Explaining sleep duration in adolescents: the impact of socio-demographic and lifestyle factors and working status

Previous studies found students who both work and attend school undergo a partial sleep deprivation that accumulates across the week. The aim of the present study was to obtain information using a questionnaire on a number of variables (e.g., socio-demographics, lifestyle, work timing, and sleep-wake habits) considered to impact on sleep duration of working (n=51) and non-working (n=41) high-school students aged 14-21 yrs old attending evening classes (19:00-22:30 h) at a public school in the city of So Paulo, Brazil. Data were collected for working days and days off. Multiple linear regression analyses were performed to assess the factors associated with sleep duration on weekdays and weekends. Work, sex, age, smoking, consumption of alcohol and caffeine, and physical activity were considered control variables. Significant predictors of sleep duration were: work (p < 0.01), daily work duration (8-10 h/day; p < 0.01), sex (p=0.04), age 18-21 yrs (0.01), smoking (p=0.02) and drinking habits (p=0.03), irregular physical exercise (p < 0.01), ease of falling asleep (p=0.04), and the sleep-wake cycle variables of napping (p < 0.01), nocturnal awakenings (p < 0.01), and mid-sleep regularity (p < 0.01). The results confirm the hypotheses that young students who work and attend school showed a reduction in night-time sleep duration. Sleep deprivation across the week, particularly in students working 8-10 h/day, is manifested through a sleep rebound (i.e., extended sleep duration) on Saturdays. However, the different roles played by socio-demographic and lifestyle variables have proven to be factors that intervene with nocturnal sleep duration. ) The variables related to the sleep-wake cycle naps and night awakenings proved to be associated with a slight reduction in night-time sleep, while regularity in sleep and wake-up schedules was shown to be associated with more extended sleep duration, with a distinct expression along the week and the weekend. Having to attend school and work, coupled with other socio-demographic and lifestyle factors, creates an unfavorable scenario for satisfactory sleep duration

Determination of Three-Dimensional Left Ventricle Motion to Analyze Ventricular Dyssyncrony in SPECT Images

A method to compute three-dimension (3D) left ventricle (LV) motion and its color coded visualization scheme for the qualitative analysis in SPECT images is proposed. It is used to investigate some aspects of Cardiac Resynchronization Therapy (CRT). The method was applied to 3D gated-SPECT images sets from normal subjects and patients with severe Idiopathic Heart Failure, before and after CRT. Color coded visualization maps representing the LV regional motion showed significant difference between patients and normal subjects. Moreover, they indicated a difference between the two groups. Numerical results of regional mean values representing the intensity and direction of movement in radial direction are presented. A difference of one order of magnitude in the intensity of the movement on patients in relation to the normal subjects was observed. Quantitative and qualitative parameters gave good indications of potential application of the technique to diagnosis and follow up of patients submitted to CRT.

Comparison of C-reactive protein and serum amyloid A protein in septic shock patients

Septic shock is a severe inflammatory state caused by an infectious agent. Our purpose was to investigate serum amyloid A (SAA) protein and C-reactive protein (CRP) as inflammatory markers of septic shock patients. Here we evaluate 29 patients in postoperative period, with septic shock, in a prospective study developed in a surgical intensive care unit. All eligible patients were monitored over a 7-day period by sequential organ failure assessment (SOFA) score, daily CRP, SAA, and lactate measurements. CRP and SAA strongly correlated up to the fifth day of observation but were not good predictors of mortality in septic shock. Copyright (C) 2008.

