Quality and validity of large animal experiments in stroke : a systematic review

An important factor for successful translational stroke research is study quality. Low-quality studies are at risk of biased results and effect overestimation, as has been intensely discussed for small animal stroke research. However, little is known about the methodological rigor and quality in large animal stroke models, which are becoming more frequently used in the field. Based on research in two databases, this systematic review surveys and analyses the methodological quality in large animal stroke research. Quality analysis was based on the Stroke Therapy Academic Industry Roundtable and the Animals in Research: Reporting In Vivo Experiments guidelines. Our analysis revealed that large animal models are utilized with similar shortcomings as small animal models. Moreover, translational benefits of large animal models may be limited due to lacking implementation of important quality criteria such as randomization, allocation concealment, and blinded assessment of outcome. On the other hand, an increase of study quality over time and a positive correlation between study quality and journal impact factor were identified. Based on the obtained findings, we derive recommendations for optimal study planning, conducting, and data analysis/reporting when using large animal stroke models to fully benefit from the translational advantages offered by these models

Extrapolating from Laboratory Behavioral Research on Nonhuman Primates Is Unjustified

Conducting research on animals is supposed to be valuable because it provides information on how human mechanisms work. But for the use of animal models to be ethically justified, it must be epistemically justified. The inference from an observation about an animal model to a conclusion about humans must be warranted for the use of animals to be moral. When researchers infer from animals to humans, it’s an extrapolation. Often non-human primates are used as animal models in laboratory behavioral research. The target populations are humans and other non-human primates. I argue that the epistemology of extrapolation renders the use of non-human primates in laboratory behavioral research unreliable. If the model is relevantly similar to the target, then the experimental conditions introduce confounding variables. If the model is not relevantly similar to the target, then the observations of the model cannot be extrapolated to the target. Since using non-human primates in as animal models in laboratory behavioral research is not epistemically justified, using them as animal models in laboratory behavioral research is not ethically justified

Probabilistic models of individual and collective animal behavior

Recent developments in automated tracking allow uninterrupted, high-resolution recording of animal trajectories, sometimes coupled with the identification of stereotyped changes of body pose or other behaviors of interest. Analysis and interpretation of such data represents a challenge: the timing of animal behaviors may be stochastic and modulated by kinematic variables, by the interaction with the environment or with the conspecifics within the animal group, and dependent on internal cognitive or behavioral state of the individual. Existing models for collective motion typically fail to incorporate the discrete, stochastic, and internal-state-dependent aspects of behavior, while models focusing on individual animal behavior typically ignore the spatial aspects of the problem. Here we propose a probabilistic modeling framework to address this gap. Each animal can switch stochastically between different behavioral states, with each state resulting in a possibly different law of motion through space. Switching rates for behavioral transitions can depend in a very general way, which we seek to identify from data, on the effects of the environment as well as the interaction between the animals. We represent the switching dynamics as a Generalized Linear Model and show that: (i) forward simulation of multiple interacting animals is possible using a variant of the Gillespie's Stochastic Simulation Algorithm; (ii) formulated properly, the maximum likelihood inference of switching rate functions is tractably solvable by gradient descent; (iii) model selection can be used to identify factors that modulate behavioral state switching and to appropriately adjust model complexity to data. To illustrate our framework, we apply it to two synthetic models of animal motion and to real zebrafish tracking data.Comment: 26 pages, 11 figure

Accepting higher morbidity in exchange for sacrificing fewer animals in studies developing novel infection-control strategies.

Preventing bacterial infections from becoming the leading cause of death by the year 2050 requires the development of novel, infection-control strategies, building heavily on biomaterials science, including nanotechnology. Pre-clinical (animal) studies are indispensable for this development. Often, animal infection outcomes bear little relation to human clinical outcome. Here, we review conclusions from pathogen-inoculum dose-finding pilot studies for evaluation of novel infection-control strategies in murine models. Pathogen-inoculum doses are generally preferred that produce the largest differences in quantitative infection outcome parameters between a control and an experimental group, without death or termination of animals due to having reached an inhumane end-point during the study. However, animal death may represent a better end-point for evaluation than large differences in outcome parameters or number of days over which infection persists. The clinical relevance of lower pre-clinical outcomes, such as bioluminescence, colony forming units (CFUs) retrieved or more rapid clearance of infection is unknown, as most animals cure infection without intervention, depending on pathogen-species and pathogen-inoculum dose administered. In human clinical practice, patients suffering from infection present to hospital emergency wards, frequently in life-threatening conditions. Animal infection-models should therefore use prevention of death and recurrence of infection as primary efficacy targets to be addressed by novel strategies. To compensate for increased animal morbidity and mortality, animal experiments should solely be conducted for pre-clinical proof of principle and safety. With the advent of sophisticated in vitro models, we advocate limiting use of animal models when exploring pathogenesis or infection mechanisms

Codimension-two bifurcations in animal aggregation models with symmetry

Pattern formation in self-organized biological aggregation is a phenomenon that has been studied intensively over the past 20 years. In general, the studies on pattern formation focus mainly on identifying the biological mechanisms that generate these patterns. However, identifying the mathematical mechanisms behind these patterns is equally important, since it can offer information on the biological parameters that could contribute to the persistence of some patterns and the disappearance of other patterns. Also, it can offer information on the mechanisms that trigger transitions between different patterns (associated with different group behaviors). In this article, we focus on a class of nonlocal hyperbolic models for self-organized aggregations and show that these models are ${{\bf O(2)}}$-equivariant. We then use group-theoretic methods, linear analysis, weakly nonlinear analysis, and numerical simulations to investigate the large variety of patterns that arise through ${{\bf O(2)}}$-symmetric codimension-two bifurcations (i.e., Hopf/Hopf, steady-state/Hopf, and steady-state/steady-state mode interactions). We classify the bifurcating solutions according to their isotropy types (subgroups), and we determine the criticality and stability of primary branches of solutions. We numerically show the existence of these solutions and determine scenarios of secondary bifurcations. Also, we discuss the secondary bifurcating solutions from the biological perspective of transitions between different group behaviors.© 2014, Society for Industrial and Applied Mathematic

Animal Models of GWAS-Identified Type 2 Diabetes Genes

More than 65 loci, encoding up to 500 different genes, have been implicated by genome-wide association studies (GWAS) as conferring an increased risk of developing type 2 diabetes (T2D). Whilst mouse models have in the past been central to understanding the mechanisms through which more penetrant risk genes for T2D, for example, those responsible for neonatal or maturity-onset diabetes of the young, only a few of those identified by GWAS, notably TCF7L2 and ZnT8/SLC30A8, have to date been examined in mouse models. We discuss here the animal models available for the latter genes and provide perspectives for future, higher throughput approaches towards efficiently mining the information provided by human genetics