The integration of genomic and DNA methylation data has been demonstrated as a powerful strategy in understanding cancer mechanisms and identifying therapeutic targets. The TCGA consortium has mapped DNA methylation in thousands of cancer samples using Illumina Infinium Human Methylation 450 K BeadChip (Illumina 450 K array) that only covers about 1.5% of CpGs in the human genome. Therefore, increasing the coverage of the DNA methylome would significantly leverage the usage of the TCGA data. Here, we present a new model called EAGLING that can expand the Illumina 450 K array data 18 times to cover about 30% of the CpGs in the human genome. We applied it to analyze 13 cancers in TCGA. By integrating the expanded methylation, gene expression, and somatic mutation data, we identified the genes showing differential patterns in each of the 13 cancers. Many of the triple-evidenced genes identified in majority of the cancers are biomarkers or potential biomarkers. Pan-cancer analysis also revealed the pathways in which the triple-evidenced genes are enriched, which include well known ones as well as new ones, such as axonal guidance signaling pathway and pathways related to inflammatory processing or inflammation response. Triple-evidenced genes, particularly TNXB, RRM2, CELSR3, SLC16A3, FANCI, MMP9, MMP11, SIK1, and TRIM59 showed superior predictive power in both tumor diagnosis and prognosis. These results have demonstrated that the integrative analysis using the expanded methylation data is powerful in identifying critical genes/pathways that may serve as new therapeutic targets. A limitation of the TCGA DNA methylation data is that it was generated using Illumina Infinium Human Methylation 450 K BeadChip (referred to as Illumina 450 K array hereinafter), which only covers about 1.5% of the CpGs in the human genome. This poor coverage restricts epigenomic analysis and many differentially modified loci are likely missed. While whole genome bisulfite sequencing (WGBS) and other technologies are available to measure DNA methylation with much higher coverage, it is unlikely to repeat the DNA methylation analysis in the large number of TCGA samples considering the expense and effort in the near future. Therefore, there is an urgent need to develop new analysis strategy to better use these data.The Cancer Genome Atlas  with a more than 10 times faster speed compared to our previous methods. Furthermore, the location distribution of the expanded CpG sites is less biased toward CpG rich regions, and the hyper/hypo-methylated ratio is also more similar to the ratio from the WGBS data. Importantly, the coverage of CpGs is significantly increased from about 1.5% of all CpGs in the human genome in the original Illumina 450 K data to about 30% after expansion.Previously, we developed a method to expand the Illumina 450 K array data by considering sequence features and local methylation profile in the neighboring CpGs.7 These triple-evidenced genes are enriched in numerous pathways, suggesting new possible targets for therapeutics. Importantly, these triple-evidenced genes can discriminate the cancer from normal samples and predict survival. In particular, nine genes, TNXB, RRM2, CELSR3, SLC16A3, FANCI, MMP9, MMP11, SIK1, and TRIM59 are important in both cancer diagnosis and prognosis; note that FANCI and SIK1 would be missed on using the original Illumina 450 K data. The EAGLING model and all of the triple-evidenced genes are available at http://114.55.236.67:8013/Integrative_Analysis/home.This new model allows integrative analysis of genetic variation, gene expression, and expanded DNA methylation to identify genes and pathways that are important for diagnosis and therapeutic treatment. We identified the triple-evidenced genes in each of the 13 TCGA cancers that have sufficient samples. The triple-evidenced genes represent the genes that are differentially methylated, differentially expressed, and associated with somatic mutation. We found that the triple-evidenced genes shared by a majority of the 13 cancers include many previously identified biomarkers or therapeutic targets.We propose here an integrative analysis strategy to identify key regulators and pathways in cancers from the TCGA data. By comparing gene expression, genetic variation, and DNA methylation data between normal and cancer samples, we extracted the triple-evidenced genes for 13 cancers and analyzed the characteristics of these genes Fig. .Fig. 1Th3 First, DNA sequence features are not included in EAGLING, which significantly improves the speed without deteriorating the performance; second, the methylation value of the closest CpG is used because of its higher precision compared to the weighted neighbor CpGs used in our previous models;3 third, more training data are included (33 versus 14 tissues/cell lines), which is expected to improve the model.In order to expand the Illumina 450 K array DNA methylation data, we previously developed prediction models based on local methylation patterns and sequence features. In this work, we proposed a new model dubbed as EAGLING that has two steps to predict methylation levels of CpGs based on the Illumina 450 K array data. First, it finds a WGBS methylome that shares the most similar local methylation profile around the CpG L under consideration, and the methylation value of the CpG in the selected WGBS methylome is taken as an input feature; second, the methylation value of the closest CpG from Illumina 450 K array is taken as another input feature. A logistic regression model was built on these two features to predict the methylation level at L  between the tumor and normal samples (see Methods for detail). We also identified the differentially expressed genes (DEGs) using the RNA-seq data and genes containing somatic mutations . Some enriched pathways are known to be important in cancer, such as MMPs , VEGF family ligand–receptor interactions, Wnt pathway, NF-kB signaling, MAPK Signaling. The top five pathways most often found in the 13 cancers are shown in Fig. For each of the 13 cancers, we checked the enriched pathways among the triple-evidenced genes using ingenuity pathway analysis (IPA)  are shown in Figure The other four enriched pathways are hepatic fibrosis/hepatic stellate cell activation, leukocyte extravasation signaling, agranulocyte adhesion, and diapedesis and atherosclerosis signaling. All of them are involved in inflammatory process or response, and their top functions are in cell-to-cell signaling and interaction, cellular movement or immune cell trafficking. The association between the development of cancer and inflammatory is well recognized,25 or sporadic breast cancer.26 The low expression level of MYL9 is correlated with a significantly reduced median survival rate in colon cancer patients and might act as clinical biomarkers for the early diagnosis of colon cancer.27 MMP9 and MMP11, both of which belong to Proteins of the matrix metalloproteinase (MMP) family, were reported as tumor biomarkers28 or associated with tumor survival,29 and targeted by an inhibitor marimastat.Several triple-evidenced genes  appear in three out of the five significantly enriched pathways and in more than half of the 13 cancers. All of these genes have been reported associated with cancers. The promoter methylation of CXCL12 was acted as a prognostic biomarker in prostate cancer patientst-test), particularly the specificity. The AUC of the cancer diagnosis with the triple-evidenced genes is about 0.85, which indicates that there are common differential gene expression and methylation features of the triple-evidenced genes that distinguish tumors from the normal samples.We then investigated whether the triple-evidenced genes are useful in distinguishing cancers from normal samples. We trained a random forest model with the selected triple-evidenced genes to discriminate the pooled cancer samples from the normal ones. As there are a large number of triple evidenced genes in all the cancers, we chose those that appear in more than half of the 10 training cancer types as the candidates. Their associated gene expression and DNA methylation levels of individual CpGs in the promoters of the selected genes in the expanded data were input features; the somatic mutation information could not be included because the mutation information for each gene in every sample was not available. The model was constructed with a cross validation strategy by sampling 10 cancer samples as training data and the remaining three cancer samples as test data for 100 times. LASSO was applied to select features in constructing each random forest model. The features are presumably important if they were most often selected in the 100 cross validations. We list the 47 features selected in more than 50 times of the cross validations in Table 30 and tested the classification performances based on the triple-evidenced genes , while the LUSC was with the lowest accuracy (87.41%). When looking into the misclassification results, KIRP is prone to be predicted as KIRC and vice versa, LUAD is prone to be predicted as LUSC and vice versa, which is reasonable because they belong to the same tumor category. Also we found that the majority of mis-classified HNSC were predicted as LUSC, and many of the mis-classified LUSC were predicted as HNSC. The interesting results were consistent with the previous reports that patients treated for head and neck squamous cell carcinoma frequently developed second primary tumors in the lung, and they shared many common patterns.33 and hepatocellular carcinoma,34 and highly expressed in breast cancer.35 Another example is CYGB expression, the fifth most important feature. CYGB shows variable expression in cancers: it is down-regulated in many cancers36 but up-regulated in lung and brain metastases, and head and neck cancer.38 The methylation level at a LAMA4 promoter CpG was found as the seventh most important feature; previously, the aberrant methylation at the LAMA4 promoter was observed in breast carcinoma39 and low methylation was associated with poor progression-free survival.40Among the top 20 features Table , 13 featp-values are 0.03 (compared with expanded methylation data), 1.2e-11 (compared with expression data) and 9.1e-6 .We also investigated whether the triple-evidenced genes are useful in predicting survival rate. For the survival data of each cancer , we applied the LASSO cox proportional hazards regression for feature selection. The candidate features include gene expression and expanded methylation data  of triple-evidenced genes identified in each cancer. The performance was assessed using 10-fold cross validation for 100 times. We compared the concordances (C-indexes) based on four different candidate features . As an example, the boxplots of the concordances of COAD in cross validation are shown in Fig. p-value = 0.00145). The results for the other 10 cancers are shown in Figure Furthermore, we focused on the gene features frequently selected among the cross validations to cluster the tumor samples. For example, using the 19 features selected in more than 20% of