Genetic Variants of Diabetes Risk and Incident Cardiovascular Events in Chronic Coronary Artery Disease

Objective: To determine whether information from genetic risk variants for diabetes is associated with cardiovascular events incidence. Methods: From the about 30 known genes associated with diabetes, we genotyped single-nucleotide polymorphisms at the 10 loci most associated with type-2 diabetes in 425 subjects from the MASS-II Study, a randomized study in patients with multi-vessel coronary artery disease. The combined genetic information was evaluated by number of risk alleles for diabetes. Performance of genetic models relative to major cardiovascular events incidence was analyzed through Kaplan-Meier curve comparison and Cox Hazard Models and the discriminatory ability of models was assessed for cardiovascular events by calculating the area under the ROC curve. Results: Genetic information was able to predict 5-year incidence of major cardiovascular events and overall-mortality in non-diabetic individuals, even after adjustment for potential confounders including fasting glycemia. Non-diabetic individuals with high genetic risk had a similar incidence of events then diabetic individuals (cumulative hazard of 33.0 versus 35.1% of diabetic subjects). The addition of combined genetic information to clinical predictors significantly improved the AUC for cardiovascular events incidence (AUC = 0.641 versus 0.610). Conclusions: Combined information of genetic variants for diabetes risk is associated to major cardiovascular events incidence, including overall mortality, in non-diabetic individuals with coronary artery disease.

An empirical evaluation of imputation accuracy for association statistics reveals increased type-I error rates in genome-wide associations

Background: Genome wide association studies (GWAS) are becoming the approach of choice to identify genetic determinants of complex phenotypes and common diseases. The astonishing amount of generated data and the use of distinct genotyping platforms with variable genomic coverage are still analytical challenges. Imputation algorithms combine directly genotyped markers information with haplotypic structure for the population of interest for the inference of a badly genotyped or missing marker and are considered a near zero cost approach to allow the comparison and combination of data generated in different studies. Several reports stated that imputed markers have an overall acceptable accuracy but no published report has performed a pair wise comparison of imputed and empiric association statistics of a complete set of GWAS markers. Results: In this report we identified a total of 73 imputed markers that yielded a nominally statistically significant association at P < 10(-5) for type 2 Diabetes Mellitus and compared them with results obtained based on empirical allelic frequencies. Interestingly, despite their overall high correlation, association statistics based on imputed frequencies were discordant in 35 of the 73 (47%) associated markers, considerably inflating the type I error rate of imputed markers. We comprehensively tested several quality thresholds, the haplotypic structure underlying imputed markers and the use of flanking markers as predictors of inaccurate association statistics derived from imputed markers. Conclusions: Our results suggest that association statistics from imputed markers showing specific MAF (Minor Allele Frequencies) range, located in weak linkage disequilibrium blocks or strongly deviating from local patterns of association are prone to have inflated false positive association signals. The present study highlights the potential of imputation procedures and proposes simple procedures for selecting the best imputed markers for follow-up genotyping studies.

Prognostic factors for progression of liver structural lesions in chronic hepatitis C patients

AIM: To evaluate the epidemiological, clinical, laboratory and histological variables capable of predicting the progression of hepatic structural disturbances in chronic hepatitis C patients during the time interval between two liver biopsies. METHODS: Clinical charts of 112 chronic hepatitis C patients were retrospectively analyzed, whereas liver biopsies were revised. Immunohistochemical detection of interferon receptor was based on the Envision-Peroxidase System. RESULTS: In the multivariate analysis, the variables in the age at first biopsy, ALT levels, presence of lymphoid aggregates and siderosis were the determinants of the best model for predicting the severity of the disease. The direct progression rate of hepatic structural lesions was significantly higher in untreated patients, intermediate in treated non-responders and lower in treated responders to antiviral therapy (non-treated vs responders, 0.22 +/- 0.50 vs -0.15 +/- 0.46, P = 0.0053). Immuno-expression of interferon receptor is not a relevant factor. CONCLUSION: The best predictors of the progression of fibrosis are age at the first liver biopsy, extent of ALT elevation, inflammation at liver histology and hepatic siderosis. Antiviral treatment is effective in preventing the progression of liver structural lesions in chronic hepatitis C patients. (C) 2008 WJG. All rights reserved.

