Evidence map›Paper›PMID 28627685›Full record

ArticleMolecular medicine reports2017

Metabolomic analysis based on 1H-nuclear magnetic resonance spectroscopy metabolic profiles in tuberculous, malignant and transudative pleural effusion.

Cheng Wang, Jingjin Peng, Yanling Kuang, Jiaqiang Zhang, Luming Dai

Open access · hybridAbstract read
In one paragraph

Article in Molecular medicine reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
1.4field-weighted citation impact, top 17% 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

12 citing papers in PubMed, 20 citations in OpenAlex.

  1. Article
  2. Article
  3. Review
  4. Article
  5. Review
  6. Review
  7. Biomarker discovery for tuberculosis using metabolomics.Frontiers in molecular biosciences · 2023
    Review
  8. Article
  9. Review
  10. Article
  11. Review
  12. Biomarkers in the diagnosis of pleural diseases: a 2018 update.Therapeutic advances in respiratory disease
    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

5 authors at 2 institutions in 1 country.

Cheng WangThe Second Department of Respiratory Medicine, The First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan 650032, P.R. China.
Jingjin PengThe Second Department of Respiratory Medicine, The First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan 650032, P.R. China.
Yanling KuangThe Second Department of Respiratory Medicine, The First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan 650032, P.R. China.
Jiaqiang ZhangThe Second Department of Respiratory Medicine, The First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan 650032, P.R. China.
Luming DaiThe Second Department of Respiratory Medicine, The First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan 650032, P.R. China.
Kunming Medical University · CNFirst Affiliated Hospital of Kunming Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Pleural effusion is a common clinical manifestation with various causes. Current diagnostic and therapeutic methods have exhibited numerous limitations. By involving the analysis of dynamic changes in low molecular weight catabolites, metabolomics has been widely applied in various types of disease and have provided platforms to distinguish many novel biomarkers. However, to the best of our knowledge, there are few studies regarding the metabolic profiling for pleural effusion. In the current study, 58 pleural effusion samples were collected, among which 20 were malignant pleural effusions, 20 were tuberculous pleural effusions and 18 were transudative pleural effusions. The small molecule metabolite spectrums were obtained by adopting 1H nuclear magnetic resonance technology, and pattern‑recognition multi-variable statistical analysis was used to screen out different metabolites. One‑way analysis of variance, and Student‑Newman‑Keuls and the Kruskal‑Wallis test were adopted for statistical analysis. Over 400 metabolites were identified in the untargeted metabolomic analysis and 26 metabolites were identified as significantly different among tuberculous, malignant and transudative pleural effusions. These metabolites were predominantly involved in the metabolic pathways of amino acids metabolism, glycometabolism and lipid metabolism. Statistical analysis revealed that eight metabolites contributed to the distinction between the three groups: Tuberculous, malignant and transudative pleural effusion. In the current study, the feasibility of identifying small molecule biochemical profiles in different types of pleural effusion were investigated reveal novel biological insights into the underlying mechanisms. The results provide specific insights into the biology of tubercular, malignant and transudative pleural effusion and may offer novel strategies for the diagnosis and therapy of associated diseases, including tuberculosis, advanced lung cancer and congestive heart failure.

Indexed as

MetabolomeProton Magnetic Resonance SpectroscopyBiomarkersDemographyDiscriminant AnalysisExudates and TransudatesFemaleHumansLeast-Squares AnalysisMaleMetabolic Networks and PathwaysMetabolomicsMiddle AgedPleural Effusion, MalignantTuberculosisBiomarkers

Identifiers

PMID28627685
PMCPMC5562006
OpenAlexW2626528112

What OpenQuestion holds

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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.