Understanding longitudinal relationships among multiple chronic conditions, limitations in activities of daily living, and health-related quality of life is important for identifying potential opportunities for health promotion and disease prevention among older adults.This study assessed longitudinal associations between multiple chronic conditions and limitations in activities of daily living on health-related quality of life among older adults (≥65 years) from 2004 through 2006, using data from the Medicare Health Outcomes Survey .Using a longitudinal path model, we found the numbers of chronic conditions at baseline and 2-year follow-up were independently associated with more limitations in activities of daily living at 2-year follow-up. In addition, more limitations in activities of daily living at 2-year follow-up were associated with worse health-related quality of life during the follow-up time period. The association between multiple chronic conditions and indices of health-related quality of life was mediated by changes in limitations in activities of daily living.Both baseline and new multiple chronic conditions led to worse health in terms of activities of daily living and health-related quality of life and should be considered important outcomes to intervene on for improved long-term health. In addition, public health practitioners should consider addressing classes of multiple chronic conditions by using interventions designed to reduce the emergence of multiple chronic conditions, such as physical activity, reductions in smoking rates, and improved and coordinated access to health care services. Health-related quality of life (HRQOL) is an important health outcome and is typically assessed by using self-reported physical and mental health indices –3. In adHHS has developed a research agenda to investigate relationships between multiple chronic conditions (MCCs) and other health outcomes ,6. HHS dFunctional limitations are associated with MCCs, especially those most prevalent in older adults  and HRQOL –13. FuncThe relationships between MCCs, functional limitations, and HRQOL include complex relationships that likely change over time and are reciprocal in nature ,13. MCCsThis study uses data from the Medicare Health Outcomes Survey (HOS) among older adults to test the following 3 hypotheses:The number of MCCs at baseline is associated with more ADL limitations at follow-up.ADL limitations at follow-up are associated with changes in HRQOL.MCCs at baseline and at follow-up are both directly and indirectly associated with changes in HRQOL.www.Medicare.gov). These plans include both seniors 65 years or older and younger people with disabilities enrolled in Medicare Part A and Part B who have chosen to enroll in managed care rather than fee-for-service coverage of hospital and medical care. Our study used data from the CMS-administered Medicare Health Outcomes Survey (HOS), which is used to inform quality improvement programs and to assess performance in Medicare managed care.The Centers for Medicare and Medicaid Services (CMS) monitors longitudinal health outcomes including HRQOL through Medicare Advantage (MA) Plans . We also excluded any respondents who used a proxy to complete the survey at either baseline or follow-up because proxy responses differ from those collected directly from patients with similar medical histories  and those with missing data  on any of the variables under study (P < .001) and mentally (P < .001) unhealthy days.Study subjects came from the HOS Cohort 7 who completed the baseline survey in 2004  and the follow-up survey in 2006 . Please see The final sample of older adults were found to be demographically similar to 2004 national census figures for adults older than 65 years of age . The finMultiple chronic conditionsThe HOS asked whether participants had ever been diagnosed with a range of chronic medical conditions. At baseline, each condition was coded as 0 (never) or 1 (diagnosed) and a summary score was created that represented the sum of the number of medical conditions experienced by each participant. Using the follow-up data, we created a dichotomous  variable that represented whether a person reported at least 1 additional chronic condition that was not reported at baseline.The analyses also controlled for numerous potential confounding medical conditions and behaviors including chronic low back pain, cancer treatment, and smoking status. The measure of chronic low back pain asked: “In the past 4 weeks, how often has low back pain interfered with your usual daily activities ?” Responses for this question on a Likert-type scale ranged from 1 (none of the time) to 5 . Cancer treatment was assessed by asking participants if they were currently under treatment  for colon, lung, breast, or prostate cancer. Smoking status was assessed by asking participants if they currently smoke every day, some days, or not at all.Activities of daily livingA 6-item ADL limitations measure was used to assess study participants’ level of functioning at baseline and follow-up. The 6 items share a common stem, followed by a list of specific activities: “Because of a health or physical problem, do you have any difficulty doing the following activities ?” . RespondThe Centers for Disease Control and Prevention (CDC) has developed 4 core HRQOL measures that have undergone cognitive testing and have demonstrated content validity, construct validity, criterion validity with the Short Form-36, predictive validity, test-retest reliability, and internal consistency ,17. Two DemographicsThe following demographic data from survey respondents were included as potential confounders in all analyses: age, annual household income, educational attainment, race, and sex. Participants’ birthdates were used to calculate an exact age at time of assessment. Participant self-reported annual household income was an ordinal variable ranging from 1  to 9 .mlr) to obtain parameter estimates and standard errors robust to nonnormality. A single model was used to simultaneously test hypothesis 1: the associations between initial MCCs and ADL limitations at follow-up (while controlling for baseline ADLs); hypothesis 2: the association between ADL limitations at follow-up and changes in HRQOL (by controlling for baseline HRQOL); and hypothesis 3: direct and indirect associations between initial and follow-up MCCs and changes in HRQOL over a 2-year period. The variances associated with both physically and mentally unhealthy days were estimated by using negative binomial regression that allows for the independent specification of the mean and the variance , and additional chronic conditions at follow-up were associated with more ADL limitations at follow-up . For hypothesis 2, ADL limitations at follow-up were associated with more physically  and mentally  unhealthy days at follow-up, after controlling for baseline levels of unhealthy days  and mentally  unhealthy days at follow-up. Baseline MCCs significantly affected changes in physical  and mental health  indirectly via ADLs at follow-up. Follow-up MCCs also significantly affected changes in physical  and mental health  indirectly via ADLs at follow-up.Our model resulted in significant associations along the paths representing our 3 hypotheses . In suppthy days . In supp3 × mean for physically unhealthy days at follow-up] – mean for physically unhealthy days at follow-up) and 0.63 additional mentally unhealthy days during the measure’s 30-day referent period. Reporting 1 or more new chronic conditions at follow-up (controlling for baseline MCCs) was associated with 2.99 more physically unhealthy days and 0.94 additional mentally unhealthy days at follow-up. Finally, reporting 3 additional ADL limitations at follow-up was associated with 10.69 additional physically unhealthy days and 3.42 additional mentally unhealthy days.Having more MCCs at baseline and reporting 1 or more additional medical conditions at follow-up were associated with more ADL limitations at follow-up. More ADL limitations at follow-up were, in turn, associated with worse HRQOL during the 2-year period. These associations can also be translated into differences in the number of unhealthy days experienced by older adults. The incident rate ratios (IRRs) associated with physically and mentally unhealthy days can be used to estimate the change in the dependent variable associated with 1 or more unit changes in the independent variable. For example, reporting 3 additional MCCs at baseline was directly associated with 2.41 additional physically unhealthy days  The number of MCCs at baseline and follow-up was associated with increases in ADL limitations at follow-up, 2) more ADL limitations at follow-up were associated with worse HRQOL as operationalized with physically and mentally unhealthy days, and 3) the association between MCCs and HRQOL was mediated by changes in ADLs. These findings provide the public health community with information regarding complex relationships between disease, disability, and HRQOL in older adults that may assist them in developing intervention strategies. These relationships can be characterized in the following manner: MCCs, both previous and incident, affect both ADLs and HRQOL. Therefore, intervening on MCCs at any point in time will likely improve both current and subsequent ADLs and HRQOL. This conclusion suggests that reducing rates of MCCs would have substantial global health benefits at any time and that interventions that reduce the likelihood of MCCs will have significant downstream benefits. These results highlight the need for broad evidence-based public health and clinical interventions that decrease rates of MCCs .Because MCCs at baseline and new MCCs during follow-up were strongly associated with subsequent increases in limitations in ADLs, prevention efforts to reduce older adults’ likelihood of developing new MCCs could forestall increases in ADL limitations. These findings support previous work  that sugFor our study, new ADLs 2 years later were independent of baseline ADLs and thus likely represent a real deterioration in health over time. Additionally, the strong relationship between MCCs and increased ADL limitations suggests that declines in function resulted from these MCCs .Besides significant indirect effects on HRQOL through ADL limitations, MCCs also directly affected changes in HRQOL during a 2-year period. The number of baseline MCCs and the changes in MCCs were both strongly associated with changes in physical and mental HRQOL. Therefore, although MCCs predict changes in ADL limitations, they also directly affect HRQOL, independent of older adults’ functional status. This study suggests that preventing MCCs will decrease the likelihood that older adults will develop additional functional limitations. Furthermore, the associations between physical activity and better HRQOL and between smoking and worse HRQOL are stronger for individuals with functional limitations than those without functional limitations . From a www.prc-han.org), in partnership with public health and the aging services networks, have been translating evidence-based programs and policies into practice in communities across the country  and on These findings support the need for practitioners to monitor and strive to reduce the number of MCCs experienced by their patients to facilitate optimal function and HRQOL among older adults. Practitioners should recognize the need for better care coordination, particularly for those with MCCs to ensure that conditions do not go untreated, to prevent the acquisition of new conditions, and to limit the burden of disease ,28. This


