Schistosoma mansoni: magnetic resonance analysis of liver fibrosis according to WHO patterns for ultrasound assessment of schistosomiasis-related morbidity

For the last two decades, ultrasound (US) has been considered a surrogate for the gold standard in the evaluation of liver fibrosis in schistosomiasis. The use of magnetic resonance imaging (MRI) is not yet standardised for diagnosing and grading liver schistosomal fibrosis. The aim of this paper was to analyse MRI using an adaptation of World Health Organization (WHO) patterns for US assessment of schistosomiasis-related morbidity. US and MRI were independently performed in 60 patients (42.1 ± 13.4 years old), including 37 men and 23 women with schistosomiasis. Liver involvement appraised by US and MRI was classified according to the WHO protocol from patterns A-F. Agreement between image methods was evaluated by kappa index (k). The correlation between US and MRI was poor using WHO patterns [k = 0.14; confidence interval (CI) 0.02; 0.26]. Even after grouping image patterns as "A-D", "Dc-E" and "Ec-F", the correlation between US and MRI remained weak (k = 0.39; CI 0.21; 0.58). The magnetic resonance adaptation used in our study did not confirm US classification of WHO patterns for liver fibrosis.

Assessment of recent HIV-1 infection by a line immunoassay for HIV-1/2 confirmation.

BACKGROUND: Knowledge of the number of recent HIV infections is important for epidemiologic surveillance. Over the past decade approaches have been developed to estimate this number by testing HIV-seropositive specimens with assays that discriminate the lower concentration and avidity of HIV antibodies in early infection. We have investigated whether this "recency" information can also be gained from an HIV confirmatory assay. METHODS AND FINDINGS: The ability of a line immunoassay (INNO-LIA HIV I/II Score, Innogenetics) to distinguish recent from older HIV-1 infection was evaluated in comparison with the Calypte HIV-1 BED Incidence enzyme immunoassay (BED-EIA). Both tests were conducted prospectively in all HIV infections newly diagnosed in Switzerland from July 2005 to June 2006. Clinical and laboratory information indicative of recent or older infection was obtained from physicians at the time of HIV diagnosis and used as the reference standard. BED-EIA and various recency algorithms utilizing the antibody reaction to INNO-LIA's five HIV-1 antigen bands were evaluated by logistic regression analysis. A total of 765 HIV-1 infections, 748 (97.8%) with complete test results, were newly diagnosed during the study. A negative or indeterminate HIV antibody assay at diagnosis, symptoms of primary HIV infection, or a negative HIV test during the past 12 mo classified 195 infections (26.1%) as recent (< or = 12 mo). Symptoms of CDC stages B or C classified 161 infections as older (21.5%), and 392 patients with no symptoms remained unclassified. BED-EIA ruled 65% of the 195 recent infections as recent and 80% of the 161 older infections as older. Two INNO-LIA algorithms showed 50% and 40% sensitivity combined with 95% and 99% specificity, respectively. Estimation of recent infection in the entire study population, based on actual results of the three tests and adjusted for a test's sensitivity and specificity, yielded 37% for BED-EIA compared to 35% and 33% for the two INNO-LIA algorithms. Window-based estimation with BED-EIA yielded 41% (95% confidence interval 36%-46%). CONCLUSIONS: Recency information can be extracted from INNO-LIA-based confirmatory testing at no additional costs. This method should improve epidemiologic surveillance in countries that routinely use INNO-LIA for HIV confirmation.

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.