Optimization of Bioactive Ingredient Extraction from Chinese Herbal Medicine Glycyrrhiza glabra: A Comparative Study of Three Optimization Models

Optimization of tetrastigma hemsleyanum extraction process based on GA-BPNN model and analysis of its antioxidant effect

Tetrastigma hemsleyanum (Tetrastigma hemsleyanum Diels et Gilg) is a valuable traditional Chinese medicine with various applications. In this study, we aimed to optimize the extraction process for the total extraction yield of five flavonoid components, namely kaempferol, quercetin, rutin, kaempferol-3-O-rutinoside, and astragalin from the Tetrastigma hemsleyanum root (THR), and explore its potential molecular mechanisms in treating oxidative diseases as well as antioxidant activity. To achieve these objectives, we employed the genetic algorithm-back propagation neural network (GA-BPNN), the Box-Behnken design (BBD) with 4-factors and 3-levels to establish the optimal ethanol extraction process for the total extraction yield of the 5 components. Using public databases, the "component core targets-disease core target genes" networks were built, as well as molecular docking. Furthermore, DPPH was used to examine the antioxidant activity of the extracts obtained from THR under the optimal extraction process. The experimental value of the total extraction yield of the 5 components achieved a maximum of 788.12 mg/kg when the ethanol concentration was 73%, the solid-liquid ratio was 26 g/mL, and the ultrasonic duration was 30 min, and the ultrasonic temperature was 76 °C. When docked with protein molecules such as 6Y8I, quercetin, and other components received moderate to high scores. When the total concentration of the 5 components was 3.033 μg/mL, the DPPH radical scavenging rate was 89.81%. Compared with the BBD method, the GA-BPNN method is more efficient and reliable for optimizing the extraction process of active ingredients in THR because of its good data-fitting ability.

An integrated strategy of spectrum-effect relationship and near-infrared spectroscopy rapid evaluation based on back propagation neural network for quality control of Paeoniae Radix Alba

The quantitative analysis of near-infrared spectroscopy in traditional Chinese medicine has still deficiencies in the selection of the measured indexes. Then Paeoniae Radix Alba is one of the famous "Eight Flavors of Zhejiang" herbs, however, it lacks the pharmacodynamic support, and cannot reflect the quality of Paeoniae Radix Alba accurately and reasonably. In this study, the spectrum-effect relationship of the anti-inflammatory activity of Paeoniae Radix Alba was established. Then based on the obtained bioactive component groups, the genetic algorithm, back propagation neural network, was combined with near-infrared spectroscopy to establish calibration models for the content of the bioactive components of Paeoniae Radix Alba. Finally, three bioactive components, paeoniflorin, 1,2,3,4,6-O-pentagalloylglucose, and benzoyl paeoniflorin, were successfully obtained. Their near-infrared spectroscopy content models were also established separately, and the validation sets results showed the coefficient of determination (R² > 0.85), indicating that good calibration statistics were obtained for the prediction of key pharmacodynamic components. As a result, an integrated analytical method of spectrum-effect relationship combined with near-infrared spectroscopy and deep learning algorithm was first proposed to assess and control the quality of traditional Chinese medicine, which is the future development trend for the rapid inspection of traditional Chinese medicine.

Response surface methodology as a tool to optimize the extraction of bioactive compounds from plant sources

Response surface methodology (RSM) is a widely used mathematical and statistical technique for modeling and optimizing the process for the extraction of bioactive compounds. This review explains the optimization approach through the use of experimental design and empirical models for response prediction and the utilization of the desirability function for multiple response optimization. This paper also reviews recent studies on the application of RSM to optimize bioactive compound extraction processes such as conventional solvent extraction, microwave-assisted extraction, supercritical fluid extraction, and ultrasound-assisted extraction. Finally, the challenges associated with the use of RSM and the efforts made to improve RSM in the extraction process are also highlighted. Overall, this review informs many aspects of RSM that are occasionally ignored or insufficiently discussed with regard to the optimization of bioactive compound extraction processes, and it summarizes significant applications where RSM proved suitable. © 2022 Society of Chemical Industry.

Non-invasive prediction of microsatellite instability in colorectal cancer by a genetic algorithm-enhanced artificial neural network-based CT radiomics signature

OBJECTIVE

The stratification of microsatellite instability (MSI) status assists clinicians in making treatment decisions for colorectal cancer (CRC) patients. This study aimed to establish a CT-based radiomics signature to predict MSI status in patients with CRC.

METHODS

A total of 837 CRC patients who underwent preoperative enhanced CT and had available MSI status data were recruited from two hospitals. Radiomics features were extracted from segmented tumours, and a series of data balancing and feature selection strategies were used to select MSI-related features. Finally, an MSI-related radiomics signature was constructed using a genetic algorithm-enhanced artificial neural network model. Combined and clinical models were constructed using multivariate logistic regression analyses by integrating the clinical factors with or without the signature. A Kaplan-Meier survival analysis was conducted to explore the prognostic information of the signature in patients with CRC.

RESULTS

Ten features were selected to construct a signature which showed robust performance in both the internal and external validation cohorts, with areas under the curves (AUC) of 0.788 and 0.775, respectively. The performance of the signature was comparable to that of the combined model (AUCs of 0.777 and 0.767, respectively) and it outperformed the clinical model constituting age and tumour location (AUCs of 0.768 and 0.623, respectively). Survival analysis demonstrated that the signature could stratify patients with stage II CRC according to prognosis (HR: 0.402, p = 0.029).

CONCLUSIONS

This study built a robust radiomics signature for identifying the MSI status of CRC patients, which may assist individualised treatment decisions.

KEY POINTS

• Our well-designed modelling strategies helped overcome the problem of data imbalance caused by the low incidence of MSI. • Genetic algorithm-enhanced artificial neural network-based CT radiomics signature can effectively distinguish the MSI status of CRC patients. • Kaplan-Meier survival analysis demonstrated that our signature could significantly stratify stage II CRC patients into high- and low-risk groups.

Atrazine-induced cell-mediated immunotoxicity in rabbits and the ameliorating role of glycyrrhizic acid

The present study aimed to explain the mechanisms involved in cell-mediated immunotoxicity of atrazine (ATR) in rabbits and to evaluate the ameliorative role of glycyrrhizic acid (GA) against such toxic effects. Forty rabbits were assigned into 4 equal groups: control, ATR, GA, and ATR + GA groups. ATR (2475 ppm) and GA (60 μg of GA/ml of water) were administrated via food and drinking water, respectively, for 60 consecutive days. The cell-mediated immunotoxicity of ATR was clarified by the induced thymus immunotoxicity through downregulation of interleukin (IL)-9 gene and interferon-γ (IFN-γ) gene expression, upregulation in caspase-3, and significant decrease in the total leukocytic and lymphocyte counts. Histopathological investigations demonstrated severe depletion of lymphoid follicles in the medulla of the thymus gland. On the other hand, co-administration of GA for group 4 improved most of the undesirable impacts of ATR. In conclusion, the alteration in IL-9/IFN-γ expression may involve ATR-induced thymocyte apoptosis which may explain the mechanisms of ATR-induced cell-mediated immunotoxicity with a possible amelioration influence of GA administration.