This paper presents the evaluation of “Moving On”, a generic self-management program for people with a chronic illness developed by Arthritis NSW. The program aims to help participants identify their need for behaviour change and acquire the knowledge and skills to implement changes that promote their health and quality of life.A prospective pragmatic randomised controlled trial involving two group programs in community settings: the intervention program (Moving On) and a control program . Participants were recruited by primary health care providers across the north-west region of metropolitan Sydney, Australia between June 2009 and October 2010. Patient outcomes were self-reported via pre- and post-program surveys completed at the time of enrolment and sixteen weeks after program commencement. Primary outcomes were change in self-efficacy , self-management knowledge and behaviour and perceived health status .A total of 388 patient referrals were received, of whom 250 (64.4%) enrolled in the study. Three patients withdrew prior to allocation. 25 block randomisations were performed by a statistician external to the research team: 123 patients were allocated to the intervention program and 124 were allocated to the control program.97 (78.9%) of the intervention participants commenced their program. The overall attrition rate of 40.5% included withdrawals from the study and both programs. 24.4% of participants withdrew from the intervention program but not the study and 22.6% withdrew from the control program but not the study. A total of 62 patients completed the intervention program and follow-up evaluation survey and 77 patients completed the control program and follow-up evaluation survey.2=4.671, p=0.031). There were no significant changes in other self-management knowledge areas and behaviours after adjusting for covariates at follow-up.At 16 weeks follow-up there was no significant difference between intervention and control groups in self-efficacy; however, there was an increase in self-efficacy from baseline to follow-up for the intervention participants . There were no significant differences in self-rated health or health distress scores between groups at follow-up, with both groups reporting a significant decrease in health distress scores. There was no significant difference between or within groups in self-management knowledge and stage of change of behaviours at follow-up. Intervention group attenders had significantly higher physical activity  and nutrition scores  at follow-up; however, these did not remain significant after adjustment for covariates. At follow-up, significantly more participants in the control group (20.8%) indicated that they did not have a self-management plan compared to those in the intervention group (8.8%) (XThe study produced mixed findings. Differences between groups as allocated were diluted by the high proportion of patients not completing the program. Further monitoring and evaluation are needed of the impact and cost effectiveness of the program.ACTRN12609000298213Australian New Zealand Clinical Trials Registry:  In 2005, chronic illnesses were responsible for 35 million deaths worldwide. Chronic conditions such as diabetes, heart disease, chronic respiratory disease, stroke and cancer account for the greatest mortality and morbidity internationally . In AustThese high rates of mortality and morbidity and associated health care costs have led to an increasing focus on the role of patients in making decisions and managing their chronic illnesses ,4 and thSince the 1980s a wide range of self-management support initiatives have been developed internationally, including care and action plans, health coaching, self-help groups, disease-specific and generic self-management education programs for both individuals and groups. There are two widely known generic chronic disease self-management group education programs: the Stanford Chronic Disease Self-management Program (CDSMP) and the Expert Patient Program (based on the Stanford CDSMP).Both programs are based on the theory of self-efficacy and consist of a six-week group education program (2.5 hour sessions each week) delivered by two trained lay leaders who implement the program according to a leader’s manual. The range of topics is broadly similar in the two programs: exercise, symptom management, diet/nutrition, fatigue and pain management, medications, community resources, managing fear, anger and depression, problem solving and communication with health care providers. Participants produce weekly action plans and set self-management goals, with support from the leaders and fellow participants.The Stanford program has been disseminated widely in the US, UK, Australia and Asia. It has been found effective in improving health outcomes and reducing costs for patients with different chronic illnesses ,6, althoParticipation in the Expert Patient Program is based on the patient’s understanding of the nature of chronic illness and does not require diagnosis or referral from a health care provider. Implemented across the UK, the program is reported to be effective in improving patient self-efficacy, energy and health-related quality of life. There is a small effect on reducing the cost to patients of health and allied services and medications and there is no reported influence on health service utilisation rates .In Australia, self-management initiatives have been supported by the Department of Health and Ageing since the late 1990s. These have included the $36.2 million Sharing Health Care Initiative (SHCI) in 2002-03 . Some SHArthritis NSW, therefore, identified the need for a generic self-management group education program that was relevant to the Australian health care system. This led to the development of “Moving On – a self-management program for people with a chronic illness” .This paper presents the evaluation of the Moving On program. Primary hypotheses were that, compared to control group, participants in the intervention group would show a greater improvement in:1. self-efficacy;2. self-management intentions and reported behaviours and lifestyle behaviours;3. health status.Secondary hypotheses were that intervention program participants would show greater improvement in scores for work and social adjustment, anxiety and depression, greater knowledge concerning use of medicines and more appropriate use of health services.The evaluation was a prospective pragmatic randomised controlled trial (RCT) comparing patients’ self-reports following participation in the Moving On intervention program and in a control program of similar intensity Table .Semi-structured interviews were also conducted by telephone with a sample of participants, program leaders and referring health care providers. The interviews were conducted after participants had completed their follow-up surveys to ascertain their views on the study and the usefulness of the intervention program. The findings of the qualitative component will be reported elsewhere.Prior to conducting the RCT, two feasibility studies were conducted: a pre-test of the intervention program modules in 2007 and a pilot study in 2008 to test the implementation and quality assurance procedures of the intervention program and the research methods to be used in the RCT .Ethics approval to conduct the study was obtained from the Human Research Ethics Committee of the Sydney West Area Health Service acting as a lead Ethics Committee for multi-site research within NSW Health Services; this approval was ratified by the Human Research Ethics Committee at the University of New South Wales.Participants were assessed for eligibility and referred to the study by a health care professional. Allocation to programs occurred after enrolment. Participants were required (1) to be aged 45-75yrs; (2) to have a chronic illness diagnosed more than 6 months previously; (3) to have a good understanding of the medical management of their illness; (4) to speak, understand and write English; (5) to agree to comply with session attendance requirements; and (6) to be able to participate in light physical activity. Patients who had a diagnosis of a mental illness (other than co-morbid anxiety or depression), cognitive impairment including dementia or substance abuse, or were in the late stage of palliative care were excluded.The upper age limit of 75 years was intended to help exclude people who were less likely to benefit from attending a self-management program due to an undiagnosed cognitive impairment or the diminishing ability to acquire new information with age, particularly for people with co-morbidities .Patients were recruited by primary health care providers  across the north-west of the Sydney metropolitan area between June 2009 and October 2010. In a small number of cases (n= 34), partners of enrolled participants were also referred. The primary health care providers were briefed individually about the aims of the study, the referral and feedback processes. The referral form required patients to provide written consent for their contact details to be forwarded to the University to enrol in the study. The study was also promoted widely within the targeted area by means of brochures in primary health care provider waiting rooms, billboards, press releases to local radio and other news media and presentations to community groups. A 1800 free-call centre was set up to handle responses to the promotion, including distribution of the referral brochure with instructions on how to join the study.Patients who were referred were sent a study package containing a letter introducing the study, an information sheet, a consent form and a baseline survey. The study package explained that if interested in participating, they should return the completed consent form and baseline survey to the university in the addressed pre-paid envelope. Following the return of their consent form and baseline survey they were to be notified which program they had been allocated to (Moving On or the control program) and the time and location of their program. Patients were invited to contact a member of the research team if they had any questions.After two weeks, if the consent form and survey had not been returned, patients received a follow- up call. Addresses were checked and an additional package sent where necessary. Referred patients not wishing to participate in the study were asked to provide their age and details of their chronic illness and were thanked for their initial interest in the study.Initial sample size calculations were based on the primary outcome of change in self-efficacy on data from the 6-item Chronic Disease Self-Efficacy Scale  for powePost hoc sample size calculations were undertaken when the study failed to reach the targeted sample size of 300. The post hoc sample size calculations for power of 80% and precision of 95% to detect a 16% difference in the mean group self-efficacy score of 5.17 (SD = 2.2) indicated a minimum sample of 88 participants in each group. For other variables the