The SF-36 is one of the most widely used self-completion measures of health status. The inclusion of the SF-36 in the first Australian national household panel survey, the Household, Income and Labour Dynamics in Australia (HILDA) Survey, provides an opportunity to investigate health inequalities. In this analysis we establish the psychometric properties and criterion validity of the SF-36 HILDA Survey data and examine scale profiles across a range of measures of socio-economic circumstance.Data from 13,055 respondents who completed the first wave of the HILDA Survey were analysed to determine the psychometric properties of the SF-36 and the relationship of the SF-36 scales to other measures of health, disability, social functioning and demographic characteristics.Results of principle components analysis were similar to previous Australian and international reports. Survey scales demonstrated convergent and divergent validity, and different markers of social status demonstrated unique patterns of outcomes across the scales.Results demonstrated the validity of the SF-36 data collected during the first wave of the HILDA Survey and support its use in research examining health inequalities and population health characteristics in Australia. While much health research focuses on objective outcome measures such as mortality or morbidity defined through clinical assessment, there is an increasing emphasis on self-reported measures of health status and health-related quality of life. Self-reported measures of health status have been included in epidemiological and community-based survey research. Their use reflects the importance of considering the patients' point of view and the multidimensional nature of health -3.The Medical Outcomes Study Short Form SF-36) is one of the most widely used, self-completion measures of health status. It was developed to meet the psychometric standards necessary for group comparisons, to enable profiling of functional health and well-being, and to quantify disease burden [6 is one The SF-36 was first adapted for use in Australia in 1992, as part of the International Quality of Life Assessment (IQOLA) Project . PreviouIn 2001, the SF-36 was included in the first wave of the Household, Income and Labour Dynamics in Australia (HILDA) Survey. This is an Australia-wide survey of approximately 7,680 households, comprising around 14,000 people aged 15 and over. The HILDA Survey is the first longitudinal household survey in Australia and is designed to provide a sound evidence base to support research and analysis of income, labour market and family dynamics. As such, it is a critical resource for social policy development. The inclusion of the SF-36 in the HILDA Survey will enable investigation of the interaction between social, economic and health measures.There is an extensive body of research demonstrating socio-economic inequalities in the distribution of physical and mental health problems -9. The SThe aim of this paper is to evaluate the psychometric properties and criterion validity of the HILDA SF-36 data. We have used the manuscript by Sanson-Fisher and Perkins  as a fraData are from the first wave of the HILDA Survey (Release 1.0), a nationally representative household panel survey. The HILDA Survey was funded by the Australian Department of Family and Community Services and managed by a consortium led by the Melbourne Institute of Applied Economic and Social Research at the University of Melbourne. The survey utilised a multi-stage sampling approach (sampling households within Census Collection Districts) and was stratified by State and part-of-State.Household Form and Household Questionnaire were completed during a personal interview with one adult member of each household. The Person Questionnaire, also administered during the personal interview, was conducted with all adult household members. Finally, the Self-Completion Questionnaire (SCQ) was provided to all respondents to the Person Questionnaire and was collected at a later date or returned by post. The SF-36 was included as the first element in the SCQ. Fieldwork for wave one of the HILDA Survey was conducted between August 2001 and January 2002.Four survey instruments were included in Wave 1. A A total of 7,682 households responded to the survey (a household response rate of 66 per cent). Within these households, there were 15,127 eligible adults. Of this group 13,969 (92%) completed the Person Questionnaire and 13,055 (86%) completed and returned an identifiable SCQ.There are some differences between the characteristics of respondents to the HILDA Survey and population estimates from the Australian Bureau of Statistics. However, these discrepancies are not large enough to discredit the data and the differences in rates of response across both sex and location are corrected by applying population weights .The first set of analyses evaluated the reliability and validity of the SF-36 scales in the HILDA Survey. Given that these analyses were concerned with the internal structure of the data rather than representativeness, we ignored the clustered and stratified nature of the data and did not take population weights into account. These analyses were conducted using SPSS version 11.7.Item-internal consistency assessed the extent to which each item measures what its associated scale measures. A correlation of 0.40 (corrected for overlap) or greater demonstrates adequate item-internal consistency . Item-diTo assess the internal consistency of scale scores, Cronbach's alpha coefficients were assessed, with a criterion of 0.7 used to define adequate internal consistency. Descriptive statistics for the eight SF-36 scales and the two summary scales were calculated, including the mean, median, range, standard deviation, skewness, kurtosis, and the per cent scoring at the lowest value (floor) and the highest value (ceiling).Construct validity of the SF-36 in the HILDA Survey was also evaluated using the principal components method of factor analysis. Results were compared with the factor structure obtained from other analysis of the SF-36 scales.The second set of analyses also examined a form of construct validity, assessing the extent to which scores on the SF-36 scales were associated with other criteria. We included in our analysis a number of measures of health and disability, ratings of satisfaction with life and health, and measures of stressful employment conditions and persistent feelings of loneliness. In addition to examining the profiles across the eight SF-36 scales, we also examined the relationship between these criterion measures and the PCS scale and the MCS scale, which were calculated according to the standard procedure outlined by Ware, Kosinski and Keller .svy procedures of STATA to account for the clustered and stratified nature of the data, and to facilitate the use of weights to overcome differential response rates and to replicate Australian population parameters. The HILDA Survey dataset contains person-level weights which, when applied to individual survey respondents, adjusts for the unequal probabilities of selection and completion of the Person Questionnaire. However, these weights do not adjust for the further attrition associated with the SCQ. This is critical as our analysis is focused upon measures from this questionnaire. We therefore, conducted a logistic regression analysis to predict completion of the SCQ using the same predictors used to derive the HILDA person-level weights  [For these analyses it was critical to take account of the complex survey design. We therefore utilised the ng type) . The proWe also examine the correspondence between SF-36 results from the HILDA Survey and data from another, large-scale Australian survey to assess the representativeness of the data. We contrast the means for each of the SF-36 scales from the HILDA Survey with those obtained by the Australian Bureau of Statistics through the National Health Survey conducted in 1995 [Based on previous analysis of the SF-36, we expected results across the eight scales to differ according to subgroups identified by demographic and socio-economic characteristics, including age, sex, marital status, educational attainment, housing tenure and employment status. We also examined differences associated with receipt of welfare payments, a focus of our research endeavours . The anaYes or No.One question from the Person Questionnaire asked respondents if they experienced a disability or health condition that had lasted, or was likely to last 6 months or more, and restricted their everyday activities. A showcard listing examples of health conditions, impairments and disabilities was presented as a prompt for respondents. Responses were either No, Yes and Can Do Nothing.Those identifying a disability or condition were asked to rate whether this limited the type or amount of work they were able to undertake. Response categories were In a series of questions from the Person Questionnaire, respondents were asked to rate their satisfaction with a range of life circumstances using an eleven-point scale with descriptive anchors at either end . Responses were categorised as either dissatisfied (if the rating was 4 or less) or satisfied/ mixed (if the rating given was at the midpoint 5 or higher).Respondents were also asked, all things considered, how satisfied they were with their life. Responses were categorised as above.The SCQ included a series of statements about current job characteristics, to which respondents rated their level of agreement or disagreement using a seven-point scale anchored at either end . One item asked respondents to rate whether they feared that the amount of job stress would make them physically ill. Respondents were categorised as experiencing significant job stress if they agreed with this statement (a rating of 5 or greater).I often feel very lonely".The final measure was also drawn from the SCQ. Using the same scale as the job stress measure, respondents were presented with a series of statements about their level of social support. Respondents were categorised as lonely if they agreed (rating of 5 or above) with the statement "Based on previous research using the SF-36, we anticipate that increasing age will be associated with poorer physical health, and that women will demonstrate poorer health than men as measured on all SF-36 scales with the possible exception of the General Health scale . Those rOf the 13,055 respondents with identifiable SCQs, 53 percent were female. The age of respondents ranged from 15 to age 90 or greater (reported age was capped at 90 in the survey data), with an average age of 43 years (SD = 17.25).A total of 11,264 respondents (86.3%) completed all SF-36 items, with a further 1,326 (10.1%) having 5 or fewer missing items. Overall, the mean number of missing items per person was 0.69, and the median was 0. Details of non-responses/missing values for each item are presented in Figure A1 of the Attached file. The rate of missing values was below 3.5 percent for all items. The highest rate of missing values was evident for items from the Physical Functioning scale, followed by the Role Physical, General Health and Role Emotional scales. Scales were calculated using standard SF-36 scoring procedures, whereby missing values were replaced by scale means where valid responses were available for at least half of the scale items. Therefore, the number of respondents with valid scale scores ranged from 12,686 for the Role Emotional scale to 13,031 for the Social Functioning scale. Analyses were conducted with the maximum number of respondents possible and, therefore, varies across the scales.Table In order to assess item-discriminant validity, items that make up a scale were correlated with other scale constructs. Item-discriminant validity is demonstrated when an item correlates higher with its own scale than with other scales. While there was some overlap in correlations between items from one scale and other scales  means for the eight scales ranged from 60.87  to 83.19 , indicaWhile relatively small, the significant differences in mean SF-36 scale scores from these two national surveys are intriguing. The most marked differences were on scales related to mental health, while the scales most strongly related to physical health  were those where no difference