Plasmodium falciparum infection prevalence were examined among schoolchildren in a highly malaria-endemic area.There is a renewed political will and financial support to eradicate malaria. Spatially-explicit risk profiling will play an important role in this endeavour. Patterns of Plasmodium spp. infections. Environmental data were obtained from satellite images and digitized maps. Bayesian variogram models for spatially-explicit risk modelling of P. falciparum infection prevalence were employed, assuming for stationary and non-stationary spatial processes.A questionnaire was administered and finger prick blood samples collected from 3,962 children, aged six to 16 years, attending 55 schools in a rural part of western Côte d'Ivoire. Information was gathered from the questionnaire on children's socioeconomic status and the use of bed nets for the prevention of malaria. Blood samples were processed with standardized, quality-controlled methods for diagnosis of P. falciparum infection was 64.9%, ranging between 34.0% and 91.9% at the unit of the school. Risk factors for a P. falciparum infection included age, socioeconomic status, not sleeping under a bed net, distance to health care facilities and a number of environmental features . After taking into account spatial correlation only age remained significant. Non-stationary models performed better than stationary models.The overall prevalence of P. falciparum prevalence data provides a useful tool for targeting malaria control intervention, and hence will play a role in the quest of local elimination and ultimate eradication of the disease.Spatial risk profiling of  Plasmodium falciparum occur each year, killing over one million individuals annually [Malaria remains a leading cause of morbidity and mortality in tropical and subtropical regions of the world. There are an estimated three billion people at risk of this disease and more than half a billion episodes of clinical annually ,2. It haannually  and the annually ,5. High-annually ,7.Predicting the abundance and spread of malaria in endemic settings, in order to develop locally-adopted malaria control strategies to lower the burden of the disease is a pressing public health issue. Recently, an audacious goal has been announced in Seattle, USA during a meeting led by the Bill and Melinda Gates Foundation, namely to eradicate malaria . This goAnopheles vectors have opened new avenues in this field of research. In particular, modelling disease and disease-related data within a Bayesian framework allows fitting of complex models in quite a flexible way. Additionally, Bayesian approaches provide computational advantages over traditional frequentist approaches via implementation of Markov chain Monte Carlo (MCMC) simulation [Advances made with geographical information system (GIS), remote sensing and geostatistical modelling to predict the spatial and temporal distribution of malaria and mulation -11. Recemulation -18.P. falciparum among schoolchildren in a rural part of western Côte d'Ivoire were explored, using Bayesian geostatistical models.In Côte d'Ivoire, malaria is one of the primary public health concerns. This is illustrated by a study carried out in the savannah zone that documented malaria being responsible for at least 60% of the consultations in hospitals and 46% in paediatric clinics . In 2005The study area is the region of Man, located in western Côte d'Ivoire. It is a mountainous region with tropical climate, including rains during eight to nine months of the year, and a dry period between November and February. The landscape is characterized in the north by rounded mountains with altitudes ranging from 200 to 1,300 m above sea level and small valleys, whereas the southern part is a river-draining plain . The fieThe study protocol was approved by the institutional research commission of the Swiss Tropical Institute  and the Centre Suisse de Recherches Scientifiques . The study was given ethical clearance from the Ministry of Health in Côte d'Ivoire. All children attending grades three to five from 57 schools in the rural parts of the study area were invited to participate.The education officers were contacted and the aims and procedures of the study were explained. After receipt of their approval, the education officers informed teachers who provided the research team with copies of the class lists, which included information of the children's name, sex and age. First, a questionnaire was administered and schoolchildren were interviewed for assets on ownership and household characteristics, and perceived symptoms and diseases with a recall period of one month. The questionnaire included 17 morbidity indicators  and 12 household assets . An asset-based approach was used to stratify schoolchildren into five socioeconomic groups ,23. An aPlasmodium, assuming a standard white blood cell (WBC) count of 8,000 per μl of blood by light microscopy. A random sample of 10% of the slides were re-examined by the senior microscopist for quality control purposes. Since more than 95% of the cases were P. falciparum infections, subsequent spatial analyses was restricted on this malaria parasite.Second, a cross-sectional survey was carried out, to collect finger prick blood samples from previously interviewed children. Two drops of blood were placed on a microscope slide and thin and thick blood films were prepared. Slides were air-dried, transferred to a laboratory in the town of Man and stained with Giemsa. The slides were then forwarded to a reference laboratory in Abidjan and analysed by experienced laboratory technicians for species-specific density of Geographical coordinates of each school were collected using a hand-held Magellan 320 global positioning system . Streets and rivers were digitized with the aid of readily available ground maps. Normalized difference vegetation index (NDVI) and land surface temperature (LST) were downloaded at 1 × 1 km spatial resolution from Moderate Resolution Imaging Spectroradiometer (MODIS) from the USGS EROS Data Centre. Rainfall estimate (RFE) data with an 8 × 8 km spatial resolution from Meteosat 7 satellite were obtained from the Africa Data Dissemination Service (ADDS). NDVI, LST and RFE were downloaded for the period of September, 2001 to August, 2002 and processed as detailed elsewhere . DistancData were entered twice and validated with EpiInfo version 6.4 . Geographical data were displayed in ArcView GIS version 3.2 . Schoolchildren were subdivided into two age groups; (i) six to 10 years, and (ii) 11 to 16 years.P. falciparum infection status variable using STATA version 9.2 . Covariates with a significance level <0.15 were built into (i) a stationary, and (ii) a non-stationary Bayesian logistic regression model for P. falciparum infection, using WinBUGS version 1.4 . The stationary geostatistical model assumed that spatial correlation is a function of distance only, whereas the non-stationary geostatistical model assumed that spatial correlation is a function of the distance and location [All covariates were fitted into bivariate logistic regression models on the location ,24. SpatYij be the P. falciparum infection status of schoolchild j in school i. It is assumed that Yij arises from a Bernoulli distribution, Yij ~Be(Pij), with probability Pij. The covariates Xij and school-specific random effect φi were modelled on the it (Pij), where β is the vector of regression coefficients.Let φi's by assuming that φ = T has a multivariate normal distribution, φ ~MVN, with variance-covariance matrix Σ. An isotropic spatial process, i.e. Σmn = σ2 exp (-udmn), was also assumed, where dmn is the Euclidean distance between schools m and n, σ2 is the geographic variability known as the sill, and u is a smoothing parameter that controls the rate of correlation decay with increasing distance. To take into account non-stationarity, the study area was partitioned in K subregions, assuming a locally stationary spatial process ωk in each subregion k = 1, ..., K, where ωk = T. In order to separate the schools into approximately equal numbers, the study area was subdivided into two subregions on a diagonal from the north-western corner to the south-eastern corner. Spatial correlation in the study area was then viewed as a mixture of the different spatial processes and the spatial random effect φi at location i was modelled as a weighted average of the subregion-specific (independent) stationary processes as follows: aik, which are decreasing functions of the distance between location i and the centroids of the subregions k [ωk ~ MVN and Ak = diag{ak1, ak2, ..., ank}. The range is defined as the minimum distance at which spatial correlation between locations is below 5%. For an exponential correlation function, it can be calculated as The spatial correlation was introduced on the egions k . Assuminβ parameters with large variances , inverse gamma priors for uk, k = 1, ..., K were chosen. MCMC simulation was employed to estimate the model parameters [Following a Bayesian model specification, prior distributions were adopted for the model parameters. Vague Normal distributions for the rameters . A singlThe deviance information criterion (DIC) was utilized to assess the model performance . For appIn the school year 2001/2002, a total of 5,448 children were registered on the class lists of grades three to five of the 57 participating rural schools. Complete questionnaire and parasitological data were obtained from 3,962 schoolchildren (72.7%) in 55 schools . All subsequent analyses are based on this final cohort , whereas infections with Plasmodium malariae and Plasmodium ovale were rare; the respective prevalences were 3.0% and 0.2%. At the unit of the school, the P. falciparum prevalence ranged from 34.0% to 91.9%  were at a significantly higher risk of having an infection with P. falciparum compared to the first wealth quintile (poorest group). Besides age and socioeconomic status, not sleeping under a bed net, distance to health care facilities, and three environmental factors  were risk factors for a P. falciparum infection.Results of the bivariate non-spatial analyses considering demographic, socioeconomic and environmental covariates, distance to health care facilities and use of bed net are summarized in Table P. falciparum prevalence, both in the stationary and the non-stationary logistic regression model, whereas NDVI was 'borderline' significant. In general, for all indicators, odds ratios (ORs) were comparable between the stationary and the non-stationary logistic regression model. The range where spatial correlation became insignificant was similar between the stationary and the non-stationary model. The non-stationary model revealed that location only had a minor leverage on the range of spatial correlation. The geographical variability was 0.3 with the stationary model, whereas in the non-stationary model there was some difference in the geographical variability between the two subregions.Results of the spatial analyses are summarized in Table Comparison of DICs suggested that both the stationary and the non-stationary model were similar with regard to their performance. Since the DIC did not give any definite information on the best-fitting models, further exploration was necessary by data training. Table P. falciparum infection prevalence among schoolchildren in a highly endemic area of rural western Côte d'Ivoire. The following covariates were significantly associated with infection: age, socioeconomic status, sleeping under a bed net, distance to health care facilities and a