The use of dried blood spots and a modified particle agglutination test for cost-effective sero-epidemiological HIV surveys

No Abstract

Characterisation of gastroenteritis associated adenoviruses in South Africa

Objective. To analyse adenovirus (Ad) numbers and types associated with paediatric gastro-enteritis in South AfricaSetting. Gauteng, 1994-1996.Methods. A total of 234 paediatric diarrhoeal stool samples were screened for Ad using commercial enzyme-linked iInmunosorbent assays (EUSAs). Adenoviral isolates were typed, where possibie, using restriction enzyme analysis.Results. Ad was detected in 23 (9.8%) specimens, of which 8 (34.8%) were found by subgroup F-specilic EUSA to contain Ad40 or 41. Six of these isolates were typed and 2 could not be typed. Of the remaining 15 specimens, 2 isolates had restriction profiles that did not correspond with known Ads, while 2were identified as Ad31 and 1 as a subgroup CAd. The remaining 10 specimens negative for Ad40/41 were noncultivable and could not be typed.Conclusions. The high percentage of non-eultivable Ads other than Ad40/41 is unusual, and may possibly indicate the prevalence of hexon variants of Ad40/41 or of emerging Ad types in South Africa

Machine learning models in electronic health records can outperform conventional survival models for predicting patient mortality in coronary artery disease

Prognostic modelling is important in clinical practice and epidemiology for patient management and research. Electronic health records (EHR) provide large quantities of data for such models, but conventional epidemiological approaches require significant researcher time to implement. Expert selection of variables, fine-tuning of variable transformations and interactions, and imputing missing values are time-consuming and could bias subsequent analysis, particularly given that missingness in EHR is both high, and may carry meaning. Using a cohort of 80,000 patients from the CALIBER programme, we compared traditional modelling and machine-learning approaches in EHR. First, we used Cox models and random survival forests with and without imputation on 27 expert-selected, preprocessed variables to predict all-cause mortality. We then used Cox models, random forests and elastic net regression on an extended dataset with 586 variables to build prognostic models and identify novel prognostic factors without prior expert input. We observed that data-driven models used on an extended dataset can outperform conventional models for prognosis, without data preprocessing or imputing missing values. An elastic net Cox regression based with 586 unimputed variables with continuous values discretised achieved a C-index of 0.801 (bootstrapped 95% CI 0.799 to 0.802), compared to 0.793 (0.791 to 0.794) for a traditional Cox model comprising 27 expert-selected variables with imputation for missing values. We also found that data-driven models allow identification of novel prognostic variables; that the absence of values for particular variables carries meaning, and can have significant implications for prognosis; and that variables often have a nonlinear association with mortality, which discretised Cox models and random forests can elucidate. This demonstrates that machine-learning approaches applied to raw EHR data can be used to build models for use in research and clinical practice, and identify novel predictive variables and their effects to inform future research

Incidence of rotavirus gastroenteritis by age in African, Asian and European children: Relevance for timing of rotavirus vaccination

© 2016 The Author(s). Published with license by Taylor & Francis. © GSK Biologicals SA.Variability in rotavirus gastroenteritis (RVGE) epidemiology can influence the optimal vaccination schedule. We evaluated regional trends in the age of RVGE episodes in low- to middle- versus high-income countries in three continents. We undertook a post-hoc analysis based on efficacy trials of a human rotavirus vaccine (HRV; Rotarix™, GSK Vaccines), in which 1348, 1641, and 5250 healthy infants received a placebo in Europe (NCT00140686), Africa (NCT00241644), and Asia (NCT00197210, NCT00329745). Incidence of any/severe RVGE by age at onset was evaluated by active surveillance over the first two years of life. Severity of RVGE episodes was assessed using the Vesikari-scale. The incidence of any RVGE in Africa was higher than in Europe during the first year of life (≤2.78% vs. ≤2.03% per month), but much lower during the second one (≤0.86% versus ≤2.00% per month). The incidence of severe RVGE in Africa was slightly lower than in Europe during the first year of life. Nevertheless, temporal profiles for the incidence of severe RVGE in Africa and Europe during the first (≤1.00% and ≤1.23% per month) and second (≤0.53% and ≤1.13% per month) years of life were similar to those of any RVGE. Any/severe RVGE incidences peaked at younger ages in Africa vs. Europe. In high-income Asian regions, severe RVGE incidence (≤0.31% per month) remained low during the study. The burden of any RVGE was higher earlier in life in children from low- to middle- compared with high-income countries. Differing rotavirus vaccine schedules are likely warranted to maximize protection in different settings

