A core curriculum in the biological and biomedical sciences for dentistry.

INTRODUCTION: The biomedical sciences (BMS) are a central part of the dental curriculum that underpins teaching and clinical practice in all areas of dentistry. Although some specialist groups have proposed curricula in their particular topic areas, there is currently no overarching view of what should be included in a BMS curriculum for undergraduate dental programmes. To address this, the Association for Dental Education in Europe (ADEE) convened a Special Interest Group (SIG) with representatives from across Europe to develop a consensus BMS curriculum for dental programmes. CURRICULUM: This paper summarises the outcome of the deliberations of this SIG and details a consensus view from the SIG of what a BMS curriculum should include. CONCLUSIONS: Given the broad nature of BMS applied to dentistry, this curriculum framework is advisory and seeks to provide programme planners with an indicative list of topics which can be mapped to specific learning objectives within their own curricula. As dentistry becomes increasingly specialised, these will change, or some elements of the undergraduate curriculum may move to the post-graduate setting. So, this document should be seen as a beginning and it will need regular review as BMS curricula in dentistry evolve.

A proteomic approach to porcine saliva.

This paper reviews recent progress in salivary animal proteomics, with special reference to the porcine proteome. Until fairly recently, most studies on saliva as a diagnostic fluid have focused on humans, primates and rodents, and the development of salivary analysis in monitoring health in farm animals including pigs has received only limited consideration. The porcine salivary proteome has been characterised by 2D-electrophoresis followed by mass spectrometry. Major and minor proteins have been identified. The use of saliva as a non-invasive biological fluid in monitoring health and disease in pigs will be reviewed, together with the potential use of proteomics for the development of biomarkers. In this review, methods of collection and the composition of porcine saliva will be considered, together with saliva handling and analysis. The overall findings indicate that there is considerable potential for the development of salivary analysis as a non-invasive diagnostic fluid in the pig, and that it offers advantages over other body fluids in this animal.

Framework for e-learning assessment in dental education: a global model for the future.

The framework presented in this article demonstrates strategies for a global approach to e-curricula in dental education by considering a collection of outcome assessment tools. By combining the outcomes for overall assessment, a global model for a pilot project that applies e-assessment tools to virtual learning environments (VLE), including haptics, is presented. Assessment strategies from two projects, HapTEL (Haptics in Technology Enhanced Learning) and UDENTE (Universal Dental E-learning), act as case-user studies that have helped develop the proposed global framework. They incorporate additional assessment tools and include evaluations from questionnaires and stakeholders' focus groups. These measure each of the factors affecting the classical teaching/learning theory framework as defined by Entwistle in a standardized manner. A mathematical combinatorial approach is proposed to join these results together as a global assessment. With the use of haptic-based simulation learning, exercises for tooth preparation assessing enamel and dentine were compared to plastic teeth in manikins. Equivalence for student performance for haptic versus traditional preparation methods was established, thus establishing the validity of the haptic solution for performing these exercises. Further data collected from HapTEL are still being analyzed, and pilots are being conducted to validate the proposed test measures. Initial results have been encouraging, but clearly the need persists to develop additional e-assessment methods for new learning domains.