the cross validations in the COAD samples  was reported to induce anti-inflammatory phenotype and could be used to treat certain autoimmune diseases,56 we speculate that it can be repurposed to treat cancers as four of the top five enriched pathways in the pan-cancers analysis are closely related to inflammatory processing or inflammation response.Among the nine genes, RRM2, MMP9, MMP11, and SIK1 are known drug targets. For example, they are inhibited by gemcitabine (RRM2), marimastat (MMP9 and MMP11) for treating several cancers. Our analyses suggested these inhibitors may be effective for the majority of the 13 cancers, which suggests possible broader applications of these inhibitors. For example, gemcitabine targeting RRM2 are validated for the treatment of non-small-cell lung cancer,We present here a method EAGLING to significantly expand the Illumina 450 K array data with a fast speed and better precision than the previous models. We have performed pan-cancer analysis on 13 TCGA cancers to identify genes with differential methylation and gene expression between cancer and normal samples as well as containing somatic mutations. These triple-evidenced genes, particularly TNXB, RRM2, CELSR3, SLC16A3, FANCI, MMP9, MMP11, SIK1, and TRIM59 show diagnostic and prognostic power. Note that FANCI and SIK1 could only be identified as triple-evidenced genes using the expanded methylation data. The pathways in which they are enriched also suggest new therapeutic targets or repurposing the existing drugs. We focused on discussing the common features among the 13 cancers but it is worth noting that the triple-evidenced genes in individual cancers can also be potential biomarkers or drug targets.We showed that the expanded methylation data allowed identification of more cancer-related genes, which led to better performance in both diagnosis and prognosis. The common patterns shared among the 13 cancers suggest that some drugs (such as gemcitabine) currently aiming to specific cancers might be useful to treat other cancers, and drugs aiming to immune diseases (such as HG-9-91-01) might be repurposed for cancer therapy.57 and TCGA and the methylation value measured by the Illumina 450 K array of the closest neighbor CpG of L was used as the second feature (x2). The local methylation profile was defined by four CpGs in the upstream and downstream 30 Kbp regions of site L  not using DNA sequence features to achieve a faster speed, (2) optimized parameters of local methylation pattern and the flanking region size, (3) the training set was significantly increased from 14 to 33 that is expected to improve the performance.In the EAGLING model, the CpG site to be predicted is denoted as L. The WGBS methylation value at L in the tissue that shows the most similar local methylation profile among the 33 tissues was used as one feature x and the 60), sensitivity (SE), specificity (SP), accuracy (ACC), andArea Under ROC Curve(AUC). For calculating SE, SP, ACC, and AUC, we defined the methylation status as + 1 if the methylation value is larger than 0.5, and the methylation status as −1 otherwise.The performance of EAGLING was assessed by leave-one-tissue-out cross validation on all the 22 autosomes. The evaluation metrics included Pearson correlation coefficient (COR), Concord  to 1) larger than 0.7 was defined as hyper-methylation, and the value less than 0.3 was defined as hypo-methylation. For comparison, the ratios of hyper/hypo-methylated CpGs of WGBS data of lung cancers were calculated.q-value of t-test < 0.05 and the absolute difference of methylation value > 0.1. We considered genes whose promoters contain any CpG covered by the original or the expanded methylation data. To identify the genes differentially methylated in each cancer, the methylation status of all the CpG sites covered in promoters were considered. For each promoter, the Fisher’s combined test was used to get the q-value to evaluate whether a gene is differentially methylated. Similar to call DMLs, genes with q-value < 0.05 and mean difference of DNA methylation >0.1 were selected as differentially methylated genes (DMGs).We compared the methylation levels of each CpG between tumor samples and the corresponding normal sample data, and defined a CpG site to be differentially methylated (DML) if the q-value of t-test is <0.05.The RNA-seq data were downloaded for the cancers listed in Table To collect genes with mutation related to the 13 cancers, we downloaded the somatic mutation level 2 data from TCGA. For each cancer, a gene that was annotated with curated somatic mutation in TCGA was considered. We extracted the gene lists with somatic mutation of all the 13 cancers for integrative analysis.To search for common features in pan-cancer analysis, all the 13 cancer samples and normal samples were combined together, respectively. A random forest model was constructed using cross validation. In each of the cross validation, the cancer and normal samples of 10 cancers were randomly selected for model training, and the remaining samples of three cancers were used for test. The cross validation was repeated for 100 times. We chose the triple-evidenced genes that appear in more than half of the 10 training cancer types as the candidate genes. Their associated gene expression and DNA methylation levels of individual CpGs in the promoters of the selected genes in the expanded data were used as input features; Both of the gene expression and the methylation levels of CpGs in the promoters were candidate features for feature selection with LASSO.To indicate whether these potential common features also reflect some differences between the cancers, a multi-class regression model was constructed with the tumor samples of the 13 cancers. The candidate features were the combination of all the features selected in any of the 100 cross validations in the above diagnosis analysis. The gene expression and the methylation levels of promoter CpGs were further selected using LASSO.In the prognostic analysis, the PRAD and THCA were not analyzed due to their limited samples with the expression, methylation, and survival data. The sample sizes of the 11 tumors are listed in Table Further information on experimental design is available in the Supplementary fileReporting Summary


This paper presents two studies exploring the latent structure of item sets used in the development of the Recovering Quality of Life mental health outcome measures: ReQoL-10 and ReQoL-20.In study 1, 2262 participants completed an initial set of 61 items. In study 2, 4266 participants completed a reduced set of 40 items. Study 2 evaluated two formats of the questionnaires: one version where the items were intermingled and one where the positively worded and negatively worded items were presented as two separate blocks. Exploratory and confirmatory factor analyses were conducted on both datasets where models were specified using ordinal treatment of the item responses. Dimensionality based on the conceptual framework and methods effects reflecting the mixture of positively worded and negatively worded items were explored. Factor invariance was tested across the intermingled and block formats.In both studies, a bi-factor model  with one general factor and two local factors (positively worded questions and negatively worded questions) was preferred. The loadings on the general factor were higher than on the two local factors suggesting that the ReQoL scale scores can be understood in terms of a general factor. Insignificant differences were found between the intermingled and block formats.The analyses confirmed that the ReQoL item sets are sufficiently unidimensional to proceed to item response theory analysis. The model was robust across different ordering of positive and negative items.The online version of this article (10.1007/s11136-018-2091-1) contains supplementary material, which is available to authorized users. The Recovering Quality of Life (ReQoL) is a self-report instrument to measure health outcomes for people with mental health difficulties . Two verThe two ReQoL measures tap seven domains that are relevant to the recovery of people with mental health issues: activity (meaningful and/or structured); belonging and relationships; choice, control and autonomy; hope; self-perception; well-being and physical health. These domains were identified in a systematic review of qualitative research on Quality of Life (QoL) in mental health and from interviews with service users –6. ServiThe items used in the ReQoL measures were generated in two stages. In stage I, items were adapted from existing measures, from qualitative interview manuscripts and new items were also generated. In all cases, the criteria suggested by Streiner and Norman were used to shortlist and refine items to enhance comprehension and avoid misinterpretation . In stagIn this paper, we present the results of factor analyses carried out to establish the latent structure of the ReQoL item sets that would be used to construct the final ReQoL measures. This was an important step for the following reasons. First, establishing a reproducible understanding of the latent structure of the ReQoL items addresses internal construct validity—the degree to which the measure assesses the themes of interest—as well as facilitates a higher fidelity and more accurate perspective when it comes to external scale validation . Second,Mental health questionnaires often report a factor solution involving two correlated factors: positive mental health and negative mental health . It is un = 3), (iii) 2 charities and (iv) from a trial cohort. An additional further 520 participants were recruited from an online panel, 200 of whom that had no mental health problem(s). As shown in Table Two sequential studies were conducted. In study 1, 1762 participants were recruited from four different sources: (i) 13 provider organisations, (ii) 3 GP surgeries . The aim was to reduce the number of items to 40 for presentation in a shorter questionnaire in subsequent study 2. The item reduction process combined insights from qualitative research and quantitative analysis and is discussed elsewhere . The 40-Participants in study 2 were presented with one of the two formats of the item set: (i) a format where the positively worded and negatively worded questions were intermingled, that is not grouped in any way (henceforth referred to as format I) and (ii) a format where all the negatively worded questions were presented as a block followed by the positively worded questions (henceforth referred to as format B). If a two-factor structure is to some extent due to intermingling of positive and negative items, we would expect a clearer two-factor structure in the intermingled format than in the block format. Each recruiting organisation used only one of the two formats. A total of 2447 (from 16 recruiting organisations) and 1819 participants (from 12 organisations) completed format I and B, respectively.The ReQoL was constructed using a conceptual framework comprising six mental health domains and a physical health domain , 6. TherSecond, exploratory