Apricot polysaccharides as new carriers to make curcumin nanoparticles and improve its stability and antibacterial activity.

Apricot polysaccharides (APs) as new types of natural carriers for encapsulating and delivering active pharmaceutical ingredients can achieve high-value utilization of apricot pulp and improve the solubility, the stability, and the antibacterial activity of insoluble compounds simultaneously. In this research, the purified APs reacted with bovine serum albumin (BSA) by the Maillard reaction, and with d-α-tocopheryl succinate (TOS) and pheophorbide A (PheoA) by grafting to fabricate two materials for the preparation of curcumin (Cur)-encapsulated AP-BSA nanoparticles (CABNs) and Cur-embedded TOS-AP-PheoA micelles (CTAPMs), respectively. The biological activities of two Cur nano-delivery systems were evaluated. APs consisted of arabinose (22.36%), galactose (7.88%), glucose (34.46%), and galacturonic acid (31.32%) after the optimized extraction. Transmission electron microscopy characterization of CABNs and CTAPMs displayed a discrete and non-aggregated morphology with a spherical shape. Compared to the unencapsulated Cur, the release rates of CABNs and CTAPMs decreased from 87% to 70% at 3 h and from 92% to 25% at 48 h, respectively. The antioxidant capacities of CABNs and CTAPMs were significantly improved. The CTAPMs exhibited a better antibacterial effect against Escherichia coli than CABNs due to the synergistic photosensitive effect between Cur and PheoA.

Effect of substrate composition on physicochemical properties of the medium-long-medium structured triacylglycerol.

BACKGROUND: Nutritional and functional qualities and applications of structured lipids (SL) depend on the composition and molecular structure of fatty acids in the glycerol backbone of triacylglycerol (TAG). However, the relationship between the substrate composition and physicochemical qualities of SL has not been revealed. The investigation aims to disclose the effect of substrate composition on the physicochemical properties of medium-long-medium structured lipids (MLM-SLs) by enzymatic interesterification of Lipozyme TLIM/RMIM. RESULTS: The medium-long-chain triacylglycerol (MLCT) yield could reach 70.32%, including 28.98% CaLCa (1,3-dioctonyl-2-linoleoyl glyceride) and 24.34% CaOCa (1,3-didecanoyl-2-oleoyl glyceride). The sn-2 unsaturated fatty acid composition mainly depended on long-chain triacylglycerol (LCT) in the substrate. The increased carbon chain length and double bond in triacylglycerol decreased its melting and crystallization temperature. The balanced substrate composition of MCT/LCT increased the size and finer crystals. Molecular docking simulation revealed that the MLCT molecule mainly interacted with the catalytic triplets of Lipozyme TLIM (Arg81-Ser83-Arg84) and the Lipozyme RMIM (Tyr183-Thr226-Arg262) by OH bond. The oxygen atom of the ester on the MLCT molecule was primarily bound to the hydrogen of hydroxyl and amino groups on the binding sites of Lipozyme TLIM/RMIM. The intermolecular interplay between MLCT and Lipozyme RMIM is more stable than Lipozyme TLIM due to the formation of lower binding affinity energy. CONCLUSION: This research clarifies the interaction mechanism between MLCT molecules and lipases, and provides an in-depth understanding of the relationship between substrate composition, molecular structure and physicochemical property of MLM-SLs. © 2023 Society of Chemical Industry.

Diagnosis of Typical Apple Diseases: A Deep Learning Method Based on Multi-Scale Dense Classification Network.

Disease has always been one of the main reasons for the decline of apple quality and yield, which directly harms the development of agricultural economy. Therefore, precise diagnosis of apple diseases and correct decision making are important measures to reduce agricultural losses and promote economic growth. In this paper, a novel Multi-scale Dense classification network is adopted to realize the diagnosis of 11 types of images, including healthy and diseased apple fruits and leaves. The diagnosis of different kinds of diseases and the same disease with different grades was accomplished. First of all, to solve the problem of insufficient images of anthracnose and ring rot, Cycle-GAN algorithm was applied to achieve dataset expansion on the basis of traditional image augmentation methods. Cycle-GAN learned the image characteristics of healthy apples and diseased apples to generate anthracnose and ring rot lesions on the surface of healthy apple fruits. The diseased apple images generated by Cycle-GAN were added to the training set, which improved the diagnosis performance compared with other traditional image augmentation methods. Subsequently, DenseNet and Multi-scale connection were adopted to establish two kinds of models, Multi-scale Dense Inception-V4 and Multi-scale Dense Inception-Resnet-V2, which facilitated the reuse of image features of the bottom layers in the classification neural networks. Both models accomplished the diagnosis of 11 different types of images. The classification accuracy was 94.31 and 94.74%, respectively, which exceeded DenseNet-121 network and reached the state-of-the-art level.

Rosanortriterpenes A-B, two new nortriterpenes from the fruits of Rosa laevigata var. leiocapus.

One new oleanane-type nortriterpene, rosanortriterpene A (1), and one new ursane-type nortriterpene, rosanortriterpene B (2), were isolated from the fruits of Rosa laevigata var. leiocapus. The structures of 1-2 were fully characterised on the basis of extensive spectroscopic analysis, including IR, HRESIMS, as well as 1D and 2D NMR spectral data (HSQC, 1H-1H COSY, and HMBC). To the best of our knowledge, this represents the first study on the chemical constituents of R. laevigata var. leiocapus. Compounds 1-2 exhibited moderate inhibitory effects on NO production in LPS stimulated RAW264.7 cells with IC50 values of 29.29 ± 3.64 and 14.28 ± 1.20 µM, respectively.

Rosanortriterpene C, a 3,24-Dinor-2,4-seco-ursane Triterpene from the Fruits of Rosa laevigata var. leiocapus.

One new 3,24-dinor-2,4-seco-ursane triterpene, rosanortriterpene C (1), together with four known compounds including 24-norursane-type nortriterpenes (2-3), 24-noroleanane-type nortriterpene (4), ursane-type triterpene (5), was isolated from the fruits of Rosa laevigata var. leiocapus. The new structure was elucidated through comprehensive spectroscopic analysis, including one dimensional (1D) and 2D NMR data, as well as electrospray ionization high resolution (HR-ESI) MS and IR spectrometry. Compounds 1-5 showed moderate anti-inflammatory activities against the production of nitric oxide (NO) in RAW264.7 cells stimulated by lipopolysaccharide (LPS) with IC50 values of 10.35 ± 0.92, 14.28 ± 1.20, 5.04 ± 1.43, 29.29 ± 3.64, and 14.37 ± 0.59 µM, respectively.

Quality evaluation of flue-cured tobacco by near infrared spectroscopy and spectral similarity method.

Herein we propose near infrared (NIR) spectroscopy as a rapid method of evaluating the quality of agricultural products. Unlike existing quantitative or qualitative models, quality similarity is characterised using spectral similarity. Key factors of the spectral similarity method were investigated, including variable selection, pre-processing and similarity measures. Sophisticated techniques were developed to ensure the reliability of similarity algorithm. The proposed method was tested by quality similarity of flue-cured tobacco samples. The results demonstrated that the quality-related factors between the target and the similar samples (determined by spectral similarity), showed high similarities. This new method has the potential to characterise product quality effectively and could be a useful new alternative to the widely used PLS models.