Efficacy and adverse effects of intravenous lignocaine therapy in fibromyalgia syndrome

BACKGROUND: To investigate the effects of intravenous lignocaine infusions (IV lignocaine) in fibromyalgia. METHODS: Prospective study of the adverse effects of IV lignocaine in 106 patients with fibromyalgia; retrospective questionnaire study of the efficacy of IV lignocaine in 50 patients with fibromyalgia. RESULTS: Prospective study: Two major (pulmonary oedema and supraventricular tachycardia) and 42 minor side-effects were reported. None had long-term sequelae. The commonest was hypotension (17 cases). Retrospective study: Pain and a range of psychosocial measures (on single 11-point scales) improved significantly after treatment. There was no effect of the treatment on work status. The average duration of pain relief after the 6-day course of treatment was 11.5 ± 6.5 weeks. CONCLUSIONS: Intravenous lignocaine appears to be both safe and of benefit in improving pain and quality of life for patients with fibromyalgia. This needs to be confirmed in prospective randomised controlled trials

Beta-2 adrenergic receptor gene polymorphisms Gln27Glu, Arg16Gly in patients with heart failure

<p>Abstract</p> <p>Background -</p> <p>Beta-2 adrenergic receptor gene polymorphisms Gln27Glu, Arg16Gly and Thr164Ile were suggested to have an effect in heart failure. We evaluated these polymorphisms relative to clinical characteristics and prognosis of alarge cohort of patients with heart failure of different etiologies.</p> <p>Methods -</p> <p>We studied 501 patients with heart failure of different etiologies. Mean age was 58 years (standard deviation 14.4 years), 298 (60%) were men. Polymorphisms were identified by polymerase chain reaction-restriction fragment length polymorphism.</p> <p>Results -</p> <p>During the mean follow-up of 12.6 months (standard deviation 10.3 months), 188 (38%) patients died. Distribution of genotypes of polymorphism Arg16Gly was different relative to body mass index (χ²= 9.797;p = 0.04). Overall the probability of survival was not significantly predicted by genotypes of Gln27Glu, Arg16Gly, or Thr164Ile. Allele and haplotype analysis also did not disclose any significant difference regarding mortality. Exploratory analysis through classification trees pointed towards a potential association between the Gln27Glu polymorphism and mortality in older individuals.</p> <p>Conclusion -</p> <p>In this study sample, we were not able to demonstrate an overall influence of polymorphisms Gln27Glu and Arg16Gly of beta-2 receptor gene on prognosis. Nevertheless, Gln27Glu polymorphism may have a potential predictive value in older individuals.</p

The cost of health professionals' brain drain in Kenya

BACKGROUND: Past attempts to estimate the cost of migration were limited to education costs only and did not include the lost returns from investment. The objectives of this study were: (i) to estimate the financial cost of emigration of Kenyan doctors to the United Kingdom (UK) and the United States of America (USA); (ii) to estimate the financial cost of emigration of nurses to seven OECD countries (Canada, Denmark, Finland, Ireland, Portugal, UK, USA); and (iii) to describe other losses from brain drain. METHODS: The costs of primary, secondary, medical and nursing schools were estimated in 2005. The cost information used in this study was obtained from one non-profit primary and secondary school and one public university in Kenya. The cost estimates represent unsubsidized cost. The loss incurred by Kenya through emigration was obtained by compounding the cost of educating a medical doctor and a nurse over the period between the average age of emigration (30 years) and the age of retirement (62 years) in recipient countries. RESULTS: The total cost of educating a single medical doctor from primary school to university is US$65,997; and for every doctor who emigrates, a country loses about US$ 517,931 worth of returns from investment. The total cost of educating one nurse from primary school to college of health sciences is US$43,180; and for every nurse that emigrates, a country loses about US$ 338,868 worth of returns from investment. CONCLUSION: Developed countries continue to deprive Kenya of millions of dollars worth of investments embodied in her human resources for health. If the current trend of poaching of scarce human resources for health (and other professionals) from Kenya is not curtailed, the chances of achieving the Millennium Development Goals would remain bleak. Such continued plunder of investments embodied in human resources contributes to further underdevelopment of Kenya and to keeping a majority of her people in the vicious circle of ill-health and poverty. Therefore, both developed and developing countries need to urgently develop and implement strategies for addressing the health human resource crisis

