Evidence map›Paper›PMID 34645804›Full record

ArticleScientific data2021

RNA-seq of peripheral blood mononuclear cells of congenital generalized lipodystrophy type 2 patients.

Yen-Hua Huang, Tzu-Chien Su, Chung-Hsing Wang, Siew-Lee Wong, Yin-Hsiu Chien, Yu-Tai Wang, Wuh-Liang Hwu, Ni-Chung Lee

Abstract readDataset
In one paragraph

Article in Scientific data, 2021. 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
–field-weighted citation impact
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.

  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

8 authors.

Yen-Hua HuangInstitute of Biomedical Informatics, National Yang-Ming University, Taipei, Taiwan.ORCID 0000-0001-7932-374X
Tzu-Chien SuInstitute of Biomedical Informatics, National Yang-Ming University, Taipei, Taiwan.
Chung-Hsing WangDepartment of Pediatrics, Children's Hospital, China Medical University, Taichung, Taiwan.
Siew-Lee WongDepartment of Pediatrics, Ditmanson Medical Foundation Chia-Yi Christian Hospital, Chia-Yi, Taiwan.
Yin-Hsiu ChienDepartment of Pediatrics and Medical Genetics, National Taiwan University Hospital, Taipei, Taiwan.ORCID 0000-0001-8802-5728
Yu-Tai WangNational Center for High-performance Computing, National Applied Research Laboratories, Hsinchu, Taiwan.ORCID 0000-0001-6677-2394
Wuh-Liang HwuDepartment of Pediatrics and Medical Genetics, National Taiwan University Hospital, Taipei, Taiwan.
Ni-Chung LeeDepartment of Pediatrics and Medical Genetics, National Taiwan University Hospital, Taipei, Taiwan. ncleentu@ntu.edu.tw.ORCID 0000-0002-5011-7499

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Illumina RNA-seq analysis was used to characterize the whole transcriptomes of peripheral blood mononuclear cells (PBMCs) from patients with congenital generalized lipodystrophy. RNA-seq information for seven patients with type 2 congenital generalized lipodystrophy (CGL2; Berardinelli-Seip congenital lipodystrophy, BSCL2) was obtained and compared with similar information for seven age- and sex-matched healthy control subjects. All seven CGL2 patients carried biallelic pathogenic mutations affecting the BSCL2 gene and had clinical symptoms of varying severity. The findings provide the whole-transcriptome signatures of PBMCs of CGL2 patients, allowing further exploration of gene expression patterns/signatures associated with the various clinical symptoms of patients with this disease.

Indexed as

RNA-SeqTranscriptomeAdolescentChildChild, PreschoolFemaleHigh-Throughput Nucleotide SequencingHumansLeukocytes, MononuclearLipodystrophy, Congenital GeneralizedMaleYoung Adult

Identifiers

PMID34645804
PMCPMC8514467

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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.