Investigating two mobile just-in-time adaptive interventions to foster psychological resilience: research protocol of the DynaM-INT study

BACKGROUND

Stress-related disorders such as anxiety and depression are highly prevalent and cause a tremendous burden for affected individuals and society. In order to improve prevention strategies, knowledge regarding resilience mechanisms and ways to boost them is highly needed. In the Dynamic Modelling of Resilience - interventional multicenter study (DynaM-INT), we will conduct a large-scale feasibility and preliminary efficacy test for two mobile- and wearable-based just-in-time adaptive interventions (JITAIs), designed to target putative resilience mechanisms. Deep participant phenotyping at baseline serves to identify individual predictors for intervention success in terms of target engagement and stress resilience.

METHODS

DynaM-INT aims to recruit N = 250 healthy but vulnerable young adults in the transition phase between adolescence and adulthood (18-27 years) across five research sites (Berlin, Mainz, Nijmegen, Tel Aviv, and Warsaw). Participants are included if they report at least three negative burdensome past life events and show increased levels of internalizing symptoms while not being affected by any major mental disorder. Participants are characterized in a multimodal baseline phase, which includes neuropsychological tests, neuroimaging, bio-samples, sociodemographic and psychological questionnaires, a video-recorded interview, as well as ecological momentary assessments (EMA) and ecological physiological assessments (EPA). Subsequently, participants are randomly assigned to one of two ecological momentary interventions (EMIs), targeting either positive cognitive reappraisal or reward sensitivity. During the following intervention phase, participants' stress responses are tracked using EMA and EPA, and JITAIs are triggered if an individually calibrated stress threshold is crossed. In a three-month-long follow-up phase, parts of the baseline characterization phase are repeated. Throughout the entire study, stressor exposure and mental health are regularly monitored to calculate stressor reactivity as a proxy for outcome resilience. The online monitoring questionnaires and the repetition of the baseline questionnaires also serve to assess target engagement.

DISCUSSION

The DynaM-INT study intends to advance the field of resilience research by feasibility-testing two new mechanistically targeted JITAIs that aim at increasing individual stress resilience and identifying predictors for successful intervention response. Determining these predictors is an important step toward future randomized controlled trials to establish the efficacy of these interventions.

Data2Dynamics: a modeling environment tailored to parameter estimation in dynamical systems

Modeling of dynamical systems using ordinary differential equations is a popular approach in the field of systems biology. Two of the most critical steps in this approach are to construct dynamical models of biochemical reaction networks for large datasets and complex experimental conditions and to perform efficient and reliable parameter estimation for model fitting. We present a modeling environment for MATLAB that pioneers these challenges. The numerically expensive parts of the calculations such as the solving of the differential equations and of the associated sensitivity system are parallelized and automatically compiled into efficient C code. A variety of parameter estimation algorithms as well as frequentist and Bayesian methods for uncertainty analysis have been implemented and used on a range of applications that lead to publications.

AVAILABILITY AND IMPLEMENTATION

The Data2Dynamics modeling environment is MATLAB based, open source and freely available at http://www.data2dynamics.org.

CONTACT

andreas.raue@fdm.uni-freiburg.de

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

[Regadenoson as a new stress agent in myocardial perfusion imaging. Initial experience in The Netherlands]

OBJECTIVE

Regadenoson is a recently approved selective adenosine-2A receptor agonist to induce pharmacological stress in myocardial perfusion imaging (MPI) procedures using a single bolus injection.

MATERIAL AND METHODS

We included 123 patients referred for MPI because of suspected coronary arterial disease (CAD). Of these, 66 patients underwent a regadenoson stress test and 57 patients underwent an adenosine stress test preceding standard myocardial SPECT imaging. Technicians, physicians and patients were asked to report their experience using questionnaires.

RESULTS

As compared to adenosine, regadenoson did not produce any atrio-ventricular block (0 vs. 10% with adenosine), but did produce minor tachycardia and minimal blood pressure changes while all other side effects were milder and shorter. There were fewer patients with severe complaints after taking regadenoson than adenosine (17% vs. 32%, respectively, p<0.01). The most frequent complaint reported was dyspnea, followed by flushing and chest pain. However, when they did occur, they usually disappeared rapidly. The overall symptom score, including severity and duration of side effects, was significantly lower after regadenoson than after adenosine (6.7±6.3 vs. 10.0±7.9, respectively; p<0.01.) SPECT imaging results were similar. The regadenoson procedure was faster and more practical.

CONCLUSION

Regadenoson, the new selective adenosine-2A receptor agonist, is a stress agent for MPI with a patient- and department friendly profile.

[Differential diagnosis of myeloproliferative neoplasms. Quantitative NF-E2 immunohistochemistry for differentiating between essential thrombocythemia and primary myelofibrosis]

BACKGROUND

Besides essential thrombocythemia (ET), polycythemia vera (PV) and primary myelofibrosis (PMF) the myeloproliferative neoplasms (MPN) defined by the World Health Organization (WHO) comprise the entity of unclassifiable MPNs (MPN, U). The exact differential diagnosis of the specific MPN entities can be challenging particularly at early stages of the diseases. So far, pathologists have had to rely only on histomorphological evaluation of bone marrow biopsies in combination with laboratory data because helpful ancillary tests are not yet available. Even molecular tests, such as JAK2 mutation analysis are not helpful particularly in the differential diagnosis of ET and PMF because both entities are associated with the V617F mutation in 50 % of the cases. Recently overexpression of the transcription factor NF-E2 in MPN was described.