Bacterial Acquisition in Juveniles of Several Broadcast Spawning Coral Species

Coral animals harbor diverse microorganisms in their tissues, including archaea, bacteria, viruses, and zooxanthellae. The extent to which coral-bacterial associations are specific and the mechanisms for their maintenance across generations in the environment are unknown. The high diversity of bacteria in adult coral colonies has made it challenging to identify species-specific patterns. Localization of bacteria in gametes and larvae of corals presents an opportunity for determining when bacterial-coral associations are initiated and whether they are dynamic throughout early development. This study focuses on the early onset of bacterial associations in the mass spawning corals Montastraea annularis, M. franksi, M. faveolata, Acropora palmata, A. cervicornis, Diploria strigosa, and A. humilis. The presence of bacteria and timing of bacterial colonization was evaluated in gametes, swimming planulae, and newly settled polyps by fluorescence in situ hybridization (FISH) using general eubacterial probes and laser-scanning confocal microscopy. The coral species investigated in this study do not appear to transmit bacteria via their gametes, and bacteria are not detectable in or on the corals until after settlement and metamorphosis. This study suggests that mass-spawning corals do not acquire, or are not colonized by, detectable numbers of bacteria until after larval settlement and development of the juvenile polyp. This timing lays the groundwork for developing and testing new hypotheses regarding general regulatory mechanisms that control bacterial colonization and infection of corals, and how interactions among bacteria and juvenile polyps influence the structure of bacterial assemblages in corals

Diversity and Strain Specificity of Plant Cell Wall Degrading Enzymes Revealed by the Draft Genome of Ruminococcus flavefaciens FD-1

Peer reviewedPublisher PD

Small area contextual effects on self-reported health: Evidence from Riverside, Calgary

<p>Abstract</p> <p>Background</p> <p>We study geographic variation within one community in the City of Calgary using a more fine-grained geographic unit than the Census tract, the Census Dissemination Area (DA). While most Riverside residents consider their neighbourhood to be a fairly cohesive community, we explore the effect of socio-economic variation between these small geographic areas on individuals' self-reported health, net of individual level determinants.</p> <p>Methods</p> <p>We merge data from the 2001 Census for Riverside, Calgary with a 2004 random telephone survey of Riverside residents. Our data are unique in that we have information on individuals from every DA wholly contained in the Riverside community. These data enable us to conduct multinomial logistic regression analyses of self-reported health using both individual-level and DA-level variables as predictors.</p> <p>Results</p> <p>We find significant variation in measures of DA socio-economic status within the Riverside community. We find that individual self-reported health is affected by variation in an index of DA-level socio-economic disadvantage, controlling for individual variation in gender, age, and socio-economic status. We investigate each aspect of the DA index of disadvantage separately, and find that average education and the percent of households that are headed by a lone parent are most important.</p> <p>Conclusions</p> <p>These findings demonstrate that, even within a cohesive community, contextual effects on health can be located at a smaller geographic level than the Census tract. Research on the effects of local area socio-economic disadvantage on health that combines administrative and survey data enables researchers to develop more comprehensive measures of social and material deprivation. Our findings suggest that both social and material deprivation affect health at the local level.</p

Multi-scale digital soil mapping with deep learning

We compared different methods of multi-scale terrain feature construction and their relative effectiveness for digital soil mapping with a Deep Learning algorithm. The most common approach for multi-scale feature construction in DSM is to filter terrain attributes based on different neighborhood sizes, however results can be difficult to interpret because the approach is affected by outliers. Alternatively, one can derive the terrain attributes on decomposed elevation data, but the resulting maps can have artefacts rendering the approach undesirable. Here, we introduce ‘mixed scaling’ a new method that overcomes these issues and preserves the landscape features that are identifiable at different scales. The new method also extends the Gaussian pyramid by introducing additional intermediate scales. This minimizes the risk that the scales that are important for soil formation are not available in the model. In our extended implementation of the Gaussian pyramid, we tested four intermediate scales between any two consecutive octaves of the Gaussian pyramid and modelled the data with Deep Learning and Random Forests. We performed the experiments using three different datasets and show that mixed scaling with the extended Gaussian pyramid produced the best performing set of covariates and that modelling with Deep Learning produced the most accurate predictions, which on average were 4–7% more accurate compared to modelling with Random Forests