The success of hematopoietic cell transplantation from an unrelated donor depends in part on the degree of Human Histocompatibility Leukocyte Antigen (HLA) matching between donor and patient. We present a structure-based analysis of HLA mismatching, focusing on individual amino acid mismatches and their effect on peptide binding specificity. Using molecular modeling simulations of HLA-peptide interactions, we find evidence that amino acid mismatches predicted to perturb peptide binding specificity are associated with higher risk of mortality in a large and diverse dataset of patient-donor pairs assembled by the International Histocompatibility Working Group in Hematopoietic Cell Transplantation consortium. This analysis may represent a first step toward sequence-based prediction of relative risk for HLA allele mismatches. Unrelated hematopoietic cell transplantations (HCTs) involving perfectly matched patient-donor pairs generally have higher success rates than those between patients and donors mismatched at one or more loci -4. As HLTo predict the effect of a single amino acid mismatch on HLA-peptide binding specificity, we build structural models for a pair of HLA sequences that differ only by the mismatch in question. One sequence is taken from a naturally occurring HLA allele; the other is a mutated variant which may not match the sequence of a known allele. Flexible-backbone peptide docking simulations are performed against each of these protein models, and binding specificity profiles are inferred from the output of these simulations. The magnitude of the difference between these predicted binding profiles is taken as the predicted specificity divergence induced by the given mismatch.pan-specific predictor NetMHCpan — which is capable of making predictions for novel HLA sequences by generalizing from experimental binding data for related alleles — was motivated by the need to make predictions for HLA-C alleles and allele-variants with little or no experimental binding data. The choice of a structural approach to HLA mismatching was also motivated in part by this lack of experimental binding data: our structure-based binding predictions rely on estimates of peptide-HLA binding affinities derived from physico-chemical potential energy functions and atomically-detailed molecular models. Furthermore, atomic level simulations of HLA-peptide complexes provide additional valuable information beyond binding affinity [To assess the impact of binding specificity changes on transplant outcome, we compared predictions of induced binding specificity divergence for a set of common amino acid mismatches in the HLA-C locus with mortality risk factors for those mismatches estimated from the clinical dataset. This comparison identified a significant correlation between the magnitude of the binding specificity change induced by a mismatch and the mortality risk associated to that mismatch. The validity of this analysis depends in part on reasonably accurate estimates of peptide binding specificity for HLA sequences and their mutated variants. To test the robustness of the observed correlation, we recalculated the binding specificity divergences using the neural network peptide binding predictor NetMHCpan-2.4 , and fouaffinity , such asaffinity .Structural features have been used previously to quantify mismatching between HLA molecules -12. AlthAs described in detail in the methods section, we assigned a relative mortality risk factor to each residue-level mismatch in the HLA-C locus based on analysis of 1,110 unrelated patient-donor HCT pairs. For example, we assigned a risk factor of 1.21 to the mismatch F versus Y at position 99, and a risk factor of 0.92 to the mismatch of G versus R at position 91. These relative risk factors compare the mortality risk for patients with the given mismatch to the mortality risk for patients without that mismatch but still mismatched elsewhere at the HLA-C locus. All patient-donor pairs analyzed were perfectly matched at HLA-A, B, DRB1, DQB1, and the other HLA-C allele (termed 9/10 matched pairs). To reduce noise in these averaged risk factors, we restricted our analysis to mismatches with high counts in the clinical dataset (details in Methods).We then used structural modeling methods to analyze the impact of each mismatch on peptide binding. We computed peptide-binding specificity profiles for an HLA-C protein containing one of the two mismatched residues at the relevant position, and for that same protein after mutating that position to the other residue. We compared the resulting profiles to yield a prediction of binding specificity divergence associated with that mismatch. This structural modeling calculation was done using as a template protein both of the two HLA-C proteins with solved X-ray crystal structures, C*03:04 and C*04:01 . Encouragingly, we found improved correlations between these revised mortality risk factors and the specificity divergences predicted from simulations . While net al. [et al. [The divergence in peptide binding specificity between two HLA molecules can be also inferred from predictions of peptide binding affinities for the two proteins. Unlike binding profile divergence, such a measure does not assume an underlying binding model; thus it allows a direct comparison between different prediction methods. Correlation of predicted peptide binding affinities was used by Nielsen et al.  as a diset al. . Specifi [et al. , we then similar , supportIn this study, we have used structural simulations to investigate the role of peptide binding specificity divergence in transplant outcome. By analyzing a large set of patient-donor pairs, we assigned mortality risk factors to individual amino acid mismatches in the HLA-C gene. We then used structural modeling simulations to predict the binding specificity changes induced by each of these mismatches. We found significant correlations between the magnitude of these predicted specificity changes and the mortality risk factors derived from the clinical data-set. For comparison, we also computed binding specificity divergences for all mismatches using the neural-network predictor NetMHCpan-2.4. These predicted specificity divergences also correlate with mortality risk, further supporting the role of peptide binding divergence in transplant outcome.Structural modeling offers several advantages as a tool for investigating HLA-peptide interactions. The predicted divergences in binding specifity can be decomposed into positional contributions , and theCritical for clinical application would be a method for arriving at predictions of risk for allele-level mismatches. If sufficiently robust and accurate, this might allow ranking of candidate donors in cases where a perfectly matched donor cannot be found. The simplest approach would be to add the predicted binding specificity divergences for each amino acid mismatch found in the allele-level mismatch of interest to arrive at a single prediction of total binding divergence. Examination of the patterns of binding specificity divergence per peptide position shown in In addition to predicting risk factors for patient-donor allele mismatches, we anticipate that further molecular modeling of HLA-peptide interactions, together with more sophisticated analysis of clinical risk factors in the dataset, may lead to insights into the mechanisms of graft -versus-host disease, graft rejection, and related complications. By analyzing peptide binding specificity changes independently from changes to the HLA-TCR and HLAKIR interaction surfaces, structural analysis has the potential to probe the molecular basis of clinical outcomes, generating experimentally testable hypotheses regarding disease processes.In order to investigate the effect of divergences in HLA-peptide binding specificity on transplant outcome, we applied a structure-based protocol for predicting peptide-HLA interactions to a large set of variant HLA molecules derived from HLA-mismatched patient-donor pairs. We sought to test the hypothesis that patient-donor pairs whose mismatched alleles are predicted to have divergent peptide-HLA binding specificity might be at greater risk for negative outcomes after transplantation. To compare structural modeling to clinical data, it was necessary to reduce the clinical data and patient-donor genotypes data down to a manageable set of relatively robust statistics. To do this, we reduced each patient/donor pair to a set of mismatched residues together with a binary survival outcome. For each residue mismatch  we combined outcomes data from all patient/ donor pairs having that mismatch in order to arrive at a single statistic for the relative risk associated with that mismatch . To make a structure-based prediction of the degree of change in peptide binding specificity associated with that mismatch, we performed peptide binding simulations on proteins having either of the two mismatched residues at the position of interest , and on those same proteins after introducing the mutation of interest in the HLA sequence. We compared the peptide binding specificity profiles before and after performing the mutation using a standard distance metric for probability distributions; the numerical result of this comparison was taken as the structural prediction of the magnitude of peptide binding specificity change associated with that mismatch.To generate a peptide binding specificity profile for a given HLA molecule, we perform twenty thousand independent, flexible-backbone peptide docking simulations. These simulations differ from standard docking simulations — which seek to predict the bound structure of a complex from the structures of the individual components  — in thade novo structure prediction. The peptide sequence is randomized at the start of each simulation and held fixed throughout the low-resolution simulation. The backbone of the peptide is built outward from the two canonical anchor positions (residues 2 and L for a peptide of length L) by assembling 3-residue fragments from proteins of known structure and similar local sequence ; a breaassembly . The oriassembly .The low-resolution modeling stage is followed by a second, high-resolution refinement stage. In this stage, all sidechain atoms (including hydrogens) are added to the low-resolution backbone model, which is subsequently refined with a Monte Carlo plus Minimization (MCM) optimization procedure in a more realistic molecular mechanics force field . Each moWe analyzed a dataset of 1,110 patient-donor pairs for which the patient and donor were mismatched at a single HLA-C allele, and matched at the other HLA-C allele and at the HLA-A, B, DRB1, and DQB1 loci (referred to as “9/10 pairs” for HLA-C); complete HLA-C protein sequences were available for each pair. Focusing on the 9/10 pairs facilitated identification of correlations between specific mismatches and outcome. As a measure of clinical outcome we focused on patient survival. Thus, as input for our analysis we had, for each patient-donor pair, two aligned HLA-C sequences and a single binary outcome variable. We mapped each pair to the set of residue-level mismatches  between their HLA-C proteins. We then tallied for each mismatch the total number of pairs with that mismatch, and the overall mortality rate for those pairs. We initially orientation in this analysis, i.e., we separated A/T mismatches at position 73 according to whether the patient had A and the donor T, or vice versa. This revealed striking asymmetry in mortality rates for a subset of the residue mismatches; for example, transplant pairs in which the patient had A and the donor had T at position 73 had a significantly lower mortality rate than pairs in which the patient had T and the donor had A. As our structure-based predictor of binding specificity divergence is inherently symmetric, we chose to symmetrize the mortality rates by taking the maximum of the two oriented mortality rates as the single mortality rate for each unoriented mismatch. In total, there were 90 distinct oriented amino acid mismatches and 50 unoriented mismatches observed in the clinical dataset.Preliminary analysis revealed that certain alleles and allele pairs contributed disproportionately to the dataset, which had the effect of potentially skewing mortality rates for mismatches that were correlated with these alleles. For example, more than half of the 239 patient donor pairs with the 91 R/G mismatch had the C*03:03/C*03:04 allele-level mismatch. To reduce these biases, we computed revised mortality rates for each mismatch by randomly sub-sampling the patient-donor pairs contributing to that mismatch so that no single allele-pair contributed more than 10 pairs. This sub-sampling was repeated 1000 times and the calculated mortality rates were averaged to yield an adjusted risk score for each mismatch.For assessing the impact of disease severity, we classified patients into three groups based on available clinical information. Diagnoses and stage of disease were categorized as low, intermediate, and high, with the high-risk category consisting of diseases that, on average, perform more poorly following transplant compared to the other two groups. The high-risk patients were taken to be any disease in relapse, CML in blast crisis, or MDS-RA with excess blasts or excess blasts in transformation.To reduce noise in the clinical risk factors, we restricted our analysis to high-counts mismatches, which we define as residue-level mismatches with at least 100 occurrences in the dataset in each orientation (patient/donor and donor/patient). We computed the predicted binding specificity divergence induced by a given mismatch by applying it within the context of a “wild-type” HLA protein of known three-dimensional structure, either C*03:04 or C*04:01 (PDB IDs 1efx  and 1qqdTable of mismatches


Objectives. To assess the induced sputum substance P (ISSP) levels in children having difficult-to-treat asthma (DA) with and without gastroesophageal reflux (GER). We aimed also to evaluate the association of GER with childhood DA, relationship of GER severity with childhood asthma control test (C-ACT), FEV1, peak expiratory flow (PEF) variability, and ISSP. Finally, we tried to evaluate esomeprazole treatment effect on C-ACT and FEV1 in children with DA. Methods. Spirometry, C-ACT, upper gastrointestinal endoscopy, and ISSP measurement were done for children with DA compared to healthy controls. Results. ISSP was high in DA with higher levels in the group having associated GER. In the latter group, ISSP and C-ACT improved significantly after esomeprazole treatment while FEV1 and PEF variability did not improve. Reflux severity was positively correlated with ISSP and negatively correlated with FEV1. Conclusions. GER was found in 49% of our patients with childhood DA. Very high ISSP levels in children with DA may be used as a marker for presence of GERD. Esomeprazole therapy improved asthma symptoms but did not improve lung function. Asthma is a common chronic complex inflammatory airway disorder characterized by variable degrees of recurring symptoms of airflow obstruction and bronchial hyperresponsiveness . AlthougThe association between asthma and gastrooesophageal reflux (GER) has been debated for decades when Sir William Osler first observed the association between worsening asthma and distended stomach in 1892 . The preGastroesophageal reflux (GER) may cause chronic respiratory disease by vagal response and tracheal aspiration of gastric contents . AspiratThe tachykinins as substance P (SP) and neurokinin A are the neuropeptides most often associated with axonal reflexes and are potent mediators of cough, bronchospasm, microvascular leakage, and mucus secretion . AsthmatThe prevalence of GERD in children with asthma has been shown to be between 19.3% and 80% in different studies . The dia1), peak expiratory flow (PEF) variability and induced sputum levels of SP. Also this study aimed to determine the effect of antireflux therapy with esomeprazole as proton pump inhibitors (PPI) on the C-ACT, FEV1, PEF variability, and induced sputum levels of SP in children having difficult-to-treat asthma with GERD.We hypothesized that measurement of induced sputum levels of substance P (SP) can be a helpful tool to anticipate presence of GERD in children with difficult-to-treat asthma. So, we aimed to assess the induced sputum substance P (ISSP) levels in children having difficult-to-treat asthma (DA) with and without gastro esophageal reflux (GER). We aimed also to detect the association of GER with the difficult-to-treat asthma in children, as well as to determine the relationship between severity of GER as assessed by upper GIT endoscopy and the childhood asthma control test (C-ACT), forced expiratory volume in the 1st second  and PEF variability ≥20% [μg beclomethasone dipropionate or equivalent per day) and long-acting β2-agonist and had received at least one course of systemic steroids in the preceding 12 months [Asthma was defined on the basis of symptoms together with documented reversible airflow obstruction  guideline 2008 with treatment being stepped up and down as appropriate.1, and peak expiratory flow (PEF) variability. The PEF variability was calculated as the percentile ratio of the difference between maximum and minimum PEF to the mean daily PEF over a period of one week, that is, (maximum PEF − minimum PEF)/ × 100 [Detailed history taking and thorough clinical examination with special stress on GIT symptoms suggestive of reflux including heart burn, acid regurgitation and food regurgitation. In asthmatic patients Childhood Asthma Control Test (C-ACT) was done. It is a seven-item child- and caregiver-completed tool with a scoring range of 0–27; higher scores indicate better control. A score of 19 or less indicates that the asthma may not be well controlled. The C-ACT is intended for use in children up to the age of 12 years . Chest Xk) × 100 . by using an Olympus GIF-P140 Pediatric Video Gastroscope with 8.5 mm diameter, and 2.2 mm channel. The endoscopic findings of esophagitis were classified according to Los Angeles classification to the following grading: Grade A (score 1): 1 or more mucosal breaks each ≤5 mm in length. Grade B (score 2): at least one mucosal break >5 mm long, but not continuous between the tops of adjacent mucosal folds. Grade C (score 3): at least one mucosal break that is continuous between the tops of adjacent mucosal folds. Grade D (score 4): mucosal break that involve at least three-fourth of the luminal circumferences. Patient took score 0 if there were no features of reflux . Upper gμg of salbutamol to minimize broncho-constriction during the induction procedure. Sputum was induced by inhalation of 3% hypertonic saline solution for 5 min using an ultrasonic nebulizer, and the subjects were encouraged to cough and expectorate sputum into sterile containers. FEV1 was measured after nebulization. Nebulization was stopped if a fall in FEV1 of >20% compared to baseline values occurred or if troublesome symptoms appeared [Sputum was collected either spontaneously or induced with hypertonic saline nebulization from all subjects. Prior to sputum induction, children inhaled 200 appeared .SP was measured using a commercially available enzyme linked immunosorbent assay . It has no significant cross-reactivity with neurokinin A, neurokinin B, or neuropeptide K. The limit of detection of this assay is 0.06 ng/mL.The group of children who had difficult-to-treat asthma without reflux received placebo identical appearing capsules containing lactose (placebo capsule/day) for 12 weeks beside the usual antiasthma medications as mentioned before.Further received medical treatment for reflux in the form of proton pumps inhibitors (PPI) for 12 weeks (esomeprazole capsule 20 mg/day) beside the usual antiasthma medications as mentioned before. Helicobacter Pylori infection and the positive cases received metronidazole and clarithromycin beside the esomeprazole (triple therapy).All children proved to have GERD either asthmatic or the control group were screened for So, all asthmatic patients with GERD received PPI and none of them received placebo because it is unethical to give placebo to them instead of proper treatment and also because if we divide them into 2 subgroups, the number of patients in each of them will be low and the power of work would be reduced.On the other hand, we gave placebo to asthmatic patients without GERD to rule out the placebo effect on improvement in the other group and to exclude the effect of better patient adherence to prescribed medications and better follow up by regular attendance to the clinic.Upper GIT endoscopy; for the group who received antireflux treatment, C-ACT, pulmonary function testing, and substance P measurement in induced sputum for both groups of patients with difficult-to-treat asthma.Parents of all patients and control subjects signed a written informed consent before enrolment into the study. The local Institutional Research Ethics Committee approved the study protocol.t-test, with P < 0.05 considered statistically significant. Wilcoxon's signed rank test was used to assess the normality of distributions of the data. The Bonferroni correction/adjustment procedure was done to avoid “significance” due to chance only, in multiple comparison with many parameters. Correlation between variables was evaluated