Prognostic index expression of cyclin-D1, cerbB-2 and VEGF: metastases vs corresponding primary cancers and metastatic vs non-metastatic adenocarcinomas

The prognostic relevance of different molecular markers in lung cancer is a crucial issue still worth investigating, and the specimens collected and analyzed represent a valuable source of material. Cyclin-D1, c-erbB-2 and vascular endothelial growth factor (VEGF) have shown to be promising as prognosticators in human cancer. In this study, we sought to examine the importance of Cyclin-D1, c-erbB-2 and VEGF, and to study the quantitative relationship among these factors and disease progression in metastases vs corresponding primary cancer, and metastatic vs non metastatic cancers. Material and Methods: We used immunohistochemistry and morphometric analysis to evaluate the amount of tumour staining for Cyclin-D1, c-erbB-2 and VEGF in 52 patients with surgically excised ademocarcinoma of the lung, and the outcome for our study was survival time until death from hematogenic metastases. Results: Metastasis presented lower c-erbB-2 expression than corresponding primary cancers (p=0.02). Cyclin-D1 and VEGF expression were also lower in metastases than in corresponding primary cancers, but this difference did not achieve statistical significance. Non-metastatic cancers also presented significantly lower Cyclin-D1 and c-erbB-2 expression than metastatic cancers (p<0.01 and p<0.01, respectively). Equally significant was the difference between higher c-erbB-2 expression by metastatic cancers compared to non-metastatic cancers (p=0.02). Considering survival in Kaplan-Maier analysis, Cyclin-D1 (p=0.04), c-erbB-2 (p=0.04) and VEGF (p<0.01) were important predictors of survival in metastatic cancers.

Cross-National Analysis of the Associations among Mental Disorders and Suicidal Behavior: Findings from the WHO World Mental Health Surveys

Background: Suicide is a leading cause of death worldwide. Mental disorders are among the strongest predictors of suicide; however, little is known about which disorders are uniquely predictive of suicidal behavior, the extent to which disorders predict suicide attempts beyond their association with suicidal thoughts, and whether these associations are similar across developed and developing countries. This study was designed to test each of these questions with a focus on nonfatal suicide attempts. Methods and Findings: Data on the lifetime presence and age-of-onset of Diagnostic and Statistical Manual of Mental Disorders, 4th Edition (DSM-IV) mental disorders and nonfatal suicidal behaviors were collected via structured face-to-face interviews with 108,664 respondents from 21 countries participating in the WHO World Mental Health Surveys. The results show that each lifetime disorder examined significantly predicts the subsequent first onset of suicide attempt (odds ratios [ORs] = 2.9-8.9). After controlling for comorbidity, these associations decreased substantially (ORs = 1.5-5.6) but remained significant in most cases. Overall, mental disorders were equally predictive in developed and developing countries, with a key difference being that the strongest predictors of suicide attempts in developed countries were mood disorders, whereas in developing countries impulse-control, substance use, and post-traumatic stress disorders were most predictive. Disaggregation of the associations between mental disorders and nonfatal suicide attempts showed that these associations are largely due to disorders predicting the onset of suicidal thoughts rather than predicting progression from thoughts to attempts. In the few instances where mental disorders predicted the transition from suicidal thoughts to attempts, the significant disorders are characterized by anxiety and poor impulse-control. The limitations of this study include the use of retrospective self-reports of lifetime occurrence and age-of-onset of mental disorders and suicidal behaviors, as well as the narrow focus on mental disorders as predictors of nonfatal suicidal behaviors, each of which must be addressed in future studies. Conclusions: This study found that a wide range of mental disorders increased the odds of experiencing suicide ideation. However, after controlling for psychiatric comorbidity, only disorders characterized by anxiety and poor impulse-control predict which people with suicide ideation act on such thoughts. These findings provide a more fine-grained understanding of the associations between mental disorders and subsequent suicidal behavior than previously available and indicate that mental disorders predict suicidal behaviors similarly in both developed and developing countries. Future research is needed to delineate the mechanisms through which people come to think about suicide and subsequently progress from ideation to attempts.