number of environmental factors. However, after accounting for spatial correlation, only age remained a significant risk factor for P. falciparum prevalence, whereas NDVI showed only 'borderline' significance. The predictive ability of the spatial models was examined using a training sample of 78% of the schools, with the non-stationary model performing better than the stationary one.The purpose of this study was to assess risk factors and small-scale spatial patterns of P. falciparum prevalence of 64.9% among six to 16-year-old children. Third, the parasitological survey was carried out over a period of several months due to the large number of schoolchildren subjected to interviews and finger prick blood sampling, which might have introduced a bias in the observed prevalence from one school to another due to seasonality. Fourth, in the absence of high-resolution data to compute distances to small standing water bodies that might serve as Anopheles breeding sites, information from digitized maps was used to obtain the distance to rivers as an indication for the distance to breeding sites. The most likely vector in this area is Anopheles gambiae and, to some extent, Anopheles funestus. The former vector species breeds in transient, sunlit and generally small pools, whereas the latter has been associated with larger, semipermanent bodies of water containing aquatic vegetation and algae [There are a number of shortcomings worth discussing. First, only a single finger prick blood sample was collected from each child for microscopic examination. Hence it is conceivable that some infections, particularly those with a low parasitaemia, were missed ,29. Second algae .P. falciparum compared to schoolchildren from the poorest households. This result is surprising given that the common expectation would be that the poorest of the poor are at highest risk of malaria [P. falciparum infection and housing conditions was evident in the present study. It is conjectured that issues related to exposure were associated to socioeconomic status, which calls for further investigation. Previous research conducted in rural Tanzania, for example, found that lack of access to health care and preventive measures, including ITNs, was associated with people's socioeconomic status [P. falciparum infection. Additionally, it was found that the risk of a P. falciparum infection was associated with distances to health care facilities. Nevertheless, after taking into account spatial correlation, the covariates socioeconomic status, distance to the nearest health care facility and sleeping under a bed net showed no significant association anymore, and hence other factor must explain the observed spatial heterogeneity of P. falciparum.The analysis presented here showed that schoolchildren from wealthier households were more likely to be infected with  malaria . Several malaria . Surprisc status . InteresP. falciparum infection in the bivariate non-spatial models. These findings are in accordance with previous studies that showed significant associations between malaria and NDVI, rainfall and distance to rivers at a broader spatial scale [Anopheles. An interesting observation in the present study was that children from schools that were located in close proximity to rivers (<500 m) were at a lower risk of a P. falciparum infection compared to more distant schools (between 500 m and 1000 m). Children from schools with distances <500 m were significantly more often reporting to sleep under a bed net, suggesting that the former observation might be partly confounded by a higher level of bed net coverage and usage due to nuisance from mosquitoes near rivers. Children enrolled in schools located at distances >1000 m of rivers were less likely to be infected with P. falciparum, which might be related to the flight range of mosquitoes, which is, on average, below 1 km [P. falciparum prevalence after accounting for spatial correlation. Hence, the current results demonstrate the importance of accounting for spatial correlation when analysing malaria prevalence data at small spatial scales as reported here. Indeed, omission of spatial correlation would have underestimated the standard errors of the covariate coefficients [P. falciparum infections rather than coarser environmental factors. These observations suggest that environmental factors are particularly salient for malaria prediction at larger spatial scales.Several environmental factors, namely NDVI, RFE and distance to rivers, were significantly associated with a al scale -35. It ilow 1 km . Interesficients . Furtherficients ,21,24,38P. falciparum can be integrated with our previous work on helminth infections for mapping P. falciparum-helminth co-infections using multinomial regression models for the simultaneous targeting of malaria and helminthic diseases [P. falciparum and hookworm have an additive impact on anaemia, implying that those high-risk groups would greatly benefit from integrated malaria and helminth control [In geostatistical modelling, the standard assumption is that there is a stationary spatial dependence in the data, which implies that the spatial correlation is a function of the distance between points and independent of the location. Bayesian non-stationary geostatistical models were employed before for the prediction of helminth infections in the same study area ,38. Gosodiseases . School- control .P. falciparum infection prevalence in a highly malaria-endemic part of sub-Saharan Africa was used. This approach can be readily adapted to other eco-epidemiological settings for spatial targeting of control interventions. In particular, it was possible to compare different geostatistical models with a large set of covariates, including demographic, socioeconomic and environmental factors, physical access to health care and bed net usage. The results suggest that the use of non-stationary models might be justified also at small-scale areas, however further research is necessary to deepen the current understanding of the fine-scale spatial heterogeneity of P. falciparum. Malaria patterns are complex and the risk of infection is influenced by many other factors that were not accounted for in this study, including malaria control interventions and genetic diversity. Specifically, vector breeding sites at small scale  may significantly influence the spatial heterogeneity in the study area [P. falciparum parasitaemia.An integrated approach that employs different data sources, GIS and remote sensing technologies and Bayesian geostatistical modelling for spatially-explicit risk profiling of udy area -43. FurtADDS: Africa Data Dissemination Service; AIC: Akaike Information Criterion; BCI: Bayesian Credible Interval; CI: Confidence Interval; DALY: Disability-Adjusted Life Year; DIC: Deviance Information Criterion; GIS: Geographical Information system; GPS: Global Positioning System; IRS: Indoor Residual Spraying; ITN: Insecticide-Treated Net; LST: Land Surface Temperature; MCMC: Markov Chain Monte Carlo; MODIS: Moderate Resolution Imaging Spectroradiometer; NDVI: Normalized Difference Vegetation Index; OR: Odds Ratio; RFE: Rainfall Estimates; WBC: White Blood CellKDS contributed to the conception and design of the study, collected the data, was responsible for quality control issues of malaria slide reading, assisted with the analysis of the data and editing of the manuscript, GR contributed to the conception and design of the study, collected the data, analysed and interpreted the data and drafted and edited the manuscript, AY was involved in the collection of the data and supervision of the field work, PV contributed to the analysis of the data and editing of the manuscript, MT contributed to the conception and design of the study, EKN and JU oversaw all aspects of the study, including conception, design, execution of the field work, interpretation of the data and editing of the manuscript. All authors read and approved the final version of the manuscript.


While incarceration has consistently been associated with a higher risk of HIV infection for individuals who use injection drugs (IDU), the effect of incarceration on the post-release risk environment remains poorly described. We sought to assess the impact of incarceration on risk factors for HIV infection after release from prison in a sample of active IDU in Vancouver, Canada.Using a prospective cohort of community-recruited IDU followed from May 1, 1996 to November 30, 2005, we examined contingency tables and performed linear growth curve analyses to assess changes in the prevalence of independent risk factors for HIV infection from before to after a period of incarceration among participants reporting incarceration and a matched control group.p < 0.05) after incarceration. In linear growth curve adjusted for age, gender and ethnicity, syringe sharing was significantly more common in those recently released from prison (p = 0.03) than in the control group.Of the 1603 participants followed-up over the study period, 147 (9.2%) were eligible for an analysis of post-incarceration risk behaviours and 742 (46.3%) were used as matched controls. Significant differences were found in one or both groups for the prevalence of frequent cocaine injection, requiring help injecting, binge drug use, residence in the HIV outbreak epicentre, sex-trade participation and syringe sharing (all In a sample of Canadian IDU, we did not observe any effect of incarceration on the prevalence of several behaviours that are risk factors for HIV infection, including intensity of drug use or participation in the sex trade. However, those recently released from prison were more likely to report syringe sharing that those in a matched control group. Incarceration is common among injection drug users (IDU) and has consistently been associated with drug-related harms, especially infection with blood-borne pathogens like hepatitis C and HIV. -3 While Although the link between imprisonment and HIV infection is robust and well described, the possible effects of incarceration on post-release behaviours of IDU and their HIV risk environment remain largely undetermined.  FindingsWe are unaware of any analyses that identify the effect of incarceration on the risk factors for HIV infection following release from prison or analyses that describe the specific individual, social and structural determinants of the post-release risk environment for IDU. Thus, in the current analysis, we sought to determine the possible effect of imprisonment on the post-release risk environment by identifying the prevalence of independent risk factors for HIV infection before and after a period of incarceration as compared to a non-incarcerated matched control group.In May 1996, the Vancouver Injection Drug User Study (VIDUS) began recruiting IDU through self-referral and street outreach. This prospective cohort study has been previously been described in detail.  