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Sensitive field-deployable diagnostic tests can assist malaria programs in achieving elimination. The performance of a new Alere™ Malaria Ag P.f Ultra Sensitive rapid diagnostic test (uRDT) was compared with the currently available SD Bioline Malaria Ag P.f RDT in blood specimens from asymptomatic individuals in Nagongera, Uganda, and in a Karen Village, Myanmar, representative of high- and low-transmission areas, respectively, as well as in pretreatment specimens from study participants from four Plasmodium falciparum-induced blood-stage malaria (IBSM) studies. A quantitative reverse transcription PCR (qRT-PCR) and a highly sensitive enzyme-linked immunosorbent assay (ELISA) test for histidine-rich protein II (HRP2) were used as reference assays. The uRDT showed a greater than 10-fold lower limit of detection for HRP2 compared with the RDT. The sensitivity of the uRDT was 84% and 44% against qRT-PCR in Uganda and Myanmar, respectively, and that of the RDT was 62% and 0% for the same two sites. The specificities of the uRDT were 92% and 99.8% against qRT-PCR for Uganda and Myanmar, respectively, and 99% and 99.8% against the HRP2 reference ELISA. The RDT had specificities of 95% and 100% against qRT-PCR for Uganda and Myanmar, respectively, and 96% and 100% against the HRP2 reference ELISA. The uRDT detected new infections in IBSM study participants 1.5 days sooner than the RDT. The uRDT has the same workflow as currently available RDTs, but improved performance characteristics to identify asymptomatic malaria infections. The uRDT may be a useful tool for malaria elimination strategies.

Here, an analysis of different acoustic features and their influence in automatic identification of hypernasality is shown. Effective feature selection method includes preprocessing of the initial feature space based on statistical independence analysis. Simultaneously, the synthesis of a specialized diagnostic feature is proposed based on analyzing the acoustic emission of the hyper nasal speech. As a result, It is obtained the acoustic features can differentiate with enough precision the pathology. However, the proposed feature does not require training samples and less computational power, as well.

Heuristical algorithms can reduce the computa- tional complexity. Such methods require of some stoping criteria (cost function). Some of these cost functions are based on statistics like univariate and multivariate methods of analysis. Dimensional reduction techniques such as Principal Component Analysis (PCA) allow to find a lower dimension transformed space based on data variance, but this procedure does not take into account information about classes separability, the direction of maximum variance does not necessarily correspond to the direction of maximum separability. In this work, we propose a feature selection algorithm with heuristic search that uses multivariate analysis of variance (MANOVA) as the cost function. This technique is put to test by classifying hypernasal from normal voices of CLP (Cleft Lip and/or Palate) patients. The classification performance, computational time and reduction ratio are also considered by the comparison with an alternate feature selection method founded on unfolding the multivariate analysis into univariate and bivariate analysis.