), PPARγ-coactivator 1α and carnitine palmitoyl transferase 1 were increased, while the concentration levels of acetyl-CoA carboxylase, fatty acid synthase and sterol regulatory element-binding protein 1 were decreased when SIRT6 was overexpressed. Notably, Res improved the basic metabolic parameters of mice fed a high-fat diet by regulating the miR-33/SIRT6 signaling pathway. Thus, it was demonstrated that the dysregulation of miR-33 could lead to lipid metabolism disorders, while Res improved lipid metabolism by regulating the expression of miR-33 and its target gene, SIRT6. Thus, Res can be used to prevent or treat hyperlipidemia and associated diseases clinically by suppressing hepatic fatty acid synthesis and increasing fatty acid ß-oxidation.

Deep learning pneumoconiosis staging and diagnosis system based on multi-stage joint approach.

BACKGROUND: Pneumoconiosis has a significant impact on the quality of patient survival due to its difficult staging diagnosis and poor prognosis. This study aimed to develop a computer-aided diagnostic system for the screening and staging of pneumoconiosis based on a multi-stage joint deep learning approach using X-ray chest radiographs of pneumoconiosis patients. METHODS: In this study, a total of 498 medical chest radiographs were obtained from the Department of Radiology of West China Fourth Hospital. The dataset was randomly divided into a training set and a test set at a ratio of 4:1. Following histogram equalization for image enhancement, the images were segmented using the U-Net model, and staging was predicted using a convolutional neural network classification model. We first used Efficient-Net for multi-classification staging diagnosis, but the results showed that stage I/II of pneumoconiosis was difficult to diagnose. Therefore, based on clinical practice we continued to improve the model by using the Res-Net 34 Multi-stage joint method. RESULTS: Of the 498 cases collected, the classification model using the Efficient-Net achieved an accuracy of 83% with a Quadratic Weighted Kappa (QWK) score of 0.889. The classification model using the multi-stage joint approach of Res-Net 34 achieved an accuracy of 89% with an area under the curve (AUC) of 0.98 and a high QWK score of 0.94. CONCLUSIONS: In this study, the diagnostic accuracy of pneumoconiosis staging was significantly improved by an innovative combined multi-stage approach, which provided a reference for clinical application and pneumoconiosis screening.

Sensitive detection of uric acid based on low-triggering-potential cathodic luminol electrochemiluminescence achieved by ReS2 nanosheets.

The majority of previously reported cathodic electrochemiluminescence (ECL) systems often required very negative potential to be carried out, which has greatly limited their applications in the sensing field. Screening high-performance cathodic ECL systems with low triggering potential is a promising way to broaden their applications. In this work, rhenium disulfide nanosheets (ReS2 NS) have been revealed as an efficient co-promoter to realize low-triggering-potential cathodic luminol ECL. One strong cathodic ECL signal appeared at a potential of -0.3 V and one anodic ECL peak was obtained at -0.15 V under the reverse potential scan, which were caused by electrogenerated reactive oxygen species (ROS) from hydrogen peroxide. The generation of strong luminol ECL at low potential was the result of the electrocatalytic effect of ReS2 NS on the reduction of H2O2. The scavenging effect of uric acid (UA) on the ROS could significantly inhibit the cathodic ECL. As a result, an ECL sensor was proposed, which showed outstanding performance for the detection of UA in the range of 10 nM to 0.1 mM with a low detection limit of 1.53 nM. Moreover, the ECL sensor was successfully applied in the sensitive detection of UA in real samples. This work provides a new avenue to establish a low-potential cathodic ECL system, which will sufficiently expand the potential application of cathodic ECL in the sensing field.

TG-Net: Using text prompts for improved skin lesion segmentation.

