Browsing by Author "Sahgal, Natasha"

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    Fungal volatile fingerprints: discrimination between dermatophyte species and strains by means of an electronic nose.
    (Elsevier, 2008-04-14) Sahgal, Natasha; Magan, Naresh
    The potential of an electronic nose (e-nose) consisting of a hybrid gas sensor array system has been examined for species discrimination and strain identification of dermatophytes which are causative pathogens for human and animal infection. Temporal volatile production patterns have been studied at a species level for a Microsporum species, two Trichophyton species and at a strain level for the two Trichophyton species. After about 120 h principal component analysis (PCA) and cluster analysis showed possible discrimination between the species from controls. Data analysis also indicated probable differentiation between the strains of T. rubrum. The same could not however be achieved for the strains of T. mentagrophytes during preliminary experiments for the same time period, signifying a good similarity between the strains of this particular species based on their volatile fingerprints. This study suggests that volatile production patterns shows promise for species and strain identification of these dermatophytic fungi thereby facilitating early diagnosis and early management of patients.
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    Microbial and non-microbial volatile fingerprints: Potential clinical applications of electronic nose for early diagnoses and detection of diseases
    (Cranfield University, 2008-01) Sahgal, Natasha; Magan, Naresh
    This is the first study to explore the potential applications of using qualitative volatile fingerprints (electronic nose) for early detection and diagnosis of diseases such as dermatophytosis, ventilator associated pneumonia and upper gastrointestinal cancer. The investigations included in vitro analysis of various dermatophyte species and strains, antifungal screening, bacterial cultures and associated clinical specimens and oesophageal cell lines. Mass spectrometric analyses were attempted to identify possible markers. The studies that involved e-nose comparisons indicated that the conducting polymer system was unable to differentiate between any of the treatments over the experimental period (120 hours). Metal oxide-based sensor arrays were better suited and differentiated between four dermatophyte species within 96 hours of growth using principal component analysis and cluster analysis (Euclidean distance and Ward’s linkage) based on their volatile profile patterns. Studies on the sensitivity of detection showed that for Trichophyton mentagrophytes and T. rubrum it was possible to differentiate between log3, log5 and log7 inoculum levels within 96 hours. The probabilistic neural network model had a high prediction accuracy of 88 to 96% depending on the number of sensors used. Temporal volatile production patterns studied at a species level for a Microsporum species, two Trichophyton species and at a strain level for the two Trichophyton species; showed possible discrimination between the species from controls after 120 hours. The predictive neural network model misclassified only one sample. Data analysis also indicated probable differentiation between the strains of T. rubrum while strains of T. mentagrophytes clustered together showing good similarity between them. Antifungal treatments with itraconazole on T. mentagrophytes and T. rubrum showed that the e-nose could differentiate between untreated fungal species from the treated fungal species at both temperatures (25 and 30°C). However, the different antifungal concentrations of 50% fungal inhibition and 2 ppm could not be separated from each other or the controls based on their volatiles. Headspace analysis of bacterial cultures in vitro indicated that the e-nose could differentiate between the microbial species and controls in 83% of samples (n=98) based on a four group model (gram-positive, gram-negative, fungi and no growth). Volatile fingerprint analysis of the bronchoalveolar lavage fluid accurately separated growth and no growth in 81% of samples (n=52); however only 63% classification accuracy was achieved with a four group model. 12/31 samples were classified as infected by the e-nose but had no microbiological growth, further analysis suggested that the traditional clinical pulmonary infection score (CPIS) system correlated with the e-nose prediction of infection in 68% of samples (n=31). No clear distinction was observed between various human cell lines (oesophageal and colorectal) based on volatile fingerprints within one to four hours of incubation, although they were clearly separate from the blank media. However, after 24 hours one of the cell lines could be clearly differentiated from the others and the controls. The different gastrointestinal pathologies (forming the clinical samples) did not show any specific pattern and thus could not be distinguished. Mass spectrometric analysis did not detect distinct markers within the fungal and cell line samples, but potential identifiers in the fungal species such as 3-Octanone, 1-Octen-3-ol and methoxybenzene including high concentration of ammonia, the latter mostly in T. mentagrophytes, followed by T. rubrum and Microsporum canis, were found. These detailed studies suggest that the approach of qualitative volatile fingerprinting shows promise for use in clinical settings, enabling rapid detection/diagnoses of diseases thus eventually reducing the time to treatment significantly.
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    Table olives volatile fingerprints: Potential of an electronic nose for quality discrimination.