between surveys was observed. It may be that the differences observed reflect different methodological approaches adopted in the two surveys, or could be due to non-sampling error. Alternatively, it could reflect a real change in the Australian population between 1995 and 2001, with poorer reported mental health occurring in more recent times. This may be the case, as changes were restricted to the measures related to mental health and therefore are not simply a general change in response bias.There is evidence which corroborates the apparent increased prevalence of mental health problems in the community. Comparison of the 1997 Australian Survey of Mental Health and Wellbeing and the 2001 National Health Survey, both of which included the 10 item Kessler Psychological Distress Scale, showed the rates of substantial psychological distress had increased by over one percent . In the The data presented in Table Age effects were also consistent with expectations. While those in older age groups reported poorer levels of health across most SF-36 scales, the decline was most pronounced in the measures related to physical health . In contrast to this pattern, however, both the Mental Health scale and Mental Component Summary Score showed improved mental health with increasing age, apart from a slight decline evident in the oldest (75+) age group. This is consistent with epidemiological survey results using a variety of mental health measures . Health The analysis showed health was associated with a range of measures of social status, however the profile across the SF-36 scales differed for the different measures. Consideration of educational attainment showed that those respondents who had less than secondary education had significantly poorer physical health, most evident on the Physical Functioning scale. Current unemployment (compared to full or part-time employment) was significantly associated with poorer health on a number of scales, but particularly the measures loading on the mental health factor . This too, is consistent with the extensive unemployment literature . HousingThe final set of comparisons examined the association between SF-36 scales and respondents' health and social circumstances as measured by other scales and items included in the HILDA Survey  were expected to be most strongly associated with poorer mental health outcomes. Consistent with expectations, those respondents in work who reported that their experience of job stress made them physically ill reported poorest health on the Vitality and Mental Health scales, while those who reported often feeling lonely had low scores on the Mental Health, Social Functioning and Vitality scales.This analysis has demonstrated the validity of the SF-36 data collected through the first wave of the HILDA Survey. The eight scales were shown to be psychometrically sound, with good internal consistency, discriminant validity and high reliability. The results supported the underlying two-factor structure, with factors related to physical and mental health. The pattern of factor loadings, variance explained, and the profile and magnitude of the scale means were consistent with previous Australian and international results. There was, however, an indication that the estimated population means on several of the SF-36 scales, particularly on scales loading on the mental health factor, were lower in the HILDA Survey than previous Australian national data collected through the National Health Survey. Self-report measures of health status can be subject to a range of biases, such as reporting bias or cohort effects. They may also be influenced by contextual factors, such as the setting in which the measure is conducted. Further consideration of these different data sources, and an examination of possible methodological and other differences, is warranted.With respect to the relationship between SF-36 scales and external criteria, we demonstrated a pattern of results consistent with expectations and previous research. We examined measures having differential effects on physical and mental health . The relationship between demographic characteristics  and SF-36 scales was consistent with previous data. Finally, we focused on health inequalities, using a range of markers of social status and examining their relationship with the SF-36 scales. While each marker of social status was associated with poorer health, the dimensions of health related to each social variable differed. Whereas educational attainment was most strongly associated with poorer physical health, housing tenure and employment status were related to mental health scales. The measure of welfare receipt, which is associated with economic hardship, was strongly associated with both poorer physical and mental health.The application of the probability weights available with the HILDA Survey data ensures the accuracy of the point-estimates of the population parameters, such as the mean, by correcting for non-response and design characteristics. However, it is also important to recognise the impact of the survey design (clustering and stratification) on the calculation of standard errors. As the sampling units in the HILDA Survey are Census Collection Districts and, within that, households, the data contradict assumptions of independence and should not be treated as a simple random sample drawn from the population. We expect more similarity between individuals within Census Collection Districts and households than between those drawn at random from the population and if these characteristics of the survey design are not taken into account the estimates of standard error will overstate accuracy.Calculation of the design effect provides an indication of the increase in error associated with the complex survey design, representing the ratio of the variance for the complex design to that obtained assuming a simple random sample. These measures range from 1.4  to 3.1 . Thus, whereas the 95 percent confidence interval around the population estimate for Physical Functioning Scale (84.49) would have been 82.04 to 82.94 had we not taken the survey design into account, our estimate was 81.77 to 83.21. The design effects were greater for the scales assessing physical health than for those loading on mental health. This suggests physical health more strongly clusters within households and areas than does mental health, perhaps as a consequence of the clustering of age (design effect of 3.8) and the strong relationship between age and physical health and disability. Note that the design effects are not evident in the population estimates in Table This analysis has provided evidence that the SF-36 data collected in the HILDA Survey is valid and supports its use as a general outcome measure of physical and mental health status. It also suggests that the results obtained using the SF-36 in the HILDA Survey can be interpreted by reference to published SF-36 normative data and comparison with previous research findings. Most critically, from our perspective, the analysis provides initial support for the proposition that the SF-36 scales and component summary scores are related in a meaningful and interpretable manner to measures of social status. This supports the use of the HILDA Survey data in our analysis of health inequalities and the effects of social exclusion in Australia.The authors declare that they have no competing interests.PB and TC participated in all aspects of the preparation of this manuscript, including data analysis, preparation of various sections of the manuscript, and commenting on drafts.The pre-publication history for this paper can be accessed here:Additional file SF36 HILDA validation.pdf – provides tables and figures used to determine item-discriminant validity and to explore the pattern of missing data across SF-36 items.Click here for file


A better understanding of the pharmacodynamics of intermittent presumptive treatment, says White, will guide more rational policymaking A better understanding of the pharmacodynamics will guide more rational policymaking Plasmodium falciparum malaria in pregnancy [Intermittent presumptive treatment (IPT) in pregnancy involves giving a curative treatment dose of an effective antimalarial drug at predefined intervals during pregnancy. IPT in pregnancy was first introduced in areas of high malaria transmission as a measure to reduce the adverse impact of regnancy ,5,6,7,8.regnancy ,10,11,12P. falciparum malaria. There are insufficient safety data on the newer antimalarials to warrant their systematic use in pregnant women. IPT with sulphadoxine-pyrimethamine (SP) has been introduced as an alternative. Antimalarial chemoprophylaxis in young children has been shown to reduce the adverse impact of P. falciparum malaria [Antimalarial chemoprophylaxis for pregnant women living in endemic areas has been recommended for many years, but in practice has been limited to the use of chloroquine and pyrimethamine ,14. Unfo malaria ,16,17, bFive randomised trials of IPT in pregnancy in East Africa have been reported ,2,3,4,5,There is no consensus on how IPT works, making planning difficult. This article argues that IPT provides mainly intermittent suppressive chemoprophylaxis . If this is correct dosing schedules should be individualised for each antimalarial depending on the drug's pharmacokinetic and pharmacodynamic properties. As increasing resistance to SP must seriously compromise IPT regimens based on this drug, the evaluation of available new effective antimalarials is needed urgently, in both high- and low-transmission areas.P. falciparum [After a treatment dose of SP (25 mg sulfadoxine/1.25 mg pyrimethamine per kilogram body weight), plasma concentrations of pyrimethamine  and sulfadoxine  decline log-linearly ,21. The lciparum ,21. For The pharmacokinetic properties of many drugs are altered in pregnancy; lower concentrations often result from an expanded volume of distribution. Strangely, despite the wide endorsement of SP IPT in pregnancy, there are no pharmacokinetic studies of sulphadoxine or pyrimethamine in pregnancy, so it is not known whether the current dosing is optimal. The absorption and disposition of many drugs are also altered in infancy, but there are very few data on antimalarial pharmacokinetics in the first year of life. For some drugs  there is insufficient information for any age group.Is the benefit of IPT gained only through clearing parasites from the placenta (“treatment effect”), or is the prevention of new infections (“prophylactic effect”) an important component? If only the treatment effect is important, then how long does the beneficial effect of eradicating an asymptomatic low-density infection persist for? If it lasts until the next infection becomes patent , then rapidly eliminated drugs will provide protection only for a few days longer than the average incubation period (about two weeks). Establishment of a new placental infection  may take longer because the placenta selects and accumulates parasites that bind to the proteoglycans chondroitin sulphate and hyaluronic acid The duration of prophylactic effect is compromised particularly by resistance. For most antimalarials the duration of antimalarial effect is a simple function of the in vivo concentration–effect (dose–response) relationship and the pharmacokinetic properties of the antimalarial drug Resistance is defined by a right shift in the concentration–effect relationship and results in reduced effects for any concentration below the MPC for resistant parasites P. falciparum malaria, retardation tends to be greatest in the first pregnancy, and often occurs without maternal illness. The greater the placental parasite burden, the greater is the reduction in birth weight. “Placental malaria”—histological evidence of placental accumulation of parasitized erythrocytes or malaria pigment deposition—has often been used as an endpoint in intervention studies, although the quantitative relationship between placental malaria and reduction in birth weight remains poorly characterised.In a high-transmission setting infections are acquired every few days or weeks throughout life see . MortaliThere are fewer data on the efficacy of IPT in infancy than in pregnancy . The phaP. vivax is also an important cause of low birth weight, and so useful preventative