Analysis of Research Status and Development Trend of Nanotoxicology of Liliaceae Medicinal Plants

Liu, Chaoqun; Yin-quan, Wang; Liu, Dongling; Yang, Tao; Tang, ZhanWen

doi:10.1155/2022/9777817

Cited by 3 publications

(3 citation statements)

References 22 publications

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“…Interestingly a striking and stochastic flower formula also was observed from one flower with 8 tepals (four inner petals and four outer sepals), 8 stamen and 4 lobed stigma at 'Valverde'. In general flowers of Lilium L. are perfect and contain six petaloid tepals (three inner petals and three outer sepals), six stamens and a superior three-celled ovary with a three-lobed stigma (Simpson, 2010;Liang and Mahadevan, 2011;Dhiman et al, 2021;Liu et al, 2022). It is necessary to share here only one flower from a plant, which had eight tepals (four inner petals and four outer sepals), eight stamens and four lobed stigma, we encountered interestingly during the study.…”

Section: (A) (B)mentioning

confidence: 86%

Agro-mophological characterization, adaptational behaviour and flower abnormalities of Lilium L. cultivars in addition to a flower with stochastic flower formula

CAVUSOGLU

2023

Not Bot Horti Agrobo

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This experiment was undertaken to determine the adaptation of Lilium L. cultivars to North-Western part of Türkiye and to define morphologic plant growth characteristics at aboveground part, including flower life and flower abnormalities. The bulbs of the three Lilium L. cultivars, ‘Profundo’, ‘Valverde’, and ‘Vonq’, were sown to perlite in pots in June, 2021, and evaluated for emergence and flowering rates, stem and plant height, stem and flower diameter, leaf and flower number, flower abnormality rates, flower fragrance degree and flower life in 2022, after one year adaptation without removing the previous sown bulbs. As the results of the cultivars differences these were determined that, at the adaptive year; the emergence rates varied between 60.42-89.58%, flowering rates changed between 43.75-87.50%, stem heights changed between 40.63-48.25 cm, plant heights changed between 52.50-59.75 cm and these data showed statistically importance. In addition to the data in the adaptive year; stem diameters, leaf numbers, flower numbers flower fragrance degree and flower longevities also showed statistical importance between cultivars. The flower abnormality rates were found between 8.3-25% in cultivars although no statistical results. Interestingly a striking, stochastic case also was observed from a flower with 8 tepals, 8 stamen and 4 lobed stigma at ‘Valverde’ contrary to well-known flower formula. It has been concluded that ‘Profundo’ has more remarkable result in terms of longevity of flower compared to the other two varieties while ‘Valverde’ and ‘Vonq’ shared the same group with some precedence in most of the agro-morphologic characteristics e.g. plant height, stem height and stem diameter. Lilium L. ‘Vonq’ showed that the highest adaptive capacity among the cultivars with the highest emergence rate (89.58) in all sowed bulbs and flowering rate (87.50) in all emerged bulbs under the study conditions in the open-air.

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Section: (A) (B)mentioning

confidence: 86%

Agro-mophological characterization, adaptational behaviour and flower abnormalities of Lilium L. cultivars in addition to a flower with stochastic flower formula

CAVUSOGLU

2023

Not Bot Horti Agrobo

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“…This article has been retracted by Hindawi following an investigation undertaken by the publisher [ 1 ]. This investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process: Discrepancies in scope Discrepancies in the description of the research reported Discrepancies between the availability of data and the research described Inappropriate citations Incoherent, meaningless and/or irrelevant content included in the article Peer-review manipulation …”

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confidence: 99%

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confidence: 99%

Retracted: Analysis of Research Status and Development Trend of Nanotoxicology of Liliaceae Medicinal Plants

International¹

2023

BioMed Research International

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Diabetes Prediction Models Based on Machine Learning

Bobo,

Xiaofei,

Zongyue

et al. 2023

Journal of Big Data and Computing

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Objective to compare the predictive efficacy of random forest, BP neural network, gradient boosting tree and plain Bayesian models for the prevalence of diabetes. Practical application: by measuring the basic indicators such as individual height, weight, triglyceride, etc., the model can be used to predict the probability of individual disease, and then targeted to improve some indicators of the body, to achieve the effect of diabetes prevention intervention, and to provide new ideas for diabetes prevention research. Methods Using the 2009 survey data from the China Health and Nutrition Survey (CHNS), the data for men and women were statistically analyzed by dividing them into four groups according to the visceral fat index (VAI). Subsequently, the processed samples were divided into training sets and test sets by 4:1, and four machine learning models, namely, random forest, BP neural network, gradient lifting tree, and naive Bayes, were constructed. The experiment was conducted using a five-fold cross validation method, and the prediction effect was evaluated through indicators such as sensitivity, accuracy, and AUC. Results One-way ANOVA showed that the differences in height, weight, waist circumference, triglycerides, high-density lipoprotein cholesterol, body mass index, fasting blood glucose, and glycosylated hemoglobin among different VAI quartile groups were statistically significant (P<0.05). Comparison of prediction effects of four models: sensitivity 75.75%, 90.77%, 76.31%, 98.57%, accuracy 74.80%, 87.82%, 74.64%, 92.00%, AUC 0.713, 0.716, 0.668, 0.676, and Jorden index 0.34, 0.27, 0.22 and 0.21. Conclusion Based on the CHNS 2009 survey data, the BP neural network model has a better effect and stability in predicting diabetes.

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Analysis of Research Status and Development Trend of Nanotoxicology of Liliaceae Medicinal Plants

Cited by 3 publications

References 22 publications

Agro-mophological characterization, adaptational behaviour and flower abnormalities of Lilium L. cultivars in addition to a flower with stochastic flower formula

Agro-mophological characterization, adaptational behaviour and flower abnormalities of Lilium L. cultivars in addition to a flower with stochastic flower formula

Retracted: Analysis of Research Status and Development Trend of Nanotoxicology of Liliaceae Medicinal Plants

Diabetes Prediction Models Based on Machine Learning

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