Plasmodium vivax: Reverse transcriptase real-time PCR for gametocyte detection and quantitation in clinical samples

The proportion of Plasmodium vivax-infected subjects that carry mature gametocytes, and thus are potentially infectious, remains poorly characterized in endemic settings. Here, we describe a quantitative reverse transcriptase (RI) real-time PCR (qRT-PCR) that targets transcripts of the mature gametocyte-specific pvs25 gene. We found mature gametocytes in 42 of 44 (95.4%) P. vivax infections diagnosed during an ongoing cohort study in northwestern Brazil. SYBR green qRT-PCR was more sensitive than a conventional RT-PCR that targets the same gene. Molecular detection of gametocytes failed, however, when dried bloodspots were used for RNA isolation and complementary DNA synthesis. Estimating the number of pvs25 gene transcripts allowed for examining the potential infectiousness of gametocyte carriers in a quantitative way. We found that most (61.9%) gametocyte carriers were either asymptomatic or had subpatent parasitemias and would have been missed by routine malaria control strategies. However, potentially undiagnosed gametocyte carriers usually had low-density infections and contributed a small fraction (up to 4%) to the overall gametocyte burden in the community. Further studies are required to determine the relative contribution to malaria transmission of long-lasting but low-density gametocytemias in asymptomatic carriers that are left undiagnosed and untreated. (C) 2012 Elsevier Inc. All rights reserved.

Clinical and Microbiologic Evaluation, by Real-Time Polymerase Chain Reaction, of Non-Surgical Treatment of Aggressive Periodontitis Associated With Amoxicillin and Metronidazole

Background: The aim of the present study is to evaluate the clinical and microbiologic changes resulting from non-surgical periodontal treatment associated with amoxicillin and metronidazole in individuals with aggressive periodontitis. Methods: Fifteen individuals with aggressive periodontitis received non-surgical periodontal treatment and 45 days after completion of treatment were treated with antibiotics. Clinical data and samples of subgingival plaque were collected at baseline, 45 days after the non-surgical periodontal treatment, and 1 month after the use of antimicrobial agents. After 3 and 6 months, only clinical data were collected. The presence and quantification of Aggregatibacter actinomycetemcomitans, Porphyromonas gingivalis (Pg), Tannerella forsythia (Tf), Treponema denticola (Td), and Dialister pneumosintes were determined by real-time polymerase chain reaction. Results: All clinical parameters, with the exception of clinical attachment level (CAL), had significantly (P<0.05) improved at the end of the third month after non-surgical therapy associated with antibiotics. There was significant (P<0.05) reduction in the quantities of Td and Tf. After 1 month, there were significant (P<0.05) reductions in the frequencies of Pg and Tf. Conclusion: Non-surgical mechanical treatment associated with the use of amoxicillin and metronidazole led to an improvement in all clinical parameters studied, except for CAL, and significantly reduced the amount of subgingival Tf and Td. J Periodontal 2012;83:744-752.

Size distributions and temporal variations of biological aerosol particles in the Amazon rainforest characterized by microscopy and real-time UV-APS fluorescence techniques during AMAZE-08

As a part of the AMAZE-08 campaign during the wet season in the rainforest of central Amazonia, an ultraviolet aerodynamic particle sizer (UV-APS) was operated for continuous measurements of fluorescent biological aerosol particles (FBAP). In the coarse particle size range (> 1 mu m) the campaign median and quartiles of FBAP number and mass concentration were 7.3x10(4) m(-3) (4.0-13.2x10(4) m(-3)) and 0.72 mu g m(-3) (0.42-1.19 mu g m(-3)), respectively, accounting for 24% (11-41%) of total particle number and 47% (25-65%) of total particle mass. During the five-week campaign in February-March 2008 the concentration of coarse-mode Saharan dust particles was highly variable. In contrast, FBAP concentrations remained fairly constant over the course of weeks and had a consistent daily pattern, peaking several hours before sunrise, suggesting observed FBAP was dominated by nocturnal spore emission. This conclusion was supported by the consistent FBAP number size distribution peaking at 2.3 mu m, also attributed to fungal spores and mixed biological particles by scanning electron microscopy (SEM), light microscopy and biochemical staining. A second primary biological aerosol particle (PBAP) mode between 0.5 and 1.0 mu m was also observed by SEM, but exhibited little fluorescence and no true fungal staining. This mode may have consisted of single bacterial cells, brochosomes, various fragments of biological material, and small Chromalveolata (Chromista) spores. Particles liquid-coated with mixed organic-inorganic material constituted a large fraction of observations, and these coatings contained salts likely from primary biological origin. We provide key support for the suggestion that real-time laser-induce fluorescence (LIF) techniques using 355 nm excitation provide size-resolved concentrations of FBAP as a lower limit for the atmospheric abundance of biological particles in a pristine environment. We also show some limitations of using the instrument for ambient monitoring of weakly fluorescent particles < 2 mu m. Our measurements confirm that primary biological particles, fungal spores in particular, are an important fraction of supermicron aerosol in the Amazon and that may contribute significantly to hydrological cycling, especially when coated by mixed inorganic material.

Fuzzy Connectedness Image Segmentation in Graph Cut Formulation: A Linear-Time Algorithm and a Comparative Analysis

A deep theoretical analysis of the graph cut image segmentation framework presented in this paper simultaneously translates into important contributions in several directions. The most important practical contribution of this work is a full theoretical description, and implementation, of a novel powerful segmentation algorithm, GC(max). The output of GC(max) coincides with a version of a segmentation algorithm known as Iterative Relative Fuzzy Connectedness, IRFC. However, GC(max) is considerably faster than the classic IRFC algorithm, which we prove theoretically and show experimentally. Specifically, we prove that, in the worst case scenario, the GC(max) algorithm runs in linear time with respect to the variable M=|C|+|Z|, where |C| is the image scene size and |Z| is the size of the allowable range, Z, of the associated weight/affinity function. For most implementations, Z is identical to the set of allowable image intensity values, and its size can be treated as small with respect to |C|, meaning that O(M)=O(|C|). In such a situation, GC(max) runs in linear time with respect to the image size |C|. We show that the output of GC(max) constitutes a solution of a graph cut energy minimization problem, in which the energy is defined as the a"" (a) norm ayenF (P) ayen(a) of the map F (P) that associates, with every element e from the boundary of an object P, its weight w(e). This formulation brings IRFC algorithms to the realm of the graph cut energy minimizers, with energy functions ayenF (P) ayen (q) for qa[1,a]. Of these, the best known minimization problem is for the energy ayenF (P) ayen(1), which is solved by the classic min-cut/max-flow algorithm, referred to often as the Graph Cut algorithm. We notice that a minimization problem for ayenF (P) ayen (q) , qa[1,a), is identical to that for ayenF (P) ayen(1), when the original weight function w is replaced by w (q) . Thus, any algorithm GC(sum) solving the ayenF (P) ayen(1) minimization problem, solves also one for ayenF (P) ayen (q) with qa[1,a), so just two algorithms, GC(sum) and GC(max), are enough to solve all ayenF (P) ayen (q) -minimization problems. We also show that, for any fixed weight assignment, the solutions of the ayenF (P) ayen (q) -minimization problems converge to a solution of the ayenF (P) ayen(a)-minimization problem (ayenF (P) ayen(a)=lim (q -> a)ayenF (P) ayen (q) is not enough to deduce that). An experimental comparison of the performance of GC(max) and GC(sum) algorithms is included. This concentrates on comparing the actual (as opposed to provable worst scenario) algorithms' running time, as well as the influence of the choice of the seeds on the output.