factor analysis (EFA) was also carried out using Geomin rotation to identify other potential factors present in the data. Finally, informed by the initial CFA and EFA, single factor, two-factor Fig. b and bi-(i)the highest MI (> 100)  or(ii)whether the pairs of items had been identified as conceptually similar at the time of item generation and from qualitative evidence , by consThe design of study 2 enabled us to compare latent factor structure for datasets collected using different item ordering formats of the item set. We used multigroup CFA approach, where the persons answering the block item order constituted one group and the persons answering the intermingled format, the other group. We estimated a two-factor CFA model with the correlation between the two factors constrained to being equal in across groups and compared this to a model where this correlation was allowed to vary between groups. A significant result would indicate that the correlations between the positive and negative factors in the two formats were different which, in turn, would indicate that the ordering of the question impacted on the results. In these analyses, factor loading and item thresholds were constrained to be equal across groups. We evaluated global fit of the model, but since our research question was operationalized as a test of the equality of factor correlations, we did not test whether each item loading or item threshold differed between the groups. Finally, we sought to establish to what extent factor solutions suggested invariance across groups within studies and across samples .Results and model fit statistics obtained for CFA factor solutions for both studies  are shown in Table Eigenvalue analysis in  identified a strong first factor and a weak second factor as shown in Table The two-factor CFA model with the factors for positively worded items and negatively worded items returned the following model fit results: . The bi-factor model also had acceptable fit . ReQoL item loadings on the general/global factor were substantially higher than the two specific/group factors. Explained common variance (ECV) values were 78.9% in study 1 and 84.5% in study 2. It was possible to identify and therefore extend our models to account for areas of strain within factor solutions, through local correlations. The model results are presented in Table The bi-factor models in both studies achieved acceptable fit  at very similar levels to the two-factor models. ECVs were 79.2% and 80.9% for general factors for study 1 and 2, respectively. In study 1, the standardised factor loadings for the 61-item set general factor were substantially higher than the two group/specific factors. The factor loadings of the general factor ranged from 0.424 (There were people I could turn to for help) to 0.892 (I felt confident in myself) while the factor loadings for the “negatively worded items-specific factor” ranged between 0.285 (I had problems with my sleep) and 0.518 (I felt panic), and 18 out of the 23 “positively worded items-specific factor” had loadings less than 0.3 (see Table S3). As shown in Table In study 1, three pairs of items  and 12 pairs of items  were identified as potential local dependence residual correlations in the two-factor and bi-factor solutions, respectively. In study 2, 10 pairs of items  and 12 pairs of items  were identified as potential local dependence residual correlations in the two-factor and bi-factor models, respectively (see Table χ2 test for difference in correlations between factors across forms was not significant (p = .378). Thus the two formats did not differ.The model fit for the intermingled and block formats was acceptable (RMSEA = 0.054 and CFI = 0.976). The common correlation of the positive and negative factors across formats was estimated at .829. A We used a combination of confirmatory and exploratory factor analyses to assess the dimensionality of the ReQoL item sets at the stage of instrument development. The results from study 1 were replicated in the study 2 adding to the robustness of results. We found that a bi-factor model with the positively worded and negatively worded questions as separate factors provided an acceptable fit. The factor loadings were substantially higher on the general factor than on the group factors and the ECV was 78.9% in study 1, and 84.5% in study 2. We have not found commonly agreed threshold for interpreting the ECV, but previous studies have concluded that scales were sufficiently unidimensional if they obtained ECV values in this range . Thus, tThe initial CFA raised questions around the hypothesis of six separate mental health themes identified in earlier work about what mattered to service users with mental health difficulties. This result and subsequent CFA results from a bi-factor model were driven by the high correlation among the themes. From the qualitative work, service users considered the themes separately but the factor analyses suggested that they all amount to one concept of quality of life. To retain the face validity of the ReQoL measure, a decision was taken by the Psychometrics advisory team to select at least one item from each of the themes despite most of the items being highly correlated.In order to capture the breadth of patient’s journey, the construction of outcome measures requires the inclusion of both positively and negatively worded items. Understandably, there has been a recent focus on framing patients’ experiences positively as exemplified, for example, in the Warwick–Edinburgh mental well-being scale (WEMWBS) . HoweverAt the time of development, we searched the literature for guidance on the ordering of positive and negative items. While there was an important literature on positive and negative items in mental health (for example see –28), we The main limitation of this paper is the fact that participants in study 2 were not randomly assigned to the intermingled and block formats of the ReQoL items. The random assignment of both formats within trust would have made gathering follow-up data needed to assess responsiveness of items reported elsewhere  logisticIn summary, the factorial structure of the items sets used to construct the ReQoL-10 and ReQoL-20 measures were found to be sufficiently unidimensional as confirmed by the good fit of the bi-factor models and therefore rendering the use of IRT methods to inform further item reduction possible.Below is the link to the electronic supplementary material.Supplementary material 1 (DOCX 59 KB)


The purpose of this study was to examine the longitudinal association between concurrent, transitory and persistent difficulties initiating and maintaining sleep (DIMS) on academic performance in children in a. longitudinal child-cohort (N = 3986) targeting school-aged children when they were 7–9 years (T1) and 11–13 years (T2) old, whilst controlling for mental health problems. DIMS were parent-reported at T1 and T2 and academic performance teacher-reported at T2. Mental health was based on child self-report at T2 using the Strength and Difficulties Questionnaire (SDQ). In all, 10.6% (n = 423) of the children had poor school performance at T2. These had more symptoms of externalizing and internalizing mental health problems (p. < 001) compared to their peers at T2. A logistic regression analysis showed that both concurrent DIMS  and persistent DIMS (at both T1 and T2) was associated with elevated risk of poor academic performance. After controlling for mental health problems, only persistent DIMS was associated with increased risk of poor academic performance. Transitory DIMS (DIMS at T1 but not at T2) did not increase the risk of later poor academic performance. A mediation analysis also revealed that the association between DIMS and poor school performance was mediated by mental health problems, in addition to an overall significant direct relative effect of DIMS on poor school performance in the persistent DIMS group. These findings support the notion that sleep problems in children are associated with impaired academic performance, and extends past findings demonstrating that sleep problems may not increase the risk of poor academic performance unless they persist over time. The negative effects of persistent sleep problems suggest that more emphasis should be put on preventive interventions of sleep problems in school-aged children. Sleep problems are common in children  and adolSleep plays an important role in fostering both short- and long-term developmental regulation of cognition, emotions and behaviour –24, as pIn a number of studies, measures of poor sleep quality has been found to be related to degraded performance on a number of cognitive tasks, particularly complex tasks involving attention  and memoThe study of the association between sleep and academic performance in children leans itself primarily on findings from cross-sectional studies . The fewhttp://uni.no/en/bergen-child-study). The protocol and the population of the BCS have been described in detail in separate pervious publications [The current sample consisted of children taking part in the first two waves (T1-T2) of the BCS, carried out in the autumn of 2002 and the spring of 2006, respectively. The BCS is a longitudinal prospective study of children’s mental health from primary academic age to adolescence in the municipality of Bergen, Norway , parents of 5661 children consented to their child’s participation, comprising a longitudinal sample of 4028 children who participated at both T1 and T2. The longitudinal sample did not differ from the sample that was lost to follow-up on their DIMS scores at T1, according to results from the Kruskal-Wallis test, with mean DIMS scores of 0.09 and 0.11 for the longitudinal sample and the sample lost to follow-up, respectively. Complete information was available for 3480 children. An additional 27 children were excluded from the analysis due to parent-reported intellectual disability. Thus, the total current study population comprised 3453 children (47.2% boys), which constituted 35.1% of the original target population, and 49.3% and 61.0% of the study population at T1 and T2, respectively. The current study population did also not differ on the DIMS T1 scores, neither from the sample lost to follow-up (K-W test (df = 1) = 0.19) nor the sample that had been excluded due to missing data or parent-reported intellectual disability (K-W test (df = 1) = 0.14). The mean DIMS scores for the current study sample and the sample excluded from the analyses were 0.09 and 0.11, respectively.Sleep problems were assessed with one question encompassing difficulties with initiating and/or maintaining sleep through parental ratings at both T1 and T2 based on the question; “Does the child have problems initiating or maintaining sleep (DIMS)?” This was rated on a three-point  scale. All children whose parents agreed or partly agreed to the question were defined as having problems with falling asleep and/or with nocturnal awakenings. This operationalization has previously been successfully applied in papers from the BCS , 56. ConAt T2 the teachers indexed