Comparison of laboratory costs of rapid molecular tests and conventional diagnostics for detection of tuberculosis and drug-resistant tuberculosis in South Africa.

BACKGROUND: The World Health Organization has endorsed the use of molecular methods for the detection of TB and drug-resistant TB as a rapid alternative to culture-based systems. In South Africa, the Xpert MTB/Rif assay and the GenoType MTBDRplus have been implemented into reference laboratories for diagnosis of TB and drug-resistance, but their costs have not been fully elucidated. METHODS: We conducted a detailed reference laboratory cost analysis of new rapid molecular assays (Xpert and MTBDRplus) for tuberculosis testing and drug-resistance testing in South Africa, and compared with the costs of conventional approaches involving sputum microscopy, liquid mycobacterial culture, and phenotypic drug sensitivity testing. RESULTS: From a laboratory perspective, Xpert MTB/RIF cost $14.93/sample and the MTBDRplus line probe assay cost$23.46/sample, compared to $16.88/sample using conventional automated liquid culture-based methods. Laboratory costs of Xpert and MTBDRplus were most influenced by cost of consumables (60-80%). CONCLUSIONS: At current public sector pricing, Xpert MTB/RIF and MTBDRplus are comparable in cost to mycobacterial culture and conventional drug sensitivity testing. Overall, reference laboratories must balance costs with performance characteristics and the need for rapid results

A self-organized model for cell-differentiation based on variations of molecular decay rates

Systemic properties of living cells are the result of molecular dynamics governed by so-called genetic regulatory networks (GRN). These networks capture all possible features of cells and are responsible for the immense levels of adaptation characteristic to living systems. At any point in time only small subsets of these networks are active. Any active subset of the GRN leads to the expression of particular sets of molecules (expression modes). The subsets of active networks change over time, leading to the observed complex dynamics of expression patterns. Understanding of this dynamics becomes increasingly important in systems biology and medicine. While the importance of transcription rates and catalytic interactions has been widely recognized in modeling genetic regulatory systems, the understanding of the role of degradation of biochemical agents (mRNA, protein) in regulatory dynamics remains limited. Recent experimental data suggests that there exists a functional relation between mRNA and protein decay rates and expression modes. In this paper we propose a model for the dynamics of successions of sequences of active subnetworks of the GRN. The model is able to reproduce key characteristics of molecular dynamics, including homeostasis, multi-stability, periodic dynamics, alternating activity, differentiability, and self-organized critical dynamics. Moreover the model allows to naturally understand the mechanism behind the relation between decay rates and expression modes. The model explains recent experimental observations that decay-rates (or turnovers) vary between differentiated tissue-classes at a general systemic level and highlights the role of intracellular decay rate control mechanisms in cell differentiation.Comment: 16 pages, 5 figure

Two novel human cytomegalovirus NK cell evasion functions target MICA for lysosomal degradation

NKG2D plays a major role in controlling immune responses through the regulation of natural killer (NK) cells, αβ and γδ T-cell function. This activating receptor recognizes eight distinct ligands (the MHC Class I polypeptide-related sequences (MIC) A andB, and UL16-binding proteins (ULBP)1–6) induced by cellular stress to promote recognition cells perturbed by malignant transformation or microbial infection. Studies into human cytomegalovirus (HCMV) have aided both the identification and characterization of NKG2D ligands (NKG2DLs). HCMV immediate early (IE) gene up regulates NKGDLs, and we now describe the differential activation of ULBP2 and MICA/B by IE1 and IE2 respectively. Despite activation by IE functions, HCMV effectively suppressed cell surface expression of NKGDLs through both the early and late phases of infection. The immune evasion functions UL16, UL142, and microRNA(miR)-UL112 are known to target NKG2DLs. While infection with a UL16 deletion mutant caused the expected increase in MICB and ULBP2 cell surface expression, deletion of UL142 did not have a similar impact on its target, MICA. We therefore performed a systematic screen of the viral genome to search of addition functions that targeted MICA. US18 and US20 were identified as novel NK cell evasion functions capable of acting independently to promote MICA degradation by lysosomal degradation. The most dramatic effect on MICA expression was achieved when US18 and US20 acted in concert. US18 and US20 are the first members of the US12 gene family to have been assigned a function. The US12 family has 10 members encoded sequentially through US12–US21; a genetic arrangement, which is suggestive of an ‘accordion’ expansion of an ancestral gene in response to a selective pressure. This expansion must have be an ancient event as the whole family is conserved across simian cytomegaloviruses from old world monkeys. The evolutionary benefit bestowed by the combinatorial effect of US18 and US20 on MICA may have contributed to sustaining the US12 gene family