In briefTo inform our analysis of HIV risk factors, we first identified all participants who reported being incarcerated in a municipal jail, provincial prison or federal penitentiary overnight or longer since initiating injection drug use, a definition consistent with previous analyses. ,33 Among2 tests and Wilcoxon rank-sum tests. We repeated these tests to compare individuals included as cases or controls to participants not included in the final analytic sample.We then examined if there were significant differences between the two groups with regards to age, gender and ethnicity  using χNext, we selected explanatory variables that had previously been identified as implicated in the transmission of HIV in this setting: daily cocaine injection (yes vs. no); daily inp-value represents the significance of the interaction term. In addition, to control for potential confounding, an a priori model fitting approach was used in which each model was adjusted for age, gender and ethnicity . All p-values were two-sided.To test for differences between pre- and post-incarceration periods between those incarcerated and the control group, we used McNemar's test to examine the proportion of individuals reporting each risk factor in each group. To test for differences over time and between groups, we constructed linear growth curve models for each risk factor in which a statistically significant trend was observed in one or both groups. Commonly used in longitudinal observational research, the linear growth curve technique enables the identification of changes over time by using an interaction term in the model to determine if those changes are significant. ,43 In eap-value < 0.01). The groups did not differ significantly with respect to gender or ethnicity. In tests comparing individuals included as cases or controls to cohort participants not included in the analyses, we found those individuals not included were significantly younger  and more likely to be female . The observed difference in the proportions of individuals reporting aboriginal ancestry (24.6% among non-included compared to 29.1%) was not statistically significant (p = 0.051).Between May 1, 1996 and November 30, 2005, 1,603 participants were recruited, including 584 (36.4%) women and 435 (27.1%) people who reported Aboriginal ancestry. At baseline, the median age of the participants was 33.0 (IQR = 26.0–40.0). The proportion of respondents at baseline and each follow-up period who reported incarceration in the previous six months is presented in Fig p > 0.05). Thus, these factors were not included in the linear growth curve analyses. For frequent cocaine injection, a significant decrease was observed in the control group (p < 0.001); the decrease among the incarcerated group was not statistically significant. A statistically significant decrease in needing help injecting was observed in both the incarcerated and control group (both p < 0.001). For binge drug use, a statistically significant decrease was observed in both the incarcerated (p < 0.005) and control (p < 0.001) groups. Fewer individuals in both the incarcerated (p = 0.016) and control (p = 0.011) groups reported living in the DTES. A significantly lower proportion of individuals reported participating in the sex trade in the incarcerated (p = 0.012) and matched (p < 0.001) groups. A statistically significant decrease in the prevalence of syringe sharing was observed in the non-incarcerated group (p < 0.001); the decrease in the incarcerated group was not statistically significant (p = 0.398).The proportion of each group reporting HIV risk factors in each period, as well as the result of McNemar's test assessing whether the proportions within groups were equal, is reported in Table p = 0.737); needing help injecting (p = 0.201); binge drug use (p = 0.273); living in the DTES (p = 0.105) or participation in the sex trade (p = 0.623) before and after a period of incarceration compared to a matched control group. However, in the linear growth curve analysis of syringe sharing, a significant difference (p = 0.033) was observed between the slopes for the incarcerated and control groups, indicating a significant decrease in prevalence in the control group but no significant decrease in the incarcerated group.The results of the linear growth curve analyses are presented in Table In this analysis, we found individual, social and structural risk factors for HIV infection were common among active IDU both before and after periods of incarceration. In a linear growth curve analysis, individuals recently released from prison were significantly more likely to report sharing contaminated sharing syringes as compared to individuals who did not report incarceration.This finding sheds further light on the relationship between incarceration and the ongoing HIV epidemic among IDU in this setting. Both qualitative and quanAlthough we were unable to determine the exact nature of the relationship, these findings suggest that some aspect of the environment or experience of incarceration leads to a greater risk of syringe sharing once individuals are released from custody. Many previous analyses from this setting and others have identified a greater risk of syringe sharing among imprisoned individuals compared with analagous samples of IDU, especially in correctional environments where access to sterile syringes is forbidden. ,32 In thTo our knowledge, this is the first analysis to evaluate the effect of incarceration on post-release rates of syringe sharing. Two previous studies measured the prevalence of syringe sharing following release from prison in Bangkok, Thailand and New We did not observe an effect of imprisonment on any of the risk factors also classified as crimes in our setting, including participation in the sex trade and illicit drug use. This is in line with previous analyses that found many IDU resume drug use following release and thatThese findings suggest a need for a number of policy reforms. As urged by other authors, we suggest that political and criminal justice interventions that seek to reduce drug use through law enforcement should be considered in light of our findings and others linking imprisonment and a greater likelihood of HIV risk behaviours. ,32,33,44This study has methodological limitations to consider when evaluating the findings. First, VIDUS is not a random sample, although it is believed to be representative of the local population of IDU. Second, although several of the surveyed behaviours may be under- or over-reported due to social desirability, we do not believe they were differentially reported by incarceration history. Although information on HIV serostatus is available for all participants, the number of incident infections during the study period among individuals included as cases or controls is too low to allow for a valid statistical analysis; thus, we have used measures of HIV risk to evalute the possible effects of incarceration on post-release transmission patterns. Most importantly, there may be differences between recently incarcerated and non-incarcerated IDU that we were unable to account for in multivariate analyses. Finally, we were not able to include the length or location  of incarceration events, nor the number of incarceration events lifetime or during the study period, in these analyses.To conclude, we evaluated the prevalence of independent risk factors for HIV infection before and after incarceration among active IDU, and, after comparison with a matched control group, observed a statistically significant relationship between syringe sharing and the post-release period. We did not find any association between incarceration and the frequency of other individual, social and structural factors, including those also defined as crimes. These findings point to the need for the ongoing development of programs both within and following prison that aim to reduce risk behaviour among IDU exposed to correctional environments.ad hoc adviser to or spoken at various events sponsored by Abbott Laboratories, Agouron Pharmaceuticals Inc., Boehringer Ingelheim Pharmaceuticals Inc., Borean Pharma AS, Bristol-Myers Squibb, DuPont Pharma, Gilead Sciences, GlaxoSmithKline, Hoffmann-La Roche, Immune Response Corporation, Incyte, Janssen-Ortho Inc., Kucera Pharmaceutical Company, Merck Frosst Laboratories, Pfizer Canada Inc., Sanofi Pasteur, Shire Biochem Inc., Tibotec Pharmaceuticals Ltd. and Trimeris Inc.M-J Milloy, Jane Buxton, Evan Wood, Kathy Li and Thomas Kerr declare they have no competing interests. Julio Montaner has received educational grants from, served as an TK and M-JM conceived the study. EW, TK, M-JM and KL designed the analysis; KL performed the statistical procedures. M-JM wrote the manuscript and incorporated all suggestions. JM contributed to conception and design of the analysis, interpretation of the data and drafting of the report. All authors approved the version to be published.The pre-publication history for this paper can be accessed here:


Blood transfusion is a common therapy for multiple trauma patients, and is often performed soon after hospital admission. It is unclear whether the need for a blood transfusion in multiply injured patients presenting with a positive blood alcohol concentration (BAC) is associated with increased morbidity/mortality, since their risk behavior differs significantly from patients with a negative BAC. In this study, we evaluated the role of blood transfusion in the treatment of BAC-positive multiple trauma patients.In a three-year period, 164 patients at a single trauma center presented with a positive BAC, and 145 met the inclusion criteria for further evaluation and regression analysis. We compared patients who were transfused (n = 76) with those who were not transfused (n = 69).In both groups, the most common causes of trauma were traffic accidents and falls. Most patients were admitted to the hospital from the scene of the accident (77.2%) and were male (89.0%). Transfused patients had a lower GCS (p ≤ .001) and her ISS (p ≤ .001), were more likely to have severe head injuries (p ≤ .001), tended to have higher BACs (p = .053), had lower hemoglobin levels and prothrombin times in the first 24 hours (p ≤ .001), had lower lactate levels, had higher rates of intubation (p ≤ .001) and ICU admission, and had longer ICU stays and artificial ventilation times (p ≤ .001). Mortality was significantly higher in transfused patients . Non-survivors were more likely to have severe head injuries; be intubated and ventilated; be older; have higher ISS scores, lactate levels, and numbers of transfusions in the first 24 hours; and have lower GCS scores, hemoglobin measurements, and prothrombin levels. In a binary logistic regression model, only age (p = .009) and ISS (p = .004) independently predicted mortality.In our single-center study, the BAC of multiple trauma patients and the number of blood transfusions they received did not predict mortality in multiple trauma patients if used as independent predictors. Prospective studies with greater sample sizes should be performed to clarify the role of blood transfusions in the outcome of this sub-population. Trauma patients with elevated blood alcohol concentrations (BAC) generally differ from other patients in terms of their risk behaviors, the likelihood of impaired consciousness, and their reaction times . They arAlthough blood transfusion is a reliable predictor of increased risk of morbidity and mortality in multiple trauma patients -7, it caProspective randomized studies to evaluate the effects of blood transfusion in acute trauma care are very difficult to perform because of ethical and legal limitations. Transfusion protocols are often based on expert opinion and not on evidence , whereasIn a three-year period (January 2004 to December 2006), data from 573 consecutive adult multiple trauma patients admitted to an urban trauma center  were collected retrospectively; 206 patients underwent blood alcohol screening, and 164 tested positive. We evaluated the diagnostic, therapeutic, and outcome characteristics of these patients and compared patients who received blood transfusions with patients who did not.Inclusion criteria