Preparing Offspring for a Dangerous World: Potential Costs of Being Wrong

Adaptive maternal responses to stressful environments before young are born can follow two non-exclusive pathways: either the mother reduces current investment in favor of future investment, or influences offspring growth and development in order to fit offspring phenotype to the stressful environment. Inducing such developmental cues, however, may be risky if the environment changes meanwhile, resulting in maladapted offspring. Here we test the effects of a predator-induced maternal effect in a predator-free postnatal environment. We manipulated perceived predation-risk for breeding female great tits by exposing them to stuffed models of either a predatory bird or a non-predatory control. Offspring were raised either in an environment matching the maternal one by exchanging whole broods within a maternal treatment group, or in a mismatching environment by exchanging broods among the maternal treatments. Offspring growth depended on the matching of the two environments. While for offspring originating from control treated mothers environmental mismatch did not significantly change growth, offspring of mothers under increased perceived predation risk grew faster and larger in matching conditions. Offspring of predator treated mothers fledged about one day later when growing under mismatching conditions. This suggests costs paid by the offspring if mothers predict environmental conditions wrongly.

The well-being model: a new drug interaction model for positive and negative effects

BACKGROUND: Drugs are routinely combined in anesthesia and pain management to obtain an enhancement of the desired effects. However, a parallel enhancement of the undesired effects might take place as well, resulting in a limited therapeutic usefulness. Therefore, when addressing the question of optimal drug combinations, side effects must be taken into account. METHODS: By extension of a previously published interaction model, the authors propose a method to study drug interactions considering also their side effects. A general outcome parameter identified as patient's well-being is defined by superposition of positive and negative effects. Well-being response surfaces are computed and analyzed for varying drugs pharmacodynamics and interaction types. In particular, the existence of multiple maxima and of optimal drug combinations is investigated for the combination of two drugs. RESULTS: Both drug pharmacodynamics and interaction type affect the well-being surface and the deriving optimal combinations. The effect of the interaction parameters can be explained in terms of synergy and antagonism and remains unchanged for varying pharmacodynamics. For all simulations performed for the combination of two drugs, the presence of more than one maximum was never observed. CONCLUSIONS: The model is consistent with clinical knowledge and supports previously published experimental results on optimal drug combinations. This new framework improves understanding of the characteristics of drug combinations used in clinical practice and can be used in clinical research to identify optimal drug dosing.