sample size was between 63 and 96 participants.Randomisation occurred throughout the enrolment period of the study. When at least six enrolments were received the participants’ ID numbers were sent for block randomisation, in an MS Excel worksheet, to a statistician (UJ) who was not otherwise involved in the study. The study coordinator recorded the allocation in the evaluation database and notified participants by telephone and letter of the details of their program, including dates, time, venue, leader’s name and contact number. Participants could choose different locations or times of their program but not which program they were allocated.Patient outcomes were self-reported via pre- and post-program surveys using both previously validated tools and questions/measures developed specifically for the study. The surveys were mailed to participants at the time of enrolment and 16 weeks after the commencement of their program. Demographic data obtained included gender, age, main chronic illness, employment status and accommodation type. Primary outcome measures were self-efficacy, self-management knowledge and behaviour and perceived health status. Secondary outcome measures were work and social adjustment, anxiety and depression and appropriateness of health service use.Self-efficacy was measured using the Self-Efficacy for Managing Chronic Disease 6-Item Scale . The scaQuestions/measures used to assess changes in participants’ self-management knowledge and lifestyle behaviours were largely developed for the study and were as follows:a. Existence of a self-management or action plan for chronic illness, measured by a single categorical yes/no question;b. Change in self-management knowledge. A set of questions devised for the study measured self-reported stage of change in adopting new ways to manage aspects of their chronic illness, including fatigue, physical activity, eating a balanced diet, coping with their chronic condition, managing stress or relaxing, improving sleep routine, communicating with health care professionals and managing medicines. The stage of change was rated using Prochaska and DiClemente’s Stages of Change Model  ;c. Sharing a self-management or action plan with a primary care provider. Participants in the Moving On Program completed a set of yes/no questions devised for the study at the end of the final session, indicating:i. willingness for the program coordinators to forward a copy of the action or self-management plan developed during the study to their general practitioner;ii. willingness for the program coordinators to forward a copy of the action or self-management plan developed during the study to the health care provider who referred them to the study (in cases where this was not the patients GP); andiii. intention to take their action or self-management plan on their next visit to their general practitioner.d. Changes in self-management behaviours were reflected by the following:i. development or use of a self-management plan: Two questions devised for the purpose of the study measured participants’ self-reported stage of change (Prochaska and DiClemente’s Stages of Change Model) in developing or updating a self-management/action plan and discussing the plan with their GP;ii. intention to make lifestyle changes: a set of questions, devised for the purpose of the study, measured participants’ self-reported stage of change (Prochaska and DiClemente’s Stages of Change Model) in relation to smoking, nutrition, alcohol and physical activity;iii. reported lifestyle changes: measured by a validated tool  in whichiv. use of a medicines list: measured by a single categorical Yes/No/NA question devised for the study; andv. seeking information about medicines: measured by seven questions devised for the purpose of the study, about participants discussing their medicines with a doctor and pharmacist, disclosing non-prescription medicines, seeking a medicine leaflet and other information pertaining to their medicines.All questions using stage of change measures were subjected to factor analysis in order to reduce complexity .Health status was measured using the Self-Rated Health Scale  and the The Work and Social Adjustment Scale was used with permission . The scaAnxiety and depression were measured using the Hospital Anxiety and Depression Scale (HADS) with permission . The scaAppropriateness of health service use was measured by a validated tool  which inThe fidelity of the intervention program implementation was monitored using a checklist devised specifically for the purposes of the study. The checklist examined the delivery of the content of the modules including whether module objectives were met, whether key topics were covered and whether module related activities such as goal setting and action or self-management planning were achieved. Fidelity monitoring also reviewed the way in which leaders presented and facilitated the modules, specifically in relation to being well-prepared to run the sessions, creating a supportive environment and facilitating discussion.Each program was subjected to two integrity checks undertaken by an independent observer using a standard checklist. For each program, the final session (session seven) received an integrity check and the other session was chosen at random. The checklist were analysed descriptively.Data were analysed on an intention to treat basis for all participants who completed the follow-up survey. This included those who completed the program and those who withdrew. Descriptive statistics were used for the demographic and baseline characteristics of participants. Univariate analyses  were performed to compare demographic characteristics, baseline and follow-up scores within and between the intervention and control groups. One-tailed tests were carried out in view of the directionality of the research hypotheses, with an alpha of 0.05.Multivariate analyses using linear and logistic regression were performed for findings that were found to be significant in the univariate analysis, adjusting for age, gender, duration of main illness, program allocation and country of birth. The findings of the multivariate analyses are presented in Table Principal component factor analysis with varimax rotation was conducted for the 14 items measuring self-management behaviours and lifestyle behaviours associated with the stages of change. The number of factors was determined using the scree test and eigen value > 1. Two component factors emerged: (1) new self-management behaviours and (2) lifestyle behaviours. The factor means, based on scale scores of 0 to 3, were 2.145 (SD= 0.970) and 2.431 (SD= 0.963) respectively .Altogether 388 referrals were received of which 250 (64.4%) enrolled in the study Figure . FeedbacA total of 22 programs (12 intervention and 10 control) were conducted between September 2009 and October 2010. In all, 25 block randomisations were performed with 123 participants randomly allocated to the intervention and 124 to the control program. An additional three participants enrolled in the study but withdrew prior to allocation and a total of 100 (40.5%) participants withdrew during the study  commenced the program of whom 62 (50.4%) attended the intervention sessions and completed the follow-up survey. These are referred to as “intervention program attenders”. A total of 55 people withdrew (44.7%) of whom 24 (19.5%) withdrew from the study and 31 (25.2%) withdrew from the intervention but remained in the study and completed the follow-up survey. They were included in the intention to treat analysis with the program attenders.Of the 124 people allocated to the control program, 97 (78.9%) commenced the program of whom 77 (62.1%) attended the control program sessions and completed the follow-up survey. These are referred to as “control program attenders”. A total of 47 people withdrew (37.9%) of whom 26 (20.9%) withdrew from the study and 19 (15.3%) withdrew from the control program but remained in the study and completed the follow-up survey. They were included in the intention to treat analysis with the program attenders.There were no significant differences in gender, age, main health condition or socio-economic status between participants who completed the programs and those who withdrew, whether in the intervention or control program. Withdrawals from 8 of the 12 intervention programs and 7 of the 10 control programs occurred due to ill health, competing life priorities, dislike of the program or, in the case of the intervention, lack of interest in a group education program.A total of 24 integrity checks were performed during implementation of the 12 Moving On Programs by a total of five independent observers. One observer performed 18 (75%) of the checks. The results of the integrity checks suggest that each session of the Program that was audited was delivered largely in line with the Program Guidelines. The audit process also highlighted areas for further development or modification as part of ongoing quality assurance. These included the length of time spent on each activity, the use of the data projector and additional leader training in facilitating group discussions and supporting participants to develop a self-management plan.Most study participants were female (65.1%) aged 55–74 yrs (78.2%), born in Australia (56.0%), spoke English at home (86.7%), lived in their own home (82.4%), resided in local government areas with the highest socio-economic decile (67.5%) and were either currently employed or retired (76.1%)   Table .Mean self-efficacy scores did not differ significantly between intervention and control groups at baseline or follow-up. There was an increase in self-efficacy score from baseline to 4 months in intervention program attenders  but no change in the control group .57.6% of participants reported that they had a self-management plan at baseline, with no significant difference between intervention and control groups. At 4 months follow-up, a significantly greater proportion of participants in the control group (20.8%) indicated that they did not have a self-management plan compared to those in the intervention group (8.8%)  were willing for the program coordinator to send it to their referring health care provider, 35 (56.5%) were not and four (6.5%) did not answer the question. Fewer were willing for it to be sent to their General Practitioner: 12 people (19.4%) were willing, 38 (61.3%) were not and 12 (19.4%) did not answer the question.With regard to the self-management plan, 11 (17.7%) were willing for it to be sent to their referring health care provider, 41 (66.1%) were not and 10 (16.1%) did not answer the question. Only one person (1.6%) was willing for it to be sent to their General Practitioner, 51 (82.3%) were not and 10 (16.1%) did not answer the question.14 program attenders (22.6%) indicated their intention to take their action or self-management plan to their next General Practitioner appointment; 22 (35.5%) did not intend to do so and 26 (41.9%) did not respond to the question.At