MOESM1 of miFRame: analysis and visualization of miRNA sequencing data in neurological disorders

Additional file 1. Pileup plots for small RNA reads mapping to miR-1285

A description of visual structure: Relations with human information processing mechanisms

Industrial Design Engineerin

Towards Personalized Cardiology: Multi-Scale Modeling of the Failing Heart

<div><p>Background</p><p>Despite modern pharmacotherapy and advanced implantable cardiac devices, overall prognosis and quality of life of HF patients remain poor. This is in part due to insufficient patient stratification and lack of individualized therapy planning, resulting in less effective treatments and a significant number of non-responders.</p><p>Methods and Results</p><p>State-of-the-art clinical phenotyping was acquired, including magnetic resonance imaging (MRI) and biomarker assessment. An individualized, multi-scale model of heart function covering cardiac anatomy, electrophysiology, biomechanics and hemodynamics was estimated using a robust framework. The model was computed on n=46 HF patients, showing for the first time that advanced multi-scale models can be fitted consistently on large cohorts. Novel multi-scale parameters derived from the model of all cases were analyzed and compared against clinical parameters, cardiac imaging, lab tests and survival scores to evaluate the explicative power of the model and its potential for better patient stratification. Model validation was pursued by comparing clinical parameters that were not used in the fitting process against model parameters.</p><p>Conclusion</p><p>This paper illustrates how advanced multi-scale models can complement cardiovascular imaging and how they could be applied in patient care. Based on obtained results, it becomes conceivable that, after thorough validation, such heart failure models could be applied for patient management and therapy planning in the future, as we illustrate in one patient of our cohort who received CRT-D implantation.</p></div

Patient-specific hemodynamics.

<p><b>A)</b> Personalized computation of arterial flow after personalization of the Windkessel parameters. <b>B)</b> Measured and computed LV pressure and volume curve in one patient, showing the high concordance between the clinical and modeling data.</p

Statistics of estimated Windkessel parameters throughout the studied population.

<p>Statistics of estimated Windkessel parameters throughout the studied population.</p

Patient-specific electrophysiology computation.

<p><b>Left Panels:</b> Computed ECG traces from the model in the patient exemplarily chosen. <b>Right Panels:</b> Computed trans-membrane potential propagation throughout the cardiac cycle (time in % of cycle length).</p

Clinical characteristics of the patient cohort with non-ischemic systolic HF.

<p>ACE: angiotensin-converting enzyme; ARB: angiotensin II receptor blocker; DCM: dilated cardiomyopathy; No: Number; NYHA: New York Heart Association; SD: Standard deviation.</p><p>Clinical characteristics of the patient cohort with non-ischemic systolic HF.</p

Overview of the modeling pipeline, from clinical data (input) to multi-scale, multi-physics cardiac models (output).

<p>The framework components are described in detail in methods.</p

Automated estimation of the 3D anatomical model.

<p><b>A)</b> Automatic segmentation of the right and left ventricle. <b>B)</b> Observed variability in cardiac anatomy (shape is color-coded on a template) estimated from the HF cohort. The representation indicates the variability in phenotypes from the cohort. <b>C)</b> After the different steps of the model computation are finished, computed intracardiac volume variations can be estimated. <b>D)</b> Fiber architecture applied to the personalized heart models.</p

Correlation between Left ventricular active force and estimated outcome of the patients.

<p>Correlation plot showing the left ventricular active force in the patients (x-axis) and their Seattle 5 Year Score (y-axis).</p