Today the SDRC are delighted to share a guest blog from Kaitlyn Hair, a PhD student from the University of Edinburgh. If you would like to write a blog for the SDRC website, get in touch
I’m a meta-researcher – which means I do research ON research. This was not a term I was familiar with until I started working with the CAMARADES (Collaborative Approach to Meta-Analysis and Review of Animal Data from Experimental Studies) group at the University of Edinburgh, having completed my undergraduate degree in Neuroscience and Psychology at the University of Glasgow.
For my PhD project, I am performing a systematic review of studies conducted in animal models of Alzheimer’s disease. Alzheimer’s disease is a devastating and incurable disorder which affects around 50 million people worldwide. Despite billions of pounds invested in research to develop much-needed treatments, and numerous reports of novel drugs reversing symptoms in animal models of the disease, no new treatments have emerged from clinical trials in the last 15 years.
Systematic reviews of preclinical studies allow us to summarise research describing the effectiveness of interventions that have been tested in animal models of disease. Statistically pooling these effect sizes from publications in a meta-analysis allows us to start to understand which interventions look more promising and under what conditions we are most likely to observe their effects. In our previous work, we have identified significant flaws in methodological quality and failures to report measures taken to reduce the risk of bias (e.g. experimenters being blinded to group allocation when assessing the outcome). These issues, combined with limited publication of negative/neutral findings, can over-inflate the perceived benefits of interventions.
The information we can gain from systematic reviews can be extremely valuable – but performing a single review can take YEARS and findings are often out of date by the time they are published, limiting their utility. Therefore, I have been building a framework to make it easier to perform and update systematic reviews of preclinical Alzheimer’s research in future. For this, we use machine learning algorithms and text-mining tools to reduce the amount of human involvement required at each stage of the review. My goal is to create a curated up-to-date summary of the literature which can be used to identify the areas where more research is required and to monitor study quality over time. Using this framework, I am now extracting data to perform a meta-analysis of experiments which measure synaptic plasticity in transgenic Alzheimer’s disease models. Changes in synaptic transmission and a loss of synapses have been linked strongly to cognitive decline in the human condition, however there is a lot of conflicting evidence about the extent of synaptic deficits in animal models of the disease. I hope to gain insights into why there are so many conflicting results in this area and inform guidelines to improve the rigor and reporting quality of these experiments.
Aside from my research, I am the social media and blog editor at BMJ Open Science (a BMJ journal focused on preclinical research that is closely aligned with medicine) and I chair a science outreach group in Edinburgh called ScotSci who organise monthly “Sci-Screen” cinema events with guest speakers on a range of scientific topics.