using Pearson's correlation coefficient.The power level of the number of cases in the study was more than 80%. Statistical analysis was performed with Statistical Package for Social Sciences (SPSS), version 16.0 . Data are presented as mean (±SD) values. Comparison between the studied groups was performed with Students 1 (% of predicted) and C-ACT was significantly lower in children with difficult-to-treat asthma than the control.The study design and the demographic data of patients and the control subjects as well as their clinical data are shown in 1 (% of predicted) between the two subgroups. However, the sputum SP was significantly higher in children with difficult-to-treat asthma and GERD than in those who have difficult-to-treat asthma without GERD.1 and both reflux severity score and induced sputum SP. C-ACT had significant negative correlations with both reflux severity score and induced sputum SP.1 (% of predicted) and PEF variability showed no significant changes. This table also showed no significant effect of the placebo treatment on children with difficult-to-treat asthma without GERD.As mentioned in the methodology section, management of asthma was done according to GINA guideline 2008 with treatment stepped up and down as required. Among the 59 children patients with difficult-to-treat asthma, seven patients required stepping up of asthma therapy (three with GERD and four without GERD) while eight patients required stepping down (four with GERD and four without GERD).Helicobacter pylori infection. Also the two control children who had GERD had concomitant infection with Helicobacter pylori.Because of the high incidence of gastro-esophageal reflux (GERD) in patients with asthma , the comThe wide difference in GERD prevalence from one study to another (between 19.3% and 80% in different studies) depends on the method of reporting of GERD  and on the patients' criteria of the studied groups . 1, and the higher PEF variability and sputum SP). With increasing severity of asthma, there is a need to increase the dose and the number of antiasthma medication. In the current study, all the asthmatic children were receiving long-acting Beta-2 agonists and high-dose inhaled corticosteroids. Asthma medications such as beta agonists and theophyllin reduce lower esophageal sphincter tone and increase gastric acid secretion [One of the points that we were concerned about in the current study was the association of GERD in a relatively homogenous group of children with difficult-to-treat asthma. Children with difficult-to-treat asthma and GERD made up to 49% of the studied patient group. Kwiecien et al. 2011 found that the intensity of GER was significantly correlated with severity and the difficulty-to-control asthma attacks in asthmatic children [ecretion . Systemiecretion . On the ecretion , 22. Being a common finding in children with difficult-to-treat asthma, presence of GERD must be ruled out in such cases. In the current study, there was a significant increase of sputum SP in children with difficult-to-treat asthma and GERD than both control children and children with difficult-to-treat asthma without GERD. In addition, the level of sputum SP significantly decreased in children with difficult-to-treat asthma and GERD after 12 weeks of treatment with the proton pump inhibitor (PPI) esomeprazole. This agreed with the work of Patterson et al. 2007, who found that sputum SP and neurokinin A (NKA) were significantly higher in adult patients with reflux than those without reflux. Also they found significantly higher levels of sputum SP and NKA in adult asthmatic patients with reflux than in adult asthmatic patients without reflux. They suggested that acid in the esophagus caused sensory nerve stimulation and release of tachykinins into the airways . Liu et 1 in the adult asthmatics [In the current study, childhood-asthma control test (C-ACT) in patients with both difficult asthma and GERD showed significant increase after 12 weeks of treatment with esomeprazole. This agreed with a number of studies done in asthmatics with GERD. Khoshoo and Haydel, 2007, showed a significant improvement in asthma symptoms and decreased requirement for asthma medication in 25 nonatopic asthmatic children treated with acid suppressor treatment . Yüksel thmatics .However, Størdal et al. 2005 found that PPI treatment did not improve asthma symptoms or lung functions in children with asthma and GERD. This dissimilarity from the result of the current study may arise from the difference in their studied group which included children with mild or moderate persistent asthma, and relatively well-controlled asthma on daily inhaled steroids; so that further improvement in asthma outcome may be difficult to be obtained. They also assessed the improvement in asthma symptoms using asthma scoring questionnaire which is different from the C-ACT. They used omeprazole 20 mg daily while in the current study we used esomeprazole 20 mg daily . Maev et al. 2003, showed that therapy of bronchial asthma associated with GERD using omeprazole in the dose equal to 40 mg per day or esomeprazole in the dose equal to 40 mg per day contributed to a reliable improvement of both pulmonary and esophageal symptoms. However, application of esomeprazole resulted in a faster reduction of bronchial obstruction and gastroesophageal reflux . 1 and PEF variability. This finding agreed with a number of studies done in both children and adults with asthma and concomitant GERD. Teichtahl et al. 1996, found no significant differences in FEV1, FVC, histamine bronchial responsiveness, and diurnal variation of PEFR between placebo and 4 weeks of 40 mg/day of omeprazole treatments in in adult patients with both asthma and GERD [1 failed to reach statistical significance [In the current study, despite the significant improvement of C-ACT after GERD treatment with esomeprazole, there was no significant improvement of FEVand GERD . The samand GERD . Althougificance . Anotherificance . Lack ofificance . In addiificance .Esomeprazole was the only antireflux therapy used during the study. It may be inadequate especially for treating cases with high reflux score. This is clearly appearing in lack of improvement of the GERD in 3 cases with reflux score of 4. Lack of prokinetic medications use during the study may be an explanation for the nonsignificant improvement in pulmonary functions as nonacidic refluxate still can trigger the airway reflexes. Another limitation in the current study is lack of determination of a cutoff point for sputum SP that can be more sensitive to detect the presence of GERD in such cases. Further studies are needed to determine the effect of poly antireflux therapy on the pulmonary function as well as to determine the cutoff point for sputum SP level with high sensitivity to detect presence of GERD in asthmatic children. Another limitation of the current study is using endoscopy for diagnosing GERD not pH or impedance studies that may be more sensitive as there will be some false-negative cases with the use of endoscopy. The endoscopy used because it was the available tool in our hospital. Finally, lack of control subjects for presence of GERD may be considered as another limitation. 1 and peak expiratory flow variability. Very high level of induced sputum substance P (SP) in children with difficult-to-treat asthma may be used as a novel marker for diagnosing GER in such children.Gastro-esophageal reflux (GER) was found in about 49% of patients with childhood difficult-to-treat asthma. Proper GER therapy can improve asthma symptoms as indicated by childhood asthma control test (C-ACT); however, it has insignificant effects on lung function as indicated by FEV


The ribosome is a large macromolecular machine, and correlated motion between residues is necessary for coordinating function across multiple protein and RNA chains. We ran two all-atom, explicit solvent molecular dynamics simulations of the bacterial ribosome and calculated correlated motion between residue pairs by using mutual information. Because of the short timescales of our simulation (ns), we expect that dynamics are largely local fluctuations around the crystal structure. We hypothesize that residues that show coupled dynamics are functionally related, even on longer timescales. We validate our model by showing that crystallographic B-factors correlate well with the entropy calculated as part of our mutual information calculations. We reveal that A-site residues move relatively independently from P-site residues, effectively insulating A-site functions from P-site functions during translation. The dynamic motion of macromolecular machines such as the ribosome is coordinated across multiple chains to accomplish complex, multi-step functions and elucidation of these motions is fundamental to understanding how these large machines work Escherichia coli). Large-scale dynamics such as motion in the GTPase association center Our current understanding of ribosome dynamics began largely by comparing EM and x-ray crystallography structures of the ribosome in various states (for example bound to co-factors or tRNA) Computational models of ribosome dynamics are challenging because of the ribosome's large size. Methods for modeling enzyme dynamics range from reduced complexity coarse-grained models to the all-atom detail of explicit solvent molecular dynamics simulations (MD) Our goal is to find features in nanosecond length all-atom MD of ribosome dynamics that are biologically relevant. Correlated motions have been previously linked to enzyme function Thermus Thermophilus bacterial ribosome Here we present data from two all-atom MD trajectories of the complete  bacterial The starting structure for molecular dynamics was the highest resolution 70S crystal structure at the time of initiating the study, the 2.8 Å 70S bacterial Thermus Thermophilus ribosome crystallized in complex with mRNA and tRNA (PDB IDs 2J02 and 2J03) Molecular dynamics was performed using the Gromacs software package In the simulation with tRNA and mRNA, the methods are the same as above except for the following changes. mRNA and the three tRNA chains were included in the simulation, with missing residues and atoms added using MODELLER The mutual information between every pair of backbone atoms was calculated as follows:Kmedians clustering was performed using the Matlab software package  using the pre-installed kmeans function with the “cityblock” parameter.Thermus thermophilus: one 53 ns trajectory of the 70S ribosome alone, and one 32 ns trajectory of the 70S ribosome with mRNA and tRNA  and without (11.2 Å) mRNA and tRNA. These equilibrated structures were the starting structures for the trajectories, and during the simulation the Calpha coordinates only shifted 2 to 3 Å on average. In the trajectory without mRNA and tRNA, the ending structure had 9.5 Å Calpha rmsd relative to the crystal structure. In the trajectory with mRNA and tRNA, the ending structure had 10.1 Å Calpha rmsd relative to the