Inferring topology from clustering coefficients in protein-protein interaction networks

BACKGROUND: Although protein-protein interaction networks determined with high-throughput methods are incomplete, they are commonly used to infer the topology of the complete interactome. These partial networks often show a scale-free behavior with only a few proteins having many and the majority having only a few connections. Recently, the possibility was suggested that this scale-free nature may not actually reflect the topology of the complete interactome but could also be due to the error proneness and incompleteness of large-scale experiments. RESULTS: In this paper, we investigate the effect of limited sampling on average clustering coefficients and how this can help to more confidently exclude possible topology models for the complete interactome. Both analytical and simulation results for different network topologies indicate that partial sampling alone lowers the clustering coefficient of all networks tremendously. Furthermore, we extend the original sampling model by also including spurious interactions via a preferential attachment process. Simulations of this extended model show that the effect of wrong interactions on clustering coefficients depends strongly on the skewness of the original topology and on the degree of randomness of clustering coefficients in the corresponding networks. CONCLUSION: Our findings suggest that the complete interactome is either highly skewed such as e.g. in scale-free networks or is at least highly clustered. Although the correct topology of the interactome may not be inferred beyond any reasonable doubt from the interaction networks available, a number of topologies can nevertheless be excluded with high confidence

Which clustering algorithm is better for predicting protein complexes?

<p>Abstract</p> <p>Background</p> <p>Protein-Protein interactions (PPI) play a key role in determining the outcome of most cellular processes. The correct identification and characterization of protein interactions and the networks, which they comprise, is critical for understanding the molecular mechanisms within the cell. Large-scale techniques such as pull down assays and tandem affinity purification are used in order to detect protein interactions in an organism. Today, relatively new high-throughput methods like yeast two hybrid, mass spectrometry, microarrays, and phage display are also used to reveal protein interaction networks.</p> <p>Results</p> <p>In this paper we evaluated four different clustering algorithms using six different interaction datasets. We parameterized the MCL, Spectral, RNSC and Affinity Propagation algorithms and applied them to six PPI datasets produced experimentally by Yeast 2 Hybrid (Y2H) and Tandem Affinity Purification (TAP) methods. The predicted clusters, so called protein complexes, were then compared and benchmarked with already known complexes stored in published databases.</p> <p>Conclusions</p> <p>While results may differ upon parameterization, the MCL and RNSC algorithms seem to be more promising and more accurate at predicting PPI complexes. Moreover, they predict more complexes than other reviewed algorithms in absolute numbers. On the other hand the spectral clustering algorithm achieves the highest valid prediction rate in our experiments. However, it is nearly always outperformed by both RNSC and MCL in terms of the geometrical accuracy while it generates the fewest valid clusters than any other reviewed algorithm. This article demonstrates various metrics to evaluate the accuracy of such predictions as they are presented in the text below. Supplementary material can be found at: <url>http://www.bioacademy.gr/bioinformatics/projects/ppireview.htm</url></p

Protein Networks Reveal Detection Bias and Species Consistency When Analysed by Information-Theoretic Methods

We apply our recently developed information-theoretic measures for the characterisation and comparison of protein–protein interaction networks. These measures are used to quantify topological network features via macroscopic statistical properties. Network differences are assessed based on these macroscopic properties as opposed to microscopic overlap, homology information or motif occurrences. We present the results of a large–scale analysis of protein–protein interaction networks. Precise null models are used in our analyses, allowing for reliable interpretation of the results. By quantifying the methodological biases of the experimental data, we can define an information threshold above which networks may be deemed to comprise consistent macroscopic topological properties, despite their small microscopic overlaps. Based on this rationale, data from yeast–two–hybrid methods are sufficiently consistent to allow for intra–species comparisons (between different experiments) and inter–species comparisons, while data from affinity–purification mass–spectrometry methods show large differences even within intra–species comparisons

Perceptions, use and attitudes of pharmacy customers on complementary medicines and pharmacy practice