MATERIALS AND METHODS

A collective of samples consisting of 163 bone marrow biopsies including 139 MPN cases was stained immunohistochemically for NF-E2 and analyzed regarding the subcellular localization of NF-E2 in erythroid progenitor cells. The results were compared between the MPN entities as well as the controls and statistical analyses were conducted.

RESULTS AND DISCUSSION

This study showed that NF-E2 immunohistochemistry and analysis of the proportion of nuclear positive erythroblasts of all erythroid precursor cells can help to distinguish between ET and PMF even in early stages of the diseases. An MPN, U case showing a proportion of more than 20 % nuclear positive erythroblasts can be classified as a PMF with 92 % accuracy.

Comprehensive estimation of input signals and dynamics in biochemical reaction networks

Motivation: Cellular information processing can be described mathematically using differential equations. Often, external stimulation of cells by compounds such as drugs or hormones leading to activation has to be considered. Mathematically, the stimulus is represented by a time-dependent input function.

Parameters such as rate constants of the molecular interactions are often unknown and need to be estimated from experimental data, e.g. by maximum likelihood estimation. For this purpose, the input function has to be defined for all times of the integration interval. This is usually achieved by approximating the input by interpolation or smoothing of the measured data. This procedure is suboptimal since the input uncertainties are not considered in the estimation process which often leads to overoptimistic confidence intervals of the inferred parameters and the model dynamics.

Results: This article presents a new approach which includes the input estimation into the estimation process of the dynamical model parameters by minimizing an objective function containing all parameters simultaneously. We applied this comprehensive approach to an illustrative model with simulated data and compared it to alternative methods. Statistical analyses revealed that our method improves the prediction of the model dynamics and the confidence intervals leading to a proper coverage of the confidence intervals of the dynamic parameters. The method was applied to the JAK-STAT signaling pathway.

Availability: MATLAB code is available on the authors' website http://www.fdmold.uni-freiburg.de/~schelker/.

Contact:max.schelker@fdm.uni-freiburg.de

Supplementary Information: Additional information is available at Bioinformatics Online.

Influence of cell-to-cell variability on spatial pattern formation

Many spatial patterns in biology arise through differentiation of selected cells within a tissue, which is regulated by a genetic network. This is specified by its structure, parameterisation and the noise on its components and reactions. The latter, in particular, is not well examined because it is rather difficult to trace. The authors use suitable local mathematical measures based on the Voronoi diagram of experimentally determined positions of epidermal plant hairs (trichomes) to examine the variability or noise in pattern formation. Although trichome initiation is a highly regulated process, the authors show that the experimentally observed trichome pattern is substantially disturbed by cell-to-cell variations. Using computer simulations, they find that the rates concerning the availability of the protein complex that triggers trichome formation plays a significant role in noise-induced variations of the pattern. The focus on the effects of cell noise yields further insights into pattern formation of trichomes. The authors expect that similar strategies can contribute to the understanding of other differentiation processes by elucidating the role of naturally occurring fluctuations in the concentration of cellular components or their properties.

Predictive mathematical models of cancer signalling pathways

Complex intracellular signalling networks integrate extracellular signals and convert them into cellular responses. In cancer cells, the tightly regulated and fine-tuned dynamics of information processing in signalling networks is altered, leading to uncontrolled cell proliferation, survival and migration. Systems biology combines mathematical modelling with comprehensive, quantitative, time-resolved data and is most advanced in addressing dynamic properties of intracellular signalling networks. Here, we introduce different modelling approaches and their application to medical systems biology, focusing on the identifiability of parameters in ordinary differential equation models and their importance in network modelling to predict cellular decisions. Two related examples are given, which include processing of ligand-encoded information and dual feedback regulation in erythropoietin (Epo) receptor signalling. Finally, we review the current understanding of how systems biology could foster the development of new treatment strategies in the context of lung cancer and anaemia.

EPILAB: a software package for studies on the prediction of epileptic seizures

A Matlab®-based software package, EPILAB, was developed for supporting researchers in performing studies on the prediction of epileptic seizures. It provides an intuitive and convenient graphical user interface. Fundamental concepts that are crucial for epileptic seizure prediction studies were implemented. This includes, for example, the development and statistical validation of prediction methodologies in long-term continuous recordings. Seizure prediction is usually based on electroencephalography (EEG) and electrocardiography (ECG) signals. EPILAB is able to process both EEG and ECG data stored in different formats. More than 35 time and frequency domain measures (features) can be extracted based on univariate and multivariate data analysis. These features can be post-processed and used for prediction purposes. The predictions may be conducted based on optimized thresholds or by applying classifications methods such as artificial neural networks, cellular neuronal networks, and support vector machines. EPILAB proved to be an efficient tool for seizure prediction, and aims to be a way to communicate, evaluate, and compare results and data among the seizure prediction community.