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Updated: Mar 19, 2026
Routine Screening Method for Microparticles in Platelet Transfusions
Published on: January 31, 2018
[Screening of Serum Markers in Primary Immune Thrombocytopenia]
Yang Zhang1, Ju Bai1, Wang-Gang Zhang1
1Department of Hematology, The Second Affiliated Hospital, Xi'an Jiaotong University Medical School, Xi'an 710004, Shanxi Province, China.
Objective:
To analyze the serum protein fingerprints of immune thrombocytopenia (ITP) patients and healthy controls by using weak cation exchange nanometer magnetic beads and MALDI-TOF-MAS technology, to identify the proteins with different expression, to establish the diagnostic model for ITP and to explore the pathogenesis of ITP.
Methods:
A total of 40 patients with ITP and 40 healthy controls were selected, the serum protein components were captured by using weak cation exchange nanaometer magnetic beads, the protein spectra of all specimens were detected by Autoflex II matrix assisted laser desorption/ionization time of flight mass spectrometry (MALDI- TOF-MS) and then the data were analyzed by CliprotoolsTM2.2 software, by which the distinct protein molecules were screened to set up ITP diagnostic model. To identify the established model, the sera of 20 ITP patients and 20 healthy controls were selected to make category and cross validations.
Results:
The detection of Clinprot system and the analysis of CliprotoolsTM2.2 software showed that about 55 protein peaks were detected with the range of 700 Da to 10 000 Da of molecular weight in the protein spectrum of serum speciments from 40 ITP patients and 40 healthy controls. Compared with healthy controls, 19 protein expression peaks with statistically significant difference were found in ITP patients (P < 0.05), among them 5 expressions were up-regulated and 14 expressions were down-regulated. The diagnostic model on basis of Supervised Neural Network Algorithm (SNN) was established through 10 MS peaks with strongest capability in ITP group and control group automatically distinguished by software, and it is expected that the sensitivity of model group reached to 100%, and the specificity to 100%. The category validation showed that this diagnostic model correctly identificed all 20 ITP patients and 20 healthy controls, and in cross validation, the model sensitivity was 100% and the specificity was 100%.
Conclusion:
The ITP SNN model ertablished by using ChinProt System with high flax and good repetition is composed of 10 protein peaks with significant difference, this model can effectively distinguish ITP patients and healthy controls.
