Inflammatory pseudotumors (IPT) are soft tissue tumors that include a diverse group of lesions characterized by inflammatory cell infiltration and variable fibrotic responses. Idiopathic orbital inflammatory pseudotumors (IOIP) are IPTs of unknown etiology that develop in the orbit. Due to the lack of well-defined pathogenic mechanisms, diagnosis and treatment of this disease remain a significant challenge. Epstein-Barr virus (EBV) infection, which causes significant lymphocyte infiltration, has been proposed to be involved in IOIP. This study tries to validate the relationship between EBV infection and the development of IOIP. Sixteen IOIP tissue samples were obtained from patients during surgical resection of the lesion. One Graves' ophthalmopathy tissue sample and 20 normal donors' plasma serves as controls. The plasma level of five EBV antibodies, including VCA-IgG, VCA-IgA, VCA-IgM, EA-IgG and EBNA1-IgG were examined. All plasma samples were EB-VCA-IgG positive and EB-VCA-IgM negative, suggesting that all people tested had been infected with EBV but not in the acute infection stage. EBV-DNA was detected in 15/16 (94%) of IOIP tissue samples despite different levels of lymphocyte infiltration and 5/16 plasma samples (31%) were detected EBV DNA positive which is higher than the normal controls (10%). Percent of positive plus suspected positive samples with one or more of the three important risk markers  is 50% of the patients (8/16) which is much higher compare with the normal controls (20%). The results further reveal the relationship between IOIP and EBV infection. Inflammatory pseudotumors (IPT), also known as inflammatory myofibroblastic tumors (IMT), are soft tissue tumors that include a diverse group of lesions characterized by inflammatory cell infiltration and variable fibrotic responses Idiopathic orbital inflammatory pseudotumors (IOIP) are one of the most common IPT diseases of the orbit, which can not only affect one's appearance due to proptosis and eyelid or conjunctival congestion, but also impair vision. After Graves' disease and lymphoproliferative disorders, IOIP is the 3rd most common ophthalmologic disease of the orbit and account for approximately 7–12.3% of all the orbital tumors viz, minor trauma, surgery and the origin of autoimmunity, the exact pathophysiology of the disease remains unknown. IOIP has been shown to be associated with the occurrence of upper respiratory tract infection, viral influenza, syphilis and paranasal sinusitis Although various etiologies of IOIP have been proposed pertaining to the origin of the lesion, EBV is a common herpesvirus that usually infects lymphocytes and epithelial cells and was specifically detected in the tumor by in situ hybridization for EBERs in lymph nodes-derived IPT, splenic IPT, liver IPT and spleen IPT In an effort to shed light on the role of EBV in the IOIP pathogenesis, EBV-DNA, EB-VCA-IgG, VCA-IgA, VCA-IgM, EA-IgG and EBNA1-IgG were used to evaluate EBV infection in IOIP patients respectively.The antibody titers of EBV, including VCA-IgG, VCA-IgA, VCA-IgM, EA-IgG and EBNA1-IgG, were examined in the above 16 IOIP patients' and 20 normal donors' plasma which are served as the control .Detection of these five kinds of antibodies may tell the stage of EBV infection of the seven IOIP patients. About two weeks after infection, anti-EA IgG, anti-VCA IgM and anti-VCA IgG-antibodies can be detected in the patient's serum. EBV-VCA IgM is produced in all primary EBV infections, whether symptomatic or asymptomatic. If antibody to EBNA is positive along with VCA-IgG, a diagnosis of a primary infection can be excluded. If the EBNA titer is negative in the presence of VCA IgG and IgM, a primary infection is likely. In this study, all IOIPs are seropositive of EB-VCA-IgG, fifteen of sixteen are EBNA1-IgG positive (93.8%), but none are VCA-IgM positive. Compared to the IOIPs, the normal donors' has a positive rate of 100%, 0%, 5%, 10%, and 90% in the detection of EBV-VCA-IgG, IgM, IgA, EA-IgG, and EBNA1-IgG. These suggest that all the sixteen IOIP patients and twenty normal donors had previous EBV infection. In most cases of reactivations, e.g. in which caused by immunosuppression, the anti-EA IgG and anti-VCA IgA antibody concentration increase. Therefore positive reaction of anti-EA-IgG and anti-VCA IgA is a noticeable marker for reactivations. In these studies, two patients are positive of both VCA-IgA and EA-IgG, one is suspected positive of both VCA-IgA and EA-IgG, one is positive of VCA-IgA and two are positive of EA-IgG &2. The pIn our former study We examined plasma EBV DNA in the above sixteen IOIPs and twenty normal donors which served as the control. Five of sixteen IOIP patients showed EBV DNA positive (31%) while only two of twenty (10%) in normal controls  & 2. AllEBV-DNA in IOIP tissues might originate from different kind of cells.To further analysis if the EBV DNA originate from inflammatory cells in the tissues only, nine of the sixteen tissue section (group T patients) were stained with hematoxylin and eosin (H & E). Histological pictures showed that tissues of Group T patients are of different types and have different levels of lymphocyte infiltration . Of the These results might not support the possibilities that EBV-DNA positive in IOIP tissues is only attributable to the presence of inflammatory cells in the tissues. It is indicated that EBV infection was not fortuitous and EBV may be an important factor in the etiopathology of IOIP.Despite of the high prevalence of EBV infection in population and the lymphocytes infiltration in IOIP tissues, it is still possible that EBV infection may be associated with IOIP etiopathologensis.in situ hybridization. A subset of IPTs, particularly those in the spleen and liver, has been shown to harbor EBV in spindle cells Several groups have reported that EBV RNA (EBERs) can be detected in lymph nodes-derived IPT, splenic IPT and liver IPT by In this study, all sixteen IOIP patients and the normal controls are EBV seropositive persons, and have been infected with EBV. EBV DNA were detected in fifteen of sixteen IOIP patients tissue samples (94%) despite different levels of lymphocyte infiltration and in five of sixteen plasma samples (31%), but not in the control Graves' ophthalmopathy tissue with the infiltration of inflammatory cells and only in two of twenty (10%) normal plasma controls. The results further reveal the relationship between IOIP and EBV infection. It is suggested that the high positive incidence of EBV-DNA detection in IOIP tissues is not only the results of the lymphocytes infiltration in inflammation response.EBV has been found in tissue of nasopharyngeal carcinoma (NPC) Since IOIP is related to EBV infection, it might have relationship with other EBV-associated diseases, especially NPC. About 13.1–29% of NPC patient are associated with eye symptoms Eye is very sensitive, any pathological changes appear in orbital cavity is easier to find than in other tissues. The intensive study of the relationship between the IOIPs and NPC or other EBV related diseases might provide an early warning for these kinds of diseases, but its essence still needs to be validated. Also, for lacking of animal model, we know very little about the re-activation and carcinogenic mechanism of EBV. IOIP patients might be at the very early stage of EBV re-activation and could serve as a good model for the study of EBV pathogenesis.In conclusion, our results further demonstrated the relationship between IOIP and EBV infection. Further investigations might be of great value in better understanding the relationship of IOIP and EBV infection.The study “Idiopathic Orbital Inflammatory Pseudotumor” was approved by the Institutional Review Board of the Institute of Orbital Diseases, and written informed consent was provided by all study participants and/or their legal guardians.Sixteen IOIP tissue samples and one Graves' ophthalmopathy tissue sample were obtained from patients during surgical resection of the lesion (IOIPs) at Beijing Armed Police General Hospital between 2009 and 2012. Twenty normal donors'plasma samples were provided by Beijing Tongren Hospital in 2011. Nine of the sixteen tissue sections (group T patients) were stained with hematoxylin and eosin (H & E). Three of the tissue sections have large number of lymphocyte infiltration, three has middle to a few lymphocyte infiltration with dense fibrous connective, three has only very few lymphocyte infiltration with wide glandular epithelium cells. The Graves' ophthalmopathy tissue sample with obvious inflammatory cell infiltration was chosen as the control. The tissue samples were immediately snap-frozen and stored in liquid nitrogen and plasma samples were stored in −80°C refrigerator. The IOIP diagnosis was made based on pathological examinations of hematoxylin-eosin-stained tissue sections.DNA was purified from tissue samples and plasma samples using the DNeasy Mini Kit  according to the manufacturer's instructions. The quality and the quantity of the DNA were assessed by agarose gel electrophoresis and spectrophotometrical analysis.EBV DNA was amplified using the EBV-DNA detection kit purchased from DaAn Gene Co. Ltd. (licensed by China State Food and Drug Administration) and crude DNA as the template in a 50 µl reaction. Nuclease-free distilled water was used as a negative control to ensure that the reagents used were not contaminated. The positive DNA control and primers for EBV were supplied with the EBV-DNA detection kit. Real-time quantitative PCR is based on the continuous optical monitoring of the progress of a fluorogenic PCR reaction. In this system, one fluorescent dye serves as a reporter (FAM), and its emission spectra is quenched by a second fluorescent dye (TAMRA). During the extension phase of PCR, the 5′ to 3′ exonuclease activity of Taq DNA polymerase cleaves the reporter from the probe, thus releasing it from the quencher and resulting in an increase in fluorescence emission at 518 nm. The reactions were subjected to 30 cycles of amplification: denaturation at 95°C for 45 sec, annealing of primers at 55°C for 45 sec, and extension of the primers at 72°C for 45 sec. Fluorescence was detected at the end of the 72°C segment in the PCR step. The results were analysed by using MxPro™ QPCR Software.EB-VCA-IgG, VCA-IgA, VCA-IgM, EA-IgG and EBNA1-IgG were analyzed with enzyme-linked immunosorbent assay (ELISA) kits . A 96-well microplate was prepared and coated with capture antibody. Samples or standards were added, then the plates were washed, horseradish peroxidase labeled detection antibody was added to each well, and washing was repeated three times. Finally, stop solution was added to each well. The optical densities of each well were quantified in 30 minutes at dual wavelengths of 450 nm using a micro-plate reader .