measures must also be effective against this infection If IPT is just a simple, albeit imperfect, way of administering chemoprophylaxis, then there are also strong arguments for evaluating this approach in low-transmission settings. The adverse impact of malaria in pregnancy is greater in low-transmission than in high-transmission settings. The reduction in the birth weight of first-borne infants is similar, but extends to the second and subsequent pregnancies; treatment failure rates are higher than in non-pregnant adults ,28,29; aP. vivax, there are no safe and effective available drugs for P. falciparum infections. Use of IPT in these areas would provide a sterner test than in a high-transmission area because there would be little or no background immunity to assist antimalarial drug efficacy.Antimalarial prophylaxis has been recommended and used in low-transmission settings, but whereas chloroquine remains generally effective against P. falciparum subpopulation in pregnancy or the delay in achieving full growth rates because of continued sub-MIC suppression. Although the MIC for drugs that are succumbing to resistance obviously varies considerably, for newer drugs such as lumefantrine or piperaquine the variance is considerably less and generalisations can be made.The dose used in IPT is usually the full age- or weight-adjusted treatment dose derived either empirically or from dosefinding studies . The dose and dosing interval should be determined by the tolerability, absorption, distribution, and elimination kinetics of the drug used, and the in vivo MIC. Unfortunately, for the two drugs that have been used for IPT (SP and amodiaquine) there are no pharmacokinetic data in pregnant women or infants. The in vivo MIC is an important measure but it is parasite specific and difficult to assess ,33,34,35If IPT is simply prophylaxis, then a rapidly eliminated artemisinin component provides very little direct benefit for the additional cost and risk . The addition of an artemisinin component would accelerate parasite clearance and prevent gametocyte production, but the benefits of this in an asymptomatic pregnant woman or child are uncertain. The main benefit would be in providing protection against the emergence of de novo resistance to the slowly eliminated drug, although, because parasitaemias tend to be low, the probabilities of de novo selection are much lower than in acute symptomatic infection. But there is a genuine concern that if monotherapies are made available, then they will be used and abused, and resistance may develop.Without a better understanding of the pharmacodynamic effects of IPT, it will be difficult to make rational improvements in this promising approach to malaria prevention. The most parsimonious explanation for its effectiveness is that IPT provides antimalarial prophylaxis that, if sufficiently lengthy and effective, is beneficial both to the pregnant woman and the infant. But how lengthy and how effective?For IPT in pregnancy the only drug that has been evaluated is SP, at a time when the drug was more effective than it is today. A significant improvement in birth weight was found in only two of four randomised trials. In a large prospective observational study conducted in western Kenya, IPT was associated with an odds ratio of 0.65  for low birth weight Is IPT safe? There is no evidence to date that IPT is harmful. But the incidence of serious adverse effects when amodiaquine  and SP  were used as antimalarial prophylaxis by Western travellers was so high that they are contraindicated Protocol S1(27 KB DOC).Click here for additional data file.Table S1(31 KB DOC).Click here for additional data file.Table S2(34 KB DOC).Click here for additional data file.Table S3(27 KB DOC).Click here for additional data file.Intermittent presumptive treatment (IPT) with sulphadoxine-pyrimethamine (SP) in pregnancy and with amodiaquine or SP in infancy has been proposed for use in areas with high levels of malaria transmission.The duration of post treatment prophylaxis is likely to be an important determinant of the benefit of IPT.Because of rapidly increasing resistance, it is very unlikely that IPT in pregnancy with SP is as effective now in east Africa as it was 5–10 years ago, when it was evaluated.More effective antimalarial drugs such as artemether-lumefantrine and particularly dihydroartemisinin-piperaquine should be evaluated for IPT in both low- and high-transmission settings.Choice of drug, dosing, and dose spacing for IPT should be based on a better understanding of pharmacokinetics and pharmacodynamics.


Post-operative complications are a significant source of morbidity and mortality for patients undergoing surgery. However, there is little research in the emerging field of perioperative medicine beyond cardiac risk stratification. We sought to determine the research priorities for perioperative medicine using a cross sectional survey of Canadian and American general internists.Surveys were electronically sent to 312 general internists from the Canadian Society of Internal Medicine and 130 internists from the perioperative medicine research interest group within the US based Society of General Internal Medicine. The questionnaire contained thirty research questions and respondents were asked to rate the priority of these questions for future study.The research topics with the highest ratings included: the need for tight control of diabetes mellitus postoperatively and the value of starting aspirin on patients at increased risk for postoperative cardiac events. Research questions evaluating the efficacy and safety of perioperative interventions had higher ratings than questions relating to the prediction of postoperative risk. Questions relating to the yield of preoperative diagnostic tests had the lowest ratings (p < 0.001 for differences across these categories).The results of this survey suggest that practicing general internists believe that interventions studies are a priority within perioperative medicine. These findings should help prioritize research in this emerging field. Over 40 million people undergo non-cardiac surgery in the United States each year . PostopeUntil now, research in this emerging field has focused primarily on cardiac risk stratification . DespiteGiven the wide spectrum of comorbidities in the surgical patient and the potential for postoperative complications, numerous research questions still need to be answered. The purpose of this study is to identify top perceived research priorities in this field using a survey of general internists practicing perioperative medicine.To obtain the opinions of general internists who practice perioperative medicine, from the Canada and the United States, all general internists within the Canadian Society of Internal Medicine (n = 312) and all members of the perioperative medicine interest group of the American based Society of General Internal Medicine (n = 130), were surveyed. Physicians were excluded if they practiced in a subspecialty rather than general medicine  or did Research questions for the survey were generated from a Medline search of perioperative medicine studies and from a focus group of five general internists active in perioperative medicine research. The questionnaire was pre-tested by four independent general internists for clarity and to confirm face validity. Modifications were made based on this pre-testing. The questionnaire was developed in English and then translated into French for French speaking Canadian physicians by a medical translator. Subsequently, a bilingual general internist validated the French translation.The questionnaire contained 30 randomly ordered research questions conceptually divided into three themes: 1. evaluating the yield of preoperative diagnostic tests (5 questions), 2. predicting postoperative risk (6 questions) and 3. determining the efficacy of perioperative interventions (19 questions). A subset of the sample (130) was asked to specifically rank the importance of these three categories as an internal check of the reliability of the survey instrument. This subset was also asked to list any other research questions that ought to be considered for future research apart from those included in the questionnaire.Respondents were asked to rate the priority for each research question to be studied in the future on a 10 point Likert scale where 1 indicates a low priority study question and 10 is a high priority research question. For questions that have been partially answered by existing studies, respondents were specifically asked to rate the priority of "further research" in these areas.The self- administered questionnaire was electronically mailed in October 2000 (faxes were sent to those without e-mail addresses). For non-responders, second and third mail-outs were sent in November and December 2000.Descriptive statistics were used to analyze the demographic data and ratings for individual questions. T-tests (two- sided) were used to compare mean response scores across different subgroups. Because of the multiple planned comparisons, the alpha was set at 0.005 to determine statistical significance. Repeated measures analysis of variance was used to analyze differences across the 3 major themes of questions using SPSS statistical software.After 3 mailings, we obtained 152 completed surveys . Thirty-three respondents were then excluded because of subspecialty status or because they did not perform perioperative consults.Respondents belonging to the Society of General Internal Medicine special interest group and Canadian Society of Internal Medicine were identical in most respects except fewer Canadian Society of Internal Medicine members had academic appointments 54% vs. 100%). Table 00%. TablResearch questions evaluating the efficacy of perioperative interventions had higher ratings followed by questions relating to prediction of postoperative risk. Questions evaluating the yield of preoperative diagnostic tests had the lowest ratings. The differences in ratings across these general categories were statistically significant (p < 0.001) and this pattern persisted regardless of academic status or volume of consults seen.Mean scores for individual research questions, based on responses where 1 indicates low priority for future research and 10 indicates high priority, ranged from a low of 3.6 (± 2.3 standard deviation) to a high of 7.2 (± 2.1 standard deviation). Mean scores for the ten highest rated individual questions are given in Table Mean scores for most questions were similar among physicians independent of their academic status or whether they performed a high volume of preoperative consults (defined as greater than 10 consults per month) or not. For several questions, however, differences in ranking according to academic status and volume of consultations were found, although none achieved statistical significance Table .There are few areas within perioperative medicine that are well studied beyond the area of predicting cardiac risk. The American Heart Association perioperative guidelines highlighted the paucity of studies on interventions to prevent postoperative cardiac events . ReflectAlthough perioperative risk stratification is well studied for identifying those at risk of cardiac events, pulmonary complications occur more frequent than cardiac complications and are associated with a longer hospital stay . ReflectSince the completion of the survey, the ratings also reflect ongoing research activity within perioperative medicine. Tight glycemic control was a high rated topic and a study examining the effect of tight glycemic control in postoperative patients in a critical care setting has been published since the completion of this survey. Also since the completion of this survey, a large multi-center trial examining the efficacy of perioperative beta blockade has been launched. Another highly rated research topic was developing a prediction rule for pulmonary complications. Recently, a study was published to identify those at increased risk of postoperative pneumonia. Two of the highly rated research priority topics have been recently published in major medical journals ,8.There are several potential limitations with this study. The low response rate may be felt to limit the generalizability to other general internists. However, views