TY  - JOUR
AU  - Perić, Mirjana
AU  - Rajković, Katarina
AU  - Milić-Lemić, Aleksandra
AU  - Živković, Rade
AU  - Arsić-Arsenijević, Valentina
PY  - 2019
UR  - https://smile.stomf.bg.ac.rs/handle/123456789/2470
AB  - Objective: The upward trend in using plant materials introduced essential oils (EOs) as a valuable, novel, bioactive antifungal agent and as an alternative to standard treatment protocol of denture stomatitis caused by Candida species. Therefore, the aim was to evaluate the antifungal activity of different EOs and to present the response surface methodology (RSM) and artificial neural network (ANN) as possible tools for optimizing and predicting EOs antifungal activity. Methods: Minimum inhibitory concentration (MIC) and Minimum fungicidal concentration (MFC) of the EOs against 3 species Candida spp. (C. albicans, C. tropicalis, C. glabrata) isolated in patients with DS were determined, together with optimization and prediction based on non-terpene and terpene content in EOs, using two mathematical models RSM and ANN-GA. Results: The highest concentrations of EO M. alternifolia inhibited (1.6-2.8 mu g/ml) and fungicided (3.5-6.0 mu g/ml) all three investigated Candida spp, while the lowest concentrations of EO C. Limon inhibited (0.2-0.5 mu g/ml) and fungicided (0.6-1.1 mu g/ml). Among the three types of Candida, C. glabrata was the most sensitive. The RSM modelling proved that MICs and MFCs statistically depend on non-terpene and terpene content in different EOs ( lt  0.0001). Both models showed that a citrus oil (EO C. Limon) with 89% content of terpenes and limonene as major constituent was more antifungal efficient. Conclusions: The investigated EOs showed a broad spectrum of anticandidal activity, also confirmed using the RSM and ANN-GA models. Since EOs can be cytotoxic in higher concentrations, models may be used for qualitative and quantitative dosage predictions of the antifungal activity of EOs.
PB  - Pergamon-Elsevier Science Ltd, Oxford
T2  - Archives of Oral Biology
T1  - Development and validation of mathematical models for testing antifungal activity of different essential oils against Candida species
VL  - 98
SP  - 258
EP  - 264
DO  - 10.1016/j.archoralbio.2018.11.029
ER  - 

@article{
author = "Perić, Mirjana and Rajković, Katarina and Milić-Lemić, Aleksandra and Živković, Rade and Arsić-Arsenijević, Valentina",
year = "2019",
abstract = "Objective: The upward trend in using plant materials introduced essential oils (EOs) as a valuable, novel, bioactive antifungal agent and as an alternative to standard treatment protocol of denture stomatitis caused by Candida species. Therefore, the aim was to evaluate the antifungal activity of different EOs and to present the response surface methodology (RSM) and artificial neural network (ANN) as possible tools for optimizing and predicting EOs antifungal activity. Methods: Minimum inhibitory concentration (MIC) and Minimum fungicidal concentration (MFC) of the EOs against 3 species Candida spp. (C. albicans, C. tropicalis, C. glabrata) isolated in patients with DS were determined, together with optimization and prediction based on non-terpene and terpene content in EOs, using two mathematical models RSM and ANN-GA. Results: The highest concentrations of EO M. alternifolia inhibited (1.6-2.8 mu g/ml) and fungicided (3.5-6.0 mu g/ml) all three investigated Candida spp, while the lowest concentrations of EO C. Limon inhibited (0.2-0.5 mu g/ml) and fungicided (0.6-1.1 mu g/ml). Among the three types of Candida, C. glabrata was the most sensitive. The RSM modelling proved that MICs and MFCs statistically depend on non-terpene and terpene content in different EOs ( lt  0.0001). Both models showed that a citrus oil (EO C. Limon) with 89% content of terpenes and limonene as major constituent was more antifungal efficient. Conclusions: The investigated EOs showed a broad spectrum of anticandidal activity, also confirmed using the RSM and ANN-GA models. Since EOs can be cytotoxic in higher concentrations, models may be used for qualitative and quantitative dosage predictions of the antifungal activity of EOs.",
publisher = "Pergamon-Elsevier Science Ltd, Oxford",
journal = "Archives of Oral Biology",
title = "Development and validation of mathematical models for testing antifungal activity of different essential oils against Candida species",
volume = "98",
pages = "258-264",
doi = "10.1016/j.archoralbio.2018.11.029"
}

