Evidence mapPaperPMID 39628902Full record

ArticleNeuropsychiatric disease and treatment2024

Bioinformatics Screening and Experimental Validation for Deciphering the Immune Signature of Late-Onset Depression.

Xinxia Wang, Yao Gao, Dan Wang, Xinzhe Du, Sha Liu

Abstract read
In one paragraph

Article in Neuropsychiatric disease and treatment, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. 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.

Xinxia Wang *Department of Psychiatry, First Hospital/First Clinical Medical College of Shanxi Medical University, Taiyuan, Shanxi, People's Republic of China.
Yao Gao *Department of Psychiatry, First Hospital/First Clinical Medical College of Shanxi Medical University, Taiyuan, Shanxi, People's Republic of China.
Dan WangShanxi Provincial Integrated TCM and WM Hospital, Taiyuan, Shanxi, People's Republic of China.
Xinzhe DuDepartment of Psychiatry, First Hospital/First Clinical Medical College of Shanxi Medical University, Taiyuan, Shanxi, People's Republic of China.
Sha LiuDepartment of Psychiatry, First Hospital/First Clinical Medical College of Shanxi Medical University, Taiyuan, Shanxi, People's Republic of China.ORCID 0000-0002-6710-8126

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Late-onset depression (LOD) is often associated with more severe cognitive impairment and a higher risk of disability and suicide. Emerging evidence suggests that immune system problems may be involved. This study aims to systematically characterize the genetic signature of LOD based on the immune landscape. Methods: The expression profile of GSE76826 was obtained from the Gene Expression Omnibus (GEO) database to gather gene expression data for 10 LOD patients and 12 healthy controls (HC). Various analyses, such as Single-Sample Gene Set Enrichment Analysis (ssGSEA) and Weighted Gene Co-expression Network Analysis (WGCNA), were used to mine key genes closely related to LOD. ImmuCellAI helped us understand differences in the immune environment between LOD patients and controls, and we used an LOD animal model to validate the relevant immune characteristics. Results: We found enriched immune pathways linked to LOD and adaptive immune responses. Using advanced bioinformatics techniques, we identified two key genes: Conclusion: We identified and validated the presence of aberrant expression of

Indexed as

APLNimmune cell infiltrationlate-onset depressionLEPmicrogliassGSEA

Identifiers

PMID39628902
PMCPMC11611703

What Socratic 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 Socratic graph.