Chemical composition and antifungal activity of essential oil from Eucalyptus smithii against dermatophytes

ABSTRACT INTRODUCTION: In this study, we evaluated the chemical composition of a commercial sample of essential oil from Eucalyptus smithii R.T. Baker and its antifungal activity against Microsporum canis ATCC 32903, Microsporum gypseum ATCC 14683, Trichophyton mentagrophytes ATCC 9533, T. mentagrophytes ATCC 11480, T. mentagrophytes ATCC 11481, and Trichophyton rubrum CCT 5507. METHODS: Morphological changes in these fungi after treatment with the oil were determined by scanning electron microscopy (SEM). The antifungal activity of the oil was determined on the basis of minimum inhibitory concentration (MIC) and minimum fungicidal concentration (MFC) values. RESULTS: The compound 1,8-cineole was found to be the predominant component (72.2%) of the essential oil. The MIC values of the oil ranged from 62.5μg·mL−1 to >1,000μg·mL−1, and the MFC values of the oil ranged from 125μg·mL−1 to >1,000μg·mL−1. SEM analysis showed physical damage and morphological alterations in the fungi exposed to this oil. CONCLUSIONS: We demonstrated the potential of Eucalyptus smithii essential oil as a natural therapeutic agent for the treatment of dermatophytosis

Road Networks Management under Uncertainty: A stochastic based model

Current pavement management systems (PMS) adopted by the Road Authorities are often very complex and data intensive. Other challenges also faced by Road Authorities in managing road networks include budget constraints and the uncertainty associated in predicting the future performance of pavements. In addition, the emphasis in pavement management has shifted from reconstructing completely new roads towards preservation of existing networks. In many cases, existing PMS do not meet these requirements. Thus, an efficient model that is able to accommodate all of those challenges needs to be developed. This paper outlines the development of a stochastic based PMS that includes a performance prediction model using Markov chains and an optimization model based on Markov Decision Processes (MDP). Combinations of pavement preservation strategies and maintenance budget levels are applied as action criteria in contrast to other stochastic models. Despite the apparent influence of uncertainty in road pavement performance during their service live, stochastic models provide promising results for enhancing current PMS. By analysing historical data, the future behaviour of road pavements under different expenditure levels and combination of routine and periodic maintenance measures can be predicted. From an optimization point of view, the utilization of constrained MDP will potentially result in cost savings. This is due to the optimality principal of the model which is capable of finding a optimal multi-year maintenance policy through the direct inclusion of additional constraints into the optimization problem. Hence, the model considers constraints and incorporates relationships between historical maintenance actions and costs. This paper also presents a methodology for developing rationale for long-term maintenance policies by integrating stochastic based performance prediction and optimization models with the experience of Road Authorities in managing roads networks