Evidence map›Paper›PMID 20012079›Full record

ArticleLung

Serum protein profiling of smear-positive and smear-negative pulmonary tuberculosis using SELDI-TOF mass spectrometry.

Qi Liu, Xuerong Chen, Chaojun Hu, Renqing Zhang, Ji Yue, Guihui Wu, Xiaoping Li, Yunhong Wu, Fuqiang Wen

Abstract readMulticenter StudyValidation Study
PubMed Publisher
In one paragraph

Article in Lung. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
1.4field-weighted citation impact, top 20% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed, 31 citations in OpenAlex.

  1. Article
  2. Article
  3. Review
  4. Review
  5. Review
  6. Article
  7. Article
  8. Article
  9. Clinical immunology and multiplex biomarkers of human tuberculosis.Cold Spring Harbor perspectives in medicine · 2014
    Review
  10. Article
  11. Immunological biomarkers of tuberculosis.Nature reviews. Immunology · 2011
    Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 5 institutions in 1 country.

Qi LiuDivision of Pulmonary Disease, State Key Laboratory of Biotherapy, Department of Respiratory Medicine, West China Hospital, West China School of Medicine, Sichuan University, Chengdu, Sichuan, 610041, People's Republic of China.
Xuerong Chen
Chaojun Hu
Renqing Zhang
Ji Yue
Guihui Wu
Xiaoping Li
Yunhong Wu
Fuqiang Wen
Sichuan University · CNTibet Autonomous Region People's Hospital · CNChengdu Third People's Hospital · CNChinese Academy of Medical Sciences & Peking Union Medical College · CNState Key Laboratory of Biotherapy

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The focus of this study was to detect novel sera biomarkers for smear-positive and smear-negative pulmonary tuberculosis and to establish respective diagnostic models using the surface-enhanced laser desorption ionization time-of-flight mass spectrometry (SELDI-TOF MS) technique. A total of 155 sera samples from smear-positive pulmonary tuberculosis (SPPTB) and smear-negative pulmonary tuberculosis (SNPTB) patients and non-tuberculosis (non-TB) controls were analyzed with SELDI-TOF MS. The study was divided into a preliminary training set and a blinded testing set. A classification tree of spectra derived from 31 SPPTB patients, 22 SNPTB patients, and 42 non-TB controls were used to develop an optimal classification tree that discriminated them respectively in the training set. Then, the validity of the classification tree was challenged with another independent blinded testing set, which included 20 SPPTB patients, 14 SNPTB patients, and 26 non-TB controls. SNPTB patients and non-TB controls also were analyzed alone using the same method. The optimal decision tree model with a panel of nine biomarkers with mass:charge ratios (m/z) of 4821.45, 3443.22, 9284.93, 4473.86, 4702.84, 3443.22, 5343.26, 3398.27, and 3193.61 determined in the training set could detect 93.55%, 95.46%, and 88.09% accuracy for classifying SPPTB patients, SNPTB patients, and non-TB controls specimens, respectively. Validation of an independent, blinded testing set gave an accuracy of 80.77% for controls, 75.00% for SPPTB, and 71.43% for SNPTB samples using the same classification tree. With the peaks displaying differences between SNPTB patients and non-TB controls, a simplified dendrogram (m/z 4821.45, 4792.74) demonstrated classification efficacy of 85.94% (sensitivity 86.36% and specificity 85.71%) for distinguishing SNPTB patients from non-TB controls. The independent blinded testing set containing 14 SNPTB patients and 26 non-TB controls gained an accuracy of 81.59% (sensitivity 78.57% and specificity 84.62%) for diagnosing SNPTB. Special proteins/peptides may change in SPPTB and SNPTB patients and those changes may be used to distinguish them with the proper discriminant analytical method and to pursue and identify some involved proteins underlying the biological process of tuberculosis.

Indexed as

Spectrometry, Mass, Matrix-Assisted Laser Desorption-IonizationAdolescentAdultAgedBiomarkersBlood ProteinsCase-Control StudiesChinaDecision TreesDiscriminant AnalysisFemaleHumansMaleMiddle AgedPredictive Value of TestsProteomicsBiomarkersBlood Proteins

Identifiers

PMID20012079
OpenAlexW2055898294

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.