BACKGROUND: Complementary medicines (CMs) are popular amongst Australians and community pharmacy is a major supplier of these products. This study explores pharmacy customer use, attitudes and perceptions of complementary medicines, and their expectations of pharmacists as they relate to these products. METHODS: Pharmacy customers randomly selected from sixty large and small, metropolitan and rural pharmacies in three Australian states completed an anonymous, self administered questionnaire that had been pre-tested and validated. RESULTS: 1,121 customers participated (response rate 62%). 72% had used CMs within the previous 12 months, 61% used prescription medicines daily and 43% had used both concomitantly. Multivitamins, fish oils, vitamin C, glucosamine and probiotics were the five most popular CMs. 72% of people using CMs rated their products as 'very effective' or 'effective enough'. CMs were as frequently used by customers aged 60 years or older as younger customers (69% vs. 72%) although the pattern of use shifted with older age. Most customers (92%) thought pharmacists should provide safety information about CMs, 90% thought they should routinely check for interactions, 87% thought they should recommend effective CMs, 78% thought CMs should be recorded in customer's medication profile and 58% thought pharmacies stocking CMs should also employ a complementary medicine practitioner. Of those using CMs, 93% thought it important for pharmacists to be knowledgeable about CMs and 48% felt their pharmacist provides useful information about CMs. CONCLUSIONS: CMs are widely used by pharmacy customers of all ages who want pharmacists to be more involved in providing advice about these products

Race/Ethnicity and gender differences in health intentions and behaviors regarding exercise and diet for adults with type 2 diabetes: A cross-sectional analysis

<p>Abstract</p> <p>Background</p> <p>Self-management is the cornerstone of diabetes control and prevention of complications; however, it is undetermined whether differences in intention to adopt healthy lifestyles and actual healthy behavior exist across race/ethnic groups. This study evaluated the differences across racial-ethnic groups in self-reported medical advice received and health intentions and behaviors among adults with type 2 diabetes mellitus.</p> <p>Methods</p> <p>A cross-sectional analysis of the 2007 SHIELD US survey ascertained self-reported health intentions and behaviors for regular exercise, diet, and weight management among Non-Hispanic Caucasian (n = 2526), Non-Hispanic African-American (n = 706), and Hispanic (n = 179) respondents with type 2 diabetes.</p> <p>Results</p> <p>A similar proportion of respondents from each race-gender group (43%-56%) reported receiving healthcare advice to increase their exercise (P = 0.32). Significantly more minorities reported an intention to follow the exercise recommendation compared with Non-Hispanic Caucasians (P = 0.03). More Non-Hispanic African-American (29%) and Hispanic (27%) men reported exercising regularly compared with other race-gender groups (P = 0.02). Significantly more Non-Hispanic Caucasian women (74%) and Hispanic women (79%) reported trying to lose weight compared with other groups (P < 0.0001).</p> <p>Conclusions</p> <p>Differences in health intentions and healthy behaviors were noted across race-gender groups. More Non-Hispanic African-American men reported an intention to follow advice on exercising and self-report of exercising regularly was also higher compared with other race-gender groups. More Hispanic men reported high physical activity levels than other groups. Despite an increased willingness to follow healthcare recommendations for diet, >50% of respondents were obese among all race-gender groups.</p

Predicting the protein-protein interactions using primary structures with predicted protein surface

<p>Abstract</p> <p>Background</p> <p>Many biological functions involve various protein-protein interactions (PPIs). Elucidating such interactions is crucial for understanding general principles of cellular systems. Previous studies have shown the potential of predicting PPIs based on only sequence information. Compared to approaches that require other auxiliary information, these sequence-based approaches can be applied to a broader range of applications.</p> <p>Results</p> <p>This study presents a novel sequence-based method based on the assumption that protein-protein interactions are more related to amino acids at the surface than those at the core. The present method considers surface information and maintains the advantage of relying on only sequence data by including an accessible surface area (ASA) predictor recently proposed by the authors. This study also reports the experiments conducted to evaluate a) the performance of PPI prediction achieved by including the predicted surface and b) the quality of the predicted surface in comparison with the surface obtained from structures. The experimental results show that surface information helps to predict interacting protein pairs. Furthermore, the prediction performance achieved by using the surface estimated with the ASA predictor is close to that using the surface obtained from protein structures.</p> <p>Conclusion</p> <p>This work presents a sequence-based method that takes into account surface information for predicting PPIs. The proposed procedure of surface identification improves the prediction performance with an <it>F-measure </it>of 5.1%. The extracted surfaces are also valuable in other biomedical applications that require similar information.</p