Evidence map›Paper›PMID 27141542›Full record

ArticleInternational journal of reproductive biomedicine2016

(1)H NMR- based metabolomics approaches as non- invasive tools for diagnosis of endometriosis.

Negar Ghazi, Mohammad Arjmand, Ziba Akbari, Ali Owsat Mellati, Hamid Saheb-Kashaf, Zahra Zamani

Open access · goldAbstract read
PubMed Full text at PMC
In one paragraph

Article in International journal of reproductive biomedicine, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed, 1 pooled it
4.0field-weighted citation impact, top 8% 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

21 citing papers in PubMed, 1 synthesis or guideline pooled it, 41 citations in OpenAlex.

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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

6 authors at 2 institutions in 1 country.

Negar GhaziDepartment of Biochemistry, Zanjan Medical University, Zanjan, Iran.; Department of Biochemistry, Pasteur Institute of Iran, Tehran, Iran.
Mohammad ArjmandDepartment of Biochemistry, Pasteur Institute of Iran, Tehran, Iran.
Ziba AkbariDepartment of Biochemistry, Pasteur Institute of Iran, Tehran, Iran.
Ali Owsat MellatiDepartment of Biochemistry, Zanjan Medical University, Zanjan, Iran.
Hamid Saheb-KashafNavid Medical Center, Tehran, Iran.
Zahra ZamaniDepartment of Biochemistry, Pasteur Institute of Iran, Tehran, Iran.
Zanjan University of Medical Sciences · IRPasteur Institute of Iran · IR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSo far, non-invasive diagnostic approaches such as ultrasound, magnetic resonance imaging, or blood tests do not have sufficient diagnostic power for endometriosis disease. Lack of a non-invasive diagnostic test contributes to the long delay between onset of symptoms and diagnosis of endometriosis.

objectiveThe present study focuses on the identification of predictive biomarkers in serum by pattern recognition techniques and uses partial least square discriminant analysis, multi-layer feed forward artificial neural networks (ANNs) and quadratic discriminant analysis (QDA) modeling tools for the early diagnosis of endometriosis in a minimally invasive manner by (1)H- NMR based metabolomics. MATERIALS AND

methodsThis prospective cohort study was done in Pasteur Institute, Iran in June 2013. Serum samples of 31 infertile women with endometriosis (stage II and III) who confirmed by diagnostic laparoscopy and 15 normal women were collected and analyzed by nuclear magnetic resonance spectroscopy. The model was built by using partial least square discriminant analysis, QDA, and ANNs to determine classifier metabolites for early prediction risk of disease.

resultsThe levels of 2- methoxyestron, 2-methoxy estradiol, dehydroepiandrostion androstendione, aldosterone, and deoxy corticosterone were enhanced significantly in infertile group. While cholesterol and primary bile acids levels were decreased. QDA model showed significant difference between two study groups. Positive and negative predict value levels obtained about 71% and 78%, respectively. ANNs provided also criteria for detection of endometriosis.

conclusionThe QDA and ANNs modeling can be used as computational tools in noninvasive diagnose of endometriosis. However, the model designed by QDA methods is more efficient compared to ANNs in diagnosis of endometriosis patients.

Indexed as

EndometriosisMetabolomicsNuclear magnetic resonance

Identifiers

PMID27141542
PMCPMC4837922
OpenAlexW2318804922

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.