Interdependent MHC-DRB exon-plus-intron evolution in artiodactyls

Exon 2 sequences of an expressed MHC-DRB locus from sheep were examined for polymorphisms in both the antigen-binding regions and the adjacent intronic mixed simple tandem repeat. Twenty-one novel exon 2 Ovar-DRB alleles were identified. Short nucleotide motifs are extensively shared between certain exon 2 regions of Ovar-DRB alleles. The simple repeat variations, the number of different amino acids at usually polymorphic sites, and the number of silent substitutions were reduced in the intraspecies analyses of sheep DRB sequences, compared with those of cattle and goats. It was paradoxical that the abundance of different sheep alleles was similar to that of cattle and goats. This paradox may be explained by postulating a relatively small number of "ancient" alleles, with the present-day Ovar-DRB alleles being generated by reciprocal exchange of nucleotide motifs. At the antigen-binding sites, new combinations of amino acids were maintained in Ovar-DRB alleles by strong positive selection. In sheep--and less pronounced in goats and cattle--the DRB alleles can be divided into two groups. In one group, silent substitutions are increased when compared with the other. This suggests separate evolutionary pathways for certain groups of DRB alleles within a species. The simple repetitive sequences are also discussed with respect to the evolution of DRB alleles.      

Proteomic analysis of novel marine bacteria using MALDI and ESI mass spectrometry.

The objective of this study was to develop a mass spectrometric protocol to search for proteins related to phototrophy in marine bacteria. The genes that produce proteins involved in conversion of light into energy have been detected by cloning-sequencing from some of these bacteria, but it was previously unknown if these proteins were actually expressed. Attaining this study's goal was complicated by the fact that the samples consisted of miniscule cell pellets, which yielded small amounts of very complex mixtures of proteins. Sample preparation and analysis were tailored to optimize the probability of detecting the proteins of interest. It has been reported that using both matrix-assisted laser desorption/ionization (MALDI) and electrospray ionization (ESI) to analyze a mixture of peptides leads to the identification of more peptides that either technique alone. In order to exploit this complementarity between ESI and MALDI for proteomic analysis, samples were analyzed using both ionization techniques. With correct choices in sample preparation and ionization process, biologically relevant proteins can be identified out of small samples containing whole proteomes.     

Modelling the cost-effectiveness of mass screening and treatment for reducing Plasmodium falciparum malaria burden

Background

Past experience and modelling suggest that, in most cases, mass treatment strategies are not likely to succeed in interrupting Plasmodium falciparum malaria transmission. However, this does not preclude their use to reduce disease burden. Mass screening and treatment (MSAT) is preferred to mass drug administration (MDA), as the latter involves massive over-use of drugs. This paper reports simulations of the incremental cost-effectiveness of well-conducted MSAT campaigns as a strategy for P. falciparum malaria disease-burden reduction in settings with varying receptivity (ability of the combined vector population in a setting to transmit disease) and access to case management.

Methods

MSAT incremental cost-effectiveness ratios (ICERs) were estimated in different sub-Saharan African settings using simulation models of the dynamics of malaria and a literature-based MSAT cost estimate. Imported infections were simulated at a rate of two per 1,000 population per annum. These estimates were compared to the ICERs of scaling up case management or insecticide-treated net (ITN) coverage in each baseline health system, in the absence of MSAT.

Results

MSAT averted most episodes, and resulted in the lowest ICERs, in settings with a moderate level of disease burden. At a low pre-intervention entomological inoculation rate (EIR) of two infectious bites per adult per annum (IBPAPA) MSAT was never more cost-effective than scaling up ITNs or case management coverage. However, at pre-intervention entomological inoculation rates (EIRs) of 20 and 50 IBPAPA and ITN coverage levels of 40 or 60%, respectively, the ICER of MSAT was similar to that of scaling up ITN coverage further.

Conclusions

In all the transmission settings considered, achieving a minimal level of ITN coverage is a “best buy”. At low transmission, MSAT probably is not worth considering. Instead, MSAT may be suitable at medium to high levels of transmission and at moderate ITN coverage. If undertaken as a burden-reducing intervention, MSAT should be continued indefinitely and should complement, not replace, case management and vector control interventions.     

Exploratory Analysis of Methods for Automated Classification of Laboratory Test Orders into Syndromic Groups in Veterinary Medicine

Background

Recent focus on earlier detection of pathogen introduction in human and animal populations has led to the development of surveillance systems based on automated monitoring of health data. Real- or near real-time monitoring of pre-diagnostic data requires automated classification of records into syndromes–syndromic surveillance–using algorithms that incorporate medical knowledge in a reliable and efficient way, while remaining comprehensible to end users.

Methods

This paper describes the application of two of machine learning (Naïve Bayes and Decision Trees) and rule-based methods to extract syndromic information from laboratory test requests submitted to a veterinary diagnostic laboratory.

