Systematic review and stratified meta-analysis of the efficacy of carnosine in animal models of ischemic stroke

Carnosine is a naturally occurring pleotropic dipeptide which influences multiple deleterious mechanisms that are activated during stroke. Numerous published studies have reported that carnosine has robust efficacy in ischemic stroke models. To further evaluate these data, we have conducted a systematic review and meta-analysis of published studies. We included publications describing in vivo models of ischemic stroke where the neuroprotective efficacy of carnosine was being evaluated through the reporting of infarct volume and/or neurological score as outcomes. Overall efficacy was evaluated using weighted mean difference random effects meta-analysis. We also evaluated for study quality and publication bias. We identified eight publications that met our inclusion criteria describing a total of 29 comparisons and 454 animals. Overall methodological quality of studies was moderate (median ¼ 4/9). Carnosine reduced infarct volume by 29.4% (95% confidence interval (CI), 24.0% to 34.9%; 29 comparisons). A clear dose-response effect was observed, and efficacy was reduced when carnosine was administered more than 6 h after ischemia. Our findings suggest that carnosine administered before or after the onset of ischemia exhibits robust efficacy in experimental ischemic stroke. However, the methodological quality of some of the studies was low and testing occurred only in healthy young male animals

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

Protocol for a systematic review and meta-analysis of data from preclinical studies employing forced swimming test:An update

Objective Forced swimming test (FST) in rodents is a widely used behavioural test for screening antidepressants in preclinical research. Translational value of preclinical studies may be improved by appraisal of the quality of experimental design and risk of biases, which remains to be addressed for FST. The present protocol of a systematic review with meta-analysis aims to investigate the quality of preclinical studies employing FST to identify risks of bias in future publications. In addition, this protocol will help to determine the effect sizes (ES) for primary and secondary outcomes according to several aspects of the FST study design. Search strategy, Screening annotation, Data management Publications reporting studies testing different classes of antidepressants in FST will be collected from Medline, SCOPUS and Web of Science databases. A broad list of inclusion criteria will be applied excluding those studies whereby FST is used as a stressor or studies reporting data from co-treatments. For assessing the quality of the included publications, the quality checklist adapted by Collaborative Approach to Meta-Analysis and Review of Animal Data from Experimental Studies will be used. If the meta-analysis seems feasible, the ES and the 95% CI will be analysed. The heterogeneity between studies will be assessed by using the χ2 statistic with n−1 degrees of freedom. Subgroup meta-analysis (metaregression, and if necessary, stratified regression) will be performed when possible according to characteristics of study design and study quality to assess their impact on efficacy of the treatments. In addition, funnel plotting, Egger regression, and ‘trim and fill’ will be used to assess the risk of publication bias. Results of this protocol will help to create rational methodological guidelines for application of FST in rodents and improve the quality and translational value of preclinical research on antidepressant discovery. Reporting A preliminary version of the present protocol has been preregistered with Systematic Review Facility (http://syrf.org.uk/). A preprint version of the current protocol has been registered with Open Science Framework (https://osf.io/9kxm4/). Results will be communicated in scientific meetings and peer-reviewed journals. We plan to conduct an anonymous and online survey within the scientific community to ask researchers about their perception of risk of bias and their experience with the publication of negative results.Fil: Ramos Hryb, Ana Belen. Universidade Federal de Santa Catarina; Brasil. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Bahor, Z.. University of Edinburgh; Reino UnidoFil: McCann, Samuel M.. University of Edinburgh; Reino UnidoFil: Senar, Eduardo Santiago. University of Edinburgh; Reino UnidoFil: Macleod, M. R.. University of Edinburgh; Reino UnidoFil: Lino de Oliveira, Cilene. Universidade Federal de Santa Catarina; Brasi

Risk of Bias in Reports of In Vivo Research: A Focus for Improvement

A systematic analysis of in vivo research reveals poor reporting of measures that reduce the risk of bias and an inverse relationship between impact factor and the reporting of randomization.</p