Predictive factors of non-deterioration of glucose tolerance following a 2-year behavioral intervention

Aim: To identify predictive factors associated with non-deterioration of glucose metabolism following a 2-year behavioral intervention in Japanese-Brazilians. Methods: 295 adults (59.7% women) without diabetes completed 2-year intervention program. Characteristics of those who maintained/improved glucose tolerance status (non-progressors) were compared with those who worsened (progressors) after the intervention. In logistic regression analysis, the condition of non-progressor was used as dependent variable. Results: Baseline characteristics of non-progressors (71.7%) and progressors were similar, except for the former being younger and having higher frequency of disturbed glucose tolerance and lower C-reactive protein (CRP). In logistic regression, non-deterioration of glucose metabolism was associated with disturbed glucose tolerance impaired fasting glucose or impaired glucose tolerance - (p < 0.001) and CRP levels <= 0.04 mg/dL (p = 0.01), adjusted for age and anthropometric variables. Changes in anthropometry and physical activity and achievement of weight and dietary goals after intervention were similar in subsets that worsened or not the glucose tolerance status. Conclusion: The whole sample presented a homogeneous behavior during the intervention. Lower CRP levels and diagnosis of glucose intolerance at baseline were predictors of non-deterioration of the glucose metabolism after a relatively simple intervention, independent of body adiposity.

Epigenetic Silencing of CRABP2 and MX1 in Head and Neck Tumors

Head and neck squamous cell carcinoma (HNSCC) is a heterogeneous disease affecting the epithelium of the oral cavity, pharynx and larynx. Conditions of most patients are diagnosed at late stages of the disease, and no sensitive and specific predictors of aggressive behavior have been identified yet. Therefore, early detection and prognostic biomarkers are highly desirable for a more rational management of the disease. Hypermethylation of CpG islands is one of the most important epigenetic mechanisms that leads to gene silencing in tumors and has been extensively used for the identification of biomarkers. In this study, we combined rapid subtractive hybridization and microarray analysis in a hierarchical manner to select genes that are putatively reactivated by the demethylating agent 5-aza-2'-deoxycytidine (5Aza-dC) in HNSCC cell lines (FaDu, UM-SCC-14A, UM-SCC-17A, UM-SCC-38A). This combined analysis identified 78 genes, 35 of which were reactivated in at least 2 cell lines and harbored a CpG island at their 5' region. Reactivation of 3 of these 35 genes (CRABP2, MX1, and SLC15A3) was confirmed by quantitative real-time polymerase chain reaction (PCR; fold change, >= 3). Bisulfite sequencing of their CpG islands revealed that they are indeed differentially methylated in the HNSCC cell lines. Using methylation-specific PCR, we detected a higher frequency of CRABP2 (58.1% for region 1) and MX1 (46.3%) hypermethylation in primary HNSCC when compared with lymphocytes from healthy individuals. Finally, absence of the CRABP2 protein was associated with decreased disease-free survival rates, supporting a potential use of CRABP2 expression as a prognostic biomarker for HNSCC patients.

Cloud condensation nuclei in pristine tropical rainforest air of Amazonia: size-resolved measurements and modeling of atmospheric aerosol composition and CCN activity

