Development of the Thalassaemia Adult Life Index (ThALI)

Abstract: Background: Beta Thalassaemia Major (βTM) is a chronic genetic illness whereby the challenges faced by patients exposes them to increased risk of psychosocial issues. Despite this, a disease-specific tool to measure the impact of this illness on adult patients has yet to be developed. Methods: In collaboration with βTM adult patients, this study aimed to develop a comprehensive, disease-specific, easy to use psychometrically sound tool to measure the impact of chelation and transfusion dependent βTM in a cross-cultural patient group in England.The Thalassaemia Life Index (ThALI) was developed in two stages – item generation and pre-testing and item reduction – in collaboration with service users. Recruited adult patients shaped the design of the instrument including its statements and subscales. Standard item reduction techniques were used to develop the instrument. Results: The final version of the ThALI encompasses 35 statements and five sub-scales - general physical health, coping, body image, appearance and confidence, social relationships and autonomy. This endorses the multidimensionality of quality of life (QoL). The factor structure of the ThALI is highly stable and its internal consistency is high (alpha = 0.87 for the overall scale; 0.83–0.94 for its subscales). The ThALI has sound scaling assumptions, acceptability and score variability. Content validity was confirmed by experts and service user interviewees. The loadings for the items retained were adequate and the item discriminant validity sound. Conclusions: The ThALI covers the impact of βTM in adult patients. Preliminary testing shows its multidimensionality to be reliable and valid. The national authentication of the tool with patients treated in Centres of Excellence will aim to provide further evidence regarding the ThALI’s psychometric properties. Once authenticated, the ThALI may be utilised in research and in clinical settings to assess the effects of new therapies and/or interventions from the patients’ perspective to inform practice and/or to identify areas of concern

Integrative modeling of transcriptional regulation in response to antirheumatic therapy

<p>Abstract</p> <p>Background</p> <p>The investigation of gene regulatory networks is an important issue in molecular systems biology and significant progress has been made by combining different types of biological data. The purpose of this study was to characterize the transcriptional program induced by etanercept therapy in patients with rheumatoid arthritis (RA). Etanercept is known to reduce disease symptoms and progression in RA, but the underlying molecular mechanisms have not been fully elucidated.</p> <p>Results</p> <p>Using a DNA microarray dataset providing genome-wide expression profiles of 19 RA patients within the first week of therapy we identified significant transcriptional changes in 83 genes. Most of these genes are known to control the human body's immune response. A novel algorithm called TILAR was then applied to construct a linear network model of the genes' regulatory interactions. The inference method derives a model from the data based on the Least Angle Regression while incorporating DNA-binding site information. As a result we obtained a scale-free network that exhibits a self-regulating and highly parallel architecture, and reflects the pleiotropic immunological role of the therapeutic target TNF-alpha. Moreover, we could show that our integrative modeling strategy performs much better than algorithms using gene expression data alone.</p> <p>Conclusion</p> <p>We present TILAR, a method to deduce gene regulatory interactions from gene expression data by integrating information on transcription factor binding sites. The inferred network uncovers gene regulatory effects in response to etanercept and thus provides useful hypotheses about the drug's mechanisms of action.</p

ENIGMA and global neuroscience: A decade of large-scale studies of the brain in health and disease across more than 40 countries

This review summarizes the last decade of work by the ENIGMA (Enhancing NeuroImaging Genetics through Meta Analysis) Consortium, a global alliance of over 1400 scientists across 43 countries, studying the human brain in health and disease. Building on large-scale genetic studies that discovered the first robustly replicated genetic loci associated with brain metrics, ENIGMA has diversified into over 50 working groups (WGs), pooling worldwide data and expertise to answer fundamental questions in neuroscience, psychiatry, neurology, and genetics. Most ENIGMA WGs focus on specific psychiatric and neurological conditions, other WGs study normal variation due to sex and gender differences, or development and aging; still other WGs develop methodological pipelines and tools to facilitate harmonized analyses of "big data" (i.e., genetic and epigenetic data, multimodal MRI, and electroencephalography data). These international efforts have yielded the largest neuroimaging studies to date in schizophrenia, bipolar disorder, major depressive disorder, post-traumatic stress disorder, substance use disorders, obsessive-compulsive disorder, attention-deficit/hyperactivity disorder, autism spectrum disorders, epilepsy, and 22q11.2 deletion syndrome. More recent ENIGMA WGs have formed to study anxiety disorders, suicidal thoughts and behavior, sleep and insomnia, eating disorders, irritability, brain injury, antisocial personality and conduct disorder, and dissociative identity disorder. Here, we summarize the first decade of ENIGMA's activities and ongoing projects, and describe the successes and challenges encountered along the way. We highlight the advantages of collaborative large-scale coordinated data analyses for testing reproducibility and robustness of findings, offering the opportunity to identify brain systems involved in clinical syndromes across diverse samples and associated genetic, environmental, demographic, cognitive, and psychosocial factors

