A 20-yr-old woman with no history of pulmonary disease presented with acute dyspnea and fever. After various investigations and treatments the symptoms persisted and a lung biopsy was performed. Pathologic analysis disclosed the presence of silicotic crystals within lung tissue and mediastinal lymph nodes. The patient admitted having repeatedly inhaled a domestic scouring powder rich in silica during the preceding 6 months. This is the first report of acute pulmonary silicosis developing after exposure to a common household product.
The 1930 International Labour Office Conference on silicosis in Johannesburg identified silicosis by setting a medicolegal framework to its nosology: as with other occupational illnesses, its medical content was fixed under economic pressure. This article follows a reading of all the proceedings of this conference (debates and reports of experts) to examine their potential impact on the etiology and nosology of other diseases, specifically sarcoidosis and pulmonary alveolar proteinosis (PAP), "idiopathic" diseases in which inorganic particles may be involved. We propose renewed study of the role of inorganic particles in these diseases. To do this, we propose to mobilize detection means such as mineralogical analysis and electron microscopy and in depth interviewing that are currently seldom used in France, in order to establish diagnosis and the potential occupational and environmental origin of these diseases.
In order to improve the quality of applications, such as recommendation or retrieval in knowledge-based service system, it is very helpful to uncover research topics of academic communities in scientific collaboration network (SCN). Previous research mainly focuses on network characteristics measurement and community evolution, but it remains largely understudied on how to uncover research topics of each community. This paper proposes a nonjoint approach, consisting of three simple steps: (1) to detect overlapping academic communities in SCN with the clique percolation method, (2) to discover underlying topics and research interests of each researcher with author-topic (AT) model, and (3) to label research topics of each community with top most frequent collaborative topics between members belonging to the community. Extensive experimental results on NIPS (neural information processing systems) dataset show that our simple procedure is feasible and efficient.