Evidence map›Paper›PMID 32904578›Full record

ArticleDisease markers2020

Urinary Peptides Associated Closely with Gestational Diabetes Mellitus.

Zhiying Hu, Yaping Tian, Jia Li, Mei Hu, Man Zhang

Open access · hybridAbstract read
In one paragraph

Article in Disease markers, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.4field-weighted citation impact, top 32% 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

3 citing papers in PubMed, 5 citations in OpenAlex.

  1. Article
  2. Article
  3. 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

5 authors at 2 institutions in 1 country.

Zhiying HuClinical Laboratory Medicine, Beijing Shijitan Hospital, Capital Medical University, Beijing 100038, China.
Yaping TianLaboratory of Translational Medicine, The First Medical Center of Chinese PLA General Hospital, Beijing 100853, China.
Jia LiClinical Laboratory Medicine, Beijing Shijitan Hospital, Capital Medical University, Beijing 100038, China.
Mei HuClinical Laboratory Medicine, Beijing Shijitan Hospital, Capital Medical University, Beijing 100038, China.
Man ZhangClinical Laboratory Medicine, Beijing Shijitan Hospital, Capital Medical University, Beijing 100038, China.ORCID https://orcid.org/0000-0001-5166-6804
Capital Medical University · CNChinese PLA General Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Gestational diabetes mellitus (GDM) is a common disease of pregnant women, which has a higher incidence in recent years. The purpose of this study is to explore urinary biomarkers that could predict and monitor gestational diabetes mellitus (GDM). Urine samples from 30 normal pregnant women and 78 GDM patients were collected and purified by weak cationic exchange magnetic beads (MB-WCX), then analyzed by matrix-assisted laser desorption ionization time of flight mass spectrometry (MALDI-TOF-MS). The urinary peptide signatures of the two groups were compared by BioExplorer software. The potential ability of the differently expressed peptides to distinguish GDM patients from normal pregnant women was evaluated by receiver operating characteristic (ROC) analysis. At last, the differently expressed peptides were identified by liquid chromatography tandem mass spectrometry (LC-MS). There were four differently expressed peptides (

Indexed as

AdultBiomarkersCase-Control StudiesChromatography, LiquidDiabetes, GestationalDiabetes Mellitus, Type 2FemaleHumansPeptidesPregnancyProteomicsROC CurveSpectrometry, Mass, Matrix-Assisted Laser Desorption-IonizationTandem Mass SpectrometryBiomarkersPeptides

Identifiers

PMID32904578
PMCPMC7456494
OpenAlexW3081329374

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.