crystal structure.We ran two all-atom MD trajectories of the complete  bacterial ribosome from alpha for protein, C4′ for RNA) and the average position of that backbone atom within a time window (DMA). DMA is one dimension (distance) over time rather than three dimensions  over time. This representation has the following properties: 1. MI between residue dynamics is direction independent, 2. MI between residue dynamics will be independent of the location of the residue relative to either the origin or to other atoms in the system, and 3. the reduced complexity is more computationally tractable than previous methods The large size of the ribosome  renders previous methods for calculating MI infeasible and thus we simplify the data into a distance from a moving average (DMA) for each residue. Previous methods used raw MD data of dynamics in the form of the  coordinates of every atom over time Mutual Information (MI) is the most general form of calculating correlation, and has been shown to capture more biologically relevant dynamics than traditional methods of calculating correlations such as principal component analysis (PCA) norm) matrix for the trajectory of the ribosome alone . Conversely, low MInorm indicates that two residues move relatively independently from one another. By definition, the highest MI is between a variable and itself, which results in a bright diagonal line through the matrix. To increase the color contrast when visualizing the matrices, the values along the diagonal are removed in all figures. The order of residues in these matrices mirrors the organization of chains in the pdb files: small subunit chains, then large subunit chains. Within the subunits, RNA chains are listed before protein chains. me alone  and the me alone . Each roCorrelation between residue root mean squared fluctuation (RMSF) and crystallographic B-factors  is commonly used to validate computational models of protein dynamics such as MD We calculated the correlation between two measurements of positional fluctuations and crystallographic B-factors for the largest ribosome chains. Both RMSF and DMA entropy  and is composed entirely of RNA nucleotides from the 23S chain of the large subunit. The PTC catalyzes peptide bond formation between the nascent peptide chain and the incoming amino acid. The PTC is located at the entrance to the 100 Å-long peptide exit tunnel, a cavity through which the nascent peptide grows and eventually exits the ribosome. The PTC binds to A- and P-site tRNA through the A- and P-loops, respectively . Each lonorm in PTC dynamics reveals a P-region and an A-region that move independently from one another, and these two regions correspond to the previously described symmetry-related regions  have coupled dynamics in both trajectories .K-means clustering of the large subunit into three clusters reveals that residues in the A-site, at the interface with the small subunit, and along the path of tRNA move independently from the rest of the large subunit. Residues coupled to A-site tRNA are at the GTPase association center, and residues coupled to P-site tRNA are in the active site and exit tunnel Mutual information between each of the three tRNAs and residues in the large subunit is calculated. Each tRNA has a different subset of residues with correlated dynamics, outlined in Correlated motion between residues is essential for enzyme functions such as interaction with ligands, catalysis, and allostery We use MI to calculate correlation because it captures both linear and non-linear correlations, and we validate this method by comparing the crystallographic B-factors  with DMA entropy . Crystallographic B-factors correlate well with DMA entropy . CorrelaTranslation of mRNA into protein by the ribosome is an inherently dynamic process. tRNA is repeatedly shuttled from the aminoacyl (A) to the peptidyl (P) to the exit (E) sites located at the interface between the large and small subunit. Large conformational changes accompany translation. One such motion occurs at the protuberances at the tRNA entrance (GTPase association center) and exit  sites that interact with incoming and outgoing tRNA, respectively. Previous simulations of correlated motion in the ribosome use low-resolution models to simulate longer timescales than the all-atom simulations described here, and captured these large scale motions Our models suggest that residues involved in P-site functions move independently from residues in A-site functions. For example, residues in the PTC active site form two distinct groups based on MI in their dynamics: A-site residues, and P-site residues. These two groups move independently despite their close spatial proximity . ResidueSupporting Information S1Calculating MI with several parameters for distance from a moving average (DMA). A range of parameters lead to the same patterns in correlated motion.(DOCX)Click here for additional data file.Table S1norm matrices.Explanation of indices used in the MI(DOCX)Click here for additional data file.


Treatment is switched before the overgrowth and archiving of mutant strains that carry resistance against the induction regimen and would limit its future re-use. In silico modelling shows that an optimal trade-off is achieved by switching treatment at The human immunodeficiency virus (HIV) can be suppressed by highly active anti-retroviral therapy (HAART) in the majority of infected patients. Nevertheless, treatment interruptions inevitably result in viral rebounds from persistent, latently infected cells, necessitating lifelong treatment. Virological failure due to resistance development is a frequent event and the major threat to treatment success. Currently, it is recommended to change treatment after the confirmation of virological failure. However, at the moment virological failure is detected, drug resistant mutants already replicate in great numbers. They infect numerous cells, many of which will turn into latently infected cells. This pool of cells represents an archive of resistance, which has the potential of limiting future treatment options. The objective of this study was to design a treatment strategy for treatment-naive patients that decreases the likelihood of early treatment failure and preserves future treatment options. We propose to apply a single, pro-active treatment switch, following a period of treatment with an induction regimen. The main goal of the  In 1996, the tremendous clinical success of highly active antiretroviral therapy had led many researchers to believe that the eradication of HIV would be feasible. However, it was soon realized that inducible pro-virus persists in latently infected cells despite ongoing therapy and that the latent reservoir prevents HIV eradication within the patients lifetime Latent infection is established when Despite the impressive improvement of antiviral therapy, many patients still experience virological failure caused by the selection of drug resistant virus populations. Current guidelines recommend changing treatment after the confirmation of virological failure. However, in the face of the rapid viral turnover this approach could be sub-optimal Induction-maintenance (IM) approaches are used for the treatment of a growing number of infectious- and neoplastic diseases Another approach to overcome the development of resistance is to alternate antiretroviral therapy We propose an approach that combines the advantages of conventional IM- and treatment alternation strategies, but minimizes their inherent disadvantages. We suggest a single, pro-active treatment switch from an inducer drug combination to a maintenance combination. The inducer drug combination should rapidly lower the viral population size and eliminate resistant mutants. Subsequently, it will be replaced by a maintenance drug regimen with a completely different resistance profile, before drug resistant strains are archived.We have previously introduced a novel model of virus dynamics and adaptation We have extended the existing viral dynamics model, described in prior to proviral genomic integration , respectively. A histogram of the derived  switching times from a total of 6000 simulations is shown in next drug combination (maintenance combination). We therefore evaluate In The derived switch-times are displayed in In order to reflect the clinical practice of HIV care, we have implemented the following routine for assessing the efficacy of the applied treatment combinations.Our virtual patients are monitored every month for efficacy assessment until virus levels fall below the limit of detection (50 HIV RNA/mL plasma). Thereafter, they are monitored every other month. Virological failure has been defined according to treatment guidelines We implemented conventional HAART in the following way: The virtual patients are initially treated with a drug combination consisting of two nucleoside reverse transcriptase inhibitors (NRTIs) and one non-nucleoside reverse transcriptase inhibitor (NNRTI) (e.g. tenofovir (TDF) + emtricitabine (FTC) + efavirenz (EFV)), until virological failure is detected, in which case treatment is changed to a second line regimen consisting of a protease inhibitor (PI), an integrase inhibitor (InI) and an entry inhibitor (EI) (e.g. ritonavir (RTV) -boosted PI + raltegravir (RLV) + maraviroc (MVR)).In the proposed induction-maintenance-strategy, patients are initially treated with a combination consisting of a PI, an InI and an EI, until In the following, we performed 1000 hybrid stochastic-deterministic simulations for each relevant parameter set  a very simple treatment strategy that can lead to significant reductions in virological failure in comparison to conventional HAART treatment. A unique drug combination (inducer combination) is used for a short time (80 days) and pro-actively switched to a maintenance combination. The purpose of the inducer combination is to decrease viral population size and thereby increase the likelihood that the subsequent therapy (maintenance) will achieve durable suppression. Clinical implementation of this novel treatment strategy requires only one additional clinical visit at 80 days in comparison with the conventional HAART therapy. The important finding of our study is, that although two drug combinations are always utilized during the proposed induction-maintenance strategy, less archiving of drug resistance occurs in comparison with a conventional treatment strategy, where a second treatment line would be applied only in the case of virological failure or toxicity. Less drug resistance archiving implies that more treatment options will be available for the follow-up and long-term management of HIV-infected patients when the proposed induction-maintenance treatment strategy is used (see current drug combination and would limit the further use of this drug combination (risk of the strategy), (ii) while at the same time, it has to be applied long enough to possibly eradicate viral mutants, which are resistant to the next drug combination (the