Evidence map›Paper›PMID 31723213›Full record

ArticleScientific reports2019

Multiplex analysis of 40 cytokines do not allow separation between endometriosis patients and controls.

Tamara Knific, Dmytro Fishman, Andrej Vogler, Manuela Gstöttner, René Wenzl, Hedi Peterson, Tea Lanišnik Rižner

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
2.5field-weighted citation impact, top 11% 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

15 citing papers in PubMed, 26 citations in OpenAlex.

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  8. Does the Use of the "ProseekInternational journal of molecular sciences · 2023
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  15. Gamma-Synuclein Levels Are Elevated in Peritoneal Fluid of Patients with Endometriosis.Medical science monitor : international medical journal of experimental and clinical research · 2020
    Article
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

7 authors at 5 institutions in 3 countries.

Tamara KnificInstitute of Biochemistry, Faculty of Medicine, University of Ljubljana, 1000, Ljubljana, Slovenia.
Dmytro FishmanInstitute of Computer Science, University of Tartu, Liivi 2, 50409, Tartu, Estonia.
Andrej VoglerDepartment of Obstetrics and Gynaecology, University Medical Centre Ljubljana, 1000, Ljubljana, Slovenia.
Manuela GstöttnerDepartment of Obstetrics and Gynecology, Medical University Vienna, 1090, Vienna, Austria.
René WenzlDepartment of Obstetrics and Gynecology, Medical University Vienna, 1090, Vienna, Austria.
Hedi PetersonInstitute of Computer Science, University of Tartu, Liivi 2, 50409, Tartu, Estonia.
Tea Lanišnik RižnerInstitute of Biochemistry, Faculty of Medicine, University of Ljubljana, 1000, Ljubljana, Slovenia. Tea.Lanisnik-Rizner@mf.uni-lj.si.
Medical University of Vienna · ATUniversity of Ljubljana · SILjubljana University Medical Centre · SIQuretec (Estonia) · EEUniversity of Tartu · EE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Endometriosis is a common gynaecological condition characterized by severe pelvic pain and/or infertility. The combination of nonspecific symptoms and invasive laparoscopic diagnostics have prompted researchers to evaluate potential biomarkers that would enable a non-invasive diagnosis of endometriosis. Endometriosis is an inflammatory disease thus different cytokines represent potential diagnostic biomarkers. As panels of biomarkers are expected to enable better separation between patients and controls we evaluated 40 different cytokines in plasma samples of 210 patients (116 patients with endometriosis; 94 controls) from two medical centres (Slovenian, Austrian). Results of the univariate statistical analysis showed no differences in concentrations of the measured cytokines between patients and controls, confirmed by principal component analysis showing no clear separation amongst these two groups. In order to validate the hypothesis of a more profound (non-linear) differentiating dependency between features, machine learning methods were used. We trained four common machine learning algorithms (decision tree, linear model, k-nearest neighbour, random forest) on data from plasma levels of proteins and patients' clinical data. The constructed models, however, did not separate patients with endometriosis from the controls with sufficient sensitivity and specificity. This study thus indicates that plasma levels of the selected cytokines have limited potential for diagnosis of endometriosis.

Indexed as

AdultBiomarkersCase-Control StudiesCytokinesEndometriosisFemaleHumansPrognosisProspective StudiesBiomarkersCytokines

Identifiers

PMID31723213
PMCPMC6853932
OpenAlexW2985316658

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.