Fecal sampling protocol to assess bumble bee health in conservation research

An increasing number of wild bee species are declining or threatened with extinction worldwide. Decline has been proposed to be caused by a combination of threats, including increasing wild bee disease prevalence and pathogen spillover from managed bees that can reduce health of wild bees. Most approaches aiming at characterizing bee health, however, require sacrificing tens to hundreds of individual bees per site or species, with reports of several thousand individuals collected per study. Considering the widespread need to assess bee health, this sampling approach is not sustainable, especially for endangered populations or species. Here, we present a non-destructive protocol to collect bumble bee faeces and assess parasite loads of wild-caught individuals. The standard protocol consists of net-capturing individual bumble bees and placing them in a 10 cm (diameter) petri dish to collect faeces. This fecal screening approach is frequently used in laboratory settings, but much less in the field, which can impair conservation research. When placing bumble bees in a previously refrigerated cooler, we successfully collected faeces for 86% individuals, while the standard protocol, as used in laboratory settings, yielded 76% success in collecting faeces. We also identified cells and spores of two common gut parasites Crithidia spp. and Vairimorpha spp. in faecal samples. The faecal sampling presented here opens future avenues to assess bee pathogen loads using molecular techniques, while collected faeces could also be used to assess bee health more broadly, including bee microbiota and bee diet

Collaborative research between academia and industry using a large clinical trial database: a case study in Alzheimer's disease

<p>Abstract</p> <p>Background</p> <p>Large clinical trials databases, developed over the course of a comprehensive clinical trial programme, represent an invaluable resource for clinical researchers. Data mining projects sponsored by industry that use these databases, however, are often not viewed favourably in the academic medical community because of concerns that commercial, rather than scientific, goals are the primary purpose of such endeavours. Thus, there are few examples of sustained collaboration between leading academic clinical researchers and industry professionals in a large-scale data mining project. We present here a successful example of this type of collaboration in the field of dementia.</p> <p>Methods</p> <p>The Donepezil Data Repository comprised 18 randomised, controlled trials conducted between 1991 and 2005. The project team at Pfizer determined that the data mining process should be guided by a diverse group of leading Alzheimer's disease clinical researchers called the "Expert Working Group." After development of a list of potential faculty members, invitations were extended and a group of seven members was assembled. The Working Group met regularly with Eisai/Pfizer clinicians and statisticians to discuss the data, identify issues that were currently of interest in the academic and clinical communities that might lend themselves to investigation using these data, and note gaps in understanding or knowledge of Alzheimer's disease that these data could address. Leadership was provided by the Pfizer Clinical Development team leader; Working Group members rotated responsibility for being lead and co-lead for each investigation and resultant publication.</p> <p>Results</p> <p>Six manuscripts, each published in a leading subspecialty journal, resulted from the group's work. Another project resulted in poster presentations at international congresses and two were cancelled due to resource constraints.</p> <p>Conclusions</p> <p>The experience represents a particular approach to optimising the value of data mining of large clinical trial databases for the combined purpose of furthering clinical research and improving patient care. Fruitful collaboration between industry and academia was fostered while the donepezil data repository was used to advance clinical and scientific knowledge. The Expert Working Group approach warrants consideration as a blueprint for conducting similar research ventures in the future.</p