In this paper, a high-dimensional pattern classification framework, based on functional associations between brain regions during resting-state, is proposed to accurately identify MCI individuals from subjects who experience normal aging. The proposed technique employs multi-spectrum networks to characterize the complex yet subtle blood oxygenation level dependent (BOLD) signal changes caused by pathological attacks. The utilization of multi-spectrum networks in identifying MCI individuals is motivated by the inherent frequency-specific properties of BOLD spectrum. It is believed that frequency specific information extracted from different spectra may delineate the complex yet subtle variations of BOLD signals more effectively. In the proposed technique, regional mean time series of each region-of-interest (ROI) is band-pass filtered ( Alzheimer's disease (AD) is one of the most prevalent dementia in older adults characterized by cognitive and intellectual deficits, which is serious enough to interfere daily life, without an effective treatment. It gets worse over time by gradually destroying brain cells, causing loss in memory and ability to reason, make judgements and communicate, and eventually causing death. AD is the most common type of dementia which accounts for 50 to 80 percent of dementia cases and definitive diagnosis can only be made with histopathological confirmation of amyloid plaques and neurofibrillary tangles. It has been reported that the incidence of AD doubles every five years after age of 65 A significant body of literature et al.Functional connectivity is defined as the temporal correlation of a neurophysiological index measured in different brain areas Models of whole-brain connectivity, which comprise networks of brain regions connected either by anatomical tracts or functional associations, have drawn a great deal of interest recently due to the increasing reliability of network characterization through neurobiological meaningful and computationally efficient measures One crucial step in resting-state fMRI signal analysis is temporal band-pass filtering. The purpose of this procedure is to minimize the effects of low frequency drift and high frequency noise. The frequency interval of band-pass filtering varies and depends on the application, but is normally within the interval of ), Band2 ([0.039–0.054 Hz]), Band5 ([0.082–0.100 Hz]) and Band1 ([0.025–0.039 Hz]). Band5 and Band1 performed the worst and no obvious differences between them. Band4 and Band2 performed slightly better than Band5 and Band1. This result explains why only features from Band2, Band4, and predominantly Band3 were selected during feature selection step.This paper investigated the diagnostic power of functional connectivity networks, derived from resting-state fMRI, for the identification of individuals with MCI from normal aging subjects. The proposed high-dimensional pattern classification framework employed an efficient frequency-specific multi-spectrum characterization of resting-state fMRI time series for accurate identification of MCI individuals. Classification accuracy was evaluated via leave-one-out cross-validation to ensure performance generalization. The classification accuracy obtained by the proposed method, involving frequency-specific multi-spectrum characterization and graph theoretic analysis, was 86.5%, which was significantly higher than the conventional full-spectrum approach. The AUC value of the proposed method was 0.863, indicating good diagnostic power, especially in view of the relatively limited number of samples available in this study. Conventional approach, which is either less sensitive to BOLD signal changes or vulnerable to noise effects, can only provide low to moderate performance as indicated by their relatively smaller AUC values. It was also found that the classification performance of individual frequency sub-bands is different indicating frequency-specific spatio-temporal information distribution in BOLD signals.A strength of our assessed cohort is that it allows us to study MCI at a relatively mild stage of cognitive impairment as indicated by very minor differences in MMSE scores between MCI patients and normal aging subjects. This provides an important distinction between our study and other studies that involve patients reporting to the clinic with significant memory and cognitive complaints.et al.Multi-spectrum characterization provides a frequency-specific description regarding the variability of the BOLD signal by decomposing the band-pass filtered time series into several smaller frequency intervals. This description reveals subtle variations of the BOLD signal and thus provides more sensitive characterization of neuronal activities during resting condition, particularly for comparison between baseline and affected BOLD signals. This is justified by the improvement of classification accuracy yielded by the multi-spectrum characterization when compared with the conventional full-spectrum approach. Further decomposing of the band-pass filtered time series, however, is restricted by the acquisition sampling frequency. It was found that each individual frequency sub-band contributed differently to classification, with Band3 ([0.054–0.068 Hz]) demonstrating the highest discriminative power. Our results based on multi-spectrum characterization are supported by findings in studies that examine the frequency-specific distribution of BLOD signal fluctuations. Many studies that examined the resting-state fMRI data by decomposing the the BOLD frequency bandwidth into separate length bands The brain regions that are selected for accurate detection of individuals with MCI includes portions of the prefrontal cortex, orbitofrontal cortex, temporal lobe, and parietal lobe, which have already been extensively reported in previous studies. These included: parts of prefrontal regions Our findings of relative asymmetric pattern, i.e., more regions in the left hemisphere were selected in training stage, is in line with some previous studies using SPECT and PET data a priori, but can only be determined from the data. Furthermore, the leave-one-out cross-validation is used in this study to guard against data overfitting, a persistent problem in high dimensionality analyses of datasets with relatively small sample sizes.It is noteworthy that the framework proposed herein is based on the data-driven concept, i.e., the set of brain measurements which optimally differentiates between MCI individuals and cognitively normal individuals cannot be known One of the methodological issues regarding post-processing of resting-state fMRI analysis is the choice of temporal filtering algorithms. Wavelet Another methodological issue that may affect the classification performance is the global correction of BOLD signal based on whole-brain signal. The whole-brain signal is defined as the average time series over all voxels in the brain and is typically removed by regression from time series at each voxel, after which the residual time series is used for further analysis Our current study is limited by the small size of available samples. Given a small sample size, statistical power is of potential concern. Although the leave-one-out cross-validation accuracy obtained may be optimistic, the limited sample size does not allow us to explore other cross-validation techniques, since the nonlinear SVM classifier will then be under-trained. Our dataset is quite diverse, and it includes both sexes and all ages between 55 to 84 for MCI patients and 55 to 88 for normal controls. However, the results obtained have to be verified in the future with larger datasets to reduce individual effects and to ensure the effectiveness of the proposed technique.In summary, resting-state fMRI has the potential for greatly increasing the clinical utility of the fMRI for diagnosis of disorders such as AD. A novel high-dimensional pattern classification method, which is based on BOLD signal contrast, has been proposed to identify individuals with MCI from normal controls. The proposed technique employs a frequency-specific multi-spectrum network characterization of the fMRI regional mean time series to effectively delineate the functional connectivity patterns at a whole brain level. Significant improvements and promising results indicate that the proposed classification framework can potentially serve as a complementary approach to clinical diagnosis of alteration in brain functions associated with cognitive impairment, especially at the early stages.All the subjects used in this study were recruited by the Duke-UNC Brain Imaging and Analysis Center (BIAC), Durham, North Carolina, USA. Written consent was obtained from all participants, and the experimental protocols were approved by the institutional ethics board at Duke University Medical Center in compliance with the Health Insurance Portability and Accountability Act. This cohort involved of 37 participants, 12 MCI patients and 25 socio-demographically matched healthy controls. All the recruited subjects were diagnosed by expert consensus panels at the Joseph and Kathleen Bryan Alzheimer's Disease Research Center (Bryan ADRC) and the Department of Psychiatry at Duke University Medical Center. Diagnosis was made by consensus with the ultimate decision by a board-certified neurologist in concert with available data from a battery of general neurological examination, neuropsychological assessment evaluation, collateral and subject symptom and functional capacity reports. The neuropsychological battery the Bryan ADRC used was a revised Consortium to Establish a Registry in Alzheimer's Disease (CERAD) which included: 1) Mini-Mental State Examination (MMSE); 2) immediate and delayed verbal memory ; 3) visual immediate memory ; 4) verbal initiation/lexical fluency ; 5) attentional/executive functions ; 6) premorbid verbal ability (Shipley Vocabulary Test); 7) Finger Oscillation Grooved Pegboard; and 8) Self Rating of Memory Function.Conformation of diagnosis for MCI if subjects met the following inclusion criteria: 1) age etc.; 3) any physical or intellectual disability affecting completion of assessments; 4) documentation of other Axis I psychiatric disorders; and 5) any prescription medication (or nonprescription drugs) with known neurological effects. Noteworthy that the diagnosis of all cases were made on clinical grounds without reference to MRI.Similarly, all healthy controls met the following criteria: 1) age An 3.0 T GE scanner  was used in scanning process to acquire resting-state fMRI volumes. Resting-state functional images of each participant were acquired axially parallel to the horizontal plane connecting the anterior and posterior commissures (AC-PC line) with echo time (TE) = Overview of Methodology: The key of our proposed multivariate high-dimensional classification framework involves an efficient characterization of resting-state fMRI time series via a two-fold description:Frequency-specific multi-spectrum characterization - to quantify relatively subtle changes of BOLD signal by decomposing the mean time series of each ROI into five distinct frequency sub-bands; andGraph theoretic analysis - to characterize topological properties and strengths of brain functional connectivity networks through neurobiologically meaningful and computationally efficient measures.An overview of the proposed MCI classification framework is summarized graphically in http://www.fil.ion.ucl.ac.uk.spm). Specifically, the first 10 acquired fMRI images of each subject were discarded to ensure magnetization equilibrium. Then, the remaining 140 images were corrected for the staggered order of slice acquisition that was used during echo-planar scanning. The correction makes the data on each slice correspond to the same point in time. The interpolated time point was chosen as the TR/2 time to minimize relative errors across each TR in the study. After acquisition time delay correction, we realigned the slice timing