were suspected high-impact trauma, hospital admission on trauma Day One, and a positive BAC. Exclusion criteria were major burns, absence of an emergency physician at the accident scene, or missing data. The study protocol was approved by the hospital's data security manager and the local ethics committee.All patients were transported to the hospital by physician-staffed ground ambulance or helicopter rescue. Some patients came directly from the scene of the accident (primary admission), and others were transported from smaller hospitals where they had received their initial treatment (secondary admission). All patients had a whole-body multi-slice CT-scan (Toshiba Aquilion 32), in accordance with the center's trauma protocol. Retrospective analysis was performed using computerized medical records, focusing on socio-demographic data and trauma- and treatment-specific variables. Personal patient information was treated in accordance with the Declaration of Helsinki.'Multiple trauma' was defined as either two or more injuries (life-threatening in combination) or one life-threatening injury . An init2 test, Student's t-test, or the Mann-Whitney U-test. The alpha level of significance was set at 5%, two-tailed. To evaluate predictors for mortality, a binary logistic regression model was employed for the whole study population. Further exploratory analyses used Spearman rank correlations. All computations were made using SPSS 13.0.Group comparisons were analyzed using the χFrom the cohort of 164 patients with a positive BAC, 19 patients were excluded: nine did not meet the requirement for the definition of multiple trauma, four had major burns, four were admitted after trauma Day One, and two had incomplete data. In the emergency department, 45 of the 145 patients had a BAC of >0.5 g/l and 36 had a BAC of >1.0 g/l. Seventy-six patients received a blood transfusion (transfusion group [TG]), and 69 patients did not (no transfusion group [NTG]). Both groups were comparable with respect to age, and most patients in both groups were male (89% of the whole study population). The characteristics of the two groups and statistical comparisons are summarized in Table The two groups did not differ in the causes of their trauma Table : in bothMost patients (77.2%) in both groups were primary admissions to the trauma center. Similar proportions of patients in both groups underwent helicopter rescue table , but theTransfused patients had lower GCS scores (p ≤ .001) and higher ISS (p ≤ .001) and were more likely to have a severe head injury (p ≤ .001). The BAC tended to be higher in TG patients (p = .053). Hemoglobin levels and prothrombin times in the first 24 hours were lower (p ≤ .001), and lactate levels were higher (p ≤ .001) in TG patients. Only 16 females were found in the study population; most of them received blood transfusions (81.3%).Higher BACs correlated with lower GCS scores, but not with other clinical variables .The overall rate of tracheal intubation was 57.9%. Transfused patients had a higher rate of tracheal intubation, especially at the accident scene and in the emergency department. The number of transport-related intubations was similar in both groups. Furthermore, transfusion was associated with a higher admission rate to the ICU, longer ICU stays (p ≤ .001), and longer artificial ventilation times (p ≤ .001).The average number of transfusions was four in the first 24 hours and 12 throughout hospitalization . The trauma contexts of deceased patients were traffic accident (n = 8), fall from height (n = 9), and shotgun injury (n = 1). Two patients died after discharge from the ICU. Non-survivors were more likely to have had a severe head injury and to have undergone tracheal intubation and artificial ventilation.Significant differences between non-survivors and survivors were: ISS, age, intubation rate, lactate level, number of transfusions in the first 24 hours and total number of transfusions, GCS scores, and hemoglobin and prothrombin levels. BAC, ICU admission, length of ICU stay, and hours of artificial ventilation were equal in both groups. There were no differences in gender, admission type, or likelihood of severe head injury.A binary logistic regression model was used to examine predictor variables for mortality Table . The potMortality was higher in transfused patients, but whether or not a patient was transfused was not a predictor of outcome in patients with a positive BAC. The higher mortality rate in these patients probably reflected the underlying severity of their injuries, with the need for transfusion being a marker of this.In our study, the BAC level did not predict mortality. One previous study found that a higher BAC is a risk factor for poor outcome in brain injury patients . Recent In a retrospective study of adult multiple trauma patients with an ISS > 12, a positive BAC combined with the presence of an illegal substance was associated with a higher rate of pneumonia and longer ventilation time, whereas alcohol alone did not exhibit those associations . There iIn prospective studies on alcohol-impaired patients involved in traffic accidents, Fabbri found the following independent risk factors for severe injury: male sex, older age, admission at night or over a weekend, and a BAC >0.5 g/l ,3,24. ThOne problem in retrospective studies involving BAC or illegal drugs in trauma is that many centers do not routinely screen for these. Only half of trauma patients are tested for alcohol, and even fewer are tested for other abused substances . IncreasThere was a higher percentage of secondary admissions in the transfused group. This may reflect a lower quality of care from less specialized centers or delays in receiving definitive treatment. Laboratory measures are helpful physiological instruments for predicting trauma severity in clinical practice. Hemoglobin as a marker of anemia, lactate as a correlate of anaerobic metabolism, and prothrombin time as a marker of coagulation status are commonly used in multiple trauma outcome studies ,28. In oIn larger, North American study populations, blood transfusion in the first 24 hours after trauma was an independent predictor of mortality, ICU admission, length of ICU stay, and systemic inflammatory response syndrome (SIRS) ,5. We liEvaluating the role of blood transfusions in severe trauma is of great public concern because the conclusions may affect patient safety and public spending on blood products . RestricOur study has several important limitations. Its retrospective character could have influenced the availability of the data collected. The decision to transfuse packed cells was at the discretion of the attending intensivist and not based on transfusion protocols or scoring systems ,32,33. AThere was a large number of patients transferred from other facilities in whom BAC-determination was performed relatively late in the course of their management, and this may have led to unreliable laboratory results  . FinallyNevertheless, the present study provides data in an under-researched field. Our findings suggest that morbidity is higher in transfused BAC-positive trauma patients and that transfusion use in BAC-positive patients correlates strongly with poorer laboratory measures and higher expenditure resulting from an increased need for intensive care. We found that ISS and age were robust predictors of overall mortality.We conclude that the level of a multiple trauma patient's BAC and the amount of packed red blood cells transfused did not predict mortality. However, more transfusions were correlated with higher injury severity and comorbidity. Prospective studies with higher sample sizes should be performed to clarify the role of blood transfusion in the outcome of this sub-population.The authors declare that they have no competing interests.MFS designed the study, collected data, reviewed the literature, and drafted the manuscript. TS performed the statistical analysis and helped to draft the manuscript. RS participated in the study design. PH collected data, participated in the study design, and helped to draft the manuscript. All authors read and approved the final manuscript.


The prevalence of obesity and overweight is increasing globally. Frequently coexisting with under-nutrition in developing countries, obesity is a major contributor to chronic disease, and will become a serious healthcare burden especially in countries with a larger percentage of youthful population. 35% of the population of Saudi Arabia are under the age of 16, and adult dietary preferences are often established during early childhood years. Our objective was to examine the dietary habits in relation to body-mass-index (BMI) and waist circumference (W_C), together with exercise and sleep patterns in a cohort of male and female Saudi school children, in order to ascertain whether dietary patterns are associated with obesity phenotypes in this population.5033 boys and 4400 girls aged 10 to 19 years old participated in a designed Food Frequency Questionnaire. BMI and W_C measurements were obtained and correlated with dietary intake.The overall prevalence of overweight and obesity was 12.2% and 27.0% respectively, with boys having higher obesity rates than girls (P ≤ 0.001). W_C and BMI was positively correlated with sugar-sweetened carbonated beverage (SSCB) intake in boys only. The association between male BMI and SSCB consumption was significant in a multivariate regression model (P < 0.0001). SSCB intake was positively associated with poor dietary choices in both males and females. Fast food meal intake, savory snacks, iced desserts and total sugar consumption correlated with SSCB intake in both boys  and girls . Older children reported eating significantly less fruit and vegetables than younger children; and less eggs, fish and cereals. Conversely, consumption of SSCB and sugar-sweetened hot beverages were higher in older versus younger children (P < 0.001). BMI and W_C were negatively correlated with hours of night-time sleep and exercise in boys, but only with night time sleep in girls, who also showed the lowest frequency of exercise.A higher intake of SSCB is associated with poor dietary choices. Male SSCB intake correlates with a higher W_C and BMI. Limiting exposure to SSCB could therefore have a large public health impact. Severaled obese .Excessive calorie intake in the form of a number of macronutrients has been associated with weight gain. Energy intake from sugar-sweetened carbonated beverages (SSCB) now accounts for a significant fraction of the total caloric intake of young people , and conGiven these observations, the influence of SSCB on diet quality can be viewed as a worldwide health concern. The aims of this study were to examine the dietary patterns that may affect anthropological factors of male and female Saudi school students between the age of 10 and 19, and to determine the prevalence of any nutritional trends which may impact on health outcomes in the future. A designed questionnaire was used to collect data regarding dietary habits, weight, height, BMI, waist circumference, physical activity and sleep patterns.2) was categorized as < 5th percentile, ≥ 5th and < 85th percentile, ≥ 85th and < 95th percentile, and ≥ 95th percentile using 2000 Centre for Disease Control gender-specific growth charts [et al [th percentile; ≥ 10th and ≤ 75th percentile; ≥ 75th and ≤ 90th percentile and ≥ 90th percentile.This was a cross-sectional study conducted during the scholastic year of 2007, which included both male and female Saudi children between the ages of 