benefit of the strategy).The time for switching between combinations de novo developed). As discussed above, we determine the abundance of mutants at the time of therapy initiation by utilizing the deterministic fix-point as starting condition for our simulations. We have shown the non-inferiority of our approach in in vivo for drugs with a large genetic barrier in vivo the inducer combination could possibly be applied for a longer time frame than the 80 days utilized in our model, if the genetic distance between wildtype and fully drug resistant mutant was greater than considered here (greater than one point mutation). However, our results demonstrate that even a very short time (80 days) in which the inducer combination is applied, can improve the clinical outcome significantly  and also on their genetic distance to the wildtype  are cleared in vivo data, Chun et al. One limitation of the proposed induction-maintenance strategy is the potential inability to eliminate viral strains, that carry resistance to the maintenance therapy. This is particularly the case, if viral mutants, which carry resistance against all (or at least the majority of) drugs in the maintenance combination, are archived in the latent reservoir prior to treatment initiation. In Infection with drug resistant strains, mainly against established drug classes, is a major, growing health concern For our strategy, we have chosen drugs from novel classes  for the inducer-combination, while we selected drugs from well-established classes for the maintenance combination . This has the following rationale: The inducer combination will only be applied for a short time (80 days), while the maintenance combination could possibly be applied for much longer periods of time . Second or third generation drugs within the established drug-classes are often more convenient to apply (e.g. once daily dosing) and are less toxic, which has important implications for the long-term management of HIV Some drug classes can cause a distinct viral load decline. In particular, the only approved InI raltegravir causes a more rapid viral load decay, compared with other HIV inhibitors Intuitively, it might be more advantageous to use drug resistance tests to guide treatment switches, instead of using a fixed time for a pro-active switch from inducer- to maintenance combination in silico study, we considered time-invariant, as well as anatomically homogeneous average drug efficacy In our Based on a recent, successful pre-exposure prophylaxis (PrEP) trial, where emtricitabine (FTC) + tenofovir (TDF) were given to high-risk individuals prior to proviral genomic integration  setting for numerical simulation. This approach (i) takes into account stochastic fluctuations in the slow reaction processes; and (ii) reduces the computational costs for the simulation of the fast (deterministic) system dynamics. We used the direct hybrid method proposed in In brief, the hybrid method comprises the following algorithmic workflow:Set initial time Generate two uniformly distributed pseudo-random variables Set Take the integer Update Set atlab−6 and an absolute error tolerance of 10−9. Our simulation code is provided in The above algorithmic scheme requires the use of numerical integrators that allow to stop integration in step (3) when a stochastic reaction event is detected at a time For the model above (eq. (3)), the reproductive numbers, which indicate the expected number of offspring in the next generation, are defined as follows: the reproductive number Since infected cells are also pathogens, which can lead to a rebound of the disease even in the absence of any virus, we also determined their basic reproductive numbers under a given treatment We have previously introduced the reproductive capacity Figure S1Time and probability of virological failure depends on pool-size of archived drug-resistant virus. A: The median time until virological failure, in relation to the number of fully-resistant archived virus . B: Probability that virological failure occurs within two years after initiation of HAART therapy as a function of the number of fully-resistant archived virus. 500 stochastic-deterministic runs were performed for each pool size of the latently infected drug-resistant reservoir. Parameter values used: (PDF)Click here for additional data file.Table S1Determination of relevant parameter space for further investigation. We assessed virological failure rates after one year of triple drug therapy for varying values of efficacy (PDF)Click here for additional data file.Source Code S1The File ‘HAART.m’ can be used to simulate the kinetics of HIV after application of conventional HAART treatment in MATLAB.(M)Click here for additional data file.Source Code S2The File ‘HIVmodel.m’ builds the original HIV model used throughout the manuscript for use in MATLAB.(M)Click here for additional data file.Source Code S3The File ‘InductionMaintenance.m’ can be used to simulate the kinetics of HIV after under the proposed induction-maintenance therapy in MATLAB.(M)Click here for additional data file.Source Code S4The File ‘PatientMonitoring.m’ contains the MATLAB implementation of routine patient monitoring.(M)Click here for additional data file.Source Code S5The File ‘ReadMeFirst.txt’ Contains a description of all supplied source code files, contact details, information on runtime and execution and a copy of the GNU public license.(TXT)Click here for additional data file.Source Code S6The File ‘SpeciesLevelsIndices.pdf’ contains an interpretation of the output generated by executing the provided MATLAB Source Code Files –S4.(PDF)Click here for additional data file.


Accurate assessment of unmet supportive care needs is essential for optimal cancer patient care. This study used confirmatory factor analysis (CFA) to test the known factor structures of the short form of Supportive Care Need Survey (SCNS-34) in Hong Kong and Taiwan Chinese patients diagnosed with colorectal cancer (CRC).360 Hong Kong and 263 Taiwanese Chinese CRC patients completed the Chinese version of SCNS-SF34. Comparative measures  tested convergent validity while known group differences were examined to test discriminant validity.The original 5-factor and recent 4-factor models of the SCNS demonstrated poor data fit using CFA in both Hong Kong and Taiwan samples. Subsequently a modified five-factor model with correlated residuals demonstrated acceptable fit in both samples. Correlations demonstrated convergent and divergent validity and known group differences were observed.While the five-factor model demonstrated a better fit for data from Chinese colorectal cancer patients, some of the items within its domain overlapped, suggesting item redundancy. The five-factor model showed good psychometric properties in these samples but also suggests conceptualization of unmet supportive care needs are currently inadequate. Unmet supportive care needs provide specific information about the physical and psycho-social needs of cancer patients Colorectal cancer (CRC) currently the second most prevalent cancer in Hong Kong's population is projected to be the most prevalent cancer within 5 years The Supportive Care Needs Survey (SCNS) was developed in Australia by Girgis and colleagues Except for the French version, studies have only examined the factorial structure of the SCNS-SF using an exploratory factor analysis in which no model is specified prior to the analysis. The main objective of the present study was to further validate the SCNS-SF by evaluating its factorial structure in a Hong Kong Chinese sample of patients with colorectal cancer (CRC). We compared the fit of the original five-factor model with the fit of the 4-factor model as proposed by Au et al Local ethics approval was independently obtained for recruitment and consent procedures from National Taiwan University Hospital and Hong Kong University/Hospital Authority HK West Cluster Institutional Review Boards and Ethics committees. All eligible participants gave fully informed written consent regarding study purpose, data confidentiality and rights to refusal and uncontested withdrawal.Chinese patients diagnosed with colorectal cancer attending a surgical out-patient clinic in Hong Kong, between September 2009–January 2012, were screened by clinicians for eligibility. Consecutive sampling was adopted. Eligibility criteria were Cantonese/Mandarin fluency, either current receiving active treatment or had completed active treatment, willingness and ability to complete the interview and age 18 years or older. Eligible participants completed face-to-face interviews administered by trained research assistants while waiting for follow-up consultation or before primary surgery. Patients lacking Cantonese/Mandarin fluency and those functionally incapable were excluded.Consenting patients were recruited from outpatient oncology and surgical clinics of a leading medical centre in northern Taiwan. Consecutive sampling was adopted. All were ≥18 years old, diagnosed and fully informed about their CRC and either still receiving or had completed active treatment, and able to communicate verbally.The SCNS-SF34 Chinese version was used The Chinese Patient Satisfaction Questionnaire (ChPSQ-9) measures out-patient clinic users' satisfaction with doctors' and nurses' performance The 14-item Hospital Anxiety and Depression Scale (HADS) The Memorial Symptom Assessment Scale–Short Form (MSAS-SF) As in the Hong Kong sample, the Chinese version of HADS was also used to assess psychological distress in the Taiwan sample.The 23-item Modified Symptom Distress Scale (SDS) was used to assess symptom distress. This scale with 23 items was modified from the symptom distress scale The above comparative measures were used to assess convergent validity. Socio-demographic and medical data were also obtained from patients and checked against their medical records.Following informed consent, participants completed a combined questionnaire orally-administered by trained research assistants to minimize respondent literacy problems.Following informed consent, SCNS-SF34 data were collected during follow-up out-patient clinic visits for cancer-related treatment, or one month after completion by trained interviewers.To assess the factorial validity of the 5-factor and the 4-factor models of the SCNS identified previously, Confirmatory factor analysis (CFA) was performed using Mplus 5.21 software Cronbach's alpha coefficient and item-to-total correlation were used to assess internal consistency with the minimal acceptable alpha specified at 0.7 Convergent validity, the extent to which theoretically-related measures are correlated with each other, was evaluated by correlating  SCNS-SF34-C domains with HADS, MSAS-PHY, MSAS-PSYCH, and ChPSQ-9 in the Hong Kong sample and with the HADS and Modified Symptom Distress scale in the Taiwan sample. We hypothesised that SCNS-SF34-C scores would correlate as follows: SCNS-34 HSI and PCS domains would positively correlate with ChPSQ-9 (poor patient