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R Saskin

Publications and source records attributed to R Saskin.

2 recordsLinked to original sources

Novel murine model of pneumococcal pneumonia: use of temperature as a measure of disease severity to compare the efficacies of moxifloxacin and levofloxacin.

Surface temperature measured by an infrared temperature-scanning thermometer was used to evaluate disease severity and predict imminent death in a murine model of pneumococcal pneumonia. We showed that a decrease in temperature was associated with increasing severity of disease and concomitant histological changes and also that a temperature of 30 degrees C or less was a predictor of death. Furthermore, viable bacterial counts in the lungs of mice euthanized at a temperature of < or = 30 degrees C were not significantly different from those seen in the lungs of mice allowed to die without intervention. These data support temperature change as a more subtle indicator of outcome than death and demonstrate that this could be used as a reliable end point for euthanasia. To test the utility of our model in a drug trial, we examined the efficacies of moxifloxacin and levofloxacin by using temperature as a measure of disease severity prior to and during treatment. Regardless of the antibiotic used, mice assessed as moderately ill (temperature > or = 32 degrees C) at the start of treatment had better clinical and bacteriological outcomes than mice assessed as severely ill (temperature < 32 degrees C). However, moxifloxacin offered better protection and greater bacterial clearance than did levofloxacin in all infected mice independent of disease severity. This model not only allows a more subtle evaluation of drug efficacy but also ensures a better degree of standardization and a more humane approach to drug efficacy studies involving animals.

Animals↗

Case-control analysis with partial knowledge of exposure misclassification probabilities.

Consider case control analysis with a dichotomous exposure variable that is subject to misclassification. If the classification probabilities are known, then methods are available to adjust odds-ratio estimates in light of the misclassification. We study the realistic scenario where reasonable guesses, but not exact values, are available for the classification probabilities. If the analysis proceeds by simply treating the guesses as exact, then even small discrepancies between the guesses and the actual probabilities can seriously degrade odds-ratio estimates. We show that this problem is mitigated by a Bayes analysis that incorporates uncertainty about the classification probabilities as prior information.

Bayes Theorem↗