[Dental pulp stem cells: characteristics, cryopreservation and therapeutic potentialities]. / Les cellules souches de la pulpe dentaire : caractéristiques, cryopréservation et potentialités thérapeutiques.

Stem cells discovery and their potential have led to the emergence of new forms of therapy with the development of bio-engineering cell and tissue methods underlying future medicine. The availability of stem cells and their preservation thus become an issue for everyone's health. Among the different sources of stem cells, those in the dental pulp have the advantage of being pluripotent, they can be cryopreserved and stored for long periods without losing their multiplication and differentiation capacities and finally they are easily accessible. The wisdom or natal teeth extracted for medical reasons are an opportunity for everyone to preserve stem cells for an autologous use. Biobanks authorized and specialized in the preparation and storage of pulp stem cells provide access to autologous regenerative medicine of tomorrow.

Indexing UMLS Semantic Types for Medical Question-Answering.

Open-domain Question-Answering (QA) systems heavily rely on named entities, a set of general-purpose semantic types which generally cover names of persons, organizations and locations, dates and amounts, etc. If we are to build medical QA systems, a set of medically relevant named entities must be used. In this paper, we explore the use of the UMLS (Unified Medical Language System) Semantic Network semantic types for this purpose. We present an experiment where the French part of the UMLS Metathesaurus, together with the associated semantic types, is used as a resource for a medically-specific named entity tagger. We also explore the detection of Semantic Network relations for answering specific types of medical questions. We present results and evaluations on a corpus of French-language medical documents that was used in the EQueR Question-Answering evaluation forum. We show, using statistical studies, that strategies for using these new tags in a QA context are to take in account the individual origin of documents.

Towards a medical question-answering system: a feasibility study.

Question-answering (QA) systems, as have been presented and evaluated in several TREC conferences, are the next generation of search engines. They combine 'traditional' Information Retrieval (IR) with Natural Language Processing (NLP) and Knowledge Engineering techniques to provide shorter, more precise answers to natural language questions. We study here the feasibility of such a system for French in the health care domain. In this purpose, we collected a corpus of student questions in oral surgery. We examined two enabling conditions: on the IR side, how to select the right keywords in a question to identify relevant material on the Web for answering this question, a prerequisite for success; and on the NLP side, whether the contents of the questions fit the conceptual model of an existing QA prototype, a favorable condition for rapid implementation. A manual Web search enabled us to devise automatable principles for building IR queries for these questions. Besides, we could design a semantic model, using UMLS Semantic Network relations, which is consistent with our prototype and covers 90% of the questions. However, the high specialization of the domain and the clinical orientation of the questions, joined with the more limited resources online in the French language, may restrain the quantity of Web material available for answering these questions.