Plasmodium vivax parasites with chloroquine resistance (CQR) are already circulating in the BrazilianAmazon. Complete single-nucleotide polymorphism (SNP) analyses of coding and noncoding sequences of the pvmdr1 and pvcrt-o genes revealed no associations with CQR, even if some mutations had not been randomly selected. In addition, striking differences in the topologies and numbers of SNPs in these transporter genes between P. vivax and P. falciparum reinforce the idea that mechanisms other than mutations may explain this virulent phenotype in P. vivax.Plasmodium vivax is the most widely distributed human malaria parasite, causing approximately 80 to 300 million clinical cases of malaria each year (17). Numerous factors indicate that this burden will increase due to the emergence and spread of chloroquine-resistant parasites (3, 17).More than 50% of the malaria cases in Latin America occur in Brazil, and P. vivax predominates as the causative agent (16,21). Notably, failures of chloroquine treatment of P. vivax malaria in the Brazilian Amazon city of Manaus have been reported recently (1). The local confirmation of the presence of active P. vivax parasites resisting chloroquine at the proposed minimal effective concentration in plasma for sensitive strains is a public health concern deserving attention.Point mutations in two digestive-vacuole membrane proteins of P. falciparum, the P. falciparum chloroquine resistance transporter (PfCRT) and multidrug resistance 1 protein (PfMDR1), have been associated with chloroquine resistance (CQR), albeit to different extents (2, 10). Orthologues of these proteins in P. vivax (P. vivax CRT-O [PvCRT-O] and PvMDR1) have been identified previously (6, 15, 18), and recently, pvmdr1 mutant alleles were suggested to be associated with both in vitro and in vivo CQR in Southeast Asia (6,20).Here, we report a single-nucleotide polymorphism (SNP) analysis of pvmdr1 and pvcrt-o genes in P. vivax isolates from chloroquine-treated patients with and without recrudescent disease in the Brazilian Amazon region. In addition to complete coding sequences, we analyzed sequences from 5Ј flanking regions and introns.Field isolates were collected during a 28-day in vivo chloroquine efficacy study conducted in the city of Manaus, Brazil (8). Plasmatic chloroquine levels in all volunteers were measured by high-performance liquid chromatography on day 3 to confirm adequate dosing and good absorption of the oral chloroquine intake (three doses of 10, 7.5, and 7.5 mg/kg of body weight in 150-mg tablet form at 24-h intervals). Clinical treatment failure was defined as the occurrence of a positive blood smear result (confirmed by PCR diagnostic analysis) on day 14, 21, or 28 and the presence of parasites in peripheral blood (collected on the same day as the positive blood smear) containing Ͼ10 ng/ml of chloroquine as determined by high-performance liquid chromatography (7). Measurements of chloroquine and its active metabolite desethylchloroquine in whole blood were not obtained, as plasma samples were c...

BackgroundHead and neck squamous cell carcinoma (HNSCC) is one of the most common malignancies in humans. The average 5-year survival rate is one of the lowest among aggressive cancers, showing no significant improvement in recent years. When detected early, HNSCC has a good prognosis, but most patients present metastatic disease at the time of diagnosis, which significantly reduces survival rate. Despite extensive research, no molecular markers are currently available for diagnostic or prognostic purposes.MethodsAiming to identify differentially-expressed genes involved in laryngeal squamous cell carcinoma (LSCC) development and progression, we generated individual Serial Analysis of Gene Expression (SAGE) libraries from a metastatic and non-metastatic larynx carcinoma, as well as from a normal larynx mucosa sample. Approximately 54,000 unique tags were sequenced in three libraries.ResultsStatistical data analysis identified a subset of 1,216 differentially expressed tags between tumor and normal libraries, and 894 differentially expressed tags between metastatic and non-metastatic carcinomas. Three genes displaying differential regulation, one down-regulated (KRT31) and two up-regulated (BST2, MFAP2), as well as one with a non-significant differential expression pattern (GNA15) in our SAGE data were selected for real-time polymerase chain reaction (PCR) in a set of HNSCC samples. Consistent with our statistical analysis, quantitative PCR confirmed the upregulation of BST2 and MFAP2 and the downregulation of KRT31 when samples of HNSCC were compared to tumor-free surgical margins. As expected, GNA15 presented a non-significant differential expression pattern when tumor samples were compared to normal tissues.ConclusionTo the best of our knowledge, this is the first study reporting SAGE data in head and neck squamous cell tumors. Statistical analysis was effective in identifying differentially expressed genes reportedly involved in cancer development. The differential expression of a subset of genes was confirmed in additional larynx carcinoma samples and in carcinomas from a distinct head and neck subsite. This result suggests the existence of potential common biomarkers for prognosis and targeted-therapy development in this heterogeneous type of tumor.