corrected fMRI time-series of each subject using a least squares approach and a 6 parameters (rigid body) spatial transformation Post-processing of the fMRI images of the resting-state fMRI images was performed using the Statistical Parametric Mapping software package  template For each frequency sub-band, we constructed a functional connectivity network by utilizing Pearson correlation to measure the interregional synchronization in BOLD signal. No global signal regression was performed in this study to avoid artifacts resulting from negative correlations. For each sub-band connectivity network, we computed the clustering coefficient of each node with respect to the other nodes in its subnetwork as feature for classification. For each subject, features from all connectivity networks were concatenated to form a large feature pool. Elements from this large feature pool were first ranked according to their Pearson correlation with respect to the clinical labels, and were further sieved to select the most discriminant features subset using a wrapper-based feature subset selection method, i.e., the SVM-RFE algorithm Frequency-Specific Multi-Spectrum Characterization: For each subject, the mean time series of each individual ROI was obtained by averaging the GM-masked fMRI time series over all voxels in that particular ROI. Temporal band-pass filtering with frequency interval , slow-4 ([0.027–0.073 Hz]), slow-3 ([0.073–0.198 Hz]) and slow-2 ([0.198–0.25 Hz]), and they found that only slow-5 and slow-4 are associated with resting-state functional connectivity while slow-3 and slow-2 are associated with respiratory and cardiac signals. It was also shown that, compared with slow-5, slow-4 has higher test-retest reliability In order to extract complex, yet subtle pathologies associated with MCI, we proposed to employ a frequency-specific multi-spectrum characterization of the regional mean time series, which utilizes multiple frequency sub-bands, in contrast to the conventional full-spectrum description, to construct functional connectivity networks. We hypothesis that by decomposing the BOLD spectrum into smaller frequency bands and performing the analysis using these sub-bands will provide a more sensitive characterization on spatio-temporal information of brain activity. In Estimation of Interregional Functional Connectivity: Functional connectivity that examines interregional correlations in neuronal variability We considered the brain regions as a set of nodes and the correlation coefficients as signed weights on the set of edges. A Fisher's In this study, we employed the weighted-graph local clustering coefficients These clustering coefficients form a feature pool from which we employed a hybrid feature selection method that combined the benefits of maximum-relevance and wrapper-based feature selection methods to determine the most discriminant features subset Classification performance of the proposed framework was evaluated using a nested full leave-one-out cross-validation strategy to ensure a relatively unbiased estimate of the generalization power of the classifiers to new subjects. We adopted the SVM classifier with non-linear radial basic function (RBF) kernel in the proposed framework. In each leave-one-out case, one subject was first left out as the testing subject, and the remaining subjects were used for feature extraction, feature selection and classifier training. Then, second or inner leave-one-out was applied within the training set, to build an ensemble classifier whose parameters were automatically optimized via grid search. Specifically, for Performance of the proposed frequency-specific multi-spectrum scheme is compared with the conventional full-spectrum scheme. The proposed framework is evaluated under different settings, i.e., GM-masked+multi-spectrum (proposed), unmasked+multi-spectrum, GM-masked+full-spectrum, and unmasked+full-spectrum. This will provide quantitative evidences to test our hypothesis that multi-spectrum analysis is more sensitive than commonly used full-spectrum analysis in capturing subtle variation of BOLD signals that is induced by pathological attacks, as well as its contribution to the prediction accuracy.The features are determined based on their contribution to the classification performance using cross-validation. The selected features will give an indication of the brain regions that potentially experience the greatest functional changes in BOLD signal. Further analysis on individual frequency sub-bands also been performed to explore the contribution of each individual sub-band in MCI classification as well as their relationships with the selected features.


Investigating psychopathological profiles of transsexuals raises a very basic methodological question: are control groups, which represent the biological or the phenotypic sex, most suited for an optimal evaluation of psychopathology of transsexuals?Male-to-female (MtF) (n=52) and female-to-male transsexuals (FtM) (n=32), receiving cross-sex hormone treatment, were compared with age matched healthy subjects of the same genetic sex (n=178) and with the same phenotypic sex (n=178) by means of the Symptom Check List-90-Revisited instrument (SCL-90-R). We performed analyses of covariance (ANCOVA) to test for group and sex effects. Furthermore, we used a profile analysis to determine if psychopathological symptom profiles of transsexuals more closely resemble genotypic sex or phenotypic sex controls.Transsexual patients reported more symptoms of psychopathological distress than did healthy control subjects in all subscales of the SCL-90-R , regardless of whether they were compared with phenotype or genotype matched controls. Depressive symptoms were more pronounced in MtF than in FtM . We could demonstrate that FtM primarily reflect the psychopathological profile of biological males rather than that of biological females (r = 0.945), while MtF showed a slightly higher profile similarity with biological females than with biological males (r = 0.698 vs. r = 0.685).Our findings suggest that phenotypic sex matched controls are potentially more appropriate for comparison with the psychopathology of transsexual patients than are genetic sex matched controls. Gender identity disorder (GID), also referred to as transsexualism, is defined as a condition in which a person with apparently normal somatic sexual differentiation is convinced that he or she is actually a member of the opposite sex. This conviction is accompanied by the unchangeable and irresistible urge to live in the other sex, both physically and socially, and by the wish to acquire the physical characteristics of the desired sex to the fullest extent possible (according to ICD-10 ). BiologTreatment protocols for patients with GID follow international consensus statements  but varyThe limited data on the long-term mortality of transsexuals reveals that MtF transsexuals suffer from an increase of 51% in mortality compared to the general male population. This can mostly be attributed to a six-fold increase in suicides which is accompanied by a higher rate of psychiatric conditions -9, and dThe prevalence and extent of psychopathology such as anxiety disorders and depression are highly sex-dependent . TherefoAll participating subjects gave written informed consent, and the study was approved by the local ethics committee of the Ludwig Maximilians University in Munich (132-08). 200 patients with the diagnosis “transsexualism” (GID) according to ICD-10 criteria, and treated at the Endocrine Outpatient Clinic of the Max Planck Institute of Psychiatry in Munich between January 1996 and December 2007, were identified through the electronic database of the Institute and consecutively invited to participate in this study  in 2009. Additionally, the clinicians and psychotherapists participating in regional quality circles for transsexualism assisted in terms of contacting some of the patients. All transsexuals underwent hormone replacement therapy in the endocrine outpatient clinic of the Max Planck Institute of Psychiatry.Our final sample consisted of 89 transsexual patients  aged 18 years or older, corresponding to a response rate of 44% (MtF) and 39% (FtM), respectively, a difference which was not significant. One MtF patient was excluded from the analysis due to ambiguous information about the biological sex. Four patients were excluded because they didn’t receive hormone replacement therapy. Finally we enrolled 84 transsexual patients  in the study. As a control sample, we selected 336 subjects with a negative lifetime history of mental axis I disorders, including any gender identity disorders, from a large cohort, the so called Munich Antidepressant Response Signature (MARS) control sample . For theQuestionnaires including general questions on the course of the sexual transition phase, treatment, social background and sexuality had been evaluated previously . The evaAnalyses were processed using PASW statistical software . The level of significance was set to be p= .05. We calculated Cohen’s f as the effect size measure. According to Cohen’s guidelines (1988), we categorized effects as small (f< .10), medium (f ≥ .25), and large (f ≥ .40). Mean group differences in terms of demographic and clinical variables were analyzed using the Student’s t-test in the case of quantitative variables, and Fisher’s exact tests with regard to dichotomous variables. We performed analyses of covariance (ANCOVA) to compare transsexual patients with their matched healthy controls (HC). The factors “group”  and “sex”  were tested for their effects on the SCL-90-R subscales . Two separate ANCOVAs were performed, one to investigate differences between transsexuals and matched healthy controls with the same genetic sex (sex_gen), and a second with respect to controls with the same phenotypic sex (sex_phen). Age was included as a covariate in these analyses. Univariate ANCOVA was used to determine the differences between MtF and FtM transsexuals in the SCL-90-R symptom subscales. We analyzed every SCL-90-R subscale regarding differences in the factor sex . In this analysis, we added age, depression diagnosis, sex reassignment surgery, age at which the transsexual patients discovered their transsexuality and the duration of hormone therapy in years, as covariates.To elucidate whether the symptom profile of transsexual patients resembles the profile of the control group of the same genetic or the same phenotypic sex, we performed a profile analysis of the total SCL-90-R outcome using Pearson correlations as proximity measure. Pairwise correlations were calculated between transsexuals  and both healthy control groups , and averaged after Fisher-Z transformation to a general proximity score of psychopathological symptoms. In the study, the mean age of MtF transsexual patients was 47.96 (SD 11.69) and 32.38 (SD 7.75) in FtM transsexuals (p<.001). The age at onset of the gender identity disorder was significantly higher in MtF  than in FtM  (p=.042). The average duration of hormone replacement therapy was 7.05 years (SD 8.01) in MtF transsexuals and 5.46 years (SD 4.50) in FtM transsexuals, which was not statistically significant. Twenty-one subjects in the FtM group had undergone complete sex reassignment surgery (65.6%), similar to the MtF group with 37 (66%). The majority of MtF (48.1%) received transdermal estrogens for hormonal therapy, while most of FtM were treated by intramuscular testosterone formulations (87.5%) . There was no significant difference in terms of the highest education level or the current employment situation. Fourteen (43.8%) of the FtM transsexuals lived in a committed relationship in comparison to eighteen in the MtF group (34.6%), though the difference was not significant. In the group of MtF transsexuals, seven patients reported a diagnosis of depression in their history (13.5%) compared to five patients (15.6%) in FtM transsexuals (p=.761) ,2.By comparing MtF and FtM transsexuals in the mean