Stress can result in anxiety, which is an unpleasant emotional state that humans working with animals wish to avoid. Anxious animals may cope poorly during scientific testing and provide inconsistent data, which is reflected in increased statistical 'noise', lowering the fidelity of results, and ultimately leading to greater numbers of animals being needed for reliable hypothesis testing. Techniques such as counterbalancing and blinding during data analyses can help to maximise precision. However, stress-related variation in data is still a major problem. For example, animals unwittingly exposed to sub-optimal housing can perform unreliably in behavioural studies. However, stress and anxiety also impact upon numerous physiological systems and generally compromise the biologic equilibrium, so can be detrimental to studies regardless of the measurement of interest. Handling laboratory mice is an unavoidable consequence of procedures and a significant cause of anxiety that has until recently has not been avoidable without using potentially even more confounding interventions (e.g. anxiolytics). However, there is now compelling evidence that 'non-aversive' handling (NAH), a relatively simple method alteration, whereby mice are lifted using a tunnel rather than by their tail can lower their anxiety; enhancing welfare, but also increasing the chances of obtaining more precise research results. The project will engage 3 local research groups (on liver disease and cancer) and 2 working internationally (cardiac disease and neuropathic pain) to test the reproducibility of NAH for both improving welfare and data precision across several scientific disciplines. These would use NAH for the first time, providing immediate uptake and knowledge transfer and an ideal opportunity to challenge common misconceptions, such as that it is too time consuming or difficult than tail-handling, and show that it can even prevent anxiety from procedures such as restraint or giving injections.
Principal investigatorDr John Roughan
Co-InvestigatorDr Tom Smulders
Professor Kenneth Sufka