baseline there was no significant difference in use of medicine lists between intervention and control participants. At 4 months follow-up more intervention participants reported keeping a medicines list . There was no change within the control group. After adjusting for covariates, there was no significant difference between groups at follow-up.There was no significant difference between or within groups with regard to medicine information-seeking behaviour.No significant differences were found between or within groups at baseline or follow-up in mean scores for self-management knowledge  (Component factor 1).No significant differences were found between or within groups at baseline or follow-up related to mean stage of change in self-management behaviour scores  (Component factor 2).Only 15 (6.0%) participants reported smoking cigarettes at baseline and 8 (4.2%) at follow-up. There was no significant change in smoking behaviour in either group.Diet scores were not significantly different between study groups at baseline. Intervention group scores increased but those in the control group did not. At follow-up those who had attended the intervention program had significantly higher scores than those who attended the control group   or ITT analysis p=0.156)  and control group ITT analyses  there was a significant within-group reduction in mean depression scores at follow-up. There was no significant difference in the mean depression scores for the intervention group from baseline to follow-up  but not in those attending the control group Table .This study shows mixed results for the Moving On program, with some positive trends in relation to self-efficacy and self-management behaviours related to increased physical activity and healthy diet by those that attended the program.Comparisons between the Moving On program and the Stanford and Expert Patient programs were limited by a lack of standardisation across the initiatives and their evaluations. The characteristics of participants were reported differently and different outcomes and measures were used in the evaluations. This is, in part, unavoidable given the different context, purpose and structures of the programs, but the use of standard methods in future research would make it easier to compare the impact of the group education programs within different contexts. Nevertheless, broad comparisons are made below.Compared with the initial evaluations of the Stanford and the Expert Patient programs, Moving On participants were slightly younger but the proportion of female participants 65-70%) was similar. The Stanford program targeted arthritis, circulatory diseases and stroke in their eligibility criteria, thus a larger proportion of Stanford participants had those illnesses compared with Moving On and the Expert Patient program -70% was ,18. IllnThe Stanford and Expert Patient program evaluations showed an increase in self-efficacy for intervention participants compared to control participants. The Moving On evaluation found that self-efficacy scores increased within the intervention group from baseline to follow-up but not between the intervention and control groups. This may in part have been a ceiling effect: Moving On participants had higher self-efficacy scores at baseline in both the intervention and control groups than participants in the Stanford and Expert Patient evaluations and so had less scope for improvement ,18. ThisIt is hypothesised that a positive change in self-efficacy would result in an increase in health status and a decrease in health service utilisation . The StaThe Moving On intervention group reported improved diet and physical activity scores compared to control program attenders. Improvement in diet was not found by the Expert Patient Program evaluation and not measured in the Stanford evaluation. All of the studies found an improvement in physical activity scores. The improvement in physical activity scores in the Moving On evaluation was a little surprising, given the focus on exercise in the control program. This may be because the Moving On program has modules that focus specifically on diet and physical activity: in the follow-up interviews (not reported here) many intervention participants reported having set diet and exercise goals after attending the program. This may be because these are tangible goals, which are consistent with ongoing GP recommendations or management.All three self-management programs were associated with improvements in health distress scores. However, in the Moving On evaluation, health distress and work and social adjustment scores improved in both the intervention and control groups. This suggests either that the influences occurred independently of program participation or that both programs had an effect. This is worth noting, given that approximately 50% of each group reported significant to severe functional impairment at baseline. These perceived improvements did not appear to be related to changes in perceptions of health status, as measured by the self-rated health scale. This may suggest that they had adjusted to living with their chronic illnesses.Other outcomes associated with the Moving On program and not evaluated by the Stanford and Expert Patient programs deserve comment. Overall, there was little difference between the two groups in the Moving On evaluation in the adoption of a range of self-management behaviours. This may reflect the stage of their chronic conditions: the average duration was nine years, during which time they may have largely adapted to living with their condition. The diversity of their conditions may also have meant that initiating some of the self-management behaviours (e.g. sleep patterns) was not relevant to all of them. This fits with the finding that at baseline some of the intervention participants were already in the action or maintenance phase of behaviour change. It is also possible that the self-management behaviours in the evaluation did not reflect the way patients managed their illnesses . This isMore intervention participants had a self-management plan at follow-up. However, the follow-up interviews with participants showed that patients had different ideas about what a self -management plan was and what its purpose was. This needs further study and more sensitive measurement.Self-management plans were sent to primary care providers with participants’ consent. This was an attempt to encourage GPs to take an active role in supporting patient self-management after completion of the program, thereby addressing the disconnection between self-management education programs and primary health care providers . This isAnxiety scores improved for participants in both the Moving On intervention and control groups, but only the control participants improved their depression scores. The reasons for this are unclear. It may have been due to the positive effects on well-being associated with exercise programs ; howeverThe study had a number of limitations. Although post hoc sample calculations suggest that there were adequate numbers of participants to detect change in primary outcomes, a high proportion of participants did not complete either the intervention or control program. This not only reduced the power but confounded the between-group analyses. Thus, although there were significant changes in self-efficacy scores for those completing the intervention program and not in those completing the control program, there was no difference between groups as allocated, especially when adjusted for covariates.A number of factors contributed to the high attrition rate 40.5%), which was higher than that reported for the Stanford program 17.8%) %, which  and the % %, whicAlthough GPs and other health care providers were generally positive about the referral process, they indicated in follow-up interviews that excluding patients aged over 75 or not proficient in English limited the number of potential participants and so the ultimate size of the study. However, these conditions are likely to have strengthened the program by excluding some who would not be able to benefit because of subtle cognitive impairment or with difficulty in understanding English.Both the Stanford and Expert Patient programs focused on primary outcomes over a six month period and the Stanford program was subsequently evaluated over 12 and 24 months. In comparison, the Moving On evaluation focused on changes in self-efficacy, health status and self-management behaviours over a four month period. A longer follow-up period (6–12 months) would be desirable.Overall, participants in the Moving On evaluation were found to be low users of health services other than general practice and the study had insufficient power to detect small changes. Therefore, the findings associated with health service use need to be interpreted with caution.Finally, the sample population of Moving On was found to be relatively homogeneous, the majority being Australian-born, English-speaking people who owned their own home in a high socio-economic index locality and who were either currently employed or retired from paid employment. Coverage of disadvantaged populations was limited. These findings are similar to other international evaluations of chronic disease self-management programs . This haOverall, the Moving On program attenders demonstrated positive trends in self-efficacy and some self-management behaviours. However, non-completion rate in both arms of the study, combined with low enrolments, reduced the study power and diluted the impact of the intervention. Further research is needed to evaluate the impact and cost effectiveness of the program, over a longer period than was possible in this study and with more diverse populations.SHCI: Sharing Health Care Initiative; COAG: Council of Australian Governments’; RCT: Randomised Controlled Trial; HADS: Hospital Anxiety and Depression Scale; ITT: Intention to treat analysis; AHS: Area Health ServiceDiana Aspinall and Nicholas Manolios are Associate Investigators and Directors on the Board of Arthritis NSW. Karen Filocamo is an Associate Investigator and the CEO of Arthritis NSW. Eloise Milthorpe is employed by Arthritis NSW as a Senior Project Officer. The authors declare that they have no competing interests.AW contributed to the conception and design of the study, data collection, analysis and interpretation and developed the draft manuscript. MH contributed to the conception and design of the study, data analysis and interpretation and development of the manuscript. LB contributed to the conception and design of the study and development of the manuscript. DA and KF contributed to the design of the study. UWJ performed the block randomizations and provided statistical advice during analysis and provided conceptual input into the draft manuscript. EM, NM, TW provided conceptual input into the draft manuscript. EM was also engaged in data collection and proof reading the manuscript. All authors read and approved the final manuscript.The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1472-6963/13/90/prepub


