Infection of a yellow baboon with simian immunodeficiency virus from African green monkeys:evidence for cross-species transmission in the wild

Many African primates are known to be naturally infected with simian immunodeficiency viruses (SIVs), but only a fraction of these viruses has been molecularly characterized. One primate species for which only serological evidence of SIV infection has been reported is the yellow baboon (Papio hamadryas cynocephalus). Two wild-living baboons with strong SIVAGM seroreactivity were previously identified in a Tanzanian national park where baboons and African green monkeys shared the same habitat (T. Kodama, D. P. Silva, M. D. Daniel, J. E. Phillips-Conroy, C. J. Jolly, J. Rogers, and R. C. Desrosiers, AIDS Res. Hum. Retroviruses 5:337-343, 1989). To determine the genetic identity of the viruses infecting these animals, we used PCR to examine SIV sequences directly in uncultured leukocyte DNA. Targeting two different, nonoverlapping genomic regions, we amplified and sequenced a 673-bp gag gene fragment and a 908-bp env gene fragment from one of the two baboons. Phylo-genetic analyses revealed that this baboon was infected with an SIVAGM strain of the vervet subtype. These results provide the first direct evidence for simian-to-simian cross-species transmission of SIV in the wild

Toward optimal implementation of cancer prevention and control programs in public health: A study protocol on mis-implementation

Abstract Background Much of the cancer burden in the USA is preventable, through application of existing knowledge. State-level funders and public health practitioners are in ideal positions to affect programs and policies related to cancer control. Mis-implementation refers to ending effective programs and policies prematurely or continuing ineffective ones. Greater attention to mis-implementation should lead to use of effective interventions and more efficient expenditure of resources, which in the long term, will lead to more positive cancer outcomes. Methods This is a three-phase study that takes a comprehensive approach, leading to the elucidation of tactics for addressing mis-implementation. Phase 1: We assess the extent to which mis-implementation is occurring among state cancer control programs in public health. This initial phase will involve a survey of 800 practitioners representing all states. The programs represented will span the full continuum of cancer control, from primary prevention to survivorship. Phase 2: Using data from phase 1 to identify organizations in which mis-implementation is particularly high or low, the team will conduct eight comparative case studies to get a richer understanding of mis-implementation and to understand contextual differences. These case studies will highlight lessons learned about mis-implementation and identify hypothesized drivers. Phase 3: Agent-based modeling will be used to identify dynamic interactions between individual capacity, organizational capacity, use of evidence, funding, and external factors driving mis-implementation. The team will then translate and disseminate findings from phases 1 to 3 to practitioners and practice-related stakeholders to support the reduction of mis-implementation. Discussion This study is innovative and significant because it will (1) be the first to refine and further develop reliable and valid measures of mis-implementation of public health programs; (2) bring together a strong, transdisciplinary team with significant expertise in practice-based research; (3) use agent-based modeling to address cancer control implementation; and (4) use a participatory, evidence-based, stakeholder-driven approach that will identify key leverage points for addressing mis-implementation among state public health programs. This research is expected to provide replicable computational simulation models that can identify leverage points and public health system dynamics to reduce mis-implementation in cancer control and may be of interest to other health areas

The need for a marker predicting benefit following cardiovascular disease risk reduction treatment

Developing a robust method to study characteristics of vascular flow using ultrasound may be useful to assess endothelial function and vasodilatation. There are four stages in this proposal. 1.The first stage is to standardise and validate the methodology to enable computational risk flow data and other flow characteristics to be used clinically. (Current Study). Further development of fluid modelling methods will enable particulate haemodynamics to be investigated, and incorporate detailed endothelial structure together with cellular pathways. 2. This should be followed up by studies in different patient groups investigating the association between the derived values and estimated risk (using other methods such as Framingham risk score). 3. Then, associated with underlying cardiovascular risk, prospective studies would be made to establish whether computational flow dynamic data can predict outcome. If successful it could prove to be a very useful marker of benefit following treatment in a clinical setting

Semantic diversity:A measure of contextual variation in word meaning based on latent semantic analysis

Semantic ambiguity is typically measured by summing the number of senses or dictionary definitions that a word has. Such measures are somewhat subjective and may not adequately capture the full extent of variation in word meaning, particularly for polysemous words that can be used in many different ways, with subtle shifts in meaning. Here, we describe an alternative, computationally derived measure of ambiguity based on the proposal that the meanings of words vary continuously as a function of their contexts. On this view, words that appear in a wide range of contexts on diverse topics are more variable in meaning than those that appear in a restricted set of similar contexts. To quantify this variation, we performed latent semantic analysis on a large text corpus to estimate the semantic similarities of different linguistic contexts. From these estimates, we calculated the degree to which the different contexts associated with a given word vary in their meanings. We term this quantity a word's semantic diversity (SemD). We suggest that this approach provides an objective way of quantifying the subtle, context-dependent variations in word meaning that are often present in language. We demonstrate that SemD is correlated with other measures of ambiguity and contextual variability, as well as with frequency and imageability. We also show that SemD is a strong predictor of performance in semantic judgments in healthy individuals and in patients with semantic deficits, accounting for unique variance beyond that of other predictors. SemD values for over 30,000 English words are provided as supplementary materials. © 2012 Psychonomic Society, Inc

Health Diplomacy the Adaptation of Global Health Interventions to Local Needs in sub-Saharan Africa and Thailand: Evaluating Findings from Project Accept (HPTN 043).