of those who practice perioperative medicine and who have a particular interest in the area, the research consumers, are important in developing a research agenda. We presume that physicians who practice within an area and who have a particular interest are more likely to respond to a survey than those who do not. Thus, the physicians who responded to the questionnaire and reported practicing perioperative medicine define the group of research consumers we were targeting. This study also only examined the beliefs of general internists on research priorities since greater than 90% of the perioperative medicine consultations are conducted by general internists rather than subspecialists in internal medicine in tertiary care centers . Also, lPerioperative medicine research is growing in response to the significant medical and economic consequences of perioperative complications. Identifying the research priorities of those who provide perioperative medical care – the consumers of research, is important. The results suggest that intervention studies are a higher priority for future research compared to studies that predict postoperative complications or determine the yield of diagnostic tests. Researchers and funding boards may use the findings of this survey to identify perceived high priority research topics within perioperative medicine.None declared.NK, TT, AF, NC, FAM, and WG contributed to the design of the project. NK and TT contributed to data collection and data analysis. NK, TT, WG contributed to writing the manuscript. AF, NC, FAM made substantive, intellectual editing contributions.The pre-publication history for this paper can be accessed here:Perioperative medicine research questions. This appendix lists all 30 perioperative research issues from the survey, along with the mean scores rated by all respondents.Click here for file


To understand mycobacterial pathogenesis analysis of gene expression by quantification of RNA levels becomes increasingly important. However, current preparation methods yield mycobacterial RNA that is contaminated with chromosomal DNA.Mycobacterium smegmatis genomic DNA is efficiently removed by DNaseI in contrast to untreated samples.After sonication of RNA samples from This procedure eliminates one of the most prevalent error sources in quantification of RNA levels in mycobacteria. Mycobacterium tuberculosis is the leading cause of death from a single infectious disease with approximately 8.8 million new cases and two million deaths per year. To understand the pathogenesis of M. tuberculosis, analysis of gene expression by relative or absolute quantification of RNA levels using microarrays and RT-PCR  becomes increasingly important [Mycobacterium smegmatis is especially refractory to lysis and therefore was chosen as a model organism.mportant . Widely mportant . Howevermportant . Since tmportant . This camportant ,6. VirtuM. smegmatis SMR5 [600 of 0.8 and mixed with 5 ml killing buffer  [-1 into several aliquots. One aliquot containing 10 μg RNA was left untreated. The second aliquot was directly treated with 10U of RNase-free DNaseI (Roche) for 1 h at 37°C, while the third aliquot was sonicated two times for 20 sec with 0.9 sec intervals at 20 % power  prior to DNaseI treatment. Between the two sonication steps the cell suspension was chilled on ice for 5 min. DNaseI was removed by precipitation with polyethylene glycol (PEG) 6000. As a control for the RNA quality, cDNA was synthesized by Omniscript reverse transcriptase and sensiscript reverse transcriptase  from total RNA (100 ng) for 35 min at 50°C followed by an inactivation step of 15 min at 95°C. The 16Sr RNA was then amplified with the primers 16S-FP (5'-TGCTACAATGGCCGGTACAAA-3') and 16S-RP (5'-GCGATTACTAGCGACGCCGACTT-3') using up to 30 cycles of 1 min at 94°C, 30 sec at 53°C, and 1 min at 72°C before a final extension step of 7 min at 72°C. As a control for DNA contamination, standard PCRs were performed, to which RNA was added after the RT inactivation step. PCR products were analysed at cycles 23, 25, 27 or 30 to check the purity of the RNA. All samples apparently contained 16S rRNA (Fig. tis SMR5  was growM. smegmatis and will be of great value for RNA preparations from other microorganisms, including M. tuberculosis.This result shows that sonication improved DNA degradation by DNaseI most likely by rendering the chromosomal DNA more accessible to enzymatic action. This work describes a simple and efficient procedure to improve the quality of RNA preparations from JS carried out the RNA preparations, the RT-PCR experiments and wrote a draft of the manuscript. JGB performed RNA preparations and RT-PCR experiments. FT participated in coordinating and supervising the study. MN conceived of the study, participated in coordinating and supervising the study, and wrote the final manuscript. All authors read and approved the final manuscript.


The rapidly increasing number of completely sequenced genomes led to the establishment of the COG-database which, based on sequence homologies, assigns similar proteins from different organisms to clusters of orthologous groups (COGs). There are several bioinformatic studies that made use of this database to determine (hyper)thermophile-specific proteins by searching for COGs containing  exclusively proteins from (hyper)thermophilic genomes. However, public software to perform individually definable group-specific searches is not available. and is linked to the COG-database. The user can freely define two groups of organisms by selecting for each of the (current) 66 organisms to belong either to groupA, to the reference groupB or to be ignored by the algorithm. Then, for all COGs a specificity index is calculated with respect to the specificity to groupA, i. e. high scoring COGs contain proteins from the most of groupA organisms while proteins from the most organisms assigned to groupB are absent. In addition to ranking all COGs according to the user defined specificity criteria, a graphical visualization shows the distribution of all COGs by displaying their abundance as a function of their specificity indexes.The tool described here exactly fills this gap. The software is accessible at This software allows detecting COGs specific to a predefined group of organisms. All COGs are ranked in the order of their specificity and a graphical visualization allows recognizing (i) the presence and abundance of such COGs and (ii) the phylogenetic relationship between groupA- and groupB-organisms. The software also allows detecting putative protein-protein interactions, novel enzymes involved in only partially known biochemical pathways, and alternate enzymes originated by convergent evolution. The COG-database has become a powerful tool in the field of comparative genomics. The construction of this data base is based on sequence homologies of proteins from different completely sequenced genomes. Highly homologous proteins are assigned to clusters of orthologous groups (COGs) ,2. Each PCOGR is written in PHP (v.4.3.3) including the domxml (v.20020815) plugin and runs on an openBSD (v.3.4) operating system at dmz.uni-wh.de in an apache (v.1.3.28) web-server environment. In addition, at the clients-side, HTML, javascript, and CSS are used.tot/Btot and B = Btot/Atot where Atot is the total number of organisms belonging to groupA and Btot is the total number of organisms belonging to groupB. After all COGs have been processed in this way, all S-values are scaled to values between 0 and 1. Then, all COGs are output in the order of their specificity indexes S. In addition, a graphical representation shows the number of COGs as a function of their S-values in discrete intervals. The total number of intervals to be displayed can be specified by the user (default = 40 for PCOGR and 7 for PKOGR).Phylogenetic COG ranking (PCOGR) is an online-tool to analyze the microbial COG, or after clicking "Switch to PKOGR", to analyze the eukaryotic KOG database. PCOGR provides a means for determining the specificity of each COG with respect to the presence of sequences from organisms belonging to a predefined group (groupA) versus the absence of sequences from organisms belonging to a second predefined reference group (groupB). For that purpose, each of the organisms can be assigned to one of the two groups or defined to be ignored by the analysis. The software then calculates a specificity index S for every individual COG. The highest ranking COGs (large S) contain sequences from the most groupA-organisms whereas the most sequences from groupB-organisms are absent. To process S for each individual COG, the algorithm starts at S = 0, adds a constant A for each groupA-organism and subtracts a constant B for each groupB-organism being present in the COG under analysis with A = A Figure A Javascript-mouseover info box intuitively explains all functions of the graphical user interface of PCOGR. Furthermore, additional information about both, organisms and output COGs, are available by the implementation of links to i. e. there are little or no proteins specific to one of the groups resulting in a specificity value of around 0.5 for most COGs. In contrast, if the two groups are rather distant, further maxima, either on the left, the right or on both sides become visible, i. e. there are group-specific proteins with S-values around 1 and/or S-values around 0. Even two single organisms can be compared by assigning the first to groupA, the second to groupB and ignoring all other organisms. For instance comparing the closely related Escherichia coli strains O157:H7 EDL933 and O157:H7 results in a prominent single peak in the middle of the graph whereas two further peaks on the edges become visible if two more distant organisms e. g. Aquifex aeolicus and Saccharomyces cerevisiae are compared.PCOGR allows detecting group-specific proteins by both ranking all COGs and graphically showing their distribution over their specificity indexes. The graphical representations can be interpreted as follows: If the two predefined groups are rather related, one expects a single peak in the middle of the graph, Distance and relationship may be interpreted either in phylogenetic or in physiologic terms. To demonstrate that physiologic relevant differences in protein distributions indeed can be detected by PCOGR, two parameter-presets are selectable: (i) a specificity ranking of hyperthermophile-specific versus non-thermophile-specific proteins as published by Makarova et al.  and of tA further attractive potential of PCOGR lies in the easy way to detect novel protein-protein interactions since physically interacting proteins should phylogenetically similarly be distributed . Thus, iNot only protein-protein interactions can be detected but also enzymes involved in the same biochemical pathway as a certain target enzyme . This poAnother attractive use of PCOGR can be to look for an alternative enzyme form catalyzing the same reaction but originated by non orthologous gene displacement (NOGD). Occurrence of NOGD in essential functions can be explored systematically by detecting complementary, rather than identical or similar, phylogenetic patterns . A rankiWith the online availability of PCOGR researchers can perform their own individual searches for group-specific proteins. This will not only allow a deeper insight into phylogenetic relationships of organisms or groups of organisms but also help to detect new highly group-specific proteins worth for isolation and further biochemical characterization. In addition, novel protein-protein interactions could be detected in silico, and this tool is also suitable to assign proteins of unknown function to partially known biochemical pathways. A further application lies in the search of alternate enzymes originated by convergent evolution.Project name: Phylogenetic COG ranking (PCOGR)Project home page: Operating system(s): Platform independentProgramming language: PHP, javascript, CSS and HTMLOther requirements: Web-browser capable to execute javascriptLicense: GNU General Public LicenseAny restrictions to use by non-academics: Contact authorsFM carried out the software development and programming work. MK conceived of the study, and participated in its design and coordination and helped to draft the manuscript. All authors read and approved the final manuscript.


