Evidence map›Paper›PMID 34751089›Full record

ArticleBioengineered2021

Monocyte-related gene biomarkers for latent and active tuberculosis.

Yu Li, Yaju Deng, Jie He

Abstract read
In one paragraph

Article in Bioengineered, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing 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

14 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Article
  5. Article
  6. Article
  7. Article
  8. Identification ofAnnals of medicine · 2024
    Article
  9. Article
  10. Review
  11. Article
  12. Article
  13. Article
  14. 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

3 authors.

Yu LiDepartment of Pulmonary and Critical Care Medicine, The Second Affiliated Hospital of Guangxi Medical University, Nanning Guangxi, China.
Yaju DengEmergency Department, Guangxi District Maternal and Child Health Hospital, Nanning, Guangxi, China.
Jie HeClinical Medical College of Chengdu Medical College, Chengdu, Sichuan, China.ORCID 0000-0003-4466-9251

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Monocytes are closely associated with tuberculosis (TB). Latent tuberculosis in some patients gradually develops into its active state. This study aimed to investigate the role of hub monocyte-associated genes in distinguishing latent TB infection (LTBI) from active TB. The gene expression profiles of 15 peripheral blood mononuclear cells (PBMCs) samples were downloaded from the gene expression omnibus (GEO) database, GSE54992. The monocyte abundance was high in active TB as evaluated by the Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) algorithm. The limma test and correlation analysis documented 165 differentially expressed monocyte-related genes (DEMonRGs) between latent TB and active TB. Functional annotation and enrichment analyses of the DEMonRGs using the database for annotation, visualization, and integration discovery (DAVID) tools showed enrichment of inflammatory response mechanisms and immune-related pathways. A protein-protein interaction network was constructed with a node degree ≥10. The expression levels of these hub DEMonRGs (

Indexed as

AdultFemaleGene Expression ProfilingGene Regulatory NetworksGenetic MarkersHumansLatent TuberculosisLeukocytes, MononuclearMaleMicroRNAsMiddle AgedMonocytesMycobacterium tuberculosisProtein Interaction MapsTranscriptomeYoung AdultGenetic MarkersMicroRNAsactive tuberculosisbiological networkgeneslatent tuberculosisMonocytes

Identifiers

PMID34751089
PMCPMC8809927

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.