with regard to SCL-90-R subscales, we found a different distribution of psychopathology see . SignifiThe mean scores of the SCL-90-R subscales are shown in First, we analyzed the effects of group  and sex (genetic men vs. genetic women) in transsexual patients, and matched healthy controls with the same genotypic sex (sex_gen). We also tested for the interaction effects in terms of group and sex. Transsexual patients had significantly higher mean scores in all SCL-90-R subscales compared to healthy controls, which was significant at p<.001 for all psychopathology subscales. Regarding sex differences in the analysis with genetic matches, we found significant main effects of sex in the following SCL-90-R-subscales: depression (p=.013), phobic anxiety (p=.001) and paranoid ideation (p=.011). The largest sex effect (f=.242) was found for the subscale phobic anxiety. There was an interaction effect in terms of group x sex_gen (p=.001) for the scale depression, with MtF patients reporting more severe depressive symptoms than FtM, as well as male and female genotype matched controls see . Next, we repeated the same analysis after exchanging the control group with healthy controls, which were matched for the phenotypic sex of the transsexual patients (sex_phen). Again, we found significant group differences in all SCL-90-R subscales (p<.001), with transsexual patients reporting significantly more psychopathology symptoms than the phenotype matched healthy control group. Significant main effects of sex appeared for the following SCL-90-R subscales: IS (p=.027), depression (p<.001), hostility (p=.035), phobic anxiety (p=.001), paranoid ideation (p=.006), and for the general psychological distress index GSI (p=.010). The strongest sex effect (f=.527) was found for the subscale depression, which could be categorized as a large effect. There was a significant interaction effect with regard to group x sex_phen (p=.013) in this subscale. Again, MtF patients reported the most severe depressive symptoms, higher than those observed in FtM, as well as in female and male subjects of the phenotype-matched controls see . There wNext, we conducted a profile analysis to determine whether psychopathological symptom profiles resemble the profile of controls with the same genotypic or phenotypic sex. Separate profile correlations between TS  and healthy controls of the same genotypic or phenotypic sex were conducted across all SCL-90-R subscales. FtM showed the closest proximity with the phenotype-matched male control group (r=.945), but also showed a high resemblance with the genotype-matched female control group (r=.895). Also MtF transsexuals had considerable, but smaller profile proximity with both control groups, with an average correlation of r=.685 with the genetically-matched control group of males and of r=.698 with the phenotypically-matched female control group.In this study, we could show that transsexual patients present with significantly higher mean scores in all psychopathology subscales of the SCL-90-R, regardless of whether they are compared with phenotype- or genotype-matched controls. The effects ranged between a medium and a large effect size. This suggests an increased burden of psychopathological distress in transsexual patients. In particular, depressive symptoms were more pronounced in MtF than in FtM, which is in accordance with the literature. Furthermore, we could demonstrate that FtM primarily reflect the psychopathological profile of biological males rather than that of biological females, while MtF showed slightly higher profile similarities with biological females rather than with biological males.By performing separate analyses to find the proper control group for transsexual patients, we could show that group effects were present in both comparisons, but sex effects differed, depending on the genetic or phenotypic sex of the controls. Both comparisons revealed sex effects for the SCL-90-R subscales depression, phobic anxiety and paranoid ideation, with larger effect sizes observed in the second analysis, when transsexual patients were matched with controls of the same phenotypic sex. In addition to these effects, women scored higher in the subscale obsessionality, and the general psychopathological scale GSI in the second analysis. Both analyses also revealed a group by sex interaction for the depression subscale, with MtF transsexuals reporting most depressive symptoms. This interaction effect was less pronounced in the second analysis as a result of the larger difference between FtM and phenotypic sex-matched controls regarding depressive symptoms.Though there are several studies on psychopathology in the transgender population -19, so fTherefore, to evaluate the similarity of symptom profiles between patients and controls, we performed a profile analysis across all SCL-90-R subscales. The best profile similarity with FtM was seen in the male control group . However, similarity with the female control group (matched for the genetic sex) could also be shown, albeit to a lower degree (r=.895). Regarding the MtF group, similarities with the phenotypically (r=.698) as well as with the genetically matched control group (r=.685) were both less pronounced. This might be explained by the more advanced age of the MtF group and the matched controls, resulting in an attenuated variability, especially, within the control group. However, we again observed slightly higher similarities between MtF and phenotypically-matched controls. While the marginal difference in the similarity scores between MtF patients and both control groups hinders a definite statement about the right control group in terms of providing the optimal psychopathological comparability, it is interesting that the similarity with the phenotypic sex-matched controls was higher for both patient samples, MtF and FtM. This is in line with the finding of more pronounced sex typical effects on the SCL-90-R subscales when transsexual patients are analyzed in combination with phenotypic sex-matched controls, suggesting that the expected sex differences become more visible in combination with this control group. The results in this study are in accordance with earlier findings on the high prevalence of depressive symptoms in the transgender population ,22. HoweOur findings also correspond well with the appearance of higher SCL-90-R psychopathology scores in female subjects ,16. The The discrepancy between SCL-90-R psychopathology scores and the history of clinical diagnosis of depression, may be explained by the way the depression diagnosis was obtained, which was from chart analysis and self-reports. This might have led to an underestimation of the true prevalence in our sample. In summary, it appears that within the transsexual population, MtF have a higher burden of depressive symptoms compared to healthy controls, but also compared to FtM transsexuals. This is in agreement with current research findings ,23. AsscSince the initiation of hormone treatment has been reported to have a fundamental impact on general well-being and mood , we deciAlthough sex steroids are widely involved in mood regulation  and may Low testosterone levels or hypogonadism has been repeatedly associated with mood disturbances in biological males . CorrespHowever, a recent study demonstrated that cross-sex hormone treatment has beneficial effects in terms of lower levels of anxiety and depressive symptoms in FtM as well as in MtF . In furtIn addition, there are also fundamental gender differences regarding social interactions and partnership integrity, further influencing psychological outcome measures . MoreoveThere have also been reports that mental health improves following sex reassignment surgery ,35,36. OTo the best of our knowledge, this is the first study investigating in detail the psychopathology of transsexuals and healthy controls assessed by SCL-90-R divided into phenotype and genotype matched subjects. In summary, we could demonstrate that transsexual patients report more symptoms of psychopathological distress than healthy control subjects, regardless of whether they are compared with phenotype or genotype matched controls. Symptoms of depression were increased in MtF transsexuals, and they are also more burdened in terms of other aspects of psychopathology than FtM. The proper control group for FtM transsexuals in terms of their profile similarity with regard to psychopathological symptoms, seems to be age-matched men rather than women, supporting the view that phenotypic sex-matched controls are the more appropriate choice. While the differences in profile similarity were less pronounced between MtF and both age-matched control groups, the overall observed sex effects are additionally supportive for selecting phenotypic sex -matched controls as the appropriate comparison group for evaluating psychopathology in transsexual patients under hormone replacement therapy.The strength of this single-center study is its well-defined study groups. However, the choice of the control samples can either be regarded as a strength or a limitation in this context. On the one hand, all controls were initially selected as healthy control groups for a depression sample and, therefore, report low scores in SCL-90-R subscales and GSI in comparison to the general population . This mi


Innate immune mechanisms that follow early recognition of microbes influence the nature and magnitude of subsequent adaptive immune responses. Early detection of microbes depends on pattern recognition receptors that sense pathogen-associated molecular patterns or microbial-associated molecular patterns . The bacterial envelope contains MAMPs that include membrane proteins, lipopeptides, glycopolymers, and other pro-inflammatory molecules. Bacteria are selected by environmental pressures resulting in quantitative or qualitative changes in their envelope structures that often promote evasion of host immune responses and therefore, infection. However, recent studies have shown that slight, adaptive changes in MAMPs on the bacterial cell wall may result in their ability to induce the secretion not only of pro-inflammatory cytokines but also of anti-inflammatory cytokines. This effect can fine-tune the subsequent response to microbes expressing these MAMPs and lead to the establishment of a commensal state within the host rather than infectious disease. In this review, we will examine the plasticity of Toll-like receptor (TLR) 2 signaling as evidence of evolving MAMPs, using the better-characterized TLR4 as a template. We will review the role of differential dimerization of TLR2 and the arrangement of signaling complexes and co-receptors in determining the capacity of the host to recognize an array of TLR2 ligands and generate different immune responses to these ligands. Last, we will assess briefly how this plasticity may expand the array of interactions between microbes and immune systems beyond the traditional disease-causing paradigm. Innate immunity is characterized by an immediate response against pathogens and is paramount in the initial control of infection. Toll-like receptors (TLRs) are thought to be among the most ancient pathogen recognition systems . Such syThe genes encoding receptors of the innate immune system are inherited and not subject to gene rearrangement like those coding for adaptive immune receptors. As a result, the innate response has been traditionally considered fairly static in its recognition of microbial patterns, and to be largely indiscriminate in promoting inflammation and priming adaptive immunity. However, emerging evidence indicates that PRR signaling can also promote anti-inflammatory responses under certain conditions –5. The pTwo views of the biology of PRR-induced responses may be considered. One is that host responses to MAMPs vary depending on the context of exposure , which ultimately determines the type of response (pro- vs. anti-inflammatory responses). Alternatively, we should not