Automated Home-Cage Behavioural Phenotyping of Mice

Neurobehavioral analysis of mouse phenotypes requires the monitoring of mouse behavior over long periods of time. Here, we describe a trainable computer vision system enabling the automated analysis of complex mouse behaviors. We provide software and an extensive manually annotated video database used for training and testing the system. Our system performs on par with human scoring, as measured from ground-truth manual annotations of thousands of clips of freely behaving mice. As a validation of the system, we characterized the home-cage behaviors of two standard inbred and two non-standard mouse strains. From this data we were able to predict in a blind test the strain identity of individual animals with high accuracy. Our video-based software will complement existing sensor based automated approaches and enable an adaptable, comprehensive, high-throughput, fine-grained, automated analysis of mouse behavior.McGovern Institute for Brain ResearchCalifornia Institute of Technology. Broad Fellows Program in Brain CircuitryNational Science Council (China) (TMS-094-1-A032

Towards a universal “baseline” characterisation of air masses for high- and low-altitude observing stations using Radon-222

We demonstrate the ability of atmospheric radon concentrations to reliably and unambiguously identify local and remote terrestrial influences on an air mass, and thereby the potential for alteration of trace gas composition by anthropogenic and biogenic processes. Based on high accuracy (lower limit of detection 10–40 mBq m–3), high temporal resolution (hourly) measurements of atmospheric radon concentration we describe, apply and evaluate a simple two-step method for identifying and characterising constituent mole fractions in baseline air. The technique involves selecting a radon-based threshold concentration to identify the “cleanest” (least terrestrially influenced) air masses, and then performing an outlier removal step based on the distribution of constituent mole fractions in the identified clean air masses. The efficacy of this baseline selection technique is tested at three contrasting WMO GAW stations: Cape Grim (a coastal low-altitude site), Mauna Loa (a remote high-altitude island site), and Jungfraujoch (a continental high-altitude site). At Cape Grim and Mauna Loa the two-step method is at least as effective as more complicated methods employed to characterise baseline conditions, some involving up to nine steps. While it is demonstrated that Jungfraujoch air masses rarely meet the baseline criteria of the more remote sites, a selection method based on a variable monthly radon threshold is shown to produce credible “near baseline” characteristics. The seasonal peak-to-peak amplitude of recent monthly baseline CO2 mole fraction deviations from the long-term trend at Cape Grim, Mauna Loa and Jungfraujoch are estimated to be 1.1, 6.0 and 8.1 ppm, respectively

Single Gene Deletions of Orexin, Leptin, Neuropeptide Y, and Ghrelin Do Not Appreciably Alter Food Anticipatory Activity in Mice

Timing activity to match resource availability is a widely conserved ability in nature. Scheduled feeding of a limited amount of food induces increased activity prior to feeding time in animals as diverse as fish and rodents. Typically, food anticipatory activity (FAA) involves temporally restricting unlimited food access (RF) to several hours in the middle of the light cycle, which is a time of day when rodents are not normally active. We compared this model to calorie restriction (CR), giving the mice 60% of their normal daily calorie intake at the same time each day. Measurement of body temperature and home cage behaviors suggests that the RF and CR models are very similar but CR has the advantage of a clearly defined food intake and more stable mean body temperature. Using the CR model, we then attempted to verify the published result that orexin deletion diminishes food anticipatory activity (FAA) but observed little to no diminution in the response to CR and, surprisingly, that orexin KO mice are refractory to body weight loss on a CR diet. Next we tested the orexigenic neuropeptide Y (NPY) and ghrelin and the anorexigenic hormone, leptin, using mouse mutants. NPY deletion did not alter the behavior or physiological response to CR. Leptin deletion impaired FAA in terms of some activity measures, such as walking and rearing, but did not substantially diminish hanging behavior preceding feeding time, suggesting that leptin knockout mice do anticipate daily meal time but do not manifest the full spectrum of activities that typify FAA. Ghrelin knockout mice do not have impaired FAA on a CR diet. Collectively, these results suggest that the individual hormones and neuropepetides tested do not regulate FAA by acting individually but this does not rule out the possibility of their concerted action in mediating FAA