the children’s academic performance by answering the question: “How do you evaluate the child’s academic performance?” on a four-point scale . All children whose teacher rated their academic performance to be “very low” or “low” constituted the poor academic performance group. This group was contrasted with the rest of the children who constituted an average-to-good academic performance group, based on their teacher’s ratings.The children’s mental health was assessed by the children’s self-report on the Strengths and Difficulties Questionnaire (SDQ) at T2 , 58. TheWe conducted Pearson chi-square tests and Kruskal–Wallis analyses for the independent samples to examine differences in demographics and self-reported mental health between children with and without poor academic performance. Non-parametric tests were chosen due to non-normality of the data. A logistic regression analysis was used to explore the association between DIMS and academic performance. Both unadjusted/crude  and analyses where all potential explanatory variables were entered simultaneously (adjusted) were conducted. The confounders we adjusted for were: Gender, Pubertal status, SES, Internalizing and Externalizing mental health problems. Finally, we conducted a fully adjusted analysis controlling for all potential confounders. Results are presented as odds ratios (OR) with 95% confidence intervals (95% CI). The mediating effects of Internalizing problems and Externalizing problems on the relationship between DIMS and poor school performance was estimated based on the Ordinary Least Squares Linear Regression model in the PROCESS macro for SPSS  yieldingAll analyses were carried out using the statistical software package (SPSS) version 24 .The National Data Inspectorate and the Regional Committee for Medical and Health Related Research Ethics in Western Norway have approved both waves of the BCS (REK 184–01 and REK 062–06). Parents provided their informed consent for participation at both T1 and T2.At T1 a total of 270 children  were reported by their parents to have difficulties initiating and maintaining sleep (DIMS), of which 132 children also had DIMS at T2. Almost one-half of the children with DIMS at T1 also had DIMS at T2, and hence constituted the Persistent DIMS group. The remaining 138 children who had DIMS at T1 but not at T2 comprised the Transitory DIMS group. An additional 265 children (7.7% of the study cohort) had DIMS at T2 but not at T1 constituted the Concurrent DIMS group. The rest of the children , who were not reported to have sleep problems at any time, comprised the No DIMS comparison group.A total of 362 children (10.5% of the current study population) were reported by their teacher to evidence poor academic performance. Comparisons between the poor academic performance and control group constituting the children with average-to-good academic performance revealed statistically significant differences in terms of DIMS, pubertal development, family economy, and level of parental education and mental health scores see . The lowThe results from the crude logistic regression analysis showed that children with transitory DIMS did not have increased risk for poor academic performance at T2, compared to the no-DIMS children  or in the models adjusting for gender, pubertal status and SES. The models additionally adjusting for mental health problems yielded an almost significantly lower risk of poor academic performance in the transitory DIMS compared to the no-DIMS group , but in the fully adjusted model, this association did not longer remain significant .Finally, persistent DIMS was a significant predictor for low academic performance, both in the crude  as well as in the fully adjusted model .The results from the path mediation analysis revealed first, as shown in The main finding in the present study was that parents’ reports of their children’s sleep problems in terms of difficulties initiating and maintaining sleep when they were 7–9 year olds was associated with the children’s teacher-reported school performance three years later. After controlling for the confounding effects of demographic and in particular internalizing and externalizing mental health problems, sleep problems were still associated with an increased risk of poor school performance, in the Persistent DIMS group. This association was partly mediated by the children’s self-reported internalizing and externalizing mental health problems. Neither the children in the Concurrent nor in the Transitory DIMS groups had any increased risk of poor school performance, after controlling for mental health problems. Finally there was no direct effect of sleep problems on school performance in these two groups, suggesting that the association between sleep problems and poor school performance was mediated by mental health problems.Children in the Persistent DIMS group had increased risk of poor school performance adjusting for the demographic and mental health factors. Neither children in the Concurrent nor in the Transitory DIMS groups had any increased risk of poor school performance compared to children in the No DIMS group, after adjusting for the demographic and mental health factors. In the transitory DIMS group, sleep problems at T1 were, after controlling for the effects of the demographic and mental health factors, associated with a reduced risk of poor school performance, although not significantly so.The finding that persistent sleep problems increased the risk of poor school performance accords with both results from the meta-analytic review of cross-sectional findings , 62, croThe association between sleep problems and school performance was mediated by the children’s self-reported level of internalizing and externalizing mental health problems. This is in line with previous findings of strong bidirectional associations both between sleep and mental health , 65, andThere was no cross-sectional association between sleep problems and school performance in the children in the Concurrent DIMS group, after controlling for confounders and the association was fully mediated by symptoms of mental health problems. The mediation through children’s self-reported level of internalizing and externalizing problems suggests that sleep problems may exert its influence on school performance through symptoms of mental health problems during childhood , possiblIn contrast to children in the Concurrent DIMS group there was a direct effect of sleep problems on school performance in the children in the Persistent DIMS group. This could be attributed to the magnitude of the sleep problems, as it is likely that children with persistent problems also are more affected by them. This is also in line with our previous findings that children in this cohort with persistent DIMS have twice the risk of fulfilling the DSM-5 criteria for insomnia when they reach late adolescence . It coulIn the transitory DIMS group, after controlling for self-reported internalizing and externalizing problems, former sleep problems actually emerged as a possible protective factor against poor school performance (see first column in The limitations of the current study should be noted. First, both sleep and academic performance were measured using single items, not targeting the nature of neither the sleep nor the academic performance problems in any depth. Second, DIMS was assessed by parent-report alone. Even though many studies find that parental reliably identifies children’s sleep problems , it is aThe main strengths of the present study were the large sample size, the longitudinal design and the multi-informant design. The present study is one of only few longitudinal prospective studies on sleep and academic performance in unselected child cohorts. As we used parent reported information on DIMS, self-reported mental health problems and teacher-report on academic performance, the risk that the results were due to common method bias , which hTaken together, these findings add to the current knowledge about the relationship between sleep problems and academic performance by examining this relationship longitudinally, and at the same time controlling for mental health problems, gender, pubertal status and socioeconomic status. We found that concurrent sleep problem, in terms of children’s difficulties initiating and maintaining sleep at age 10–12 years did not increase the risk for poor academic performance after controlling for mental health problems. However, if the sleep problems persisted from age 7–9 until age 10–12, it increased the risk for poor academic performance, even after controlling for mental health problems. These findings emphasize the importance of assessing sleep problems in young children. While children’s sleep problems often are overlooked , these p


Musculoskeletal (MSK) conditions are a common presentation in primary care. This study sought to determine the prevalence of MSK conditions in primary care in Ontario and to validate the extent to which health administrative date billing codes accurately represent MSK diagnoses.De-identified electronic medical records (EMR) from the DELPHI database in southwestern Ontario, which contains 2493 patients ) and 21,964 encounters  were used for the analyses. Outcomes included: validation measures of agreement between International Classification of Diseases (ICD-9) diagnostic codes  and International Classification of Primary Care (ICPC) diagnoses defined as the reference standard, time to first ICD-9 code, prevalence, and healthcare utilization.p < .0001); referrals ; and prescriptions for opioids .There were 2940 true positive MSK encounters with primary care practitioners for 998 patients. Performance of the ICD-9 diagnostic codes included sensitivity = 76.5%, specificity = 95.2%, PPV = 94.6%, and NPV = 78.7%, compared to the ICPC reference standard. The majority of 998 patients were coded with both an ICPC and ICD-9 MSK code at their first or second encounter (67.4%). However, 23.5% of patients with the ICPC reference standard MSK were never coded with ICD-9. Four-year prevalence of MSK was 52.3% and varied by age . Patients at MSK encounters had a higher number of: investigations  contains supplementary material, which is available to authorized users. Musculoskeletal conditions are a common presentation among patients in primary care , yet theCanada’s health care system provides universal, single-payer, government-funded medical care including primary care. In Ontario, health administrative data offer rich and plentiful information about patients’ health problems and their health care utilization; many conditions have been studied using health administrative data . Health The first objective of this study was to determine the prevalence of musculoskeletal conditions in primary care patients, who were characterized according to their demographic and clinical characteristics, including healthcare utilization. The second study objective was to measure the extent to which OHIP diagnostic codes for musculoskeletal conditions  agreed with the separate set of codes defined as the reference standard. We