satisfaction) because they both measures support received from the health care system. PSY and PDL domains would positively correlate with HADS, , MSAS-PHY, MSAS-PSYCH, Modified Symptom Distress scale  because these measures assess the experience of physical and psychological concerns.t-test was used to examine these hypotheses.We tested Lehmann, Koch & Mehnart's A total of 360/416 Hong Kong Chinese patients were eligible to participate in this study. Their mean age was 65.7 years (SD = 11.1)  and 227 A total of 263/298 Taiwanese Chinese patients were eligible to participate in the study. Of the 263 Taiwanese CRC patients, 150 were male (57%) and 113 were female (43%), with a mean age of 58.4 years  . Two-fifThere was no missing SCNS data in the Taiwan sample, whereas only 0.19% of data SCNS were missing in the Hong Kong sample. No significant differences were found between patients for whom all SCNS items were complete and those with missing data in terms of medical and socio-demographic characteristics.Both original 5-factor SCNS-SF 34 and 4-factor SCNS-33-C models were tested using the Hong Kong sample, then cross-validated using the Taiwan sample. Since the 4-factor model did not demonstrate a better fit than the original 5-factor model, modification indices were used to improve the fit of the original five-factor model The modified model based on the Hong Kong sample was substantially improved revealing an adequate fit to the data. The fit of the modified model based on the Taiwan sample also improved, but the model only reached a marginally adequate fit to the data. The standardized factor loadings of the 5-factor model for Hong Kong and Taiwan samples are presented in The reliability of the 5-factor model of the SCNS-SF is presented in In the Taiwan sample, item internal consistency  was seen for all items within each domain, excepting the PDL domain. The proportion of items meeting the .40 criterion in the PDL needs domain was 80%. In contrast, for the Hong Kong sample only the HSI domain had all items meeting the criterion, with the proportion of items meeting the .40 criterion in the PDL, PSY, SEX, and PCS domains ranging from 60% to 90%.In the Hong Kong sample, item-other scale correlations showed that 100% of all the items correlate more strongly with their own domain items than with other domains' items, supporting item discriminant validity. Likewise, in the Taiwan sample, most items, ranging from 72.7% to 100%, in each domain correlated significantly more with their own domain than with other domains.To assess scale convergent validity, the five-factor model of the SCNS-SF was correlated with measures of anxiety, depression, and symptom distress in both the Hong Kong and Taiwan samples, and patient satisfaction with clinical staff in the Hong Kong sample only . As hypot-test to examine known group differences in both Hong Kong and Taiwan samples. Significant differences emerged between patients who were aged under 65 and 65 or above for all domains except PDL domain in both samples. Younger patients reported more unmet HSI , PSY , PCS  and SEX domains  needs than did older patients. In contrast, except for Hong Kong female patients reporting greater unmet PSY  and PCS  needs and Hong Kong male patients reported greater unmet SEX needs , SCNS domain scores did not differ by gender (We next compared the 5-factor SCNS-SF scores by age and gender using Student's y gender .Previous studies examining factor structures of the SCNS-SF34 and its validity as a measure of unmet needs in different samples used mixed groups comprising cancer patient with various diagnoses With the exception of the French version of the SCNS-SF Since the 4-factor model did not fit better than the original 5-factor model, the original 5-factor model was chosen for closer examination to improve fittingness. Similar to Bredart et al's French version of the SCNS-SF Nevertheless, the modified 5-factor SCNS-SF demonstrated an acceptable fit model in both Hong Kong and Taiwan CRC samples. Also, the Chinese version of the SCNS-SF demonstrated good internal consistency for the five domains. Though the SEX domain demonstrated weak internal consistency in the Hong Kong sample. This is due to the item “to be given information about sexual relationships”; assessing information needs correlated weakly with the other two items which assess changes in sexuality. Item internal consistency was also supported by the fact that most items correlated more strongly with their own domains. Item discriminant validity was supported as almost all items correlated higher with its own domain than with other domains.The Chinese version of the SCNS-SF also showed good convergent validity in both Hong Kong and Taiwan samples demonstrated by strong positive correlation between psychological unmet needs and psychological distress; strong positive correlation between physical and daily living unmet needs and physical symptom distress. Consistent with previous studies on women with breast cancer from France Known group comparison demonstrated good discriminant validity. CRC patients younger than 65 years reported stronger unmet needs across all domains, except physical and daily living domain compared to older patients, similar to breast cancer patients The present study showed the extent of unmet supportive care was primarily related to health system and information aspects of care in both Hong Kong and Taiwanese samples. Unmet supportive care in relation to sexual need was minimal in both samples. This is consistent with previous studies demonstrating that patterns of unmet supportive care needs differ across cultures or health care services among Caucasians, Japanese, and Chinese The main strength of the present study is the inclusion of two datasets from samples of similar cultural background but geographically diverse Chinese populations which enables us to test the factorial validity across two samples. On the other hand, this study is limited to the recruitment of Hong Kong and Taiwanese Chinese colorectal patients based on one regional public hospital in Hong Kong and Taiwan respectively. A broader sample frame would have been preferable. Secondly, this cross-sectional study prohibited test-retest reliability assessment.In summary, the present study found that the modified 5-factor structure for 34 items of the Chinese version of the SCNS-SF best fitted the data for two independently recruited samples of CRC patients of Chinese ethnicity. While the internal reliability and clinical validity of the SCNS-SF is consistently demonstrated across studies, the item redundancy limited the factorial validity of the instrument. Hence, caution should be taken using SCNS-SF to assess supportive care needs in other cultural or cancer-type contexts.


The classification of breast cancer patients into risk groups provides apowerful tool for the identification of patients who will benefit fromaggressive systemic therapy. The analysis of microarray data has generatedseveral gene expression signatures that improve diagnosis and allow riskassessment. There is also evidence that cell proliferation-related geneshave a high predictive power within these signatures.Drosophila melanogaster genesrequired for either the maintenance of chromosome integrity (36 genes) ormitotic division (72 genes).We thus constructed a gene expression signature (the DM signature) using thehuman orthologues of 108 The DM signature has minimal overlap with the extant signatures and is highlypredictive of survival in 5 large breast cancer datasets. In addition, weshow that the DM signature outperforms many widely used breast cancersignatures in predictive power, and performs comparably to otherproliferation-based signatures. For most genes of the DM signature, anincreased expression is negatively correlated with patient survival. Thegenes that provide the highest contribution to the predictive power of theDM signature are those involved in cytokinesis.This finding highlights cytokinesis as an important marker in breast cancerprognosis and as a possible target for antimitotic therapies. A reliable prediction of the outcome of a breast cancer is extremely valuableinformation for deciding a therapeutic strategy. The analysis of gene expressionprofiles obtained with microarrays has allowed identification of gene sets, orgenetic “signatures”, that are strongly predictive of poor prognosis(see Bottom-up (or unsupervised) signatures are developed using sets of genes thought tobe involved in specific cancer-related processes and do not rely on patients'gene expression data. Examples of these signatures are the “Woundsignature” that includes genes expressed in fibroblasts after serum additionwith a pattern reminiscent of the wound healing process Drosophila S2 cellsDrosophila genes identified in thescreen Drosophilagenes have unambiguous human orthologs. Here we show that these 108 human genesconstitute an excellent signature to predict breast cancer outcome. ThisDrosophila mitotic signature, or “DM signature”,has minimal overlap with pre-existing gene signatures and outperforms most of themin predictive power.Tumors are characterized by frequent mitotic divisions and chromosome instability. Inaddition, several independent studies have shown that mitotic activity in breastcancer samples from lymph node-negative patients positively correlates with poorprognosis D. melanogaster mitotic genes described in Drosophila genes. However, thedegree of homology between these human genes and theirDrosophila counterparts was not sufficient for inclusion inHomologene.The 142 http://www.rii.com/publications/2002/nejm.html. For theAffymetrix-based datasets, we obtained gene expression values from the raw data,using MAS 5.0 algorithm as implemented in the Simpleaffy We used the following publicly available breast cancer datasets: NKI survival library of R, whether the cumulative probabilityof survival is significantly different between the two groups.To determine whether the expression profiles of the genes included in the DMsignature are significantly and robustly correlated with the disease outcome weused the following procedure on the datasets mentioned above: (a) selecting themicroarray probes unambiguously associated to the signature genes; (b) creatingtwo groups of patients by Pearson correlation-based hierarchical clustering,using only the expression profiles of the probes selected in step a; (c)determining by a standard log-rank test, as implemented in thehttp://www.rii.com/publications/2002/nejm.htmlFor all datasets we divided the patients into two groups (good- and poor-outcome)based on their status at five years. We then calculated the prognostic scoresfor outcome prediction at five years using the following procedures. For the70-gene signature, the score of a patient is the cosine-correlation of theexpression profile of genes with good-prognosis found in http://microarray-pubs.stanford.edu/woundFor the Wound and IGS signatures, the score of a patient is given by the Pearsoncorrelation of the expression profile of the signature genes. For the Woundsignature the core serum response centroid is available at For the CIN z(g) is the z-score of probesetg computed in the Pawitan dataset andx is the logarithmic expression level of probesetg in patient p. The Affymetrix probesetsthat comprise the DM signature together with their z-scores arereported in For the DM signature, the prognostic score of a patient is givenbyhttp://biostat.mc.vanderbilt.edu/s/Hmisc. The Pawitan and NKIdatasets were not used in this comparison because they were involved in thetraining of the DM and 70-gene signatures, respectively.We used Receiver Operating Characteristic ROC curves to compare the scalablescores on three datasets . The area underthe curves and the related standard error were computed using the Hmisc libraryand programs available at g to the difference in scorebetween poor- and good-prognosis patients is defined asP(g) (G(g)) is the logarithmic expressionof the probeset averaged on all poor (good) prognosis patients andz(g) is the z-score of the probeset. Givena subset of the DM signature (e.g. cytokinesis-related genes),we used a Mann-Whitney U test to compare the contribution of the probesetsincluded in the subset to the contribution of all the other probesets.The contribution of each probeset Drosophila genes required for chromosome integrity and forthe fidelity of mitotic division Drosophila S2 cultured cells. Phenotypic analysis ofdsRNA-treated cells allowed the identification of 142 genes representative ofthe entire spectrum of functions required for proper transmission of geneticinformation. 