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dc.creatorPerić, Mirjana
dc.creatorRajković, Katarina
dc.creatorMilić-Lemić, Aleksandra
dc.creatorŽivković, Rade
dc.creatorArsić-Arsenijević, Valentina
dc.date.accessioned2020-07-02T13:27:08Z
dc.date.available2020-07-02T13:27:08Z
dc.date.issued2019
dc.identifier.issn0003-9969
dc.identifier.urihttp://smile.stomf.bg.ac.rs/handle/123456789/2470
dc.description.abstractObjective: 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.en
dc.publisherPergamon-Elsevier Science Ltd, Oxford
dc.relationinfo:eu-repo/grantAgreement/MESTD/Basic Research (BR or ON)/175034/RS//
dc.rightsrestrictedAccess
dc.sourceArchives of Oral Biology
dc.subjectCandido speciesen
dc.subjectEssential oilsen
dc.subjectResponse surface methodologyen
dc.subjectArtificial neural networken
dc.titleDevelopment and validation of mathematical models for testing antifungal activity of different essential oils against Candida speciesen
dc.typearticle
dc.rights.licenseARR
dcterms.abstractМилић-Лемић, Aлександра; Рајковић, Катарина; Живковић, Раде; Aрсић-Aрсенијевић, Валентина; Перић, Мирјана;
dc.citation.volume98
dc.citation.spage258
dc.citation.epage264
dc.citation.other98: 258-264
dc.citation.rankM22
dc.identifier.wos000457664000036
dc.identifier.doi10.1016/j.archoralbio.2018.11.029
dc.identifier.pmid30530237
dc.identifier.scopus2-s2.0-85057870829
dc.identifier.rcubconv_3600
dc.type.versionpublishedVersion


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