Clinical Chemistry and Laboratory Medicine (CCLM)
Published in Association with the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM)
Editor-in-Chief: Plebani, Mario
Ed. by Gillery, Philippe / Lackner, Karl J. / Lippi, Giuseppe / Melichar, Bohuslav / Payne, Deborah A. / Schlattmann, Peter / Tate, Jillian R.
IMPACT FACTOR increased in 2015: 3.017
Rank 5 out of 30 in category Medical Laboratory Technology in the 2014 Thomson Reuters Journal Citation Report/Science Edition
SCImago Journal Rank (SJR) 2015: 0.873
Source Normalized Impact per Paper (SNIP) 2015: 0.982
Impact per Publication (IPP) 2015: 2.238
New serum biomarkers for detection of tuberculosis using surface-enhanced laser desorption/ionization time-of-flight mass spectrometry
1Institute of Cell Biology, Zhejiang University, Hangzhou, P.R. China
2Department of Clinical Laboratory, Taizhou Municipal Hospital, Taizhou, P.R. China
3Hangzhou Red Cross Hospital, Hangzhou, P.R. China
4Jilin Academy of Traditional Chinese Medicine Sciences, Changchun, P.R. China
5The Sixth Hospital of Shaoxing, Shaoxing, P.R. China
Citation Information: Clinical Chemistry and Laboratory Medicine. Volume 49, Issue 10, Pages 1727–1733, ISSN (Online) 1437-4331, ISSN (Print) 1434-6621, DOI: https://doi.org/10.1515/CCLM.2011.634, June 2011
- Published Online:
Background: New technologies for the early detection of tuberculosis (TB) are urgently needed. Pathological changes within an organ might be reflected in proteomic patterns in serum. The aim of the present study was to screen for the potential protein biomarkers in serum for the diagnosis of TB using proteomic fingerprint technology.
Methods: Proteomic fingerprint technology combining protein chips with surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS) was used to profile the serum proteins from 50 patients with TB, 25 patients with lung disease other than TB, and 25 healthy volunteers. The protein fingerprint expression of all the serum samples and the resulting profiles between TB and control groups were analyzed with the Biomarker Wizard system.
Results: A total of 30 discriminating m/z peaks were detected that were related to TB (p<0.01). The model of biomarkers constructed by the Biomarker Patterns Software based on the three biomarkers (2024, 8007, and 8598 Da) generated excellent separation between the TB and control groups. The sensitivity was 84.0% and the specificity was 86.0%. Blind test data indicated a sensitivity of 80.0% and a specificity of 84.2%.
Conclusions: The data suggested a potential application of SELDI-TOF MS as an effective technology to profile serum proteome, and with pattern analysis, a diagnostic model comprising three potential biomarkers was indicated to differentiate people with TB and healthy controls rapidly and precisely.
Here you can find all Crossref-listed publications in which this article is cited. If you would like to receive automatic email messages as soon as this article is cited in other publications, simply activate the “Citation Alert” on the top of this page.