Bounded survival

The useful and flexible “generic” family of parametric survival functions has been extended to cover the case of a hypothesized bound on the lifespan. The resulting model can be considered to be analogous to a simple system of three chemical reactions where survivors are lost, and where hazard (risk) and senescence are increased. Tables of one, two, and three parameter special cases, including single hit, Gompertz, Weibull, extreme value, and Burr functions, are presented. An application to a published life table is briefly discussed.     

Analysis of survival data with cross-effects of survival functions

We present a model and semiparametric estimation procedures for analysis of survival data with cross-effects (CE) of survival functions. Finite sample properties of the estimators are analyzed by simulation. A goodness-of-fit test for the proportional hazards model against the CE model is proposed. The well known data concerning effects of chemotherapy and radiotherapy on the survival times of gastric cancer patients is analyzed as an example.     

Early Bacillus anthracis-macrophage interactions: intracellular survival survival and escape

This study describes early intracellular events occurring during the establishment phase of Bacillus anthracis infections. Anthrax infections are initiated by dormant endospores gaining access to the mammalian host and becoming engulfed by regional macrophages (Mφ). During systemic anthrax, late stage events include vegetative growth in the blood to very high titres and the synthesis of the anthrax exotoxin complex, which causes disease symptoms and death. Experiments focus on the early events occurring during the first few hours of the B. anthracis infectious cycle, from endospore germination up to and including release of the vegetative cell from phagocytes. We found that newly vegetative bacilli escape from the phagocytic vesicles of cultured Mφ and replicate within the cytoplasm of these cells. Release from the Mφ occurs 4–6 h after endospore phagocytosis, timing that correlates with anthrax infection of test animals. Genetic analysis from this study indicates that the toxin plasmid pXO1 is required for release from the Mφ, whereas the capsule plasmid pXO2 is not. The transactivator atxA , located on pXO1, is also found to be essential for release, but the toxin genes themselves are not required. This suggests that Mφ release of anthrax bacilli is atxA regulated. The putative 'escape' genes may be located on the chromosome and/or on pXO1.     

Apoptosis and survival

The term apoptosis first appeared in the biomedical literature in 1972, to delineate a structurally distinctive mode of cell death responsible for cell loss within living tissues. The cardinal morphological features are cell shrinkage, accompanied by transient but violent bubbling and blebbing from the surface, and culminating in separation of the cell into a cluster of membrane-bounded bodies. Changes in several cell surface molecules also ensure that, in tissues, apoptotic cells are immediately recognised and phagocytosed by their neighbours. However, it is important to note that apoptosis is only one form of cell death and the particular death pathway that is the most important determinant for cancer therapy is not necessarily that which has the fastest kinetics, as is the bias in many laboratories, but rather that which displays the most sensitive dose-response relationship.     

Mutation for survival

Adaptive mutations appear in response to selection. In the best-studied system, the two most controversial issues were resolved this year. The mutations are neither Lamarckian nor a peculiarity of bacterial sex, as had been suggested. They occur genome-wide in a hypermutable subpopulation of stressed cells. Genomic ‘hot’ and ‘cold’ regions may explain previous failures to detect similar mutations in other systems and at other sites. Stationary phase specific limitation of mismatch repair has also been discovered.     

Modeling survival of experimentally virus-infected laboratory animals--exploration of the survival diagram

In a recent paper (Sühnel & Veckenstedt, 1989, J. theor. Biol. 137, 27) we have proposed a new method of plotting survival data from experimentally virus-infected laboratory animals; the survival diagram. In this diagram two experiments, for which the mean number of virions inoculated is kept fixed but other parameters may vary, are compared. The variations in two basic quantities of survival analysis are simultaneously displayed: the standard mean survival time and the relative mean challenge virus dose, which is via a dose-response relation interrelated with the fraction of animals dying. It is analyzed in which manner variations in the kinetic parameters and the critical virus level necessary to produce a particular effect influence the location of the points of comparison in the survival diagram. The analysis presented is a prerequisite for further applications of this diagram and of the underlying mathematical model.     

Cracking the survival code

Modifications of histones, the chief protein components of the chromatin, have emerged as critical regulators of life and death. While the “apoptotic histone code” came to light a few years ago, accumulating evidence indicates that autophagy, a cell survival pathway, is also heavily regulated by histone-modifying proteins. In this review we describe the emerging “autophagic histone code” and the role of histone modifications in the cellular life vs. death decision.     

Fundamentals of survival data

Survival data stand out as a special statistical field. This paper tries to describe what survival data is and what makes it so special. Survival data concern times to some events. A key point is the successive observation of time, which on the one hand leads to some times not being observed so that all that is known is that they exceed some given times (censoring), and on the other hand implies that predictions regarding the future course should be conditional on the present status (truncation). In the simplest case, this condition is that the individual is alive. The successive conditioning makes the hazard function, which describes the probability of an event happening during a short interval given that the individual is alive today (or more generally able to experience the event), the most relevant concept. Standard distributions available (normal, log-normal, gamma, inverse Gaussian, and so forth) can account for censoring and truncation, but this is cumbersome. Besides, they fit badly because they are either symmetric or right skewed, but survival time distributions can easily be left-skewed positive variables. A few distributions satisfying these requirements are available, but often nonparametric methods are preferable as they account better conceptually for truncation and censoring and give a better fit. Finally, we compare the proportional hazards regression models with accelerated failure time models.