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Lipidomic analysis is able to measure simultaneously thousands of compounds belonging to a few lipid classes. In each lipid class, compounds differ only by the acyl radical, ranging between C10:0 (capric acid) and C24:0 (lignoceric acid). Although some metabolites have a peculiar pathological role, more often compounds belonging to a single lipid class exert the same biological effect. Here, we present a lipidomics workflow that extracts the tandem mass spectrometry data from individual files and uses them to group compounds into structurally homogeneous clusters by chemical structure hierarchical clustering analysis (CHCA). The case-to-control peak area ratios of the metabolites are then analyzed within clusters. We created two freely available applications to assist the workflow: FragClust to generate the tables to be subjected to CHCA, and TestClust to perform statistical analysis on clustered data. We used the lipidomics data from the plasma of Alzheimer's disease (AD) patients in comparison with healthy controls to test the workflow. To date, the search for plasma biomarkers in AD has not provided reliable results. This article shows that the workflow is helpful to understand the behavior of whole lipid classes in plasma of AD patients.

The microstructural alterations suffered during the process of drawing deformation and subsequent annealing of pearlitic steel wires, were evaluated by scanning electron microscopy and atomic force microscopy. The deformed material showed the curling structure in cross section while, in the longitudinal section, the lamellae was aligned with the drawing direction. The microstructural characterization of deformed samples also allowed observing an interlamellar spacing reduction and the intermediate lamellae alignment process. After the heat treatment at 1000ºC for 5 min the microstructure was restored, however, few recrystallized grains were observed. The recovery was the dominant phenomenon, due to factors associated with curling structure that inhibited recrystallization.

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