forget that MAMPs are subject to evolutionary modification as a result of selective pressures by host defense mechanisms, and that this evolution will ultimately expand the array of ligands to a given PRR and the signals that it can deliver. However, co-evolution of both MAMPs as well as host responses can explain the two opposite outcomes of PRR signaling. In this review, we will focus only on TLRs, and specifically on TLR2 and TLR4, to review the plasticity of signaling by these receptors and its biological implications.Drosophila and its role in controlling fungal infections  on the extracellular N-terminal domain . This LRToll-like receptors are constitutively expressed on monocytes, macrophages, and DCs, while certain TLRs are also expressed on other cell types such as neutrophils, mast cells, epithelial cells, and B cells . TLR1, 2Through TLRs, the host can recognize a wide variety of microbial ligands including nucleic acids, lipids, lipoproteins, and polysaccharides. TLRs can be grouped according to their recognition of similar MAMPs. For example, TLRs 3, 7, 8, 9, and 13 all recognize nucleic acids. However, TLRs can also recognize a repertoire of structurally unrelated ligands. TLR4 has long been recognized as the receptor for LPS, but also recognizes heat-shock proteins , glycoprToll-like receptors function as important immune receptors that, in coordination with other PRRs, turn on innate mechanisms of immunity, including inflammation. It is thus expected that changes in TLR ligand binding and signaling capacity will translate in changes in innate immunity. This is well illustrated by observations that certain polymorphisms in TLRs have been associated with increased susceptibility to a myriad of infectious diseases , 24 and Staphylococcus aureus  have helped to identify genetic factors that reveal host susceptibilities to impairment of gut immune homeostasis . HoweverAlthough the pro-inflammatory effects of TLR4 signaling have been described in detail, the anti-inflammatory responses induced by TLR4 activation are much less characterized. The cell compartment where TLR4 signaling occurs seems to be an important factor when it comes to differentiating between pro- and anti-inflammatory signaling. Contrary to TLR4 pro-inflammatory signaling at the cell surface, TLR4 signaling from endosomal compartments induces the secretion of the anti-inflammatory cytokine IL-10 . This coOther signaling events downstream of TLR4 activation leading to this anti-inflammatory response are unclear. The MAP kinases p38 and JNK have been shown to be necessary for IL-10 secretion by bladder epithelial cells (BECs) . InhibitTrypanosoma cruzi, hemagglutinin protein from measles virus, and phospholipomannan from Candida albicans . The en in Ref. .d-alanine residues, and are responsible for the stimulating properties of this molecule when tested with synthetic LTA  decreases its recognition by innate immune receptors, playing a role in immune evasion. Strains with this gene knocked out displayed increased sensitivity to lysozyme, impaired survival in macrophages, and impaired virulence in vivo in BALB/c and C57/BL6J mice  are many and structurally diverse, albeit sharing lipid moieties . The varin vitro recombinant systems, using chimeric proteins of the extracellular domain of CD4 fused with the transmembrane and intracellular domains of TLR2, and may not take into account other regulatory factors associated with natural ligand-induced TLR2 dimerization . Another adapter linked to TLR-induced PI3K activation is the B-cell adapter for PI3K (BCAP). It has been shown that the TIR domain of BCAP interacts with MyD88 and MAL to suppress production of pro-inflammatory cytokines in bone-marrow-derived macrophages  is an autoimmune bone disorder, and patients were shown to have impaired IL-10 expression downstream of TLR4  survival  was involved in correcting CD4+ T cell deficiency and the TH1/TH2 imbalance in germ-free mice   and other PRRs is emerging in the crosstalk between the host and its microbiome. TLR2 has been linked to the establishment and regulation of the microbiota in different sites. One such site is the skin, where staphylococcal LTA has been shown to down-regulate inflammatory cytokine release by keratinocytes in a TLR2-dependent manner . Anotherree mice . The sigechanism . PSA wasesponses . It has stridia) , 112.The immunological environment in the gut and other sites where the host interacts with its microbiome is a critical site for host-microbe co-evolution and dynamic selection of MAMPs and PRRs. There is an advantage for the host to evolve sophisticated mechanisms to differentially respond to molecular patterns associated with symbiosis or pathogenicity. The involvement of TLR2 in regulating the gut microbiota and preventing dysbiosis may be just one example of the complexity of TLR ligand co-evolution, providing additional impetus for further investigation of the plasticity of TLR2 signaling and potential avenues to exploit it.in vivo in response to bacteria. However, the therapeutic promise shown by synthetic TLR2 ligands as well as the emerging significance of TLR2 in regulating the gut microbiota, among other reasons, justify further investigation into the plasticity of TLR2-mediated MAMP recognition and downstream signaling.While the pro-inflammatory responses activated upon detection of Gram-negative and Gram-positive bacteria by TLRs have been well characterized, the TLR-triggered anti-inflammatory responses are only beginning to be elucidated. The current literature regarding these new responses focus on TLR4 but TLR2 is especially interesting to study in this context given the diversity in receptor complexes and the crosstalk between downstream signaling cascades. The challenges ahead are not trivial: (i) the array of ligands for TLR2 may be larger than originally suspected, and its characterization may be an ongoing exercise as suggested by the evidence supporting their evolving nature; (ii) the relevance of various TLR2 ligands in the context of infection is not clear; and (iii) the tools used to study TLR2 activation both structurally and functionally are limited and may not recapitulate the full range of responses following TLR2 activation The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


We compared the readmission rates and the pattern of readmission among patients discharged against medical advice (AMA) to control patients discharged with approval over a one-year follow-up period.A retrospective matched-cohort study of 656 patients(328 were discharged AMA) who were followed for one year after their initial hospitalization at an urban university-affiliated teaching hospital in Vancouver, Canada that serves a population with high prevalence of addiction and psychiatric disorders. Multivariate conditional logistic regression was used to examine the independent association of discharge AMA on 14-day related diagnosis hospital readmission. We fit a multivariate conditional negative binomial regression model to examine the readmission frequency ratio between the AMA and non-AMA group.AMA patients were more likely to be homeless (32.3% vs. 11%) and have co-morbid conditions such as psychiatric illnesses, injection drug use, HIV, hepatitis C and previous gastrointestinal bleeding. Patients discharged AMA were more likely to be readmitted: 25.6% vs. 3.4%, p<0.001 by day 14. The AMA group were more likely to be readmitted within 14 days with a related diagnosis than the non-AMA group . Patients who left AMA were more likely to be readmitted multiple times at one year compared to the non-AMA group . There was also higher all-cause in-hospital mortality during the 12-month follow-up in the AMA group compared to non-AMA group .Patients discharged AMA were more likely to be homeless and have multiple co-morbid conditions. At one year follow-up, the AMA group had higher readmission rates, were predisposed to multiple readmissions and had a higher in-hospital mortality. Interventions to reduce discharges AMA in high-risk groups need to be developed and tested. Discharge against medical advice (AMA) makes up 1 to 2% of overall discharges in an acute hospital setting and are known to be higher with certain patient populations et al showed that patients discharged AMA from the general medicine ward at Beth Israel Hospital in Boston were more likely to be younger, male, and lack health care coverage compared to their counterparts. Not having a primary care physician or admission to hospital with high alcohol level was also associated with discharges AMA et al demonstrated significantly higher readmission rates in the AMA group compared with the non-AMA group within the first 15 days. They also showed that up to 40% of patients discharged AMA were readmitted during the first 90 days. History of drug or alcohol abuse was significantly associated with higher rates of discharge AMA Younger age, male sex, poor social support, lack of health care coverage, psychiatric illness, drug or alcohol abuse are frequently associated with discharge AMA We undertook our study at St. Paul's Hospital, a tertiary care, university-affiliated teaching hospital in Vancouver, British Columbia that serves one of the poorest neighbourhoods in Canada with high rates of addiction, homelessness and HIV In this study, we compared the readmission rates and the pattern of readmission among patients discharged AMA to control patients discharged with approval from the general medical and HIV services over a one-year follow-up period. We hypothesized that the rate of hospital readmission would be higher than reported in previous studies given the high prevalence of addiction disorders in our patient population and their increased risk of discharges AMA. These findings may have implications for other hospitals that serve similar marginalized populations in terms of understanding the magnitude of this problem and potential strategies to ameliorate it since often studies of AMA patients are often focused on one chronic physical or mental health condition.The Providence Health Care Research Ethics Board approved this study in December 2009 and because this was a retrospective chart review, they waived the need for patient informed consent.st and December 31st, 2008 from the St. Paul's Hospital Health Records. Discharge status  was recorded for all patients by the nursing unit coordinator and included in the medical record. Patients were determined to leave AMA if they did not return to hospital within six hours after obtaining physician permission to go on a pass. The unit coordinator would assign a discharge AMA to the patient if they have not returned by six hours unless a longer period has been authorized by the physician. Patients were also designated as discharge AMA if they simply left the hospital without physician permission and did not return within six hours. Each patient discharged AMA was matched with a patient discharged with approval according to 10-year age group, gender and Case Mix Group (CMG+), which codes for the primary reason for the hospital length of stay The study design was a retrospective matched-cohort. We obtained a list of patients discharged against medical advice from either the general medicine or the HIV services between January 1The data were extracted from St. Paul's Hospital electronic medical record database, Sunrise Clinical Manager®. We abstracted patient information including age, gender, housing situation, co-morbidities, medication list, most responsible diagnosis and length of stay for each admission for the 2008 calendar year. The number of readmissions and in-hospital mortality was recorded up to 12 months following the index admission. Housing situation was classified to homeless, single room occupancy hotel, apartment or house. Major categories under co-morbidities included psychiatric illnesses , neurologic conditions , alcoholism, injection drug use status, HIV status, hepatitis C status, congestive heart