New antigen microarray technology enables parallel recording of antibody reactivities with hundreds of antigens. Such data affords system level analysis of the immune system's organization using methods and approaches from network theory. Here we measured the reactivity of 290 antigens (for both the IgG and IgM isotypes) of 10 healthy mothers and their term newborns. We constructed antigen correlation networks (or immune networks) whose nodes are the antigens and the edges are the antigen-antigen reactivity correlations, and we also computed their corresponding minimum spanning trees (MST) – maximal information reduced sub-graphs. We quantify the network organization (topology) in terms of the network theory divergence rate measure and rank the antigen importance in the full antigen correlation networks by the eigen-value centrality measure. This analysis makes possible the characterization and comparison of the IgG and IgM immune networks at birth (newborns) and adulthood (mothers) in terms of topology and node importance.Comparison of the immune network topology at birth and adulthood revealed partial conservation of the IgG immune network topology, and significant reorganization of the IgM immune networks. Inspection of the antigen importance revealed some dominant  antigens in the IgG and IgM networks at birth, which retain their importance at adulthood. The recently introduced new antigen microarray chip enables detection in parallel of the patterns of antibodies binding to hundreds of antigens, and so provides a system-level view of the antibody repertoire N(N-1) links) to a representative sub-graph (that contains only N-1 links). Hence, generating the maximum information immune networks (or immune trees) by the MST, makes it possible to investigate the essential organization motifs, such as the network topological organization.Here, we extended the study of these data by applying graph and network theory analysis methods We assessed and compared the topological organization of the IgG and IgM immune trees of the mothers and newborns. Next, the networks of the two subject groups, mothers and newborns, were compared by employing the widely used divergence rate measure Much effort has been devoted to assessing the importance of nodes in complex biological networks such as gene transcriptional regulatory networks, protein interaction networks and neural networks. The commonly used measures of node importance include node degree, node centrality, betweenness, and node vulnerability score Blood samples were obtained by random availability from 10 healthy women at the onset of term labor and from 10 serum samples of the cord blood of their newborns, in the course of normal procedures. All samples were collected with informed consent and approval by the Institutional Review Board (Helsinki Committee) of the Tel-Aviv Sourasky Medical Center. The newborns were healthy at the term of pregnancy (weeks 38–42) and normal in development and weight for gestational age. The blood samples were allowed to clot at room temperature. After centrifugation, sera were collected and stored at −20°C 305 antigens were spotted on each microarray, as described previously Antigen microarrays were prepared and studied as described previously Antigen reactivity was defined by the mean intensity of the 4 replicates binding to that antigen on the microarray; however, antigen intensities with mean value lower than 1000 in removed from the datasets leaving us with 290 antigens. Each chip was then normalized by its mean reactivity divided by the standard deviation. This was done in order to account for differences in total protein concentrations that affect the background intensity level et al.Following Madi i and j of subject n and i and j. Note that the antigen-antigen correlations (or for simplicity the antigen correlations) for all pairs of antigen define a symmetric correlation matrix whose i and j.Where Similar to neural and gene networks, the immune system can exhibit activated and inhibited reactivity responses; both positive and negative antigen correlations contain important information. To ease the calculations while retaining the information about negative correlations, we start by transforming the Pearson correlation values i and j of the correlation matrix after reordering. In the reordering process, the elements i is {j it is {i with all other antigens and the correlations of antigen j with all other antigens. To ease the following calculations the meta-correlation matrix MC was transformed to the range of . The meta-correlations were then used to generate the normalized correlations i and j, given by Following Baruchi et al. The collective normalization further signifies features and can reveal collective motifs related to functional connectivity in the network In the current work we represented the correlation matrices by employing network theory approaches In network theory, an adjacency matrix commonly describes the network topology by containing the information of whether a link between two nodes exists or not. In the case of activity networks  Defined this way, the distance In correlation based networks, such as the one described here, a weight, which is monotonically related to the correlation coefficient of each pair of elements, can be associated with each link. Therefore one can directly associate a weighted complete graph with the correlation matrix among N elements of interest.i and j have a distance shorter than a threshold level, and i and j are linked if they are strongly correlated - the correlation between them is above a threshold level. Determining which threshold to use is not a simple task. Here we have chosen to use a measure of normalized STD of the principle eigenvector.Another approach to extract relevant information is by reduction of the weighted adjacency matrix into a reduced binary matrix, the elements of which are assigned values A as described above. We diagonalize the adjacency matrix, and focus on the principle eigenvector . The centrality of each variable is defined as the weight of each variable in the principle eigenvector. We then compute the STD of the components for the principle eigenvector, and normalize it by the number of variables with a non-zero component. This allows us to search for a threshold that gives a compromise between high values of centrality and a small number of variables; in so doing, we can identify a significant sub-group of variables with the highest centrality in the network.We begin by choosing a correlation threshold, and use it to create an adjacency matrix We then calculate the normalized STD of the eigenvalue centrality for different thresholds, ranging from 0 to 0.95 correlations. We test the normalized STD as a function of the correlation threshold for each of the datasets. The evaluation continues by investigating the second derivative of the resulting normalized STD's. This process revealed that the fluctuations in the second derivative of the STD begin at thresholds above 0.79 and more specifically above 0.82 for the maternal IgM, above 0.82 for the cord IgM, above 0.79 for the maternal IgG and above 0.86 for the cord IgG , contains too much irrelevant information (links that correspond to very weak correlations); hence, relevant information can be obscured Here we applied the commonly used Kruskal algorithm et al. We performed quantitative comparisons between the IgM and the IgG immune trees  of the newborns and the mothers using the divergence rate measure developed by Lee Y is assigned the value y. The Conditional Entropy X and Y and X. The conditional entropy X and Y as The divergence rate measure developed by Lee and Kim, 2006 Motivated by this idea, Lee and Kim i to all its neighborhoods nodes i to the group of nodes i in graph i in the graph Where i to node j corresponds to a topological distance 1 from i to j and we take the neighborhoods nodes i. The topological distance between two nodes that are not directly connected by an edge is the number of directed edges of the shortest path connecting the two nodes.Note that in the informative sub-graphs studied here, each directed link from node We investigated the antigen correlation matrices of the IgG and IgM isotype antigen-reactivity data of 10 pairs of mothers and their newborns. We also studied the combined (or integrated) IgG and IgM correlation networks. The immune trees for the mothers and their newborns were calculated separately and compared.Antibodies of the IgM isotype are produced by B cells in the first phase of an antibody immune response, and IgM antibodies have been proposed to regulate the development of IgG autoantibodies Quantified comparison between the MSTs (immune trees) revealed that the maternal  and newbAnalyses of node centrality  (8 out of 10 for the mothers and 7 out of 10 for the cords) see . In contTo decipher the development of node centrality between birth and adulthood, we present in As mentioned above, the IgG isotypes are transferred during pregnancy from the mothers to the fetus. Hence, the findings of different IgG central nodes in the mothers and in the cords networks suggest that the IgG antibodies are transferred in a selective way. Such selective transfer can lead to the differences between the mothers and newborns IgG immune networks discovered here.To test the consistency between the previously discovered antigen cliques (subgroups of highly correlated antigens) To decipher the functional relations of the immune cliques with other antigens we re-plotted in In The immune trees described above were derived from the reactivities of the groups of mothers and newborns. To compare the antigen-reactivity networks of individuals within a group, we superimposed on the maternal IgM immune tree , the perWe constructed and analyzed the networks of autoantibody reactivities present in the blood sera of two groups of individuals – healthy mothers who had just given birth and their term newborn babies. This type of network analysis provides a powerful tool for simplifying complex systems, such as the immune system and for studying their components and their most informative interactions in order to identify their structure, topology and functions emerging from the organization of the collective of elements Mothers and newborns repertoires manifest generally different network architectures. In the mothers' immune tree the IgG and IgM nodes are largely integrated, but remain distinct clusters in the newborns. The greater difference between IgM and IgG repertoires at birth could be explained by the fact that the congenital IgM and IgG repertoires may develop independently. After birth, the IgM and IgG repertoires together are influenced by the external antigenic environment. This result further suggests that natural maturation of the immune system from the newborn to young adulthood might lead to the evolution of greater coordination between antibody reactivities of the IgG and IgM isotypes. As was