Sequence similarity searching is a powerful tool to help develop hypotheses in the quest to assign functional, structural and evolutionary information to DNA and protein sequences. As sequence databases continue to grow exponentially, it becomes increasingly important to repeat searches at frequent intervals, and similarity searches retrieve larger and larger sets of results. New and potentially significant results may be buried in a long list of previously obtained sequence hits from past searches.ReHAB (Recent Hits Acquired from BLAST) is a tool for finding new protein hits in repeated PSI-BLAST searches. ReHAB compares results from PSI-BLAST searches performed with two versions of a protein sequence database and highlights hits that are present only in the updated database. Results are presented in an easily comprehended table, or in a BLAST-like report, using colors to highlight the new hits. ReHAB is designed to handle large numbers of query sequences, such as whole genomes or sets of genomes. Advanced computer skills are not needed to use ReHAB; the graphics interface is simple to use and was designed with the bench biologist in mind.This software greatly simplifies the problem of evaluating the output of large numbers of protein database searches. Advances in technology have increased the speed and reduced the cost of DNA sequencing. This has resulted in a dramatic increase in the number of sequences contributed by both large sequencing centres and individual laboratories to sequence databases. Public biological sequence databases are growing at an ever-increasing rate, with 9 million new sequences being added to GenBank from August 2002 to August 2003 alone . CurrentSequence similarity searching is a powerful tool to help develop testable hypotheses in the quest to characterize genes and other DNA sequences and infer structural, functional or evolutionary relationships. Researchers interested in identifying new matches to query sequences, which may be a few genes or even whole genomes, must search through massive amounts of alignment data to retrieve new and interesting matches. In order to keep up with the growing databases, the researcher must submit the same queries periodically. However, the new results, no matter how significant, are often buried in a long list of results that were previously obtained on past searches.hits") obtained; these searches are performed on a regular schedule against updated versions of the sequence database. It then compares the sequences in the new PSI-BLAST result with the ReHAB hits database to identify new matches resulting from recently deposited sequences. The complete ReHAB hits database can then be queried by date using a simple GUI to allow the researcher to easily identify new hits; these are highlighted, and pairwise or multiple alignments can be performed to assess the quality of the match. As well as filtering out results that have been found previously, the ReHAB browser can filter out hits against sequences that are identical to the sequences being submitted as queries (such as orthologs of the query sequence).ReHAB (Recent Hits Acquired from BLAST) is a new software package that was developed to address these problems. ReHAB performs PSI-BLAST  searchesReHAB is designed to be a practical tool for searching NCBI database updates with large numbers of query sequences. For example, our laboratory uses it with all open reading frames (ORFs) from fully sequenced poxvirus genomes (over 7000 query sequences). As the number of sequenced virus genomes continues to increase, the number of hypothetical ORFs of unknown function also expands. This is particularly true for large viruses like poxviruses, baculoviruses, and herpesviruses that possess many virulence genes that are not part of the core set of genes that define a virus family . There aOther programs have been previously created to deal with this particular issue, including DBWatcher , SEALS , Swiss-SReHAB was implemented for the Java platform to simplify the support of multiple operating systems including Linux, Microsoft Windows, Solaris, and Mac OS X. Users initially access and launch the application (client) from a web page using Java Web Start, which also automatically downloads updated versions as they become available. This ensures users are taking advantage of improvements or added features in the latest software version. Furthermore, coding in Java allows interoperability with existing applications developed in our laboratory, including Base-By-Base . Our chohits, including biological sequences, alignments between them, and other categorization and annotation data; (2) a Java server that provides access to programs which cannot be run locally by the client on arbitrary user workstations, such as NCBI BLAST and EMBOSS [ReHAB consists of four main components Figure : (1) a Md EMBOSS  utilitiehit subjects; (2) information on each query/subject pair (hit), gathered from individual search results and alignment programs ; (3) organizing information, such as which query sequences belong to which organisms; and (4) other caching information, used to speed performance of server-side program functions. To reduce the amount of required storage space, actual alignments are not stored, but are regenerated for presentation when the user selects the specific query sequence or query-target pair to be viewed. Query sequences, which are entered using a simple FASTA-like format that includes additional annotation information in the identifying line, need only be submitted once to ReHAB since they are stored for future search cycles.There are four types of information stored in the ReHAB database: (1) biological sequences and their annotations, both those used as queries in BLAST searches and those which have been returned as The work of running PSI-BLAST searches is done in batch mode by the NCBI blastpgp program against a local copy of the NCBI non-redundant (NR) protein database. PSI-BLAST is performed for three iterations without filtering procedures (such as for low-complexity regions). Hits with an E-value less than 0.001 are used to generate the scoring matrix for the subsequent cycle. To increase speed, searches with query sequences that result in no new hits are terminated after the first cycle. Those with new hits scoring below the threshold continue to the third cycle. PSI-BLAST was chosen because it is a more sensitive search method than BLASTP. The searches do not need to be performed on the same machine on which the database or server components are installed. XML output from blastpgp is parsed and relevant information about each hit is stored in the ReHAB database. In addition to scores and identifying information, target sequences are copied into the ReHAB database to ensure that they are available for analysis in the future, even if they are no longer available from NCBI. This is important because, although NCBI does not actually remove sequences from its database, it may change the identifier of a sequence if it is corrected, updated, or merged with another identical entry. Any changes to a pre-existing entry are added to the database, but it is not registered as a new hit.The server component consists of Java RMI classes that provide remote access to local facilities, and a loading program that registers those classes with an RMI Registry installed on the server from which the client will be downloaded. Requirement for the server is a system that can support Java 1.4.1 and MySQL 4.0.The Java Swing client component allows a user to browse the information collected in the database by the back-end program. When the client is downloaded and launched from a website, it connects to the server and database specified in its configuration file.The client program visually presents summary information about hits added to the database, and allows the user to quickly locate new, relevant hits and the sequences involved. There are five main views available in the client: (1) The management console lists the available databases, and has options for creating new databases or adding files to existing databases, (2) The Hits Browser window lists the organisms for which query sequences have been added in the database, and allows users to select filtering and highlighting options, (3) a Hits Summary, which displays the results in a table with highlighting to mark new hits, and (4) an HTML output or (5) a Hits Manager that displays detailed information about retrieved sequences and alignments.what new sequences match my sequences since the last time I searched?" In the example of our work, the query sequences are all the ORFs of the fully sequenced poxvirus genomes . These sequences are used to query the NR NCBI protein database and a MySQL database of all hits is generated and stored  will also be available in the near future. The hits database can then be accessed by double-clicking on the database name or by selecting "browse by organism" in the Action menu. This opens a new window to present browsing, sorting, and highlighting options  by selecting the appropriate radio button. The results are presented in a new window, using colors to indicate new hits .Since all new hits can be viewed in two ways. Selecting "HTML Report" launches the user's default web browser and displays the "hit-list" in familiar BLAST-style . In contrast to the usual BLAST output, which presents the local alignment found by BLAST, a global alignment produced by Needle [Hits Manager" window (Figure Global" button for global alignments produced by the EMBOSS program Needle or the "Local" button for local alignments generated by the EMBOSS program Water [Base-By-Base" button; the software automatically retrieves the appropriate sequences from the ReHAB hits database, performs a ClustalW alignment and passes the resulting multiple alignment to Base-By-Base, which functions as an alignment viewer and editor [Hits Manager can be viewed in FASTA format by clicking the "Show" button and can be copied from this window using standard keystrokes.Information about the e Figure . Hits ary Needle ,13 is shw Figure . Here, sam Water ,14. Multd editor . Finallyhit sequences that are also present in the query database when the "Don't Show My Own Sequences" option is selected; such sequences will not be shown or highlighted in the Hits Results window. However, these sequences and their alignments with the query can still be visualized in the HTML Report and Hits Manager windows, thus allowing the user to access all the available information. This feature becomes essential when new poxvirus genomes are added to the public database, since a large fraction of the queries will hit proteins in the new genome and signal a "new hit" report when there may be no other new hits in the database. Although these are clearly high scoring matches, they are expected and therefore must be masked in the analysis if the full value of ReHAB is to be realized.Unless a sequence has not been deposited in the public database, a sequence similarity search will return results including the query sequence itself, as well as nearly identical sequences that are orthologs of the query. ReHAB can block the highlighting of browser window (Figure Group by Annotation" list loads the new category in the list on the left side of the window. This sorting allows the user to quickly find query sequences of particular interest. For example, one may be interested in looking at only sequences from the Ankyrin family. Results can then be viewed and analyzed as described above.In the w Figure , the usequery sequences and the hits results databases by laboratory name, and browsing of results could be by the researcher's name (Figure laboratory name would replace virus family, and user names would replace organism names. All query sequences would be searched in the same batch process, and each individual could then browse their sequences of interest. Users interested in establishing their own ReHAB database should contact the authors for assistance.Researchers can use ReHAB to search databases with their own set of query sequences. In the example of our research, it is most practical to organize the query sequences by organism and protein family. Other researchers, however, may find other naming schemes to be more useful; no changes to the program or database are required. For example, a research group could organize e Figure . Individhits by comparing results from previously run searches to those with a recently updated database. ReHAB allows researchers to query the NR protein database with large numbers of sequences and can highlight, sort, and analyze results in a user-friendly graphical interface. It can also be used to rapidly create multiple alignments with any set of sequences returned by a BLAST search. This enables researchers to recognize new significant sequence matches in the mass of results generated by high throughput database search protocols.The goal of this project was to build a software package to aid in the identification of new results returned from sequence similarity searches. To this end, we developed ReHAB, a tool that highlights new Project name: ReHABProject home page: Operating systems: All platforms supporting Sun's JRE version 1.4.1 or compatibleProgramming languages: Java, SQLOther requirements: Java 1.4 or higherLicense: GNU General Public LicenseRestrictions for non-academic use: Contact corresponding AuthorCU described and specified the features of and problems to be solved by ReHAB, tested the program and provided usage examples. JW implemented the software, both the Java components and the database schemata used to store alignment results. DJE tested the program and provided usage examples. All authors contributed to writing of the manuscript.