failure, diabetes, chronic obstructive pulmonary disease, previous gastrointestinal bleeding, and soft tissue infections (cellulitis and abscess). These co-morbidities were selected as these are common conditions seen among patients admitted to the general medical ward at our hospital. They were abstracted from the standardized discharge summary that includes pre-admit co-morbidities and post-admit co-morbidities. The past medical history section of the initial admission note was also reviewed for the above co-morbidities. We created a co-morbidity count indicator variable with three levels: 0–1, 2–3 and ≥4 co-morbidities.As this was matched case-cohort study using individual matching, we compared baseline characteristics between the AMA and non-AMA group using the McNemar's tests for categorical variables and Wilcoxon signed-rank test for continuous variables. Conditional logistic regression models were fitted to estimate the impact of AMA on the occurrence of hospital readmission related to the index diagnosis. Homelessness, injection drug use, HIV, soft tissue infections, psychiatric illness and the co-morbidity count indicator were included in the model as potential confounding variables. The effect of AMA discharge on the hospital readmission frequency in the one-year follow-up period was evaluated using the conditional negative binomial model adjusting for the aforementioned pre-selected potential confounding variables. Analyses were performed with the SAS software version 9.2. All reported p-values are two-sided.Of the 2792 discharges during the calendar year of 2008 from the general medicine and the HIV services, 451 (16.1%) were discharges AMA incurred by 328 patients. Discharge AMA from the general medicine service and the HIV service was 17.8% and 15.4%, respectively. Among hospital discharges from the general medical and HIV services in 2008, we found 16.1% of them were discharges AMA, which is higher than previous reports A 2002 nationwide survey in the United States revealed that 1.4% of all discharges were discharges AMA We also found a high in-hospital all-cause mortality of 6.7% in our study among patients with left AMA during the 12-month follow-up period. Glasgow et al reported a higher 30-day mortality of 0.75% Veterans Administration patients who left AMA vs. non-AMA patients (p = 0.001) over a 5-year period. When they modeled the 60-day mortality, they found a significant association between AMA and mortality  It is unclear how many of the discharges AMA in our sample were potentially preventable. More than half of the subsequent readmissions following an index case of leaving AMA resulted in another discharge AMA as shown in This study has several limitations. As a single-site study, our results may not be generalizable to all hospitals but may reflect the situation of other hospitals that serve a substantial portion of indigent patients with significant co-morbidities such as addiction, homelessness and HIV. We did not track patient admissions to other hospitals or ambulatory clinic visits during the one-year follow-up period, or take into account deaths outside the hospital. As a result, the readmission rates reported in this study as well as mortality rate are likely conservative estimates. We did not assess the reliability of the abstracted co-morbidity diagnoses and did not have complete data to use the well-validated Charlson index to adjust for co-morbidity. We were unable to adjust for severity of illness in our analysis due to differences in diagnoses between cases and controls and typically severity of illness measures are disease specific. Furthermore, we did not match the cases and controls for co-morbidity and these between-group differences may contribute to the higher observed mortality in the AMA group. Lastly, we were unable to provide insight regarding the reasons for patients leaving AMA, which are crucial to understand in order to address this significant problem.In summary, this retrospective matched cohort study found that patients discharged AMA had poor housing situations and multiple co-morbid conditions such as HIV, addiction and psychiatric illness. They had higher readmission rates particularly during the first 14 days post-discharge and were predisposed to multiple readmissions and repeated discharges AMA in the following year. Despite greater health care utilization, the AMA group had poorer health outcomes, most notably higher in-hospital mortality highlighting the importance of developing and testing interventions to reduce AMA discharges in vulnerable populations. A prospective trial is needed to study the effectiveness of interventions such as Alfandre's approach


To describe out-of-pocket costs of inpatient care for children under 5 years of age in district hospitals in Kenya.A total of 256 caretakers of admitted children were interviewed in 2-week surveys conducted in eight hospitals in four provinces in Kenya. Caretakers were asked to report care seeking behaviour and expenditure related to accessing inpatient care. Family socio-economic status was assessed through reported expenditure in the previous month.Seventy eight percent of caretakers were required to pay user charges to access inpatient care for children. User charges  were 59% of total out-of-pocket costs, while transport costs  and medicine costs  were 36% and 5%, respectively. The mean total out-of-pocket cost per paediatric admission was US$ 14.1 . Out-of-pocket expenditures on health were catastrophic for 25.4%  of caretakers interviewed. Out-of-pocket expenditures were regressive, with a greater burden being experienced by households with lower socio-economic status.Despite a policy of user fee exemption for children under 5 years of age in Kenya, our findings show that high unofficial user fees are still charged in district hospitals. Financing mechanisms that will offer financial risk protection to children seeking care need to be developed to remove barriers to child survival. Access to hospital care plays an important role in improving child survival . The cosThe study was conducted in 2008 as part of a larger study to improve the quality of paediatric inpatient care in district hospitals in Kenya (The characteristics of the admitted children are shown in P = 0.025) but did not vary significantly with diagnosis. Out-of-pocket costs were catastrophic for 25.4%  of households. The proportion of catastrophic health expenditures was higher in households in the lower socio-economic group ) than in the higher socio-economic group ).Household monthly expenditure on food was 79% of the total monthly expenditure on essentials ; expenditures on rent constituted 11%, and on education 10% . The majOur findings show that 78% of caretakers paid user fees for inpatient paediatric care for sick children. The mean out-of-pocket costs for inpatient paediatric care were US$ 14.1 (95%CI 11.9–16.2), which is almost three times higher than reported out-of-pocket costs for district hospital paediatric inpatient care in Tanzania (US$ 5.5) . This level of healthcare expenditure is arguably high and comparable to findings in Tanzania of 35.4% .This study has a number of limitations. Data on household monthly expenditures depend on recalled expenditure over a period of a month and are subject to recall biases. Out-of-pocket costs reported are lower than actual costs, given that we did not collect information on other direct costs (e.g. expenses for food and upkeep of any accompanying relatives). The study also only focused on out-of-pocket costs associated with hospital admissions and left out costs associated with outpatient visits and with those who did not seek care in hospitals; these costs would be important in giving a comprehensive picture of out-of-pocket healthcare costs associated with child illnesses. Also, whereas catastrophic health expenditures are conventionally calculated based on annual health expenditure and annual consumption expenditure, the data available allowed for a calculation based on monthly household and health expenditures. These limitations notwithstanding the data presented are potentially useful as inputs in costing and/or cost-effectiveness models that require patient cost and suggest there are significant-out-of pocket costs associated with paediatric admission care in district hospitals in Kenya, which offer a barrier to access to care.Policy makers need to mitigate the adverse effects of such expenditures, for example, by extending insurance coverage to the uninsured or by increasing efforts to implement exemption mechanisms. Our findings reinforce observations of tension between policy makers and health facility managers in resource-limited settings, with managers trying to provide services with limited resources and hence forced to disregard policies that threaten their revenue streams.


We assessed the severity of the 2009 influenza pandemic by comparing pandemic mortality to seasonal influenza mortality. However, reported pandemic deaths were laboratory-confirmed – and thus an underestimation – whereas seasonal influenza mortality is often more inclusively estimated. For a valid comparison, our study used the same statistical methodology and data types to estimate pandemic and seasonal influenza mortality.We used data on all-cause mortality  and influenza-like-illness (ILI) incidence (0.8% coverage). Data was aggregated by week and age category. Using generalized estimating equation regression models, we attributed mortality to influenza by associating mortality with ILI-incidence, while adjusting for annual shifts in association. We also adjusted for respiratory syncytial virus, hot/cold weather, other seasonal factors and autocorrelation. For the 2009 pandemic season, we estimated 612 (range 266–958) influenza-attributed deaths; for seasonal influenza 1,956 . 15,845 years-of-life-lost were estimated for the pandemic; for an average seasonal epidemic 17,908. For 0–4 yrs of age the number of influenza-attributed deaths during the pandemic were higher than in any seasonal epidemic; 77 deaths (range 61–93) compared to 16 deaths (range 0–45). The ≥75 yrs of age showed a far below average number of deaths. Using pneumonia/influenza and respiratory/cardiovascular instead of all-cause deaths consistently resulted in relatively low total pandemic mortality, combined with high impact in the youngest age category.The pandemic had an overall moderate impact on mortality compared to 10 preceding seasonal epidemics, with higher mortality in young children and low mortality in the elderly. This resulted in a total number of pandemic deaths far below the average for seasonal influenza, and a total number of years-of-life-lost somewhat below average. Comparing pandemic and seasonal influenza mortality as in our study will help assessing the worldwide impact of the 2009 pandemic. The 2009 influenza A/H1N1 pandemic has led to major additional surveillance and control efforts by public health authorities worldwide. Assessing the severity of the pandemic has been complicated. A concrete measure of severity would be the number of deaths  and years-of-life-lost due to the pandemic compared to the numbers caused by seasonal epidemics. However, the numbers of pandemic deaths reported to WHO Another methodological issue is that young children seem to have been disproportionately affected by the pandemic, whereas the elderly have been relatively spared Two studies have estimated age-specific pandemic and seasonal influenza mortality for the US Nevertheless, associating all-cause mortality to ILI-incidence can be complicated by annual heterogeneity in the association between mortality and ILI. In an earlier study we already reported that such annual heterogeneity in association is likely to be due to differences in severity per ILI case driven by viral factors such as dominant (sub)types (influenza A(H1N1), A(H3N2) or B) and/or antigenic characteristics In this study we compared the 2009 pandemic mortality to the 10 preceding