Perić, M., Rajković, K., Milić-Lemić, A., Živković, R.,& Arsić-Arsenijević, V.. (2019). Development and validation of mathematical models for testing antifungal activity of different essential oils against Candida species. in Archives of Oral Biology
Pergamon-Elsevier Science Ltd, Oxford., 98, 258-264.
https://doi.org/10.1016/j.archoralbio.2018.11.029

Perić M, Rajković K, Milić-Lemić A, Živković R, Arsić-Arsenijević V. Development and validation of mathematical models for testing antifungal activity of different essential oils against Candida species. in Archives of Oral Biology. 2019;98:258-264.
doi:10.1016/j.archoralbio.2018.11.029 .

Perić, Mirjana, Rajković, Katarina, Milić-Lemić, Aleksandra, Živković, Rade, Arsić-Arsenijević, Valentina, "Development and validation of mathematical models for testing antifungal activity of different essential oils against Candida species" in Archives of Oral Biology, 98 (2019):258-264,
https://doi.org/10.1016/j.archoralbio.2018.11.029 . .

TY  - CONF
AU  - Barac, Aleksandra
AU  - Pekmezović, Marina
AU  - Rajković, Katarina
AU  - Rakočević, Zoran
AU  - Janović, Aleksa
AU  - Bracanović, Đurđa
AU  - Stošović, Rajica
AU  - Spirić, Tomić, V
PY  - 2016
UR  - https://smile.stomf.bg.ac.rs/handle/123456789/2085
PB  - Wiley-Blackwell, Hoboken
C3  - Allergy
T1  - Mathematical modeling and image analysis: possible clinical application in practice as a predictor of fungal rhinosinusitis
VL  - 71
SP  - 426
EP  - 426
UR  - https://hdl.handle.net/21.15107/rcub_smile_2085
ER  - 

@conference{
author = "Barac, Aleksandra and Pekmezović, Marina and Rajković, Katarina and Rakočević, Zoran and Janović, Aleksa and Bracanović, Đurđa and Stošović, Rajica and Spirić, Tomić, V",
year = "2016",
publisher = "Wiley-Blackwell, Hoboken",
journal = "Allergy",
title = "Mathematical modeling and image analysis: possible clinical application in practice as a predictor of fungal rhinosinusitis",
volume = "71",
pages = "426-426",
url = "https://hdl.handle.net/21.15107/rcub_smile_2085"
}

Barac, A., Pekmezović, M., Rajković, K., Rakočević, Z., Janović, A., Bracanović, Đ., Stošović, R.,& Spirić, T. V.. (2016). Mathematical modeling and image analysis: possible clinical application in practice as a predictor of fungal rhinosinusitis. in Allergy
Wiley-Blackwell, Hoboken., 71, 426-426.
https://hdl.handle.net/21.15107/rcub_smile_2085

Barac A, Pekmezović M, Rajković K, Rakočević Z, Janović A, Bracanović Đ, Stošović R, Spirić TV. Mathematical modeling and image analysis: possible clinical application in practice as a predictor of fungal rhinosinusitis. in Allergy. 2016;71:426-426.
https://hdl.handle.net/21.15107/rcub_smile_2085 .

Barac, Aleksandra, Pekmezović, Marina, Rajković, Katarina, Rakočević, Zoran, Janović, Aleksa, Bracanović, Đurđa, Stošović, Rajica, Spirić, Tomić, V, "Mathematical modeling and image analysis: possible clinical application in practice as a predictor of fungal rhinosinusitis" in Allergy, 71 (2016):426-426,
https://hdl.handle.net/21.15107/rcub_smile_2085 .