A few studies have examined relationships between mercury exposures and adverse immunological reactions in humans, but there is little evidence of mercury-associated autoimmunity in humans.Mercury is an immunotoxic metal that induces autoimmune disease in rodents. Highly susceptible mouse strains such as SJL/N, A.SW, B10.S  were measured by indirect immunofluorescence. Anti-fibrillarin autoantibodies (AFA) were measured by immunoblotting.In a gold mining site, there was a high prevalence of ANA and ANoA: 40.8% with detectable ANoA at ≥1:10 serum dilution, and 54.1% with detectable ANA . In a riverine town, where the population is exposed to methylmercury by fish consumption, both prevalence and levels of autoantibodies were lower: 18% with detectable ANoA and 10.7% with detectable ANA. In a reference site with lower mercury exposures, both prevalence and levels of autoantibodies were much lower: only 2.0% detectable ANoA, and only 7.1% with detectable ANA. In the gold mining population, we also examined serum for AFA in those subjects with detectable ANoA (≥1:10). There was no evidence for mercury induction of this autoantibody.This is the first study to report immunologic changes, indicative of autoimmune dysfunction in persons exposed to mercury, which may also reflect interactions with infectious disease and other factors. Mercury has been recognized as a significant environmental and public health problem for more than 40 years, primarily for its effects on the developing nervous system, as expressed in tragic episodes of human poisoning in Japan and Iraq . Awarene2H5Hg+), the active compound in thimerosal and other medical compounds, induces in a dose-dependent pattern all the features of systemic autoimmunity that have been described after exposure to mercuric chloride (HgCl2) [0), through dermal or eye contact, as ethylmercury, and by absorption through the gastrointestinal track, primarily as methylmercury (CH3Hg+) through ingestion of contaminated fish [0 vapor easily crosses the pulmonary alveolar membranes to enter the circulatory system, where it is primarily bound to red blood cells, and is rapidly distributed to the central nervous system, and the kidneys [2+ by glutathione [In rodent models exposure to inorganic and organic mercury has a range of immunotoxic effects, functionally associated with decreased cell-mediated immunity and the induction of autoimmunity . These e (HgCl2) . Mercuryted fish . Inhaled kidneys . Mercurytathione . After etathione ,10.0 vapor may involve deposition of autoantibodies to basement membrane proteins in the glomerulus [+CD45RA), T-cell proliferation, and plasma IgM were decreased, while IgG levels were increased, relative to controls [In the literature, no cases of frank autoimmune disease have been reported in persons exposed to mercury, occupationally or environmentally . A few somerulus ,14. In aomerulus . No studomerulus ,16. In acontrols .The goal of this study was to test the hypothesis that exposures to methylmercury and/or inorganic mercury may have effects on specific markers of mercury-induced autoimmunity, that is, ANA and antinucleolar (ANoA) autoantibodies, and in a subset of subjects anti-fibrillarin (AFA) autoantibodies. ANoA autoantibodies, a marker found in some human autoimmune diseases , have beWe were able to conduct this study in collaboration with an ongoing epidemiological surveillance of mercury exposures in Amazonian Brazil, where populations are exposed to both inorganic and organic mercury associated with gold mining activities ,24. In AIn this study we analyzed autoantibodies and mercury exposures in three populations from the state of Pará, Brazil. These groups were exposed to different types of mercury in different settings, with exposure to other risk factors not all of which were determined. Therefore, these may contribute to the observed differences among communities, in addition to mercury exposures. We report here that exposures to mercury are associated with significant increases in the prevalence of elevated serum ANoA.Rio-Rato, a garimpo or gold mine site, most of the population was directly involved in gold extraction and refining, resulting in relatively high but often episodic exposures to inorganic mercury, similar to those described by us and others [Jacareacanga, a riverine community on the Tapajós River several hundred km downstream from the region of active gold mining in Pará, the inhabitants consume fish known to be contaminated with methylmercury [Tabatinga, located on the lower Amazon River east of the Tapajós, the population has no direct or indirect contact with gold mining, and fish collected in this village have levels of methylmercury [To test our hypothesis we examined three separate populations, selected from ongoing studies of mercury exposures and health status being conducted by FUNASA , under the leadership of Dr Santos of the Evandro Chagas Institute. The communities in our study were chosen from this surveillance database on the basis of differences in exposures to mercury and other risk factors in Pará, Brazil. At d others ,26. Thisd others . At Jacalmercury ,29,30. Tlmercury . Finallylmercury  within tlmercury . TabatinThe overall design of the mercury surveillance studies conducted by Dr Santos is a community based, cross sectional survey of Brazilian populations in Amazonia, focused on the states of Pará, Amazonas, Acre, and Rondônia, including gold mining sites, riverine communities, and populations without exposure to mercury. The study design is described in detail by Santos and colleagues ,32. In aSubjects' exposure to mercury was determined in two ways. First, information was gathered by questionnaire on occupational history (contact with and use of mercury in gold mining), and/or fish consumption (by weekly frequency and predominant types of fish consumed). Second, mercury concentrations were measured in biologic compartments. For persons in Tabatinga and Jacareacanga with chronic exposure via fish consumption, hair mercury (μg Hg/g of hair) was used as the exposure biomarker as recommended . Hair saBlood samples were collected by venipuncture and sera were separated on site by centrifuge, aliquoted and immediately frozen on liquid nitrogen for transport by air to the IEC in Belem (Pará). Aliquots of frozen serum were stored at -80°C and then transferred on dry ice to Baltimore via air transport accompanied by Dr Silbergeld. All analyses were done under blinded conditions.The serum samples were stored at -80°C until analysis. Each aliquot was thawed and 10 μL taken for analysis by indirect immunofluorescence (IIF) microscopy using commercially available slides prepared from human epithelial cells (HEp-2) as substrate (INOVA Diagnostics) following the methods of Burek and Rose . The sliThe antigen proteins were obtained from rat liver nuclei , and theThe concentration of serum autoantibodies is expressed in terms of the dilution factor at which fluorescence could still be detected. Detection of autoantibodies at a serum dilution of ≥1:40 is considered "positive" for most clinical uses . HoweverMeans for continuous variables (median for variables with skewed distributions) and percents for categorical variables were computed for the descriptive analysis in our data. Chi Square test was used to compare categorical variables and Student's t-test was used for continuous variables. In Jacareacanga we stratified hair mercury levels based on World Health Organization guidance (≤8 or ≥8 μg/g hair). In Tabatinga we stratified exposure by the observed median level (5.57 μg/g) since most hair mercury concentrations were below 8 μg/g. In Rio-Rato we used urine mercury levels based on WHO guidance (≤5 or ≥5 μg/L). We used the mean age for each population to stratify by age. Logistic regression modeling was used to evaluate the effect of mercury exposures on prevalence of ANA and ANoA (for 1:10 and 1:40 cutoffs), while controlling for age, sex, prevalent malaria, past history of malaria, and occupation. All data were analyzed using the SAS v.8.1 statistical package.Because of substantial differences among the populations and sites, we present the results for each site separately.Tabatinga is a typical riverine community in the lower Amazon. The community sample consisted of 98 adults, with 73% females, and a mean age of 44 years, Table . This coThe prevalence of detectable ANA and ANoA in the Tabatinga samples was very low Table . Most meJacareacanga is a riverine settlement of approximately 500 persons, located on the mid-Tapajós River. The 140 subjects consisted of 54% women and had a mean age of 25 years . Persons from Tabatinga, with the lowest range of mercury exposures, had lower prevalence of detectable ANA or ANoA, and in those few persons with detectable autoantibodies, the concentrations were low. Nonetheless, Tabatinga subjects had significantly elevated mercury levels, as compared to North America populations ,38, whicIt is possible that exposure to inorganic mercury may be more "autoimmunogenic" than exposure to methylmercury, as shown in mice models . In contWe examined the serum ANA and ANoA results at both 1:10 and 1:40 dilutions. The results in Jacareacanga and Rio-Rato subjects are clearly different from studies of healthy individuals in the US and Brazil . InterprIn Rio-Rato and Jacareacanga subjects, other risk factors were related to elevations in ANoA and ANA. In Rio-Rato, time spent at the site and in gold mining was positively correlated with likelihood of elevated ANoA. This variable, time spent at the site, may represent length of exposure to both mercury and malaria infection. In Jacareacanga, there was a positive relationship between malaria (any past reported cases) and likelihood of elevated ANoA. We examined these potential biological interactions among mercury, malaria, and autoimmune biomarkers further, because of studies demonstrating that repeated malaria infections are associated with increased levels of autoantibodies, including ANA, presumably due to cytotoxic damage and exposure of intracellular epitopes ,45. OtheIt is difficult to draw any firm conclusions from these analyses, since the malaria data in Jacareacanga were based upon unconfirmed self-reports. We could not test this hypothesis in the Rio-Rato group, since almost all subjects had prevalent malaria and extensive histories of past infection.Plasmodium yoelii) and impairs acquisition of immunity to murine malaria in the Nussenzweig model [We have reported a suggested correlation between mercury exposures and number of past malaria infections among gold miners in another gold mine settlement, in Brazil, at Piranha . We haveig model . Mercuryig model ,49.Finally, we may speculate as to why we have been able to observe associations between mercury exposures and these biomarkers of autoimmune dysfunction in these populations, while most clinical or epidemiological studies of mercury and immunotoxicity have been negative or only weakly positive -13. In tOur study is the first to report a correlation between biomarkers (ANA and ANoA) and mercury exposure in humans. In addition, co-exposures to mercury and infectious diseases, including malaria, may set the stage for eliciting discernible alterations in immune function. Whether such co-exposures increase the risks of autoimmune disease will require further studies, which are underway.ANA – antinuclear autoantibodiesANoA – antinucleolar autoantibodiesAFA – anti-fibrillarin autoantibodiesIIF – indirect immunofluorescenceThe authors declare that they have no competing interests.IA Silva carried out the analysis of autoantibodies, participated in the statistical analysis, and drafted the manuscript. JF Nyland helped in autoantibody analysis and technical editing of the manuscript; A Gorman helped in autoantibody analysis; AM Ventura, JM de Sousa, and ECO Santos carried out the field studies, malaria assessments, and mercury analysis. CL Burek and NR Rose guided the antibody analysis. EK Silbergeld participated in the design and coordinated the study; she also participated in the Jacareacanga field study, and in the draft of the manuscript. All authors read and approved the final manuscript.The pre-publication history for this paper can be accessed here:


Why is that many infectious diseases, like cholera, malaria, and meningococcal meningitis, show seasonal patterns? And how can we accurately determine these patterns? Meningococcal meningitis in western Africa shows recurrent seasonal patterns every year. Epidemics typically start at the beginning of February and last until May. We can try to explain the observed patterns on the basis of some seasonally varying environmental factor that favors disease transmission. Air dryness produced by strong dust winds is the most likely candidate.PLoS Medicine is a remarkable exception But while there are qualitative “stories” of this kind in the literature for many seasonal phenomena, convincing quantitative evidence to support them remains largely elusive. Instead, we tend to see weak associations between environmental and transmission variables when measured by simple, linear correlations. The study of meningococcal meningitis in Mali by Sultan and colleagues in this issue of Why is a focus on the seasonality of infectious diseases and its variation from year to year so important? Isn't it more important for us to instead understand the effects of long-term climate change on human health?At first sight, understanding seasonal patterns seems disconnected from understanding the impact of long-term climate change. However, seasonal patterns are one major pathway for the subtle but potentially drastic effects of climate change on disease dynamics. Long-term climate change affects seasonal patterns through the lengthening of the transmission season and the crossing of environmental and demographic thresholds that underlie seasonal outbreaks One important difficulty in uncovering seasonal drivers of infectious diseases is to identify the appropriate scale of analysis. The relationship between disease and climate described by Sultan and colleagues only becomes apparent at large spatial scales. The authors argue that these large scales are necessary to eliminate “idiosyncratic” variability in the relationship between cases and climate at the local level. In other words, there are only weak correlations between seasonal variations and climate variables at small scales because of the multiple other factors that play a local role and act as noise.But we should be cautious about the suggestion that appropriate larger scales will always resolve the problem of local variability and present strong linear associations between climate and disease. Public health measures might require predictions not only at national and regional scales, but also at a variety of smaller scales.Moreover, one important source of variation in how infectious diseases respond to climate is the fraction of susceptible individuals in the population. This fraction varies over time as the result of immunity acquired by previous infection, and by the input of births and migrants into the pool of susceptible people. The constant waxing and waning of this pool of hosts underlies the intrinsic potential of the population dynamics of infectious diseases to oscillate and create epidemic outbreaks. The tendency of these intrinsic cycles to go up and down in synchrony at different locations in space will determine whether susceptibility levels act as noise at small scales or, alternatively, whether their effect must be considered in conjunction with climate at larger scales. Because the number of susceptible individuals is a hidden variable in most epidemiological analyses, recently proposed methods for its reconstruction from data on cases must be combined with studies on climate variation if we are to understand the interaction between susceptibility levels and climate variation ,4,5.The problem of scale also arises when we need to identify the appropriate timing  to detect strong associations between disease outbreaks and environmental covariates. This is particularly important when strong couplings between environment and transmission occur only transiently. This seems to be the case for cholera in Bangladesh, where couplings are strong during El Niños, but considerably weaker the rest of the time Besides scale, specific seasonal drivers are often elusive because of the simpler reason that in nature seasonality is ubiquitous. Multiple and covarying drivers have been proposed for the seasonal nature of cholera, including temperature, rainfall, and plankton blooms Ecologists have considered seasonality in mathematical models of the population dynamics of infectious diseases. Models of populations with seasonally forced, dynamic interactions  reveal an array of possible responses, from simple yearly cycles, through cycles that repeat with longer periods, to irregular chaotic fluctuations. Some models also predict intermittent switching between different dynamic infectious disease behaviors. But typical models consider only simple seasonal forcing functions . There are some important exceptions to this—some models do incorporate more complicated seasonal forcing functions that describe the actual processes underlying the seasonal drivers of transmission. Examples are models of childhood diseases that describe the regular stopping and starting of school terms ,10,11, aThe explicit way in which models treat seasonal environmental drivers may be critical in addressing the links between within-year seasonal cycles and those of longer period that are observed in many infectious diseases. For meningococcal meningitis, we still need to examine the connection between the seasonal association described by Sultan and colleagues and the previously proposed role of humidity in inter-annual cycles Sultan and colleagues' study is exceptional in that it illustrates a clear relationship between an external environmental variable and the initiation of disease outbreaks. In contrast, many studies seeking environmental drivers are plagued by the many confounding factors, particularly the impact that other components of global change have on the transmission dynamics of infectious diseases. Thus, when we examine datasets for malaria, we must also consider the evolution of drug resistance and a growing human population that is increasingly forced to live in areas that are marginal for agricultural production but optimal for malaria transmission.Given this complexity, a serious limiting factor to quantitative analyses and predictive models of ecological and disease patterns is the lack of long-term disease records with similar data collected over a network of spatial locations. The handful of extremely valuable records that have allowed progress in understanding long-term patterns in disease dynamics pale in comparison to the spatiotemporal coverage available for climate studies and modeling. The need to resolve these issues of scale and confounding variability only underscores the urgency and importance of maintaining and developing systematic surveillance programs for infectious diseases around the world.


Mention greenhouse gases to most people and they're apt to think of carbon dioxide, fossil fuels, and big cars. Though carbon dioxide emissions are the major source of greenhouse gases, methane is far more effective at trapping heat in the atmosphere. Like increasing carbon dioxide levels, rising levels of atmospheric methane have been attributed to human activity, mostly in the form of landfills, natural gas and oil processing (about 60%), domesticated livestock (cattle account for about 75% of livestock contributions), and rice fields .Ruminants—from cows and water buffalo to llamas and vicunas—emit methane gas as a natural by-product of their digestive process, which confers a unique ability to digest cellulose. Ruminants don't digest cellulose directly, but depend on a variety of microbes living in their rumen (main stomach) to do it for them. These microbes ferment cellulose, breaking it down into products the ruminant can digest. During this process, some microbes—bacteria called methanogens—produce methane, which ruminants expel by eructation (otherwise known as belching).PLoS Biology, a multidisciplinary team spanning the fields of genomics, bioinformatics, microbiology, evolutionary biology, and molecular biology report the complete genome sequence of Methylococcus capsulatus and shed light on the metabolism and biology of this ubiquitous microbe.Luckily, there are microbes, called methanotrophs, that consume methane. A type of aerobic bacteria, methanotrophs oxidize methane as an energy and carbon source using the enzyme methane monooxygenase. They've been found in soils, landfills, sediments, hotsprings, and peat bogs, among other environments. Methanotrophs have been the subject of increasing interest because they can use methane as a sole source of carbon and energy—which means they play a major role in global carbon cycles—and could dramatically reduce biologically generated methane emissions. They've also been the focus of bioremediation efforts aimed at environmental decontamination. And now, with the publication of the first complete genome sequence of a methanotroph, such efforts will be all the easier. In this issue of M. capsulatus genome comprises about 3.3 million base pairs—which is about average for a free-living bacterium—with an estimated 3,120 genes. The genome appears well-equipped to meet the specialized needs of this methanotroph, with what appear to be multiple pathways involved in the metabolism of methane and duplications of genes that code for methane monooxygenases, which are essential for the first step of methane oxidation.Contained in a single, circular molecule, the M. capsulatus employs different pathways depending on the availability of molecules needed to sustain cellular activities. Most surprising, the researchers note, was evidence that this methane specialist can turn into a sort of metabolic generalist—with a capacity to use sugars, hydrogen, and sulfur—and appears able to survive reduced oxygen levels. These genome-based hypotheses will require experimental validation, the authors note, but could have important implications for M. capsulatus ecology—including what environments might be amenable to methanotroph-mediated bioremediation.Ward et al. also found evidence of “genomic redundancy” in methane oxidation pathways, suggesting that M. capsulatus genome provides a platform for investigating the details of methanotroph biology and its potential as a biotech workhorse. It may also guide efforts to harness this bacterium's penchant for methane to reduce global greenhouse gas emissions, to degrade chlorinated hydrocarbons and other persistent pollutants, and to produce protein for animal feed.The genomes of important microbial players in the carbon cycle—including microbes involved in photosynthesis and methanogenesis (methane production)—have already been sequenced. With the addition of a sequenced methanotroph genome, scientists can systematically investigate different genes and regulatory elements to better understand how these methane consumers fit into this global cycle. The