seasonal epidemics in the Netherlands (population size 16.5 million) by attributing age-specific all-cause mortality to influenza in 1999–2010, using the modeled association of weekly all-cause mortality with ILI-incidence. We also calculated years-of-life-lost from the age-specific influenza mortality estimates.st and to end on June 30th, since influenza epidemics normally occur around wintertime.We obtained weekly mortality data from Statistics Netherlands (100% coverage) for the period 1999–2010, aggregated by week and age category . In addition to all-cause mortality, physician-reported primary cause of death data were available for analysis. Weekly ILI-incidence data for 1999–2010 were obtained from a sentinel network of general practitioners  To adjust for possible mortality caused by pathogens other than influenza, we also investigated total weekly counts of RSV, as reported by a routine laboratory surveillance system  and patient age were not available www.knmi.nl) and defined weight variables for cold or warm weeks as the number of degrees Celsius below 0°C and above 17°C respectively.Another contributor to (excess) mortality can be weather conditions, such as hot and/or cold temperatures Similar to We then used the model coefficients to estimate the weekly number of ILI-attributed deaths by age category. Based upon these weekly estimates of influenza-attributed deaths, we calculated the annual influenza-attributed numbers of deaths, scaled to the 2009 population. Finally, we compared the pandemic mortality to the influenza mortality in earlier years.We also performed a sensitivity analysis to evaluate the impact of our model choices on the influenza-attributed mortality; we evaluated the impact of in- or excluding RSV or the hot/cold weather variables in the model, using ILI-incidence vs influenza detections as a measure of influenza activity, and using pneumonia/influenza (ICD-10 codes J09-J18) or respiratory/cardiovascular (J00-J99 and I00-I99) deaths as explained variables instead of all-cause deaths.We calculated the years-of-life-lost (YLL) Fitting the regression models with annual ILI-coefficients to our data showed heterogeneity in the influenza-attributed mortality incidence both among years, and among age categories. In the models, for all age categories the (lagged) ILI-incidence was included as explanatory variable for all-cause mortality. The annual ILI regression coefficients showed significant heterogeneity between years for all age categories . Some of the annual ILI regression coefficients estimates were below zero – reflecting the mild influenza impact in those years, as also observed in Sensitivity analysis showed that RSV and hot/cold weather only minimally influenced the influenza-attributed mortality. Replacing ILI-incidence by influenza detections in the models did not change the estimates much either. When replacing all-cause mortality by respiratory/cardiovascular or pneumonia/influenza deaths, the estimates for pandemic and seasonal influenza deaths were much lower; but pandemic mortality was consistently estimated lower than during an average seasonal influenza epidemic, with a relatively low impact in the elderly combined with a relatively high impact in the youngest age category. See th revision, ICD-10 code I42) and spinal muscular atrophy (ICD-10 code G12.9) were overrepresented during the pandemic exclusively (P≤0.002 and P≤0.012 respectively). Intracerebral haemorrhage in neonates was also disproportionately high during the pandemic , but also in 2004/2005 although then only borderline significant. The two other causes of death that showed elevations  seemed unlikely to be related to the pandemic, since the first had been increasing since 2007 and the second showed elevations for two other years in the study. See Because of the increased number of influenza-attributed deaths in the 0–4 yrs of age during the pandemic year, we further explored the distribution of age and reported cause of death in this age category. After excluding neonatal deaths, deaths at <1 yrs of age occurred relatively more often during the pandemic than in earlier seasonal influenza years , but to a lesser extent this was also observed for the seasonal epidemic in 2000/2001. Among the reported causes of death, cardiomyopathy  compared to most seasonal epidemics.We were able to compare pandemic to seasonal influenza mortality by using the same statistical methodology and data types for both pandemic and seasonal influenza years. Our use of annual instead of constant ILI-coefficients made it possible to estimate the influenza-attributed mortality per year, adjusted for shifts in the association of ILI-incidence and mortality.We found a large range of total numbers of deaths and years-of-life-lost for the 10 seasonal epidemics. In 2003/2004 the influenza A/Fujian/411/02(H3N2) drift variant emerged and Fujian-like H3N2 viruses remained the dominant subtype in the next 3 years For the 0–4 yrs of age, we estimated 77 pandemic deaths, which is higher than for any seasonal influenza epidemic in the study. Remarkably, in the Netherlands only 5 laboratory-confirmed deaths in 0–4 yrs of age were reported during the pandemic, including one child <1 yrs of age with a medical condition The higher proportion of deaths in <1 yrs of age (excluding neonates) that we observed, suggests that a disproportionate fraction of the 77 estimated influenza-attributed deaths may have occurred in infants <1 yrs of age who may be less likely to show sufficiently specific symptoms of influenza infection to prompt requests for influenza laboratory diagnostics. In England and Argentina, high mortality rates in infants have been reported among pediatric deaths due to laboratory-confirmed influenza A(H1N1) 2009 infection Our observation of 8 deaths by cardiomyopathy during the pandemic in the 0–4 yrs of age, and 5 deaths by spinal muscular atrophy – even if confirmed to be related to pandemic influenza infection – cannot quantitatively explain our estimate of 77 pandemic deaths. Nevertheless, cardiomyopathy and spinal muscular atrophy are not unlikely to be related to influenza. Heart disease is a known complication of influenza and has also been specifically reported for pandemic influenza A(H1N1) 2009 Unlike two earlier Dutch studies on the impact of the pandemic In the other Dutch study Several methods can be used to estimate influenza deaths from mortality data, like annual regression methods Nevertheless, our estimates for influenza mortality can be affected by the way we chose to model the association of mortality with influenza activity in the population; another study Some consider mortality categories such as pneumonia/influenza and respiratory/cardiovascular deaths more specific for influenza than all-cause mortality Finally, the residual life expectancy that we used to estimate the years-of-life-lost may be lower for people with underlying medical conditions , leading to a potential overestimation of years-of-life-lost. In an earlier study on laboratory-confirmed pandemic deaths, modifying the life expectancy based on reported underlying disease resulted in a reduction of years-of-life-lost by over a third We found that the pandemic had an overall moderate impact on mortality compared to the 10 preceding seasonal epidemics; disproportionate mortality in young children was observed, as opposed to low mortality in ≥75 yrs of age. This resulted in a far below average total number of pandemic deaths compared to seasonal influenza, but a total number of years-of-life-lost only somewhat below average. To assess the impact of future pandemics, attributing age-specific all-cause mortality to influenza should be preferred above comparing laboratory-confirmed deaths to historical estimates for seasonal influenza. For other countries and continents, pandemic mortality should be estimated relative to seasonal influenza as well, to assess the worldwide impact of the 2009 pandemic.Figure S1Weekly all-cause mortality by age category and ILI-incidence in time.(DOC)Click here for additional data file.Appendix S1Details on regression models.(DOC)Click here for additional data file.Appendix S2Sensitivity analysis.(DOC)Click here for additional data file.Appendix S3Deaths in 0–4 yrs of age during the pandemic compared to seasonal influenza; distribution of age and causes of death.(DOC)Click here for additional data file.


Estrogen forms a complex with the estrogen receptor (ER) that binds to estrogen response elements (EREs) in the promoter region of estrogen-responsive genes, regulates their transcription, and consequently mediates physiological or tumorigenic effects. Thus, sequence variants in EREs have the potential to affect the estrogen-ER-ERE interaction. In this study, we examined the hypothesis that genetic variations of EREs are associated with breast cancer development.This case-control study involved 815 patients of Asian descent with incident breast cancer and 821 healthy female controls. A total of 13,737 ERE sites in the whole genome predicted by a genome-wide computational algorithm were blasted with single-nucleotide polymorphism (SNP) sequences. Twenty-one SNPs located within 2,000 bp upstream or within introns 1 and 2 of putative genes and with a minor allele frequency greater than 5% were identified and genotyped. Frequencies of SNPs were compared between cases and controls to identify SNPs associated with cancer susceptibility.NRCAM which codes for a cell adhesion molecule, and SNPs of ESR1, the gene coding for ER, on breast cancer risk was found. Interestingly, this combined effect was more significant in women who had experienced a longer period of lifetime estrogen exposure, supporting a hormonal etiology of this SNP in breast tumorigenesis.A significant combined effect of rs12539530, an ERE SNP in intron 2 of ESR1 in determining susceptibility of breast cancer development.Our findings provide support for a role of genetic variation in ERE- The roles of estrogen receptor α (ERα) in initiating tumor development in breast cancer, regulating progression and determining therapeutic protocols and efficacy are well documented -3. AlthoXenopus vitellogenin gene identified a minimal ERE core sequence of 5'-GGTCANNNTGACC-3' [cis-regulatory modules, to predict all possible EREs in the genome. The prediction algorithm of PReMod takes into account the fact that, in higher eukaryotes, cis-regulatory regions often contain several phylogenetically conserved binding sites for different transcription factors [Early work on the TGACC-3' . Since tTGACC-3' ,10. By sTGACC-3' . To distTGACC-3' . In thisTGACC-3' , a new d factors ,14, and ESR1 [This case-control study is part of an ongoing cooperative study aimed at understanding the causes of breast cancer in Taiwan. Breast cancer in Taiwan is characterized by low incidence, early tumor onset, hormone dependency and novel genomic alterations -17. We sESR1 , the genESR1 -21.Experienced research nurses were assigned to administer a structured questionnaire to both patients and controls. The information collected has been described, and the validity of the questionnaire has been addressed and confirmed, in our previous studies -21.At the end of the interview, blood was taken for DNA isolation and genotyping. All samples were examined by laboratory personnel who were blinded to the case/control status of specimens. DNA was extracted from the peripheral blood samples of patients and controls using DNA purification kits .The PReMod database is a genome-wide/transcription factor-wide collection of more than 100,000 computational predicted transcriptional regulatory modules within the human genome ,14. ThesSNPs were genotyped in all samples using Sequenom iPLEX technology . Positive, negative and duplicate controls were included on all plates, with genotypes being autocalled by specialized software  and subsequently confirmed by visual assessment of the data. All assays were performed by individuals blinded to the