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Objectives: to rank according to their significance MeSH terms automatically extracted from Internet sites in the framework of a French project, VUMeF, a contribution to the NLM' UMLS project. Material and methods: scores are affected to key-words of a given document on the basis of the Semantic Network of the UMLS and frequencies of co-occurring major terms in the Medline literature. If N is the number of major terms of a document, and n is the number of major terms retrieved in the N first terms ranked in descending order according to their scores, the measure of the achievement of the method is n/N. Results: a set of 1444 randomized documents have been extracted from Medline. For each document we computed the retrieved major terms among the first N terms with two methods: a statistical method using only frequencies given by co-occurrences, and our method that uses furthermore the UMLS semantic network. In 34% of cases corresponding to documents indexed by about 16 key-words, about 3 major terms among them, our method produces a better precision (7%) than the statistical method. Discussion: the rough calculation of the proportion of retrieved major terms should be enhanced by the use of a probability law allowing to enlarge the list of terms to select taking into account both the number of major terms and the total number of key-words used to index each document. [ABSTRACT FROM AUTHOR]

This paper is focused on designing and developing a teaching environment associated with an introductory course in evidence-based medicine designed for undergraduates. Attempting to break away from the traditional educational model based on acquisition of factual knowledge, we developed a software tool centred on content management, student assessment and feedback. We ran this course on an educational website, using chat rooms and electronic mail for trainer–trainee communication over one academic year. The website served as a repository for knowledge and information and presented freestanding web-based interactive study modules and a non-interactive degree course. Trainers and trainees responded favourably to the computer applications and the Internet. This project demonstrated the feasibility of computer-aided learning and the advantages of distance teaching over the Internet. [ABSTRACT FROM AUTHOR]

Object: Pituitary adenomas are usually benign tumors; however, some behave aggressively and metastasize. Until now, no specific marker of aggressive behavior or malignancy has been found. The polysialylated neural cell adhesion molecule (NCAM), which is highly expressed in embryonic tissues such as the brain and pituitary, is detected in some and neuroendocrine tumors. Because polysialylation has been implicated in the regulation of cell growth and migration, polysialylated NCAM expression has been considered as a prognostic marker in such tumors., Methods: In the present study, the authors analyzed polysialylated NCAM expression in 82 pituitary tumors from humans: 49 secreting adenomas, 32 nonfunctioning adenomas, and one growth hormone and prolactin-secreting carcinoma associated with acromegaly and spinal and liver metastases. Based on immunohistochemical analyses, the tumors were classified as somatotropic (22 tumors), prolactinoma (14 tumors), corticotropic (17 tumors), and gonadotropic or so-called null cell adenomas (28 tumors). Assessment of polysialylated NCAM was performed using three different methods (immunohistochemical analysis, Western blot analysis, and enzyme-linked immunosorbent assay) with a specific mouse monoclonal immunoglobulin M (Men B) that recognizes polysialic acid on NCAM. Tumoral NCAM expression was also evaluated with the aid of immunohistochemical analysis. Using this method, NCAM and polysialylated NCAM were studied in six healthy pituitaries. In addition, correlations were investigated using three statistical methods (chi-square test, nonparametric Mann-Whitney U-test, and principal component analysis) to compare tumoral polysialylated NCAM expression and seven parameters (tumor size and type, intrasphenoidal or cavernous sinus invasion, Ki-67 index, mitoses, and patient age and sex). Neural cell adhesion molecules were expressed in the healthy anterior pituitary and in all tumors. In contrast, polysialylated NCAM was not found in the healthy pituitary gland, but was expressed in 46.3% of typical pituitary tumors and 85% of the tumors selected as highly aggressive, including one carcinoma and three tumors with histological characteristics that raised suspicion of malignancy. There was no significant correlation between polysialylated NCAM expression and tumor size, tumor type, Ki-67 index, mitoses, or patient age and sex. In contrast, the expression of polysialylated NCAM, which was sensitive to endoneuraminidase-N treatment, was strongly correlated with tumor invasion (p < 0.0001)., Conclusions: In pituitary tumors in humans, expression of polysialylated NCAM is strongly related to tumor invasion and confirms the clinical diagnosis of aggressiveness.

The quality criteria of guidelines are identified. Taking these criteria into account we have built a platform to improve computable guideline effectiveness and facilitate their production. Our implementation is based on the GLIF Model (GuideLine Interchange Format). We have designed software components to edit, test, index, perform and present guidelines in a user-friendly Web architecture. These components are based on XML specifications in order to preserve their shareability and their re-usability. Several guidelines have been developed using our approach. We discuss the advantages of such an implementation and future evolutions so as to improve guideline integration within the clinician's workflow.