10 and 19, randomly selected from 450 intermediate and secondary schools in different regions of the capital city of Riyadh, corresponding to 2.7% of the Riyadh student population for that year. The study protocol was reviewed and approved by the Institutional Review Board (IRB) and because all data were collected anonymously, a waiver of consent was granted by the Research Ethics Committee of the King Faisal Specialist Hospital & Research Centre. Parents were notified in writing about the objectives of the study and were invited to contact their respective schools to ask questions or to withdraw their child from the study. A total of 10,000 subjects were interviewed, and after removing subjects with missing/incomplete data, 9433 anonymized entries were included in the study, aged between 10 and 19 years, with an overall male to female ratio of 1.14. Anthropometric measurements of weight, height and waist circumference were carried out by the same team of experienced nurses and one attending physician in order to avoid inter-examiner variability. Weight was measured in light clothing and without shoes using a digital scale, and was recorded to the nearest 100 g. Height was measured as the distance from the top of the head to the bottom of the feet without shoes using a fixed stadiometer. BMI-for-age , previously translated into Arabic and including colored pictures of the food items under investigation was used in this study. Children were briefed as to how to complete the questionnaire prior to filling out their response. The variables studied were related to 7-day recall of intake of several types of commonly available fast food meals such as beef burger meal with French fries and a choice of either sugar-sweetened or diet carbonated beverage, chicken burger meal, chicken nugget meal or fried chicken meal. Other items such as apple pie, frozen desserts and pizza were included in the survey on the basis of their availability at fast food restaurants. Major nutrient sources queried included eggs, cheese, fish, selected fruits  and vegetables , bread, sweetened and unsweetened cereal, sweet and savory snacks and drink consumption. Items included in the questionnaire were selected from a larger list of food and drink items, the frequency of consumption having previously been tested in a small pilot study performed with 420 Riyadh school children during the preceding scholastic year. The survey included questions related to SSCB intake either as part of a fast food meal, or alone. Other items included as independent variables based on their possible relationship with beverage intake were donuts, muffin/cake, ice cream, and savory snacks such as potato crisps and popcorn). Local foods were also featured in the questionnaire in the form of shawarma, and a typical rice and meat dish (Kabsa). For tea, coffee and milk, number of cups consumed per week were recorded. The number of spoonfuls of sugar added to beverages (5 g per serving) was also included in the questionnaire. The nutrient content of these food items was calculated from the USDA National Nutrient Database for Standard Reference  and veriThe frequency of physical activity was monitored. Students were asked to report the number of occasions per week that they took part in exercise consisting of 30 minutes or more of moderate activity. Students were also asked to record their normal duration of night-time and day-time sleep.et al [Only forms with a complete set of valid data were included in this study, in which we aimed to sample 10,000 children. The response rate was 94.4%, with the remaining subjects electing not to participate. All statistical analyses were performed using SPSS version 13.0 . One way ANOVA with Tukey's posthoc test was used to compare differences in means of BMI, W_C, food intake frequency, exercise frequency and sleep patterns amongst gender within the three age groups (n = 9433). Percentage data were compared using the z-test for column proportions. Statistical significance was set at P ≤ 0.05 for all tests. For the correlation analysis, the data was filtered for possible over and under reporting by mean intake ± 1 standard deviation  of mean et al . Followith and <95th percentile) was 15.5%, whereas obese (BMI ≥ 95th percentile) children constituted 21.1% of the study population. Regardless of age or gender, approximately 55% of this population had a BMI-for-age within the normal range of between ≥ 5th and < 85th percentile. Male and female underweight (≤ 5th BMI-for-age percentile) children accounted for 10% and 6.8% of the population respectively. Overweight children (between the 85th and 95th BMI-for-age percentile) accounted for 14.4.% of males and 16.7% of females, with the remainder  having BMI-for-age values of ≥ 95th percentile . The numbers of obese male, but not female children also increased with age, so that there were higher numbers of obese children aged 16-19 years than at 10-13 years (P < 0.001). Waist Circumference (W_C) measurements showed similar gender differences, with a higher percentage of boys achieving W_C scores in the ≥ 90th percentile compared to girls, regardless of age group , with a male: female ratio of 1.14 to 1  was 15.5%, whereas obese children constituted 21.0% of the population, which is higher than previous reports [th percentile and waist-circumference scores ≥ 90th percentile compared to girls. Increased prevalence of obesity amongst boys aged under 11 compared to girls was recently noted in a report based on National data from the Health Survey for England 2005 [In this study we surveyed BMI, W_C, dietary habits and exercise/sleep patterns of a cohort of 9433 male and female Saudi school children aged 10-19, since many aspects of adult physical and nutritional behavior are often established during childhood and adolescence [and 2005 . Within and 2005 . Howeveret al [Our study indicated that the frequency of reported consumption of SSBC, added sugar in hot beverages, total sugar and number of local rice dishes increased with age in both male and female children, suggesting a trend towards sugar-rich foods and away from healthier food choices with advancing age. This was accompanied by a decrease in reported weekly consumption of milk (both full fat and low fat), fruit, vegetables, fish, eggs and cereal. These observations tend to confirm other studies which show that the quality of children's diets decrease over time  possiblyet al . Converset al ,58, and et al . Severalet al , and secet al . The invet al .Our study indicated that fast food meals were consumed by Riyadh school children at an average of 4.5 times a week. This frequency contrasts markedly with data from the southwestern region of Saudi Arabia , in whicThe frequency of exercise occasions decreased with advancing age, and was inversely correlated with BMI and W_C in boys only, in keeping with previous observations . Femaleset al [et al [et al [The usual limitations of this cross-sectional study is that causal relationships between various types of food and beverage consumption and body measurements cannot be ascertained, but can only be used to generate hypotheses which may be evaluated by future prospective randomized trials if necessary. The present study is further limited by the accuracy of the self-reported dietary intake, a feature shared by many studies of this nature. The issue of reporting bias has recently been addressed by Savage et al , who conl [et al , whereasl [et al  found thThis study of 9433 school children points to an association between SSCB intake, W_C and BMI in boys age 10-19. Secondly, SSCB intake correlates with poor dietary choices such as fast food meals, savory snacks, and ice cream desserts in both males and females. Conversely, milk consumption was inversely correlated with BMI and W_C, and positively associated with fruit, vegetable, dates, eggs and cheese intake in both genders. SSCB and fast food meal intake may be important factors in evaluating the relationship between overall dietary intake and dietary choices, particularly in the adolescent population. Our data points to a need for further studies into the effect of dietary choices on the growing trend towards obesity.SSCB: sugar-sweetened carbonated beverage; DCB: Diet carbonated beverage; W_C: waist circumference; WHO: World Health organization; %DV: percentage Daily Value; SD: standard deviation.The authors declare that they have no competing interests.KSC conceived of the study and participated in its design, data analysis, interpretation, presentation and drafted the manuscript. MZZ participated in the data collection and analysis, and in the generation of figures and tables for the manuscript. SS participated in the data collection and analysis. MS oversaw statistical considerations and KA-R supervised data collection. FA-M participated in the study design, manuscript drafting and data analysis and interpretation. All authors read and approved the final manuscript.The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2458/10/234/prepub


Recently, it has been assumed that resistance of Plasmodium to chloroquine increased malaria mortality. The study aimed to assess the impact of chemoresistance on mortality attributable to malaria in a rural area of Senegal, since the emergence of resistance in 1992, whilst chloroquine was used as first-line treatment of malaria, until the change in national anti-malarial policy in 2003.The retrospective study took place in the demographic surveillance site (DSS) of Niakhar. Data about malaria morbidity were obtained from health records of three health care facilities, where diagnosis of malaria was based on clinical signs. Source of data concerning malaria mortality were verbal autopsies performed by trained fieldworkers and examined by physicians who identified the probable cause of death.From 1992 to 2004, clinical malaria morbidity represented 39% of total morbidity in health centres. Mean malaria mortality was 2.4‰ and 10.4‰ among total population and children younger than five years, respectively, and was highest in the 1992-1995 period. It tended to decline from 1992 to 2003 .Contrary to what has been observed until 1995, mortality attributable to malaria did not continue to increase dramatically in spite of the growing resistance to chloroquine and its use as first-line treatment until 2003. Malaria morbidity and mortality followed parallel trends and rather fluctuated accordingly to rainfall. The mean body temperature was significantly higher among patients with a clinical diagnosis of malaria than non-malarial patients , mainly attributed to a decrease in malaria-specific morbidity (81‰)  , then it decreased until 2004 (9.6‰). Mortality attributable to malaria followed the same general tendency, but presented two peaks, in 1995 (3.6‰) and 2002 (2.9‰). Malaria mortality fell to 1‰ in 2004  . Among children less than 15 years, malaria mortality presented a declining trend, although not significant (p = 0.062). It was respectively 12.5‰, 10.6‰ and 9.8‰ for 0-4 years (p = 0.117). Concerning more particularly children aged 1 to 4 years, it was respectively 13.3‰, 11.7‰ and 9.0‰ (p = 0.039). It was 3.3‰, 2.0‰ and 1.8‰ for 5-9 years (p = 0.011) and 0.2‰, 0.2‰ and 0.7‰ for 10-14 years (p = 0.09). In 2004, malaria mortality decreased to 4.3‰, 0.4‰ and 0.0‰ in each age group respectively. Although CQ resistance was continuously growing during the period of study, malaria mortality tended to decline in the area  that give the most probable cause of death, estimated by physicians according to a standardized