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Biomedical subjects

H M Marks

Publications and source records attributed to H M Marks.

7 recordsLinked to original sources
PubMed2005

Accounting for inherent variability of growth in microbial risk assessment.

Risk assessments of pathogens need to account for the growth of small number of cells under varying conditions. In order to determine the possible risks that occur when there are small numbers of cells, stochastic models of growth are needed that would capture the distribution of the number of cells over replicate trials of the same scenario or environmental conditions. This paper provides a simple stochastic growth model, accounting only for inherent cell-growth variability, assuming constant growth kinetic parameters, for an initial, small, numbers of cells assumed to be transforming from a stationary to an exponential phase. Two, basic, microbial sets of assumptions are considered: serial, where it is assume that cells transform through a lag phase before entering the exponential phase of growth; and parallel, where it is assumed that lag and exponential phases develop in parallel. The model is based on, first determining the distribution of the time when growth commences, and then modelling the conditional distribution of the number of cells. For the latter distribution, it is found that a Weibull distribution provides a simple approximation to the conditional distribution of the relative growth, so that the model developed in this paper can be easily implemented in risk assessments using commercial software packages.

Bacteria↗
PubMed2001

Modeling non-linear survival curves to calculate thermal inactivation of salmonella in poultry of different fat levels.

Survival curves of a cocktail of eight serotypes of Salmonella in ground poultry of different fat levels (1-12%), when heated rapidly to specified temperatures (58-65 degrees C), were examined. Because many of the survival curves were concave, values for two parameters: the asymptotic D-value and the "lag" times were estimated and used to develop secondary models for estimating the time needed to obtain a 7 log10 relative reduction as a function of fat level and temperature. To compute the necessary time, at a given temperature and fat level, the estimated lag time should be added to the product of 7 and the estimated asymptotic D-value. A model was also developed for estimating the standard error of the estimated times, so that upper confidence bounds for the necessary times can be computed. It was found that lag times increase with higher fat levels. The effect of fat on D-values depended on the species; it is estimated that, for a given increase of fat level, the increase of the D-value would be greater for ground chicken than that for ground turkey. In addition, there was a statistically significant species effect on D-values, with higher D-values for ground turkey than for ground chicken at the higher temperatures studied. The thermal death curves displayed a non-linear tendency, however, for estimation purposes, a linear curve was assumed. There was not a statistically significant interaction effect of fat levels and temperatures on D-values, thus, for modeling, it was assumed that z-values were not dependent on the fat levels. The z-values for ground chicken and turkey were estimated to be 5.5 degrees C and 6.1 degrees C, respectively, and are statistically significantly different. These findings should have substantial practical importance to food processors of cooked poultry, allowing them to vary their thermal treatment of ready-to-eat poultry products in a safe manner.

Animals↗
PubMed1998

Topics in microbial risk assessment: dynamic flow tree process.

Microbial risk assessment is emerging as a new discipline in risk assessment. A systematic approach to microbial risk assessment is presented that employs data analysis for developing parsimonious models and accounts formally for the variability and uncertainty of model inputs using analysis of variance and Monte Carlo simulation. The purpose of the paper is to raise and examine issues in conducting microbial risk assessments. The enteric pathogen Escherichia coli O157:H7 was selected as an example for this study due to its significance to public health. The framework for our work is consistent with the risk assessment components described by the National Research Council in 1983 (hazard identification; exposure assessment; dose-response assessment; and risk characterization). Exposure assessment focuses on hamburgers, cooked a range of temperatures from rare to well done, the latter typical for fast food restaurants. Features of the model include predictive microbiology components that account for random stochastic growth and death of organisms in hamburger. For dose-response modeling, Shigella data from human feeding studies were used as a surrogate for E. coli O157:H7. Risks were calculated using a threshold model and an alternative nonthreshold model. The 95% probability intervals for risk of illness for product cooked to a given internal temperature spanned five orders of magnitude for these models. The existence of even a small threshold has a dramatic impact on the estimated risk.

Analysis of Variance↗
PubMed1995

Revisiting "the origins of compulsory drug prescriptions".

It has been argued that today's prescription drug market originated in the arbitrary acts of the US Food and Drug Administration (FDA), which in 1938 issued regulations creating a class of drugs that could be sold by prescription only. On the basis of the FDA's administrative records, I argue that the 1938 regulations on prescription drug labeling were initiated by industry and then agreed to by the FDA; that contemporaries understood and accepted the reasons for restricting the use of certain drugs; and that the subsequent evolution of these regulations is best understood as an FDA effort to limit industry abuses of the prescription labeling system. This decade-long war of position ended when drug manufacturers persuaded the US Congress to enshrine their version of prescription labeling in law in a highly politicized struggle over government's role in the economy.

Drug Industry↗