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Artificial Intelligence Assisted Ultrasonic Extraction of Total Flavonoids from Rosa sterilis.

ABSTRACT: Flavonoids in Rosa sterilis were studied. The flavonoids in Rosa sterilis were extracted by ultrasonic method, and the extraction conditions were modeled and optimized by response the surface methodology and the artificial intelligence method. The results show that the ultrasonic method can effectively extract total flavonoids, and the extraction rate is close to the prediction value of ANN-GA algorithm, which proves the rationality of the model. The order of the effects of the parameters on the experiment was material liquid ratio > extraction power > extraction time > ethanol concentration. In addition, the scavenging effects of flavonoids on DPPH, O2-· and ·OH were also determined, and these indicated that flavonoids have strong antioxidant activities. The kinetics of the extraction process was studied by using the data of the extraction process, and it was found that the extraction process conformed to Fick's first law.

SUBMITTER: Liu J

PROVIDER: S-EPMC8270336 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature

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Artificial Intelligence Assisted Ultrasonic Extraction of Total Flavonoids from Rosa sterilis.

Liu Jing J Li Chaochan C Ding Guijie G Quan Wenxuan W

Molecules (Basel, Switzerland) 20210623 13

Flavonoids in Rosa sterilis were studied. The flavonoids in Rosa sterilis were extracted by ultrasonic method, and the extraction conditions were modeled and optimized by response the surface methodology and the artificial intelligence method. The results show that the ultrasonic method can effectively extract total flavonoids, and the extraction rate is close to the prediction value of ANN-GA algorithm, which proves the rationality of the model. The order of the effects of the par ...[more]

PMID: 34201870

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Project description:Nymphaea lotus L. is the medicinal plant that has long been used for food, cosmetics and traditional medicines in Africa and Asia since ancient times. Its flavonoids and other interesting phytochemical compounds from rhizome, leaf and the whole flowers have been reported in the previous published research. However, stamens, which are essential for reproductive functions, may also represent new alternative sources of potential antioxidant flavonoids, as investigated in this study. The innovative green chemistry methods, i.e., ultrasound-assisted extraction (UAE) as well as a macroporous resin (MPR) purification procedure, were employed in this current research. Using a full factorial design coupled to three-dimensional (3D) surface plot methodology, the influence of three variables, namely aqEtOH concentration (ranging from 50 to 100% (v/v), US frequency (ranging from 0 (no US applied) to 45 kHz), and the extraction duration (ranging from 20 to 60 min), were evaluated. Five MPRs with different surface areas, average pore diameters, matrix types and polarities were also investigated for the purification of total flavonoids. The optimal UAE condition is 90% (v/v) aqEtOH with 34.65 khz ultrasonic frequency and 46 min of extraction duration. Compared with the conventional heat reflux extraction (HRE) method, a significant 1.35-fold increase in total flavonoids content was obtained using optimized UAE conditions (169.64 for HRE vs. 235.45 mg/g dry weight for UAE), causing a 2.80-fold increase when this UAE associated with MPR purification (475.42 mg/g dry weight). In vitro cell free antioxidant activity of N. lotus stamen extracts and in cellulo antioxidant investigation using yeast model showed the same trend, indicating that the best antioxidant flavonoid can be found in UAE coupled with MPR purification. Moreover, in the yeast model, the expression of key antioxidant genes such as SIR2 and SOD2 were expressed at the highest level in yeast cells treated with the extract from UAE together with MPR purification. Consequently, it can be seen that the UAE combined with MPR purification can help enhance the flavonoid antioxidant potential of the stamens extract from this medicinal species.

Project description:BackgroundSmall-incision lenticule extraction (SMILE) is a surgical procedure for the refractive correction of myopia and astigmatism, which has been reported as safe and effective. However, over- and under-correction still occur after SMILE. The necessity of nomograms is emphasized to achieve optimal refractive results. Ophthalmologists diagnose nomograms by analyzing the preoperative refractive data with their individual knowledge which they accumulate over years of experience. Our aim was to predict the nomograms of sphere, cylinder, and astigmatism axis for SMILE accurately by applying machine learning algorithm.MethodsWe retrospectively analyzed the data of 3,034 eyes composed of four categorical features and 28 numerical features selected from 46 features. The multiple linear regression, decision tree, AdaBoost, XGBoost, and multi-layer perceptron were employed in developing the nomogram models for sphere, cylinder, and astigmatism axis. The scores of the root-mean-square error (RMSE) and accuracy were evaluated and compared. Subsequently, the feature importance of the best models was calculated.ResultsAdaBoost achieved the highest performance with RMSE of 0.1378, 0.1166, and 5.17 for the sphere, cylinder, and astigmatism axis, respectively. The accuracies of which error below 0.25 D for the sphere and cylinder nomograms and 25° for the astigmatism axis nomograms were 0.969, 0.976, and 0.994, respectively. The feature with the highest importance was preoperative manifest refraction for all the cases of nomograms. For the sphere and cylinder nomograms, the following highly important feature was the surgeon.ConclusionsAmong the diverse machine learning algorithms, AdaBoost exhibited the highest performance in the prediction of the sphere, cylinder, and astigmatism axis nomograms for SMILE. The study proved the feasibility of applying artificial intelligence (AI) to nomograms for SMILE. Also, it may enhance the quality of the surgical result of SMILE by providing assistance in nomograms and preventing the misdiagnosis in nomograms.

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Artificial Intelligence Assisted Ultrasonic Extraction of Total Flavonoids from Rosa sterilis.

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Artificial Intelligence Assisted Ultrasonic Extraction of Total Flavonoids from <i>Rosa sterilis</i>.

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