PubMed Health⌕ Search
Home/Archive/PubMed

PubMed · 6259738

Mutation caused by human phagocytes.

S A Weitzman·T P Stossel
View original source ↗Export metadata ↓

Abstract

Histidine-requiring mutants of Salmonella typhimurium TA100 were incubated with human peripheral blood leukocytes. More of these bacteria reverted to histidine independence than controls not incubated with cells. Phagocyte-rich suspensions were mutagenic, while heat-killed cells, lymphocytes, or mixed blood leukocytes of a patient with chronic granulomatous disease were not. Production of reactive oxygen metabolites could explain the capacity of phagocytes to induce mutation.

Explore related subjects

Granulomatous Disease, ChronicHumansLeukocytesMutagenicity TestsMutationOxygenPeroxidesPhagocytesSalmonella typhimuriumSuperoxidesJournal ArticleResearch Support, Non-U.S. Gov'tResearch Support, U.S. Gov't, P.H.S.

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

S A Weitzman, T P Stossel. 1981-05-01. Mutation caused by human phagocytes.. https://doi.org/10.1126/science.6259738

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

PubMed2004

Accelerated rates regression models for recurrent failure time data.

In this article, we formulate a semiparametric model for counting processes in which the effect of covariates is to transform the time scale for a baseline rate function. We assume an arbitrary dependence structure for the counting process and propose a class of estimating equations for the regression parameters. Asymptotic results for these estimators are derived. In addition, goodness of fit methods for assessing the adequacy of the accelerated rates model are proposed. The finite-sample behavior of the proposed methods is examined in simulation studies, and data from a chronic granulomatous disease study are used to illustrate the methodology.

Granulomatous Disease, Chronic↗
PubMed2004

Nonparametric prediction of event times in randomized clinical trials.

In clinical trials with planned interim analysis, it can be valuable for logistical reasons to predict the times of landmark events such as the 50th and 100th event. Bagiella and Heitjan (Stat Med 2001; 20: 2055-63) proposed a parametric prediction model for failure-time outcomes assuming exponential survival and Poisson enrollment. When little is known about the distributions of interest, there is concern that parametric prediction methods may be biased and inefficient if their underlying distributional assumptions are invalid. We propose nonparametric approaches to make point and interval predictions for landmark dates during the course of the trial. We obtain point predictions using the Kaplan-Meier estimator to extrapolate the survival probability into the future, selecting the time when the expected number of events is equal to the landmark number. To construct prediction intervals, we use a simulation strategy based on the Bayesian bootstrap. Monte Carlo results demonstrate the superiority of the nonparametric method when the assumptions underlying the parametric model are incorrect. We demonstrate the methods using data from a trial of immunotherapy of chronic granulomatous disease.

Granulomatous Disease, Chronic↗