Automatic skin segmentation is an efficient method for the early diagnosis of skin cancer, which can minimize the missed detection rate and treat early skin cancer in time. However, significant variations in texture, size, shape, the position of lesions, and obscure boundaries in dermoscopy images make it extremely challenging to accurately locate and segment lesions. To address these challenges, we propose a novel framework named TG-Net, which exploits textual diagnostic information to guide the segmentation of dermoscopic images. Specifically, TG-Net adopts a dual-stream encoder-decoder architecture. The dual-stream encoder comprises Res2Net for extracting image features and our proposed text attention (TA) block for extracting textual features. Through hierarchical guidance, textual features are embedded into the process of image feature extraction. Additionally, we devise a multi-level fusion (MLF) module to merge higher-level features and generate a global feature map as guidance for subsequent steps. In the decoding stage of the network, local features and the global feature map are utilized in three multi-scale reverse attention modules (MSRA) to produce the final segmentation results. We conduct extensive experiments on three publicly accessible datasets, namely ISIC 2017, HAM10000, and PH2. Experimental results demonstrate that TG-Net outperforms state-of-the-art methods, validating the reliability of our method. Source code is available at https://github.com/ukeLin/TG-Net.

A Recommendation System for Prosumers Based on Large Language Models.

As modern technologies, particularly home assistant devices and sensors, become more integrated into our daily lives, they are also making their way into the domain of energy management within our homes. Homeowners, now acting as prosumers, have access to detailed information at 15-min or even 5-min intervals, including weather forecasts, outputs from renewable energy source (RES)-based systems, appliance schedules and the current energy balance, which details any deficits or surpluses along with their quantities and the predicted prices on the local energy market (LEM). The goal for these prosumers is to reduce costs while ensuring their home's comfort levels are maintained. However, given the complexity and the rapid decision-making required in managing this information, the need for a supportive system is evident. This is particularly true given the routine nature of these decisions, highlighting the potential for a system that provides personalized recommendations to optimize energy consumption, whether that involves adjusting the load or engaging in transactions with the LEM. In this context, we propose a recommendation system powered by large language models (LLMs), Scikit-llm and zero-shot classifiers, designed to evaluate specific scenarios and offer tailored advice for prosumers based on the available data at any given moment. Two scenarios for a prosumer of 5.9 kW are assessed using candidate labels, such as Decrease, Increase, Sell and Buy. A comparison with a content-based filtering system is provided considering the performance metrics that are relevant for prosumers.

Exploring and Analyzing the Systemic Delivery Barriers for Nanoparticles.

Most nanomedicines require efficient in vivo delivery to elicit diagnostic and therapeutic effects. However, en route to their intended tissues, systemically administered nanoparticles often encounter delivery barriers. To describe these barriers, we propose the term "nanoparticle blood removal pathways" (NBRP), which summarizes the interactions between nanoparticles and the body's various cell-dependent and cell-independent blood clearance mechanisms. We reviewed nanoparticle design and biological modulation strategies to mitigate nanoparticle-NBRP interactions. As these interactions affect nanoparticle delivery, we studied the preclinical literature from 2011-2021 and analyzed nanoparticle blood circulation and organ biodistribution data. Our findings revealed that nanoparticle surface chemistry affected the in vivo behavior more than other nanoparticle design parameters. Combinatory biological-PEG surface modification improved the blood area under the curve by ~418%, with a decrease in liver accumulation of up to 47%. A greater understanding of nanoparticle-NBRP interactions and associated delivery trends will provide new nanoparticle design and biological modulation strategies for safer, more effective, and more efficient nanomedicines.

MR2CPPIS: Accurate prediction of protein-protein interaction sites based on multi-scale Res2Net with coordinate attention mechanism.

Proteins play a vital role in various biological processes and achieve their functions through protein-protein interactions (PPIs). Thus, accurate identification of PPI sites is essential. Traditional biological methods for identifying PPIs are costly, labor-intensive, and time-consuming. The development of computational prediction methods for PPI sites offers promising alternatives. Most known deep learning (DL) methods employ layer-wise multi-scale CNNs to extract features from protein sequences. But, these methods usually neglect the spatial positions and hierarchical information embedded within protein sequences, which are actually crucial for PPI site prediction. In this paper, we propose MR2CPPIS, a novel sequence-based DL model that utilizes the multi-scale Res2Net with coordinate attention mechanism to exploit multi-scale features and enhance PPI site prediction capability. We leverage the multi-scale Res2Net to expand the receptive field for each network layer, thus capturing multi-scale information of protein sequences at a granular level. To further explore the local contextual features of each target residue, we employ a coordinate attention block to characterize the precise spatial position information, enabling the network to effectively extract long-range dependencies. We evaluate our MR2CPPIS on three public benchmark datasets (Dset 72, Dset 186, and PDBset 164), achieving state-of-the-art performance. The source codes are available at https://github.com/YyinGong/MR2CPPIS.