Atmospheric aerosol particles serving as cloud condensation nuclei (CCN) are key elements of the hydrological cycle and climate. We have measured and characterized CCN at water vapor supersaturations in the range of S=0.10-0.82% in pristine tropical rainforest air during the AMAZE-08 campaign in central Amazonia. The effective hygroscopicity parameters describing the influence of chemical composition on the CCN activity of aerosol particles varied in the range of kappa approximate to 0.1-0.4 (0.16+/-0.06 arithmetic mean and standard deviation). The overall median value of kappa approximate to 0.15 was by a factor of two lower than the values typically observed for continental aerosols in other regions of the world. Aitken mode particles were less hygroscopic than accumulation mode particles (kappa approximate to 0.1 at D approximate to 50 nm; kappa approximate to 0.2 at D approximate to 200 nm), which is in agreement with earlier hygroscopicity tandem differential mobility analyzer (H-TDMA) studies. The CCN measurement results are consistent with aerosol mass spectrometry (AMS) data, showing that the organic mass fraction (f(org)) was on average as high as similar to 90% in the Aitken mode (D <= 100 nm) and decreased with increasing particle diameter in the accumulation mode (similar to 80% at D approximate to 200 nm). The kappa values exhibited a negative linear correlation with f(org) (R(2)=0.81), and extrapolation yielded the following effective hygroscopicity parameters for organic and inorganic particle components: kappa(org)approximate to 0.1 which can be regarded as the effective hygroscopicity of biogenic secondary organic aerosol (SOA) and kappa(inorg)approximate to 0.6 which is characteristic for ammonium sulfate and related salts. Both the size dependence and the temporal variability of effective particle hygroscopicity could be parameterized as a function of AMS-based organic and inorganic mass fractions (kappa(p)=kappa(org) x f(org)+kappa(inorg) x f(inorg)). The CCN number concentrations predicted with kappa(p) were in fair agreement with the measurement results (similar to 20% average deviation). The median CCN number concentrations at S=0.1-0.82% ranged from N(CCN,0.10)approximate to 35 cm(-3) to N(CCN,0.82)approximate to 160 cm(-3), the median concentration of aerosol particles larger than 30 nm was N(CN,30)approximate to 200 cm(-3), and the corresponding integral CCN efficiencies were in the range of N(CCN,0.10/NCN,30)approximate to 0.1 to N(CCN,0.82/NCN,30)approximate to 0.8. Although the number concentrations and hygroscopicity parameters were much lower in pristine rainforest air, the integral CCN efficiencies observed were similar to those in highly polluted megacity air. Moreover, model calculations of N(CCN,S) assuming an approximate global average value of kappa approximate to 0.3 for continental aerosols led to systematic overpredictions, but the average deviations exceeded similar to 50% only at low water vapor supersaturation (0.1%) and low particle number concentrations (<= 100 cm(-3)). Model calculations assuming aconstant aerosol size distribution led to higher average deviations at all investigated levels of supersaturation: similar to 60% for the campaign average distribution and similar to 1600% for a generic remote continental size distribution. These findings confirm earlier studies suggesting that aerosol particle number and size are the major predictors for the variability of the CCN concentration in continental boundary layer air, followed by particle composition and hygroscopicity as relatively minor modulators. Depending on the required and applicable level of detail, the information and parameterizations presented in this paper should enable efficient description of the CCN properties of pristine tropical rainforest aerosols of Amazonia in detailed process models as well as in large-scale atmospheric and climate models.

Gene Expression Complex Networks: Synthesis, Identification, and Analysis

Thanks to recent advances in molecular biology, allied to an ever increasing amount of experimental data, the functional state of thousands of genes can now be extracted simultaneously by using methods such as cDNA microarrays and RNA-Seq. Particularly important related investigations are the modeling and identification of gene regulatory networks from expression data sets. Such a knowledge is fundamental for many applications, such as disease treatment, therapeutic intervention strategies and drugs design, as well as for planning high-throughput new experiments. Methods have been developed for gene networks modeling and identification from expression profiles. However, an important open problem regards how to validate such approaches and its results. This work presents an objective approach for validation of gene network modeling and identification which comprises the following three main aspects: (1) Artificial Gene Networks (AGNs) model generation through theoretical models of complex networks, which is used to simulate temporal expression data; (2) a computational method for gene network identification from the simulated data, which is founded on a feature selection approach where a target gene is fixed and the expression profile is observed for all other genes in order to identify a relevant subset of predictors; and (3) validation of the identified AGN-based network through comparison with the original network. The proposed framework allows several types of AGNs to be generated and used in order to simulate temporal expression data. The results of the network identification method can then be compared to the original network in order to estimate its properties and accuracy. Some of the most important theoretical models of complex networks have been assessed: the uniformly-random Erdos-Renyi (ER), the small-world Watts-Strogatz (WS), the scale-free Barabasi-Albert (BA), and geographical networks (GG). The experimental results indicate that the inference method was sensitive to average degree k variation, decreasing its network recovery rate with the increase of k. The signal size was important for the inference method to get better accuracy in the network identification rate, presenting very good results with small expression profiles. However, the adopted inference method was not sensible to recognize distinct structures of interaction among genes, presenting a similar behavior when applied to different network topologies. In summary, the proposed framework, though simple, was adequate for the validation of the inferred networks by identifying some properties of the evaluated method, which can be extended to other inference methods.

