Ultrasonic-Assisted Extraction of Flavonoids from Juglans mandshurica Maxim.: Artificial Intelligence-Based Optimization, Kinetics Estimation, and Antioxidant Potential

Enrichment and Evaluation of Antitumor Properties of Total Flavonoids from Juglans mandshurica Maxim

Flavonoids are important secondary metabolites found in Juglans mandshurica Maxim., which is a precious reservoir of bioactive substances in China. To explore the antitumor actions of flavonoids (JMFs) from the waste branches of J. mandshurica, the following optimized purification parameters of JMFs by macroporous resins were first obtained. The loading concentration, flow rate, and loading volume of raw flavonoid extracts were 1.4 mg/mL, 2.4 BV/h, and 5 BV, respectively, and for desorption, 60% ethanol (4 BV) was selected to elute JMFs-loaded AB-8 resin at a flow rate of 2.4 BV/h. This adsorption behavior can be explained by the pseudo-second-order kinetic model and Langmuir isotherm model. Subsequently, JMFs were identified using Fourier transform infrared combined with high-performance liquid chromatography and tandem mass spectrometry, and a total of 156 flavonoids were identified. Furthermore, the inhibitory potential of JMFs on the proliferation, migration, and invasion of HepG2 cells was demonstrated. The results also show that exposure to JMFs induced apoptotic cell death, which might be associated with extrinsic and intrinsic pathways. Additionally, flow cytometry detection found that JMFs exposure triggered S phase arrest and the generation of reactive oxygen species in HepG2 cells. These findings suggest that the JMFs purified in this study represent great potential for the treatment of liver cancer.

Provenance and family variations in early growth of Manchurian walnut (Juglans mandshurica Maxim.) and selection of superior families

This study, conducted in China in November 2020, was aimed at exploring the variations in growth traits among different provenances and families as well as to select elite materials of Juglans mandshurica. Thus, seeds of 44 families from six J. mandshurica provenances in Heilongjiang and Jilin provinces were sown in the nursery and then transplanted out in the field. At the age of 5 years, seven growth traits were assessed, and a comprehensive analysis was conducted as well as selection of provenance and families. Analysis of variance revealed statistically significant (P < 0.01) differences in seven growth traits among different provenances and families, thereby justifying the pursuit of further breeding endeavors. The genetic coefficient of variation (GCV) for all traits ranged from 5.44% (branch angle) to 21.95% (tree height) whereas the phenotypic coefficient of variation (PCV) ranged from 13.74% (tapering) to 38.50% (branch number per node), indicating considerable variability across the traits. Further, all the studied traits except stem straightness degree, branch angle and branch number per node, showed high heritability (Tree height, ground diameter, mean crown width and tapering, over 0.7±0.073), indicating that the variation in these traits is primarily driven by genetic factors. Correlation analysis revealed a strong positive correlation (r > 0.8) between tree height and ground diameter (r = 0.86), tree height and mean crown width (r = 0.82), and ground diameter and mean crown width (r = 0.83). This suggests that these relationships can be employed for more precise predictions of the growth and morphological characteristics of trees, as well as the selection of superior materials. There was a strong correlation between temperature factors and growth traits. Based on the comprehensive scores in this study, Sanchazi was selected as elite provenance. Using the top-percentile selection criteria, SC1, SC8, DJC15, and DQ18 were selected as elite families. These selected families exhibit genetic gains of over 10% in tree height, ground diameter and mean crown width, signifying their significant potential in forestry for enhancing timber production and reducing production cycles, thereby contributing to sustainable forest management. In this study, the growth traits of J. mandshurica were found to exhibit stable variation, and there were correlations between these traits. The selected elite provenance and families of J. mandshurica showed faster growth, which is advantageous for the subsequent breeding and promotion of improved J. mandshurica varieties.

Ultrasound-assisted extraction of emodin from Rheum officinale Baill and its antibacterial mechanism against Streptococcus suis based on CcpA

Emodin was extracted from Rheum officinale Baill by ultrasound-assisted extraction (UAE), and ethanol was chosen as the suitable solvent through SEM and molecular dynamic simulation. Under the optimum conditions (power 541 W, time 23 min, liquid to material ratio 13:1 mL/g, ethanol concentration 83 %) predicted by RSM, the yield of emodin was 2.18 ± 0.11 mg/g. Moreover, ultrasound power and time displayed the significant effects on the extraction process. Extracting dynamics analysis indicated that the extraction process of emodin by UAE conformed to Fick's second diffusion law. The results of antibacterial experiments suggested that emodin can damage cell membrane and inhibit the expression of cps2A, sao, mrp, epf, neu and the hemolytic activity of S. suis. Biolayer interferometry and FT-IR multi-peak fitting assays demonstrated that emodin induced a secondary conformational shift in CcpA. Molecular docking and molecular dynamics confirmed that emodin bound to CcpA through hydrogen bonding (ALA248, GLU249, GLY129 and ASN196) and π-π T-shaped interaction (TYR225 and TYR130), and the mutation of amino acid residues affected the affinity of CcpA to emodin. Therefore, emodin inhibited the sugar utilization of S. suis through binding to CcpA, and CcpA may be a potential target to inhibit the growth of S. suis.

Bridging artificial intelligence and fucoxanthin for the recovery and quantification from microalgae

Fucoxanthin is a carotenoid that possesses various beneficial medicinal properties for human well-being. However, the current extraction technologies and quantification techniques are still lacking in terms of cost validation, high energy consumption, long extraction time, and low yield production. To date, artificial intelligence (AI) models can assist and improvise the bottleneck of fucoxanthin extraction and quantification process by establishing new technologies and processes which involve big data, digitalization, and automation for efficiency fucoxanthin production. This review highlights the application of AI models such as artificial neural network (ANN) and adaptive neuro fuzzy inference system (ANFIS), capable of learning patterns and relationships from large datasets, capturing non-linearity, and predicting optimal conditions that significantly impact the fucoxanthin extraction yield. On top of that, combining metaheuristic algorithm such as genetic algorithm (GA) can further improve the parameter space and discovery of optimal conditions of ANN and ANFIS models, which results in high R2 accuracy ranging from 98.28% to 99.60% after optimization. Besides, AI models such as support vector machine (SVM), convolutional neural networks (CNNs), and ANN have been leveraged for the quantification of fucoxanthin, either computer vision based on color space of images or regression analysis based on statistical data. The findings are reliable when modeling for the concentration of pigments with high R2 accuracy ranging from 66.0% - 99.2%. This review paper has reviewed the feasibility and potential of AI for the extraction and quantification purposes, which can reduce the cost, accelerate the fucoxanthin yields, and development of fucoxanthin-based products.