shown the networks are also characterized by negative relationships (correlations). It can be seen that the links within a single branch can turn from positive to negative, and vice versa. This finding indicates that both negative and positive relations between antigen reactivities participate in connectivity throughout the immune tree network.Comparison of network topology between birth and adulthood reveals partial conservation of the IgG immune network topology between birth and adulthood and significant reorganization of the IgM immune networks. The similarity between the IgG immune trees of the mothers and newborns is expected as is explained above The reactivities to central antigens in the mothers and newborns show both similarities and differences in both IgG and IgM isotypes. For example, antibodies to cytokines are central in the IgM trees of both mothers and newborns, but the maternal cytokine hub is INF-gamma, and the newborns' central cytokine is IL-4. Antibodies to heat shock proteins such as HSP60 are hubs in both maternal and newborns' IgM trees, but the HSP60 peptide epitopes differ in each tree . Thus, tCliques of antigen reactivities, previously revealed by correlation analysis The results presented here illustrate the efficiency of the present method in revealing new and possibly important motifs of the immune system. For example the findings that show the persistence of central antigens from birth (newborns) to adulthood (mothers) might account for the reports that IgM repertoires show little change from early age In general, the results presented here are consistent with the concept of the Immunological Homunculus, the idea that healthy immune repertoires contain certain T cells and B cells that have been positively selected to respond to key body molecules to form a functional “internal image” of the body The analysis of immune system network architecture shown here and elsewhere Figure S1Normalized eigenvalue centrality STD, as function of correlation threshold. Calculated for the maternal IgM (A), cords IgM (B), maternal IgG (C), and cords IgG (D).(TIF)Click here for additional data file.Figure S2Second derivative of the normalized eigenvalue centrality STD, as function of correlation threshold. Calculated for the maternal IgM (A), cords IgM (B), maternal IgG (C), and cords IgG (D). In all four cases, there is a significant change in normalized STD for thresholds larger than 0.79 and more specifically 0.79 for the cords' IgM, 0.85 for the cords' IgG, 0.89 for the maternal IgG and 0.85 for the maternal IgM.(TIF)Click here for additional data file.Figure S3Illustration of the Kruskal algorithm.(TIF)Click here for additional data file.Figure S4The integrated correlation matrices of the IgM and IgG datasets. Shown for (A) the mothers and (B) the cords. In each matrix, the IgM isotypes are in the top left frame and the IgG isotypes are in the bottom right frame. Both frames (isotypes) are ordered using a dendrogram algorithm demonstrating the relationships between correlated groups of antibodies of both isotypes.(TIF)Click here for additional data file.Figure S5Antigen centrality values in descending order. Calculated for the maternal IgM (A), cord IgM (B), maternal IgG (C), and cord IgG (D). Marked in red dots are the numbers of antigens whose centrality values constitute 30 percent of the total centrality values in the network. Note that for clearer visualization zero values were removed prior to plotting the data.(TIF)Click here for additional data file.Figure S6Zipf plots of node centrality values. Presented in descending order for the maternal IgM (A), cords' IgM (B), maternal IgG (C), and cords' IgG (D). Note that zero values were removed prior to plotting the data and the plots are presented in log scale.(TIF)Click here for additional data file.Figure S7Semi-log plots of node centrality values. Presented in descending order for the maternal IgM (A), cords IgM (B), maternal IgG (C), and cords IgG (D). Note that zero values were removed prior to plotting the data and the plots are presented in semi-log scale.(TIF)Click here for additional data file.Figure S8Zipf plots of the descending sorted eigenvalues of the correlation matrices . Calculated for the maternal IgM (A), cords IgM (B), maternal IgG (C), and cords' IgG (D). Note that the plots are presented in log scale.(TIF)Click here for additional data file.Figure S9Zipf plots of the descending sorted eigenvalues of the correlation matrices . Calculated for the maternal IgG and IgM (A), cords IgG and IgM (B). Note that the plots are presented in log scale.(TIF)Click here for additional data file.Figure S10Robustness of the trees. We calculated the distance from all nodes to all others and subtracted it from the original calculated distance , this process was repeated 100 times and the results were plotted for (A) the maternal IgG and (B) maternal IgM.(TIF)Click here for additional data file.Figure S11Maternal conservation of FIGs. For each of these randomly “trimmed” trees, we also calculated the average distance between all the nodes (members) within each FIG and subtracted it from the original calculated distance . (A) maternal IgM and (b) maternal IgG.(TIF)Click here for additional data file.Supporting Information S1Complete list of antigens spotted on the antigen chip.(PDF)Click here for additional data file.Supporting Information S2The eigen-values' entropy of the correlation matrices.(DOC)Click here for additional data file.Supporting Information S3Calculation of the node centrality.(DOC)Click here for additional data file.Supporting Information S4Construction of the immune trees by the Kruskal algorithm.(DOC)Click here for additional data file.Supporting Information S5The integrated correlation matrices.(DOC)Click here for additional data file.Supporting Information S6The topological statistics of the immune trees.(DOC)Click here for additional data file.Supporting Information S7The central nodes – the network hubs.(DOC)Click here for additional data file.Supporting Information S8The immune tree robustness.(DOC)Click here for additional data file.


In recent years, several massive earthquakes have occurred across the globe. Multidetector computed tomography (MDCT) is reliable in detecting spinal injuries. The purpose of this study was to compare the features of spinal injuries resulting from the Sichuan earthquake with those of non-earthquake-related spinal trauma using MDCT.Features of spinal injuries of 223 Sichuan earthquake-exposed patients and 223 non-earthquake-related spinal injury patients were retrospectively compared using MDCT. The date of non-earthquake-related spinal injury patients was collected from 1 May 2009 to 22 July 2009 to avoid the confounding effects of seasonal activity and clothing. We focused on anatomic sites, injury types and neurologic deficits related to spinal injuries. Major injuries were classified according to the grid 3-3-3 scheme of the Magerl (AO) classification system.P < 0.001). Cervical spinal injuries were more common in the control group (risk ratio (RR) = 2.12, P < 0.001), whereas lumbar spinal injuries were more common in the earthquake-related spinal injuries group . The major types of injuries were significantly different between these cohorts (P = 0.002). Magerl AO type A lesions composed most of the lesions seen in both of these cohorts. Type B lesions were more frequently seen in earthquake-related spinal injuries (RR = 1.27), while we observed type C lesions more frequently in subjects with non-earthquake-related spinal injuries .A total of 185 patients (82.96%) in the earthquake-exposed cohort experienced crush injuries. In the earthquake and control groups, 65 and 92 patients, respectively, had neurologic deficits. The anatomic distribution of these two cohorts was significantly different (Spinal injuries sustained in the Sichuan earthquake were located mainly in the lumbar spine, with a peak prevalence of type A lesions and a high occurrence of neurologic deficits. The anatomic distribution and type of spinal injuries that varied between earthquake-related and non-earthquake-related spinal injury groups were perhaps due to the different mechanism of injury. The magnitude 8.0 Sichuan earthquake that happened at 2:28 PM Beijing time on 12 May 2008 injured an estimated 374,643 people. A retrospective study of the features of 223 patients with spinal injuries was performed using multidetector computed tomography (MDCT). This study relied on the 2,728 patients with earthquake-related spinal injuries seen in a key university hospital [Spinal injuries account for 13.0-15.2% of earthquake-related trauma patients ,5. In geClassification based on the information provided by MDCT should provide concise information regarding the severity of the injury and guide the choice of treatment. We used MDCT alone to evaluate the difference between spinal injuries from earthquake victims and those from other causes, which may help in clinical management algorithms. We cite better disaster preparedness as an overarching goal of this investigation.The study was approved by the ethics committee of our medical school, and because of the retrospective nature of this study, informed consent was waived. Of the 2,728 patients with earthquake-related spinal injuries seen in our hospital, we consecutively enrolled patients into the exposed cohort according to the following criteria: (1) the etiology of the injuries was associated with the 2008 Sichuan earthquake, (2) spinal injuries were evaluated using MDCT, and (3) the patients had not received related surgical treatment before the spinal MDCT scan. To avoid bias, we excluded patients injured in an earthquake-related motor vehicle accident. With the exception that the etiology of spinal injury was not associated with the earthquake, similar inclusive criteria were used to enroll patients into the cohort of patients with other common injuries (unexposed cohort). By 4 June 2008, we had enrolled all 223 patients with clinically worrisome spinal injuries from the Sichuan earthquake who met the inclusive criteria. We selected an unexposed cohort similar in size to the exposed cohort of 223 consecutive patients. To avoid the confounding effect of seasonal activity and clothing, our unexposed cohort was collected during a similar time frame of 1 May 2009 to 22 July 2009.z-axis from the upper two vertebrae to the lower two vertebrae of the target vertebrae. Scanning parameters were 140 kV, 250 mAs, rotation time 1.0 s, pitch 1.235 and collimation of 64 × 0.625 mm for the Philips Brilliance 64 MDCT scanner; 120 kV, 250 mAs, rotation time 0.75 s, pitch 1.0 and collimation of 16 × 0.75 mm for the Siemens Somatom Sensation 16-MDCT scan; and 120 kV, 180 mAs, rotation time 0.75 s, pitch 1.5 and collimation of 4 × 1.0 mm for the Siemens Somatom Plus 4 MDCT scan. The axial image data were reconstructed in 1- to 2-mm thickness and 0.7- to 1.0-mm increments and transferred to syngo Workflow software of the picture achieving and communicating system work