The Catalysed Decarboxylation of Oxaloacetic Acid

The nature of the chelate compounds formed by transition metal ions with oxaloacetic acid in aqueous solution, has been investigated spectrophotometrically and potentiometrically. The mechanism of the catalysed reaction has been clarified. Thermodynamic information on the ketonic chelate compounds, which are the catalytically active species in decarboxylation, has been obtained by measuring association constants for dimethyloxaloacetic acid (II) (which cannot enolise),and comparing these with the known association constants for oxaloacetic acid (I), HO2C.CO.CH2.CO2H (I); HO2C.CO.C(Me)2.CO2H (II). Spectrophotometric studies have demonstrated the presence of enolic chelate compounds which are not decarboxylated. Approximate values for the proportion of enolic complex for oxaloacetate chelates of Ca2+, Mn2+, Zn2+, Co2+, Ni2+ and Cu2+ have been obtained. Spectrophotometrie measurements on the chelate compounds of oxaloacetic acid (I) and its ethyl ester (III), HO2C.CO.CH2.CO2Et (III) which cannot decarboxylate, have shown that oxaloacetate chelate compounds are formed very rapidly. The rise of optical density (270 mmu) with time to a maximum; produced by addition of some metal ions to aqueous solutions of oxaloacetic acid, is due to the production of an enolic pyruvate intermediate. The mechanism of decarboxylation, may be represented by, (diagram redacted) The changes of optical density with time are consistent with the above reaction scheme. Inhibition of decarboxylation at high copper ion concentrations has been found to occur, and the results are related to previous potentiometric studies of the copper chelates. Inhibition at high pH (> 6) is due to the production of kinetically inactive enolic complexes. The aniline catalysed decarboxylation of oxaloacetic acid has been studied by manometric, spectrophotometric, and potentiometric methods. Experiments with the half ester of oxaloacetic acid (III),have shown that in aqueous solution, the intermediate is the ketimine hydrate (A). Kinetic measurements have demonstrated that the rate of the aniline catalysed decarboxylation passes through a maximum at around pH 4. The pH-Rate profile is consistent with a catalytically active species (B), the fall in rate at pH greater than being attributed to ionisation according to the equation (equation redacted) Kinetic measurements have shown that the ketimine hydrate is present only in small amounts, under the experimental conditions used, and that it loses CO2 in the rate-determining step. In aqueous solution the mechanism is of the type, (diagram redacted) In ethanol, experiments with esters (III) and (IV) EtO2C.CO.CH2.CO2Et (IV) have shown that the catalytically active species is the ketimine (C). This compound is formed in quantitative yield. The aniline salt of compound (a), and the diethyl ester derivative of (C) have been isolated. The formation of the ketimine has been studied spectrophotometrically and shown to be kinetically second order. The rate of formation of the ketimine is equal to the rate of decarboxylation, indicating that in ethanol, the formation of the ketimine is the rate-controlling step in decarboxylation. Metal ion and amine catalysis have been compared with the metal ion activated enzymatic decarboxylation of some biologically important keto acids

The Malaria Atlas Project: Developing Global Maps of Malaria Risk

The primary goal of the recently launched Malaria Atlas Project is to develop the science of malaria cartography

Satellite Imagery in the Study and Forecast of Malaria

More than 30 years ago, human beings looked back from the Moon to see the magnificent spectacle of Earthrise. The technology that put us into space has since been used to assess the damage we are doing to our natural environment and is now being harnessed to monitor and predict diseases through space and time. Satellite sensor data promise the development of early-warning systems for diseases such as malaria, which kills between 1 and 2 million people each year

