Dermatophytes were earlier reported to respond well to anti-fungal agents;however, an upsurge in resistance with the high cost of these agents increased the use of medicinal plants for treatment. This study investigat...Dermatophytes were earlier reported to respond well to anti-fungal agents;however, an upsurge in resistance with the high cost of these agents increased the use of medicinal plants for treatment. This study investigated the sensitivity pattern of dermatophytes to oral anti-fungal drugs and aqueous leaf extract of the plant, <em>Acacia nilotica</em>. The extract was tested against seven strains of dermatophytes <em>Arthroderma otae</em>, <em>Trichophyton interdigitale</em>, <em>Trichophyton mentagrophyte</em>, <em>Microsporum ferrugineum</em>, <em>Arthroderma vespertilii</em>, <em>Arthroderma quadrifidum</em>, and <em>Arthroderma multifidum</em>, previously isolated from diabetic patients. The minimum inhibitory and fungicidal concentrations of the plant extracts and the standard antifungal agents were evaluated using modifications of the broth macro dilution method of the National Committee for Clinical Laboratory Standards M38-A2 protocol. There was a significant difference in the Minimum Inhibitory concentrations (MIC) of the dermatophytes to the three antifungal drugs tested (p < 0.001). The dermatophytes were mostly susceptible to itraconazole followed by Nystatin. All the dermatophytes tested were resistant to griseofulvin. <em>Acacia nilotica</em> had an inhibitory effect on all the dermatophytes tested, and showed anti-fungal activity in a dose-dependent relationship between 0.625 - 1.25 mg/ml. Though the inhibitions of the dermatophytes were significantly higher with the standard anti-fungal drugs as compared to the plant extract (p < 0.001);however, the dermatophyte, <em>Arthroderma quadrifidum</em>, which was resistant to all the anti-fungal drugs, had the highest inhibition with <em>A. nilotica</em>. Some circulating dermatophyte strains in Nigeria are griseofulvin and/or itraconazole resistant which may influence the spread of infection and <em>A. nilotica</em> aqueous leaf extract showed a strong anti-dermatophytic activity.展开更多
In this study,we developed multiple hybrid machine-learning models to address parameter optimization limitations and enhance the spatial prediction of landslide susceptibility models.We created a geographic informatio...In this study,we developed multiple hybrid machine-learning models to address parameter optimization limitations and enhance the spatial prediction of landslide susceptibility models.We created a geographic information system database,and our analysis results were used to prepare a landslide inventory map containing 359 landslide events identified from Google Earth,aerial photographs,and other validated sources.A support vector regression(SVR)machine-learning model was used to divide the landslide inventory into training(70%)and testing(30%)datasets.The landslide susceptibility map was produced using 14 causative factors.We applied the established gray wolf optimization(GWO)algorithm,bat algorithm(BA),and cuckoo optimization algorithm(COA)to fine-tune the parameters of the SVR model to improve its predictive accuracy.The resultant hybrid models,SVR-GWO,SVR-BA,and SVR-COA,were validated in terms of the area under curve(AUC)and root mean square error(RMSE).The AUC values for the SVR-GWO(0.733),SVR-BA(0.724),and SVR-COA(0.738)models indicate their good prediction rates for landslide susceptibility modeling.SVR-COA had the greatest accuracy,with an RMSE of 0.21687,and SVR-BA had the least accuracy,with an RMSE of 0.23046.The three optimized hybrid models outperformed the SVR model(AUC=0.704,RMSE=0.26689),confirming the ability of metaheuristic algorithms to improve model performance.展开更多
文摘Dermatophytes were earlier reported to respond well to anti-fungal agents;however, an upsurge in resistance with the high cost of these agents increased the use of medicinal plants for treatment. This study investigated the sensitivity pattern of dermatophytes to oral anti-fungal drugs and aqueous leaf extract of the plant, <em>Acacia nilotica</em>. The extract was tested against seven strains of dermatophytes <em>Arthroderma otae</em>, <em>Trichophyton interdigitale</em>, <em>Trichophyton mentagrophyte</em>, <em>Microsporum ferrugineum</em>, <em>Arthroderma vespertilii</em>, <em>Arthroderma quadrifidum</em>, and <em>Arthroderma multifidum</em>, previously isolated from diabetic patients. The minimum inhibitory and fungicidal concentrations of the plant extracts and the standard antifungal agents were evaluated using modifications of the broth macro dilution method of the National Committee for Clinical Laboratory Standards M38-A2 protocol. There was a significant difference in the Minimum Inhibitory concentrations (MIC) of the dermatophytes to the three antifungal drugs tested (p < 0.001). The dermatophytes were mostly susceptible to itraconazole followed by Nystatin. All the dermatophytes tested were resistant to griseofulvin. <em>Acacia nilotica</em> had an inhibitory effect on all the dermatophytes tested, and showed anti-fungal activity in a dose-dependent relationship between 0.625 - 1.25 mg/ml. Though the inhibitions of the dermatophytes were significantly higher with the standard anti-fungal drugs as compared to the plant extract (p < 0.001);however, the dermatophyte, <em>Arthroderma quadrifidum</em>, which was resistant to all the anti-fungal drugs, had the highest inhibition with <em>A. nilotica</em>. Some circulating dermatophyte strains in Nigeria are griseofulvin and/or itraconazole resistant which may influence the spread of infection and <em>A. nilotica</em> aqueous leaf extract showed a strong anti-dermatophytic activity.
基金supported by the Basic Research Project of the Korea Institute of Geoscience and Mineral Resources(KIGAM)Project of Environmental Business Big Data Platform and Center Construction funded by the Ministry of Science and ICT。
文摘In this study,we developed multiple hybrid machine-learning models to address parameter optimization limitations and enhance the spatial prediction of landslide susceptibility models.We created a geographic information system database,and our analysis results were used to prepare a landslide inventory map containing 359 landslide events identified from Google Earth,aerial photographs,and other validated sources.A support vector regression(SVR)machine-learning model was used to divide the landslide inventory into training(70%)and testing(30%)datasets.The landslide susceptibility map was produced using 14 causative factors.We applied the established gray wolf optimization(GWO)algorithm,bat algorithm(BA),and cuckoo optimization algorithm(COA)to fine-tune the parameters of the SVR model to improve its predictive accuracy.The resultant hybrid models,SVR-GWO,SVR-BA,and SVR-COA,were validated in terms of the area under curve(AUC)and root mean square error(RMSE).The AUC values for the SVR-GWO(0.733),SVR-BA(0.724),and SVR-COA(0.738)models indicate their good prediction rates for landslide susceptibility modeling.SVR-COA had the greatest accuracy,with an RMSE of 0.21687,and SVR-BA had the least accuracy,with an RMSE of 0.23046.The three optimized hybrid models outperformed the SVR model(AUC=0.704,RMSE=0.26689),confirming the ability of metaheuristic algorithms to improve model performance.