case-control status of the samples. As a quality control, we repeated the genotyping on 10% of the samples, and all genotype scoring was performed and checked separately by one reviewer who was unaware of the case-control status. The concordance rate for replicate samples was 100%.2 goodness-of-fit test or a Fisher's exact test. The χ2 test for a 2 × 3 contingency table was used to compare genotype frequency between patients and controls. To take account of multiple comparisons, these associations were also assessed using the permutation test provided in the Haploview software tool , run using 10,000 permutations. The association of susceptibility genotypes and breast cancer risk was further evaluated with simultaneous consideration of established risk factors for breast cancer or other significant risk factors in multivariate logistic regression models. Biologic plausibility was the most important criterion for inclusion of variables in the model. Therefore, we included all established risk factors for breast cancer in the statistical models: age, family history of breast cancer, age at menarche, parity and age at first full-term pregnancy (FFTP). Adjusted ORs (aORs) and 95% CIs for genotypes were then estimated.Univariate and multivariate analyses were used to determine the risk factors for breast cancer in this series of study participants, and the odds ratio (OR) and corresponding 95% confidence intervals (95% CIs) were estimated. For individual ERE SNPs, genotype frequencies were assessed for departure from the Hardy-Weinberg equilibrium using either a χESR1 polymorphism in our study participants that we published recently [ESR1-ERE interaction or estrogen-ESR1-ERE interaction in determining breast cancer development. A combination of the joint method and stratified analysis [ESR1 and ERE was associated with breast cancer formation. A joint effect of ESR1-ERE on increased breast cancer risk was explored using conventional logistic regression, a test evaluating whether a statistically significant increase in risk was observed with specific combinations of putative high-risk genotypes in these SNPs (measured by the β estimates from this regression model). In addition, we stratified our study participants on the basis of ESR1 genotype and examined whether breast cancer risk associated with ERE SNPs was particularly significant in specific ESR1 genotype subsets of women. Because we were especially interested in the relationship between the joint effect of ESR1-ERE and breast cancer risk within categories of risk factors representing different levels of estrogen exposure, we performed stratified analysis to test this hypothesis. Therefore, if the identified joint effect of ESR1-ERE SNPs initiated breast cancer by the formation of the estrogen-ER complex, then the relationship between breast cancer risk and the joint effect would not be the same in women who had experienced different lengths of estrogen exposure. This was evaluated by calculating the risk (aOR) of breast cancer associated with the joint effect of ESR1-ERE SNPs in women with a longer or shorter period of total estrogen exposure. For menopausal women, total estrogen exposure was calculated using the formula (age at menopause - age at menarche - years of full-term pregnancy) and, for premenopausal women, age at menopause in this formula was replaced by age at recruitment into this study.We made use of the information on the recently  and explanalysis -21 was aThe risk profile of breast cancer in our study participants was similar to that found in our previous studies -21 and iA total of 13,737 sites  in the whole genome were predicted to be ERE-related sequences using the PReMod algorithm. After blasting these data with online information available from SNP data sets , 322 ER-binding sites were identified as potentially harboring SNPs. One hundred ten of these 322 SNPs were found to contain no variant allele in the Chinese population, resulting in 212 SNPs, 21 of which met our criteria and were genotyped in patients and controls  compared with women carrying the homozygous wild-type genotype, as well as that the carrying of one additional risk allele was associated with a significant increase in risk   or not expressing ESR1 (MDA-MB-231 cells) and examined whether the expression of these putative ER-regulated genes is ER-dependent. The results for NRCAM were more promising, as they showed that this gene could be expressed in an ER-positive breast cancer cell  and between rs12539530 and rs7739506 (P = 0.09) are in line with the suggestion that an interaction between rs12539530 and ESR1 polymorphism is linked to breast cancer risk. To confirm this interaction, we used the joint method and calculated the risk of breast cancer associated with both rs12539530 and ESR1 SNPs using a set of dummy variables representing different combinations of genotypes of ESR1 and rs12539530. In contrast to the increased, but not significant, risk associated with either a high-risk genotype of ESR1 or rs12539530 alone, the greatest risk was found in those patients harboring high-risk genotypes of both rs12539530 and ESR1 SNPs in all women combined  and activator protein 1 (AP-1). After blasting these binding sites with the sequences of 30 SNPs known to reside in this block as reported by HapMap, some SNPs were found to be located within these binding sites, so the second possibility cannot be totally excluded. However, we are prompted to suggest the breast tumorigenic contribution of rs12539530 on the basis of the findings that (1) rs12539530 and ESR1 SNPs jointly increased breast cancer susceptibility and (2) this joint effect was more significant in women with a longer period of estrogen exposure. In addition, this suggestion is mechanistically plausible and is similar to the finding that rs10736303, the SNP generating a putative ERE in intron 2 of FGFR2, has been identified as the most significant gene determining breast cancer susceptibility in recent genome-wide association studies (GWASs) [NRCAM, the gene putatively regulated by rs12539530, has been suggested to be upregulated in ER-positive, but not in ER-negative, breast cancer cell lines [In considering whether our findings represent a true association between this SNP in ERE and breast cancer, the most important issue is the interpretation of the identified association between SNPs and the trait. Because the SNP identified is in an intron, it does not affect amino acid coding and therefore probably does not directly affect protein function. In addition, the observed association between breast cancer and this SNP could be due to the presence of linkage disequilibrium (LD) between this SNP and other SNPs in exons  or in regulatory regions (affecting the expression of these genes). However, the first of these two possibilities is less likely, as we checked the LD block in which rs12539530 is located in the Chinese population in HapMap , which s (GWASs) -31. In all lines , and thiNRCAM is located, has never been reported to be important with regard to breast cancer risk determination in GWASs. This might be partially explained by the relatively low-penetrance effect of SNPs of estrogen-regulating genes. Distinct from those alleles identified by current GWAS, these polymorphic alleles of estrogen-regulating genes would predispose carriers to only a moderately increased risk of developing cancer. Thus, the significance of such SNPs depends not only on their own effect but also on the interaction between the target genes and other functionally related genes  or the promoting effect of reproductive risk factors reflecting estrogen exposure. Our findings are in line with this suggestion.Recent GWASs based on technological advances in high-throughput genotyping have provided information regarding the LD of neighboring polymorphisms. These GWASs have made possible the use of a few hundred thousand SNPs as tags for all other variants, as well as the biobanking of tens of thousands of specimens, allowing their immediate use in whole genome research. These studies have led to the mapping of novel susceptibility loci for many common traits, including breast cancer ,31. InteThis study is a hybrid of candidate gene and genome-wide approaches in which we took advantage of both designs. The well-defined roles of the ER during breast tumorigenesis makes it reasonable to assume that polymorphic genetic variants of EREs, central nodes in the ER pathway, might underlie the variations seen between individuals in their susceptibility to breast cancer. This candidate mechanism lends critical support to the biological plausibility and tumorigenic relevance of our findings. In addition, our genotyping of SNPs on the basis of genome-wide predicted EREs provided a unique opportunity to comprehensively examine putative ERE sites without depending on a prior hypothesis. The successful identification of rs12539530 by using these combined methods suggests that this is a promising approach to identifying the breast tumorigenic contribution of EREs on a genome-wide scale on the basis of information generated by recent technological advances. For example, in our ongoing study, we are exploring the breast tumorigenic role of putative EREs identified using chromatin immunoprecipitation-based methods ,33. The NRCAM, which codes for a cell adhesion molecule, and SNPs of ESR1, the gene coding for ER, on breast cancer risk was found. Our findings provide support for a role of ESR1-ERE polymorphism in determining susceptibility of breast cancer development. This knowledge will be helpful for directing the focus of future experimental studies.Though the promise of personalized medicine, in which the risk and the course of diseases and the efficacy of treatment protocols would be predicted on the basis of a person's genotype, must been tempered with caution, validated molecular tests assessing the patient's germline DNA already drive therapeutic decision-making. On the basis of the well-documented role of ER in breast cancer development and progression, this study explored whether genetic variations in EREs, the sequences bound by ER to activate the transcriptional regulation of target genes, are associated with susceptibility for breast cancer. Notably, the ERE sites genotyped were based on genome-wide prediction, providing a unique opportunity to comprehensively examine putative ERE sites without depending on a prior hypothesis. A significant combined effect of rs12539530, an ERE SNP in intron 2 of CI: confidence interval; ER: estrogen receptor; ERα: estrogen receptor α; ERE: estrogen response element; FFTP: first full-term pregnancy; FTP: full-term pregnancy; GWAS: genome-wide association studies; LD: linkage disequilibrium; OR: odds ratio; SNP: single nucleotide polymorphism.Financial support was provided by grant NSC 98-2628-B-016-001-MY3 (to JCY) from the National Science Council, Taiwan and by grants TSGH-C97-7-S03, DOD-98-28-03 and DOD-98-36 from the Tri-Service General Hospital (to JCY and HMH).The authors declare that they have no competing interests.NRCAM expression in breast cancer cell lines. SD and CWC performed ESR1 genotyping.JCY and CYS participated in the study design and coordination, carried out the participant recruitment and drafted the manuscript. CNH and PEW carried out the genotyping and data analysis. BYB contributed to searching ERE websites using PReMod. HMH, STC and GCH carried out the participant recruitment. WCC and LYH contributed to the validation of Tables S1 and S2. Table S1 presents genotype frequencies of sequence variants of estrogen response element (ERE)-related sequences in breast cancer patients and controls and the adjusted odds ratio (aOR) in relation to breast cancer risk. Table S2 presents breast cancer risk associated with genotypic polymorphism of rs12539530, a single-nucleotide polymorphism (SNP) in the estrogen response element (ERE)-related sequence, stratified by genotypes of ESR1, the estrogen receptor gene.Click here for file