Spatial prediction of Plasmodium falciparum prevalence in Somalia

BACKGROUND Maps of malaria distribution are vital for optimal allocation of resources for anti-malarial activities. There is a lack of reliable contemporary malaria maps in endemic countries in sub-Saharan Africa. This problem is particularly acute in low malaria transmission countries such as those located in the horn of Africa. METHODS Data from a national malaria cluster sample survey in 2005 and routine cluster surveys in 2007 were assembled for Somalia. Rapid diagnostic tests were used to examine the presence of Plasmodium falciparum parasites in finger-prick blood samples obtained from individuals across all age-groups. Bayesian geostatistical models, with environmental and survey covariates, were used to predict continuous maps of malaria prevalence across Somalia and to define the uncertainty associated with the predictions. RESULTS For analyses the country was divided into north and south. In the north, the month of survey, distance to water, precipitation and temperature had no significant association with P. falciparum prevalence when spatial correlation was taken into account. In contrast, all the covariates, except distance to water, were significantly associated with parasite prevalence in the south. The inclusion of covariates improved model fit for the south but not for the north. Model precision was highest in the south. The majority of the country had a predicted prevalence of or = 5% prevalence were predominantly in the south. CONCLUSION The maps showed that malaria transmission in Somalia varied from hypo- to meso-endemic. However, even after including the selected covariates in the model, there still remained a considerable amount of unexplained spatial variation in parasite prevalence, indicating effects of other factors not captured in the study. Nonetheless the maps presented here provide the best contemporary information on malaria prevalence in Somalia.AMN is supported by the Wellcome Trust as a Research Training Fellow (#081829). SIH is supported by the Wellcome Trust as Senior Research Fellow (#079091). RWS is supported by the Wellcome Trust as Principal Research Fellow (#079081). AMN, SIH and RWS acknowledge the support of the Kenyan Medical Research Institute

Malaria in Kenya's Western Highlands

Reemergence of epidemics in tea plantations will likely result in antimalarial-drug resistance

Defining the relationship between Plasmodium falciparum parasite rate and clinical disease: statistical models for disease burden estimation

<p>Abstract</p> <p>Background</p> <p>Clinical malaria has proven an elusive burden to enumerate. Many cases go undetected by routine disease recording systems. Epidemiologists have, therefore, frequently defaulted to actively measuring malaria in population cohorts through time. Measuring the clinical incidence of malaria longitudinally is labour-intensive and impossible to undertake universally. There is a need, therefore, to define a relationship between clinical incidence and the easier and more commonly measured index of infection prevalence: the "parasite rate". This relationship can help provide an informed basis to define malaria burdens in areas where health statistics are inadequate.</p> <p>Methods</p> <p>Formal literature searches were conducted for <it>Plasmodium falciparum </it>malaria incidence surveys undertaken prospectively through active case detection at least every 14 days. The data were abstracted, standardized and geo-referenced. Incidence surveys were time-space matched with modelled estimates of infection prevalence derived from a larger database of parasite prevalence surveys and modelling procedures developed for a global malaria endemicity map. Several potential relationships between clinical incidence and infection prevalence were then specified in a non-parametric Gaussian process model with minimal, biologically informed, prior constraints. Bayesian inference was then used to choose between the candidate models.</p> <p>Results</p> <p>The suggested relationships with credible intervals are shown for the Africa and a combined America and Central and South East Asia regions. In both regions clinical incidence increased slowly and smoothly as a function of infection prevalence. In Africa, when infection prevalence exceeded 40%, clinical incidence reached a plateau of 500 cases per thousand of the population <it>per annum</it>. In the combined America and Central and South East Asia regions, this plateau was reached at 250 cases per thousand of the population <it>per annum</it>. A temporal volatility model was also incorporated to facilitate a closer description of the variance in the observed data.</p> <p>Conclusion</p> <p>It was possible to model a relationship between clinical incidence and <it>P. falciparum </it>infection prevalence but the best-fit models were very noisy reflecting the large variance within the observed opportunistic data sample. This continuous quantification allows for estimates of the clinical burden of <it>P. falciparum </it>of known confidence from wherever an estimate of <it>P. falciparum </it>prevalence is available.</p

Spatial prediction of Plasmodium falciparum prevalence in Somalia

BACKGROUND: Maps of malaria distribution are vital for optimal allocation of resources for anti-malarial activities. There is a lack of reliable contemporary malaria maps in endemic countries in sub-Saharan Africa. This problem is particularly acute in low malaria transmission countries such as those located in the horn of Africa. METHODS: Data from a national malaria cluster sample survey in 2005 and routine cluster surveys in 2007 were assembled for Somalia. Rapid diagnostic tests were used to examine the presence of Plasmodium falciparum parasites in finger-prick blood samples obtained from individuals across all age-groups. Bayesian geostatistical models, with environmental and survey covariates, were used to predict continuous maps of malaria prevalence across Somalia and to define the uncertainty associated with the predictions. RESULTS: For analyses the country was divided into north and south. In the north, the month of survey, distance to water, precipitation and temperature had no significant association with P. falciparum prevalence when spatial correlation was taken into account. In contrast, all the covariates, except distance to water, were significantly associated with parasite prevalence in the south. The inclusion of covariates improved model fit for the south but not for the north. Model precision was highest in the south. The majority of the country had a predicted prevalence of &lt; 5%; areas with &gt; or = 5% prevalence were predominantly in the south. CONCLUSION: The maps showed that malaria transmission in Somalia varied from hypo- to meso-endemic. However, even after including the selected covariates in the model, there still remained a considerable amount of unexplained spatial variation in parasite prevalence, indicating effects of other factors not captured in the study. Nonetheless the maps presented here provide the best contemporary information on malaria prevalence in Somalia