station .Among the earthquake-exposed cohort, spinal CT scans of 10, 175 and 38 patients were performed using the Philips Brilliance 64-slice MDCT , the Siemens Somatom Sensation 16-slice MDCT and the Siemens Somatom Plus 4-slice MDCT , respectively. Among the unexposed common injury cohort, the 223 spinal CT scans were performed using the Siemens Somatom Sensation 16-slice MDCT. Each patient underwent MDCT along the All MDCT scans were read independently by two experienced radiologists  on a syngo Workflow PACS workstation on which we obtained axial images and multiplanar reformatted sagittal and coronal images. Surface-shaded display was used to evaluate the vertebral fractures and the deformation of the spinal column. Discrepancies in interpretation were resolved by consensus.At each vertebral site, the presence of fractures in the vertebral body, including the transverse processes, spinous processes, articular processes and isthmus of the vertebrae, as well as the degree of spinal canal narrowing, were evaluated, with differences resolved by consensus. We categorized the injury patterns and regions of spinal injuries and correlated these findings with the mechanism of injury and the clinician-evaluated Frankel level . Spinal Major spinal injuries were categorized according to the Magerl (AO) classification because it allows categorization of injuries to all relevant parts of the spine Figures , 2, 3, 4Spinal canal narrowing was measured in all injured vertebrae at the point of greatest narrowing and was defined as 0 , 1 , 2  and 3 , respectively . The neuU test for gender, age  and anatomic distribution of injury . Additionally, the anatomic distributions and types of injuries were compared using the χ2 test, and the patient's age and spinal canal narrowing degrees were compared using the Mann-Whitney U test. The Kruskal-Wallis H test was performed to compare the spinal canal narrowing degrees between different AO types of injuries. We accepted two-tailed P values less than 0.05 as a statistically significant difference.Data for each patient were entered into a Microsoft Excel database . We performed data analysis on a personal computer using the SPSS statistical package . We determined the differences between the earthquake-exposed and non-earthquake-exposed cohorts using risk ratios (RRs) and the Mann-Whitney U test = 6.418 and P < 0.001). We also found that young patients more commonly represented in the non-earthquake-related injury cohort .The earthquake-exposed cohort included 119 (53.36%) female patients and 104 (46.64%) male patients, with ages ranging from 3 to 89 years  including 56, 118 and 49 patients grouped by ages <35, 35-64 and >64 years, respectively. The mean hospital stay was 13.6 days, ranging from 1-203 days. Only one 74-year-old female patient died as a result of heart failure from septic shock related to thoracoabdominal blunt trauma. The non-earthquake-related cohort included 53 (23.77%) female patients and 170 (76.23%) male patients whose ages ranged from 1.8 to 88 years  including 80, 124 and 19 patients in the age groups <35, 35-64 and >64 years, respectively. The mean hospital stay was 11 days and ranged from 1-113 days. Three patients died, including one patient whose spinal injury was combined with a head injury, one patient with an injury of cervical spine due to a gunshot wound and one patient with multiorgan injury. Male patients were statistically more common in the non-earthquake-related spinal injury cohort . The cervical spinal injuries presented more frequently in patients with non-earthquake-related spinal injuries than in patients with earthquake-related injuries , including both the cervical major spinal injuries    sustained crush injuries and 38 had fallen with resultant injury. For the non-earthquake-related spinal injury cohort, on the other hand, the proximate causes of injury included falls (90 patients), motor vehicle accidents (71 patients), crush injuries (26 patients), assault and gunshot wounds. The numbers and anatomic distribution of spinal injuries detected in the earthquake-exposed and non-earthquake-exposed cohorts are listed in Table % sustain2 = 15.250, P = 0.002). Type C lesions were seen more commonly in the non-earthquake-related spinal injury patients than in the earthquake-related spinal injury patients . However, the incidence of type B lesions was relatively higher in patients with earthquake-related injuries (20.27% of AO type fractures) than in those with non-earthquake-related spinal injuries (RR = 1.27). Associated type A3 fractures were detected in 31 earthquake-related type B lesions (72.09% of the 43 type B lesions that associate with type A fractures). Other categories of major injuries are listed in Table Of the 252 earthquake-related major spinal injuries, types A, B and C lesions were detected in 155, 45 and 22 vertebrae, respectively, which was significantly different from the findings in the non-earthquake-related cohort, in whom types A, B and C lesions were detected in 127, 36 and 44 vertebrae, respectively . The degree of spinal canal narrowing increased significantly from Magerl type A through type C injuries  . Furthermore, the incidence of neurological deficit increased significantly from Magerl type A through type C: neurological deficit was associated with 21.99% of type A lesions, 40.74% of type B lesions and 69.70% of type C lesions .Neurologic deficits such as decreased perineal sensation, limb anesthesia, bladder dysfunction, muscle weakness and paraplegia occurred in 65 patients  in the earthquake-related cohort, with a Frankel level of A, B, C and D in 14, 8, 24 and 19 patients, respectively. For the non-earthquake-related cohort, neurologic deficits occurred in 92 patients , with a Frankel level of A, B, C and D in 41, 12, 24 and 15 patients, respectively. Spinal canal narrowing was much higher in patients with neurologic deficits than in patients without neurologic deficits (Table Spinal injuries are common in both trauma associated with earthquakes and other major blunt traumas -5. A spiWe found young male patients were more commonly involved in non-earthquake-related spinal injuries, which concurs with previous data. This difference between the cohort populations can probably be attributed to the fact that falls and motor vehicle injuries are the two primary causes of non-earthquake-related spinal injuries. Most work-related falls from elevated height occur in young males, and drivers injured in motor vehicle collisions also tend to be young males, consistent with previous Chinese data. In the earthquake, however, agile young males would be more likely to escape danger and withstand vertebral trauma. On the other hand, as the earthquake occurred during working hours in the mountainous area of China, most of the males were working and alert. In contrast, unemployed and older adult females were at home and relatively relaxed. Thus, the sudden, intense earthquake would be more likely to injure the latter group. Our results indicate that it was necessary to increase attention to female and older adult patients in the ward preparation and staff scheduling.As described by Velez and Newell , the majet al. [The mechanisms of spinal fractures include simple flexion, flexion-distraction, flexion and compression, extension, "shearing" forces and rotation . Of theset al. , type A et al. ,20. The According to the AO classification system applied in the present study, earthquake-related type B lesions occurred more often than other types, with a RR of 1.27 as compared to common spinal injuries. We hypothesized that the forces of falling objects on the victims who were most likely in anterior flexion, combined with the axial loading or vertical force, might invoke a flexion and distraction component. The axis of rotation adjacent to the posterior vertebral body cortex may result in associated anterior compression. When the axis of rotation is beyond the posterior longitudinal ligament, combined type A3 compression fractures tended to occur. In this manner, all three sections of the spinal column can fail because of flexion and distraction with initial axial loading, resulting in an unstable type B lesion. The high incidence of associated type A3 fractures suggests that spinal crush injuries are different from seat belt or chance fractures, in which the initial force is flexion and distraction and the fulcrum is mainly the anterior column [et al. [Clinically, neurologic sequelae serve as one of the most important factors in assessing spinal injuries and clinical management ,9,15,16.et al. . Our stuMinor injuries compose about 16.95% of all spinal fractures . The cliOur study has limitations. Since our hospital is a major medical center in the southwest region of China, some severely injured patients were transferred from the local hospital and may have had progression to neurologic sequelae in the process of being transported to our site. Although we selected the data consecutively to try our best to avoid selection bias, the comparatively high incidence of neurologic deficits in the non-earthquake-related cohort perhaps is due to this type of bias.Spinal injuries sustained in the Sichuan earthquake were mainly caused by crush injuries and included a high incidence of neurologic deficits. The thoracolumbar region is the most common site of spinal fracture, and type A fractures are the most common type of major injury. The major injuries of patients with non-earthquake-related spinal injuries were equally distributed among the cervical, thoracic and lumbar spine, with a comparatively high incidence of type C lesions, which were significantly different from the distribution observed among patients with earthquake-related injuries, which perhaps were due to the special mechanism associated with the earthquake.• The leading cause of spinal injuries in the Sichuan earthquake was crush injuries.• Earthquake-related spinal injuries were mainly involved in the thoracolumbar region and resulted in type A fractures with a high incidence of neurologic deficits.• The causes of non-earthquake-related spinal injuries were significantly different from earthquake-related injuries, including falls, motor vehicle accidents, crush injuries, assaults and gunshot wounds.• Non-earthquake-related spinal injuries were equally distributed among the cervical, thoracic and lumbar spine, with a comparatively high incidence of type C lesions.AO: Arbeitsgemeinschaft fur Osteosynthesefragen; CI: confidence interval; CT: computed tomography; MDCT: multidetector computed tomography; PACS: picture achieving and communicating system; RR: risk ratio.The authors declare that they have no competing interests.ZGY and ZHD conceived and organized the study. ZHD, TWC, ZGC, QLW and WD participated in the acquisition of data. ZGY, ZHD, TWC, ZGC and JCD performed the statistical analysis and interpretation of data. ZGY, ZHD, TWC, ZGC and JCD participated in the drafting of the manuscript. All authors read and approved the final version of the manuscript.


