ABSTRACT
We explored the correlations between the color difference values [ΔL~*(lightness), Δa~*(red-green), Δb~*(yellow-blue)] and the content of four active components(including sesquiterpenoids and polyacetylenes) in the powder of Atractylodes lancea and A. chinensis, aiming to provide reference for the quality evaluation of Atractylodis Rhizoma and establish a qualitative model that can distinguish between A. lancea and A. chinensis based on the chromatic values. The tristimulus values(L~*, a~*, and b~*) of 23 batches of A. lancea and A. chinensis were measured by a color difference meter. The content of atractylenolide Ⅱ, β-eudesmol, atractylodin, and atractylone in the 23 batches of samples were measured by high performance liquid chromatography(HPLC). Principal component analysis(PCA) and partial least squares-discriminant analysis(PLS-DA) were performed to establish the qualitative models for distinguishing between A. lancea and A. chinensis. SPSS was employed to analyze the correlations between the tristimulus values and the content of the four index components. The results showed that the established PCA and PLS-DA models can divide the A. lancea and A. chinensis samples into two regions, and the tristimulus values of A. lancea and A. chinensis were positively correlated with the content of β-eudesmol and atractylodin. Therefore, the PCA and PLS-DA models can successfully identify A. lancea and A. chinensis, and the appearance color can be used to quickly predict the internal quality of Atractylodis Rhizoma. This study provides a reference for the quality evaluation of Atractylodis Rhizoma and the modern research on the color of Chinese medicinal materials.
Subject(s)
Atractylodes , Sesquiterpenes, Eudesmane , Drugs, Chinese Herbal , Rhizome , ExcipientsABSTRACT
OBJECTIVE@#To predict the trends for fine-scale spread of Oncomelania hupensis based on supervised machine learning models in Shanghai Municipality, so as to provide insights into precision O. hupensis snail control.@*METHODS@#Based on 2016 O. hupensis snail survey data in Shanghai Municipality and climatic, geographical, vegetation and socioeconomic data relating to O. hupensis snail distribution, seven supervised machine learning models were created to predict the risk of snail spread in Shanghai, including decision tree, random forest, generalized boosted model, support vector machine, naive Bayes, k-nearest neighbor and C5.0. The performance of seven models for predicting snail spread was evaluated with the area under the receiver operating characteristic curve (AUC), F1-score and accuracy, and optimal models were selected to identify the environmental variables affecting snail spread and predict the areas at risk of snail spread in Shanghai Municipality.@*RESULTS@#Seven supervised machine learning models were successfully created to predict the risk of snail spread in Shanghai Municipality, and random forest (AUC = 0.901, F1-score = 0.840, ACC = 0.797) and generalized boosted model (AUC= 0.889, F1-score = 0.869, ACC = 0.835) showed higher predictive performance than other models. Random forest analysis showed that the three most important climatic variables contributing to snail spread in Shanghai included aridity (11.87%), ≥ 0 °C annual accumulated temperature (10.19%), moisture index (10.18%) and average annual precipitation (9.86%), the two most important vegetation variables included the vegetation index of the first quarter (8.30%) and vegetation index of the second quarter (7.69%). Snails were more likely to spread at aridity of < 0.87, ≥ 0 °C annual accumulated temperature of 5 550 to 5 675 °C, moisture index of > 39% and average annual precipitation of > 1 180 mm, and with the vegetation index of the first quarter of > 0.4 and the vegetation index of the first quarter of > 0.6. According to the water resource developments and township administrative maps, the areas at risk of snail spread were mainly predicted in 10 townships/subdistricts, covering the Xipian, Dongpian and Tainan sections of southern Shanghai.@*CONCLUSIONS@#Supervised machine learning models are effective to predict the risk of fine-scale O. hupensis snail spread and identify the environmental determinants relating to snail spread. The areas at risk of O. hupensis snail spread are mainly located in southwestern Songjiang District, northwestern Jinshan District and southeastern Qingpu District of Shanghai Municipality.
Subject(s)
Animals , Bayes Theorem , China/epidemiology , Ecosystem , Gastropoda , Supervised Machine LearningABSTRACT
On February 2022, WHO released the evidence-based guideline on control and elimination of human schistosomiasis, with aims to guide the elimination of schistosomiasis as a public health problem in disease-endemic countries by 2030 and promote the interruption of schistosomiasis transmission across the world. Based on the One Health concept, six evidence-based recommendations were proposed in this guideline. This article aims to analyze the feasibility of key aspects of this guideline in Chinese national schistosomiasis control program and illustrate the significance to guide the future actions for Chinese national schistosomiasis control program. Currently, the One Health concept has been embodied in the Chinese national schistosomiasis control program. Based on this new WHO guideline, the following recommendations are proposed for the national schistosomiasis control program of China: (1) improving the systematic framework building, facilitating the agreement of the cross-sectoral consensus, and building a high-level leadership group; (2) optimizing the current human and livestock treatments in the national schistosomiasis control program of China; (3) developing highly sensitive and specific diagnostics and the framework for verifying elimination of schistosomiasis; (4) accelerating the progress towards elimination of schistosomiasis and other parasitic diseases through integrating the national control programs for other parasitic diseases.