Modelling the global constraints of temperature on transmission of Plasmodium falciparum and P. vivax

<p>Abstract</p> <p>Background</p> <p>Temperature is a key determinant of environmental suitability for transmission of human malaria, modulating endemicity in some regions and preventing transmission in others. The spatial modelling of malaria endemicity has become increasingly sophisticated and is now central to the global scale planning, implementation, and monitoring of disease control and regional efforts towards elimination, but existing efforts to model the constraints of temperature on the malaria landscape at these scales have been simplistic. Here, we define an analytical framework to model these constraints appropriately at fine spatial and temporal resolutions, providing a detailed dynamic description that can enhance large scale malaria cartography as a decision-support tool in public health.</p> <p>Results</p> <p>We defined a dynamic biological model that incorporated the principal mechanisms of temperature dependency in the malaria transmission cycle and used it with fine spatial and temporal resolution temperature data to evaluate time-series of temperature suitability for transmission of <it>Plasmodium falciparum </it>and <it>P. vivax </it>throughout an average year, quantified using an index proportional to the basic reproductive number. Time-series were calculated for all 1 km resolution land pixels globally and were summarised to create high-resolution maps for each species delineating those regions where temperature precludes transmission throughout the year. Within suitable zones we mapped for each pixel the number of days in which transmission is possible and an integrated measure of the intensity of suitability across the year. The detailed evaluation of temporal suitability dynamics provided by the model is visualised in a series of accompanying animations.</p> <p>Conclusions</p> <p>These modelled products, made available freely in the public domain, can support the refined delineation of populations at risk; enhance endemicity mapping by offering a detailed, dynamic, and biologically driven alternative to the ubiquitous empirical incorporation of raw temperature data in geospatial models; and provide a rich spatial and temporal platform for future biological modelling studies.</p

The risks of malariainfection in Kenya in 2009

BACKGROUND: To design an effective strategy for the control of malaria requires a map of infection and disease risks to select appropriate suites of interventions. Advances in model based geo-statistics and malaria parasite prevalence data assemblies provide unique opportunities to redefine national Plasmodium falciparum risk distributions. Here we present a new map of malaria risk for Kenya in 2009. METHODS: Plasmodium falciparum parasite rate data were assembled from cross-sectional community based surveys undertaken from 1975 to 2009. Details recorded for each survey included the month and year of the survey, sample size, positivity and the age ranges of sampled population. Data were corrected to a standard age-range of two to less than 10 years (PfPR2-10) and each survey location was geo-positioned using national and on-line digital settlement maps. Ecological and climate covariates were matched to each PfPR2-10 survey location and examined separately and in combination for relationships to PfPR2-10. Significant covariates were then included in a Bayesian geostatistical spatial-temporal framework to predict continuous and categorical maps of mean PfPR2-10 at a 1 x 1 km resolution across Kenya for the year 2009. Model hold-out data were used to test the predictive accuracy of the mapped surfaces and distributions of the posterior uncertainty were mapped. RESULTS: A total of 2,682 estimates of PfPR2-10 from surveys undertaken at 2,095 sites between 1975 and 2009 were selected for inclusion in the geo-statistical modeling. The covariates selected for prediction were urbanization; maximum temperature; precipitation; enhanced vegetation index; and distance to main water bodies. The final Bayesian geo-statistical model had a high predictive accuracy with mean error of -0.15% PfPR2-10; mean absolute error of 0.38% PfPR2-10; and linear correlation between observed and predicted PfPR2-10 of 0.81. The majority of Kenya's 2009 population (35.2 million, 86.3%) reside in areas where predicted PfPR2-10 is less than 5%; conversely in 2009 only 4.3 million people (10.6%) lived in areas where PfPR2-10 was predicted to be &gt; or =40% and were largely located around the shores of Lake Victoria. CONCLUSION: Model based geo-statistical methods can be used to interpolate malaria risks in Kenya with precision and our model shows that the majority of Kenyans live in areas of very low P. falciparum risk. As malaria interventions go to scale effectively tracking epidemiological changes of risk demands a rigorous effort to document infection prevalence in time and space to remodel risks and redefine intervention priorities over the next 10-15 years