hypothesized that, compared to the reference standard, OHIP diagnostic codes would demonstrate moderate sensitivity and high specificity for musculoskeletal conditions.Deliver Primary Healthcare Information) Electronic Medical Record (EMR) database. This database is located at the Centre for Studies in Family Medicine at Western University, London, Ontario, Canada and contains 29,303 patient records from 23 participating family physicians at 10 practice sites across the southwestern region of the province of Ontario [This retrospective cohort study used de-identified data from the DELPHI  to code encounter diagnoses for a random selection of one patient per day until a maximum of 10% of their patients were prospectively coded as part of the research study . A totaln = 21,964 encounters coded by 17 physicians at seven practice sites.For this study, 4 years of data were extracted between July 1st, 2006 and June 30th, 2010. Of the ICPC cohort, 2493 patients were eligible for inclusion in our study by having at least one encounter with both ICPC coding and an International Classification of Diseases version 9 (ICD-9) diagnostic code recorded for the Ontario Health Insurance Program billing. These patients had at total of The presence of a musculoskeletal ICPC symptom or diagnosis code recorded by the physician in the EMR at the end of a patient encounter was considered the reference standard for this study (hereafter referred to as the ‘ICPC reference standard’). A list of ICPC codes for musculoskeletal conditions were selected and approved by two clinician researchers  through consensus. All of the symptoms and diagnoses in the Musculoskeletal Chapter were selected as the ICPC reference standard to identify patients with musculoskeletal conditions . Four adCongruent with the selection of the ICPC list of musculoskeletal conditions, the list of OHIP ICD-9 diagnostic billing codes (hereafter referred to as the ‘ICD-9 billing codes’) was also developed through a consensus process. The Ontario Ministry of Health and Long Term Care’s (2016) Resource Manual for Physicians contains an alphabetical list of diagnoses with three digit ICD-9 codes that was used to identify musculoskeletal conditions . All codThe following patient characteristics were extracted: sex , age in years, age groups , patient’s rural/urban residence using the first three digits of their postal code  and a comparison of the characteristics of patients with and without musculoskeletal conditions was done using Pearson Chi-Square statistics for categorical factors and using ANOVA for the continuous factor of age in years using a significance level of patient was the unit of analysis where the first occurrence of an ICPC reference standard code was captured for each patient. Then, the presence of an ICD-9 billing code was identified at either the same encounter or at an encounter subsequent within the 4 years of data. The second approach used the encounter as the unit of analysis where the occurrence of an ICPC reference standard and ICD-9 billing code were captured for each individual encounter. For each of these two approaches, sensitivity, specificity, positive predictive value and negative predictive value were calculated as performance measures.For the validation, the ICPC reference standard was compared to the ICD-9 billing codes to validate the extent to which the ICD-9 billing code represented the prevalence of musculoskeletal conditions. This validation analysis was conducted using two approaches. First, the The total number of visits and mean number of months it took for a patient to receive their first ICD-9 billing code after an ICPC reference standard code were calculated. The remainder of patients (without an ICD-9 billing code) were assessed for their distribution across practices and physicians in the database to assess data quality and observing whether there was only one physician or practice who did not use any ICD-9 billing codes for musculoskeletal conditions. In addition, the amount of time subsequent to the last ICPC reference standard visit was measured to determine if patients had additional visits in the database where they would have an opportunity to receive an ICD-9 billing code.The mean number of visits to the family physician was calculated for patients with and without musculoskeletal conditions. Healthcare utilization was compared across encounters using investigations ordered, referrals made and opioid prescriptions. For investigations and referrals, overall utilization was measured as well as the proportion of investigations and referrals related to musculoskeletal conditions. Overall opioid prescriptions were measured. For all three utilization measures, encounters for patients with and without musculoskeletal conditions (using the ICPC reference standard) were compared as a whole and by age group using Pearson Chi-square testing. The proportion of encounters with a referral to each type of medical specialty was calculated.All analyses were performed in SPSS Version 24 .Of the 2493 patients in the selected cohort, 1305 were identified as having musculoskeletal conditions (defined with the ICPC reference standard). Table The prevalence of musculoskeletal conditions was 52.3% across the 4 years of the cohort. In any given year, the prevalence ranged from 36.5 to 43.0% of visiting patients having at least one encounter for musculoskeletal conditions (results not shown). Additional file Table A further examination of the 307 false negative patients found that they were distributed across practices and physicians; i.e., there was no one practice or physician responsible for the majority of false negatives. In addition, we examined the number of visits these 307 patients had following their first ICPC reference standard encounter to ensure there was sufficient opportunity for the recording of ICD-9 billing codes. The vast majority (66.4%) had five or more subsequent visits where they could be coded with an ICD-9 billing code. A minority (6.2%) had no visits after their first ICPC reference standard coded visit, which left them only the one visit in which to receive an ICD-9 billing code.p < .0005). The most frequent referrals for musculoskeletal and non-musculoskeletal encounters are shown in Additional file In terms of healthcare utilization, patients with musculoskeletal conditions had a significantly higher mean of 3.1 family physician visits per year (standard deviation 3.1) compared to patients without musculoskeletal conditions  (results not shown in a table). Fig. This study set out to determine the prevalence of musculoskeletal conditions in primary care patients, and health care utilization for those with musculoskeletal conditions compared to those without. Using a reference standard definition, this study found a high overall prevalence of musculoskeletal conditions (52%). Prevalence increased in each subsequent age group until 64 years where it dropped for those 65 plus years. Prevalence was calculated over a four-year period and would be expected to be higher than annual prevalence rates reported in the literature. Annual rates were also calculated, and over 4 years the lowest rate was 36.5%.For the purposes of comparison, few studies were identified that provided prevalence estimates for musculoskeletal conditions overall. Studies varied with respect to the study population and with specific kinds or subsets of musculoskeletal conditions. For example, an Ontario study that examined musculoskeletal conditions overall in the general population using health administrative data found a prevalence of 22.3% per year . A studyThis current study also determined that patients with musculoskeletal conditions had more visits to the family physician and at those visits they received more investigations, more referrals, and more prescriptions for opioids than those without musculoskeletal conditions. The one other Ontario study regarding health care utilization  also fouThis study validated one definition for musculoskeletal conditions in primary care, both acute and chronic; by comparing the reference standard to the diagnostic billing codes using ICD-9. In Canada, these ICD-9 codes are often the only diagnostic billing codes available in EMR data and so are the only method for identifying patients with musculoskeletal conditions in primary care EMR data and in health administrative data. The definition for musculoskeletal conditions using ICD-9 codes had good sensitivity overall (76.5) and across age groups. Sensitivity was higher for those between the ages of 18 and 64, compared to the young and the old age groups. The proportion of patients who were false negatives increased in each age group from 6.3% of 0–17 year olds to 17.5% of those 65+ years. This variation by age may be due to the fact that older patients may be presenting for other conditions at their visits and these may be the conditions the physician chooses for the diagnostic billing code. The study found half of the patients with musculoskeletal conditions received a musculoskeletal diagnosis at the first visit, and approximately another quarter receiving a diagnosis in two or more visits. The remaining 25% never received a diagnosis for musculoskeletal conditions using ICD-9 codes.Future research will validate definitions for acute and chronic musculoskeletal conditions separately. The ICD-9 codes for musculoskeletal conditions do not always make distinctions between what are acute musculoskeletal and what are chronic musculoskeletal conditions. As an example, there is one code for ‘knee sprain/pain’. This code could be used for an acute sprain or for on-going knee pain. Additionally, acute conditions can continue over time and move from being acute to either resolving or to continuing on to a chronic condition. Therefore, future research will use an episode of care approach that considers the length of time for particular musculoskeletal conditions to move from acute to chronic.any musculoskeletal condition, the physician would have coded some variation on a musculoskeletal condition. We had two physicians confirm the list of all acceptable musculoskeletal conditions to ensure all musculoskeletal conditions coded by ICPC would be captured. The motivation for the study was to determine the extent to which physicians did capture musculoskeletal conditions in health administrative data. A second important consideration is that the use of ICD-9 codes is decreasing. In the United States, for example, the use of ICD-10 has been mandated since 2015 [The data for this study are from 2006 to 2010; this suggests potential limitations. First, it is possible that there has been a change in coding behavior since that time. Within our own DELPHI database, we did find an initial improvement in coding behavior from its inception in October 2005 to March 2006 as physicians were trained in coding , 20. Thince 2015 . While hnce 2015 , 23. ThiIn Ontario, physicians send only one ICD-9 billing code to the provincial health insurance system. This one code becomes part of the health administrative data for the province. Because