Fig. 1: Substrate material characteristics and schematics of extraction and machine-learning workflow.

a Transmission electron microscopy (TEM) image of ferric particles (n ≥ 3 randomly selected) and selected area electron diffraction (SAED) pattern (inset) showing polycrystalline structure. Scale bar = 100 nm. b Scanning electron microscopy (SEM) images (n ≥ 3 randomly selected) of ferric particles showing nanoscale surface roughness and large-scale uniformity (inset). Scale bars = 100 nm in b and 1 μm in the inset of b. c Schematic workflow for the extraction of serum metabolic patterns by ferric particle-assisted laser desorption/ionization mass spectrometry (LDI MS). Fifty nanolitres of native serum was consumed for direct analysis without pre-treatment procedures. Only Na⁺-adducted and K⁺-adducted metabolites can be selectively detected with the coexistence of high concentration of peptides and proteins. d Schematic outline for the sparse regression machine learning of serum metabolic patterns (X). The sparse regression method was used to build calculation models with sparsely constrained \(\bar \beta\) towards the diagnosis of early-stage LA (\(\overrightarrow {\mathbf{y}}\)). Each square and its colour in X corresponded to one m/z feature and its signal intensity in serum metabolic patterns.