DREAM4: Combining Genetic and Dynamic Information to Identify Biological Networks and Dynamical Models

Current technologies have lead to the availability of multiple genomic data types in sufficient quantity and quality to serve as a basis for automatic global network inference. Accordingly, there are currently a large variety of network inference methods that learn regulatory networks to varying degrees of detail. These methods have different strengths and weaknesses and thus can be complementary. However, combining different methods in a mutually reinforcing manner remains a challenge.We investigate how three scalable methods can be combined into a useful network inference pipeline. The first is a novel t-test-based method that relies on a comprehensive steady-state knock-out dataset to rank regulatory interactions. The remaining two are previously published mutual information and ordinary differential equation based methods (tlCLR and Inferelator 1.0, respectively) that use both time-series and steady-state data to rank regulatory interactions; the latter has the added advantage of also inferring dynamic models of gene regulation which can be used to predict the system's response to new perturbations.Our t-test based method proved powerful at ranking regulatory interactions, tying for first out of methods in the DREAM4 100-gene in-silico network inference challenge. We demonstrate complementarity between this method and the two methods that take advantage of time-series data by combining the three into a pipeline whose ability to rank regulatory interactions is markedly improved compared to either method alone. Moreover, the pipeline is able to accurately predict the response of the system to new conditions (in this case new double knock-out genetic perturbations). Our evaluation of the performance of multiple methods for network inference suggests avenues for future methods development and provides simple considerations for genomic experimental design. Our code is publicly available at http://err.bio.nyu.edu/inferelator/

Nonlinear Analysis of Motor Activity Shows Differences between Schizophrenia and Depression: A Study Using Fourier Analysis and Sample Entropy

The purpose of this study has been to describe motor activity data obtained by using wrist-worn actigraphs in patients with schizophrenia and major depression by the use of linear and non-linear methods of analysis. Different time frames were investigated, i.e., activity counts measured every minute for up to five hours and activity counts made hourly for up to two weeks. The results show that motor activity was lower in the schizophrenic patients and in patients with major depression, compared to controls. Using one minute intervals the depressed patients had a higher standard deviation (SD) compared to both the schizophrenic patients and the controls. The ratio between the root mean square successive differences (RMSSD) and SD was higher in the schizophrenic patients compared to controls. The Fourier analysis of the activity counts measured every minute showed that the relation between variance in the low and the high frequency range was lower in the schizophrenic patients compared to the controls. The sample entropy was higher in the schizophrenic patients compared to controls in the time series from the activity counts made every minute. The main conclusions of the study are that schizophrenic and depressive patients have distinctly different profiles of motor activity and that the results differ according to period length analysed

Daily rhythms of the sleep-wake cycle

The amount and timing of sleep and sleep architecture (sleep stages) are determined by several factors, important among which are the environment, circadian rhythms and time awake. Separating the roles played by these factors requires specific protocols, including the constant routine and altered sleep-wake schedules. Results from such protocols have led to the discovery of the factors that determine the amounts and distribution of slow wave and rapid eye movement sleep as well as to the development of models to determine the amount and timing of sleep. One successful model postulates two processes. The first is process S, which is due to sleep pressure (and increases with time awake) and is attributed to a 'sleep homeostat'. Process S reverses during slow wave sleep (when it is called process S'). The second is process C, which shows a daily rhythm that is parallel to the rhythm of core temperature. Processes S and C combine approximately additively to determine the times of sleep onset and waking. The model has proved useful in describing normal sleep in adults. Current work aims to identify the detailed nature of processes S and C. The model can also be applied to circumstances when the sleep-wake cycle is different from the norm in some way. These circumstances include: those who are poor sleepers or short sleepers; the role an individual's chronotype (a measure of how the timing of the individual's preferred sleep-wake cycle compares with the average for a population); and changes in the sleep-wake cycle with age, particularly in adolescence and aging, since individuals tend to prefer to go to sleep later during adolescence and earlier in old age. In all circumstances, the evidence that sleep times and architecture are altered and the possible causes of these changes (including altered S, S' and C processes) are examined

Using Interviews in CER Projects: Options, Considerations, and Limitations

Interviews can be a powerful chemistry education research tool. Different from an assessment score or Likert-scale survey number, interviews can provide the researcher with a way to examine and describe what we cannot see, aspects such as feelings, thoughts, or explanations of thinking or behavior. Most people have no doubt seen countless interviews on TV news and talk shows. These sessions might convey interviewing as a spontaneous, easy, and straightforward process. However, using interviews as a meaningful research tool requires considerable thought, preparation, and practice. This chapter provides a general introduction to the use of interviews as a tool within a chemistry education research context. The chapter provides a general introduction to the use of interviews as a research tool including how to plan, conduct, and analyze interviews. It highlights important considerations for designing and conducting fruitful interviews, provides examples of different ways in which interviews have been used effectively in chemistry education research, and supplies additional references for the reader who wants to delve more deeply into particular topics