primary care visits often include the discussion of multiple issues, it is expected that musculoskeletal conditions would not get coded for all visits where they are discussed. Other billing codes may be chosen by the physician for various reasons such as the relative importance of the condition to the patient’s health or the reimbursement schedule for particular conditions. This is why we examined multiple visits to determine at what point musculoskeletal conditions were coded. Other health systems may have the ability to code multiple billing codes and, for those contexts, it might be expected that the sensitivity of one billing code may be higher.An additional limitation of the cohort used in this study is the small sample size relative to the Ontario health system. The results may not be generalizable to the entire province; however, the primary care practices in the DELPHI database contain urban and rural practices, and the patient characteristics are similar to that of the Ontario population . As wellThis study determined the prevalence of musculoskeletal conditions in primary care in Ontario using a reference standard definition, and described the health care utilization for these conditions. The study highlighted the value of using primary care ICPC codes to validate a definition for musculoskeletal conditions. Future studies could use this method to validate musculoskeletal conditions coded with ICD-10. Retrospective studies, where ICD-9 codes are still prevalent, can use this validated definition for research concerning musculoskeletal conditions in both primary care EMR data and population-based health administrative data. It is important to note that the prevalence of musculoskeletal conditions using only ICD-9 codes was under-estimated.Additional file 1:Table S1. Prevalence of the Top 20 ICPC Coded Musculoskeletal Conditions (n = 2493 patients). Table S2. Prevalence of the Top 20 ICD9 Coded Musculoskeletal Conditions (n = 2493 patients) (DOCX 14 kb)Additional file 2:Table S3. Most Frequent Referrals at ICPC Reference Standard Musculoskeletal Coded Encounters Compared to Non Musculoskeletal Encounters (DOCX 13 kb)


Recent advances in computational tools have allowed the study of the relationship between the genotype and phenotype in many diseases including β-thalassemia. Due to high prevalence of β-thalassemia, these analyses have helped to understand the molecular basis of the disease in a better way. In this direction, a relational database, named HbVar, was developed in 2001 by a collective academic effort to provide quality and up-to-date information on the genomic variations leading to hemoglobinopathies and thalassemia. The database recorded details about each variant including the altered sequence, hematological defects, its pathology, and its occurrence along with references. In the present study, an attempt was made to investigate nondeletion mutations in the HBB picked up from HbVar and their effects using the in silico approach. Our study investigated 12 nucleotides insertion mutations in six different altered sequences. These 12 extra nucleotides led to the formation of a loop in the protein structure and did not alter its function. It appears that these mutations act as ‘silent' mutations. However, further in vitro studies are required to reach definitive conclusions.Beta-thalassemia is described as a group of hereditary blood disorders characterized by abnormalities in the synthesis of beta chains of hemoglobin. These anomalies result in different phenotypes ranging from moderate to severe clinical symptoms to no symptoms at all. Most of the defects in hemoglobin arise directly from the mutations in the structural  These are the most common inherited disorders in humans with nearly 7% of the human population acting as carriers of the mutations in globin genes. Single nucleotide substitutions in the coding or regulatory regions of these genes can lead to varying degrees of defects in their expression [ HBB gene belongs to the β-globin gene cluster that encodes β-globin polypeptide. It is located in the short arm of chromosome 11 and contains two introns and three exons. Molecular defects in human HBB may result in structural defects causing abnormality in hemoglobins, such as HbS, HbC, and HbD, or may result in absence or reduced synthesis of β-globin chains causing β-thalassemia [ HBB may involve substitution, deletion, or insertion of one or multiple nucleotides within the gene or its flanking regions resulting in anemia and low RBC production [β- Thalassemia is inherited as an autosomal recessive trait and its clinical manifestation can be divided as thalassemia major, intermedia, and thalassemia minor (trait) [ HBB lead to complete inactivation of the gene resulting in the absence of β-globin chains (β0), in turn, leading to the most severe form of thalassemia. Other mutations allow production of β-globin chains in varying proportions leading to β+ thalassemia. This case is most commonly found in the Middle East, Central Asia, Mediterranean countries, India, and southern China and in some parts of Africa and South America [Hemoglobinopathies are genetic disorders caused by single-gene variations in the pression . The HBBlassemia . Mutatiooduction . β- Thal (trait) . Some mu America . It is o America . The moshttp://globin.cse.psu.edu/globin/hbvar/) is the oldest and most appreciated database of hemoglobin variants and thalassemia mutations established in 2001 [Due to the high prevalence of variable phenotypes of thalassemia and striking heterogeneity of its molecular defects, various strategies were employed to investigate the molecular mechanisms of this disease. Due to recent advances in computational tools, in silico analysis has become one of the chosen methods to investigate links between genomic and resulting phenotypic characteristics in thalassemia. HbVar ( in 2001 . It is a in 2001 . HbVar h HBB exons using the in silico approach. We aimed to search the HbVar database to select sequences with uncharacterized insertion mutations and study their effects on the structure and function of β-globin protein.The objective of this study is to investigate the effects of insertion mutations in thehttp://globin.bx.psu.edu/hbvar) were used. Using this database, we identified uncharacterized HBB sequences containing nondeletion mutations and picked them up for in silico investigation. The sequence of a wild HBB (gene ID 3043) was taken as reference (https://www.ncbi.nlm.nih.gov/gene/?term=3043). The potential 5' and 3' sites in the gene sequences were identified using the Human Splicing Finder software [https://web.expasy.org/translate/) was used to translate the nucleotide sequences into amino acids, and the types of mutations as well as their respective positions were noted. Mutations located in the defined donor and acceptor splice sites were included here. The relative strength of the sites obtained from the bioinformatics tool was given values between 0 to 100. Splice sites with high value were considered functional. Homology modeling of the wild type and mutant sequences was performed to compare the 3D structures of the proteins using the SWISS-MODELER [To perform this study, data from the HbVar database (software . These p-MODELER .+) were added for neutralization. The solvated systems were then subjected to 5000 steps of energy minimization using the steepest descent method to remove the steric clashes. Convergence was achieved in the energy minimization when the maximum force was smaller than 1000 kJ mol−1 nm−1. The NPT ensemble was performed for 1000 ps at 300 K. The production simulation was executed at 300 K for 30 ns for the wild type and mutant proteins. Protein visualization and superimposition were performed using PyMOL software (https://pymol.org/2/). Root mean square deviation was analyzed using the PyMOL align module.The molecular dynamic simulation of the wild type and mutant proteins was performed using the GROMACS software . The forMultiple sequence alignment between reference and mutated gene sequences showed that at least 12 nucleotides have been inserted in the sequences 1, 2, 3, 4, 5, and 6 from position 93 to 104 as compared to the wild type . The insVariations among these inserted nucleotides gave rise to changes in amino acids in protein sequences. Twelve nucleotides in total gave rise to four amino acids where different variants have been identified .α-helical chains (The construction of the 3D structure of the HBB protein from the given mutated sequences showed that the inserted amino acids formed a loop structure (secondary structure) in the protein. The homology modeling of the wild type and mutant type HBB protein showed that the mutated segment did not form any well-defined secondary structure. The mutated segment formed a loop and connected two l chains .The stability and properties of the wild type and its mutant structures were studied by explicit solvent MD stimulation. Root mean square deviations have been calculated between different structures to form a complete picture of deviation in the structures of mutated proteins from the wild type . The rooConstructing a relationship between the genotype and phenotype experimentally is an important aspect of research , but it β-thalassemia affects the people of the Gulf, Middle Eastern, and Mediterranean regions [β- thalassemia is prevalent, though there are variations in the frequency of the gene and in the type of mutations [ HBB and their frequencies in the Saudi population [Generally,  regions . Especiautations . Many prpulation –21. HBB are linked to the gene physically and form different alleles of the gene, but some mutations that affect the gene expression, while also segregating the HBB cluster, have also been identified [ HBB is well characterized, some mutations in this gene recorded in the HbVar database are poorly understood and have not been properly studied before. In this study, we picked up such mutations and performed in silico analysis to understand their effect on the protein structure and function.Most of the mutations affecting the expression of theentified , 23. Alt HBB account for a large number of the β-thalassemia alleles [β-thalassemia are very mild, in that carriers (heterozygotes) of such alleles are almost normal with no apparent signs of the disease, except for imbalanced synthesis of globin chains [β-thalassemia to cause milder forms [ HBB [The nondeletion forms of defects in the alleles . These i alleles . Some ofn chains , 26. Oneer forms . Other ‘ms [ HBB , 26.The present study aimed to understand the nondeletion mutations using in silico tools. We identified 6 different sequences carrying insertion mutation of 12 nucleotides from position 93 to 104 in the gene sequence. Variations also occurred among the inserted nucleotides among these 6 different sequences. In silico analysis showed that these inserted nucleotides translated into 4 additional amino acids. These additional amino acids acquired a loop formation in the 3D structure of the protein . The difα atoms of the wild type and mutant proteins depicted in the 3D structure of the Seq1 mutant protein, are seen to be more compact and stable, as compared to the wild type and Seq6 proteins. This data suggests that the insertion mutations in HBB protein might be affecting its overall structure and function as shown in Seq1 and Seq6, but more intensive studies are required to fully understand the scope of these effects. We are yet to determine how, if at all, these mutations affect the flexibility of HBB protein and whether this loss affects protein function and to what extent. In vitro studies will further assess the functional behavior of mutated proteins.Furthermore, the studies on radius of gyration of C