Large-scale sequence comparison is a powerful tool for biological inference in modern molecular biology. Comparing new sequences to those in annotated databases is a useful source of functional and structural information about these sequences. Using software such as the basic local alignment search tool (BLAST) or HMMPFAM to identify statistically significant matches between newly sequenced segments of genetic material and those in databases is an important task for most molecular biologists. Searching algorithms are intrinsically slow and data-intensive, especially in light of the rapid growth of biological sequence databases due to the emergence of high throughput DNA sequencing techniques. Thus, traditional bioinformatics tools are impractical on PCs and even on dedicated UNIX servers. To take advantage of larger databases and more reliable methods, high performance computation becomes necessary.We describe the implementation of SS-Wrapper (Similarity Search Wrapper), a package of wrapper applications that can parallelize similarity search applications on a Linux cluster. Our wrapper utilizes a query segmentation-search (QS-search) approach to parallelize sequence database search applications. It takes into consideration load balancing between each node on the cluster to maximize resource usage. QS-search is designed to wrap many different search tools, such as BLAST and HMMPFAM using the same interface. This implementation does not alter the original program, so newly obtained programs and program updates should be accommodated easily. Benchmark experiments using QS-search to optimize BLAST and HMMPFAM showed that QS-search accelerated the performance of these programs almost linearly in proportion to the number of CPUs used. We have also implemented a wrapper that utilizes a database segmentation approach (DS-BLAST) that provides a complementary solution for BLAST searches when the database is too large to fit into the memory of a single node.Used together, QS-search and DS-BLAST provide a flexible solution to adapt sequential similarity searching applications in high performance computing environments. Their ease of use and their ability to wrap a variety of database search programs provide an analytical architecture to assist both the seasoned bioinformaticist and the wet-bench biologist. Evolution can be measured and studied on a number of different scales, one of which is through the determination and comparison of genetic sequence information. Current-day gene sequences in living organisms have arisen through modifications of an array of ancestral sequences. Duplication with modification is the central paradigm of protein evolution, wherein new proteins and/or new biological functions are fashioned from earlier ones. . To deteThe basic local alignment search tool (BLAST) ,4 is by While BLAST provides a balance between search sensitivity and speed, in many cases a researcher would like to detect more distant sequence similarities by employing search strategies that maximize the sensitivity of the search. A profile-based comparison which, for example, compares a sequence to a hidden Markov model (HMM) representing an empirically derived estimate of all possible evolutionary changes for a protein of a particular function, generally permits identification of a much higher proportion of distantly related sequences . There aTaking advantage of larger databases and more sensitive searching methods necessitates the use of high performance computing (HPC) platforms. Traditionally, HPC has been synonymous with high-priced vector or parallel supercomputers, but rapid advances in microprocessor and network bandwidth technologies are changing the definition . ClusterThe objective of this study was to implement a generic wrapper application that could deploy similarity searching applications on a Linux cluster. Design criteria for the wrapper included the ability to deploy applications without the need to alter the original application and the ability to increase the speed of the underlying application in a linear manner dependent on the number of cluster nodes available. To meet these objectives, tools in the SS-Wrapper package were written in C/C++ using the message passing interface (MPI) . Tools iUsing multiple processors is practical only when the computational task is too large to complete on a single processor in a reasonable amount of time. In the case of database searching, the database to be searched may be too large or the set of query sequences used in the search may be too large to accomplish the search on a workstation with a single or even double or quad processor architecture. Since the search must compare every query sequence to every database sequence, parallelizing the process can be accomplished by three different methods. The first method is to split the query sequence file into smaller subsets and apply each subset to one particular node of the cluster in a search against the entire database. The second method calls for the database to be split into a series of smaller files, one of which is distributed to each node of the cluster; then the entire file of query sequences is searched against each of the database segments. Finally, it is possible to use a combination of query sequence splitting and database splitting to accomplish the search.Using the query splitting approach does not require any modification to the output generated by search programs such as BLAST. When utilizing the database splitting approach, however, a correction must be made to the reported E-value for any particular hit. Most similarity comparison tools provide a statistical measure  that gives an indication of the statistical significance of a match between the query sequence and a particular database hit. This statistical measure is generally influenced by the size of the search space  and therefore the E-value needs to be recalculated when only a portion of the database is being searched. An added complication is that some search tools require that the database be intact. For example, PSI-BLAST  is a varIn contrast to database splitting, query splitting offers greater flexibility in that the query file segments can more easily be adaptively distributed to the nodes of the cluster according to the load on each particular node during the search process. As any one node becomes available, another segment of the query file can be distributed to that node during the search process, which improves performance. It is difficult to predict the workload on any one node before the search begins, and the time required to complete a program running on a cluster depends on the processor that finishes last. Adaptive distribution of the workload maximizes resource utilization. Optimizing resource utilization is dependent on finding a balance between having larger numbers of smaller tasks versus increased startup and communication overhead due to distribution of the required query and database files to each node. For this reason, for our query splitting wrapper (QS-search), we adopted a hybrid strategy wherein approximately 90% of the total workload is evenly divided and distributed to each node at the beginning of the search using a modified bucket algorithm (described below). Then the remaining 10% of the workload is divided and distributed to each node as it becomes available after completing its previous task. This strategy is accomplished using a master-slave model, where one node is set aside to act as the master, which is responsible for distributing the workload and supervising the slave nodes, which perform the computations.In general, the query splitting approach seems to be superior to the database splitting approach due to higher performance and fewer post-search processing tasks. Database splitting does provide a distinct advantage when the database is too large to fit into the physical memory of a single node ,14. NCBIAs indicated above, a modified bucket algorithm is used to split up the query sequences for QS-search, and a similar algorithm is used to split up the database sequences for DS-BLAST. The modified bucket algorithm works as follows: First, the sequences are sorted according to length. In the first cycle, sequences are placed one at a time into each bucket in descending order of length. In the next two cycles, individual sequences continue to be placed into each bucket after first reversing the order of the buckets. Bucket order is reversed every two cycles and the process continues until all sequences have been placed into a bucket. At the end of this process, each bucket contains nearly the same number of sequences, and the total length of all sequences in any one bucket is also approximately the same as the total sequence length of any other bucket. In the end, therefore, the file of query sequences or the file of database sequences is evenly divided in both length and number.The BLAST E-value is a function of the size of the effective search space, which is dependent on three factors: the number of sequences in the database, the total combined length of all sequences in the database, and the length of the query sequence ,18. FiguThe QS-search executable (qssearch) provides the same interface for all search tools. The command line is as follows:qssearch -c <command> -q <query> -d <database> -o <output> -l <local scratch> -x <database files>DS-BLAST uses two executables: dsblast and dsformatdb. dsformatdb is responsible for splitting the database into fragments according to the modified bucket algorithm and then formats these fragments using the NCBI formatdb executable.The command line for dsformatdb is as follows:dsformatdb -n <number> -c <command> -d <database> -p <path>The command line for dsblast is as follows:dsblast -o <output> -c <command> -l <local scratch> -d <database> -q <query>The command-line variables are as follows:• -c command: normal command line used for the underlying application including all desired options• -q query: query filename in fasta format• -d database: database filename• -o output: output filename• -l local scratch: temporary directory on each node• -x database files: a space-delimited list of the database file names generated by the search program's formatting utility (formatdb for BLAST)• -n number: desired number of database fragments• -p path: directory to store database fragments.All benchmark experiments were performed on a Linux cluster in the Department of Engineering at the University of Alabama at Birmingham . The cluFigure Figure We also compared the performance of DS-BLAST to that of mpiBLAST  version To increase the speed and efficiency of sequence similarity search programs, we have developed the SS-Wrapper package, a series of wrapper applications that supports the deployment of sequence similarity searches on high-performance computing clusters. QS-search implements a query sequence splitting approach for the deployment of NCBI BLAST and HMMPFAM. It also will support other similarity search programs, including all variants of NCBI BLAST  as well as all options provided by the blastall executable. Because this implementation does not alter the original program, program updates and new programs should be easily accommodated. The output from QS-search is effectively identical to that produced by the underlying program. QS-search is designed to provide optimal load balancing and maximize resource usage when using computer clusters. The performance gain approaches linearity in proportion to the number of processors employed.When the database is too large to fit into the physical memory of a single node in the cluster, a database splitting approach should outperform the query splitting approach used by QS-search ,14. TherSS-Wrapper provides a suite of tools that makes large sequence similarity searches feasible by deploying the search on a Linux cluster. These tools permit the bioinformatics community to take advantage of the power of high-performance cluster computing. Other tools such as Disperse  and Turbelliotl@uab.edu for information on obtaining the software. All tools have been tested on an IBM Intel® processor-based Linux cluster with LAM/MPI [The SS-Wrapper package is freely available under the Artistic License described in the Open Source Initiative. The source code can be downloaded via ftp . Contact LAM/MPI  and shouCW was responsible for the conception, design, implementation, and testing of the SS-Wrapper package. EJL contributed to its conception and testing and provided overall project coordination. Both authors have read and approved the final manuscript.