44 of these genes were required to prevent spontaneous chromosomebreakage. The remaining 98 genes specified a variety of mitotic functionsincluding those required for spindle assembly, chromosome segregation andcytokinesis We have recently carried out an RNAi-based screen to detectDrosophila genes, according to Homologene Drosophila counterparts, are listed in Drosophila genes identified in the screen To construct the DM signature we identified the human homologues of theseThe DM signature shares very few genes with pre existing signatures. Weconsidered the top- down 70-gene signature 25 of the 108 genes of the DM signature are included in the list of genesperiodically expressed during the cell cycle in HeLa cells For a preliminary assessment of the predictive power and robustness of the DMsignature we used six publicly available breast cancer datasets: (i) NKI, whichcontains expression data from primary breast tumors of 295 consecutive,relatively young (age<52 yrs) patients H3F3A, PPAN-P2RY11 and KIF4) were not representedin the Affymetrix platform, we performed our analyses on 105 genes. For eachdataset, patients were divided into two groups based on the expression profilesof the genes in the DM signature using hierarchical clustering. Differences insurvival probability between the two groups were then evaluated with a standardlog-rank test on Kaplan-Meier curves. Although most of these gene expression data were generated using the samemicroarray platform, and could in principle be merged in a single dataset asrecently described As mentioned above, the DM signature contains two broad classes of genes, namely72 mitotic genes (71 in platform) and 36 genes required for the maintenance ofchromosome integrity (34 in platform). To determine the relative contribution ofthese two gene classes to the predictive power of the DM signature, we performedthe analysis using the two categories of genes separately. Both gene groupsturned out to be independently predictive of survival .HoweverP = 3e−6) and in a gliomadataset P = 0.0170).However, the DM signature is not significantly predictive in other lungcancer We also asked whether the DM signature is predictive of survival in other tumorsbesides breast cancer. Using the hierarchical clustering approach describedabove, we found that the DM signature is predictive of survival in a large lungcancer dataset Subdivision of patients into risk groups using the unsupervised clustering-basedapproach described above allows assessment of the predictive power of a genesignature, but does not allow specificity (fraction of low-risk patientscorrectly classified) and sensitivity (fraction of high-risk patients correctlyclassified) to be tuned according to specific requirements. However, such tuningis important in clinical applications, because the misclassification of ahigh-risk patient is potentially more harmful than the misclassification of alow-risk patient. Indeed, the 70-gene signature z-score. The Coxz-score measures the correlation between the expression patternof a gene and survival of the patient. A positive (negative)z-score indicates negative (positive) correlation between thegene expression level and patient's survival time.We determined a scalable prognostic score for the DM signature, using a proceduresimilar to that employed by Wang and co-workers z-scores for the Affymetrix probesets associated with theDM signature  (We used the Pawitan dataset as training set and computed the Cox U test) .Thus, aWe then compared the DM signature score with the scores of 6 other scalablesignatures for performance in predicting cancer outcome at 5 years. For thisanalysis we used ROC curves generated with the Affymetrix datasets not employedfor training . The scores of the CIN Since the DM signature and the two other proliferation-based signatures performsimilarly in predicting outcome at 5 years see , we compWe also performed multivariate Cox analysis to ascertain whether the DM signaturepredicts survival independently of other molecular and clinical tumor markers.The results for the Miller dataset , which ivs 0.67, and 0.678vs 0.683, respectively). Thus, we conclude that the DMsignature is a robust predictor of survival in lymph-node negative patients.The patients that would benefit the most from an effective prognostic predictorare those with lymph-node negative breast cancers. The Wang dataset includesonly lymph-node negative patients, while the Miller and Sotiriou-Desmedtdatasets include both node-positive and negative patients. Therefore weevaluated the performance of the DM signature on the patients of the latterdatasets by computing the AUC under ROC curves at the five-year endpoint. Forboth the Miller and the Sotiriou-Desmedt studies, the AUC values obtained forthe lymph node-negative patients were very similar to the values obtained forthe entire datasets  turned out to contribute to the difference inscore significantly more than other genes . The function of these genes is highly conserved,as they are required for cytokinesis in both Drosophila andhumans  are involved in multiple mitotic stages and are requiredfor cytokinesis ASPM and PLK1 are involved in human cellcytokinesis We next asked whether any of the phenotypic classes identified by the RNAi screen and PLK1 .The Droz-score is PIAS1, an E3 ligase involved in sumoylation of DNA repair proteinsincluding BRCA1 In the DM signature, there are a few genes whose expression is positivelycorrelated with survival . The genWe have shown that the DM signature is highly predictive of survival in five majorbreast cancer datasets. The DM signature contains two classes of genes required forcell proliferation: genes that maintain the integrity of mitotic chromosomes andgenes that mediate mitotic division. Cell proliferation-associated genes have beenpreviously used to construct several cancer signatures, and large subsets of thistype of genes are included in most supervised signatures Consistent with this idea, we found that the DM signature has a predictive power forbreast cancer outcome similar to that of two other proliferation-based signatures,the CIN signature What is the basis of the high prognostic value of the DM signature and why does itoutperform many of the extant signatures? We propose that the high performance ofthe DM signature reflects its specifically high content in genes truly involved incell proliferation. The proliferation-associated genes in other signatures have beenselected on the basis of their periodic expression pattern during the cell cycle andinclude several genes that, although periodically expressed, are not involved inbasic cell cycle processes The idea that survival of breast cancer patients is negatively correlated with thefrequency of dividing cells within a tumor sample is not novel. Indeed, it has beenshown that a correct measure of the mitotic activity ANLN, CIT,ECT2, KIF23, PRC1, RACGAP1, ASPM, KIF18A andPLK1) contributes to the predictive power of the DM signaturesignificantly more than the other genes. All cytokinesis genes display high positivez-scores, indicating that their increased expression isnegatively correlated with survival. Strikingly, there is evidence thatANLN, ECT2, PRC1,RACGAP1, ASPM, and PLK1 areupregulated in a variety of human cancers and that their overexpression oftencorrelates with poor outcome Click here for additional data file.Figure S2Distribution of the z-scores of the genes of the DM signature compared to thedistribution of z-scores of all genes represented in five breast cancerdatasets. Distribution of the z-scores of the genes of the DM signaturecompared to the distribution of z-scores of all genes represented in fivebreast cancer datasets. The z-scores were obtained using Cox univariateanalysis. Note that the distribution of the signature genes is shiftedtowards positive values.(0.28 MB PDF)Click here for additional data file.Table S1Ranking of the Affymetrix probesets of the DM signature according to theirz-scores. The Affymetrix probesets associated with the DM signature genesare ranked according to their Cox z-score computed on the training dataset(Pawitan). The contribution to the difference in score between poor and goodprognosis patients in the other datesets is also reported. The phenoclustersassociated with the Drosophila genes (0.06 MB XLS)Click here for additional data file.Table S2Predictive power of the DM signature in cancers other than breast. TheP-values were obtained from the log-rank test by comparing the cumulativeprobability of survival of clusters of patients in other cancer types.(0.01 MB XLS)Click here for additional data file.Table S3Differently classified patients by the three proliferation-based signatures.For each dataset and pair of proliferation-based signatures, we report thenumber of patients classified in different outcome groups, using scorecutoffs corresponding to the same sensitivity.(0.01 MB XLS)Click here for additional data file.Table S4Cox multivariate analysis for the NKI, Sotiriou-Desmedt and Wang datasets.The analysis shows that the DM signature is a predictor independent ofseveral clinical parameters.(0.01 MB XLS)Click here for additional data file.Table S5Cox multivariate analysis for the Miller dataset. The analysis shows that theCIN and Proliferation signatures are predictors independent of severalclinical and molecular parameters.(0.01 MB XLS)Click here for additional data file.