What it is

LOVAMAP stands for LOcal Void Analysis of MAP scaffolds, and it’s a custom piece of software we’ve developed to analyze important geometrical features of our MAP material – or any granular material for that matter!

We quickly learned that cells like to hang out inside of MAP scaffolds while they re-build tissue, and that got us thinking – if cells are spending time in the void space between the microparticles, then we should study this space to figure out the best design for promoting tissue repair. But studying the void space among packed particles is a complicated business, and we couldn’t help ourselves – we developed LOVAMAP to go above and beyond when it comes to MAP characterization.

Who helps us

We’ve partnered with Ninjabyte Computing to generate simulated MAP scaffolds that are so realistic, we do a double-take.

You know what’s easier than spending hours in lab fabricating hydrogel microparticles? Pressing a button to simulate hydrogel microparticles. Ninjabyte Computing is equipped with state-of-the-art technology to bring our wildest MAP scaffold fantasies to life. By running LOVAMAP on simulated MAP scaffolds, we can learn more faster. This is why Ninjabyte Computing deserves its very own page here.

How it works

Void Space Analysis

Read in formatted data

Void Space Analysis Landmarks First Image

Extract geometrical landmarks

Void Space Analysis Landmarks Second Image

Segment void space

Void Space Analysis Landmarks Third Image

Return descriptor data

Void Space Analysis Landmarks Fourth Image

Generate images & GIFs

What it offers

LOVAMAP outputs 3 types of descriptors that characterize MAP scaffolds:

Void Space Analysis - Global

GLOBAL DESCRIPTORS

Void Space Analysis - ridges

INTERUNIT DESCRIPTORS

Void Space Analysis - Subs

SUBUNIT DESCRIPTORS

Get the data

We’re sharing the data that accompanies our freshly-submitted manuscript about LOVAMAP. You can download the raw descriptor data for each particle composition category, as well as the statistical measurements for each distribution-descriptor.

Reach out if you’d like some help sifting through the data. And if you use these numbers in your own research, a citation is all the thanks we need 🙂

Homogeneous, rigid spheres

Binary mixtures of rigid spheres, 40 + 100 µm

Binary mixtures of rigid spheres, 60 + 100 µm

Heterogeneous, rigid spheres

Square packing

Hexagonal packing

Homogeneous, soft-body spheres

Binary mixtures of rigid and soft spheres

Atypical particles

Isotropy / Anisotropy ellipsoids