questionnaire and procedure, indicating the context of death and the history of the disease, and filled in by a trained fieldworker during his interview with the relatives of the deceased. It was shown that validity of VA strongly depended on the pathology responsible for the death . Accordiet al  in Kenyal [et al  showed sNevertheless, in the absence of an effective national death register at the population level, and for rural regions where most deaths occur at home, the VA method proved to be a satisfactory tool, and the only one that can currently overcome the lack of reliable data on mortality causes. In the Niakhar DSS, VA methodology was constant throughout the period of study, using standardized questionnaires and causes of deaths being identified by at least two physicians, which allows considering that it is a valid tool for trend computation.However, periodicity of the collection of demographic events -including deaths- changed from weekly to quarterly in 1997, due to the end of vaccine trials that were conducted in the study area during the previous period (1987-1996). This might have led to underreporting of deaths explaining the low figures in malaria mortality that same year. Nevertheless, morbidity figures, from a different source of data -i.e. dispensaries- showed the same drop in 1997, and rainfall level was also low and could have induced a lower malaria transmission.Tests of trends in malaria mortality were repeated, excluding the year 1997, and showed similar results .et al [Chemoresistance may induce a rise in malaria-specific mortality, resulting from therapeutic failures -as suggested by Trape et al ,4 until et al [Mortality attributable to malaria presented two peaks in 1995 and 2002 and revealed a maximum of deaths during the first period of the study (1992-1995). Studies conducted by Trape et al  showed tet al . The maiet al [Etard et al  also shoThe high malaria mortality observed in 1992-1995 can also be partly attributed to the inappropriate treatment of cerebral malaria carried out in dispensaries at this time. Intravenous quinine was diluted with saline, which could induce severe hypoglycaemia leading to death. Since 1995, isotonic glucose replaced saline in the treatment of severe malaria.Morbidity and mortality come from two different sources of data -dispensaries vs. entire DSS population, but are consistent with each other. Without any information on case-fatality rates, morbidity data were used, hypothesizing that, if the case-fatality rate increased with chemoresistance, mortality rates would be susceptible to increase faster than morbidity rates. Conversely, parallel trends were observed for both indicators from 1992 to 1998, and a discrepancy occurred from 1999 to 2001, with an increase in morbidity concomitant with a decrease in mortality  and could also have contributed to the decline in mortality.During the period of study, other factors susceptible to have an impact on malaria morbidity and mortality were not present or trivial, except an IPTi (Intermittent Preventive Treatment in infants) trial conducted by Cisse in 2002, which affected 535 children aged 2-59 months (less than 10% of the whole DSS population of children aged<5 years) treated with one dose of AQ/SP ,29. FurtFinally, no major change in socio-economic status of residents, or in patients' health-seeking behaviour occurred, that could have impacted malaria morbidity and mortality.et al [Malaria appeared to be the main cause of deaths among children below 15 years of age . The number of deaths attributable to malaria increased from 1992 to 1995, as stated by Trape et al  but stabMorbidity and mortality attributable to malaria presented similar trends over the time and were concordant with rainfall rather than chemoresistance patterns. Thus, while the impact of chloroquine resistance on malaria mortality is probable, it is certainly not the only cause nor even the predominant cause, at least in this mesoendemic malarial area.New national policy regarding malaria treatment from 2004 and reliable diagnosis of malaria using routine rapid diagnosis tests in health centres from 2007 should be helpful for the malaria surveillance and the understanding of the causes of morbidity and mortality fluctuations. Although chemoresistance must remain a concern in order to improve the management of malaria, other factors, such as climate and those affecting risks of malaria transmission, should also be considered.The authors declare that they have no competing interests.AM participated in the study design, data collection, analysis and interpretation, and drafted the manuscript. AD contributed to the study design, data collection and interpretation, and revision of the manuscript. AMa contributed to the study design and supervision, and update and analysis of Niakhar DSS database. MC participated in data interpretation, writing, and critical review of the manuscript. PA contributed to the study design, supervision of data collection, and revision of the manuscript. ON participated in the supervision of data collection, and designed the data entry screen for health records collection. He participated to the revision of the manuscript. BMM and BG participated in data interpretation and revision of the manuscript. JPC was the conceptor of the study; he contributed to data interpretation, preparation and review of the manuscript. He is the guarantor of the paper. All authors read and approved the final manuscript.


Understanding the compositional dynamics of genomes and their coding sequences is of great significance in gaining clues into molecular evolution and a large number of publically-available genome sequences have allowed us to quantitatively predict deviations of empirical data from their theoretical counterparts. However, the quantification of theoretical compositional variations for a wide diversity of genomes remains a major challenge.To model the compositional dynamics of protein-coding sequences, we propose two simple models that take into account both mutation and selection effects, which act differently at the three codon positions, and use both GC and purine contents as compositional parameters. The two models concern the theoretical composition of nucleotides, codons, and amino acids, with no prerequisite of homologous sequences or their alignments. We evaluated the two models by quantifying theoretical compositions of a large collection of protein-coding sequences , yielding consistent theoretical compositions across all the collected sequences.We show that the compositions of nucleotides, codons, and amino acids are largely determined by both GC and purine contents and suggest that deviations of the observed from the expected compositions may reflect compositional signatures that arise from a complex interplay between mutation and selection via DNA replication and repair mechanisms.This article was reviewed by Zhaolei Zhang (nominated by Mark Gerstein), Guruprasad Ananda (nominated by Kateryna Makova), and Daniel Haft. Compositional biases in the contexts of nucleotides, codons, and amino acids are found among bacteria -4, fungiCandidatus Carsonella ruddii PV) to 75%  [Nucleotide compositions are highly variable among genomes, and the guanine-plus-cytosine (G + C or simply GC) content differs dramatically from one species to another -27, parts 2CP-C) ,29. The s 2CP-C) . In addis 2CP-C) ,31-38, ps 2CP-C) ,40 that Here we present two models that calculate theoretical compositions of nucleotides, codons, and amino acids in quantitative ways. Our models assume that mutation and selection act at the level of nucleotide (rather than codon or amino acid) , take ini and between R and Ri, where i represents codon position  (see Models). We inferred these empirical relationships . Both models . The expected and observed codon frequencies were plotted against GC content over all collected sequences , respectively [Empirical relationships between GC content and codon (amino acid) usage have been widely reported but explained in most of the cases less comprehensively. Here we show that each codon as well as each nucleotide in cellular genomes follows a very similar trend when GC content varies Figures , 3, 4, aectively --can givTherefore, our models first adopt GC and purine contents as two important compositional elements and consider heterogeneous mutation and selection forces acting at all three codon positions. As testified across a wide variety of species, the models provided consistent compositions, quantitatively recapturing the empirical relationships with changing GC and purine contents. Our results, especially in the various changing trends (most of them are not linear) further validated that mutations (dominated by GC content variations) and selections (dominated by purine content variations) mainly act at the level of nucleotides rather than codons or amino acids in accordance with previous studies ,41,52. Ac, R}, {GC, TC} = {S, Rc}, {AT, TC} = {Sc, Rc}. As assumed, S and R is an independent pair, which leads to Sc and R, S and Rc, Sc and Rc are also independent pairs (see Models). Therefore, the variants, {Sc, R}, {S, Rc}, {Sc, Rc}, are in essence equivalent to our models.Our models have several variants. Since they are built on the basis of GC and purine contents and thus symbolized as {GC, AG} or {S, R}, their variants can also be represented by S and R: {AT, AG} = {SThe expected compositions predicted by our models, however, sometimes deviate in various degrees from the observed. Such deviations can be caused by complex evolutionary mechanisms  and decConsidering nucleotide compositions at all three codons positions Figure , four nuEscherichia coli, fruit fly, rice, yeast, and mammal (see Methods). When we take the top 10 highly-exchangeable pairs in all five matrices, the four among the top are (1) Ala ↔ Ser, (2) Ala↔ Thr (threonine), (3) Ala ↔ Val, and (4) Val ↔ Ile (isoleucine)  conversion matrices. For instance, Ala and Val  are the two most departed amino acids in all the collected sequences. Namely, in comparison to expectations, there are a surplus of alanine and a deficit of valine. Since amino acids are exchanged at different frequencies due to their compositional relevance at nucleotide level, it is possible that deviations of these two amino acids are highly related to such exchangeability. Therefore, we constructed five amino acid exchange matrices that are based on five different datasets in Here we have presented two models that theoretically quantify expected compositions of nucleotides, codons, and amino acids, based merely on GC and purine contents (which are easily computed from input sequences). We evaluated the two models on a large collection of protein-coding sequences across the three domains of life. Our results show that the two models are capable of yielding consistent expected compositions. In addition, our results indicate that deviations of the observed from the expected compositions are signatures resulted from complex interplays between mutation and selection. Therefore, our models represent a promising theoretical framework for compositional studies.We devise two models (denoted as Model 1 and Model 2) that theoretically quantify expected compositions of nucleotides, codons, and amino acids, beginning with only GC and purine contents, which are easily derived from input sequences. To provide a better description, we detailed the computational procedures in Figure z|z ∈ X or z ∈ Y}; (2) intersection of X and Y, denoted X ∩ Y, is the set of all elements that are members of both