A DERMATOLOGICAL EVALUATION OF SYNTHETIC PYRETHROID INSECTICIDES (PARESTHESIA, FENVALERATE, PERMETHRIN)

In this investigation, differences in parasthesia were detected by human participants between synthetic pyrethroids with a cyano group in the (S)-configuration of the 3-phenoxybenzyl alcohol of their molecular structure (fenvalerate) and those that do not (permethrin). A strong relationship was noted between insecticidal potency and degree of induced cutaneous sensation for the alpha-cyano and non-cyano pyrethroids, with a prominent difference between the two. A linear correlation between concentration and degree of induced dysesthesia was observed for both pyrethroids. Regressing the cutaneous sensation on the common logarithm of concentration resulted in a regression equation of Y = 84.0 + 31.0X(,1) for fenvalerate and Y = 27.5 + 15.8X(,1) for permethrin. An evaluation for dermal cytotoxicity in albino rabbits yielded a slight increase in cutaneous perfusion as indicated both visually and by laser Doppler velocimetry. However, no significant difference was detected in edema or thermal variation. Histopathological alterations were minimal after repeated daily applications with the majority of changes involving acanthosis. A highly efficacious therapeutic agent for pyrethroid exposure was noted to be dl-alpha tocopherol acetate. An impressive degree of inhibition of parasthesia resulted from the topical application of vitamin E acetate, with a therapeutic index of almost 100%. ^

An empirical evaluation of the Random Forests classifier models for variable selection in a large-scale lung cancer case-control study

Random Forests™ is reported to be one of the most accurate classification algorithms in complex data analysis. It shows excellent performance even when most predictors are noisy and the number of variables is much larger than the number of observations. In this thesis Random Forests was applied to a large-scale lung cancer case-control study. A novel way of automatically selecting prognostic factors was proposed. Also, synthetic positive control was used to validate Random Forests method. Throughout this study we showed that Random Forests can deal with large number of weak input variables without overfitting. It can account for non-additive interactions between these input variables. Random Forests can also be used for variable selection without being adversely affected by collinearities. ^ Random Forests can deal with the large-scale data sets without rigorous data preprocessing. It has robust variable importance ranking measure. Proposed is a novel variable selection method in context of Random Forests that uses the data noise level as the cut-off value to determine the subset of the important predictors. This new approach enhanced the ability of the Random Forests algorithm to automatically identify important predictors for complex data. The cut-off value can also be adjusted based on the results of the synthetic positive control experiments. ^ When the data set had high variables to observations ratio, Random Forests complemented the established logistic regression. This study suggested that Random Forests is recommended for such high dimensionality data. One can use Random Forests to select the important variables and then use logistic regression or Random Forests itself to estimate the effect size of the predictors and to classify new observations. ^ We also found that the mean decrease of accuracy is a more reliable variable ranking measurement than mean decrease of Gini. ^