Results

High performance (F₁-macro = 0.9995) was achieved through the use of a rule-based syndrome classifier, based on rule induction followed by manual modification during the construction phase, which also resulted in clear interpretability of the resulting classification process. An unmodified rule induction algorithm achieved an F_1-micro score of 0.979 though this fell to 0.677 when performance for individual classes was averaged in an unweighted manner (F_1-macro), due to the fact that the algorithm failed to learn 3 of the 16 classes from the training set. Decision Trees showed equal interpretability to the rule-based approaches, but achieved an F_1-micro score of 0.923 (falling to 0.311 when classes are given equal weight). A Naïve Bayes classifier learned all classes and achieved high performance (F_1-micro = 0.994 and F_1-macro = .955), however the classification process is not transparent to the domain experts.

Conclusion

The use of a manually customised rule set allowed for the development of a system for classification of laboratory tests into syndromic groups with very high performance, and high interpretability by the domain experts. Further research is required to develop internal validation rules in order to establish automated methods to update model rules without user input.     

The Association of Income with Health Behavior Change and Disease Monitoring among Patients with Chronic Disease

Background

Management of chronic diseases requires patients to adhere to recommended health behavior change and complete tests for monitoring. While studies have shown an association between low income and lack of adherence, the reasons why people with low income may be less likely to adhere are unclear. We sought to determine the association between household income and receipt of health behavior change advice, adherence to advice, receipt of recommended monitoring tests, and self-reported reasons for non-adherence/non-receipt.

Methods

We conducted a population-weighted survey, with 1849 respondents with cardiovascular-related chronic diseases (heart disease, hypertension, diabetes, stroke) from Western Canada (n = 1849). We used log-binomial regression to examine the association between household income and the outcome variables of interest: receipt of advice for and adherence to health behavior change (sodium reduction, dietary improvement, increased physical activity, smoking cessation, weight loss), reasons for non-adherence, receipt of recommended monitoring tests (cholesterol, blood glucose, blood pressure), and reasons for non-receipt of tests.

Results

Behavior change advice was received equally by both low and high income respondents. Low income respondents were more likely than those with high income to not adhere to recommendations regarding smoking cessation (adjusted prevalence rate ratio (PRR): 1.55, 95%CI: 1.09–2.20), and more likely to not receive measurements of blood cholesterol (PRR: 1.72, 95%CI 1.24–2.40) or glucose (PRR: 1.80, 95%CI: 1.26–2.58). Those with low income were less likely to state that non-adherence/non-receipt was due to personal choice, and more likely to state that it was due to an extrinsic factor, such as cost or lack of accessibility.

Conclusions

There are important income-related differences in the patterns of health behavior change and disease monitoring, as well as reasons for non-adherence or non-receipt. Among those with low income, adherence to health behavior change and monitoring may be improved by addressing modifiable barriers such as cost and access.     

Spatial and temporal variation in malaria transmission in a low endemicity area in northern Tanzania

Background

Current detection or screening for malaria infection necessitates drawing blood by fingerprick or venipuncture, which poses risks and limitations for repeated measurement. This study presents PCR detection of Plasmodium falciparum in human urine and saliva samples, and illustrates this potential application in genotyping malaria infections.

Methods

Urine and saliva were obtained from 47 thick film positive and 4 negative individuals one day after collection of blood slides and filter paper blood spots. P. falciparum DNA was extracted from blood, urine and saliva, in separate groups, using the Chelex method or Qiagen DNEasy^® kit (urine and saliva only). Blood, urine and saliva extracts were subjected to PCR in separate batches. Amplicons from the various sample types were examined for MSP2 polymorphisms and restriction fragment patterns on DHFR amino acid codon 59.

Results and discussion

Malaria infections exhibited primarily low-grade parasite densities, with a geometric mean of 775 asexual parasites/μl. Regularly matching polymorphic MSP2 genotypes were found between the corresponding urine, saliva and peripheral blood amplicons of each individual, with different inter-individual polymorphic genotypes. Amplicon yields were significantly dependent on DNA extraction method, parasite density and primer set (p < 0.001). A Qiagen^® kit extraction had more than 2× higher amplicon yield than the Chelex method, for both urine and saliva. Amplicon yields were 1.6 fold higher from saliva than urine. For each unit increase in log parasite density, the probability of amplicon enhanced 1.8 fold. Highest amplicon yields were obtained from the primer set with the shortest PCR product.

Conclusion

P. falciparum infection is detectable by PCR on human urine and saliva samples. Subject to further refinement of extraction technique and amplicon yields, large-scale malaria parasite screening and epidemiological surveys could be possible without the need to collect blood and use of needles or sharps.     

Comparison of normalisation methods for surface-enhanced laser desorption and ionisation (SELDI) time-of-flight (TOF) mass spectrometry data

Background  

Mass spectrometry for biological data analysis is an active field of research, providing an efficient way of high-throughput proteome screening. A popular variant of mass spectrometry is SELDI, which is often used to measure sample populations with the goal of developing (clinical) classifiers. Unfortunately, not only is the data resulting from such measurements quite noisy, variance between replicate measurements of the same sample can be high as well. Normalisation of spectra can greatly reduce the effect of this technical variance and further improve the quality and interpretability of the data. However, it is unclear which normalisation method yields the most informative result.