Autor: |
Giuseppe Maulucci, Ermanno Cordelli, Alessandro Rizzi, Francesca De Leva, Massimiliano Papi, Gabriele Ciasca, Daniela Samengo, Giovambattista Pani, Dario Pitocco, Paolo Soda, Giovanni Ghirlanda, Giulio Iannello, Marco De Spirito |
Jazyk: |
angličtina |
Rok vydání: |
2017 |
Předmět: |
|
Zdroj: |
PLoS ONE, Vol 12, Iss 9, p e0184109 (2017) |
Druh dokumentu: |
article |
ISSN: |
1932-6203 |
DOI: |
10.1371/journal.pone.0184109 |
Popis: |
Glycosylation, oxidation and other post-translational modifications of membrane and transmembrane proteins can alter lipid density, packing and interactions, and are considered an important factor that affects fluidity variation in membranes. Red blood cells (RBC) membrane physical state, showing pronounced alterations in Type 1 diabetes mellitus (T1DM), could be the ideal candidate for monitoring the disease progression and the effects of therapies. On these grounds, the measurement of RBC membrane fluidity alterations can furnish a more sensitive index in T1DM diagnosis and disease progression than Glycosylated hemoglobin (HbA1c), which reflects only the information related to glycosylation processes. Here, through a functional two-photon microscopy approach we retrieved fluidity maps at submicrometric scale in RBC of T1DM patients with and without complications, detecting an altered membrane equilibrium. We found that a phase separation between fluid and rigid domains occurs, triggered by systemic effects on membranes fluidity of glycation and oxidation. The phase separation patterns are different among healthy, T1DM and T1DM with complications patients. Blood cholesterol and LDL content are positively correlated with the extent of the phase separation patterns. To quantify this extent a machine learning approach is employed to develop a Decision-Support-System (DSS) able to recognize different fluidity patterns in RBC. Preliminary analysis shows significant differences(p |
Databáze: |
Directory of Open Access Journals |
Externí odkaz: |
|