In this work, we develop a quatitative marker of progressing myelination for assessment preterm neonatal brain maturation based on novel automatic segmentation method for myelin-like signals on T2-weighted magnetic resonance images. Firstly we define a segmentation protocol for myelin-like signals. We then develop an expectation-maximization framework to obtain the automatic segmentations of myelin-like signals with explicit class for partial volume voxels whose locations are configured in relation to the composing pure tissues via second-order Markov random fields. The proposed segmentation achieves high Dice overlaps of 0.83 with manual annotations. The automatic segmentations are then used to track volumes of myelinated tissues in the regions of the central brain structures and brainstem. Finally, we construct a spatio-temporal growth models for myelin-like signals, which allows us to predict gestational age at scan in preterm infants with root mean squared error 1.41 weeks.Myelination is considered to be an important developmental process during human brain maturation and closely correlated with gestational age. Quantitative assessment of the myelination status requires dedicated imaging, but the conventional T Process of myelination is initiated during intra-uterine development, progressing from brainstem and deep structures, which are partly myelinated prior to birth, towards peripheral brain regions  that mostly myelinate during the first year, and continue throughout life4. Patterns of myelination were initially observed through histological studies5 and more recently, T1- and T2-weighted Magnetic Resonance Imaging (MRI) has been established as the primary method for qualitative assessment of myelination during prenatal and early postnatal period4.Human brain maturation involves a complex series of morphological, structural and functional changes. Among these changes is the process of myelin growth and axon ensheathment known as myelination, which facilitates electrical conduction in the neural system7. Impaired myelination in posterior limb of internal capsule (PLIC) is considered a sign of developmental delay in the newborn8 and is one of the cerebral abnormalities observed on MRI of preterm infants at term-equivalent age linked to very preterm birth9. Previous work has also suggested that absolute volume of myelinated white matter (WM) at term equivalent age is decreased in preterm infants10. Diffusion MRI has shown decrease in fractional anisotropy (FA) in PLIC in preterm infants at term equivalent age11 which may reflect either reduction in myelination or decreased coherence of WM fibers12. A recent study of a small cohort of preterm infants suggest that decrease in FA is accompanied by increase in T2 relaxation times in very preterm infants compared to late preterm infants at term equivalent age13. White matter abnormalities observed on MRI of preterm infants have been linked to neurodevelopmental impairment, such as motor and cognitive delay16.Preterm birth and other perinatal insults often lead to injury to immature WM and subsequent hypomyelination1 and T2 relaxation times17. However, their time courses are different. T1 shortening starts already in “pre-myelination” stage and is thought to be linked to the bounding of free water molecules to the accumulating building blocks of myelin (choloesterol and glicolipids). On the other hand, shortening of T2 relaxation times seems to correspond to tightening of the myelin sheath around the axons and consequent redistribution of free water, reflecting true maturation of the myelin17. T1 and T2-weighted weighted MRI remain the most common modalities for imaging neonatal brain19 and qualitative descriptions of myelination using these modalities21 are consistent with histological observations2. Though specialised sequences for imaging of myelin have been proposed , they are not routinely available and their application in preterm and neonatal population would currently be limited to small prospective studies. On the other hand, availability of quantitative markers of myelin using standard T2-weighted imaging would facilitate quantitative evaluation of mature myelin in large-scale studies of neurodeveoplment in preterm and neonatal populations.Progressing myelination results in shortening of T2-weighted neonatal brain MRI as myelin-like signals (MLS). One way of developing a quantitative markers of myelination is to develop an automatic method for segmentation of MLS, that can be further utilised in volumetric studies and spatio-termporal modelling of progressing myelination. Automatic segmentation of MLS is challenging and there is currently a lack of dedicated methods for segmenting MLS during preterm and neonatal period. Recently developed methods for neonatal brain segmentation in the NeoBrainS12 challenge28 all showed great promise in segmenting a number of brain structures in neonatal brain MRI. However, none of them performed well in segmenting myelination29. Most neonatal brain segmentation methods adapt the approaches developed for adults34, and use a probabilistic atlas or manual annotations to obtain prior information on the expected tissue locations. However, myelin is not included in any of the existing neonatal brain atlases37 or manual annotation database38, and neither it is considered in segmentation protocol in Developing Human Connectomme Project39. Another issue that complicates accurate segmentation of MLS in perinatal period is its small volume compared to resolution of the MRI. Partial volume (PV) effect (two or more tissues mixing in a single voxel) therefore needs to be taken into account. Many methods for PV modeling were previously proposed45, however they all require prior information about location of mixing tissues, which is currently not available for MLS.In this study, we refer to the tissue that is likely to contain myelin in T2-weighted neonatal brain images that does not require any probabilistic atlas or manual annotation of myelin. Similarly to Ledig et al.41 we model PV between MLS and backgournd (BKG) voxels using second-order Markov random fields (MRFs) within an expectation-maximization (EM) framework. We introduce an explicit PV class whose locations are configured in relation to MLS and BKG using a 3D connectivity tensor. We distinguish between MLS and other tissue with similar intensity ranges by defining an anatomical region of interest (ROI) for MLS based on anatomical knowledge. Our method achieves automatic MLS segmentations of high Dice coefficients (DCs)46 with respect to the manual annotations for 16 preterm infants at one-week intervals between 29 and 44 weeks gestational age (GA). The proposed segmentation method is applied to T2-weighted scans of 114 preterm infants to develop quantitative markers of myelination. Firstly, we perform volumetric analysis of the progressing myelination and show that myelination increases in deep brain region while it is stable in brainstem. Secondly, we build a spatio-temporal model of progressing myelination, which we compare to the qualitative studies of myelination21. Finally we show that the spatio-termporal atlas of progressing myelination enables us to predict gestational age at scan with high accuracy and is thus a potential quantitative marker of developmental delay.In this paper we propose an automatic method of MLS on T2-weighted neonatal scans. The details of the subjects, acquisition protocol and the segmentation method can be found in the Methods section. The volumes calculated from the resulting segmentations in the deep brain region and brainstem were plotted against GA in Fig. R2 scores.Automatic segmentation of the MLS was applied to 114 T48. This suggest that the MLS volume increased in the brainstem mainly due to the overall brain growth, and the increase in the deep brain region was attributed to both brain growth and new myelination.We found that the MLS volume appeared to grow exponentially with GA in the deep brain region , to evaluate whether MLS were suitable markers of GA. The individual segmentations registered to the reference space were compared with models for each time-point using sum of squared errors and the GA with smallest error between the model and the segmentation was chosen as the final GA estimate for each subject.The estimated GAs were plotted against the nominal values in Fig. 2-weighted MRI. We have proposed a novel dedicated method for segmentation of myelin-like signals with high reproducibility comparable to the intra-rater variability. The achieved Dice overlap with manual segmentations 0.83 can be considered particularly high given small size of myelinated structures compared to the voxels size. This was achieved by defining a clear segmentation protocol and designing a dedicated method to reproduce the protocol reliably. We have used Gaussian Mixture Model (GMM) to estimate the intensity profiles of myelinated and non-myelinated tissues to overcome the problem of qualitative nature T2-weighted MRI. Modelling of partial volume effect proved to be particularly important, as GMM alone did not produce reliable results. On the other hand, modeling the partial volume by an extra class and constraining its location using Markov Random Fields resulted in a stable segmentation method. We have demonstrated, that resulting segmentations can be used to produce quantitative markers of myelination by calculating volumes of myelinated tissues in absolume terms (e.g. in mL) or as a proportion of a pre-defined region of interest, to account for individual differences in brain sizes. Additionally, modelling of typical patterns of myelination during this age-range and accurate method for estimation of myelination status in form of predicted GA has been proposed. This method might have potential in uncovering developmental delays in this age group. Other studies have explored myelination paterns in infancy and childhood56, but this is the first such detailed study during preterm neonatal period.In this work we proposed quantitative markers of the myelination patters in preterm neonates with GA between 29 and 44 weeks using T1. We have overcome this issue by defining a segmentation protocol based on the available literature21 and creating a ROI “deep brain region” to separate myelinating structures from the structures that are more likely to be composed of densely packed grey matter. Inherently, uncertainties in the existing anatomical knowledge also exist in the proposed