Study-based global health interventions, especially those that are conducted on an international or multi-site basis, frequently require site-specific adaptations in order to (1) respond to socio-cultural differences in risk determinants, (2) to make interventions more relevant to target population needs, and (3) in recognition of 'global health diplomacy' issues. We report on the adaptations development, approval and implementation process from the Project Accept voluntary counseling and testing, community mobilization and post-test support services intervention. We reviewed all relevant documentation collected during the study intervention period (e.g. monthly progress reports; bi-annual steering committee presentations) and conducted a series of semi-structured interviews with project directors and between 12 and 23 field staff at each study site in South Africa, Zimbabwe, Thailand and Tanzania during 2009. Respondents were asked to describe (1) the adaptations development and approval process and (2) the most successful site-specific adaptations from the perspective of facilitating intervention implementation. Across sites, proposed adaptations were identified by field staff and submitted to project directors for review on a formally planned basis. The cross-site intervention sub-committee then ensured fidelity to the study protocol before approval. Successfully-implemented adaptations included: intervention delivery adaptations (e.g. development of tailored counseling messages for immigrant labour groups in South Africa) political, environmental and infrastructural adaptations (e.g. use of local community centers as VCT venues in Zimbabwe); religious adaptations (e.g. dividing clients by gender in Muslim areas of Tanzania); economic adaptations (e.g. co-provision of income generating skills classes in Zimbabwe); epidemiological adaptations (e.g. provision of 'youth-friendly' services in South Africa, Zimbabwe and Tanzania), and social adaptations (e.g. modification of terminology to local dialects in Thailand: and adjustment of service delivery schedules to suit seasonal and daily work schedules across sites). Adaptation selection, development and approval during multi-site global health research studies should be a planned process that maintains fidelity to the study protocol. The successful implementation of appropriate site-specific adaptations may have important implications for intervention implementation, from both a service uptake and a global health diplomacy perspective

Optimizing the colour and fabric of targets for the control of the tsetse fly Glossina fuscipes fuscipes

Background: Most cases of human African trypanosomiasis (HAT) start with a bite from one of the subspecies of Glossina fuscipes. Tsetse use a range of olfactory and visual stimuli to locate their hosts and this response can be exploited to lure tsetse to insecticide-treated targets thereby reducing transmission. To provide a rational basis for cost-effective designs of target, we undertook studies to identify the optimal target colour. Methodology/Principal Findings: On the Chamaunga islands of Lake Victoria , Kenya, studies were made of the numbers of G. fuscipes fuscipes attracted to targets consisting of a panel (25 cm square) of various coloured fabrics flanked by a panel (also 25 cm square) of fine black netting. Both panels were covered with an electrocuting grid to catch tsetse as they contacted the target. The reflectances of the 37 different-coloured cloth panels utilised in the study were measured spectrophotometrically. Catch was positively correlated with percentage reflectance at the blue (460 nm) wavelength and negatively correlated with reflectance at UV (360 nm) and green (520 nm) wavelengths. The best target was subjectively blue, with percentage reflectances of 3%, 29%, and 20% at 360 nm, 460 nm and 520 nm respectively. The worst target was also, subjectively, blue, but with high reflectances at UV (35% reflectance at 360 nm) wavelengths as well as blue (36% reflectance at 460 nm); the best low UV-reflecting blue caught 3× more tsetse than the high UV-reflecting blue. Conclusions/Significance: Insecticide-treated targets to control G. f. fuscipes should be blue with low reflectance in both the UV and green bands of the spectrum. Targets that are subjectively blue will perform poorly if they also reflect UV strongly. The selection of fabrics for targets should be guided by spectral analysis of the cloth across both the spectrum visible to humans and the UV region

Quantitative model for inferring dynamic regulation of the tumour suppressor gene p53

Background: The availability of various "omics" datasets creates a prospect of performing the study of genome-wide genetic regulatory networks. However, one of the major challenges of using mathematical models to infer genetic regulation from microarray datasets is the lack of information for protein concentrations and activities. Most of the previous researches were based on an assumption that the mRNA levels of a gene are consistent with its protein activities, though it is not always the case. Therefore, a more sophisticated modelling framework together with the corresponding inference methods is needed to accurately estimate genetic regulation from "omics" datasets. Results: This work developed a novel approach, which is based on a nonlinear mathematical model, to infer genetic regulation from microarray gene expression data. By using the p53 network as a test system, we used the nonlinear model to estimate the activities of transcription factor (TF) p53 from the expression levels of its target genes, and to identify the activation/inhibition status of p53 to its target genes. The predicted top 317 putative p53 target genes were supported by DNA sequence analysis. A comparison between our prediction and the other published predictions of p53 targets suggests that most of putative p53 targets may share a common depleted or enriched sequence signal on their upstream non-coding region. Conclusions: The proposed quantitative model can not only be used to infer the regulatory relationship between TF and its down-stream genes, but also be applied to estimate the protein activities of TF from the expression levels of its target genes