Non protein-coding RNAs (ncRNAs) are a research hotspot in bioinformatics. Recent discoveries have revealed new ncRNA families performing a variety of roles, from gene expression regulation to catalytic activities. It is also believed that other families are still to be unveiled. Computational methods developed for protein coding genes often fail when searching for ncRNAs. Noncoding RNAs functionality is often heavily dependent on their secondary structure, which makes gene discovery very different from protein coding RNA genes. This motivated the development of specific methods for ncRNA research. This article reviews the main approaches used to identify ncRNAs and predict secondary structure.

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Down syndrome is one of the most common genetic disorders caused by chromosome abnormalities in humans. Among other physical characteristics, certain facial features are typically associated in people with Down syndrome. We investigate the problem of Down syndrome detection from a collection of face images. As the main contribution, a compact geometric descriptor is used to extract facial features from the images. Experiments are conducted on an available dataset to demonstrate the performance of the proposed methodology.

There is an urgent need for expanding the number of brain banks serving psychiatric research. We describe here the Psychiatric Disorders arm of the Brain Bank of the Brazilian Aging Brain Study Group (Psy-BBBABSG), which is focused in bipolar disorder (BD) and obsessive compulsive disorder (OCD). Our protocol was designed to minimize limitations faced by previous initiatives, and to enable design-based neurostereological analyses. The Psy-BBBABSG first milestone is the collection of 10 brains each of BD and OCD patients, and matched controls. The brains are sourced from a population-based autopsy service. The clinical and psychiatric assessments were done by an expert team including psychiatrists, through an informant. One hemisphere was perfused-fixed to render an optimal fixation for conducting neurostereological studies. The other hemisphere was comprehensively dissected and frozen for molecular studies. In 20 months, we collected 36 brains. A final report was completed for 14 cases: 3 BDs, 4 major depressive disorders, 1 substance use disorder, 1 mood disorder NOS, 3 obsessive compulsive spectrum symptoms, 1 OCD and 1 schizophrenia. The majority were male (64%), and the average age at death was 67.2 ± 9.0 years. The average postmortem interval was 16 h. Three matched controls were collected. The pilot stage confirmed that the protocols are well fitted to reach our goals. Our unique autopsy source makes possible to collect a fairly number of high quality cases in a short time. Such a collection offers an additional to the international research community to advance the understanding on neuropsychiatric diseases.

An advantage of using eye tracking for diagnosis is that it is non-invasive and can be performed in individuals with different functional levels and ages. Computer/aided diagnosis using eye tracking data is commonly based on eye fixation points in some regions of interest (ROI) in an image. However, besides the need for every ROI demarcation in each image or video frame used in the experiment, the diversity of visual features contained in each ROI may compromise the characterization of visual attention in each group (case or control) and consequent diagnosis accuracy. Although some approaches use eye tracking signals for aiding diagnosis, it is still a challenge to identify frames of interest when videos are used as stimuli and to select relevant characteristics extracted from the videos. This is mainly observed in applications for autism spectrum disorder (ASD) diagnosis. To address these issues, the present paper proposes: (1) a computational method, integrating concepts of Visual Attention Model, Image Processing and Artificial Intelligence techniques for learning a model for each group (case and control) using eye tracking data, and (2) a supervised classifier that, using the learned models, performs the diagnosis. Although this approach is not disorder-specific, it was tested in the context of ASD diagnosis, obtaining an average of precision, recall and specificity of 90%, 69% and 93%, respectively.