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

Recent advancements in ultrasound-assisted biomolecule extraction from prokaryotic and eukaryotic cells: a review

With numerous advantages over conventional techniques, ultrasound-assisted extraction (UAE) has become a viable method for the effective extraction of biomolecules from prokaryotic and eukaryotic cells. The fundamentals and workings of UAE are examined in this review, focusing on current developments, including how these impact the extraction of proteins, lipids, enzymes, and other bioactive compounds. UAE not only enhances cell disruption and mass transfer, leading to improved extraction yields, but also preserves the integrity of the extracted bioactive molecules under optimized conditions, making it a preferred choice in Biochemistry and Biotechnology. Additionally, this review explores recent innovative approaches that combine ultrasound with other techniques like enzymatic digestion, supercritical CO2, deep eutectic solvents, and Three-Phase Partitioning (UA-TPP) etc, to further enhance extraction efficiency. The differences in extraction effectiveness between prokaryotic and eukaryotic cells are attributed to cellular structure and ultrasonic conditions. Overall, this review highlights UAE's promise as a viable and efficient substitute for biomolecule extraction concerning prokaryotic and eukaryotic cells while bringing up areas that need additional research and development.

Optimization of ultrasonic-assisted extraction of total flavonoids from Oxalis corniculata by a hybrid response surface methodology-artificial neural network-genetic algorithm (RSM-ANN-GA) approach, coupled with an assessment of antioxidant activities

The objective of this research endeavor is to refine the ultrasonic-assisted extraction technique for total flavonoids from Oxalis corniculata (TFO), utilizing a synergistic approach combining response surface methodology (RSM) and artificial neural network integrated with genetic algorithm (RSM-ANN-GA). The optimized extraction parameters determined through RSM yielded a TFO concentration of 13.538 mg g-1 under the following conditions: an ethanol concentration of 61.95%, a liquid-solid ratio of 41.06 mL g-1, an ultrasonic power setting of 351.57 W, and an ultrasonic exposure duration of 58.95 minutes. Conversely, the RSM-ANN-GA approach identified an even more refined set of conditions, achieving a TFO concentration of 13.7844 mg g-1, with an ethanol concentration of 58.93%, a liquid-solid ratio of 41.16 mL g-1, an ultrasonic power of 350.22 W, and an ultrasonic exposure time of 58.18 minutes. These findings underscore the superior predictive accuracy and enhanced extraction efficiency offered by the RSM-ANN-GA model over the conventional RSM method. Furthermore, the study demonstrated that TFO possesses a potent antioxidant effect, as evidenced by its ability to scavenge DPPH, hydroxyl, and superoxide anion free radicals in vitro, highlighting its potential as a valuable source of natural antioxidants.

A hybrid RSM-BPNN-GA approach for optimizing ultrasound-assisted deep eutectic solvents extraction conditions for Mesona chinensis benth. and investigation of the extraction mechanism

Mesona chinensis Benth (MCB) is the source of the most commonly consumed herbal beverage in Southeast Asia and China and is thus an economically important agricultural plant. Therefore, optimal extraction and production procedures have significant commercial value. Currently, in terms of green chemistry, researchers are investigating the use of greener solvents and innovative extraction techniques to increase extract yields. This study represents the first investigation of the optimal conditions for ultrasound-assisted deep eutectic solvent (DES) extraction from MCB. The major factors influencing ultrasound-assisted DESs were optimized using the response surface methodcentral-genetic algorithm-back propagation neural networks. This model demonstrated superior predictability and accuracy compared to the RSM model. Various types of DESs were used for the extraction of MCB constituents, with choline chloride-ethylene glycol resulting in the highest yield. The optimal conditions for maximal extraction were the use of choline chloride-ethylene glycol (1:4) as the solvent with a 40% water content, an extraction duration of 60 min at 60°C, and maintaining a leaf-to-solvent ratio of 20 mL/g. Noticeable enhancements in Van der Waals forces and more robust interactions between DESs and the target chemicals were observed relative to those seen with ethanol (70%, v/v) or water. This investigation not only introduced an environmentally friendly approach for highly efficient extraction from MCB but also identified the mechanisms underlying the improved extraction efficacy. These findings have the potential to contribute to the broader utilization of MCB and provide valuable insights into the extraction mechanisms utilizing deep eutectic solvents. PRACTICAL APPLICATION: This work describes an efficient and green ultrasound-assisted deep eutectic solvent (DES) method for Mesona chinensis Benth (MCB) extraction. Molecular dynamics was used to examine the intermolecular interactions between the solvent and the extracted compounds. It is anticipated that green and environmentally friendly solvents, such as DESs, will be used in further research on foods and their bioactive components. With the development of the herbal tea industry, new products made of MCB are becoming increasingly popular, thus gradually making it a research hotspot.

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.