Evidence map›Paper›PMID 38291465›Full record

ArticleMolecular cytogenetics2024

Chromosomal microarray analysis for prenatal diagnosis of uniparental disomy: a retrospective study.

Chenxia Xu, Miaoyuan Li, Tiancai Gu, Fenghua Xie, Yanfang Zhang, Degang Wang, Jianming Peng

Open access · goldAbstract read
In one paragraph

Article in Molecular cytogenetics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
10.0field-weighted citation impact, top 2% 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

8 citing papers in PubMed, 9 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. 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 3 institutions in 1 country.

Chenxia Xu *Prenatal Diagnosis Center, Boai Hospital of Zhongshan, Zhongshan, Guangdong, China.
Miaoyuan Li *Department of Urology, The People's Hospital of Zhongshan, Zhongshan, Guangdong, China.
Tiancai GuPrenatal Diagnosis Center, Boai Hospital of Zhongshan, Zhongshan, Guangdong, China.
Fenghua XiePrenatal Diagnosis Center, Boai Hospital of Zhongshan, Zhongshan, Guangdong, China.
Yanfang ZhangPrenatal Diagnosis Center, Boai Hospital of Zhongshan, Zhongshan, Guangdong, China.
Degang WangPrenatal Diagnosis Center, Boai Hospital of Zhongshan, Zhongshan, Guangdong, China.
Jianming PengPrenatal Diagnosis Center, Boai Hospital of Zhongshan, Zhongshan, Guangdong, China. pengjm2001@sina.com.
Boai Hospital of Zhongshan · CNJinan University · CNSouthern Medical University · CN

Funding

Zhongshan Science and Technology Bureau 221022094598062
6 · The paper itself

Abstract

backgroundChromosomal microarray analysis (CMA) is a valuable tool in prenatal diagnosis for the detection of chromosome uniparental disomy (UPD). This retrospective study examines fetuses undergoing invasive prenatal diagnosis through Affymetrix CytoScan 750 K array analysis. We evaluated both chromosome G-banding karyotyping data and CMA results from 2007 cases subjected to amniocentesis.

resultsThe detection rate of regions of homozygosity (ROH) ≥ 10 Mb was 1.8% (33/2007), with chromosome 11 being the most frequently implicated (17.1%, 6/33). There were three cases where UPD predicted an abnormal phenotype based on imprinted gene expression.

conclusionThe integration of UPD detection by CMA offers a more precise approach to prenatal genetic diagnosis. CMA proves effective in identifying ROH and preventing the birth of children affected by imprinting diseases.

Indexed as

Chromosomal microarrayPrenatal diagnosisRegions of homozygositySNP arraysUniparental disomy

Identifiers

PMID38291465
PMCPMC10826057
OpenAlexW4391357594

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.