3 resultados para Modified truncation approach
em Université de Montréal, Canada
Resumo:
Background: Routine screening of scoliosis is a controversial subject and screening efforts vary greatly around the world. METHODS: Consensus was sought among an international group of experts (seven spine surgeons and one clinical epidemiologist) using a modified Delphi approach. The consensus achieved was based on careful analysis of a recent critical review of the literature on scoliosis screening, performed using a conceptual framework of analysis focusing on five main dimensions: technical, clinical, program, cost and treatment effectiveness. FINDINGS: A consensus was obtained in all five dimensions of analysis, resulting in 10 statements and recommendations. In summary, there is scientific evidence to support the value of scoliosis screening with respect to technical efficacy, clinical, program and treatment effectiveness, but there insufficient evidence to make a statement with respect to cost effectiveness. Scoliosis screening should be aimed at identifying suspected cases of scoliosis that will be referred for diagnostic evaluation and confirmed, or ruled out, with a clinically significant scoliosis. The scoliometer is currently the best tool available for scoliosis screening and there is moderate evidence to recommend referral with values between 5 degrees and 7 degrees. There is moderate evidence that scoliosis screening allows for detection and referral of patients at an earlier stage of the clinical course, and there is low evidence suggesting that scoliosis patients detected by screening are less likely to need surgery than those who did not have screening. There is strong evidence to support treatment by bracing. INTERPRETATION: This information statement by an expert panel supports scoliosis screening in 4 of the 5 domains studied, using a framework of analysis which includes all of the World Health Organisation criteria for a valid screening procedure.
Resumo:
Background: Swine influenza is a highly contagious viral infection in pigs affecting the respiratory tract that can have significant economic impacts. Streptococcus suis serotype 2 is one of the most important post-weaning bacterial pathogens in swine causing different infections, including pneumonia. Both pathogens are important contributors to the porcine respiratory disease complex. Outbreaks of swine influenza virus with a significant level of co-infections due to S. suis have lately been reported. In order to analyze, for the first time, the transcriptional host response of swine tracheal epithelial (NPTr) cells to H1N1 swine influenza virus (swH1N1) infection, S. suis serotype 2 infection and a dual infection, we carried out a comprehensive gene expression profiling using a microarray approach. Results: Gene clustering showed that the swH1N1 and swH1N1/S. suis infections modified the expression of genes in a similar manner. Additionally, infection of NPTr cells by S. suis alone resulted in fewer differentially expressed genes compared to mock-infected cells. However, some important genes coding for inflammatory mediators such as chemokines, interleukins, cell adhesion molecules, and eicosanoids were significantly upregulated in the presence of both pathogens compared to infection with each pathogen individually. This synergy may be the consequence, at least in part, of an increased bacterial adhesion/invasion of epithelial cells previously infected by swH1N1, as recently reported. Conclusion: Influenza virus would replicate in the respiratory epithelium and induce an inflammatory infiltrate comprised of mononuclear cells and neutrophils. In a co-infection situation, although these cells would be unable to phagocyte and kill S. suis, they are highly activated by this pathogen. S. suis is not considered a primary pulmonary pathogen, but an exacerbated production of proinflammatory mediators during a co-infection with influenza virus may be important in the pathogenesis and clinical outcome of S. suis-induced respiratory diseases.
Resumo:
The main objective of this letter is to formulate a new approach of learning a Mahalanobis distance metric for nearest neighbor regression from a training sample set. We propose a modified version of the large margin nearest neighbor metric learning method to deal with regression problems. As an application, the prediction of post-operative trunk 3-D shapes in scoliosis surgery using nearest neighbor regression is described. Accuracy of the proposed method is quantitatively evaluated through experiments on real medical data.