Cold-induced vasoconstriction at forearm and hand skin sites: the effect of age

During mild cold exposure, elderly are at risk of hypothermia. In humans, glabrous skin at the hands is well adapted as a heat exchanger. Evidence exists that elderly show equal vasoconstriction due to local cooling at the ventral forearm, yet no age effects on vasoconstriction at hand skin have been studied. Here, we tested the hypotheses that at hand sites (a) elderly show equal vasoconstriction due to local cooling and (b) elderly show reduced response to noradrenergic stimuli. Skin perfusion and mean arterial pressure were measured in 16 young adults (Y: 18–28 years) and 16 elderly (E: 68–78 years). To study the effect of local vasoconstriction mechanisms local sympathetic nerve terminals were blocked by bretylium (BR). Baseline local skin temperature was clamped at 33°C. Next, local temperature was reduced to 24°C. After 15 min of local cooling, noradrenalin (NA) was administered to study the effect of neural vasoconstriction mechanisms. No significant age effect was observed in vasoconstriction due to local cooling at BR sites. After NA, vasoconstriction at the forearm showed a significant age effect; however, no significant age effect was found at the hand sites. [Change in CVC (% from baseline): Forearm Y: −76 ± 3 vs. E: −60 ± 5 (P < 0.01), dorsal hand Y: −74 ± 4 vs. E: −72 ± 4 (n.s.), ventral hand Y: −80 ± 7 vs. E: −70 ± 11 (n.s.)]. In conclusion, in contrast to results from the ventral forearm, elderly did not show a blunted response to local cooling and noradrenalin at hand skin sites. This indicates that at hand skin the noradrenergic mechanism of vasoconstriction is maintained with age

Brain structural correlates of insomnia severity in 1053 individuals with major depressive disorder : results from the ENIGMA MDD Working Group

It has been difficult to find robust brain structural correlates of the overall severity of major depressive disorder (MDD). We hypothesized that specific symptoms may better reveal correlates and investigated this for the severity of insomnia, both a key symptom and a modifiable major risk factor of MDD. Cortical thickness, surface area and subcortical volumes were assessed from T1-weighted brain magnetic resonance imaging (MRI) scans of 1053 MDD patients (age range 13-79 years) from 15 cohorts within the ENIGMA MDD Working Group. Insomnia severity was measured by summing the insomnia items of the Hamilton Depression Rating Scale (HDRS). Symptom specificity was evaluated with correlates of overall depression severity. Disease specificity was evaluated in two independent samples comprising 2108 healthy controls, and in 260 clinical controls with bipolar disorder. Results showed that MDD patients with more severe insomnia had a smaller cortical surface area, mostly driven by the right insula, left inferior frontal gyrus pars triangularis, left frontal pole, right superior parietal cortex, right medial orbitofrontal cortex, and right supramarginal gyrus. Associations were specific for insomnia severity, and were not found for overall depression severity. Associations were also specific to MDD; healthy controls and clinical controls showed differential insomnia severity association profiles. The findings indicate that MDD patients with more severe insomnia show smaller surfaces in several frontoparietal cortical areas. While explained variance remains small, symptom-specific associations could bring us closer to clues on underlying biological phenomena of MDD

Identifying Biological Network Structure, Predicting Network Behavior, and Classifying Network State With High Dimensional Model Representation (HDMR)

This work presents an adapted Random Sampling - High Dimensional Model Representation (RS-HDMR) algorithm for synergistically addressing three key problems in network biology: (1) identifying the structure of biological networks from multivariate data, (2) predicting network response under previously unsampled conditions, and (3) inferring experimental perturbations based on the observed network state. RS-HDMR is a multivariate regression method that decomposes network interactions into a hierarchy of non-linear component functions. Sensitivity analysis based on these functions provides a clear physical and statistical interpretation of the underlying network structure. The advantages of RS-HDMR include efficient extraction of nonlinear and cooperative network relationships without resorting to discretization, prediction of network behavior without mechanistic modeling, robustness to data noise, and favorable scalability of the sampling requirement with respect to network size. As a proof-of-principle study, RS-HDMR was applied to experimental data measuring the single-cell response of a protein-protein signaling network to various experimental perturbations. A comparison to network structure identified in the literature and through other inference methods, including Bayesian and mutual-information based algorithms, suggests that RS-HDMR can successfully reveal a network structure with a low false positive rate while still capturing non-linear and cooperative interactions. RS-HDMR identified several higher-order network interactions that correspond to known feedback regulations among multiple network species and that were unidentified by other network inference methods. Furthermore, RS-HDMR has a better ability to predict network response under unsampled conditions in this application than the best statistical inference algorithm presented in the recent DREAM3 signaling-prediction competition. RS-HDMR can discern and predict differences in network state that arise from sources ranging from intrinsic cell-cell variability to altered experimental conditions, such as when drug perturbations are introduced. This ability ultimately allows RS-HDMR to accurately classify the experimental conditions of a given sample based on its observed network state