Optimization of treatment with interferon beta in multiple sclerosis. Usefulness of automatic system application criteria

Background. A software based tool has been developed (Optem) to allow automatize the recommendations of the Canadian Multiple Sclerosis Working Group for optimizing MS treatment in order to avoid subjective interpretation. METHODS: Treatment Optimization Recommendations (TORs) were applied to our database of patients treated with IFN beta1a IM. Patient data were assessed during year 1 for disease activity, and patients were assigned to 2 groups according to TOR: "change treatment" (CH) and "no change treatment" (NCH). These assessments were then compared to observed clinical outcomes for disease activity over the following years. RESULTS: We have data on 55 patients. The "change treatment" status was assigned to 22 patients, and "no change treatment" to 33 patients. The estimated sensitivity and specificity according to last visit status were 73.9% and 84.4%. During the following years, the Relapse Rate was always higher in the "change treatment" group than in the "no change treatment" group (5 y; CH: 0.7, NCH: 0.07; p < 0.001, 12 m - last visit; CH: 0.536, NCH: 0.34). We obtained the same results with the EDSS (4 y; CH: 3.53, NCH: 2.55, annual progression rate in 12 m - last visit; CH: 0.29, NCH: 0.13). CONCLUSION: Applying TOR at the first year of therapy allowed accurate prediction of continued disease activity in relapses and disability progression.

Meta-analysis of studies analyzing the role of human papillomavirus in the development of bladder carcinoma

PURPOSE We aimed to ascertain the degree of association between bladder cancer and human papillomavirus (HPV) infection. MATERIALS AND METHODS We performed a meta-analysis of observational studies with cases and controls with publication dates up to January 2011. The PubMed electronic database was searched by using the key words "bladder cancer and virus." Twenty-one articles were selected that met the required methodological criteria. We implemented an internal quality control system to verify the selected search method. We analyzed the pooled effect of all the studies and also analyzed the techniques used as follows: 1) studies with DNA-based techniques, among which we found studies with polymerase chain reaction (PCR)-based techniques and 2) studies with non-PCR-based techniques, and studies with non-DNA-based techniques. RESULTS Taking into account the 21 studies that were included in the meta-analysis, we obtained a heterogeneity chi-squared value of Q(exp)=26.45 (p=0.383). The pooled odds ratio (OR) was 2.13 (95% confidence interval [CI], 1.54 to 2.95), which points to a significant effect between HPV and bladder cancer. Twenty studies assessed the presence of DNA. The overall effect showed a significant relationship between virus presence and bladder cancer, with a pooled OR of 2.19 (95% CI, 1.40 to 3.43). Of the other six studies, four examined the virus's capsid antigen and two detected antibodies in serum by Western blot. The estimated pooled OR in this group was 2.11 (95% CI, 1.27 to 3.51), which confirmed the relationship between the presence of virus and cancer. CONCLUSIONS The pooled OR value showed a moderate relationship between viral infection and bladder tumors.

Assessment of Severe Apnoea through Voice Analysis, Automatic Speech, and Speaker Recognition Techniques

This study is part of an ongoing collaborative effort between the medical and the signal processing communities to promote research on applying standard Automatic Speech Recognition (ASR) techniques for the automatic diagnosis of patients with severe obstructive sleep apnoea (OSA). Early detection of severe apnoea cases is important so that patients can receive early treatment. Effective ASR-based detection could dramatically cut medical testing time. Working with a carefully designed speech database of healthy and apnoea subjects, we describe an acoustic search for distinctive apnoea voice characteristics. We also study abnormal nasalization in OSA patients by modelling vowels in nasal and nonnasal phonetic contexts using Gaussian Mixture Model (GMM) pattern recognition on speech spectra. Finally, we present experimental findings regarding the discriminative power of GMMs applied to severe apnoea detection. We have achieved an 81% correct classification rate, which is very promising and underpins the interest in this line of inquiry.