Pasos Adelante is a lifestyle intervention program facilitated by community health workers (promotores) targeting chronic disease prevention and control in Mexican Americans. Initial studies of Pasos Adelante indicated significant improvements in self-reported nutrition and physical activity. This study examined whether Pasos Adelante participants living in a US border community showed improvements in selected physiological measures after participating in the program and whether changes were maintained at 3-month follow-up.The program took place in 12-week sessions from January 2005 to May 2008 and included walking groups and education targeting nutrition and physical activity. Questionnaires, anthropometric measures, and laboratory tests were conducted at baseline (n = 305), conclusion of program (n = 254), and 3-month follow-up (n = 221).P = .04), waist and hip circumference (P < .001), diastolic and systolic blood pressure (P < .001), and total cholesterol (P = .008) from baseline to program conclusion. No values worsened significantly between program conclusion and follow-up, except systolic blood pressure. Glucose levels improved between conclusion and follow-up (P = .01).Participants demonstrated decreases in body mass index (These results support the initial findings of improvements in participants' self-reported physical activity and nutrition patterns through changes in objective measures. This evidence-based program demonstrates the potential for a promotores-facilitated chronic disease prevention and control program to improve physical health and targets both primary and secondary prevention in Hispanic communities and organizations. Mexican American weight patterns were first documented in 1982 to 1984, when an estimated 60% of adults were overweight or obese compared to 44% for non-Hispanic whites .The rate of diabetes among Hispanics is more than 50% higher than that of non-Hispanic whites . By 2050Randomized, controlled trials demonstrate that lifestyle changes can delay and even prevent the onset of type 2 diabetes ,10 and CGuide to Community Preventive Services were incorporated in Pasos Adelante: social support in community settings  and group education (recommended for diabetes management)  9-week curriculum . The intagement) ,17. In ahttp://azprc.arizona.edu).Details of the development and contents of the curriculum are available . The curParticipants in the Pasos Adelante program were residents of Douglas, Arizona. Douglas is located in the southeast corner of the state approximately 50 miles west of the New Mexico border and directly on the border between Arizona and Sonora, Mexico. Historically, Douglas was known for its smelter operations that processed copper from nearby mines until the operations shut down in 1987. According to the 2000 US Census, the population is approximately 86% Hispanic, and approximately 9,532 people are aged 18 years or older , state government , and private industry . Despite the changing economic conditions and national spotlight because of illegal immigration and drug trafficking, the community has worked hard to create a healthy environment for its residents. In 1996, community members became concerned about perceived high rates of diabetes in their community and approached University of Arizona College of Public Health faculty  who had lived in the community 20 to 35 years recruited participants and facilitated the intervention. Two of the 3 promotoras had facilitated the Pasos Adelante program in their community for a 1-year pilot phase. The third promotora shadowed the more experienced promotoras before facilitating sessions. All 3 promotoras attended a 1-week, half-day training for facilitating the Su Corazón, Su Vida curriculum at a national conference. Ongoing trainings included quarterly refresher trainings and local and national conferences.Using opportunistic methods, promotoras recruited cohorts of approximately 30 participants by attending public events such as health fairs and Rotary club meetings. Over time, waiting lists were established for the program, and most recruitment occurred through referrals from former participants. The only inclusion criteria were that participants be aged 18 or older and consider themselves residents of the Douglas community. Participants for this project were recruited from January 2005 through February 2008. One cohort completed the program before the next cohort was recruited.All study protocols were approved by the University of Arizona institutional review board. Interviewer training consisted of multiple day-long reviews of the questionnaire, including discussions of the intent of each question to ensure that all personnel fully understood the questions. A training manual documenting the intention of each question was developed and provided to all staff conducting interviews. Training emphasized the need to strictly follow questions as worded and appropriate prompts for questions. New staff observed multiple administrations of the interview by an experienced interviewer. They were then observed for their first few interviews to ensure consistency.Baseline measures were taken between 1 month before participation in the Pasos Adelante program and the second week of class. Participants gave their informed consent before completing a baseline survey, having their anthropometric measures taken, and being referred for a fasting blood draw. Interviews and anthropometric measures were conducted by project personnel at a local church, the site of the Pasos Adelante classes. Anthropometric measures took place in the church kitchen in a semiprivate location. Participants were referred for a fasting blood draw at the local hospital. Measures were repeated within 6 weeks after the conclusion of the program and again at a 3-month follow-up. The promotoras who facilitated Pasos Adelante conducted interviews at baseline did not interview the participants at the conclusion of the program or 3-month follow-up assessments to reduce the potential for bias. These data were collected by other project staff. The promotoras took the anthropometric and physiological measures at each assessment but did not have access to prior results.The project questionnaire consisted of 70 questions that assessed demographic characteristics, physical activity, dietary practices, and medical history, including chronic disease, access to medical care, depression symptoms, and health-related quality of life. Questionnaires were interviewer-administered in the participants' preferred language, either Spanish or English, by bilingual personnel on paper forms. Interview length averaged 45 minutes .One of the authors (L.S.) trained project staff members to conduct standardized height, weight, waist and hip circumference, blood pressure, and pulse measurements . Height Fasting blood samples were analyzed for total serum cholesterol, blood glucose, and triglyceride levels at a single certified laboratory. Initial funding was for a baseline and 3-month follow-up but did not allow for blood measures at the conclusion of the program. Funding was obtained in the second year of the study for all 3 measurement periods. Therefore, 102 participants were not eligible for blood draws at all 3 assessment periods.2 or Fisher exact tests. We compared the mean baseline age for completers and noncompleters using the t test.We analyzed selected demographic variables for all participants with baseline assessments. To assess the extent of study completion bias, baseline demographic variables for study completers were compared with baseline variables for participants who did not complete all 3 assessment periods using χAnthropometric and laboratory measures were summarized using the mean, standard deviation, minimum, and maximum values. The mean of the 3 measures taken at each assessment period for each anthropometric variable was used as the point estimate for each participant. If a fourth measure was collected, the mean of the 3 measures with the smallest standard deviation was used as the point estimate.A linear mixed-effects model was used to assess the effect of time (independent variable) on each physiological measure. The model included all participants with at least a baseline measure regardless of the number of additional measures. Because the study population was homogeneous for sex, age, ethnicity, and language preference, we did not pursue models to control for these demographic variables.P < .05. All statistical procedures were conducted with SAS version 9.2 .To assess movement between body mass index (BMI) categories, we collapsed the categories of normal/overweight and obese/extremely obese and assessed the differences in proportions in each category from baseline to conclusion of the program and from conclusion to 3-month follow-up. The McNemar test for paired differences was used to test the null hypothesis that the proportions of patients who switched categories were equal. Significance was set at P < .001), and more had health insurance (P = .045), but these 2 groups did not differ for any other demographic variables or health indicators (data not shown).Between January 2005 and February 2008, 327 people enrolled in the Pasos Adelante program. Twenty-two (7%) had participated in another intervention targeting diabetes prevention or control before participating in Pasos Adelante and were excluded from statistical analyses presented in this article . Of the Mean values for all anthropometric measures, total and HDL cholesterol, and glucose show a downward trend from baseline to program conclusion and additional declines at follow-up in BMI and hip circumference . Time ofP = .03) (data not shown).The percentage of participants who moved from the obese or extremely obese category to the overweight or normal category was not significant at the conclusion of the program but had become significant by follow-up  allowed for participation of friends and family members, all of whom were the focus of the intervention but made assessment challenging. Some of the participants enrolled in the