    (Elsevier, 2008-09-25) Panagou, Efstathios Z.; Sahgal, Natasha; Magan, Naresh; Nychas, George-John E.
    In the present work, the potential of an electronic nose to differentiate the quality of fermented green table olives based on their volatile profile was investigated. An electronic gas sensor array system comprising a hybrid sensor array of 12 metal oxide and 10 metal ion-based sensors was used to generate a chemical fingerprint (pattern) of the volatile compounds present in olives. Multivariate statistical analysis and artificial neural networks were applied to the generated patterns to achieve various classification tasks. Green olives were initially classified into three major classes (acceptable, unacceptable, marginal) based on a sensory panel. Multivariate statistical approach showed good discrimination between the class of unacceptable samples and the classes of acceptable and marginal samples. However, in the latter two classes there was a certain area of overlapping in which no clear differentiation could be made. The potential to discriminate green olives in the three selected classes was also evaluated using a multilayer perceptron (MLP) neural network as a classifier with an 18–15–8–3 structure. Results showed good performance of the developed network as only two samples were misclassified in a 66-sample training dataset population, whereas only one case was misclassified in a 12-sample test dataset population. The results of this study provide promising perspectives for the use of a low-cost and rapid system for quality differentiation of fermented green olives based on their volatile profile.
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    Trichophyton species: use of volatile fingerprints for rapid identification and discrimination.
    (Blackwell Publishing, 2006-12) Sahgal, Natasha; Monk, Barry; Wasil, Mohammad; Magan, Naresh
    Background: Fungal infection of the skin is a common clinical problem, and laboratory confirmation of the diagnosis is important to ensure appropriate treatment. The identification of the species of fungus is also important, because different fungal species have different modes of transmission, and this may be of importance both in preventing re-infection or in avoidance of infection of others. Objective: This study examined the potential of using volatile production patterns for the detection and discrimination between four Trichophyton species (T. mentagrophytes, T. rubrum, T. verrucosum and T. violaceum) in vitro on solid media and in broth culture. Methods: Two different sensor array systems (conducting polymer and metal oxide sensors) were examined for comparing the qualitative volatile fingerprints produced by these species over periods of 24-120 hrs in the headspace. The relative sensitivity of detection of two of the species (T. mentagrophytes, T. rubrum) was determined for log1 to log7 inoculum levels over the same time period. Results: The conducting polymer based system was unable to differentiate between species based on volatile fingerprints over the experimental period. However, metal oxide-based sensor arrays were found to be able to differentiate between the four species within 96 hrs of growth using PCA analysis which accounted for approximately 93% of the data in PC1 and 2 based on the qualitative volatile production patterns. This differentiation was confirmed by the Cluster analysis of the data using Euclidean distance and Ward’s linkage. Studies of the sensitivity of detection showed that for T. mentagrophytes and T. rubrum it was possible to differentiate between log3, log5 and log7 inoculum levels within 96 hrs. Conclusions: This is the first detailed study of the use of qualitative volatile fingerprints for identification and discrimination of dermatophytes. This approach could have potential for rapid identification of patient samples reducing significantly the time to treatment.
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    Use of volatile fingerprints for rapid screening of antifungal agents for efficacy against dermatophyte Trichophyton species
    (Elsevier Science B.V., Amsterdam., 2010-04-29T00:00:00Z) Naraghi, Kamran; Sahgal, Natasha; Adriaans, Beverley; Barr, Hugh; Magan, Naresh
    The potential of using an electronic nose (E-nose) as a rapid technique for screening the responses of dermatophytes to antifungal agents was studied. In vitro, the 50% and 90% effective concentration (EC) values of five antifungal agents including fungicides and antioxidant mixtures against Trichophyton rubrum and Trichophyton mentagrophytes were obtained by mycelial growth assays. The qualitative volatile production patterns of the growth responses of these fungi to the EC values incorporated into solid media were analysed after 96-120h incubation at 25°C using headspace analyses using five replicates per treatment. Overall, results, using principal components analysis and cluster analysis, demonstrated that it was possible to differentiate between various treatments within 96-120h of growth. The EC50 values were discriminated from the controls while the EC90 concentration treatments were often grouped with the agar blanks because of very slow growth. This study showed that potential exists for using qualitative volatile patterns as a rapid screening method for antifungal agents against micro-organisms. This approach could significantly improve and facilitate the monitoring of antimicrobial drug activities and infection control programmes and perhaps also for monitoring of drug resistance buildup in microbial populations