Do essential oils from plants occurring in the Brazilian Caatinga biome present antifungal potential against dermatophytoses? A systematic review

Pereira, Fillipe de Oliveira; Teixeira, Anna Paula de Castro; Medeiros, Francinalva Dantas de

doi:10.1007/s00253-021-11530-5

Cited by 2 publications

(2 citation statements)

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“…The main compounds in the C. zehntneri oil are estragole and anethole (Fontenelle et al 2008), which may be responsible for inhibiting the growth of dermatophyte and saprophyte fungi. However, the inhibitory concentrations against fungi have been high, i.e., between 25,000 and 100,000 µg mL -1 (Pereira et al 2021). In this study, the concentration of 2,000 µg mL -1 was moderately effective in inhibiting the growth of the fungi evaluated, showing an excellent potential in controlling phytopathogenic fungi.…”

Section: Resultsmentioning

confidence: 58%

Ethanolic extract of Copaifera, Croton and Lippia on the control of phytopathogenic fungi

Pires

Silva

Sousa

et al. 2023

Pesqui. Agropecu. Trop.

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The search for vegetable extracts for phytosanitary control has been expanded to find new active ingredients to control plant diseases. This study aimed to evaluate the in vitro effect of the fixed constituents of Copaifera luetzelburgii, Croton zehntneri and Lippia lasiocalycina, at the concentrations of 2, 20, 200 and 2,000 μg mL-1, on the percentage of mycelial growth inhibition of Colletotrichum siamense, C. truncatum, Fusarium sacchari, F. udum, Lasiodiplodia theobromae and Thielaviopsis ethacetica, as well as the conidium concentration of C. siamense, F. sacchari and F. udum produced in culture medium with all the extracts. The tested ethanolic extract, especially at the highest concentration, inhibited the percentage of mycelial growth and/or conidium concentration of the evaluated fungi. The other concentrations showed low inhibitory effects or no activity against the fungi. The average values for percentage of mycelial growth inhibition of the ethanolic extract from L. lasiocalycina, C. zehntneri and C. luetzelburgii against the six fungi were 62.5, 53.4 and 51.0 %, respectively. The ethanolic extract of L. lasiocalycina showed the most significant effect on the percentage of mycelial growth inhibition and conidia concentration. The fixed constituents of C. luetzelburgii, C. zehntneri and L. lasiocalycina at 2,000 μg mL-1 showed to be efficient in inhibiting the mycelial growth of C. siamense, C. truncatum, F. sacchari, F. udum, L. theobromae and T. ethacetica, and inhibit the conidia production of C. siamense, F. sacchari and F. udum.

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Section: Resultsmentioning

confidence: 58%

Ethanolic extract of Copaifera, Croton and Lippia on the control of phytopathogenic fungi

Pires

Silva

Sousa

et al. 2023

Pesqui. Agropecu. Trop.

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“…The aim was to identify a time series function, which might identify patterns and seasonality in specific vegetables, as well as explore supply and demand variables ( Bagis et al., 2012 ) ( Jha and Sinha, 2013 ) ( Young, 2019 ). In addition, many researchers proposed a methodology that uses Average Pearson Correlation (APC) and Coefficient of Variance (CV) to determine indications that reveal crop price fluctuation ( Pereira et al., 2021 ). All these methods require the dataset to be extremely clearly described, which is difficult to generate in the context of Bangladesh.…”

Section: Related Workmentioning

confidence: 99%

Ensemble machine learning-based recommendation system for effective prediction of suitable agricultural crop cultivation

et al. 2023

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Agriculture is the most critical sector for food supply on the earth, and it is also responsible for supplying raw materials for other industrial productions. Currently, the growth in agricultural production is not sufficient to keep up with the growing population, which may result in a food shortfall for the world’s inhabitants. As a result, increasing food production is crucial for developing nations with limited land and resources. It is essential to select a suitable crop for a specific region to increase its production rate. Effective crop production forecasting in that area based on historical data, including environmental and cultivation areas, and crop production amount, is required. However, the data for such forecasting are not publicly available. As such, in this paper, we take a case study of a developing country, Bangladesh, whose economy relies on agriculture. We first gather and preprocess the data from the relevant research institutions of Bangladesh and then propose an ensemble machine learning approach, called K-nearest Neighbor Random Forest Ridge Regression (KRR), to effectively predict the production of the major crops (three different kinds of rice, potato, and wheat). KRR is designed after investigating five existing traditional machine learning (Support Vector Regression, Naïve Bayes, and Ridge Regression) and ensemble learning (Random Forest and CatBoost) algorithms. We consider four classical evaluation metrics, i.e., mean absolute error, mean square error (MSE), root MSE, and R2, to evaluate the performance of the proposed KRR over the other machine learning models. It shows 0.009 MSE, 99% R2 for Aus; 0.92 MSE, 90% R2 for Aman; 0.246 MSE, 99% R2 for Boro; 0.062 MSE, 99% R2 for wheat; and 0.016 MSE, 99% R2 for potato production prediction. The Diebold–Mariano test is conducted to check the robustness of the proposed ensemble model, KRR. In most cases, it shows 1% and 5% significance compared to the benchmark ML models. Lastly, we design a recommender system that suggests suitable crops for a specific land area for cultivation in the next season. We believe that the proposed paradigm will help the farmers and personnel in the agricultural sector leverage proper crop cultivation and production.

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Do essential oils from plants occurring in the Brazilian Caatinga biome present antifungal potential against dermatophytoses? A systematic review

Cited by 2 publications

References 60 publications

Ethanolic extract of Copaifera, Croton and Lippia on the control of phytopathogenic fungi

Ethanolic extract of Copaifera, Croton and Lippia on the control of phytopathogenic fungi

Ensemble machine learning-based recommendation system for effective prediction of suitable agricultural crop cultivation

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