X and Y, viz., X ∩ Y = {z|z ∈ X and z ∈ Y}; (3) complement of set X relative to set U, denoted Xc, is the set of all members of U that are not members of X, viz., Xc = {z|z ∉ X}. For simplicity, the four nucleotide  contents are denoted as A, T, G, and C, respectively. Since our models are built at the nucleotide level, the universal set U is defined as U = {A, T, G, C}. GC and purine contents are denoted as S and R, respectively, where S = G ∪ C and R = A ∪ G. Notations used to describe in our models are summarized in Table To describe the two models, let us briefly recapitulate several pertinent elements in probability theory. Suppose that the universal set is U, and X or Y are two subsets of U, there are three common operations: 1) union of X and Y, denoted X ∪ Y, is the set of all elements that are a member of X, or Y, or both, viz., X ∪ Y = { union ofc and R, S and Rc, and Sc and Rc [In this study, S and R are assumed statistically independent. Statistical independence of two variables probabilistically means that the occurrence  of one variable does not change the probability for that  of the other. According to the Chargaff's rules again, S varies broadly whereas R always centers at 50%, implying the independence between S and R. As S and R are statistically independent , S ∩ R can be quantitatively expressed by SR, namely, S ∩ R = SR, which is also applicable to the three pairs: Sc and Rc . As a rec and Rc ,40.i and Ri, where i is the codon position, i = 1, 2, 3. The two models proposed in this study differ only in how to compute Si and Ri. As S and R are calculated from each individual sequence, Model 1 deduces Si and Ri from empirical relationships between S and Si and between R and Ri, where Si and Ri are based on the linear relationships derived from multiple species [i and Ri from the input sequence, in a manner similar to S and R. After Si and Ri being obtained, nucleotide contents at the three codon positions are inferred in the same way,Mutation and selection forces are assumed to act at nucleotide level, which also alter codon and amino acid usages ,41. Cons species ,37,67,68i, Ti, Gi, and Ci represent their corresponding contents at the three codon positions .where A1y2z3, normalized by the sum over all non-stop codons, 1, y2 and z3 are calculated from Equations 5-8. Amino acid composition relates closely to codon composition; for a given amino acid, its expected composition is the sum over all its codons.Therefore, for any sense codon xyz,x,y,z ∈ {A, T, G, C}, its expected composition is given by the product of its constituent nucleotide frequencies xModel 1, as it incorporates empirical relationships, is a combination of empirical and mechanistic, whereas Model 2 is purely mechanistic without any prior knowledge  [We retrieved species and their corresponding codon usage totals from Codon Usage Database (/codon/) , which wEscherichia coli ; (2) fruit fly ; (3) rice (Oryza indica and O. sativa); (4) mammal ; and (5) yeast . For the first four groups, proteins sequences and their corresponding orthologous relationships were downloaded from Ensembl Genomes  and BioMart (http://www.biomart.org), respectively. For the fifth group (yeast), proteins sequences and their corresponding orthologous relationships were retrieved from Fungal Orthogroups Repository . One-to-many or many-to-many orthologous relationships were excluded from this analysis, in order to avoid ambiguous or questionable orthologs. We then aligned protein sequences for each dataset group by using T-Coffee [E. coli, 6302 in fruit fly, 16606 in rice, 6606 in mammal and 2026 in yeast. Finally, based on these orthologous alignments for each group, we constructed five amino acid exchange matrices.To construct amino acid exchange matrix, we defined five dataset groups: (1) T-Coffee  and aligThe authors declare that they have no competing interests.ZZ designed the two models and drafted the manuscript. JY supervised the research and revised the manuscript. Both authors read and approved the final manuscript.Zhaolei Zhang, Donnelly Centre for Cellular & Biomolecular Research (CCBR), University of Toronto, Toronto, Canada I thought the Introduction could be further clarified, please explicitly present and describe the Chargaff's first and second rules . And please formerly define "purine content" and explain what is the expected value for it.We expanded our descriptions on the Chargaff's rules and purine content in the Introduction.I don't think it is S and R are independent, since S = G/C and R = A/G. It may not affect the analysis, but the authors need to clarify this.As defined, statistical independence of two variables means that the occurrence of one variable makes it neither more nor less probable that the other occurs. GC and purine contents are assumed independent in this study, which is based on Chargaff's rules that for simplicity, A = T and G = C; therefore, purines are expected to be 50% since A+T+G+C = 2(A+G) = 100%, in contrast to broad variation of GC content. That is, in theory, GC (purine) content does not influence purine (GC) content variation. In reality, it is observed that species with very close purine (or GC) contents have variations in their GC (or purine) contents. For example, Streptococcus mutans UA159 and Rubrobacter xylanophilus DSM 9941 are similar in purine content (52%) but different in GC content , and Bartonella quintana str. Toulouse and Clostridium thermocellum ATCC 27405 are similar in GC content (40%) but different in purine content . Although GC and purine contents share nucleotide G in common, it does not mean that they are dependent. Suppose that two contents X and Y have no nucleotide in common, viz., mutually exclusive, for example, GC and AT, the increase (or decrease) of one content leads to the decrease (or increase) of the other. If two contents are mutually exclusive, they cannot be independent and vice versa [We clarified this point more clearly in the Models.ce versa . We clarHere the null model is that the frequency of a codon is the product of the frequency of individual nucleotide at each position. This is not very accurate since it is known genomes have severe dinucleotide biases.Our models adopt GC (S) and purine (R) contents to model genome compositional dynamics for nucleotides, codons and amino acids and therefore, dinucleotide bias is in fact represented primarily at the nucleotide level. Although it is not fully considered in codon composition, dinucleotide bias is accurately captured at the nucleotide level. For instance, given a sequence containing severe dinucleotide bias, CGCGCG......, it is observed that S = 100% and R = 50%. According to equations 1-4, the expected contents of four nucleotides are A = (1-S)R = 0, T = (1-S)(1-R) = 0, G = SR = 50%, and C = S(1-R) = 50%, which is consistent with the observed compositions and captures well the severe dinucleotide bias.i? Are you referring to the observed ratio between these two variables? I see the authors provided some details in the Discussion session , it may help the readers to move them up front.Can the authors elaborate on these empirical relations between S and Si are not the observed ratio between the two variables, but the linear relationships derived from all genomes in individual domains of life . We accepted the reviewer's suggestion to move the text from the Discussion to the Models and elaborated on the description of the empirical relationships more clearly.I can understand that the GC% could be mostly uniform along the chromosome for prokaryotes and lower eukaryotes such as fly and nematodes; but for vertebrates like frog, chicken and mammals, it is known the chromosomes can be broken into regions (100 Kb at least) of uniform GC content, i.e. the so-called "isochore" concept. Also different chromosomes may have slightly different GC%, at least in mammalian genomes. It appears that the author took the GC% of the entire genome to predict nucleotide frequency. At least the authors should comment on this issue and how this would influence the analysis (by doing some simple experiments). Perhaps this is why the scatter plots for eukaryotes are not as linear as for bacteria and archaea.We agree. We added the description on the influence of isochores on eukaryotes in the Discussion and accordingly cited two relevant references I understand and agree with the results presented, i.e. the nucleotide and codon compositions are influenced by GC% and purine content (AG%), but it is not clear to me how the authors can link GC% with mutations, and purine content variation with selections. The authors need to provide sufficient background on this, instead of referring the readers to a list of references.In the context of protein-coding sequences, mutation primarily creates new substitutions, which leads to a broad variation in GC content. The same substitutions are often selected and cater for a particular amino acid composition with specific physicochemical properties or functional consequences in biologists' term. As reported by several studies and mentioned in our manuscript, purines also have a determinative role in amino acid physicochemical properties. Therefore, purine content should also be relevant with selection although it may be limited to a narrow range of content variation. We expanded our description on this point in the Discussion.Minor issues not for publicationNotation: I suggest the authors use the notation of GC% instead of GC when referring to the nucleotide content.Page 2, line 4, "have been allowing us" -> "have allowed us"Page 2, line 6, quantization -> quantificationPage 2, line 9, "take into account"Page 6, line 15, typo "their the"Page 8, line 11, typo "again consistent"We addressed the minor issues in the revised manuscript.Guruprasad Ananda, Huck Institute for the Life Sciences, Pennsylvania State University, Pennsylvania, USA This manuscript describes two models to quantify genome compositional dynamics for a wide range of genomes, both of which incorporate GC and purine contents as compositional parameters. These models are evaluated by comparing predicted with observed compositions across three domains of life and these comparisons yield a number of (mostly) descriptive results. First, this work demonstrates the combined role of GC content and purine content in contributing to different compositions at the levels of nucleotides, codons and amino acids. Next, it reiterates previous findings about the role of mutations and selection acting at the nucleotide-level in causing differences between predicted and observed compositions. Interestingly, the deviation of observed from expected compositions is decoupled for the three codon-positions and underlying nucleotides (A/T vs. C/G) and is used as a measure of the strength of selection acting at each position/nucleotide. These deviations are also used to assess the degree of selection acting on different amino acids, the results of which seem closely consistent with other findings.Although the models are described quite well, the same cannot be said about results and discussion. In particular, since the deviation of observed from expected compositions is used to derive biological inferences, I think it'd be good to provide some sort of a quantitative measurement that gives an idea about the extent of this deviation. For instance, since the deviations are used to assess the degree of selection at different codon positions, having a value to these deviations can help in understanding the relative quantitative differences in the strengths of selection at the