program did not have overt health risks such as a diagnosed chronic disease or symptoms, and entered the program with all normal laboratory values, but 86% of them were overweight or obese at baseline.Our study has several limitations. Because the study design lacked a randomized control group and called for opportunistic recruitment methods, alternative explanations for change in the outcomes of interest must be considered. First, opportunistic recruitment may have resulted in a selection bias for participants. However, the goal of this intervention was to test the effectiveness of a behavior change intervention in a real-world setting such as might occur if an agency offered the program to the general public. The overrepresentation of older women in health promotion programs is frequently documented. Second, participants may have engaged in other nutrition and physical activity–focused interventions concurrently with Pasos Adelante, but our knowledge about the program offerings in this community suggests that this explanation is unlikely. A secular trend toward decreasing body measures over a 12-week period was theoretically possible, but our findings are in contrast to general secular trends. The US trend for weight has been increasing, not decreasing as seen in our participants. For example, between data collected for 1988 to 1994 and those for 2008 the prevalence of obesity increased among Mexican American men from 24% to 36% and among Mexican American women from 35% to 45% . The mosThe Pasos Adelante program demonstrated results that are comparable to other programs facilitated by community health workers that targeted high-risk people. For example, Balcazar and colleagues conducted a randomized controlled trial targeting people with at least 1 CVD risk factor using the Su Corazón, Su Vida curriculum (the basis for the Pasos Adelante curriculum) during a 4-month period . The parIn conclusion, participants in the Pasos Adelante program demonstrated significant decreases in key risk factors for CVD and diabetes, many of which were maintained 12 weeks after completion of the program. These results support the initial findings of improvements in self-reported physical activity and nutrition patterns . This pr


TRAIL, a member of the TNF ligand family, was shown to selectively kill cancer cells and, therefore, to participate in the cell-mediated immunity against tumors. However, TRAIL is down-regulated in a variety of cancers. Furthermore, PRAME  is frequently over expressed in a wide variety of malignant diseases. It was shown that PRAME, in a complex with a member of the polycomb group, EZH2, can function as a transcriptional repressor of retinoic acid receptor. Interestingly, TRAIL expression can be positively regulated by retinoic acid. Previous studies performed by us revealed that TRAIL is down-regulated and PRAME is up-regulated during development of chronic myeloid leukemia (CML) and that their normal levels are restored after complete cytogenetic remission (CCR). There was a significant, negative correlation between the expression of PRAME and TRAIL in CML patients. Over expression of BCR-ABL in the acute promyelocytic leukemia cell line HL-60 increased the levels of PRAME and decreased the levels of TRAIL. Knocking-down of either PRAME or EZH2 in K562 CML cell line resulted in TRAIL up-regulation.We are continuing this study in solid tumors and sarcomas, through qRT-PCR and tissue microarray (TMA) immunohistochemistry, using samples from human cell lines and cancer patients.Using the publicly available Oncomine Research platform, we found that PRAME was up- and TRAIL was down-regulated in several cancers. Literature data were validated by TMA immunohistochemistry, in tumor samples from patients with lung, prostate, breast and kidney cancers, melanoma and sarcoma. We are performing qRT-PCR assays to validate deregulated mRNA expression in several tumor cell lines and primary patient samples.These initial data, showing PRAME overexpressed in tumors, accompanied by a decreased expression of TRAIL, corroborate our hypothesis that the presence of a complex consisting of PRAME and EZH2 is responsible for the negative transcriptional regulation of TRAIL in cancer.FAPESP and CNPq.


Mycoplasma pneumoniae and Chlamydophila pneumoniae during Q fever are rare. We conducted a retrospective serological study of M. pneumoniae and C. pneumoniae by enzyme-linked immunosorbent assay (ELISA), a method widely used in clinical practice, in 102 cases of acute Q fever, 39 cases of scrub typhus, and 14 cases of murine typhus. The seropositive  and seroconversion rates  of M. pneumoniae IgM, but not M. pneumoniae IgG and C. pneumoniae IgG/IgM, in acute Q fever were significantly higher than in scrub typhus and murine typhus. Another ELISA kit also revealed a high seropositivity (49.5%) and seroconversion rate (33.3%) of M. pneumoniae IgM in acute Q fever. The temporal and age distributions of patients with positive M. pneumoniae IgM were not typical of M. pneumoniae pneumonia. Comparing acute Q fever patients who were positive for M. pneumoniae IgM (59 cases) with those who were negative (43 cases), the demographic characteristics and underlying diseases were not different. In addition, the clinical manifestations associated with atypical pneumonia, including headache , sore throat , cough , and chest x-ray suggesting pneumonia , were unchanged between the two groups. Clinicians should be aware of the high seroprevalence of M. pneumoniae IgM in acute Q fever, particularly with ELISA kits, which can lead to misdiagnosis, overestimations of the prevalence of M. pneumoniae pneumonia, and underestimations of the true prevalence of Q fever pneumonia.Q fever is serologically cross-reactive with other intracellular microorganisms. However, studies of the serological status of  Coxiella burnetii  and 43 negative cases [42.2%]). There were no significant differences among demographic data, underlying diseases, or clinical symptoms and signs between the 2 groups of patients (p=0.255), sore throat , and cough , were not different. In imaging findings, laboratory examinations, and responses to treatment, there were no differences between the patients with and without serum M. pneumoniae IgM (p=0.258). Similarly, no significant differences were found by comparing the same variables listed in M. pneumoniae IgM (data not shown). To investigate the possible clinical differences between acute Q fever with and without serum patients . It is nniae IgM . The ratM. pneumoniae [C. pneumoniae [M. pneumoniae IgM and C. pneumoniae IgM, respectively  [C. pneumoniae IgM were also higher than the percentage of C. pneumoniae pneumonia in CAP in Taiwan (7.1%-13.0%) [M. pneumoniae and C. pneumoniae IgM in the present study did not result from background seroprevalence. These results had two important impacts. First, in clinical practice, rickettsioses may be misdiagnosed as atypical pneumonia if only M. pneumoniae and C. pneumoniae antibodies are tested. Second, in the investigation of M. pneumoniae and C. pneumoniae ELISA kits, the sera from rickettsioses should be included for evaluating specificity and possible false-positivity of the ELISA kits.Various degrees of sensitivity, specificity, and cross-reactivity were reported in several commercially available kits of eumoniae -19 and Ceumoniae ,21, but ectively . The sers (6.0%) , adults s (6.0%) , and coms (6.0%) ,25 in Ta%-13.0%) ,25. AccoM. pneumoniae pneumonia belong to atypical pneumonia, but serological studies for one in the other are rare [M. pneumoniae IgM (SeroMPTM) [M. pneumoniae IgM [M. pneumoniae had false positivity for M. pneumoniae IgM in 4 Q fever controls  [C. burnetii and M. pneumoniae or C. pneumoniae have rarely been reported in studies of CAP [C. burnetii was the most frequently isolated co-pathogen, but the pathogens were not clearly described [C. pneumoniae and M. pneumoniae, respectively [M. pneumoniae IgM suggests a recent infection, which indicates that as many as 50-60% acute Q fever cases could be diagnosed as M. pneumoniae infection without testing for Q fever. From the epidemiological perspective, this would underestimate and overestimate the true prevalence of Q fever and M. pneumoniae infections, respectively.Both Q fever pneumonia and are rare ,12,19. Aniae IgM . In a sts of CAP ,27. In aescribed . In a stM. pneumoniae IgM and C. pneumoniae, respectively  and zoonotic (C. burnetii) atypical pneumonia is low [M. pneumoniae pneumonia, which generally predominates in autumn among children and young adults [M. pneumoniae pneumonia were not different between acute Q fever patients with and without serum M. pneumoniae IgM  (M. pneumoniae IgM (50.6%)  and IgG seroconversion (28.6% and 11.1%) as measured by the two ELISA kits   and sero (50.6%)  in acute (50.6%)  are not ISA kits , indicat.8%  and boratory ,34.M. pneumoniae and C. pneumoniae infections. Whether a similar phenomenon exists with other kits needs further investigation. The studied cases were mainly distributed across southern Taiwan, and the results require a large-scale study involving other countries to be confirmed. The hypothesis of serological cross-reactivity derived from clinical observations needs further investigation by basic medical research in a professional laboratory.This retrospective clinical and serological study has several limitations. We only tested 2 ELISA kits commonly used in Taiwan for the serological diagnosis of M. pneumoniae IgM by ELISA. However, their clinical manifestations were not different from those that were serum negative for M. pneumoniae IgM. In addition, approximately 25% of acute Q fever cases were serum positive for C. pneumoniae IgM. Clinicians should be aware of the high seroprevalence of M. pneumoniae IgM, particularly using ELISA kits, in acute Q fever. This observation could lead to misdiagnosis, overestimation of the prevalence of M. pneumoniae infection, and underestimations of the true prevalence of Q fever pneumonia.In conclusion, nearly 60% of acute Q fever cases were serum positive for