Feature selection environment for genomic applications

Background: Feature selection is a pattern recognition approach to choose important variables according to some criteria in order to distinguish or explain certain phenomena (i.e., for dimensionality reduction). There are many genomic and proteomic applications that rely on feature selection to answer questions such as selecting signature genes which are informative about some biological state, e. g., normal tissues and several types of cancer; or inferring a prediction network among elements such as genes, proteins and external stimuli. In these applications, a recurrent problem is the lack of samples to perform an adequate estimate of the joint probabilities between element states. A myriad of feature selection algorithms and criterion functions have been proposed, although it is difficult to point the best solution for each application. Results: The intent of this work is to provide an open-source multiplataform graphical environment for bioinformatics problems, which supports many feature selection algorithms, criterion functions and graphic visualization tools such as scatterplots, parallel coordinates and graphs. A feature selection approach for growing genetic networks from seed genes ( targets or predictors) is also implemented in the system. Conclusion: The proposed feature selection environment allows data analysis using several algorithms, criterion functions and graphic visualization tools. Our experiments have shown the software effectiveness in two distinct types of biological problems. Besides, the environment can be used in different pattern recognition applications, although the main concern regards bioinformatics tasks.

Land planarians (Platyhelminthes) as a model organism for fine-scale phylogeographic studies: understanding patterns of biodiversity in the Brazilian Atlantic Forest hotspot

The Brazilian Atlantic Forest is one of the richest biodiversity hotspots of the world. Paleoclimatic models have predicted two large stability regions in its northern and central parts, whereas southern regions might have suffered strong instability during Pleistocene glaciations. Molecular phylogeographic and endemism studies show, nevertheless, contradictory results: although some results validate these predictions, other data suggest that paleoclimatic models fail to predict stable rainforest areas in the south. Most studies, however, have surveyed species with relatively high dispersal rates whereas taxa with lower dispersion capabilities should be better predictors of habitat stability. Here, we have used two land planarian species as model organisms to analyse the patterns and levels of nucleotide diversity on a locality within the Southern Atlantic Forest. We find that both species harbour high levels of genetic variability without exhibiting the molecular footprint of recent colonization or population expansions, suggesting a long-term stability scenario. The results reflect, therefore, that paleoclimatic models may fail to detect refugia in the Southern Atlantic Forest, and that model organisms with low dispersal capability can improve the resolution of these models.

Transient effects of stretching exercises on gait parameters of elderly women

This study aimed to analyse the effects of a single stretching exercise session on a number of gait parameters ill elderly participants in all attempt to determine whether these exercises can influence the risk of fall. Fifteen healthy women living in the community Volunteered to participate in the study. A kinematic gait analysis was performed immediately before and after a session of static stretching exercises applied oil hip flexor/extensor muscles. Results showed a significant influence of stretching exercises on a number of gait parameters, which have previously been proposed as fall predictors. Participants showed increased gait velocity, greater step length and reduced double Support time during stance after performing stretching exercises, suggesting improved stability and mobility. Changes around the pelvis (increased anterior-posterior tilt and rotation range of motion) resulting from the stretching exercises were suggested to influence the gait parameters (velocity, step length and double support time). Therefore, stretching exercises were shown to be a promising strategy to facilitate changes in gait parameters related to the risk of fall. Some other gait variables related to the risk of fall remained Unaltered (e.g., toe clearance). The stable pattern of segmental angular velocities was proposed to explain the stability of these unchanged gait variables. The results indicate that stretching exercises, performed oil a regular (daily) basis, result in gait adaptations which can be considered as indicative of reduced fall risk. Other Studies to determine whether regular stretching routines are an effective strategy to reduce the risk of fall are required. (C) 2008 Elsevier Ltd. All rights reserved.