three codon-positions.This study focuses on modeling genome compositional dynamics and we described the deviations of the observed from the expected compositions in the Discussion. Further investigation of these deviations is attractive, but beyond the scope of this study. This suggestion would be an excellent suggestion for next step. In fact, these deviations can be estimated by several measures  and we have been working on an algorithm to statistically quantify these deviations, which will be summarized into another paper soon.The paper does a decent job at describing the role of varying GC content as a determinant of compositions at all levels - as seen in Results, Discussion and Figures. However, the same cannot be said about the role of purine content. The graphs of expected vs. observed compositions are always plotted for GC content, although the models use both GC and purine contents as parameters. One doesn't get a clear picture of the effect of purine content on compositional dynamics. This therefore clouds the results to an extent.Compared to GC content, purine content varies at a relatively small range and the role of purine content in determining compositions is not significant as GC content. However, it does not mean that purine content is not important. The small range of purine content owes to strong selection for particular amino acids, since purine content has a determinative role in amino acid physic-chemical properties. Its slight departure from 50% can lead to diversely different compositions, as indicated by equations 1-8. We clarified our description on purine content in the Discussion.It is mentioned that Alanine and Valine are the two most departed amino acids . From Fig. S5, it becomes apparent why Valine is listed as one of the most departed amino acids; however it's hard to draw this inference for Alanine. As mentioned above, quantifying the deviations can help better understand such differences. Also, it would be interesting to provide a table indicating the amount of deviation per amino acid, and one can then easily compare this information with the data from the exchangeability matrices to understand to what extent the two are correlated.We agree that providing the expected and observed frequencies of amino acids would help better understand the deviations. Thus, we summarized the information as Additional file 5.On the whole, I would recommend this manuscript to be published as a Research article in Biology Direct with the aforementioned modifications.Minor issues:Results, 1st paragraph, 6th line: of empirical...Results, 3rd paragraph (Codon composition),5th line: one of the codons listed doesn't match the ones in Figure 10th line: It should be TGC and GCC that correlate positively with GC content, right?11th line: consistent with the observed...Discussion, 5th paragraph (Considering nucleotide compositions.......)Although this part of the discussion is quite important and interesting, it is not well phrased. Please try to break up the long sentence about A/C vs. G/T.Also the terms 'across' and 'between' the positions are slightly misleading. Please rephrase.We accepted the minor issues and corrected the wording accordingly.Daniel Haft, The J. Craig Venter Institute, Rockville, Maryland, USARevised review of Daniel Haft:Evolutionary change to genomic content includes mechanisms of lateral gene transfer, duplication, gene loss, insertion, deletion, and point mutation, all of which can alter the GC content of an organism. The most common point mutations, but by no means the only ones, are transitions, in which a nucleotide maintains its identity as a purine, or as a pyrimidine, but base pairs switch between AT and GC. Some organisms show extremely strong biases to either AT richness or GC richness, especially between genes and in the third position of each codon.An earlier study by Knight, Freeland, and Landweber asks whether selection, with its effects on amino acid usage and their encoding by particular codons, drives GC bias in coding regions, or whether mutational biases towards a particular GC content more strongly drive patterns of codon usage and amino acid usage. The difference between these two models is that viewing GC mutational bias as the driver would allow a very simple equation to predict most of the variance seen in codon frequencies and amino acid frequencies from species to species. Knight, et al. indeed find that the single parameter of GC content can explain 71-87% of the variance across species in the differential usage of synonymous codons for a given amino acid, and 71-79% of the variance in the usage of different amino acids. The fact that codon positions 1, 2, and 3 respond so differently to overall coding region GC content in their models shows a balance between forces of mutation and selection. Their work, on the whole, nicely reveals this balance through examination of GC content alone.The present study largely reiterates the work of Knight, Freeland, and Landweber, although coding regions for analysis are taken from roughly twice as many species, and graphs are shown for every codon and every amino acids in supplementary figures. In this study, it is noted that average purine content in coding regions, which represent just one strand at a time, may vary slightly in a narrow band around 50%, with the extremes among 686 bacteria never going below 48% or above 59%. The deviation of purine content from 50%, therefore, theoretically allows for a slight enhancement of the power of models based only on GC content to predict actual codon frequencies and therefore amino acid frequencies. However, the authors do not quantify the degree to which adding consideration of coding strand purine content explains additional variance in codon and amino acid frequencies. Without some quantifications of how much purine content improves the models, it is impossible for readers to judge the central claims of the paper. For this reason, the work being reported here is incomplete.The purpose of this paper is to model compositional dynamics. GC and purine contents are regarded as two essential parameters in quantifying compositions, e.g., for four nucleotides, A = (1-S)R, T = (1-S)(1-R), G = SR, C = S(1-R). It can be seen from these equations that purine content has an important contribution to the model; as mentioned in Models, "a special case when R = 0.5, can lead to ". Our model for this special case when R = 0.5, is equivalent to the model proposed by Knight et, al (2001) that considers GC content alone.The paper has several serious errors in its exposition and response to review comments.1. The computational method relies on a change of coordinates for describing base composition from one based on T, A, G, and C to one described by just two variables, GC content and purine content. Changing coordinate systems is fine, but calling the two measures "independent" is not, especially because of the logic used: "According to the Chargaff's rules again, S varies broadly whereas R always centers at 50%, implying the independence between S and R." This becomes an empty rationale for claiming independence when the actual deviation from 50% becomes a main theme of the paper. The deviation of purine content from 50%, and the deviation of codon position 2 GC content from overall GC content have many of the same drivers, and are not independent. It would be better simply to say that equations 1-8 allow for the four nucleotide frequencies to be described by just two parameters.We agree purine content deviates from 50%, which can be observed across the sequences. The statement "Purine content always centers on 50%" is theoretically derived from Chargaff's rules that A = T and G = C, and thus A + T + G + C = 2(A + G) = 100%. In practice, although purine content is not always 50%, the deviation range is relatively small, nearly 10% below or above 50%, whereas GC content varies broadly from ~20% to ~80%. The assumption of the independence of GC and purine contents, therefore, is based on practical observations. For example, in bacteria, Streptococcus mutans UA159 and Rubrobacter xylanophilus DSM 9941 are similar in purine content (52%) but different in GC content , and Bartonella quintana str. Toulouse and Clostridium thermocellum ATCC 27405 are similar in GC content (40%) but different in purine content .2. The response to reviewer 1 about dinucleotide bias is deeply flawed. The claim is that GCGCGC is handled correctly, but in fact it is handled the same as GGGCCC, despite the different codons and amino acid frequencies that result. Any evolutionary process that drives an alteration in dinucleotide frequencies (for a given GC content) will cause their predictive models to be off systematically. Fits by the models, in fact, are not as good as suggested qualitatively in the discussion. The codons selected for display in Figure We agree that the deviations of the expected compositions from the observed result from a complex interplay of mutation and selection and relate closely to evolutionary mechanisms, such as, dinucleotide abundance. We accepted the reviewer's point to expand our discussion on these issues in the Discussion and cite a relevant reference "Karlin and Burge (1995). Trends Genet. 11: 283-290".3. Reviewer 2 joined me in requesting quantitative measures of how well the models fit. The response that the "algorithm to statistically quantify these deviations ... will be summarized into another paper soon" is not satisfying. Modeling why the fits are wrong may be beyond the scope of this paper, but measuring whether the models fit is actually essential.Sequences from different species undergo differential evolutionary processes, consequently resulting in diverse compositions, even for sequences having same GC and purine contents. For different sequences, therefore, the fitness of the models would vary at different degrees, suggesting diverse interplays of mutation and selection forces acting on these sequences. For a given composition, the models fit differently across different sequences, which can be influenced by several factors, most likely linked to the structure of the genetic code and physicochemical properties of amino acid, protein structure, the abundance of iso-accepting transfer RNAs, and translation efficiency and/or accuracy. To focus on our topics on modeling compositions, we only limited the description by taking the deviations of Ala and Val as examples in the Discussion.4. The authors persist in describing collections of coding region sequences as "genomes" throughout much of the manuscript, when "collected coding region sequences" would be better.We accepted the reviewer's suggestion and revised our wording throughout the manuscript.Overall, this paper does not make a strong case that tracking purine content in coding regions provides useful new insights or new working models for the study of molecular evolution.Correlations between genome-wide GC content and GC contents at three codon positions and between genome-wide purine content and purine contents at three codon positions.Click here for fileExpected and observed nucleotide compositions across the three domains of life .Click here for fileExpected and observed codon compositions across the three domains of life .Click here for fileExpected and observed amino acid compositions across the three domains of life .Click here for fileComparison between expected and observed amino acid compositions.Click here for fileEscherichia coli, fruit fly, rice, yeast, and mammalAmino acid exchange matrices in .Click here for file