Sandwiched ReS2 nanocables with dual carbon coating for efficient K+/Na+ storage performance.

In this work, the nanocables of few-layered ReS2 nanosheets sandwiched between carbon nanotubes (CNTs) and nitrogen-doped amorphous carbon (NC) coating (i.e., CNT@ReS2@NC) are synthesized as high-performance anodes of both potassium-ion batteries (PIBs) and sodium-ion batteries (SIBs). The CNT@ReS2@NC nanocables with dual carbon modifications have the several advantages for efficient K+/Na+ storage. The few-layered ReS2 nanosheets with a wide interlayer spacing of 0.64 nm contribute to accelerated reaction kinetics for fast K+/Na+ intercalation/extraction. The carbon nanotube skeleton with a hollow interior can effectively relieve the volume change and serve as a robust conductive network to boost structural stability. The NC layer provides rich defects as active sites and suppresses the shuttle effect of polysulfides produced in discharge/charge processes. Consequently, the CNT@ReS2@NC nanocables possess outstanding electrochemical performance in both PIBs and SIBs owing to the synergistic effect from the different components. A long cycling lifespan of 3500 cycles with a maintained discharge capacity of 125 mAh/g is achieved for CNT@ReS2@NC at 1 A/g in PIBs. In SIBs, it can keep a high capacity of 202 mAh/g over 3000 cycles at 5 A/g. Moreover, the CNT@ReS2@NC||Na3V2(PO4)3 full cell can exhibit remarkable cycling performance, yielding a low capacity decay rate of 0.019 % per cycle over 1000 cycles at 2C.

Preparation and functional properties of rice bran globulin-chitooligosaccharide-quercetin-resveratrol covalent complex.

BACKGROUND: As the major protein (approximately 36%) in rice bran, globulin exhibits excellent foaming and emulsifying properties, endowing its useful application as a foaming and emulsifying agent in the food industry. However, the low water solubility restricts its commercial potential in industrial applications. The present study aimed to improve this protein's processing and functional properties. RESULTS: A novel covalent complex was fabricated by a combination of the Maillard reaction and alkaline oxidation using rice bran globulin (RBG), chitooligosaccharide (C), quercetin (Que) and resveratrol (Res). The Maillard reaction improved the solubility, emulsifying and foaming properties of RBG. The resultant glycosylated protein was covalently bonded with quercetin and resveratrol to form a (RBG-C)-Que-Res complex. (RBG-C)-Que-Res exhibited higher thermal stability and antioxidant ability than the native protein, binary globulin-chitooligosaccharide or ternary globulin-chitooligosaccharide-polyphenol (only containing quercetin or resveratrol) conjugates. (RBG-C)-Que-Res exerted better cytoprotection against the generation of malondialdehyde and reactive oxygen species in HepG2 cells, which was associated with increased activities of antioxidative enzymes superoxide dismutase (SOD), catalase (CAT) and glutathione peroxidase (GSH-Px) through upregulated genes SOD1, CAT, GPX1 (i.e. gene for glutathione peroxidase-1), GCLM (i.e. gene for glutamate cysteine ligase modifier subunit), SLC1A11 (i.e. gene for solute carrier family 7, member 11) and SRXN1 (i.e. gene for sulfiredoxin-1). The anti-apoptotic effect of (RBG-C)-Que-Res was confirmed by the downregulation of caspase-3 and p53 and the upregulation of B-cell lymphoma-2 gene expression. CONCLUSION: The present study highlights the potential of (RBG-C)-Que-Res conjugates as functional ingredients in healthy foods. © 2024 Society of Chemical Industry.

RES-CMCNPs Enhance Antioxidant, Proinflammatory, and Sensitivity of Tumor Solids to γ-Irradiation in EAC-Bearing Mice.

OBJECTIVES: Resveratrol (Res) is a bifunctional compound found in numerous plants, including grapes and mulberries. Nanotechnology has promising applications in medicine. The ability of various nanomaterials to serve as radiosensitizers against tumor cells were reported in several manuscripts. The present investigation aimed to assess the antitumor and radiosensitizing effects of Res-CMCNPs on EAC-bearing mice.