We have recently implemented this method through a custom device and software. This study has been performed with the support from @SysBioGroup, @CONICETDialoga, @agenciaidiar y @Htal_Britanico.

Since it is solely based on the patient´s surface electrocardiogram, our method can be easily implemented in any scenario where cardiac synchrony needs to be assessed. This is of particular interest during CRT candidate selection, device implant and post implant optimization.

We tested our dyssynchrony index against mechanical dyssynchrony (assessed by strain echocardiography): It proved useful in predicting mechanical dyssynchrony. We also verified its efficacy in identifying the most efficient activation sequence.

We found a cut-off value for the dyssynchrony index, separating synchronous from dyssynchronous populations.

When tested in patients with abnormal conduction patterns, our index reflected the magnitude of the dyssynchrony commonly associated to these types of conduction.

We found a correlation between voltage and speed in control subjects. From this observation we built a “dyssynchrony index” quantifying the deviation from the expected behavior.

Impairment of the electrical synchrony of the heart results in mechanical dysfunction. There is still no reliable, universal method to assess cardiac electrical synchrony. We developed a pipeline for the automated reconstruction of the vectorcardiogram from a single surface ECG.

By recreating a FRAP experiment in silico, we found that the infinite-domain model wrongly estimates diffusion coefficients when the domain is smaller than the Crossover length. (7/9)

We evaluated whether the infinite-domain model gives an accurate estimation of diffusion coefficients when fitted to finite spatial profiles, a procedure typically followed in Fluorescence Recovery After Photobleaching (FRAP) experiments. (6/9)

Moreover, we found that the time it takes to reach the steady state (and its Standard deviation!) differs between small and large tissues. (5/9)

We propose an observable accounting for the shape of the morphogen spatial distributions. By comparing a finite tissue with an infinite domain, this shape factor allowed us to define a Crossover length of ~3 morphogen characteristic lengths. (4/9)

We found a Crossover length in a Reaction-Diffusion model. While above the crossover, assuming an infinite domain renders a good approximation; below the crossover, a finite domain should be used. (3/9)

We analytically investigated the spatiotemporal distributions of morphogens predicted by the well-known reaction–diffusion model in a finite one-dimensional domain, as a proxy for a biological tissue, and compared it with the solution of the infinite-domain model. (2/9)