Improving Risk Adjustment for Mortality After Pediatric Cardiac Surgery: The UK PRAiS2 Model

BACKGROUND: Partial Risk Adjustment in Surgery (PRAiS), a risk model for 30-day mortality after children's heart surgery, has been used by the UK National Congenital Heart Disease Audit to report expected risk-adjusted survival since 2013. This study aimed to improve the model by incorporating additional comorbidity and diagnostic information. METHODS: The model development dataset was all procedures performed between 2009 and 2014 in all UK and Ireland congenital cardiac centers. The outcome measure was death within each 30-day surgical episode. Model development followed an iterative process of clinical discussion and development and assessment of models using logistic regression under 25 × 5 cross-validation. Performance was measured using Akaike information criterion, the area under the receiver-operating characteristic curve (AUC), and calibration. The final model was assessed in an external 2014 to 2015 validation dataset. RESULTS: The development dataset comprised 21,838 30-day surgical episodes, with 539 deaths (mortality, 2.5%). The validation dataset comprised 4,207 episodes, with 97 deaths (mortality, 2.3%). The updated risk model included 15 procedural, 11 diagnostic, and 4 comorbidity groupings, and nonlinear functions of age and weight. Performance under cross-validation was: median AUC of 0.83 (range, 0.82 to 0.83), median calibration slope and intercept of 0.92 (range, 0.64 to 1.25) and -0.23 (range, -1.08 to 0.85) respectively. In the validation dataset, the AUC was 0.86 (95% confidence interval [CI], 0.82 to 0.89), and the calibration slope and intercept were 1.01 (95% CI, 0.83 to 1.18) and 0.11 (95% CI, -0.45 to 0.67), respectively, showing excellent performance. CONCLUSIONS: A more sophisticated PRAiS2 risk model for UK use was developed with additional comorbidity and diagnostic information, alongside age and weight as nonlinear variables

Effectiveness of moving on: an Australian designed generic self-management program for people with a chronic illness

Background: This paper presents the evaluation of “Moving On”, a generic self-management program for people with a chronic illness developed by Arthritis NSW. The program aims to help participants identify their need for behavior change and acquire the knowledge and skills to implement changes that promote their health and quality of life. Method: A prospective pragmatic randomised controlled trial involving two group programs in community settings: the intervention program (Moving On) and a control program (light physical activity). Participants were recruited by primary health care providers across the north-west region of metropolitan Sydney, Australia between June 2009 and October 2010. Patient outcomes were self-reported via pre- and post-program surveys completed at the time of enrolment and sixteen weeks after program commencement. Primary outcomes were change in self-efficacy (Self-efficacy for Managing Chronic Disease 6-Item Scale), self-management knowledge and behaviour and perceived health status (Self-Rated Health Scale and the Health Distress Scale). Results: A total of 388 patient referrals were received, of whom 250 (64.4%) enrolled in the study. Three patients withdrew prior to allocation. 25 block randomisations were performed by a statistician external to the research team: 123 patients were allocated to the intervention program and 124 were allocated to the control program. 97 (78.9%) of the intervention participants commenced their program. The overall attrition rate of 40.5% included withdrawals from the study and both programs. 24.4% of participants withdrew from the intervention program but not the study and 22.6% withdrew from the control program but not the study. A total of 62 patients completed the intervention program and follow-up evaluation survey and 77 patients completed the control program and follow- up evaluation survey. At 16 weeks follow-up there was no significant difference between intervention and control groups in self-efficacy; however, there was an increase in self-efficacy from baseline to follow-up for the intervention participants (t=−1.948, p=0.028). There were no significant differences in self-rated health or health distress scores between groups at follow-up, with both groups reporting a significant decrease in health distress scores. There was no significant difference between or within groups in self-management knowledge and stage of change of behaviours at follow-up. Intervention group attenders had significantly higher physical activity (t=−4.053, p=0.000) and nutrition scores (t=2.315, p= 0.01) at follow-up; however, these did not remain significant after adjustment for covariates. At follow-up, significantly more participants in the control group (20.8%) indicated that they did not have a self-management plan compared to those in the intervention group (8.8%) (X2=4.671, p=0.031). There were no significant changes in other self-management knowledge areas and behaviours after adjusting for covariates at follow-up. Conclusions: The study produced mixed findings. Differences between groups as allocated were diluted by the high proportion of patients not completing the program. Further monitoring and evaluation are needed of the impact and cost effectiveness of the program. Trial registration: Australian New Zealand Clinical Trials Registry: ACTRN1260900029821