HIV-1 subtypes and drug resistance in children during antiretroviral therapy in Brazil.

We evaluate the genetic characterization of 132 HIV-1 pol sequences from children and adolescents undergoing antiretroviral therapy in Northeast Brazil. Phylogenetic and recombination analyses were performed using the maximum likelihood method using SeaView version 4 and SIMPLOT software. Most individuals harbored HIV-1 B (84.8%) and BF recombinants (9.8%), although other non-B subtypes were detected: HIV-1 C (1.5%), HIV-1 F (2.4%), and BC recombinants (1.5%). Antiretroviral resistance was 47% (95% confidence interval [CI]: 38.7%-55.4%). Non-nucleoside reverse transcriptase inhibitors (NNRTIs) showed higher frequencies of primary mutations, with 40.9% (95% CI: 32.9%-49.4%), followed by nucleoside reverse transcriptase inhibitors (NRTI) and protease inhibitors (PIs) with 34.8% (95% CI: 27.3-43.3) and 6.1% (95% CI: 3.1%-11.5%), respectively. Among NRTIs, higher resistance levels were observed for abacavir, emtricitabine, and lamivudine; for NNRTI, nevirapine and efavirenz. The most common primary mutations found were M184V (29.5%), K103N (25%), M41L (9.8%), T215Y (8.3%), and G190A (8.3%). Our findings highlight the importance of surveillance of resistance mutations, which contributes to the continuous updating and implementation of preventive measures to decrease mother-to-child-transmission and transmitted drug resistance.

Classification of breast tissue in mammograms using efficient coding.

BACKGROUND: Female breast cancer is the major cause of death by cancer in western countries. Efforts in Computer Vision have been made in order to improve the diagnostic accuracy by radiologists. Some methods of lesion diagnosis in mammogram images were developed based in the technique of principal component analysis which has been used in efficient coding of signals and 2D Gabor wavelets used for computer vision applications and modeling biological vision. METHODS: In this work, we present a methodology that uses efficient coding along with linear discriminant analysis to distinguish between mass and non-mass from 5090 region of interest from mammograms. RESULTS: The results show that the best rates of success reached with Gabor wavelets and principal component analysis were 85.28% and 87.28%, respectively. In comparison, the model of efficient coding presented here reached up to 90.07%. CONCLUSIONS: Altogether, the results presented demonstrate that independent component analysis performed successfully the efficient coding in order to discriminate mass from non-mass tissues. In addition, we have observed that LDA with ICA bases showed high predictive performance for some datasets and thus provide significant support for a more detailed clinical investigation.