As I recall, they did deep immune profiling of patient and HC blood. They then ran a Machine Learning classification algorithm on the samples. The algorithm separated into LC vs non-LC. This had 94% correlation with the patient's self-reported diagnosis (i.e included in study with or without active symptomatology).
UMAP is Uniform Manifold Approximation and Projection for Dimension Reduction. See UMAP Dimensionality Reduction — An Incredibly Robust Machine Learning Algorithm.
Inclusion criteria for the Long COVID group were age ≥ 18 years; previous confirmed or probable COVID-19 infection (according to World Health Organization guidelines); and persistent symptoms > 6 weeks following initial COVID-19 infection. Inclusion criteria for enrollment of healthy controls were age ≥ 18 years, no prior COVID-19 infection, and completion of a brief, semi-structured verbal screening with research staff confirming no active symptomatology. Inclusion criteria for convalescent controls were age ≥ 18 years; previous confirmed or probable prior COVID-19 infection; and completion of a brief, semi-structured verbal screening with research staff confirming no active symptomatology.
UMAP embedding of study participants with all collected immunological features demonstrated a clear visual separation between people with Long COVID and those without.
UMAP is Uniform Manifold Approximation and Projection for Dimension Reduction. See UMAP Dimensionality Reduction — An Incredibly Robust Machine Learning Algorithm.