segmentation methodology. Nevertheless, gradual increase of the MLS in our spatio-termporal model supports the hypothesis that we are indeed detecting developing myelin.Inherent limitation of any study of myelination patters stems from lack of specificity of MRI sequences towards myelin. Histological studies consistently demonstrate that many MRI sequences (including quantitative MRI such as relaxometry and MTI) are sensitive to myelin, but none is specific, which means that intensity patterns linked to degree of myelination could also be attributed to other underlying biological processes1 and T2-weighted imaging. Given that shortening of T1 and T2 relaxation times determine different stages of myelination process17, their joint analysis could provide more detailed staging of myelination of different brain structures56, resulting in further insights into brain maturation during perinatal period.Interesting direction for future work would be to quantify myelination progress using combination of TThough our segmentation method offers quatitative measure of volume of myelinated tissue, it cannot determine proportion of myelin within each voxel. Quantitative methods such as relaxometry and MTI could produce even more accurate quantification of myelin maturation once large databases in this age-range become available. Our results however show that the proposed methods can be nevertheless a useful marker of brain maturation during this period of brain development and has particular applications in large-scale studies to determine consequences of preterm birth on developing myelin.2-weighed scans of 95 preterm infants were acquired between 29 and 44 weeks GA at Hammersmith Hospital, London, UK. Ethical permission for this study was granted by the Hammersmith and Queen Charlotte’s and Chelsea Research Ethics Committee (07/H0707/101). Informed written parental consent was obtained prior to imaging. All methods and experiments were performed in accordance with relevant guidelines and regulations. T2-weighted fast spin-echo brain images were acquired on a 3 T Philips Intera system with repetition time = 8700 ms, echo time = 160 ms and voxel sizes = 0.86 mm × 0.86 mm × 1 mm. Full information about the subjects is given in Table 114 T48. Cerebellum was not included in this study. Since the myelinated nuclei can have similar intensities to the densely organized GM in the basal ganglia which are thought not to contain myelin between 29 and 44 weeks GA21, we create a custom ROI containing the posterior limbs of the internal capsule (PLIC), ventrolateral nuclei (VLN) and subthalamic nuclei (STN), referred to as deep brain region in this paper. The MLS segmentation protocol is defined based on both image intensities and anatomical knowledge21. We delineate three structures, PLIC, VLN and STN in the the deep brain region. In the brainstem we define the segmentation protocol mainly based on intensities because the image resolution is insufficient to identify the detailed anatomical structures. An example of the segmentation protocol is shown in Fig. Our segmentation protocol focuses on central brain structures  and brainstem for assessing MLS because in these regions the myelination is present between 29 and 44 weeks GA2-weighted neonatal brain MR images is shown in Fig. 31 of manually annotated brain masks, and segment the skull-stripped images using the Statistical Parametric Mapping (SPM) software 30. The spatial priors are provided by a publicly available (brain-development.org/brain-atlases) 4D probabilistic neonatal brain atlas35. Subsequently, we obtain the bias-corrected images as well as the segmentations of the cortical GM, WM, deep gray matter (DGM), brainstem, cerebellum and CSF.The overall pipeline proposed for MLS segmentation on T21, we extract the ROI masks for the deep brain region from the DGM segmentations in order to prevent misclassifications by the intensity-based segmentation model. We first manually delineate the deep brain region on the T2-weighted image of a single reference subject at 36 weeks GA. We register the reference image with the dilated DGM region of each subject using free-form deformation (FFD) non-rigid registration50, and then transform the manual delineation of the deep brain region from the reference space to each individual subject’s space.We use the automatic SPM segmentations of the brainstem directly as the ROI mask. The DGM segmentations include the basal ganglia, thalami, hippocampi and amygdalae. Since the myelinated nuclei can have similar intensities to the densely organized GM in the basal ganglia which is considered not to contain myelin between 29 and 44 weeks GAμk and variances y is the voxel intensity and k is the class index. We approximate the PV class mean as the arithmetic mean of the composing pure tissue means μMLS and μBKG, and assume that the variances of all three classes are identical:The segmentation of myelin like signals is performed in Expectation Maximisation framework. We model three classes, MLS, BKG and PV, with their intensities defined by a Gaussian distribution 57, the ML estimations for the means of the MLS and BKG classes result in the following system of linear equations (see more details in the appendix):By substituting Eqs. k are updated aspik is the posterior probability of voxel i belonging to class k, yi is the observed intensity of voxel i, K the number of classes, and m the iteration number. The above equations comprise the M-step of Expectation Maximisation algorithm.The variances 57:The E-step of the expectation maximisation algorithm calculates the posterior probabilies, which represent probabilistic segmentation. The probabilistic segmentation is regularised with the spatial prior zi indicates to which class voxel i belongs, and ek is a unit vector of length K whose components are equal to zero except the kth component. The probabilities in the 26-neighborhood i are denoted as The symbol pjk is the probability that neighbor j belongs to class k, and dij the Euclidean distance between voxel i and neighbor j. The element of the connectivity tensor, denoted as k1 and k2 are both present in the neighborhood of class k. By assigning a large value to k1 and k2 are both high in the neighborhood. This would result in a large value of the energy function i less probable to belong to class k.Here k of the current voxel and classes k1 and k2 of a pair of neighboring voxels:k is the same as the pair. We assign a penalty t1 if k is a pure tissue class different from the pair to encourage the PV class as MLS and BKG are both in the triplet. We assign a penalty t3 if k is PV as there is no evidence for PV in the triplet. This effectively prevents over-estimation of the PV class.t2 if k is MLS or BKG, and set the penalty to 0 if k is PV. We aim to encourage the PV class by deliberately penalizing the MLS and BKG classes if they are both present in the neighborhood.k.We assign the penalties t1, t2 and t3 needed to be determined empirically and were set to 0.05, 0.03 and 0.01 by optimising the agreement with manual segmentation using Dice score46.The values of penalties 57 is less than 0.01%. The resultant PPMs are converted into hard segmentations using the maximum-vote rule. In addition, we reclassify the PV voxels as one of the composing pure tissues by calculating the fraction of the MLS class, denoted as fi, at PV voxel i:μMLS and μBKG are the means of the MLS and BKG classes, and yi the observed intensity. PV voxels with fractions above 0.5 are reclassified as MLS and combined with the voted hard segmentation of the MLS class to form the final segmentation.The EM algorithm is initialized by assigning 6 percent voxels with lowest intensities to MLS and rest to BKG class in deep brain region, while 25th percentile is used for brainstem. These values were estimated from manual segmentations. Tissue classification is achieved by interleaving the E-step and M-step until the relative change in the objective function of log likelihoodThe automatic segmentation method of MLS was evaluated against manual annotations of MLS for 16 preterm infants, each at a different time point, between 29 and 44 weeks GA at one-week intervals. Eight of the 16 subjects, from 30 weeks GA onward at two-week intervals, have a repeated manual segmentation delineated by the same rater in order to assess the intra-rater reliability. The quality of these manual annotations was confirmed by a clinical expert.We compared agreement with manual segmetations for three different methods: Gaussian Mixture Model with 2 classes MLS and BKG (GMM), Gaussian Mixture Model with explicit PV modelling but no spatial regularisation (GMM-PV) and the proposed three class model with spatial regularisation using MRF (GMM-PV-MRF). The proposed method clearly outperformed the other methods as well as the initial tresholding as shown in Tables p-values of two-tailed Student’s t-tests with the intra-rater DCs at the 5% significance level. It can be seen that only GMM-PV-MRF performed equally well as a human rater in both ROIs, and demonstrated no significant differences with respect to the repeated manual annotations.We also assessed the intra-rater reliability as DCs between two sets of manual annotations that were available for eight of the 16 test subjects at two-week intervals between 30 and 44 weeks GA. We evaluated the automated model-based methods with respect to each manual set. The average DCs over the eight subjects are shown in Tables 29, achieved Dice overlap 0.16–0.69 for 30 weeks GA and 0.23–0.68 for 40 weeks GA. None of the method achieved Dice overlap over 0.6 for both age groups.We achieved high Dice overlaps of 0.83 comparable to the intra-rater variability. This proves that our method is reliable and reproducible across the target age-range. In contrast, the Dice overlap achieved by the methods that entered Neonatal Segmentation Challenge49. First we registered the T2-weighted image of each individual subject with the dilated ROI of the single reference subject at 36 weeks GA using FFD non-rigid registration50. This was the same reference subject used to create the ROI masks of the deep brain region. We used normalized mutual information (NMI) as the similarity measure and 10 mm B-spline control point spacing. The automatic MLS segmentations were then transformed accordingly from each subject’s space to the common reference space. Lastly, we constructed the growth model in each ROI by fitting a voxelwise logistic function to the transformed segmentations. Details of constructing of the spatio-temporal models can be found in our previous work49.We constructed MLS growth models in the deep brain region and brainstem using voxelwise logistic regression based on the automatic segmentations computed using GMM-PV-MRF for the 114 subjectsWe further used the MLS growth models in the deep brain region and brainstem to predict GAs of the 114 preterm infants. The age estimates were determined by minimizing the sum of squared differences (SSD) between each individual transformed segmentation and the average growth model constructed in a leave-one-out procedure.