The generation of new mouse models of human disease is accelerating rapidly, due to the completion of whole-genome sequencing efforts and technological advances in the manipulation of the mouse genome. We sought to investigate manpower issues in the provision of histopathology expertise for mouse functional genomics and compared this to the perceived demand from principal investigators (PIs). Through the European Commission (EC)-funded PRIME pathology training initiative, two questionnaires were devised to collect information from pathologists and EC-funded PIs on the current provision of mouse histopathology expertise in Europe and the demands for this service. We find that pathological analysis is being performed almost exclusively by professionally qualified pathologists, generally employed in clinical diagnostic posts, where the work is undertaken as collaboration outside of their contractual commitments but without previous training in veterinary or comparative pathology. The results indicate that there is a lack of both trainees and provision of specialist training in this field. Unsurprisingly, the availability of diagnostic expertise and advice falls far short of the number of genetically engineered mice (GEM) being generated for analysis. We analyse these results with reference to previous studies and discuss solutions for the future recruitment, training and funding for pathologists in mouse functional genomics in Europe.
The nature of ophthalmic services, and the education and qualifications of nurses, a national survey This paper reports on a national survey xindertaken by the Royal College of Nursmg (RCN) Ophthalmic Nursing Forum to assess, first, the nature of ophthalmic services and, second, skiU-mix and educational opportunities of nurses working with ophthalmic patients A questionnaire was formulated and tested by members of the committee, with research and statistical support from the School of Nursing Studies, University of Manchester The questionnaire was sent to the total population of ophthalmic umts/hospitals in the UK [n = 168) Following descriptive statistical data analysis it was concluded that m smaller
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